{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "113058f9-8324-4368-99ed-b088987c683a",
   "metadata": {},
   "source": [
    "# Comprehensive analysis of Vostok station data\n",
    "Seasonal-diurnal diagram, hour crossections, adjustment using meteoparameters"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5870ae4e-8a7d-4d76-a7e0-a542ee0fbd25",
   "metadata": {},
   "source": [
    "### Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c4d7641a-fe60-4177-95d8-9f10e7622f87",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.transforms as tf\n",
    "from matplotlib import cm, colors, colormaps\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy.stats as st\n",
    "\n",
    "import datetime as dt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb2d634e-ccc5-4fd2-a9e8-05d314f12c1b",
   "metadata": {},
   "source": [
    "### Loading source PG datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "80dbd8bc-6286-4b35-8de7-900551e1026e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# potential gradient values measured at Vostok station in (1998–2001)\n",
    "# units are V·m^(−1)\n",
    "# source: Burns et al. (2012), Table 2\n",
    "\n",
    "vostok_old_data = (\n",
    "    np.array([195, 201, 205, 192, 188, 195,\n",
    "              209, 198, 209, 195, 193, 192])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c63b60de-82fa-4dcd-af67-a3d4738a0431",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load hourly data derived from 10-second intervals \n",
    "# \"Field\" column contains PG values [V/m] without scale and calibration\n",
    "df_10s = pd.read_csv('./data/Vostok/vostok_hourly_from_10_s_without_calibration_and_empty.tsv', \n",
    "                     sep='\\t', parse_dates=['Datetime']).set_index('Datetime')\n",
    "\n",
    "# Add a new column 'Mark' to label this dataframe's data as originating from 10-second averages\n",
    "df_10s[\"Mark\"] = '10s'  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d2d83b75-97ce-42ba-8a99-8597f84d7946",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load hourly data derived from 5-minute intervals \n",
    "# \"Field\" column contains PG values [V/m] without scale and calibration\n",
    "df_5min = pd.read_csv('./data/Vostok/vostok_hourly_from_5_min_without_calibration_and_empty.tsv', \n",
    "                      sep='\\t', parse_dates=['Datetime']).set_index('Datetime')\n",
    "\n",
    "# Add a new column 'Mark' to label this dataframe's data as originating from 5-minute averages\n",
    "df_5min[\"Mark\"] = '5min'  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "26da1848-ad4f-4e58-8a75-d02428fb12c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load earlier dataset (1998-2004) \n",
    "earlier_df_src = pd.read_csv('./data/Vostok/vostok_1998_2004_hourly_80percent_all.tsv', \n",
    "                             sep='\\t', parse_dates=['Datetime']).set_index('Datetime')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d126b8b1-0b66-4817-a556-8f33860608ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Note that all the aforementioned files are averaged with 80% data availability, which \n",
    "# means that the hourly value was calculated only when at least 80% of the readings were \n",
    "# available (at least 288 10-second averages or at least 9 5-minute averages)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7de3455e-ba80-420d-8fec-8f82ee28804a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Combine the two dataframes: fill in gaps in the 10-second data with the 5-minute data.\n",
    "df_src = df_10s.combine_first(df_5min)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf820033-d3f9-4432-a425-db7bcf047a4d",
   "metadata": {},
   "source": [
    "### Helper functions and variables for PG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "42979690-97b5-4e86-8777-db48896539ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "def std_error(avg_val, avg_sqr, counter):\n",
    "    \"\"\"\n",
    "    Estimate the standard error from the average value\n",
    "    and the average value of the square.\n",
    "    \n",
    "    :param avg_val: the average value\n",
    "    :param avg_sqr: the average square value\n",
    "    :param counter: the size of the sample\n",
    "    \n",
    "    :return: the standard error\n",
    "    \"\"\"\n",
    "\n",
    "    return np.sqrt((avg_sqr - avg_val**2) / (counter - 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b13f968c-f9db-4b05-86ae-0e997383f9c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def pass_fair_weather(df, scale_factor=None):\n",
    "    \"\"\"\n",
    "    Filter and adjust potential gradient (PG) data to represent fair weather conditions.\n",
    "\n",
    "    :param df: The input dataframe containing 'Field' values for hourly averaged potential gradient (PG) data\n",
    "               and 'Count' values indicating the number of valid measurements per day.\n",
    "    :param scale_factor: A factor used to normalize the 'Field' values to mitigate external influences\n",
    "                         such as nearby metal structures (default is None, implying no scaling).\n",
    "\n",
    "    :return: A dataframe with hourly data filtered to likely represent fair weather conditions only.\n",
    "    \"\"\"\n",
    "\n",
    "    # normalize 'Field' values by the scale_factor if provided.\n",
    "    if scale_factor:\n",
    "        df[\"Field\"] = df[\"Field\"] / scale_factor\n",
    "    \n",
    "    # exclude records with non-positive PG values\n",
    "    df = df[df[\"Field\"] > 0]\n",
    "    \n",
    "    # exclude records with PG values > 300 V/m\n",
    "    df = df[df[\"Field\"] <= 300]\n",
    "    \n",
    "    # retain only days with complete hourly data (24 counts)\n",
    "    df[\"Count\"] = df[\"Field\"].resample('D').transform('count')\n",
    "    df = df[df[\"Count\"] == 24]\n",
    "    \n",
    "    # compute diurnal PG stats\n",
    "    df['Emax'] = df['Field'].resample('D').transform('max')\n",
    "    df['Emin'] = df['Field'].resample('D').transform('min')\n",
    "    df['Emean'] = df['Field'].resample('D').transform('mean')\n",
    "    \n",
    "    # exclude days with peak-to-peak amplitude over 150% of the daily mean\n",
    "    df['Var'] = ((df[\"Emax\"]-df[\"Emin\"]))/df[\"Emean\"]\n",
    "    df = df[df['Var'] <= 1.5]\n",
    "    \n",
    "    # # debug output for '5min' marked data counts per year\n",
    "    # (averaged from 5-min data) included in individual years \n",
    "    # z = df[df.Mark == '5min']\n",
    "    # print(z.groupby(z.index.year).count()[\"Mark\"])\n",
    "    \n",
    "    # remove temporary columns used for calculations\n",
    "    df = df[[\"Field\"]]\n",
    "\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "350d271c-0337-4e2b-89df-f90a42671d5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_seasonal_var_params(cond_df, key=\"Field\"):\n",
    "    \"\"\"\n",
    "    Calculate parameters necessary for assessing the seasonal \n",
    "    variability of fair weather potential gradients (FW PG).\n",
    "\n",
    "    :param cond_df: DataFrame containing conditioned data with a datetime \n",
    "                    index and at least one numerical column.\n",
    "    :param key: Column label in 'cond_df' which contains the potential \n",
    "                gradient data (default is 'Field').\n",
    "\n",
    "    :return: A list containing arrays of:\n",
    "             monthly mean PG values, \n",
    "             counts of fair weather days, and \n",
    "             adjusted monthly mean square PG values.\n",
    "    \"\"\"\n",
    "    # compute the monthly mean of fair weather PG\n",
    "    mon_pg = cond_df[key].groupby(cond_df.index.month).mean().to_numpy().flatten()\n",
    "\n",
    "    # count the number of fair weather days for each month\n",
    "    mon_counter = cond_df.groupby(cond_df.index.month).count().to_numpy().flatten()\n",
    "\n",
    "    # sum((daily mean FW PG)**2)/(count FW days) over months\n",
    "    mon_pg_sqr = cond_df[key].pow(2).groupby(cond_df.index.month).sum().to_numpy().flatten() / mon_counter\n",
    "\n",
    "    return [mon_pg, mon_counter, mon_pg_sqr]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "89b9f394-8d80-4db6-bd44-0b9778b03f9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "month_name = [\"J\", \"F\", \"M\", \"A\", \"M\", \"J\", \"J\", \"A\", \"S\", \"O\", \"N\", \"D\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f1c0ca4-15bb-448e-b85b-1f6b3a8a555f",
   "metadata": {},
   "source": [
    "### Applying fair weather criteria "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d1f85dec-45b7-47b2-b421-d528776fff39",
   "metadata": {},
   "outputs": [],
   "source": [
    "# apply the fair weather filter to the newdataset with a scale factor of 3 to normalize field values.\n",
    "df = pass_fair_weather(df_src, scale_factor=3)\n",
    "\n",
    "# apply the fair weather filter to earlier dataset data with a scale factor of 1 for normalization.\n",
    "earlier_df = pass_fair_weather(earlier_df_src, scale_factor=1)\n",
    "\n",
    "# Optional: Adjust the scale factor to 1.35 for earlier data to equalize the mean values of FW histograms across datasets.\n",
    "# earlier_df = pass_fair_weather(earlier_df, scale_factor=1.35)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fbce521b-c249-4441-b962-9a7991fb521f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # debug figure for check the mean values of FW histograms\n",
    "# fig, ax = plt.subplots(1,1)\n",
    "# df.hist(ax=ax,bins=600, color='red')\n",
    "# earlier_df.hist(ax=ax,bins=600, color='blue')\n",
    "# plt.xlim(-200,500)\n",
    "# plt.xlabel('V/m')\n",
    "# plt.ylabel('Counts')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4aebf84b-3335-450e-8379-bee1f0f01c05",
   "metadata": {},
   "source": [
    "### Resampling daily fair-weather datasets "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c339ed78-e4ba-43a9-89b8-85e8a0c57236",
   "metadata": {},
   "outputs": [],
   "source": [
    "# resample the filtered data to calculate the daily mean of FW PG\n",
    "# then drop any days with missing data to ensure completeness of the dataset\n",
    "\n",
    "daily_df = df.resample('D').mean().dropna()\n",
    "daily_edf = earlier_df.resample('D').mean().dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "a0aa9a7f-c65a-46f7-bb85-fcd1c133e4be",
   "metadata": {},
   "outputs": [],
   "source": [
    "# saving intermediate data for plotting in other scripts\n",
    "\n",
    "np.save('./data/Vostok/vostok_diurnal_2006_2020.npy', \n",
    "        df.groupby(df.index.hour)\n",
    "            .mean().dropna().to_numpy().flatten())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5a58975-053a-4162-bccb-f0dbef39b0ed",
   "metadata": {},
   "source": [
    "### Figure: Seasonal variation (new data) for different years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "6d6b5a71-6dd3-4471-b081-f3606c7b6956",
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate seasonal variation parameters for specific years: \n",
    "# average FW PG, count of FW days, and the sum of squares of FW PG hourly values.\n",
    "\n",
    "data, data_counter, data_sqr = np.array(\n",
    "    [\n",
    "        calculate_seasonal_var_params(daily_df),\n",
    "        calculate_seasonal_var_params(daily_df[daily_df.index.year <= 2012]),\n",
    "        calculate_seasonal_var_params(daily_df[daily_df.index.year > 2012])\n",
    "    ]\n",
    ").swapaxes(0,1)\n",
    "\n",
    "data_counter = data_counter.astype(int)\n",
    "\n",
    "# saving intermediate data for plotting in other scripts\n",
    "np.savez('./data/Vostok/vostok_2006_2020_results', mean=data, counter=data_counter, sqr=data_sqr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4e29c652-918d-47e2-ac67-1d0c2a898eea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 14), constrained_layout=False)\n",
    "ax = [None for _ in range(3)]\n",
    "ax[0] = fig.add_subplot(4, 4, (2, 3))\n",
    "for n in range(2, 4):\n",
    "    ax[n-1] = fig.add_subplot(4, 4, (2*n + 1, 2*n + 2))\n",
    "\n",
    "low = [100] + [80] * 2\n",
    "high = [180] * 3\n",
    "step = [20] * 3\n",
    "coeff = [1] * 3\n",
    "caption = ['Vostok station, 2006–2020',\n",
    "           'Vostok station, 2006–2012',\n",
    "           'Vostok station, 2013–2020']\n",
    "col = ['orangered'] * 3\n",
    "\n",
    "for n in range(3):\n",
    "    for axis in ['top', 'bottom', 'left', 'right']:\n",
    "        ax[n].spines[axis].set_linewidth(0.5)\n",
    "    ax[n].tick_params(length=6, width=0.5, axis='y')\n",
    "    ax[n].tick_params(length=0, width=0.5, axis='x')\n",
    "    ax[n].grid(color='0.', linewidth=0.5, axis='y')\n",
    "\n",
    "    ax[n].set_xlim((-0.5, 11.5))\n",
    "    ax[n].set_xticks(np.arange(12))\n",
    "    ax[n].set_xticklabels(month_name, fontsize='large', va='top')\n",
    "\n",
    "    ax[n].set_ylim((low[n], high[n]))\n",
    "    ax[n].set_yticks(np.arange(low[n], high[n] + step[n] / 2, step[n]))\n",
    "    ax[n].set_yticklabels((np.arange(low[n], high[n] + step[n] / 2,\n",
    "                                     step[n]) / coeff[n]).astype(int),\n",
    "                          fontsize='large')\n",
    "    ax[n].set_ylabel('Monthly mean fair-weather\\npotential gradient, V/m',\n",
    "                     fontsize='large')\n",
    "\n",
    "    ax[n].set_title(caption[n], fontsize='large')\n",
    "\n",
    "    ax[n].annotate('', xy=(12, np.min(data[n])), xycoords='data',\n",
    "                   xytext=(12, np.max(data[n])), textcoords='data',\n",
    "                   annotation_clip=False,\n",
    "                   arrowprops=dict(\n",
    "                       arrowstyle='<|-|>,head_length=0.8,head_width=0.3',\n",
    "                       patchA=None, patchB=None, shrinkA=0., shrinkB=0.,\n",
    "                       connectionstyle='arc3,rad=0.', fc='black',\n",
    "                       linewidth=0.5\n",
    "                   ))\n",
    "\n",
    "    ampl = (np.max(data[n]) - np.min(data[n])) / \\\n",
    "        np.sum(data[n] * data_counter[n]) * np.sum(data_counter[n])\n",
    "    ax[n].text(12.2, (np.min(data[n]) + np.max(data[n])) / 2,\n",
    "               f'{ampl * 100:.0f}%',\n",
    "               fontsize='large', ha='left', va='center', rotation=270)\n",
    "\n",
    "for n in range(3):\n",
    "    ax[n].bar(np.arange(12), data[n],\n",
    "              yerr=std_error(data[n],\n",
    "                             data_sqr[n],\n",
    "                             data_counter[n]),\n",
    "              width=0.8, color=col[n])\n",
    "\n",
    "for n in range(3):\n",
    "    ax[n].text(-0.3, 1.05, chr(ord('a') + n),\n",
    "               fontsize='x-large',\n",
    "               fontweight='semibold', ha='left', va='bottom',\n",
    "               transform=ax[n].transAxes)\n",
    "\n",
    "fig.subplots_adjust(hspace=0.3, wspace=1.6)\n",
    "\n",
    "for n in range(3):\n",
    "    for m in range(12):\n",
    "        ax[n].annotate(f'{data_counter[n, m]}',\n",
    "                       xy=(m-0.15, ax[n].get_ylim()[0] + 3),\n",
    "                       rotation=270, ha='center', va='bottom',\n",
    "                       fontsize='large', color='0.')\n",
    "\n",
    "fig.savefig('figures_two_parts/pg_total_partial.eps', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b65fb2a-8f3d-4d23-a8a2-c4c7f031c910",
   "metadata": {},
   "source": [
    "### Figure: Diurnal-Seasonal Diagram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "19eddef4-207d-4fcd-af6b-fc3fcd02e7cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a seasonal-diurnal array by grouping the data by hour of the day and month, \n",
    "# then calculating the mean FW PG for each combination.\n",
    "\n",
    "sd_df = df.groupby([df.index.hour, df.index.month]).mean()\n",
    "sd_df.index.set_names(['hour', 'month'], inplace=True)\n",
    "\n",
    "vostok_pg_data_to_plot = sd_df.values.reshape(24,12).T\n",
    "vostok_mean_pg_data = vostok_pg_data_to_plot.mean(axis=1).reshape(12,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5b03d7c6-e23e-45a9-8188-024d21851176",
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate seasonal var params (avg FW PG, count FW days, \n",
    "# sum of squares of FW PG) for specific years\n",
    "data, data_counter, data_sqr = np.array(\n",
    "    [\n",
    "        calculate_seasonal_var_params(daily_df),\n",
    "        calculate_seasonal_var_params(df[df.index.hour == 3]),\n",
    "        calculate_seasonal_var_params(df[df.index.hour == 9]),\n",
    "        calculate_seasonal_var_params(df[df.index.hour == 15]),\n",
    "        calculate_seasonal_var_params(df[df.index.hour == 21]),\n",
    "    ]\n",
    ").swapaxes(0,1)\n",
    "\n",
    "data_counter = data_counter.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "762c01a9-2ee5-428c-b331-ef44d1801127",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5+aFYsWJYvnw5OnXqhMePH8PS0hJeXl64c+cOwsLCsGTJEpQoUQIAYGVlpdLr16NHD8yYMQOhoaGoWbOmbHlaWhq2bduGhg0bonTp0khKSkKTJk1w7949DBs2DM7Ozti+fTt8fX0RFxeHkSNHAgAOHToEHx8fNGvWDPPmzQOQfk/I33//jZEjR6JRo0YYMWIEli9fjkmTJqFy5coAIPt3+vTpmDFjBpo3b44hQ4bgv//+w+rVq3HhwgX8/fffKFKkSKbbsXfvXnTu3BndunXD77//Dl1d3UzrpaSkoFWrVkhOTsbw4cNha2uLp0+fYu/evYiLi4OZmRmCg4PRv39/uLm5YeDAgQCAsmXLAlDus5LTNn5J2dc2Q2hoKBISEjBo0CBIJBLMnz8fXl5eePDgQZavj6qeP38OALLPFQBcuXIFxsbGCtvh5uYmK2/QoAGePn2Kly9fonbt2grturm54c8//5Q9P3z4MADAwMAAtWvXxqVLl6Cvr4+OHTsiICAAFhYWedoOIQRevHgBFxcX2TJV4svK8+fP5V4bqVSKa9euoV+/fpm2efDgQSQkJKBYsWK53BLts379euzbtw9r167F3bt3UaxYMXh4eODXX3+Fvb29psPTGj169MCmTZuwbds2DBs2TLb87du3OHDgAHx8fGBkZIQXL16gfv36+PDhA0aMGAFLS0ts2rQJnp6e2LFjBzp27AgAWLduHUaMGIHOnTtj5MiR+PjxI65du4Zz586he/fuOR5blDm+Z2bt2rUYPHgwJk2ahNmzZ2dZ7+XLl2jZsiWsrKwwYcIEmJubIyoqSjbvrpWVFVavXo0hQ4agY8eO8PLyAgBUr14dQPox6MGDB+jbty9sbW1x48YNrF27Fjdu3MDZs2chkUhUPn4q+9pmmDt3LnR0dDBmzBjEx8dj/vz56NGjB86dO5f1G62irPbRFSpUgKmpqVzdjH30P//8g1KlSuHff//Fp0+fFPaB+vr6qFGjBq5cuSJbdvjwYZiamuLp06f48ccfcefOHRgbG6NXr15YsmQJDA0N87Qd79+/x/v37+W2Q5X4MpNf+/3MbNiwAYMGDUL9+vXh7++PBw8eyO7lLVWqVK7azMrGjRvx8eNHDBw4EAYGBnLHx65du8LZ2Rm//fYbLl++jPXr18Pa2lr2PQwAVq9eDRcXF3h6ekJPTw979uyBn58fpFIphg4dmquYTp48iYiICNn6v/32G9q1a4dx48YhICAAfn5+iI2Nxfz589GvXz8cPXpUtu7Ro0fRpk0b1KpVC9OmTYOOjg42btyIpk2b4uTJk7LPrbLf0TMMHz4cxYsXx7Rp0xAVFYWlS5di2LBhWd7XrXGazpS/Jhm/Vhw+fFi8evVKPHnyRGzZskVYWloKIyMjER0dLf755x8BQPTv319u3TFjxggA4ujRo7Jleb309/nz50JPT0/8+OOPcsunT58udxbs89gbNGigcMYms0sOz5w5IwCI//u//5Mt8/f3FwDEuXPnZMtevnwpzMzMcjyjGhwcLHR0dBTuE1mzZo0AIP7++2/ZMgBCX19f3Lt3T7bs6tWrAoBYsWKFbJm6Ll1ydXUVDg4Ocr+S/vXXX3KXwyxdulQAEJs3b5bVSUlJEfXq1RMmJibi3bt3QgghRo4cKUxNTbM9K5bVZbEvX74U+vr6omXLlnKxrFy5UgAQv//+u2zZ52dUd+7cKYoUKSIGDBiQ4y+9V65cEQDE9u3bs62X1aW/yn5Wsrv098vPhrKvbcavqpaWluLt27eyurt37xYAxJ49e7LdJmUlJyeLKlWqCGdnZ7kz0x4eHqJMmTIK9RMTEwUAMWHCBCFE+tmQL1+PDGPHjhUAZGdfPD09ZdvUo0cPsWPHDjFlyhShp6cn6tevL6RSaZ62JTg4WAAQGzZskC1TJb7MnDhxQkgkEjFlyhTZsoxLEWfOnKlQP+NM9O3bt/O0LfRt+vTpk7Czs1O4dDPj2JFxGWTG8enzY0xCQoJwdnYWTk5Osn1jhw4dcrxtIqtjiyrH98/PqC5btkxIJBIxa9asHLd3165dmV659bnsLv3NbB8dFhYmAIgTJ07kuI0ZsX++/1f2tc04o1q5cmW5yzKXLVsmACjcM5lbb968EdbW1qJhw4Zyy11cXETTpk0V6t+4cUMAEGvWrBFC/O/49PnrkaFLly7C1tZW9rx69eqiaNGiomjRomL48OFi586dYvjw4QKA8Pb2zvO2zJo1SwAQR44ckS1TJb7M5Md+PzMpKSnC2tpa1KhRQ+79Xrt2rcJVguo4o2pqaqpwZjDjjOrnYwIIIUTHjh2FpaWl3LLM/jZatWqlcFxX5YyqgYGB3N9QYGCgACBsbW1l312EEGLixIlyf29SqVSUL19etGrVSu44/+HDB+Hs7CxatGiRbdyZfe/K+J7fvHlzuTZHjRoldHV1ZVfNFTYqT09DOWvevDmsrKxQqlQpeHt7w8TEBLt27YK9vb3sV6nRo0fLrfPzzz8DAPbt26e2OI4cOYJPnz7Bz89Pbvnw4cOzXGfAgAEKZ9s+PyOampqKN2/eoFy5cjA3N8fly5dlZX/++Sfq1q0r+5UHSP8FtkePHjnGun37dlSuXBmVKlXC69evZY+mTZsCACIjI+XqN2/eXHYWD0j/tdjU1DRfRhDt2bMnoqOjceLECdmy0NBQ6Ovry0a6/vPPP2FrawsfHx9ZnSJFishG4Tt+/DiA9JE8ExMTsxxlMDuHDx9GSkoK/P39oaPzvz/dAQMGwNTUNNPPTlhYGLp164ZBgwYhMDBQbr3MmJmZAQAOHDiADx8+qByjsp8VVSj72mbo1q0bihcvLnvesGFDAFDbZ2PYsGG4efMmVq5cCT29/12UkpSUBAMDA4X6Gb+oJyUlyf2rTN2MERxdXV2xefNmdOrUCTNnzsSsWbNw+vRpHDlyJNfbcfv2bQwdOhT16tWTm9NTlfi+9PLlS3Tv3h3Ozs4YN26cWtokyo6uri68vb1x5swZuVHEQ0NDYWNjg2bNmgFI34+4ubmhQYMGsjomJiYYOHAgoqKicPPmTQDp++jo6GhcuHBB5Vhyc3yfP38+Ro4ciXnz5uGXX37JsY+M0aD37t2L1NRUlWP8fB/98eNHvH79GnXr1gWAPO2jlXltM/Tt2xf6+vqy5+rcR0ulUvTo0QNxcXFYsWKFXJm69tGf76vev3+PDx8+oHfv3li+fDm8vLywfPlyDBo0CFu2bMHdu3dzvS0nTpzAjBkz0LVrV9l3IVXj+1J+7PezcvHiRbx8+RKDBw+We799fX1l3zXUqVOnTlme9R88eLDc84YNG+LNmzd49+6dbNnnfxvx8fF4/fo1GjdujAcPHiA+Pj5XMTVr1gxOTk6y53Xq1JHF+vkVRBnLM/4G/vnnH9y9exfdu3fHmzdvZN+JExMT0axZM5w4cQJSqVQhbmW+dw0cOBASiUTutUhLS8OjR49ytY35jYlqPli1ahUOHTqEyMhI3Lx5Ew8ePECrVq0AAI8ePYKOjg7KlSsnt46trS3Mzc3V+kHJaOvLviwsLOS+yH/O2dlZYVlSUhKmTp2KUqVKwcDAACVKlICVlRXi4uLk/ngfPXqE8uXLK6xfsWLFHGO9e/cubty4ASsrK7lHhQoVAKR/Af5c6dKlFdooXrx4vsw96O3tDV1dXYSGhgJIP7jv2rULbdq0kb2OGdv+ZSKYcRloxnvh5+eHChUqoE2bNnBwcEC/fv3w119/KRVHRhtfvp76+vooU6aMwmfn4cOH6NmzJzp16oQVK1bI7Ziy4uzsjNGjR2P9+vUoUaIEWrVqhVWrVim9k1b2s6IKZV/bDF9+NjLeI3V8NhYsWIB169Zh1qxZaNu2rVyZkZFRpvNHZ1xmn3EwyfhXlbqfJ+kA0L17dwDp0xMA6QfV58+fZ/rIbBqd58+fw8PDA2ZmZtixY4fcj1OqxPe5xMREtGvXDgkJCdi9ezdMTEzy3CaRMjJ+DM3YR0dHR+PkyZOyfTeQvp/I7Fj05X5k/PjxMDExgZubG8qXL4+hQ4fi77//VioOVY/vx48fx/jx4zF+/HiMHTtWqT4aN26MTp06YcaMGShRogQ6dOiAjRs3Kj13/du3bzFy5EjY2NjAyMgIVlZWsuN+XvbRyry2GfJzHz18+HD89ddfWL9+Pb777ju5MnXtoz/fV+W0jz5z5gyA9Nc9q310Zm7fvo2OHTuiatWqWL9+vcJ2KBvf5/Jjv5+djPf9y++FRYoUQZkyZVRqSxmZfX/NoMxn7u+//0bz5s1hbGwMc3NzWFlZYdKkSQBy/7fxZb8ZCfqXlz1nLM+IJ+MHjj59+ih8L16/fj2Sk5NlMan6vSs///7yg8r3qFLO3NzcMr3O/3PKJA2akNmOaPjw4di4cSP8/f1Rr149mJmZQSKRwNvbW/aLTl5JpVJUq1YNixcvzrT8yz/qrO6xFEKoJZ7PWVtbo0WLFti5cydWrVqFPXv2ICEhQakzxZm19c8//+DAgQPYv38/9u/fj40bN6J3797YtGmTWuO2s7ODnZ0d/vzzT1y8eDHHz2SGRYsWwdfXF7t378bBgwcxYsQI/Pbbbzh79iwcHByyXbcgPis5ya/PRlBQEMaPH4/BgwdneubDzs4OkZGREELI/X1nzIFXsmRJWb3Pl38uJiYGFhYWsl+1M9b58v50a2trAP87sIwcOTLLz8/Dhw/lftGNj49HmzZtEBcXh5MnT8r6+Hw7lI0vQ0pKCry8vHDt2jUcOHAAVatWlSvPWCerNj/fViJV1apVC5UqVUJYWBgmTZqEsLAwCCFytY+uXLky/vvvP+zduxd//fUXdu7ciYCAAEydOhUzZsxQqg1lj+8uLi6Ii4tDcHAwBg0alO0X7c/b3rFjB86ePYs9e/bgwIED6NevHxYtWoSzZ8/K/UCUma5du+L06dMYO3YsatSoARMTE0ilUrRu3Vrr99EzZsxAQEAA5s6di169eimU29nZ4enTpwrLVd1Hf76vKlmyJG7cuJHjPtrLy0vh6p8MX273kydP0LJlS5iZmeHPP/9UuHdflfgy5Md+P79JJJJMPxOZ/fgKZJ9I5/SZu3//Ppo1a4ZKlSph8eLFKFWqFPT19fHnn39iyZIluf7byKrfnOLJ6G/BggWoUaNGpnUz/tZV/d5VkN+f1YGJagFzdHSEVCrF3bt35QZdefHiBeLi4uDo6ChbltdkNqOte/fuyR0A37x5o9IvJzt27ECfPn2waNEi2bKPHz8qTATu6OiY6WUu//33X459lC1bFlevXkWzZs3UlsSr88eAHj164K+//sL+/fsRGhoKU1NTtG/fXlbu6OiIa9euQSqVyp35u337tqw8g76+Ptq3b4/27dtDKpXCz88PgYGBmDJlCsqVK5dl3Blt/Pfff3K/RqakpODhw4do3ry5XH1DQ0Ps3bsXTZs2RevWrXH8+HG5wROyU61aNVSrVg2//PILTp8+jR9++AFr1qyRDfKRVYzKflZUeW9UeW3zy+7du9G/f394eXlh1apVmdapUaMG1q9fj1u3bqFKlSqy5RkDhGQcbOzt7WFlZYWLFy8qtHH+/Hm5g1KtWrWwbt06hS9Xz549A/C/wU3GjRuHnj17ZhqXra2t7P8fP35E+/btcefOHRw+fFguzgyqxAekH1B79+6NI0eOYNu2bWjcuLHCejo6OqhWrVqmbZ47dw5lypT5pgZSIvXr0aMHpkyZgmvXriE0NBTly5eHq6urrNzR0THTY1Fm+xFjY2N069YN3bp1k/0I8+uvv2LixIkwNDTMdh+t7PEdSB/oZ8eOHWjQoAGaNWuGU6dOKf2DTd26dVG3bl38+uuvCA0NRY8ePbBlyxb0798/y/hiY2Nx5MgRzJgxA1OnTpUtz+y4reo+WtnXNr+sWrUK06dPh7+/P8aPH59pnRo1aiAyMhLv3r2TG1Dpy3101apVoaenh4sXL6Jr166yeikpKfjnn3/kltWqVQuHDh3C06dP5c4qf7mPXrRokVLfu968eYOWLVsiOTkZR44ckSWQn1MlPiB/9vvKyHjf7969K3fpcmpqKh4+fKhwxvtLxYsXz/Ry8Py4RHXPnj1ITk5GRESE3BnHL287KygZt7aZmpoqfLf7krLfu7QVL/0tYBmXCy5dulRuecaZRA8PD9kyY2PjTD9oqampuH37dqa/fH2uWbNm0NPTw+rVq+WWr1y5UqWYdXV1FX5pWbFihcKvWm3btsXZs2dx/vx52bJXr14hJCQkxz66du2Kp0+fYt26dQplSUlJshGIVWFsbAwAavlj/fHHH1G0aFEEBARg//798PLykhvNr23btnj+/LncqGmfPn3CihUrYGJiIvvy/ubNG7l2dXR0ZKMxZlxyk1XczZs3h76+PpYvXy73fmzYsAHx8fFyn50MZmZmOHDggOyscMYI1Fl59+4dPn36JLesWrVq0NHRkbskKKvPprKfFVXeG2Vf2/xy4sQJeHt7o1GjRggJCcnyPt8OHTqgSJEiCAgIkC0TQmDNmjWwt7dH/fr1Zcs7deqEvXv34smTJ7JlR44cwZ07d2T3PWe0aWBggI0bN8r9MppxKViLFi0AAFWqVEHz5s0zfWR8TtPS0tCtWzecOXMG27dvR7169bLcZmXjA9J/zd26dSsCAgJkI4xmpnPnzrhw4YLcF6H//vsPR48eVWiTSFUZZ0+nTp2Kf/75R+Fsatu2bXH+/HnZpZhA+uXqa9euhZOTk+zL+5f7aH19fVSpUgVCCNk9oVntv1Q5vmdwcHDA4cOHkZSUhBYtWij0/6XY2FiFfWxGEpGxj84Y5fPL+DLOpHy5/pfxAqrvo5V5bfPL1q1bMWLECPTo0SPLq7KA9H1QWloa1q5dK1uWnJyMjRs3ok6dOrIrt8zMzNC8eXNs3rwZCQkJsrrBwcF4//693P4qIyncsGGDXF/r16+Hnp4emjRpAiA9oc1qH50hMTERbdu2xdOnT/Hnn39meiuVqvHl135fGbVr14aVlRXWrFmDlJQU2fKgoCClPldly5bF7du38erVK9myq1evKn0pvioy+9uIj4/Hxo0b1d6XMmrVqoWyZcti4cKFsrEqPvf5a6Ls9y5txTOqBey7775Dnz59sHbtWsTFxaFx48Y4f/48Nm3ahB9//BHu7u6yurVq1cLq1asxe/ZslCtXDtbW1mjatCmePn2KypUro0+fPggKCsqyLxsbG4wcORKLFi2Cp6cnWrdujatXr2L//v0oUaKE0r+YtmvXDsHBwTAzM0OVKlVw5swZHD58WDbdToZx48YhODgYrVu3xsiRI2XT02ScEctOr169sG3bNgwePBiRkZH44YcfkJaWhtu3b2Pbtm04cOCA0peuZqhVqxYAYPLkyfD29kaRIkXQvn17GBsby6Z5iYyMlB1IsmNiYoIff/xRdg/Ul1+CBg4ciMDAQPj6+uLSpUtwcnLCjh078Pfff2Pp0qWys0X9+/fH27dv0bRpUzg4OODRo0dYsWIFatSoIfsFvkaNGtDV1cW8efMQHx8PAwMDNG3aFNbW1pg4cSJmzJiB1q1bw9PTE//99x8CAgLg6uqa5Rm1EiVK4NChQ2jQoAGaN2+OU6dOZTn9xdGjRzFs2DB06dIFFSpUwKdPnxAcHAxdXV106tRJ7rU9fPgwFi9ejJIlS8LZ2Rl16tRR+rOS3TZ+SdnXVhVRUVFwdnbO8W/o0aNH8PT0hEQiQefOnbF9+3a58urVq8t+aHBwcIC/vz8WLFiA1NRUuLq64o8//sDJkycREhIid7nNpEmTsH37dri7u2PkyJF4//49FixYgGrVqqFv376yera2tpg8eTKmTp2K1q1b48cff8TVq1exbt06+Pj4yJ0xysnPP/+MiIgItG/fHm/fvsXmzZvlyj///Cgb39KlSxEQEIB69eqhaNGiCm127NhR9oXXz88P69atg4eHB8aMGYMiRYpg8eLFsLGxkQ02Q5Rbzs7OqF+/Pnbv3g1AcR89YcIEhIWFoU2bNhgxYgQsLCywadMmPHz4EDt37pT9ANWyZUvY2trihx9+gI2NDW7duoWVK1fCw8NDtq/J6tiiyvH9c+XKlcPBgwfRpEkTtGrVCkePHlWYQiXDpk2bEBAQgI4dO6Js2bJISEjAunXrYGpqKkuUjYyMUKVKFWzduhUVKlSAhYUFqlatiqpVq6JRo0aYP38+UlNTYW9vj4MHD+Lhw4cK/WR3/PySsq+tKo4dOwZ3d3dMmzYN06dPz7Le+fPn0bt3b1haWqJZs2YKP4zXr19fdgVSnTp10KVLF0ycOBEvX75EuXLlsGnTJkRFRSkkmr/++ivq16+Pxo0bY+DAgYiOjsaiRYvQsmVLtG7dWlbv+++/R79+/fD777/j06dPaNy4MY4dO4bt27dj4sSJKt3S0KNHD5w/fx79+vXDrVu3cOvWLVlZxncQVePLj/0+ANntJJ8PYPalIkWKYPbs2Rg0aBCaNm2Kbt264eHDh9i4caNS96j269cPixcvRqtWrfDTTz/h5cuXWLNmDVxcXOQGQVKHli1byq52GzRoEN6/f49169bB2to6x5NC+UFHRwfr169HmzZt4OLigr59+8Le3h5Pnz5FZGQkTE1NsWfPHgDKf0fXWgU8yvBXLWPo5+yGjRcifULwGTNmCGdnZ1GkSBFRqlSpTCcEf/78ufDw8BDFihWTG8o7YyjuzKYH+dKnT5/ElClThK2trTAyMhJNmzYVt27dEpaWlmLw4MFKxR4bGyv69u0rSpQoIUxMTESrVq3E7du3M530+9q1a6Jx48bC0NBQ2Nvbi1mzZokNGzbkOD2NEOlDmc+bN0+4uLgIAwMDUbx4cVGrVi0xY8YMuQm68f8nUf5SZvHMmjVL2NvbCx0dHbkYfv75ZyGRSMStW7dyfA0z7Nu3TwAQdnZ2mU7z8uLFC9nrpK+vL6pVqyY31LoQQuzYsUO0bNlSWFtbC319fVG6dGkxaNAgERMTI1dv3bp1okyZMkJXV1dhGpeVK1eKSpUqiSJFiggbGxsxZMgQERsbK7f+59PTZLh3756ws7MTlStXFq9evcp0Gx88eCD69esnypYtK5s42t3dXRw+fFiu3u3bt0WjRo2EkZGR3GdRlc9KVtuY2WdDmdf280m/v4Qvpmr4999/5aaMyUrGdApZPb6c/iEtLU3MmTNHODo6Cn19feHi4iI3rc7nrl+/Llq2bCmKFi0qzM3NRY8ePcTz588V6kmlUrFixQpRoUIF2f7il19+ESkpKdnG/qXGjRtnuy25ia9Pnz7Ztvnl1BZPnjwRnTt3FqampsLExES0a9dO3L17V6XtIMpKxlRHbm5umZbfv39fdO7cWZibmwtDQ0Ph5uYm9u7dK1cnMDBQNGrUSFhaWgoDAwNRtmxZMXbsWLljkBBZH1uUPb5/Pj1NhnPnzolixYqJRo0aZTrlhBBCXL58Wfj4+IjSpUsLAwMDYW1tLdq1aycuXrwoV+/06dOiVq1aQl9fX25fFR0dLTp27CjMzc2FmZmZ6NKli3j27Fmm+7OstjGz/bkyr23G/vTL6c8ym5pkz549clPGZCXju0tWjy+PE0lJSWLMmDHC1tZWGBgYCFdXV/HXX39l2vbJkydF/fr1haGhobCyshJDhw6Vm1IkQ0pKipg+fbpwdHQURYoUEeXKlRNLlizJNu7MODo6Zrkdn0/Hokp8+bHfF0KIEiVKiLp16yq1XQEBAcLZ2VkYGBiI2rVrixMnTigc5zP7DAghxObNm0WZMmWEvr6+qFGjhjhw4ECW09NkduzPmJ7my+88GZ+bz49RERERonr16sLQ0FA4OTmJefPmid9//12p76+Zyey7alaxZvW3ceXKFeHl5SXbHzk6OoquXbvKTVek7PeurL7nZ/Sd2XSBhYFEiEJ69yzlm7i4OBQvXhyzZ8/G5MmTNR2ORri5ucHR0VHhDBl9GwICAjBu3Djcv39fYRAMIiLSrHHjxiEsLAz37t0r8EF8KHs3b96Ei4sL9u7dm+nl7ETqxEt/v3JJSUkKI6Fl3I+izCWvX6N3797h6tWrah9ll7RHZGQkRowYwSSViKgQioyMxJQpU5ikFkKRkZGoV68ek1QqEDyj+pULCgpCUFAQ2rZtCxMTE5w6dQphYWFo2bIlDhw4oOnwiIhIC2Xcv3bu3DmcP38esbGx2LhxI3x9fRXqbtu2DYsXL8bt27ehq6uLqlWrYty4cQpfdKVSKRYuXIjVq1cjJiYGFSpUwMSJExXmqCQiom8DR/39ylWvXh16enqYP38+/P39cfLkSYwcORI7d+7UdGhERKSlXr9+jZkzZ+LWrVvZTjOxYsUKdOvWDSVKlMDcuXMxZcoUxMfHo127dggPD5erO3nyZIwfPx4tWrTAihUrULp0aXTv3h1btmzJ780hIqJCiGdUiYiISCXJycmIjY2Fra0tLl68CFdX10zPqFaoUAHm5uY4d+6cbKT5d+/ewd7eHk2bNpWN0vv06VM4Oztj4MCBsinUhBBo3LgxHj58iKioqCwnqicioq8Tz6gSERGRSgwMDGBra5tjvXfv3sHa2lpuOjRTU1OYmJjIjZ+we/dupKamws/PT7ZMIpFgyJAhiI6Olpujk4iIvg1MVImIiChfNGnSBH/99RdWrFiBqKgo3L59G0OHDkV8fDxGjhwpq3flyhUYGxvL5pPO4ObmJisnIqJvyzcz6m9YWBjCwsKyLN9z6AQk5qULJBbxWvkJoDNISjzLh0jUzLKQx/hG9dc913LxWhQzi1N/HFkoZfh1XvHvYCTN1XrRSQX3m92Tj5KcK30hId48d53l4jNf2SINN27cyF1/KpDYGkNSwijnikqqLGwKJG5SzfLly/H69WuMGDECI0aMAACUKFECR44cQb169WT1YmJiYGNjI3fmFQDs7OwAAM+eZb1Pzen4fvToUZiYmORlM4iItJqlpaVWHiO/mUTVx8cn25EDdeyqQ7fO4AKJJS2ym8rr6NbZmg+RqJdOk22aDiFb0mNdC6yv3LwWZb+/nA+RZK6XQ0qB9VWQepbK3XZtflJEzZFkLThaX+V1blypmqu+cvOZLyv9K1d9qUpSwghF+uduuzJT9qip2toi9SlatCgqVqwIBwcHtGvXDgkJCViyZAm8vLxw8uRJlCtXDkD6VGqZTUViaGgoK89KTsd3T09PRERE5HFLiIi0l6enp6ZDyJVvJlElIiKigtWlSxfo6elhz549smUdOnRA+fLlMXnyZGzdmv4jrJGREZKTkxXW//jxo6yciIi+LbxHlYiIiNTuwYMH+OuvvxR+ybewsECDBg3w999/y5bZ2dnh+fPn+HIigpiYGABAyZIFeOsGEREVCkxUiYiISO1evHgBAEhLS1MoS01NxadPn2TPa9SogQ8fPuDWrVty9c6dOycrJyKib4tWJapBQUGQSCSyh6GhIUqWLIlWrVph+fLlSEhI0HSIREREBKBcuXLQ0dHB1q1b5c6URkdH4+TJk/j+++9lyzp06IAiRYogICBAtkwIgTVr1sDe3h7169cv0NiJiEjztPIe1ZkzZ8LZ2Rmpqal4/vw5jh07Bn9/fyxevBgRERGoXr26pkMkIiL6qq1cuRJxcXGyEXn37NmD6OhoAMDw4cNhZWWFfv36Yf369WjWrBm8vLyQkJCAgIAAJCUlYeLEibK2HBwc4O/vjwULFiA1NRWurq74448/cPLkSYSEhEBXV1cj20hERJqjlYlqmzZtULt2bdnziRMn4ujRo2jXrh08PT1x69YtDrxARESUjxYuXIhHjx7JnoeHhyM8PBwA0LNnT5iZmWH16tX47rvvsGHDBlli6urqiv/7v/9Do0aN5NqbO3cuihcvjsDAQAQFBaF8+fLYvHkzunfvXnAbRUREhYZWXfqbnaZNm2LKlCl49OgRNm/erOlwiIiIvmpRUVEQQmT6cHJyAgDo6elh2LBhuHLlChISEpCQkICjR4/C3d1doT0dHR1MnDgRUVFRSE5OxvXr19GjR48C3ioiIiosvppEFQB69eoFADh48KCGIyEiIlJOUlISRo8eLTeFCxER0bfuq0pUHRwcYGZmhvv372s6FCIiIqUYGRkhMDBQNkouERERfWWJKgCYmJhw9F8iItIqtWrVwvXr1zUdBhERUaHx1SWq79+/R7FixTQdBhERkdKWLl2KLVu2YP369XLzixIREX2rtHLU36xER0cjPj4e5cqV03QoRERESvP19YWOjg4GDRqEESNGwN7eXmH0eolEgqtXr2ooQiIiooL1VSWqwcHBAIBWrVppOBIiIiLlWVhYwNLSEhUrVtR0KERERIXCV5OoHj16FLNmzYKzszOHsyciIq1y7NgxTYdARERUqGhlorp//37cvn0bnz59wosXL3D06FEcOnQIjo6OiIiIgKGhoaZDJCIiIiIiolzSykR16tSpAAB9fX1YWFigWrVqWLp0Kfr27cuBlIiISCu9e/cOAQEBiIyMxMuXLxEYGAg3Nze8ffsWQUFB8PT05BgMRET0zdCqRNXX1xe+vr6aDoOIiEitoqOj0bhxYzx58gTly5fH7du38f79ewDp968GBgbi0aNHWLZsmYYjJSIiKhhalagSERF9jcaOHYuEhAT8888/sLa2hrW1tVz5jz/+iL1792ooOiIiooL3zSSqYWFhCAsLy7JcJMUVWCy67lsLrK/c0GmyTdMhUCHVs1RKgfVlFSlUX+le7voahWSV13nlLsldZ7kQjMu5Wu9GblY6apqrvlRVzCwOZb/P3XZl6mgT9bWlAQcPHsSoUaNQpUoVvHnzRqG8TJkyePLkiQYiIyIi0oxvJlH18fGBj49PluU6dtULMBoiIqL/SUpKgpWVVZblCQkJBRgNERGR5uloOgAiIqJvXZUqVXDixIksy//44w98//33BRgRERGRZjFRJSIi0jB/f39s2bIF8+bNQ3x8PABAKpXi3r176NWrF86cOYNRo0ZpOEoiIqKC881c+ktERFRY9ezZE48ePcIvv/yCyZMnAwBat24NIQR0dHQwZ84c/Pjjj5oNkoiIqABpXaIaFBSEvn37wsDAAPfv34e9vb1ceZMmTfD69Wtcv35dQxESERGpbvLkyejVqxd27tyJe/fuQSqVomzZsvDy8kKZMmU0HR4REVGB0rpENUNycjLmzp2LFStWaDoUIiIitShdurTaL/F9/PgxHjx4gNjYWAihOJq2l5eXWvsjIiJSB61NVGvUqIF169Zh4sSJKFmypKbDISIiUov3799nmVSWLl1a6XYeP36Mfv36ITIyEgAybU8ikSAtLS33wRIREeUTrU1UJ02ahO7du2Pu3LlYvny5psMhIiLKtY8fP2LGjBnYsGFDpvOoZlAlqezTpw/OnDmDCRMmoE6dOjAzM1NHqERERAVCaxNVZ2dn9O7dG+vWrcOECRN4VpWIiLSWn58fNm3ahB9//BENGzZE8eLF89zm2bNnMX78eMyYMUMNERIRERUsrU1UgfSBJ/7v//4P8+bNw7JlyzQdDhERUa6Eh4ejf//+CAwMVFubDg4Oakl4iYiINEGr51EtU6YMevXqhbVr1yImJkbT4RAREeWKRCJBzZo11drmmDFjsGHDBnz48EGt7RIRERUErT6jCgC//PILgoODMXfuXJ5VJSIirdShQwccPnwYgwYNUlubgwYNQlpaGsqXL4/OnTvDwcEBurq6cnUkEonaRxkmIiJSB61PVMuUKYOePXti7dq1mDBhgqbDISIiytHbt2/lnk+ZMgVdu3bFwIEDMWjQIJQuXVohqQQACwsLpfu4fv065s+fj5iYmCyncmOiSkREhZXWJ6pA+lnVzZs3Y968eZoOhYiIKEclSpSARCKRWyaEwJUrV7Bhw4Ys11Nl1N+BAwciPj4egYGBHPWXiIi0zleRqJYtWxY9e/ZEYGAgHB0doaf3VWwWERF9paZOnaqQqKrbP//8gxkzZmDAgAH52g8REVF++GoyusmTJyM4OBj//fcfXFxcNB0OERFRlqZPn57vfTg7O+d7H0RERPlFq0f9/Vy5cuXQs2dPTYdBRESksn79+uHcuXNZlp8/fx79+vVTqc0ZM2Zg1apVePLkSV7DIyIiKnBad0bV19cXvr6+mZYFBQUhKCioQOMhIiLKq6CgIDRv3hx16tTJtPzhw4fYtGkTfv/9d6XbPHHiBMzNzVGxYkU0b94cpUqVynTUX46YT0REhZHWJapERETfmmfPnsHIyEildVauXCn7/969ezOtw0SViIgKq28mUQ0LC0NYWFiW5ULvIXSabCvAiAgAXL6/nKv1blypqfI6X+v728shRdMhFB6bC64rK4hcrdfTveDer2Dk4u/raBO1x5GZUoZCrZ/dSLW1VHB2796N3bt3y56vXbsWhw8fVqgXFxeHw4cPw9XVVaX2pVJpnmMkIiLSlG8mUfXx8YGPj0+W5TpVSxRgNERE9K27efMmtm/fDiD9zOa5c+dw6dIluToSiQTGxsZo1KgRFi9enGObO3bsQMuWLWFqapovMRMRERWUbyZRJSIiKkwmTpyIiRMnAgB0dHSwYcMGdO/ePU9t+vj4QEdHBz/88AM8PDzQtm1bVK5cWR3hEhERFaivZtRfIiIibSWVSvOcpALAq1evsGnTJpQqVQrz589H1apVUaZMGQwfPhwHDhxAcnKyGqIlIiLKf0xUiYiIvhLm5ubw9vbGpk2b8Pz5c5w+fRq9evXC2bNn0bZtW1haWqJ9+/YIDAzktDVERFSoaW2ieuPGDfTs2RP29vYwMDBAyZIl0aNHD9y4cUPToREREals//79aNGiBSwtLaGnpwddXV2FhyokEgnq1KmDGTNm4MKFC3j27BlWrFgBQ0NDjB8/Hk5OTqhWrRomTpyI//77L5+2ioiIKHe0MlENDw9HzZo1ceTIEfTt2xcBAQH46aefEBkZiZo1a2LXrl2aDpGIiEhpO3fuRLt27fDixQt4e3tDKpXCx8cH3t7eMDIyQvXq1TF16tQ89WFjY4O+ffti+/bteP36NQ4fPozWrVsjIiIC27Z9naOiExGR9tK6wZTu37+PXr16oUyZMjhx4gSsrKxkZSNHjkTDhg3Rq1cvXLt2DWXKlNFgpERERMr57bff4ObmhlOnTiE2NharV69Gv3790LRpU0RFRaFu3bpwdnbOsZ0xY8agV69e+O6777Ktp6enB3d3d7i7u2PBggVITU1V16YQERGphdadUV2wYAE+fPiAtWvXyiWpAFCiRAkEBgYiMTER8+fP11CEREREqrl58ya8vb2hq6sLPb3035AzkkcnJyf4+flh3rx5ObazePFi1KxZEy4uLvjtt98QFRWlVP9FihTJdexERET5QesS1T179sDJyQkNGzbMtLxRo0ZwcnLCvn37CjgyIiKi3ClatCj09fUBpA+IZGBggJiYGFm5jY0NHj58mGM7d+7cwdSpUyGVSjF58mSULVsWDRo0wJo1a/DmzZt8i5+ItE9iYiIkEgkkEgkSExM1HQ6RAq1KVOPj4/Hs2bMcL2mqXr06oqOjkZCQUECRERER5V7FihVx8+ZN2fMaNWogODgYnz59wsePHxEaGorSpUvn2E65cuUwbdo03Lp1CxcvXoS/vz8ePXoEPz8/lCxZEp6enti6dSuSkpLyc3OIiIjyTKsS1YzEs1ixYtnWyyh/9+5dvsdERESUVx07dsTu3btl85xOnjwZx44dg7m5OaysrHDy5ElMmDBBpTZr1qyJRYsW4fHjxzhy5Ah69+6Nv//+Gz4+PrCxsUHv3r1x4MCB/NgcIiKiPNOqRDUjAc3pTKmyCS0REVFhMGbMGDx+/BgGBgYAgHbt2uHYsWMYMGAABg0ahCNHjsDX1zdXbUskEri7u2PdunV4/vw5/vjjD/zwww/YvHkzPDw81LgVRERE6qNVo/6amZnBzs4O165dy7betWvXYG9vD1NT0wKKjIiISL0aNmyY5XgMuZGSkoK9e/ciNDQUx44dA5B+7ysREVFhpFVnVIH0X5kfPnyIU6dOZVp+8uRJREVFoV27dgUcGRERUd48ffoUYWFhWLZsGaKjowEAUqkUb9++RVpamsrtCSFw+PBh9OvXDzY2NujcuTMOHTqEbt264eDBg3jy5Im6N4GIiEgttC5RHTt2LIyMjDBo0CCFEQzfvn2LwYMHo2jRohg7dqyGIiQiIlKNEAKjR4+Gs7MzevTogdGjR+POnTsA0m9ncXJywooVK5Ru7/z58/D394e9vT1atWqFkJAQNGzYEFu2bMGLFy8QFBSE5s2bQ0cnd18D3r9/j2nTpqF169awsLCARCJBUFBQpnWlUilWr16NGjVqwMjICJaWlmjatCmuXr2qUG/+/PlwdnaGoaEhqlevjrCwsFzFR/TNaynJ+eFp8r/6nibKrUPfhCdPnqBVq1aaDkO7Lv0FgPLly2PTpk3o0aMHqlWrhp9++gnOzs6IiorChg0b8Pr1a4SFhaFs2bKaDpWIiEgpCxYswLJlyzB+/Hg0a9YMLVq0kJWZmZnBy8sLO3fuhL+/f7btTJ06FWFhYXjw4AGEEKhfvz6mTJmCbt26wcLCQm3xvn79GjNnzkTp0qXx3XffyS4lzky/fv0QEhKC3r17Y9iwYUhMTMSVK1fw8uVLuXqTJ0/G3LlzMWDAALi6umL37t3o3r07JBIJvL291RY7ERFlb82aNThy5AjevXun0VsptS5RBYAuXbqgUqVK+O2332TJqaWlJdzd3TFp0iRUrVpV0yESEREpbd26dejduzfmzJmT6Xyn1atXx/79+3NsZ/bs2ahUqRJmzpyJHj16wMnJKR+iBezs7BATEwNbW1tcvHgRrq6umdbbtm0bNm3ahPDwcHTs2DHL9p4+fYpFixZh6NChWLlyJQCgf//+aNy4McaOHYsuXbpAV1c3X7aFiIjkbdu2DVKpFMHBwRg6dKjG4tDKRBUAqlWrhtDQUE2HQURElGdPnjxB/fr1syw3NjZWasq1S5cu4fvvv1dnaJkyMDCAra1tjvUWL14MNzc3dOzYEVKpFElJSTA2Nlaot3v3bqSmpsLPz0+2TCKRYMiQIejevTvOnDmDBg0aqHUbiIhI0T///IPY2FgIIbB27VqNJqpad48qERHR18ba2jrbgY0uXbqE0qVL59hOQSSpynr37h3Onz8PV1dXTJo0CWZmZjAxMUGZMmWwbds2ubpXrlyBsbExKleuLLfczc1NVk5ERPlvwYIFsit7Xrx4gXv37mksFq09o6qqsLCwbAdlKIm3+Ln9WZXaDI7Wz2tYX5VeDikF15mDau/V16xnqQJ83QvQK3fVB22w2izyIZIstM7dalY9VY9xVM/kXPXV0131z8ZPuepJdQ5GUrV+diPV1pJmeHl5Yc2aNfD19YWZmRmA9DOKAHDw4EEEBQVh3LhxmgxRZffv34cQAlu2bIGenh7mz58PMzMzLFu2DN7e3jA1NUXr1ul/SDExMbCxsZFtcwY7OzsAwLNnz7LsJ6fj+5kzZ+Dp6amGLSLSIkr+ttO+RPq/PtnP/Pg//Fv6qqWkpMjNrPLixQssXbpUdktGQftmElUfHx/4+PhkWV61Ge99ISIizZgxYwYiIyNRo0YNNGzYEBKJBPPmzcOUKVNw5swZfP/995g0aZKmw1TJ+/fvAQBv3rzB2bNnUadOHQCAp6cnnJ2dMXv2bFmimpSUBAMDA4U2DA0NZeVZyen47unpiYiIiFxvB5FWyq8Revm3pJWU/bEuIiJCYZyEffv2Yfny5bkeJT4veOkvERGRhpmZmeHs2bMYN24cnj59CkNDQxw/fhxxcXGYNm0aTp48iaJFi2o6TJUYGRkBAJydnWVJKgCYmJigffv2OH/+PD59+iSrm5yseOXAx48f5doiIqL8s3TpUiQmJsote/v2LY4ePaqReJioEhERFQJGRkb45Zdf8M8//yAxMRFJSUm4fv06pk6dqpWJWsmSJQEANjY2CmXW1tZITU2VfSGys7PD8+fPIYT8pfExMTFybRERUf549eoVHj58qLD83bt3WLBggQYiYqJKRESkcQcOHJBdKvu1KFmyJGxtbfH06VOFsmfPnsHQ0BDFihUDANSoUQMfPnzArVu35OqdO3dOVk5ERPknMDAQz58/z7Tsxo0bSo08r25amaj++++/6Ny5MxwdHWFoaAh7e3u0aNECK1as0HRoREREKmvTpg2KFy+O2rVrY9SoUQgPD8erV6/ytc9+/frhl19+ydd+unXrhidPnuDQoUOyZa9fv8bu3bvRtGlT2T1PHTp0QJEiRRAQECCrJ4TAmjVrYG9vn+3UPURElHebN2+GVCrNtOzly5cIDg4u4Ii0cDCl06dPw93dHaVLl8aAAQNga2uLJ0+e4OzZs1i2bBmGDx+u6RCJiIhUcvbsWZw4cQKnTp1CcHAwli1bBolEggoVKqBhw4ayh5OTk9r6DAoKAgAsX74cI0eOxKxZs1Raf+XKlYiLi5ONyLtnzx5ER0cDAIYPHw4zMzNMnDgR27ZtQ6dOnTB69GiYmZlhzZo1SE1NxZw5c2RtOTg4wN/fHwsWLEBqaipcXV3xxx9/4OTJkwgJCYGuLgc8JM1JTEyEiYkJgPRBwjKbC5hIm0VFReHZs2ey2yyEEIiLi0Px4sUBAGlpaQgLCyvwOVW1LlH99ddfYWZmhgsXLsDc3Fyu7OXLl5oJioiIKA/c3Nzg5uaGMWPGAABu3ryJkydP4uTJk/jrr7+wYcMGSCQS2eBD6vDw4UO8f/8ex48fx7Fjx1Ref+HChXj06JHseXh4OMLDwwEAPXv2hJmZGWxsbHDq1CmMGTMGS5YsQWpqKurVq4fNmzfju+++k2tv7ty5KF68OAIDAxEUFITy5ctj8+bN6N69e562k4iIsufk5ITXr1/LnsfHx2PkyJGyHzQBaOQHQ61LVO/fvw8XFxeFJBVIH5yBiIhIm338+BEvX77Ey5cv8eLFC8TGxkIIgbJly6q1H0dHRwCAi4sL/Pz8VF4/KipKqXplypSRJbDZ0dHRwcSJEzFx4kSVYyEiorzR19eX+7+urq7cMk3QuntUHR0dcenSJVy/fl3ToRAREanF3r17MX78eNSvXx9mZmZo3rw5du7cicqVK+P3339HTEwM7ty5o1KbM2fOzPZYeePGDcycOTOvoRMREeULrTujOmbMGLRp0wY1atSAm5sbGjZsiGbNmsHd3R1FihTRdHhEREQq8/T0hK6uLjp16oRffvkFP/zwA8zMzPLU5vTp01GuXDlUrVo10/Lr169jxowZmDp1ap76ISIiyg9ad0a1RYsWOHPmDDw9PXH16lXMnz8frVq1gr29PSIiIjQdHhERkco8PDxgamqKbdu2YciQIRg6dCjWrl2rMF2LOr19+1bjl3URERFlRevOqAKAq6srwsPDkZKSgqtXr2LXrl1YsmQJOnfujH/++QdVqlTRdIhERERK27NnD4D0s5wZgyjNmjULz549g4WFBX744Qc0bNgQP//8c7btnDhxQm5gpPDwcNy7d0+hXlxcHLZu3Ypq1aqpdTuItFJLSfblaZ/939MEUGZMmYMiLxEREbQ0Uc2gr68PV1dXuLq6okKFCujbty+2b9+OadOmaTo0IiIilVWtWhVVq1bFkCFDkJycjLCwMMybNw8RERHYs2dPjolqZGQkZsyYAQCQSCRyI/F+qUqVKpx/nIiICi2tTlQ/V7t2bQBATEyMhiMhIiJS3fv37/H333/jxIkTOHnyJC5cuICUlBTo6emhbt26aNiwYY5tjBs3DsOGDYMQAtbW1lizZg06deokV0cikaBo0aIwNDTMr00hIiLKM61LVCMjI9GkSRNIJPKXafz5558AgIoVK2oiLCIiolyrVasWrl27hrS0NJiYmKBevXqYNGkSGjZsiDp16sDIyEipdoyMjGR1Hz58CCsrKxQtWjQ/QyciIsoXWpeoDh8+HB8+fEDHjh1RqVIlpKSk4PTp09i6dSucnJzQt29fTYdIRESkEicnJ/Tq1QsNGzbE999/Dx2dvI91mDFPKhFpj8TERJiYmABIv8rC2NhYwxERaY7WJaoLFy7E9u3b8eeff2Lt2rVISUlB6dKl4efnh19++QXm5uaaDpGIiEglO3fuVHubQgisXbsWGzZswIMHDxAbG6tQRyKR4NOnT2rvm+hrYqwLiBaajoLo26N1iWrr1q3RunVrTYdBRESUb2JjY9GpUycsWrQI33//fa7aGDduHBYvXowaNWqgZ8+eKF68uJqjJCIiyj9al6gSERF97VJSUnDs2LFMz4Iqa9OmTejUqRO2bdumxsiIiIgKxjeTqIaFhSEsLCzLcoPHUoy6l6xSmz3dU3IVy+YnX+cE6z1Lqf56WEVynrEMr9xzmMeNctYzl+ttVmsU6u8rl/FZ9Sy8f1/6r/n3n9+SkpLQvHlzTYdBRMR7bylXvplE1cfHBz4+PlmWe9ZlkkBERF+PZs2a4cKFCxg4cKCmQyEiIlLZN5OoEhERFUZJSUmYPHky3N3d0b59ewDp08z06dMHJUuWzHW7AQEBaNWqFebMmYNBgwbB0tJSXSETUW60VOKkSNpn//c0AXSVaPcgr06hrxMTVSIiIg0yMjJCYGAgqlSpIltmamqKjRs35qndihUrQiqVYsqUKZgyZQoMDQ2hqyv/rVcikSA+Pj5P/RAREeUHJqpEREQaVqtWLVy/fl2tbXbq1AkSCW9rISIi7aTViWpAQACGDh0KNzc3nDt3TtPhEBER5crSpUvRtm1bVK1aFb6+vtDTy/vhOSgoKO+BERHlhJc0Uz7R6kQ1JCQETk5OOH/+PO7du4dy5cppOiQiIiKV+fr6QkdHB4MGDcKIESNgb28PIyMjuToSiQRXr17VUIREREQFS0fTAeTWw4cPcfr0aSxevBhWVlYICQnRdEhERES5YmFhgYoVK6JRo0aoU6cOHBwcYGlpKfewsLBQud3Hjx9j8ODBqFixIooXL44TJ04AAF6/fo0RI0bgypUr6t4UIsoDY11AtEh/GCtz1pHoK6a1Z1RDQkJQvHhxeHh4oHPnzggJCcG0adM0HRYREZHKjh07pvY2b968iYYNG0IqlaJOnTq4d+8ePn36BAAoUaIETp06hcTERGzYsEHtfRMREeWV1p5RDQkJgZeXF/T19eHj44O7d+/iwoULmg6LiIioUBg3bhzMzc1x584dbN68GULI3+/l4eGBkydPaig6IvqW8Ewx5YZWnlG9dOkSbt++jRUrVgAAGjRoAAcHB4SEhMDV1VXD0REREWUv4xLcRo0ayT3PSUZ9ZfuYOnUqrKys8ObNG4Xy0qVL4+nTp0q3R0REVJC0MlENCQmBjY0N3N3dAaQPMNGtWzds3rwZixYtUpgnjoiIqDBp0qQJJBIJkpKSoK+vL3ueFSEEJBIJ0tLSsqzzJalUiqJFi2ZZ/urVKxgYGKgUNxERUUHRukQ1LS0NW7Zsgbu7Ox4+fChbXqdOHSxatAhHjhxBy5YtNRghERFR9iIjIwEA+vr6cs/VqWbNmti3bx/8/PwUyj59+oQtW7agbt26au+XiIhIHbQuUT169ChiYmKwZcsWbNmyRaE8JCSEiSoRERVqjRs3zva5OkycOBHt2rXDkCFD4O3tDQB48eIFDh8+jDlz5uDWrVtYuXKl2vslIiJSB61LVENCQmBtbY1Vq1YplIWHh2PXrl1Ys2aNwvxzRERE35I2bdogKCgII0eOxNq1awEAPXv2hBACpqam+L//+z+V7nklIiIqSFqVqCYlJSE8PBxdunRB586dFcpLliyJsLAwREREoFu3bhqIkIiIKHc+fvyInTt34vLly4iPj4dUKpUrl0gkKk8l06tXL3h5eeHgwYO4d+8epFIpypYti1atWqFYsWLqDJ+IiEittCpRjYiIQEJCAjw9PTMtr1u3LqysrBASEsJElYiItMajR4/g7u6OqKgomJubIz4+HhYWFoiLi0NaWhpKlCgBExOTXLVtbGyMjh07qjliIiKi/KVViWpISAgMDQ3RokWLTMt1dHTg4eGBkJAQvHnzBpaWlgUcIRERkerGjh2L+Ph4nD17FmXKlIG1tTW2bt2KH374AcuXL8fKlStx4MCBbNt4/PgxgPRpZz5/npOM+kRERIWJViWqEREROdbZuHEjNm7cWADREBERqcfRo0fh5+cHNzc3vH37FkD6lDQGBgYYO3Ysbt26BX9/f+zbty/LNpycnOSmvMl4nhNVprwhIiIqKFqVqBIREX2NPnz4ACcnJwCAqakpJBIJ4uPjZeX16tXDmDFjsm3j999/h0QiQZEiReSeE32NEhMTZZfDv3//HsbGxhqOiIjU7ZtJVMPCwhAWFpZl+aMU4JW7agf0zU/08xqW0oKjC66vXg4puVovN69HT/fc9fU1KsjP09cqt58nKwg1R6JmPQuwL8UB1fNFSgnV97nZKqC480vp0qURHR0NANDT04O9vT3Onj0LLy8vAMDNmzdhaGiYbRu+vr7ZPiciItIm30yi6uPjAx8fnyzLW7fjr85ERKQZTZs2xe7duzFt2jQA6Unmb7/9htjYWEilUgQHB6N3794ajpKIiKjgfDOJKhERUWE1YcIEXLhwAcnJyTAwMMCkSZPw7Nkz7NixA7q6uujevTsWL16cbRszZ85UuV+JRIIpU6bkNmwiIqJ8w0SViIiogEVERKB27dooWbIkgPRLfz8ffdfQ0BDr16/H+vXrlW5z+vTpCssy7lEVQigsF0IwUSUiokJLR9MBEBERfWs6duyIY8eOyZ6XKVNGqZHtsyOVSuUeT548QbVq1eDj44Pz588jPj4e8fHxOHfuHLy9vfHdd9/hyZMnedwSonzSUpL9w/OzeYU9TXKu35K3eBHlp23btsHLywuvXr3Kse7+/ftx8+bNHOtpdaIaFBQEiUSCixcvajoUIiIipRUrVgxxcXGy51FRUXj//r1a+xg6dCjKly+PzZs3o3bt2ihWrBiKFSsGV1dXhISEoGzZshg6dKha+yQiom/Thg0b8O7dO1hZWeVYd8WKFZleBfQlXvpLRERUwNzc3PDrr7/ixYsXMDMzAwD8+eefeP78eZbrSCQSjBo1Suk+jh49innz5mVZ3qxZM4wfP175oImIiLLw77//YvDgwQrL79y5gyVLlmDRokUoWrQogPQp14KCgnJsk4kqERFRAQsICEDv3r0xa9YsAOlJaGhoKEJDQ7NcR9VE1dDQEGfOnMGQIUMyLT99+nSOU94QEREp482bN7C3t1dYHh0djcDAQMyePVuWqJYsWTLbH2YzMFElIiIqYOXKlcPp06fx8eNHvHz5Ek5OTli6dCk6dOigtj569OiB5cuXw9zcHMOHD0fZsmUBAPfv38fy5csRGhqKESNGqK0/IiL6dhUrVizT5PPZs2cAgPj4eFhaWgIAPnz4IEtas8NElYiISEMMDQ1RunRpTJs2DU2bNoWjo6Pa2p43bx5ev36NlStXYtWqVdDRSR+WQiqVQggBHx+fbC8NJsqNxMREmJikD3T0/v17GBsb50s/xrqAaJEvTRNRLtSqVQshISEYP3489PT+l2Ju27YNOjo62L17N0aNGgUhBHbu3IkqVark2CYTVSIiIg2bNm2a2tvU19dHcHAwxo4diz///BOPHj0CADg6OqJNmzb47rvv1N4nERF9m0aMGAFPT0/UqVMHffr0gZGREXbs2IFixYqhU6dOGDduHCIjI/HkyRNcvXpVqZHumagSERF9xapXr47q1atrOgwiIvqKeXh4YPHixZg8ebJsPAVXV1cEBwfDwMAABgYGOHz4MMzNzbF27Vq0a9cuxzaZqBIREREREVGejBw5EgMGDMDNmzdhZmaG8uXLy8r+7//+T+X2tHoeVSIiIsra/v370aJFC1haWkJPTw+6uroKDyIiInUpWrQoateuLZek5hbPqBIREX2Fdu7cia5du8LFxQXe3t5YvXo1unfvDiEEdu/ejfLly+PHH3/UdJikTVpKcq6T9tn/PU2AnH4LOSjyEhGRgoIa0IvkHT9+HM7OzihdunS29V69eoWAgAC0bdsWrq6u2dblGVUiIqKv0G+//QY3NzdcuXIFM2bMAAD069cPISEhuH79OmJiYuDs7Jyrtt+/f49p06ahdevWsLCwgEQiyXHy9tTUVFSpUgUSiQQLFy5UKJdKpZg/fz6cnZ1haGiI6tWrIywsLFfxERFRwXJ3d0dwcHCO9UxNTfHbb79h48aNOdblGVUiIqJCIC0tDQcOHMCDBw8QGxsLIeTPNEkkEkyZMkXp9m7evInffvsNurq6sqkCUlNTAQBOTk7w8/PDvHnz0Lt3b5Vjff36NWbOnInSpUvju+++w7Fjx3JcZ8WKFXj8+HGW5ZMnT8bcuXMxYMAAuLq6Yvfu3ejevTskEgm8vb1VjpGIiAqORKLEFRcADAwMUL16dZw/fz7HulqdqGYcxHmPDRERabOLFy+iU6dOiI6OVkhQM6iaqBYtWhT6+voAAHNzcxgYGCAmJkZWbmNjg4cPH+YqXjs7O8TExMDW1hYXL17M8fKtly9fYubMmRg/fjymTp2qUP706VMsWrQIQ4cOxcqVKwEA/fv3R+PGjTF27Fh06dKFx3oiokJu7969eP78eY71EhIScP/+/RzraXWimpCQACD9FDIREZG28vPzQ1JSEv744w80bNgQ5ubmeW6zYsWKuHnzpux5jRo1EBwcjJ49e+LTp08IDQ3N8V6irBgYGMDW1lbp+hMmTEDFihXRs2fPTBPV3bt3IzU1FX5+frJlEokEQ4YMQffu3XHmzBk0aNAgV7ESEVHBOH/+vFJnSpWl1YnqhQsXYGxsDEdHR02HQkRElGvXrl3Dr7/+ivbt26utzY4dO2L58uVYuHAhDAwMMHnyZHTo0AHm5uaQSCRITEzE77//rrb+snL+/Hls2rQJp06dyvLSsCtXrsDY2BiVK1eWW+7m5iYrZ6JKRBzQq3CbOXMmJk+erLb2tDJR3blzJ44dO4aQkBD0799fdu9NdsLCwrIdlCHmmepx9CyVovpKADY/0Vd5nV4OueurIOX29aB0fP3yzioylwebzeqNg0hVDg4OWV7ym1tjxozBmDFjZM/btWuHY8eOITw8HLq6uvDw8IC7u7ta+/ySEALDhw9Ht27dUK9ePURFRWVaLyYmBjY2NgqJrJ2dHQDg2bOsD9I5Hd/PnDkDT09P1YMnRVeUq9a+RPq/PteUqJzVe6NkXyrRdF/50Q/7UvDps11pl6uAXk65LfcPhZZWJqpjxoxBQkICfvrpJyxZskSpdXx8fODj45Nleet2yt0ATEREpG7jx4/HwoULMXDgQLXczpKcnIwDBw7AyckJ1atXly1v2LAhGjZsmOf2lRUUFIR///0XO3bsyLZeUlISDAwMFJYbGhrKyrOS0/Hd09MTERERSkZM2VLmbJaqsnpvvsa+8qMf9qUgMQ0wOZr+/+3fAcY5nVH9BvYPBfFjXePGjdV+latWJqq5HfyBiIioMEpISICJiQnKlSsHb29vlCpVSmHwIIlEglGjRinVnr6+Prp06YJly5bJJaoF6d27d5g4cSLGjh2LUqVKZVvXyMgIycnJCss/fvwoKyciosLr6NGjam9TKxNVIiKir8nnl+hmjHr7JVUSVYlEgvLly+P169dqiS83Fi5ciJSUFHTr1k12yW90dDQAIDY2FlFRUShZsiT09fVhZ2eHyMhICCHkLv/NGKW4ZMmSBR4/ERFpFhNVIiIiDcuPK4UmTZqE0aNHo0uXLqhYsaLa28/J48ePERsbCxcXF4WyOXPmYM6cObhy5Qpq1KiBGjVqYP369bh16xaqVKkiq3fu3DkA6SMWExEpw1gXEC00HQV97unTp5g+fTouXLgAIyMjeHh4YNy4cbIp1LKi1YlqUFAQ+vbtiwsXLqB27dqaDoeIiChX8mP0+rNnz8LS0hJVq1ZFkyZN4OTkpHAJrUQiwbJly9TeNwCMGDECP/74o9yyly9fYtCgQfD19UWHDh3g7OwMAOjQoQNGjRqFgIAA2RllIQTWrFkDe3t71K9fP19iJCKi/JWWloY2bdoAAAYNGoS4uDgsXLgQL168wIoVK7JdV6sTVSIiIsrc55cQHzlyJNM6eUlUV65cibi4ONmIvHv27JFd2jt8+HDUrFkTNWvWlFsn4xJgFxcXuSTWwcEB/v7+WLBgAVJTU+Hq6oo//vgDJ0+eREhIiML9ukREVLi8fPkS1tbWCstv3LiB69ev49q1a6hatSqA9IHy5s2bx0SViIhIG1y7dg0rVqzA5cuXER8fD6lUKlcukUhw//59pdv7cn11W7hwIR49eiR7Hh4ejvDwcABAz549YWZmplJ7c+fORfHixREYGIigoCCUL18emzdvRvfu3dUaNxERqV+FChUwefJk+Pv7o0iRIrLl+vr6kEgkSEhIkC179+5dpiO9f4mJKhERkYYdO3YMrVu3RvHixVG7dm1cuXIFTZs2xcePH3HmzBm4uLigVq1amg5TTlZzombHyckpy/lidXR0MHHiREycODGPkdGXEhMTYWJiAgB4//49jI2NNRwREX1tQkNDMXr0aKxfvx6LFi1Cu3btAACVKlVCo0aN0KlTJ/j4+ODt27cICwvDlClTcmxTJ7+DJiIiouxNnToVZcqUwX///YeNGzcCSB8M6dSpUzh9+jSio6PRtWtXDUdJRESUubZt2+L69esYNGgQevXqhTZt2uD27dsAgH379mHEiBF48OABUlJSsGnTJkyePDnHNpmoEhERadjly5fx008/wdTUVHY/ZlpaGgCgTp06GDRokFK/Pn9OR0cHurq62T6MjY1RsWJFDB48WKXLiomIiL6kp6eH0aNH4969eyhdujS+//57jB49GqmpqZgwYQJ27dqFkJAQdOvWTan2mKgSERFpmJ6eHooVKwYAMDc3R5EiRfDy5UtZeZkyZXDz5k2V2pw6dSqqV68OXV1dtGvXDv7+/vD394eHhwd0dXVRo0YN+Pn5oUqVKti4cSNq1qyJq1evqnW7iIjo22NpaYnAwECcPXsWly5dQoUKFbBu3bosb/3ICu9RJSIi0rBy5crh7t27ANIHTapUqRJ27dqFHj16AEi/bMrW1lalNkuWLInXr1/j9u3bKFOmjFzZvXv30KRJE1SpUgULFizA3bt3Ua9ePUyaNAn79u1Tz0YREdE35dWrV/jzzz/x4sUL2NnZoV27djh+/Dh27NiBsWPHIjAwEEuXLkWDBg2Uao9nVImIiDSsbdu2CAsLw6dPnwAAo0ePRnh4OMqXL4/y5csjIiICgwYNUqnNBQsWYOjQoQpJKpCeGA8dOhS//fYbAKB8+fIYPHgwTp8+nfeNISKib87Bgwfh7OyM8ePHIyIiAtOnT4ezszNOnTqFzp074/bt2+jYsSPatm0LHx8f2XRm2eEZVSIiIg2bMmUKRo4cKbs/tU+fPtDV1cXOnTuhq6uLyZMnw9fXV6U2o6OjoaeX9WFeT08PT548kT13cnJCcnJyruInDWopyblO2mf/9zQBlJmW9qBql+gR0bdt1KhR8PDwQFhYGHR00s+Fzp8/H2PGjMHZs2dhYGCAyZMno1+/fhg3bhwqV64sN2VNZnhGlYiISMOKFCkCS0tLSCT/Szp69uyJXbt2YceOHSonqQDg4uKC1atX48WLFwplz58/x+rVq+Hi4iJb9uDBA5UvLyYiIgLSjyv16tWTJakA0KRJEzx79kyunp2dHYKDg3Hw4MEc2+QZVSIiokIiOTkZly9fxsuXL/HDDz+gRIkSuW5r4cKFaNOmDcqVK4cff/wR5cqVA5B+f+off/yB1NRU/P777wCAjx8/IigoCG3atFHLdhAR0belQ4cOmD17NgCgSpUqePbsGebPn4+OHTtmWr9evXo5tqnViWrGyFEZl0oRERFpq+XLl2P69OmIj48HABw6dAhNmzbF69evUalSJcyfPx/9+vVTur0mTZrg9OnTmDZtGsLDw5GUlAQAMDQ0RPPmzTF9+nTUrFlTtuzLX72JiIiUFRAQAFtbW6xYsQLPnz+Hvb09evToodR8qVnR6kQ147pmU1NTDUdCRESUexs3boS/vz+8vb3RsmVLuYS0RIkSaNq0KbZs2aJSogoA33//PSIiIiCVSmXT3VhbW8tdmkVERLmTmJgIExMTAMD79+9hbGys4Yg0x9DQEHPmzMGcOXPU1qZWJ6oXLlyAsbExHB0dNR0KERFRri1atAgdOnRAaGgo3rx5o1Beq1YtLF++PNft6+jo8P7Tb5ixLiBaaDoKIiLVaGWiunPnThw7dgwhISHo379/tqMaZggLC0NYWFiW5dejJNj8RF+lOIKjVatP2qmwv8+9HFIKrK+epQqur9x45a7E6JeZcVdvHIWFqvu0ghSdpIPNT4poOoxC4969exgxYkSW5RYWFpkmsERERF8rrUxUx4wZg4SEBPz0009YsmSJUuv4+PjAx8cny/KqzXifKxERaYa5uTlev36dZfnNmzd5RpSIiL4pWpmoPnz4UNMhEBERqU3btm2xdu1a+Pn5KZTduHED69atU/n+VCIiIm2mlYkqERHR12T27NmoU6cOqlativbt20MikWDTpk34/fffsXPnTtjZ2WHq1KmaDpOI6NvRUonbidI++7+nCZDTBZoHRV4i+uZw2D8iIiINK1myJC5duoTWrVtj69atEEIgODgYe/bsgY+PD86ePZunOVWJiIi0jVadUQ0KCkLfvn0BACdPnkSDBg3kyoUQKF26NKKjo+Hh4YG9e/dqIkwiIiKVWVtbY/369Vi/fj1evXoFqVQKKysrpaeSOXHiRK76bdSoUa7WIyIiyk9alahmMDQ0RGhoqEKievz4cURHR8PAwEBDkREREeWdlZWVyus0adIEEonyI18LISCRSJCWlpZzZSIiogKmlYlq27ZtsX37dixfvlxuaprQ0FDUqlUr25ETiYiICqPExETs3LkTDx48QGxsLISQv5dJIpFg2bJlWa4fGRmZ3yESEREVGK1MVH18fLBr1y4cOnQIbdq0AQCkpKRgx44d+OWXX/I0KToREVFBO3LkCLp06YK4uLgs6+SUqDZu3DgfIiMioqwY6wKihaaj+Hpp5WBKTk5OqFevHsLCwmTL9u/fj/j4eHh7e2swMiIiItUNHToUxsbGOHDgAOLi4iCVShUevESXiIi+JVp5RhUAunfvjokTJyIpKQlGRkYICQlB48aNUbJkSU2HRkREpJLHjx9j3rx5aNFCvT/Nf/z4ETt37sTly5cRHx8PqVQqVy6RSLBhwwa19klERKQOWpuodu3aFf7+/ti7dy9at26NvXv38pJfIiLSStWrV0d8fLxa23z06BHc3d0RFRUFc3NzxMfHw8LCAnFxcUhLS0OJEiVgYmKi1j6JiIjURSsv/QXSR0Rs3rw5QkNDER4ejrS0NHTu3FnTYREREals3rx5CAgIwMWLF9XW5tixYxEfH4+zZ8/izp07EEJg69ateP/+PebNmwcjIyMcOHBAbf0RERGpk9aeUQXSL/8dMGAAnj9/jjZt2sDc3FzTIREREamscePGWLp0KerVq4fKlSujVKlS0NXVlasjkUiwe/dupds8evQo/Pz84Obmhrdv3wJIn5LGwMAAY8eOxa1bt+Dv7499+/apdVuIiEi9EhMTZVfAvH//HsbGxhqOqGBodaLasWNHDBo0CGfPnsXWrVs1HQ4REVGu7Ny5Ez179kRaWhqio6ORkJCgUEeVOVIB4MOHD3BycgIAmJqaQiKRyF1eXK9ePYwZMyZPcRMREeUXrU5UTUxMsHr1akRFRaF9+/aaDoeIiChXJkyYgIoVK2Lnzp2oUKGCWtosXbo0oqOjAQB6enqwt7fH2bNn4eXlBQC4efMmDA0N1dIXERGRuml1ogoAffr00XQIREREefLs2TMsWLBAbUkqADRt2hS7d+/GtGnTAAC+vr747bffEBsbC6lUiuDgYPTu3Vtt/REREamT1ieqRERE2s7V1RWPHz9Wa5sTJkzAhQsXkJycDAMDA0yaNAnPnj3Djh07oKuri+7du2Px4sVq7ZOIiHKhZQ63dnw+jbanCaCbZc3/OSjyElGhoFWJqq+vL3x9fXOsFxUVle+xEBERqcuKFSvQvn171KxZE127dlVLm6VLl0bp0qVlzw0NDbF+/XqsX79eLe0TERHlJ61KVImIiL5GPXr0wKdPn+Dj44MBAwbAwcEh01F/r169qqEIiYiICtY3k6iGhYUhLCwsy/IHT4DgaP0CieXGlZoF0k9BC8ZlTYdA3ziryMJ/mcsrd9VGbgWAzU8KZt9EmmNhYQFLS0uUL18+123MnDkTEokEkydPho6ODmbOnJnjOhKJBFOmTMl1n0RERPnlm0lUfXx84OPjk2W52Q/fzEtBRESFzLFjx/LcxvTp0yGRSDB+/Hjo6+tj+vTpOa7DRJWIiAorZmdERERfAalUmu1zIiLSTsa6gGih6SgKno6mAyAiIiIiIiL6HBNVIiKir5Curi5CQ0OzLN+6davCgE1ERESFhVYnqgEBAZBIJKhTp46mQyEiIipUhMh+cLG0tDRIJKoP7kVERFQQtDpRDQkJgZOTE86fP4979+5pOhwiIqJCJatE9N27dzhw4ABKlChRwBEREREpR2sT1YcPH+L06dNYvHgxrKysEBISoumQiIiINGrGjBnQ1dWFrq4uJBIJevbsKXv++aN48eIIDg6Gt7e3pkMmIiLKlNaO+hsSEoLixYvDw8MDnTt3RkhICKZNm6bpsIiIiFRWuXJl9OrVCz169ICjo2Ou23Fzc4Ofnx+EEAgICECLFi1QoUIFuToSiQTGxsaoVasWvLy88ho6ERFRvtDqRNXLywv6+vrw8fHB6tWrceHCBbi6umo6NCIiIpWUKlUK06ZNw9SpU1G/fn307t0bXbp0gZmZmUrttGnTBm3atAEAJCYmYvDgwRzHgYiItJJWXvp76dIl3L59W3bJUoMGDeDg4MDLf4mISCsdPHgQ0dHRWLBgAZKSkjBw4EDY2tqic+fO2L17N1JTU1Vuc+PGjUxSiYhIa2nlGdWQkBDY2NjA3d0dQPplTN26dcPmzZuxaNEiDrdPRERax8bGBqNGjcKoUaPw33//YfPmzQgLC8OuXbtgbm6Obt26oWfPnqhfv77SbaalpeHAgQN48OABYmNjFUYClkgkmDJliro3hYiIKM+0LlFNS0vDli1b4O7ujocPH8qW16lTB4sWLcKRI0fQsmVLDUZIRESUNxUrVsSsWbMwdOhQjBw5Etu3b8eaNWsQGBiIMmXKwN/fH0OGDIGOTtYXRl28eBGdOnVCdHR0llPVMFElIqLCSusS1aNHjyImJgZbtmzBli1bFMpDQkKYqBIRkdZKTEzErl27sHnzZhw9ehQA0K5dO/Tu3Rv6+vpYu3YtRowYgWvXriEwMDDLdvz8/JCUlIQ//vgDDRs2hLm5eQFtARERaZOPHz/ixIkTsueJiYl49uwZDh48KFtmY2OD7777rkDj0rpENSQkBNbW1li1apVCWXh4OHbt2oU1a9bAyMhIA9ERERGpLuMS3c2bNyMiIgIfPnxArVq1sGjRIvj4+MjNd+rp6YlJkyZh1apV2Saq165dw6+//or27dsXxCaQihITE2FiYgIAeP/+PYyNjTUcERF9q+Li4tClSxd8+vQJACCEQEpKCs6ePQsAkEqlqFu3LiIjIws0Lq0aTCkpKQnh4eFo164dOnfurPAYNmwYEhISEBERoelQiYiIlGZra4v27dvj1KlTGD58OG7cuIELFy5g+PDhcklqhurVqyMhISHbNh0cHLK85Dev3r9/j2nTpqF169awsLCARCJBUFCQXB2pVIqgoCB4enqiVKlSMDY2RtWqVTF79mx8/Pgx03Y3bNiAypUrw9DQEOXLl8eKFSvyJX4iIvofW1tbVKxYER8+fMCHDx+QlJSEtLQ02XM9PT2MHj26wOPSqkQ1IiICCQkJ8PT0zLS8bt26sLKy4ui/RESkVTw8PHDw4EE8evQIv/32GypXrpxtfW9vb0il0mzrjB8/HuvWrcO7d+/UGSoA4PXr15g5cyZu3bqV5aVgHz58QN++ffHq1SsMHjwYS5cuhZubG6ZNm4Y2bdooJNGBgYHo378/XFxcsGLFCtSrVw8jRozAvHnz1B4/ERHJGz58eJZXpFpaWsqmPitIWnXpb0hICAwNDdGiRYtMy3V0dODh4YGQkBC8efMGlpaWBRwhERGR6r48G6kOCQkJMDExQbly5eDt7Y1SpUopjIovkUgwatQoldu2s7NDTEwMbG1tcfHixUznMNfX18fff/8tN0rxgAED4OTkhGnTpuHIkSNo3rw5gPQrpiZPngwPDw/s2LFDVlcqlWLWrFkYOHAgihcvrnKcRESknC5dumDy5Ml48uSJQlnTpk2hp1fwaaNWJarKXNK7ceNGbNy4sQCiISIiyp3Hjx/nar3SpUsrXXfMmDGy/69cuTLTOrlNVA0MDGBra5ttHX19/Uyn0unYsSOmTZuGW7duyRLVyMhIvHnzBn5+fnJ1hw4dipCQEOzbtw89e/ZUOU4iIlKOoaEhatWqpZCoWllZ4eeff9ZITFqVqBIREX0NnJycIJFIVF4vLS1N6bqfT+FWmDx//hwA5O69vXLlCgCgdu3acnVr1aoFHR0dXLlyRbsS1ZZKvLefv5WeJoAyU8AfzJ97jomIAGDcuHE4ceIE3r59K1tWokQJuLi4aCSebyZRDQsLQ1hYWJbl7x4b4caVmiq1KT3WNa9hFUo6Tbblaj1VXz+S5/L9ZU2HQCp45a56kgEAm5/oq7xOcLTq6+SWQwH18+SjRK3bVVBxq8vvv/+eq0RVFY6Ojvnafm7Nnz8fpqamcvc7xcTEQFdXF9bW1nJ19fX1YWlpiWfPnmXZXk7H9zNnzmQ5tkW+uZJzlU+f5ZxdrgJ6ynwcMtsOJfrKlYLqK6v35mvsS9vfq6+1L01/Lgq6r2zUrVsXxYsXlyWqenp66Nu3r7ojU9o3k6j6+PjAx8cny3KdqoqjKhIREeUHX1/fAuvr6dOnOHHiBF6+fIlOnTrBwcEBaWlpiI+Ph5mZmcJ9q/lpzpw5OHz4MAICAuTmdU1KSoK+fuY/XBgaGiIpKSnLNnM6vnt6ehb8bABKnFFNTANM0qfJxfbvAGNl3obMtkOZs7e5UVB9ZfXefI19aft79bX2penPRQH0peyPdRKJBF26dMG8efMghICNjQ1++ukn9cemJK0a9ZeIiIiUI4TA6NGj4ezsjB49emD06NG4c+cOgPTpZZycnAp0+petW7fil19+wU8//YQhQ4bIlRkZGSElJSXT9T5+/Fhgc6MnJiZCIpFAIpEgMTGxQPokIipMhg0bBhsbGwBAuXLlYGFhobFYvpkzqkRERIVZbGwswsLC8ODBA8TGxipM3yKRSLBhwwal21uwYAGWLVuG8ePHo1mzZnIj5puZmcHLyws7d+6Ev7+/ujYhS4cOHULv3r3h4eGBNWvWKJTb2dkhLS0NL1++lLv8NyUlBW/evEHJkiXzPUYiIgLs7e1ha2uLV69eaWwQpQxMVImIiDTswIED6Ny5MxITE2FqaprpVCyq3tO6bt069O7dG3PmzMGbN28UyqtXr479+/fnOmZlnTt3Dh07dkTt2rWxbdu2TKc4qFGjBgDg4sWLaNu2rWz5xYsXIZVKZeVfE2NdQGQ+2x4RkUYNGjQII0aM0MjcqZ/Tqkt/g4KCZJfknDp1SqFcCIFSpUpBIpGgXbt2GoiQiIhIdT///DNsbW1x9epVxMXF4eHDhwqPBw8eqNTmkydPMp0eJoOxsTHevXuX19CzdevWLXh4eMDJyQl79+7N8hLepk2bwsLCAqtXr5Zbvnr1ahQtWhQeHh75GicREf1Pz549MWXKFI3Mnfo5rTyjamhoiNDQUDRo0EBu+fHjxxEdHQ0DAwMNRUZERKS6e/fuYcGCBahWrZra2rS2ts504vYMly5dUmle1i+tXLkScXFxshF59+zZg+joaADA8OHDoaOjg1atWiE2NhZjx47Fvn375NYvW7Ys6tWrByD9HtVZs2Zh6NCh6NKlC1q1aoWTJ09i8+bN+PXXX9VzjxSnjCEiUoqJiQmmTJmi6TC0M1Ft27Yttm/fjuXLl8tl+qGhoahVqxZev36tweiIiIhUU758eSQkJKi1TS8vL6xZswa+vr4wMzMD8L/Lhw8ePIigoCCMGzcu1+0vXLgQjx49kj0PDw9HeHg4AMjmPM1IlCdMmKCwfp8+fWSJKgD4+fmhSJEiWLRoESIiIlCqVCksWbIEI0eOzHWMRESkvbTq0t8MPj4+ePPmDQ4dOiRblpKSgh07dqB79+4ajIyIiEh1s2fPRkBAAKKiotTW5owZM2BnZ4caNWqgd+/ekEgkmDdvHho0aIA2bdqgevXqmDRpUq7bj4qKghAi04eTkxOcnJyyLBdCICgoSKHNAQMG4Pbt20hOTsa9e/fg7++f7/PNEhFR4aSVZ1SdnJxQr149hIWFyW7y3b9/P+Lj4+Ht7Y3ly5drOEIiIqKsjRgxQmGZlZUVKleujBYtWqBUqVIK85tKJBIsW7ZM6T7MzMxw9uxZLFq0CDt27IChoSGOHz+OsmXLYtq0aRg7dmyBTftCRESkKq1MVAGge/fumDhxIpKSkmBkZISQkBA0btyYQ9gTEVGht3LlyizL9u7dm+lyVRNVIP3ez19++QW//PKLSut9qzgSLxFR4aGVl/4CQNeuXZGUlIS9e/ciISEBe/fu5WW/RESkFaRSqcqPtLS0nBv+TNOmTXHkyJEsyyMjI9G0adO8bgoREVG+0NpE1crKCs2bN0doaCjCw8ORlpaGzp07azosIiIilT1+/BhJSUlZliclJeHx48cqtXns2DG8ePEiy/KXL1/i+PHjKrVJRERUULQ2UQXSL//dv38/1qxZgzZt2sDc3FzTIREREanM2dkZu3btyrI8IiICzs7OKreb3UBE9+7dQ7FixVRuk4iIqCBo7T2qANCxY0cMGjQIZ8+exdatWzUdDhERUa4Ikf1cnKmpqdDRyfm35U2bNmHTpk2y57Nnz8a6desU6sXFxeHatWto27at6sESEREVAK1OVE1MTLB69WpERUWhffv2mg6HiIhIae/evUNcXJzs+Zs3bzK9vDcuLg5btmyBnZ1djm1++PABr169kj1PSEhQSHAlEgmMjY0xePBgTJ06NfcbQERElI+0OlEF0icMJyIi0jZLlizBzJkzAaQnj/7+/vD398+0rhACs2fPzrHNIUOGYMiQIQDSLydetmwZPD091RYzERFRQdH6RJWIiEgbtWzZEiYmJhBCYNy4cfDx8UHNmjXl6mSc/axVqxZq166tUvsPHz5UZ7hEREQFSqsSVV9fX/j6+uZYLyoqKt9jISIiyot69eqhXr16AIDExER06tQJVatWVXs/x48fx759+/Do0SMAgKOjIzw8PNC4cWO190VERKQuWpWoEhERfY2mTZum9jZTUlLg4+ODP/74A0II2cj4cXFxWLRoETp27IiwsDAUKVJE7X0TERHl1TeTqIaFhSEsLCzLcnHLHqnTdhZgRIVXWmS3XK2n686Rl/PiRi7XC8ZltcaRbV/R+gXWVy+HFNVXKqf+OLISfCZ3r8WNKzVzrvQF6bGuueorNxzwV4H0kxBvjhtX1Hf20EFtLWlObGwswsLC8ODBA8TGxiqMBCyRSLBhwwal25sxYwZ27dqFMWPG4Oeff4aNjQ2A9PlTFy1ahAULFmDmzJmYNWuWWreDiIhIHb6ZRNXHxwc+Pj5ZlkuKtC7AaIiIiP7nwIED6Ny5MxITE2FqaorixYsr1MluTtTMhIaGok+fPpg/f77ccmtra8ybNw8vXrxAcHAwE1UiIiqUvplElYiIqLD6+eefYWtri/DwcFSrVk0tbcbExKBOnTpZltepUwdbtmxRS19ERETqlvPs4URERJSv7t27hxEjRqgtSQUABwcHHDt2LMvy48ePw8Hha7homoiIvkZMVImIiDSsfPnySEhIUGubffr0wbZt2zB48GD8999/SEtLg1QqxX///YchQ4Zg+/btSo2kT0REpAlamagGBQVBIpFk+pgwYYKmwyMiIlLJ7NmzERAQoNbp1SZNmoTevXtj7dq1qFKlCgwNDWFgYIAqVaogMDAQvXv3xqRJk9TWHxERkTpp9T2qM2fOhLOzs9yy/JiDjoiIKD8dOXIEVlZWqFy5Mlq0aIFSpUpBV1dXro5EIsGyZcuUblNXVxdBQUEYPXo09u3bh8ePHwNIn0e1bdu2qF69ulq3gYiISJ20OlFt06YNateurekwiIiI8mTlypWy/+/duzfTOqomqhmqV6/OpJSIiLSOVieqREREXwOpVJpvbV+/fh1//vmn7LJiZ2dntG7dWq0DNxEREambVieq8fHxeP36tdyyEiVKaCgaIiKiwiM5ORmDBg1CcHAwhBDQ0UkflkIqlWLChAno0aMH1q9fD319fQ1HSkREpEirE9XmzZsrLBNCaCASIiKivHv48CH279+PR48eAUi/n7RNmzYK4zEoY/z48fi///s/+Pn5Yfjw4ShbtiwkEgnu3buH5cuXY/Xq1bCwsMDSpUvVvBVERER5p9WJ6qpVq1ChQgVNh0FERJRnP//8M5YtW6ZwGbCOjg78/f2xcOFCldrbvHkzevXqJXf/KwBUrFgRq1atwrt377B582YmqkREVChpdaLq5ubGwZSIiEjrLVq0CEuWLEHnzp3x888/o3LlygCAW7duYcmSJViyZAns7e0xatQopdtMTU1F3bp1syyvX78+9uzZk+fYiYiI8oNWzqNKRET0NVm3bh08PT2xbds21KlTB6ampjA1NUWdOnWwZcsWtG/fHoGBgSq12apVKxw4cCDL8r/++gstW7bMa+hERET5gokqERGRhkVFRaFVq1ZZlrdq1Uo2aq+yZs2ahYcPH8LLywtHjhzBo0eP8OjRIxw+fBgdO3bEo0ePMGvWLLx9+1buQUREVBho9aW/REREXwNra2tcvXo1y/KrV6/CyspKpTYzLh/+999/sXv3brmyjIEHq1SporBeWlqaSv0QERHlByaqREREGtalSxcsW7YMTk5OGD58OIyNjQEAiYmJWLlyJdavXw9/f3+V2pw6dSokEkk+REtERJT/mKgSERFp2KxZs/DPP/9g0qRJmDp1KkqWLAkAePbsGT59+gR3d3fMnDlTpTanT5+eD5ESEREVDK1MVH19feHr66vpMIiIiNSiaNGiOHLkCHbv3i03j2rr1q3Rtm1btG/fnmdHiYjom6KViSoREdHXqEOHDujQoYOmwyAiItK4byZRDQsLQ1hYWNYVpDEqt9mynmWuYjl45k2u1iPKzI0rNVVex+X7y/kQiXoFR+trOgStlxbZTfWVGv+l/kAy86YkpMe6qrHBAoqbiIiICsQ3k6j6+PjAx8cny3JJkdYFGA0REX3rPD09VaovkUgURu8lIiL6Wn0ziSoREVFhsnfvXhgaGsLW1lY2XUx2eI8qERF9S5ioEhERaYC9vT2ePn2KEiVKoHv37vD29oatra2mwyIiIioUdDQdABER0bfoyZMniIyMxPfff49Zs2ahVKlSaN68OTZu3IiEhARNh0dERKRRWp2oBgUFQSKR4OLFi5oOhYiISGWNGzdGYGAgnj9/jh07dsDS0hLDhg2DtbU1vLy8sGPHDiQnJ2s6TCIiogKn1YkqERHR16BIkSLo0KEDtm7dihcvXsiS127dumH+/PmaDo+IiKjAMVElIiIqJJKTk3HgwAHs3r0bV65cgaGhIZycnDQdFhERUYFjokpERKRBUqkUBw4cgK+vL2xsbODj44OkpCSsW7cOL1++RK9evTQdIhERUYHjqL9EREQacPr0aYSGhmL79u148+YN6tatizlz5qBr164oUaKEpsMjIiLSKCaqREREGtCgQQMYGRmhbdu28PHxkV3i+/jxYzx+/DjTdWrWrFmAERIREWkOE1UiIiINSUpKws6dOxEeHp5tPSEEJBIJ0tLSCigyIiIizWKiSkREpAEbN27UdAhERESFFhNVIiIiDejTp4+mQyAiIiq0OOovERERERERFSpMVImIiEgl79+/x7Rp09C6dWtYWFhAIpEgKCgo07q3bt1C69atYWJiAgsLC/Tq1QuvXr1SqCeVSjF//nw4OzvD0NAQ1atXR1hYWD5vCRERFVZMVImIiEglr1+/xsyZM3Hr1i189913WdaLjo5Go0aNcO/ePcyZMwdjxozBvn370KJFC6SkpMjVnTx5MsaPH48WLVpgxYoVKF26NLp3744tW7bk9+YQEVEhpNX3qAohAAC6uroajoSIiOjbYWdnh5iYGNja2uLixYtwdXXNtN6cOXOQmJiIS5cuoXTp0gAANzc3tGjRAkFBQRg4cCAA4OnTp1i0aBGGDh2KlStXAgD69++Pxo0bY+zYsejSpQuP9URE3xitPqOakJAAADA1NdVwJERERN8OAwMD2Nra5lhv586daNeunSxJBYDmzZujQoUK2LZtm2zZ7t27kZqaCj8/P9kyiUSCIUOGIDo6GmfOnFHvBhARUaGn1YnqhQsXYGxsDEdHR02HQkRERJ95+vQpXr58idq1ayuUubm54cqVK7LnV65cgbGxMSpXrqxQL6OciIi+LVp56e/OnTtx7NgxhISEoH///tDTy3kzwsLCsh2UwcL0An74oa86w8zS9+Wewt7evkD6evq0APt68pVuVwH19TQ4oeC2Kbj8V/f6aUtfDrnqa0PutqvxXyqvcvnyZdX7yYXKFmkoK805PmVf5/v376sjLFKjmJgYAOmXCX/Jzs4Ob9++RXJyMgwMDBATEwMbGxtIJBKFegDw7NmzLPvJ6fh++fJleHp6Aobtc7MZOfP0VFzGvvLWz9fal7a/V19rX5r+XBRAX9p6jNTKRHXMmDFISEjATz/9hCVLlii1jo+PD3x8fLIs9/T0REREhLpCzBb7Yl+a7Id9sa+c+ioIN27cUKpeQW47qVdSUhKA9MuEv2RoaCirY2BgIPs3u3pZyen4TkRE2kkrE9WHDx9qOgQiIiLKhpGREQAgOTlZoezjx49ydYyMjJSqR0RE3w6tvkeViIiICqeMy3YzLgH+XExMDCwsLGRnUe3s7PD8+XPZaP6f1wOAkiVL5nO0RERU2DBRJSIiIrWzt7eHlZUVLl68qFB2/vx51KhRQ/a8Ro0a+PDhA27duiVX79y5c7JyIiL6tjBRJSIionzRqVMn7N27F0+ePJEtO3LkCO7cuYMuXbrIlnXo0AFFihRBQECAbJkQAmvWrIG9vT3q169foHETEZHmaeU9qkRERKRZK1euRFxcnGxE3j179iA6OhoAMHz4cJiZmWHSpEnYvn073N3dMXLkSLx//x4LFixAtWrV0Lfv/0bad3BwgL+/PxYsWIDU1FS4urrijz/+wMmTJxESEgJdXV2NbCMREWkOz6gSERGRyhYuXIgpU6Zg9erVAIDw8HBMmTIFU6ZMQWxsLACgVKlSOH78OMqWLYsJEyZg/vz5aNu2LQ4dOqQwyu/cuXMxZ84cHDhwAEOHDkVUVBQ2b96M7t27F+h2BQUFQSKRZHrJsrr7yOwxYcIEtbZ/6tQphXIhBEqVKgWJRIJ27drlub/PBQQEQCKRoE6dOmptF9DsdhXE56Kg+8rP9+pz//77Lzp37gxHR0cYGhrC3t4eLVq0wIoVK9Te140bN9CzZ0/Y29vDwMAAJUuWRI8ePZQeaV4ZGe+PoaEhnj59qlDepEkTVK1aVa19ZTwMDQ1RsmRJtGrVCsuXL0dCQoJa+imseEb1/yvIoe3ZF/vSZD/si30Vlr6UUdjiof+JiopSqp6LiwsOHDiQYz0dHR1MnDgREydOzGNk2mPmzJlwdnaWW6auL7hA+vQ+oaGhaNCggdzy48ePIzo6OtMpgfIqJCQETk5OOH/+PO7du4dy5cqpvQ9NbNfXqCDeq9OnT8Pd3R2lS5fGgAEDYGtriydPnuDs2bNYtmwZhg8frra+wsPD4ePjAwsLC/z0009wdnZGVFQUNmzYgB07dmDLli3o2LGj2vpLTk7G3Llz8yXh/lLGviI1NRXPnz/HsWPH4O/vj8WLFyMiIgLVq1fP9xg0QhARERGREEKIjRs3CgDiwoULWttHRvteXl6iRIkSIjU1Va58wIABolatWsLR0VF4eHiord8HDx4IACI8PFxYWVmJ6dOnq61tITS3XZ/3nZ+fi4LsK7/fqwxt27YVVlZWIjY2VqHsxYsXauvn3r17omjRoqJSpUri5cuXcmWvXr0SlSpVEsbGxuL+/ft57ivj/alRo4YwMDAQT58+lStv3LixcHFxyXM/n/eV2WfhyJEjwsjISDg6OooPHz6opb/Chpf+EhEREX2FfHx88ObNGxw6dEi2LCUlBTt27MiXS6pDQkJQvHhxeHh4oHPnzggJCVF7H0DBb9fXqKDeq/v378PFxQXm5uYKZdbW1mrrZ8GCBfjw4QPWrl0LKysrubISJUogMDAQiYmJmD9/vtr6nDRpEtLS0jB37ly1tamKpk2bYsqUKXj06BE2b96skRjyGxNVIiIiIg2Ij4/H69ev5R7q5OTkhHr16iEsLEy2bP/+/YiPj4e3t7da+wLSkx8vLy/o6+vDx8cHd+/exYULF9TeT0Fv19eooN4rR0dHXLp0CdevX1d725/bs2cPnJyc0LBhw0zLGzVqBCcnJ+zbt09tfTo7O6N3795Yt26dbFC5gtarVy8AwMGDBzXSf35jokpERESkAc2bN4eVlZXcQ926d++OP/74A0lJSQDSE5TGjRujZMmSau3n0qVLuH37tixRbNCgARwcHPLtTF1BbdfXqCDfqzFjxuDDhw+oUaMG6tevj/Hjx+PgwYNITU1VWx/x8fF49uwZvvvuu2zrVa9eHdHR0WodgGjy5Mn49OkT5s2bp7Y2VeHg4AAzMzPcv39fI/3nNyaqRERERBqwatUqHDp0SO6hbl27dkVSUhL27t2LhIQE7N27N98u+7WxsYG7uzsAQCKRoFu3btiyZQvS0tLU3l9BbdfXqCDfqxYtWuDMmTPw9PTE1atXMX/+fLRq1Qr29vaIiIhQSx8ZiWexYsWyrZdR/u7dO7X0CwBlypRBr169sHbtWsTExKitXVWYmJh8taP/MlElIiIi0gA3Nzc0b95c7qFuVlZWaN68OUJDQxEeHo60tDR07txZrX2kpaVhy5YtcHd3x8OHD3Hv3j3cu3cPderUwYsXL3DkyBG19gcUzHZ9jTTxXrm6uiI8PByxsbE4f/48Jk6ciISEBHTu3Bk3b97Mc/sZCWhOyZqyCa2qfvnlF3z69Elj96q+f/9e7dtUWHzTiWpycjLGjx+PkiVLwsjICHXq1MmXXzMvXLiAYcOGwcXFBcbGxihdujS6du2KO3fuqL2vzPz666+QSCRqHfL+c5cvX4anpycsLCxQtGhRVK1aFcuXL1d7P3fv3oW3tzccHBxQtGhRVKpUCTNnzsSHDx9y3eb79+8xbdo0tG7dGhYWFpBIJAgKCsq07q1bt9C6dWuYmJjAwsICvXr1wqtXr9Tal1QqRVBQEDw9PVGqVCkYGxujatWqmD17Nj5+/Jgv25UhNTUVVapUgUQiwcKFC9Xej1QqxerVq1GjRg0YGRnB0tISTZs2xdWrV9Xe17Zt21C3bl2Ym5vD0tISjRs3Vum+FFX+ZvPyuVCmH3V9JnKzH8rNZ4KICp/u3btj//79WLNmDdq0aZPpwDZ5cfToUcTExGDLli0oX7687NG1a1cAyNfLf/8fe3cdFlX2/wH8PXSniIhKKgp2x1rYK2Kv2Ll2rWthY3cXxooidqzdsbIW6uruWmuiooQoISkw5/eHv5mv49AMDOr79Tzz6Jx75pzPHS7z4cy995z83K9vkbp+VgCgo6ODGjVqYO7cuVi3bh1SUlKwd+/ePLdramoKGxsb/PPPP5nW++eff2BrawsTE5M89/k5R0dH9OjRQy1nVUNCQhATE5MvSwsVBt/1Oqp9+vTBvn37MHr0aJQuXRp+fn748ccfceHCBaW1ufJiwYIFuHz5Mjp37oyKFSsiLCwMq1evRtWqVXHt2rV8G0ACnw7guXPnwtDQMF/aP336NNq0aYMqVapg6tSpMDIywtOnTxESEqLSfl69eoWaNWvC1NQUw4cPh4WFBa5evYrp06fj1q1bOHToUK7ajYyMxMyZM1GqVClUqlQJFy9eTLdeSEgIGjRoAFNTU8ydOxdxcXFYvHgx/v33XwQFBUFHR0clfSUkJKBv376oXbs2Bg8ejKJFi8r389y5czh//jwkEonK9utzq1atwsuXL7Osl9t++vXrh4CAAPTq1QvDhw9HfHw8bt++jYiICJX2tWrVKowcORKtW7fG/PnzkZSUBD8/P3h4eGD//v3o0KFDln1l93c2r8dFdvpR1TGRm8+h3BwTRFT4tG/fHoMGDcK1a9ewe/dulbcfEBCAokWLYs2aNUrbDhw4gIMHD2L9+vXQ19dXab/5vV/fInX9rL5UvXp1AFDZwM7DwwMbN27En3/+me7f8IGBgQgODsagQYNU0t+XpkyZgu3btxf4var+/v4AgBYtWhRovwVG3evjqMv169cFALFo0SJ5WWJionBychJ16tRRaV+XL18WycnJCmWPHj0Surq6onv37irt60tdunQR7u7uKl3TSSYmJkZYW1uL9u3bi7S0NJW2/aU5c+YIAOLu3bsK5b169RIAxPv373PVblJSkggNDRVCCHHjxg0BQGzZskWp3pAhQ4S+vr548eKFvOzMmTMCgPD19VVZX8nJyeLy5ctKr/Xx8REAxJkzZ1S6XzLh4eHC1NRUzJw5U+n3QhX97N69W75eW25lt6/SpUuLGjVqCKlUKi+LiYkRRkZGwtPTM1t9Zfd3Nq/HRXb6UdUxkdPPodweE0Rfu29pHdXP2/fz8xMzZsxQWG9RFeuNJiQkCGNjY9GvX790t1++fFkAELt27cpTP0IU7H5lp+/8kl99FeTPSub8+fMK+VhmwYIFAoBYunSpSvp59OiR0NfXF66uriIyMlJh27t374Srq6swMDAQT548yXNfGf18+vTpI/T09ISLi0uBrqPq4OAgEhMTVdJfYfPdXvq7b98+aGpqYuDAgfIyPT099O/fH1evXsWrV69U1lfdunWVzqyULl0abm5uePDggcr6+dKlS5ewb98+LF++PF/a37FjB8LDwzFnzhxoaGggPj4eUqk0X/qS3fhubW2tUG5jYwMNDY1sndFMj66uLooVK5Zlvf3798PDwwOlSpWSlzVt2hRlypTBnj17VNaXjo4O6tatq1Tevn17AMj28ZLd/ZKZOHEiXFxc0KNHj2y/Jif9LF26FDVr1kT79u0hlUoRHx+fo35y0ldsbCyKFi2qcJbRxMQERkZG2f6GOLu/s3k9LrLTj6qOiZx+DuX0mJgxYwYkEgkePXqEHj16wNTUFFZWVpg6dSqEEHj16hXatm0LExMTFCtWDEuWLFF4fXJyMqZPnw5nZ2fo6uqiZMmSGD9+PJKTkxXqbdmyBe7u7ihatCh0dXXh6uqKdevWKcVjb28PDw8P/Pnnn6hZsyb09PTg6OiIbdu2ZWt/iL41vXv3xvTp01V+puzw4cP48OEDPD09091eu3ZtWFlZ5dslpfm1X98idfysRowYAScnJ/z666/YuHEj1qxZg+7du2PSpEmwt7dH3759VdJP6dKlsXXrVjx+/BgVKlTA1KlT8dtvv2HatGmoUKECnjx5An9/fzg5Oamkv/RMnjwZKSkp+O+//1Te9okTJ7B9+3b4+flhwYIFaNGiBZo2bQpra2scPnwYenp6Ku+zMPhuB6q3b99GmTJllK5Tr1mzJgDgzp07+dq/EALh4eEoUqRIvrSflpaGESNGYMCAAahQoUK+9HH27FmYmJjg9evXcHFxgZGREUxMTDBkyJAc3TuXHY0aNQIA9O/fH3fu3MGrV6+we/durFu3DiNHjsy3S5sB4PXr14iIiJBfpvK5mjVr4vbt2/nWt0xYWBgA5MvxEhQUhK1bt2L58uXZuoQ0p2JjYxEUFIQaNWpg0qRJMDU1hZGRERwdHbM9yM+JRo0a4eTJk1i1ahWCg4Px8OFDDBs2DDExMRg1alSu2/3ydza/jovsfjao4pjIqK+8HBNdunSBVCrF/PnzUatWLcyePRvLly9Hs2bNYGtriwULFsDZ2Rljx47FpUuXAHy6D9fT0xOLFy9GmzZtsGrVKrRr1w7Lli1Dly5dFNpft24d7OzsMGnSJCxZsgQlS5bE0KFD072M7cmTJ+jUqROaNWuGJUuWwNzcHH369MG9e/dy+E7R90QIAQDQ1NRUcyRfh4CAAOjp6aFZs2bpbtfQ0EDr1q1x8uRJvHv3roCjU52CPC7yqy91/KwWL16Mxo0b4/jx4xgzZgzGjBmDoKAgDB06FNevX1fpfcWdO3fGrVu30KhRI2zevBmDBw/Gxo0b0bBhQ9y6dStbt/7khbOzc46/8M+uadOmoWfPnhg0aBCWL18OIQSWL1+Of/75J19vIVQ7tZ3LVTM3Nzfh7u6uVH7v3j0BQKxfvz5f+/f39xcAxObNm/Ol/dWrVwtTU1MREREhhBD5culvxYoVhYGBgTAwMBAjRowQ+/fvFyNGjBAAhJeXl0r7EkKIWbNmCX19fQFA/pg8ebLK2s/oclJZ+bZt25ReM27cOAFAJCUlqaSvjDRt2lSYmJiIqKioHPWTVV9SqVTUrFlTdO3aVQghxPPnz3N9mWdG/fz1118CgLC0tBTW1tZi7dq1IiAgQNSsWVNIJBJx4sQJle5TeHi4aNKkicJxUqRIEXHlypUc9/O5L39n8+O4SK+fjOTlmMisr9weE9OnTxcAxMCBA+VlqampokSJEkIikYj58+fLy6OiooS+vr7o3bu3PA4NDQ0RGBio0Ob69esFAIVLnz+/vE+mRYsWwtHRUaHMzs5OABCXLl2Sl0VERAhdXV3x66+/Zrov9H1bsWKFAKCSSwTp21GQxwWPQaJPvtvJlBITE6Grq6tULjt1LltAOj/IzvDUqVMHvXv3Vnn77969w7Rp0zB16tR8WTxcJi4uDgkJCRg8eLB8lt8OHTrg48eP8PX1xcyZM1G6dGmV9Wdvb48GDRqgY8eOsLS0xLFjxzB37lwUK1YMw4cPV1k/X5IdC1kdL+ltV4W5c+fi7NmzWLt2rcpnNPTz88O///6Lffv2qbTdz8XFxQH4dFxeu3YNtWrVAgB4enrCwcEBs2fPRsuWLVXWn4GBAVxcXFCiRAl4eHjgw4cPWLZsGTp06IDAwMBczYyX3u9sfhwX2f1sUMUxkVFfeT0mBgwYIP+/pqYmqlevjpCQEPTv319ebmZmBhcXFzx79gwAsHfvXpQrVw5ly5ZFZGSkvJ67uzsA4MKFC/LLnz+/vC8mJgYpKSlo2LAhTp06hZiYGJiamsq3u7q6on79+vLnVlZWCv0SpefGjRswNDSEnZ2dukOhQqQgjwseg0SffLcDVX19faV7nwDIL1nNr3sdwsLC0Lp1a5iamsrvk1W1KVOmwMLCAiNGjFB525+TvUddu3ZVKO/WrRt8fX1x9epVlQ1Ud+3ahYEDB+LRo0coUaIEgE+DYqlUigkTJqBr166wtLRUSV9fku2nOo6X3bt3Y8qUKejfvz+GDBmi0rZjY2Ph7e2NcePGoWTJkipt+3Oy98bBwUE+SAU+LVDdpk0bbN++HampqdDSUs3HUefOnaGlpYUjR47Iy9q2bYvSpUtj8uTJOZ4ZMqPfWVUfF9n9bFDFMZFRX6o4Jj6/Xxf4tGyAnp6e0uXFpqam8kvLHj9+jAcPHmT4xdrnM0NfvnwZ06dPx9WrV5WWpvpyoPplLABgbm6OqKionO0UfRf279+PixcvIiAgAAMGDFDZZxJ93QryuOAxSKTou/0NsLGxwevXr5XKZdNkFy9eXOV9xsTEoFWrVoiOjkZgYGC+9PH48WNs2LABy5cvx5s3b+TlSUlJSElJQXBwMExMTGBhYZHnvooXL4579+4pTXBUtGhRAFDpH4Nr165FlSpV5INUGU9PT/j5+eH27dv5slA68OlYAdKfQj00NBQWFhb5cjb1zJkz6NWrF1q3bo3169ervP3Fixfj48eP6NKlC4KDgwFAvqxQVFQUgoODUbx48VxPVCUjO86/PE6AT8dKSkoK4uPjFQYYufXs2TOcPHkSGzZsUCi3sLDADz/8gMuXL+eovcx+Z1V5XGT3s0EVx0RmfanimEhvgJ3RoFv8/31YUqkUFSpUwNKlS9OtJxs0P336FE2aNEHZsmWxdOlSlCxZEjo6Ojh+/DiWLVumNJlbVv0SfW7s2LH48OED+vfvj2XLlqk7HCokCvK44DFIpOi7HahWrlwZFy5cQGxsrMKEStevX5dvV6WkpCS0adMGjx49wtmzZ+Hq6qrS9mVev34NqVSKkSNHYuTIkUrbHRwcMGrUKJXMBFytWjWcOXNGPpmSjGyArMrLjsPDw2Fubq5UnpKSAgBITU1VWV9fsrW1hZWVFW7evKm0LSgoSOXHCvDpOGzfvj2qV6+OPXv25Mu3qi9fvkRUVBTc3NyUts2dOxdz587F7du387x/xYsXR7FixdL9YujNmzfQ09ODsbFxnvqQCQ8PB/BpMrEvpaSk5Og4yep3VlXHRXY/G1RxTGTVV0EdE19ycnLC33//jSZNmmQ6edORI0eQnJyMw4cPK5wtvXDhgkrjoe/T8+fP1R0CFUIFeVzwGCRS9N3O+tupUyekpaUpnHlJTk7Gli1bUKtWLZVeCpmWloYuXbrg6tWr2Lt3L+rUqaOytr9Uvnx5HDx4UOnh5uaGUqVK4eDBgwr3iuXFTz/9BADYvHmzQvmmTZugpaUln6lXFcqUKYPbt2/j0aNHCuU7d+6EhoYGKlasqLK+0tOxY0ccPXpUYdmic+fO4dGjR+jcubNK+3rw4AFat24Ne3t7HD16NN8uKx45cqTSceLr6wsA6NOnDw4ePAgHBweV9NWlSxe8evUKZ86ckZdFRkbi0KFDcHd3h4aGaj6KnJ2doaGhgd27dyucNQsJCUFgYCCqVKmSrXay+zub1+Miu/2o4pjITl8FeUx87qeffsLr16+xceNGpW2JiYny5YxkZ0g//9nGxMRgy5YtKo+JiIiI1Ou7PaNaq1YtdO7cGd7e3oiIiICzszO2bt2K4OBgpYFXXv366684fPgw2rRpg/fv32P79u0K21U5lXWRIkXQrl07pXLZGdT0tuVWlSpV0K9fP/z2229ITU1Fw4YNcfHiRezduxfe3t4qvbR53LhxOHHiBOrXr4/hw4fD0tISR48exYkTJzBgwIA89bV69WpER0fLzwQfOXJEfrnjiBEjYGpqikmTJmHv3r1o3LgxRo0ahbi4OCxatAgVKlTI0RpgWfWloaGBFi1aICoqCuPGjcOxY8cUXu/k5JTtLzqy6qtq1aqoWrWqwmtkl3u6ubll+1jJzvvn7e2NPXv2oGPHjhgzZgxMTU2xfv16pKSkYO7cudnqJzt9WVlZoV+/fti0aROaNGmCDh064MOHD1i7di0SExPh7e2drX6y+zub1+MiO/18+PBBJcdEdvpS1TGRUz179sSePXswePBgXLhwAfXq1UNaWhoePnyIPXv24NSpU6hevTqaN28OHR0dtGnTBoMGDUJcXBw2btyIokWLpnsJNhEREX3F1DvpsHolJiaKsWPHimLFigldXV1Ro0YNcfLkSZX307BhQ4WlMr58FIT8WJ5GCCE+fvwoZsyYIezs7IS2trZwdnYWy5YtU3k/Qghx/fp10apVK1GsWDGhra0typQpI+bMmSNSUlLy1K5sGYv0Hs+fP5fXu3v3rmjevLkwMDAQZmZmonv37iIsLEylfcmWAsnoIVvOQ5X79bncLE+T3X6ePn0q2rdvL0xMTIS+vr5wd3cXQUFB2e4nu32lpKSIVatWicqVKwsjIyNhZGQkGjduLM6fP5/tfnLyO5uX4yI7/ajqmMjt51BOl6d5+/atQnnv3r2FoaFhuvF8/pn08eNHsWDBAuHm5iZ0dXWFubm5qFatmvDx8RExMTHyeocPHxYVK1YUenp6wt7eXixYsED89ttvSsebnZ2daN26dbr9NmzYMNN9ISIiIvWTCMFZJYiIiIiIiKjw+G7vUSUiIiIiIqLCiQNVIiIiIiIiKlQ4UCUiIiIiIqJChQNVIiIiIiIiKlQ4UCUiIiIiIqJChQNVIiIiIiIiKlQ4UCUiIiIiIqJChQNVIiIiIiIiKlQ4UCUiIiIiIqJChQNVokLC3t4eHh4e6g6DiIiIiEjtOFClb9aMGTMgkUgQGRmZ7vby5cujUaNGAIBGjRpBIpFk+ZgxYwYAIC0tDVu2bEGjRo1gYWEBXV1d2Nvbo2/fvrh582YB7SERERER0bdJS90BEBUGkydPxoABA+TPb9y4gZUrV2LSpEkoV66cvLxixYpITExEhw4dcPLkSTRo0ACTJk2ChYUFgoODsWfPHmzduhUvX75EiRIl1LErRERERERfPQ5UiQA0a9ZM4bmenh5WrlyJZs2ayc+6ygwfPhwnT57EsmXLMHr0aIVt06dPx7Jly/I52oKRkJAAAwMDdYdBRERERN8hXvpLlAMhISHw9fVFs2bNlAapAKCpqYmxY8fm6Wzqn3/+iZo1a0JPTw+Ojo7Ytm2bUp1nz56hc+fOsLCwgIGBAWrXro1jx44p1PHz84NEIkFwcLBC+cWLFyGRSHDx4kV5WaNGjVC+fHncunULDRo0gIGBASZNmgQAuHnzJlq0aIEiRYpAX18fDg4O6NevX673j4iIiIgoKzyjSpQDJ06cQGpqKnr27Jkv7T958gSdOnVC//790bt3b/z222/o06cPqlWrBjc3NwBAeHg46tati4SEBIwcORKWlpbYunUrPD09sW/fPrRv3z5Xfb979w6tWrWCl5cXevToAWtra0RERKB58+awsrLCxIkTYWZmhuDgYBw4cECVu01EREREpIADVaIcePDgAQCgQoUK+dL+f//9h0uXLqF+/foAgJ9++gklS5bEli1bsHjxYgDA/PnzER4ejsDAQPzwww8AgJ9//hkVK1bEmDFj0LZtW2ho5PxiibCwMKxfvx6DBg2Sl/3++++IiorC6dOnUb16dXn57Nmz87KbRERERESZ4qW/RDkQGxsLADA2Ns6X9l1dXeWDVACwsrKCi4sLnj17Ji87fvw4atasKR+kAoCRkREGDhyI4OBg3L9/P1d96+rqom/fvgplZmZmAICjR48iJSUlV+0SEREREeUUB6r0XZNIJDmqb2JiAgD48OFDfoSDUqVKKZWZm5sjKipK/vzFixdwcXFRqiebnfjFixe56tvW1hY6OjoKZQ0bNkTHjh3h4+ODIkWKoG3bttiyZQuSk5Nz1QcRERERUXZwoErfLD09PQBAYmJiutsTEhLkdbKrbNmyAIB///03b8FlQFNTM91yIUSO28poEJ6WlpZuub6+frpt7Nu3D1evXsXw4cPx+vVr9OvXD9WqVUNcXFyOYyIiIiIiyg4OVOmbZWdnB+DTfZ9fSkhIwKtXr+R1sqtVq1bQ1NTE9u3bVRJjbtjZ2aW7Tw8fPpRvBz6diQWA6OhohXq5OeNau3ZtzJkzBzdv3kRAQADu3buHXbt25bgdIiIiIqLs4ECVvllNmjSBjo4O1q1bB6lUqrBtw4YNSE1NRatWrXLUZsmSJfHzzz/j9OnTWLVqldJ2qVSKJUuWICQkJE+xZ+bHH39EUFAQrl69Ki+Lj4/Hhg0bYG9vD1dXVwCAk5MTAODSpUvyemlpadiwYUO2+4qKilI6m1u5cmUA4OW/RERERJRvOOsvfbOKFi2KadOmYcqUKWjQoAE8PT1hYGCAK1euYOfOnWjevDnatGmT43aXLFmCp0+fYuTIkThw4AA8PDxgbm6Oly9fYu/evXj48CG8vLzk9SUSCRo2bKiwbmleTJw4ETt37kSrVq0wcuRIWFhYYOvWrXj+/Dn2798vn/HXzc0NtWvXhre3N96/fw8LCwvs2rULqamp2e5r69atWLt2Ldq3bw8nJyd8+PABGzduhImJCX788UeV7A8RERER0Zc4UKVv2uTJk2Fvb4/Vq1dj5syZSE1NhYODA3x8fDBhwoRcLeNiYGCAEydOwM/PD1u3bsWsWbOQkJCA4sWLw93dHQEBAbC1tQUA+X2cNjY2Ktsna2trXLlyBRMmTMCqVauQlJSEihUr4siRI2jdurVC3YCAAAwaNAjz58+HmZkZ+vfvj8aNG6NZs2bZ6qthw4YICgrCrl27EB4eDlNTU9SsWRMBAQFwcHBQ2T4REREREX1OInIzSwsRZcvx48fh4eGBv//+O9/WXiUiIiIi+tbwHlWifHThwgV4eXlxkEpERERElAM8o0pERERERESFCs+oEhERERERUaHCgSoREREREREVKhyoEhERERERUaHCgSoREREREREVKhyoEhERERERUaHCgSoREREREREVKhyoEhERERERUaHCgSoREREREREVKhyoEhERERERUaHCgSoREREREREVKhyoEhERERERUaHCgSoREREREREVKhyoEhERERERUaHCgSoREREREREVKhyoEhERERERUaGipe4ACovixXsiNTVG3WEoeBv1Ud0hKJEUeaPuEJRZFr6YjE2j1R2CkpJ6Qt0hKCmhL1V3CEp0ItUdQTpC1B1AOl6rOwBFpj16wN/fX91hEBERkYpwoPr/UlNjULXqYXWHoeD01XfqDkGJZq3d6g5BiUajPeoOQYlTlb/UHYKSniUK3xcfPUoWvpisLhS+AT22qzuAdBSymDxjCtcXjURERJQ3vPSXiIiIiIiIChUOVImIiIiIiKhQ4UCViIiIiIiIChUOVImIiIiIiKhQ4UCViIiIiIiIChUOVImIiIiIiKhQ4UCViIiIiIiIChUOVImIiIiIiKhQ4UCViIiIiIiIChUOVImIiIiIiKhQ4UCViIiIiIiIChUOVImIiIiIiKhQ4UCViIiIiIiIChUOVImIiIiIiKhQ4UCViIiIiIiIChUOVImIiIiIiKhQ4UCViIiIiIiIChUOVImIiIiIiKhQkQghhLqDKAzq168Pc3NzdYeh4PXr17C1tVV3GAoYU/YwpuxhTNnDmLIWFRWFwMBAdYdBREREKqKl7gAKC3Nzcxw+fFjdYSjw9PRkTNnAmLKHMWUPY8qewhaTp6enukMgIiIiFeKlv0RERERERFSocKBKREREREREhQoHqkRERERERFSocKBKREREREREhQoHqkRERERERFSocKBKREREREREhQoHqv+va9eu6g5BCWPKHsaUPYwpexhT9hS2mApbPERERJQ3EiGEUHcQRERERERERDI8o0pERERERESFCgeqREREREREVKhwoEpERERERESFCgeqREREREREVKhwoEpERERERESFynczUL116xZatmwJExMTGBsbo3nz5rhz5066da9cuYIffvgBBgYGKFasGEaOHIm4uLhc9x0XF4fp06ejZcuWsLCwgEQigZ+fX7p1Hzx4gJYtW8LIyAgWFhbo2bMn3r59q1RPKpVi4cKFcHBwgJ6eHipWrIidO3eqPKagoCAMHToU1apVg7a2NiQSSabtbt68GeXKlYOenh5Kly6NVatWqTQmqVQKPz8/eHp6omTJkjA0NET58uUxe/ZsJCUlqSUmANi4cSMaNmwIa2tr6OrqwsHBAX379kVwcLDaYvpcSkoKXF1dIZFIsHjxYqXtBXU89enTBxKJROlRtmxZtcUk62vdunWoXLky9PX1YWlpCXd3d/z9999qiSm990j2aNasmVpiAoA9e/agdu3aMDMzg6WlJRo2bIhjx44p1ctLTDmJZ/Xq1ShXrhx0dXVha2uLMWPGID4+XqXxEBERkZqI78CtW7eEnp6eKF26tFi8eLFYuHChsLe3FyYmJuLhw4cKdW/fvi309PRElSpVxLp168TkyZOFrq6uaNmyZa77f/78uQAgSpUqJRo1aiQAiC1btijVe/XqlShSpIhwcnISK1asEHPmzBHm5uaiUqVKIjk5WaHuxIkTBQDx888/iw0bNojWrVsLAGLnzp0qjWn69OlCW1tbVKtWTZQpU0ZkdsisX79eABAdO3YUGzZsED179hQAxPz581UW04cPHwQAUbt2bTF79myxYcMG0bdvX6GhoSEaNWokpFJpgcckhBBDhgwRvXv3FosXLxabN28WU6ZMEdbW1qJIkSLi9evXaonpc0uWLBGGhoYCgFi0aJHS9oI6nnr37i10dXWFv7+/wuPw4cNqi0kWl5aWlujXr5/YuHGjWL58uejdu7c4ffq0WmL68v3x9/cXo0aNEgDEwoUL1RLTypUrBQDRunVrsW7dOrFs2TJRqVIlAUDs379fZTFlN57x48cLAKJTp05i3bp1YsSIEUJLS0s0b95cqW5e3yMiIiIqeN/FQPXHH38U5ubmIjIyUl725s0bYWRkJDp06KBQt1WrVsLGxkbExMTIyzZu3CgAiFOnTuWq/6SkJBEaGiqEEOLGjRuZDnb09fXFixcv5GVnzpwRAISvr6+8LCQkRGhra4thw4bJy6RSqahfv74oUaKESE1NVVlMYWFhIiEhQQghxLBhwzIcqCYkJAhLS0vRunVrhfLu3bsLQ0ND8f79e5XElJycLC5fvqz0Wh8fHwFAnDlzpsBjysjNmzcFADFv3jy1xhQeHi5MTU3FzJkz0x2oFuTx1Lt3b2FoaJhlewUZ0+7duwUAceDAgUITU3r69+8vJBKJePXqlVpiKl26tKhRo4bCl0ExMTHCyMhIeHp6qiym7MTz5s0boaWlJXr27KlQvmrVKgFA4YsPVbxHREREVPC+i0t/AwMD0bRpU1haWsrLbGxs0LBhQxw9elR+WW9sbCzOnDmDHj16wMTERF63V69eMDIywp49e3LVv66uLooVK5Zlvf3798PDwwOlSpWSlzVt2hRlypRR6PvQoUNISUnB0KFD5WUSiQRDhgxBSEgIrl69qrKYrK2toa+vn2W9Cxcu4N27dwoxAcCwYcMQHx+f7uWBuYlJR0cHdevWVSpv3749gE+XThd0TBmxt7cHAERHR6s1pokTJ8LFxQU9evRId3tBHk8yaWlpiI2NzXB7Qca0dOlS1KxZE+3bt4dUKk330tGCjulLycnJ2L9/Pxo2bIgSJUqoJabY2FgULVpU4fJ/ExMTGBkZKXxG5DWm7MRz9epVpKamwsvLS6Fc9nzXrl0qi4eIiIjU47sYqCYnJ6c72DIwMMDHjx9x9+5dAMC///6L1NRUVK9eXaGejo4OKleujNu3b+dbjK9fv0ZERIRS3wBQs2ZNhb5v374NQ0NDlCtXTqmebHtBk/X5ZfzVqlWDhoZGvscUFhYGAChSpIhaY3r37h0iIiJw8+ZN9O3bFwDQpEkTtcUUFBSErVu3Yvny5RneX1zQx1NCQgJMTExgamoKCwsLDBs2TOke8IKKKTY2FkFBQahRowYmTZoEU1NTGBkZwdHRUemLKXX+3h0/fhzR0dHo3r272mJq1KgRTp48iVWrViE4OBgPHz7EsGHDEBMTg1GjRhVoTMnJyQCg9LluYGAA4NOcBAUZDxEREamelroDKAguLi64du0a0tLSoKmpCQD4+PEjrl+/DuDTIBEAQkNDAXw62/olGxsbBAYG5luMWfX9/v17JCcnQ1dXF6GhobC2tlYaeMhe++bNm3yLMyOhoaHQ1NRE0aJFFcp1dHRgaWmZ7zEtXLgQJiYmaNWqlVpjsrW1lf8RbWlpiZUrVypMflOQMQkhMGLECHTp0gV16tTJcGKngjyebGxsMH78eFStWhVSqRQnT57E2rVr8ffff+PixYvQ0tIq0JiePn0KIQR27doFLS0tLFy4EKamplixYgW8vLxgYmKCli1bFmhM6QkICICuri46deqkUF6QMa1cuRKRkZEYOXIkRo4cCeDTF0Pnzp1DnTp1CjQmFxcXAMDly5fRuHFjebnsM1r2mV5Q8RAREZHqfRcD1aFDh2LIkCHo378/xo8fD6lUitmzZ8sHh4mJiQr/6urqKrWhp6cn354fsupbVkdXV1f+b2b1ClpiYiJ0dHTS3Zbf793cuXNx9uxZrF27FmZmZmqN6cSJE0hKSsKDBw+wfft2pctICzImPz8//Pvvv9i3b1+m9QryeJo3b57Ccy8vL5QpUwaTJ0/Gvn375JduFlRMsjO57969w7Vr11CrVi0AgKenJxwcHDB79mz5QFVdv3exsbE4duwYfvzxR4Xju6BjMjAwgIuLC0qUKAEPDw98+PABy5YtQ4cOHRAYGAhnZ+cCi6lq1aqoVasWFixYAFtbWzRu3BgPHjzAkCFDoK2trdBHYfy8JCIioqx9F5f+Dh48GJMmTcKOHTvg5uaGChUq4OnTpxg/fjwAwMjICMD/LiOTnRH7XFJSUrbu1cytrPr+vI6+vn626hUkfX19fPz4Md1t+fne7d69G1OmTEH//v0xZMgQtcfUuHFjtGrVCmPGjMHevXvh4+OD1atXF3hMsbGx8Pb2xrhx41CyZMlM66r7ePrll1+goaGBs2fPFnhMsnYcHBzkg1Tg02dCmzZtEBQUhNTU1AKN6Uv79+9HUlKS0mW/BR1T586d8fLlS/j5+aFTp07o27cvLl68iI8fP2Ly5MkFHtP+/ftRqVIl9OvXDw4ODmjTpg1++uknVKlSRf6ZXpDxEBERkWp9FwNVAJgzZw7Cw8MRGBiIf/75Bzdu3IBUKgUAlClTBsD/LgWTnWn9XGhoKIoXL55v8WXVt4WFhfysgI2NDcLCwiCEUKoHIF/jzIiNjQ3S0tIQERGhUP7x40e8e/cuX2I6c+YMevXqhdatW2P9+vWFIqbPOTk5oUqVKggICCjwmBYvXoyPHz+iS5cuCA4ORnBwMEJCQgAAUVFRCA4Olg+Y1X08ydYtff/+vbysoGKStWNtba20rWjRokhJSZGfFVfX+xQQEABTU1N4eHgobSuomJ49e4aTJ0/C09NTodzCwgI//PADLl++XOAx2dra4s8//8SjR49w6dIlhISEYOHChXj16pX8M70g4yEiIiLV+m4GqgBgbm6OH374ARUqVAAAnD17FiVKlEDZsmUBAOXLl4eWlhZu3ryp8LqPHz/izp07qFy5cr7FZmtrCysrK6W+gU8T4nzed+XKlZGQkKAwwy0A+T23+RlnRmR9fhn/zZs3IZVKVR7T9evX0b59e1SvXh179uyR39uozpjSk5iYiJiYmAKP6eXLl4iKioKbmxscHBzg4OCA+vXrA/h0qbSDgwPu378vj0mdx9OHDx8QGRkJKysreVlBxVS8eHEUK1ZM4Z5GmTdv3kBPTw/GxsYFGtPnQkNDceHCBXTs2DHdy1cLKqbw8HAAn2Zr/lJKSor8rHNBxiRTunRp1K9fH8WKFcP9+/cRGhqKpk2bqi0eIiIiUo3vaqD6ud27d+PGjRsYPXo0NDQ+vQ2mpqZo2rQptm/fjg8fPsjr+vv7Iy4uDp07d87XmDp27IijR4/i1atX8rJz587h0aNHCn23bdsW2traWLt2rbxMCIH169fD1tY23eVb8pu7uzssLCywbt06hfJ169bBwMAArVu3VllfDx48QOvWrWFvb4+jR49meOleQcWUmpqKqKgopfKgoCD8+++/CjP8FlRMI0eOxMGDBxUevr6+AIA+ffrg4MGDcHBwAFBwx1NSUpLC75XMrFmzIISQ3wtakDEBQJcuXfDq1SucOXNGXhYZGYlDhw7B3d1d/vmgjt+7Xbt2QSqVpnvZb0HG5OzsDA0NDezevVvhzGRISAgCAwNRpUqVAo/pS1KpFOPHj4eBgQEGDx6s9niIiIgob76LyZQuXbqEmTNnonnz5rC0tMS1a9ewZcsWtGzZUmFZBeDTJcJ169ZFw4YNMXDgQISEhGDJkiVo3ry5wh/SObV69WpER0fLZ5g8cuSI/FLMESNGwNTUFJMmTcLevXvRuHFjjBo1CnFxcVi0aBEqVKggX+oEAEqUKIHRo0dj0aJFSElJQY0aNfD7778jMDAQAQEB8pmNVRHTixcv4O/vD+B/ZwFnz54NALCzs0PPnj0BfLp8c9asWRg2bBg6d+6MFi1aIDAwENu3b8ecOXNgYWGhkpg0NDTQokULREVFYdy4cUrrjjo5OclnIC2omIQQKFmyJLp06QI3NzcYGhri33//xZYtW2BqaoqpU6fK2yqomKpWrYqqVasqvEY266+bmxvatWsnLy+o4ykqKgpVqlRB165d5VcxnDp1CsePH0fLli3Rtm3bAo/J1NQU3t7e2LNnDzp27IgxY8bA1NQU69evR0pKCubOnauWmGQCAgJQvHhxNGrUKN22CiomKysr9OvXD5s2bUKTJk3QoUMHfPjwAWvXrkViYiK8vb1VGlN23qNRo0YhKSkJlStXRkpKCnbs2CFfjunztahV9R4RERFRARPfgSdPnojmzZuLIkWKCF1dXVG2bFkxb948kZycnG79wMBAUbduXaGnpyesrKzEsGHDRGxsbJ5isLOzEwDSfTx//lxe7+7du6J58+bCwMBAmJmZie7du4uwsDCl9tLS0sTcuXOFnZ2d0NHREW5ubmL79u0qj+nChQsZ1mnYsKFSmxs2bBAuLi5CR0dHODk5iWXLlgmpVKqymJ4/f57hdgCid+/eBR5TcnKyGDVqlKhYsaIwMTER2traws7OTvTv31/hZ1uQMaVH9t4tWrRIaVtBHE9RUVGiR48ewtnZWRgYGAhdXV3h5uYm5s6dKz5+/KiWmGSePn0q2rdvL0xMTIS+vr5wd3cXQUFBao3p4cOHAoAYM2ZMpu0VVEwpKSli1apVonLlysLIyEgYGRmJxo0bi/Pnz6s8puzEs2XLFlGpUiVhaGgojI2NRZMmTdKNRVXvERERERUsiRBfzDBBREREREREpEbf7T2qREREREREVDhxoEpERERERESFCgeqREREREREVKhwoEpERERERESFCgeqREREREREVKhwoEpERERERESFCgeqREREREREVKhwoEpERERERESFCgeqREREREREVKhwoEqkAhKJBDNmzMjx6y5evAiJRIKLFy+qPKb88OV++vn5QSKRIDg4WG0xEREREdG3hwNVKlCygY3soaenhzJlymD48OEIDw/PcXtr166Fn5+f6gNNx/Hjx3M1GKW8SUhIwIwZM3I9mP/rr78gkUgwZcqUDOs8fvwYEokEY8aMUSiPioqClpYW9uzZk6u+iYiIiCh3OFAltZg5cyb8/f2xevVq1K1bF+vWrUOdOnWQkJCQo3YKeqDq4+OT7rbExMRMB0Lfqp49eyIxMRF2dnb51kdCQgJ8fHxyPVCtWrUqypYti507d2ZYZ8eOHQCAHj16KJSfOnUKEokEzZs3z1XfRERERJQ7HKiSWrRq1Qo9evTAgAED4Ofnh9GjR+P58+c4dOiQukPLFT09PWhpaak7jHRJpVIkJSXlS9uamprQ09ODRCLJl/ZVpXv37nj27BmuXbuW7vadO3eibNmyqFq1qkL58ePHUa9ePZiZmRVAlEREREQkw4EqFQru7u4AgOfPnwMAUlNTMWvWLDg5OUFXVxf29vaYNGkSkpOT5a+xt7fHvXv38Mcff8gvJW7UqJF8e3R0NEaPHo2SJUtCV1cXzs7OWLBgAaRSqbxOcHAwJBIJFi9ejA0bNsj7q1GjBm7cuCGv16dPH6xZswYAFC5dlvny3s0XL15g6NChcHFxgb6+PiwtLdG5c+c83ct58eJFVK9eHXp6enBycoKvry9mzJihNEiUSCQYPnw4AgIC4ObmBl1dXZw8eRIAsHjxYtStWxeWlpbQ19dHtWrVsG/fPqW+kpOT8csvv8DKygrGxsbw9PRESEiIUr2M7lE9ceIE6tevD0NDQxgbG6N169a4d++eQp0+ffrAyMgIr1+/Rrt27WBkZAQrKyuMHTsWaWlpAD79fKysrAAAPj4+8vc9p5dgd+/eHcD/zpx+7tatW/jvv//kdWSkUilOnjyJ1q1by8tk7+3evXvh6uoKfX191KlTB//++y8AwNfXF87OztDT00OjRo147y4RERFRLhXOU0D03Xn69CkAwNLSEgAwYMAAbN26FZ06dcKvv/6K69evY968eXjw4AEOHjwIAFi+fDlGjBgBIyMjTJ48GQBgbW0N4NPlog0bNsTr168xaNAglCpVCleuXIG3tzdCQ0OxfPlyhf537NiBDx8+YNCgQZBIJFi4cCE6dOiAZ8+eQVtbG4MGDcKbN29w5swZ+Pv7Z7k/N27cwJUrV+Dl5YUSJUogODgY69atQ6NGjXD//n0YGBjk6P25ffs2WrZsCRsbG/j4+CAtLQ0zZ86UD+K+dP78eezZswfDhw9HkSJFYG9vDwBYsWIFPD090b17d3z8+BG7du1C586dcfToUYUB2YABA7B9+3Z069YNdevWxfnz5xW2Z8bf3x+9e/dGixYtsGDBAiQkJGDdunX44YcfcPv2bXksAJCWloYWLVqgVq1aWLx4Mc6ePYslS5bAyckJQ4YMgZWVFdatW4chQ4agffv26NChAwCgYsWKOXr/HBwcULduXezZswfLli2DpqamfJts8NqtWzeF19y4cQNv377Fjz/+qFAeGBiIw4cPY9iwYQCAefPmwcPDA+PHj8fatWsxdOhQREVFYeHChejXrx/Onz+fo1iJiIiICIAgKkBbtmwRAMTZs2fF27dvxatXr8SuXbuEpaWl0NfXFyEhIeLOnTsCgBgwYIDCa8eOHSsAiPPnz8vL3NzcRMOGDZX6mTVrljA0NBSPHj1SKJ84caLQ1NQUL1++FEII8fz5cwFAWFpaivfv38vrHTp0SAAQR44ckZcNGzZMZPQrA0BMnz5d/jwhIUGpztWrVwUAsW3bNnnZhQsXBABx4cKFdNuVadOmjTAwMBCvX7+Wlz1+/FhoaWkpxQRAaGhoiHv37im182VcHz9+FOXLlxfu7u7yMtn7P3ToUIW63bp1U9pP2c/z+fPnQgghPnz4IMzMzMTPP/+s8NqwsDBhamqqUN67d28BQMycOVOhbpUqVUS1atXkz9++favUb26sWbNGABCnTp2Sl6WlpQlbW1tRp04dpfpTp04VdnZ2CmUAhK6urnx/hRDC19dXABDFihUTsbGx8nJvb2+F94aIiIiIso+X/pJaNG3aFFZWVihZsiS8vLxgZGSEgwcPwtbWFsePHwcApRlYf/31VwDAsWPHsmx/7969qF+/PszNzREZGSl/NG3aFGlpabh06ZJC/S5dusDc3Fz+vH79+gCAZ8+e5Wr/9PX15f9PSUnBu3fv4OzsDDMzM/z11185aistLQ1nz55Fu3btULx4cXm5s7MzWrVqle5rGjZsCFdX10zjioqKQkxMDOrXr68Qk+z9HzlypMJrR48enWWsZ86cQXR0NLp27arwvmtqaqJWrVq4cOGC0msGDx6s8Lx+/fq5ft8z06VLF2hraytc/vvHH3/g9evXSpf9Ap/eh/TOIjdp0kThrHCtWrUAAB07doSxsbFSeX7sCxEREdG3jpf+klqsWbMGZcqUgZaWFqytreHi4gINjU/fm7x48QIaGhpwdnZWeE2xYsVgZmaGFy9eZNn+48eP8c8//2R4aWxERITC81KlSik8lw1ao6Kisr1Pn0tMTMS8efOwZcsWvH79GkII+baYmJgctRUREYHExESl9wNAumXAp0td03P06FHMnj0bd+7cUbjf9/P7XGXvv5OTk8JrXVxcsoz18ePHAP53z/GXTExMFJ7r6ekp/YzMzc1z/b5nxtLSEi1atMDBgwexfv166OnpYceOHdDS0sJPP/2kUDcsLAx//fUXZs6cqdTOl8eKqakpAKBkyZLplufHvhARERF96zhQJbWoWbMmqlevnmmdvMwkK5VK0axZM4wfPz7d7WXKlFF4/vk9i5/7fICZEyNGjMCWLVswevRo1KlTB6amppBIJPDy8lKYzCm/fH7mVCYwMBCenp5o0KAB1q5dCxsbG2hra2PLli3pTjKUG7J98/f3R7FixZS2fzkzckbve37p0aMHjh49iqNHj8LT0xP79+9H8+bNlQbLJ06cgJ6eHho3bqzURkYxq/oYIiIiIvqecaBKhY6dnR2kUikeP36McuXKycvDw8MRHR2tsGZnRoNZJycnxMXFoWnTpiqLKycD53379qF3795YsmSJvCwpKQnR0dE57rdo0aLQ09PDkydPlLalV5aR/fv3Q09PD6dOnYKurq68fMuWLQr1ZO//06dPFc6i/vfff1n2ITsLW7RoUZW996pc+sbT0xPGxsbYsWMHtLW1ERUVle5lv8eOHUPjxo3THfATERERUf7jPapU6MhmWf1yZt6lS5cCgMJ9g4aGhukO/n766SdcvXoVp06dUtoWHR2N1NTUHMdlaGgof31WNDU1lc6krVq1Sr7sSk5oamqiadOm+P333/HmzRt5+ZMnT3DixIkctSORSBRiCA4Oxu+//65QT3bf68qVKxXKv/x5pKdFixYwMTHB3LlzkZKSorT97du32Y5XRjZDcm4G+V/S19dH+/btcfz4caxbtw6GhoZo27atQp2UlBScOXMm27McExEREZHq8YwqFTqVKlVC7969sWHDBkRHR6Nhw4YICgrC1q1b0a5dO4XLMatVq4Z169Zh9uzZcHZ2RtGiReHu7o5x48bh8OHD8PDwQJ8+fVCtWjXEx8fj33//xb59+xAcHIwiRYrkKK5q1aoB+DTJUIsWLaCpqQkvL69063p4eMDf3x+mpqZwdXXF1atXcfbsWfnyOzk1Y8YMnD59GvXq1cOQIUOQlpaG1atXo3z58rhz50622mjdujWWLl2Kli1bolu3boiIiMCaNWvg7OyMf/75R16vcuXK6Nq1K9auXYuYmBjUrVsX586dy9bZWxMTE6xbtw49e/ZE1apV4eXlBSsrK7x8+RLHjh1DvXr1sHr16hztu76+PlxdXbF7926UKVMGFhYWKF++PMqXL4/g4GA4ODigd+/e8PPzy1Z7PXr0wLZt23Dq1Cl0795d/gWEzJ9//onY2FgOVImIiIjUiANVKpQ2bdoER0dH+Pn54eDBgyhWrBi8vb0xffp0hXrTpk3DixcvsHDhQnz48AENGzaEu7s7DAwM8Mcff2Du3LnYu3cvtm3bBhMTE5QpUwY+Pj7yiW5yokOHDhgxYgR27dqF7du3QwiR4UB1xYoV0NTUREBAAJKSklCvXj2cPXsWLVq0yNX7Ua1aNZw4cQJjx47F1KlTUbJkScycORMPHjzAw4cPs9WGu7s7Nm/ejPnz52P06NFwcHDAggULEBwcrDBQBYDffvsNVlZWCAgIwO+//w53d3ccO3ZMacKg9HTr1g3FixfH/PnzsWjRIiQnJ8PW1hb169dH3759c7X/mzZtwogRI/DLL7/g48ePmD59OsqXL4+4uDgAgI2NTbbbcnd3h42NDUJDQzOc7dfV1VXhEnMiIiIiKlgSwZk+iL5a7dq1w7179+Sz7X5v1q5di/Hjx+Pp06ewtrZWSZuurq7w8PDAwoULVdIeEREREeUc71El+kokJiYqPH/8+DGOHz+ORo0aqSegQuDChQsYOXKkygapHz9+RJcuXXJ95peIiIiIVINnVIm+EjY2NujTpw8cHR3x4sULrFu3DsnJybh9+zZKly6t7vCIiIiIiFSG96gSfSVatmyJnTt3IiwsDLq6uqhTpw7mzp3LQSoRERERfXN4RpWIiIiIiIgKFd6jSkRERERERIUKB6pERERERERUqHCgSkRERERERIUKB6pERERERERUqHCgSkRERERERIUKB6pERERERERUqHCgSkRERERERIUKB6pERERERERUqHCgSkRERERERIUKB6pERERERERUqHCgSkRERERERIUKB6pERERERERUqHCgSkRERERERIUKB6pERERqZm9vD4lEAolEgj59+qg7HCIiIrXjQJWIiIiIiIgKFQ5UiYiIiIiIqFDhQJWIiIiIiIgKlVwPVP/55x8MHjwY5cqVg7GxMfT19WFvb48OHTrg7NmzqoyRiIjoq5ecnAwfHx84OTlBV1cX9vb2mDhxIuLj49UdGhERUaGjlZsXLV68GBMnTkRaWppC+YsXL/DixQuUKlUKTZs2VUmAREREX7u0tDR4eHgofJH74sULLFiwABcuXEBSUpIaoyMiIip8cjxQ/f333zFu3Lj/NaClhc6dO6Ns2bIIDQ3FuXPnVBogERHR12716tUKg1QnJyf89NNPePfuHbZs2YKUlBQ1RkdERFT45HigOmfOHPn/NTU18ccff6Bu3bryMqlUipcvX6omOiIiom/A+vXr5f83MzNDUFAQLCwsAAA//PADevXqpa7QiIiICqUc3aOakJCAW7duyZ+3adNGYZAKABoaGrC3t1dJcERERF+7uLg4PHz4UP68TZs28kEqAHTr1g3a2trqCI2IiKjQytFANSoqCkII+XMHBweVB0Rkb28PDw8PdYeRK/b29ujTp4+6wyCiQiQ6OlrhubW1tcJzTU1NWFpaFmBE9LVjniSi70GOBqrm5uaQSCTy58+fP1d5QKQanp6eMDAwwIcPHzKs0717d+jo6ODdu3cq6/fKlSuYMWOG0h9mhUVCQgJmzJiBixcv5rqNwr6PMu/evcOiRYvQoEEDWFlZwczMDLVr18bu3bvTrZ+cnIwJEyagePHi0NfXR61atXDmzJl06165cgU//PADDAwMUKxYMYwcORJxcXHp1v3rr7/g6ekJCwsLGBgYoHz58li5cmWO9uXcuXPo168fypQpAwMDAzg6OmLAgAEIDQ3NdXw3btzA8OHD4ebmBkNDQ5QqVQo//fQTHj16lG6bDx48QMuWLWFkZAQLCwv07NkTb9++zdF+fO7gwYNo0aIFihcvDl1dXZQoUQKdOnXC3bt3c92mTLNmzSCRSDB8+PBs1c+s7r59+yCRSHDx4kVcvHgREokkWw+Zp0+fYtCgQXB0dISenh5MTExQr149rFixAomJiXne16+BqampwvPw8HCF52lpaSr9HKbsYZ5MH/Mk86SMuvMkAOzatQtVq1aFnp4erKys0L9/f0RGRua4naCgIAwdOhTVqlWDtra2Qp5Kz+bNm1GuXDno6emhdOnSWLVqVbb6mTFjBiQSSYYxli9fHo0aNQIANGrUKFv5dMaMGQA+5YotW7agUaNGsLCwkM8e37dvX9y8eTPb78VXReRQ9erVBQABQGhpaYlr164pbJdKpSI4ODinzZKK7dq1SwAQW7duTXd7fHy8MDQ0FG3atFFpv4sWLRIAxPPnz3Pdhp2dnWjdurXqgvrM27dvBQAxffr0XLeR2T4mJSWJjx8/5j5AFTpy5IjQ1tYWbdu2FcuXLxerV68WjRs3FgDEtGnTlOp7eXkJLS0tMXbsWOHr6yvq1KkjtLS0RGBgoEK927dvCz09PVGlShWxbt06MXnyZKGrqytatmyp1OapU6eEjo6OqFWrlli6dKnYsGGDmDBhghg3blyO9qVatWrCwcFBjB8/XmzcuFF4e3sLY2NjYW1tLUJDQ3MVX8eOHUWxYsXEiBEjxMaNG8WsWbOEtbW1MDQ0FP/++69C3VevXokiRYoIJycnsWLFCjFnzhxhbm4uKlWqJJKTk3O0LzI+Pj6iS5cuYv78+WLTpk1i9uzZwtHRUejr64s7d+7kqk0hhNi/f78wNDQUAMSwYcOy9ZrM6u7du1cAEBcuXBBhYWHC399f4VGiRAlRtmxZpXIhhDh69KjQ19cXZmZmYuTIkWLDhg1i9erVwsvLS2hra4uff/451/v5tSlbtqw8d5qbm4t3797Jt23btk2+DYDo3bu3+gL9jjBPpo95knlSRt15cu3atQKAaNKkiVizZo3w9vYWBgYGomLFiiIxMTFHbU2fPl1oa2uLatWqiTJlyojMhkDr168XAETHjh3Fhg0bRM+ePQUAMX/+/Gz1A0C8ffs23e1ubm6iYcOGQgghTp8+rZA3R44cKQCISZMmKZT//fffIiEhQbRs2VIAEA0aNBCLFi0SmzdvFlOnThUuLi5CIpGIV69e5eg9+RrkeKB68OBBhYSqra0tunXrJmbOnCmGDx8u3NzcxKhRo/IhVMqJhIQEYWxsLFq0aJHu9h07dggAYteuXSrt93tPwIXJs2fPlL40kkqlwt3dXejq6oq4uDh5+fXr1wUAsWjRInlZYmKicHJyEnXq1FFoo1WrVsLGxkbExMTIyzZu3CgAiFOnTsnLYmJihLW1tWjfvr1IS0vL07788ccfSm388ccfAoCYPHlyruK7fPmyUvJ89OiR0NXVFd27d1coHzJkiNDX1xcvXryQl505c0YAEL6+vnnat8+FhYUJLS0tMWjQoFy9PjExUdjb24uZM2fmy0A1PZ8n3c89e/ZMGBkZibJly4o3b94obX/8+LFYvnx5tuL7FixdulQhdzo5OYlJkyaJgQMHCm1tbQ5U1YB5Mn3Mk8yTMurMk8nJycLMzEw0aNBASKVSefmRI0cEALFy5coctRcWFiYSEhKEEEIMGzYsw4FqQkKCsLS0VPr96t69uzA0NBTv37/PtJ+cDFS/lFm+lcW8bNkypW2pqali0aJFHKjKLFy4UGhqaiok1s8fHKgWDr179xZaWloiPDxcaZuHh4cwNjaW/9I+ffpUdOrUSZibmwt9fX1Rq1YtcfToUaXXrVy5Uri6usrPklSrVk0EBAQIIf73y/nlQ5aoUlJSxMyZM4Wjo6PQ0dERdnZ2wtvbWyQlJSn0kV4C9vPzE5qammLs2LGZ7vONGzdE8+bNhaWlpdDT0xP29vaib9++Qgghnj9/nm58smT8999/i969ewsHBwehq6srrK2tRd++fUVkZKS8/az20c7OTumPzOy8txcuXBAAxO7du8Xs2bOFra2t0NXVFe7u7uLx48eZ7nNOrVy5UgAQ//zzj7xs3LhxQlNTUyFpCSHE3LlzBQDx8uVLIcSnpKqlpaX0TW9ycrIwMjIS/fv3l5etW7dOABD3798XQggRFxeX50T8JQsLC9GhQwf585zEl5GqVauKqlWrKpQVLVpUdO7cWalumTJlRJMmTXIZvTKpVCpMTExEly5dcvV6Hx8fUapUKZGQkKD2gergwYMFAHH58uXshv9NS0lJEe7u7ul+flSoUEEUKVKEA1U1YJ5knkwP82TmCiJP3rp1SwAQa9asUdpmZGQk6tatm+M2ZTIbqB47dkwAEMeOHVMov3LligAgv2IoI/kxUH316pXQ0tISzZo1y7Tvb1GO7lGVGTduHG7duoWBAwfCxcUFBgYG0NXVha2tLdq0aYPWrVvnpllSse7duyM1NRV79uxRKH///j1OnTqF9u3bQ19fH+Hh4ahbty5OnTqFoUOHYs6cOUhKSoKnpycOHjwof93GjRsxcuRIuLq6Yvny5fDx8UHlypVx/fp1AECHDh3QtWtXAMCyZcvg7+8Pf39/WFlZAQAGDBiAadOmoWrVqli2bBkaNmyIefPmwcvLK9P92LBhA/r27YuJEydi0aJFGdaLiIhA8+bNERwcjIkTJ2LVqlXo3r07rl27BgCwsrLCunXrAADt27eXx9ehQwcAwJkzZ/Ds2TP07dsXq1atgpeXF3bt2oUff/xRPolYVvv4pey+tzLz58/HwYMHMXbsWHh7e+PatWvo3r17pu9PToWFhQEAihQpIi+7ffs2ypQpAxMTE4W6NWvWBADcuXMHAPDvv/8iNTUV1atXV6ino6ODypUr4/bt2/Kys2fPwsTEBK9fv4aLiwuMjIxgYmKCIUOGICkpKc/7ERcXh7i4OIX9yEl86RFCIDw8XKHN169fIyIiQqlN4NP7k1WbWYmOjsbbt2/x77//YsCAAYiNjUWTJk1y3M7Lly8xf/58LFiwAPr6+nmKSRWOHDkCR0dHpZnhv1daWlo4duwYpk2bBgcHB2hra6NkyZL45ZdfEBgYCENDQ3WH+F1inmSeTA/zZMYKKk8mJycDQLr5TF9fH7dv34ZUKs1xu1mRxfrlvlSrVg0aGhp5zvm5ceLECaSmpqJnz54F3rfaqXecTPkpNTVV2NjYKF2SIrv2XnZ5x+jRowUAhXssPnz4IBwcHIS9vb382722bdsKNze3TPvM6HKfO3fuCABiwIABCuVjx44VAMT58+flZZ9/U7xixQohkUjErFmzstxf2WXpN27cyLBOZpc0yb41/9zOnTsFAHHp0qUs91EW++ffFGf3vZV9U1yuXDmFy2xWrFghACjdC5Jb7969E0WLFhX169dXKHdzcxPu7u5K9e/duycAiPXr1wsh/vdt3+fvh0znzp1FsWLF5M8rVqwoDAwMhIGBgRgxYoTYv3+/GDFihAAgvLy88rwvs2bNEgDEuXPn5GU5iS89/v7+AoDYvHmzvOzGjRsCgNi2bZtS/XHjxgkASmc7csLFxUV+xsHIyEhMmTIlV9+od+rUSeEbZqjxjGpMTIwAINq2bZut/onUhXlSGfMk82RmCipPvn37VkgkEqUzvA8fPpTnzM/P5OdEZmdUhw0bJjQ1NdPdZmVlleXPJT/OqP7yyy8CgLh9+3amfX+LcnVGlb4Ompqa8PLywtWrVxEcHCwv37FjB6ytreVnbY4fP46aNWvihx9+kNcxMjLCwIEDERwcjPv37wP4tEh9SEgIbty4keNYjh8/DgAYM2aMQvmvv/4KADh27JjSaxYuXIhRo0ZhwYIFmDJlSpZ9mJmZAQCOHj2KlJSUHMf4+bd2SUlJiIyMRO3atQF8mpEvN7L73sr07dsXOjo68uf169cHADx79ixX/X9OKpWie/fuiI6OVpq9LjExEbq6ukqv0dPTk2///N+M6n4+i2tcXBwSEhLQq1cvrFy5Eh06dMDKlSsxaNAg7Nq1C48fP871vly6dAk+Pj746aef4O7urrAf2Y3vSw8fPsSwYcNQp04d9O7dO9ttfl4nN7Zs2YKTJ09i7dq1KFeuHBITE5GWlpajNi5cuID9+/dj+fLluY5DlWJjYwEAxsbGao6EKHPMkznDPMk8WVB5skiRIvjpp5+wdetWLFmyBM+ePUNgYCC6dOkiX3c6P2aOT0xMVDi+PpfV+5NfvuecyoHqN052OcyOHTsAACEhIQgMDISXlxc0NTUBAC9evICLi4vSa8uVKyffDgATJkyAkZERatasidKlS2PYsGG4fPlytuJ48eIFNDQ04OzsrFBerFgxmJmZyfuQ+eOPPzBhwgRMmDAB48aNy1YfDRs2RMeOHeHj44MiRYqgbdu22LJli/zykay8f/8eo0aNgrW1NfT19WFlZSVfKzgmJiZbbXwpu++tTKlSpRSem5ubA/i0hnFejRgxAidPnsSmTZtQqVIlhW36+vrpvk+yS49kf5zI/s2o7ud/xMj+L7sETKZbt24AgKtXrwL49L6HhYWl+0jPw4cP0b59e5QvXx6bNm1S2o/sxve5sLAwtG7dGqampti3b5/8dyM7bX5eJzfq1KmDFi1aYMiQITh16hS2b98Ob29v+fa3b9+m+97IpvxPTU3FyJEj0bNnT9SoUSPXcWQlq6n8Pye7NC6zZT+ICgvmSeZJGebJwpUnfX198eOPP2Ls2LFwcnJCgwYNUKFCBbRp0wbApy80ZLGl93j//n2O+9TX18fHjx/T3ZbZ+5MTOcmnwPedUzlQ/cZVq1YNZcuWxc6dOwEAO3fuhBAiV/dzlCtXDv/99x927dqFH374Afv378cPP/yA6dOnZ7uN7P5yurm5wcXFBf7+/tler1cikWDfvn24evUqhg8fjtevX6Nfv36oVq1ahmuXfe6nn37Cxo0bMXjwYBw4cACnT5/GyZMnASBf7oNIz+cf/J8T/3/vT275+Phg7dq1mD9/frr3ONjY2KS71pqsrHjx4vJ6n5d/WVdW7/PXWFtbK9QrWrQogP/9UdGhQwfY2Nik+/jSq1ev0Lx5c5iamuL48eNK3y7mJD6ZmJgYtGrVCtHR0Th58qRSnazalK1lpgrm5uZwd3dHQECAvKxGjRrpvjeyQem2bdvw33//YdCgQQgODpY/gE9JLTg4GAkJCZn2q6urm+G3xLLXyr4Vzw4TExMUL15cJWvCEuU35knmSYB5Mr34ZNSVJ01NTXHo0CG8ePECf/zxB4KDg+Hv74/Q0FD5urey/tN7yO6tzgkbGxukpaUhIiJCofzjx4949+5duu/P57I6g5yQkJCjfAoAZcuWBfDp/uLvDQeq34Hu3bvj7t27+Oeff7Bjxw6ULl1a4cyLnZ0d/vvvP6XXPXz4UL5dxtDQEF26dMGWLVvw8uVLtG7dWj7xAZBxgrWzs4NUKlW6jCU8PBzR0dEKfQCfLvk4e/YstLW10aRJE7x58ybb+1u7dm3MmTMHN2/eREBAAO7du4ddu3ZlGl9UVBTOnTuHiRMnwsfHB+3bt0ezZs3g6OioVDcn34Tl5L3NL2vWrMGMGTMwevRoTJgwId06lStXxqNHj+SXl8jIJgCpXLkygE8LVWtpaSktLP3x40fcuXNHXg/49Mcf8GmShc/JfpayiTWWLFmCM2fOpPv43Lt379C8eXMkJyfj1KlT6SbonMQHfPp2tE2bNnj06BGOHj0KV1dXpTZtbW1hZWWV7mLaQUFBSm3mVWJiosKZiYCAgHTfG9lg9uXLl0hJSUG9evXg4OAgfwCfBrEODg44ffp0pn1mdJwCkJfn9Fj18PDA06dP5WcEiAoz5knmSebJwpsnS5UqhQYNGsDOzg7R0dG4desWmjZtKt+e0XuzZMmSHPcli/XLfbl58yakUmmW+yI7XtM7phMSEvDq1ascH9OtWrWCpqYmtm/fnqPXfRPUe4ssFYRnz57JJzYBIGbMmKGwXTaRwZUrV+RlcXFxwtHRUWEig/RuWh83bpzQ0NAQsbGxQoj/TbX+5Q3fskkiBg4cqFA+fvz4TCeJePz4sShWrJhwdXXN8qb59+/fK6y1JcT/JjlYvXq1EELIl+34cgkl2eQvX743Q4cOVZpUIqN9lMWe3iQRWb23skki9u7dq9CebKmALVu2ZLrvGdm1a5fQ0NAQ3bt3V3pvPnft2jUBKK4Pl5SUJJydnUWtWrUU6rZs2VLY2NjIf+ZCCLFp0yYBQJw4cUJe9tdffwkAolu3bgqv79q1q9DS0hKvX7/O9n7ExcWJmjVrCmNjY3Hz5s1M62Y3vtTUVOHp6Sm0tLSUpqH/0uDBg4W+vr58+QEhhDh79qwAINatW5ft/fhcesthPH/+XBgbGytN4pGZBw8eiIMHDyo9AIgff/xRHDx4MN11TD83evRooampqfTeRkVFCTs7O1G5cuUMX5vRxBBPnjwRhoaGwtXVVYSFhaW7/XtaR5UKN+ZJ5knmycKXJzPqR0NDQwQFBeW6jazWUbWwsBAeHh4K5T169BAGBgbi3bt3mbYdHh4udHR0RIcOHZQmRly2bJkAIH7//fd0X5vZ5IWyJd/SWz82LS1NLF68mOuo0terbt268lnSvlxvLCwsTFhbWwtTU1MxdepUsWzZMlG5cmUhkUjEgQMH5PWqVq0qfvzxRzFnzhyxadMm8euvvwpdXV3Rpk0beZ2goCD5H8jbtm0TO3fulC+Y3bt3bwFA/PTTT2LNmjXy5+3atVOI58v14f755x9hYWEhqlWrprR+2eeWLVsmSpcuLcaPHy98fX3F4sWLhYuLizAxMRHPnj2T13N1dRXFihUTa9asETt37pTPFNigQQNhYGAgJk+eLNauXSvatWsnKlWqpJSAM9vHLxNwdt/bnCRgWd2sFmO/fv260NHREVZWVuK3334T/v7+Co+nT58q1O/cubN8bTVfX19Rt25doaWlJf744w+Ferdu3RK6urqiSpUqYt26dWLy5MlCT09PNG/eXCmGfv36KfzMO3fuLAAIb2/vTGP/kuyPx379+intx8GDB3MV36hRowQA0aZNG6U2v1wn7eXLl8LS0lI4OTmJlStXirlz5wpzc3NRoUKFdNc3tLOzy3KfihYtKrp27SoWLFggNmzYIMaNGycsLCyEnp6eStYeRQ5m/Q0LCxO2trbCwMBA/PLLL8LX11dMnz5d2NnZCR0dHYU/kL+U2QyGhw4dEnp6esLc3FyMGjVKbNy4UaxZs0Z0795d6OjoKP1BTqROzJPMk8yThStPzps3T3Tv3l2sXLlSrF27VjRv3lwAELNnz87ReyOEEMHBwWLWrFli1qxZolatWgKA/PmXsxWvWbNGABCdOnUSGzduFL169RIAxJw5c7LV1+zZswUAUa9ePbFgwQKxatUq0bVrVwFANG/ePMOZ/TMbqMbHx4tmzZoJAKJRo0Zi8eLFYvPmzWL69OnC1dVVaGhoiJCQkBy/L4UdB6rfCdkvXc2aNdPdLlts28zMTOjp6YmaNWsqLbbt6+srGjRoICwtLYWurq5wcnIS48aNU0qKs2bNEra2tkJDQ0NhevqUlBTh4+MjHBwchLa2tihZsmS2FzK/fv26MDY2Fg0aNEh3enwhPn0z2bVrV1GqVCmhq6srihYtKjw8PJS+Wbxy5YqoVq2a0NHRUUhkISEhon379sLMzEyYmpqKzp07izdv3qSb7DLax8wWMs/svc1JAj5y5IjCVPgZ2bJli/yPrvQeX377nJiYKMaOHSuKFSsmdHV1RY0aNcTJkyfTbTswMFDUrVtX6OnpCSsrKzFs2DCFb2ZlPn78KGbMmCHs7OyEtra2cHZ2FsuWLcs07vTY2dlluB/pJbvsxNewYcNM358v3b17VzRv3lwYGBgIMzMz0b1793TPFBYpUkTUrl07y32aPn26qF69ujA3NxdaWlqiePHiwsvLS2GB+bzIyUBViE/H/4ABA4Stra3Q0tKSf6N87dq1TF+X2UBVCCEePXokfv75Z2Fvby90dHSEsbGxqFevnli1alWelvUhUjXmyf9hnmSeFEL9efLo0aPys8QGBgaidu3aYs+ePdl/Uz4jO37Se6SXwzZs2CBcXFyEjo6OcHJyEsuWLcv0jPuXtm/fLmrXri0MDQ2Frq6uKFu2rPDx8ck072W1HFxqaqrYtGmTqF+/vjA1NRXa2trCzs5O9O3b95tdukYiRB7vPieiAjV+/Hjs3LkTT548UdkkPqQa9+/fh5ubG44ePYrWrVurOxwiou8S82ThxTxJOcHJlIi+MhcuXMDUqVOZfAuhCxcuoE6dOky+RERqxDxZeDFPUk589wPVuLg4TJ8+HS1btoSFhQUkEgn8/PzSrbtnzx7Url0bZmZmsLS0RMOGDdNdgFsqlWLhwoVwcHCAnp4eKlasKJ/2niivbty4gYEDB6o7DErHsGHDcOXKFXWHQZTvmDupMGOeLLyYJyknvvuBamRkJGbOnIkHDx4oLe78uVWrVqFLly4oUqQI5s+fj6lTpyImJgYeHh44cOCAQt3JkydjwoQJaNasGVatWoVSpUqhW7du8qnfiYiIvmbMnURElN+++3tUk5OTERUVhWLFiuHmzZuoUaMGtmzZgj59+ijUK1OmDMzMzHD9+nX5+mCxsbGwtbWFu7s7Dh06BODTWlgODg4YOHAgVq9eDQAQQqBhw4Z4/vw5goODM1ysmoiI6GvA3ElERPntuz+jqquri2LFimVZLzY2FkWLFlVYxNrExARGRkbQ19eXlx06dAgpKSkYOnSovEwikWDIkCEICQnB1atXVbsDREREBYy5k4iI8tt3P1DNrkaNGuHkyZNYtWoVgoOD8fDhQwwbNgwxMTEYNWqUvN7t27dhaGiIcuXKKby+Zs2a8u1ERETfA+ZOIiLKLS11B/C1WLlyJSIjIzFy5EiMHDkSAFCkSBGcO3cOderUkdcLDQ2FtbW1wrfHAGBjYwMAePPmTYZ97Ny5M9OJI86fPw8jI6O87AYR0VfN0tIS9+7dU3cYlE3MnURE6ve15k4OVLPJwMAALi4uKFGiBDw8PPDhwwcsW7YMHTp0QGBgIJydnQEAiYmJ6U6HrqenJ9+eka5du6Jr164Zbvf09MThw4fzuCdERF8vT09PdYdAOcDcSUSkfl9r7uRANZs6d+4MLS0tHDlyRF7Wtm1blC5dGpMnT8bu3bsBAPr6+khOTlZ6fVJSknw7ERHR94C5k4iIcov3qGbDs2fPcPLkSaVvIywsLPDDDz/g8uXL8jIbGxuEhYXhy8mUQ0NDAQDFixfP/4CJiIjUjLmTiIjyggPVbAgPDwcApKWlKW1LSUlBamqq/HnlypWRkJCABw8eKNS7fv26fDsREdG3jrmTiIjyggPVbHB2doaGhgZ2796t8G1vSEgIAgMDUaVKFXlZ27Ztoa2tjbVr18rLhBBYv349bG1tUbdu3QKNnYiISB2YO4mIKC94jyqA1atXIzo6Wj6r4JEjRxASEgIAGDFiBKysrNCvXz9s2rQJTZo0QYcOHfDhwwesXbsWiYmJ8Pb2lrdVokQJjB49GosWLUJKSgpq1KiB33//HYGBgQgICOCC5URE9E1g7iQiovwkEV/eEPIdsre3x4sXL9Ld9vz5c9jb2yM1NRXr16/H5s2b8eTJEwBAjRo1MHXqVDRu3FjhNVKpFAsWLICvry9CQ0NRunRpeHt7o3v37nmKkzMXEtH3jp+DhQdzJxHR1+Fr/RzkQPUr8rUeZEREqsLPQcopHjNE9L37Wj8HVXqPamJiIsaMGaMwDT0RERFljLmTiIhImUoHqvr6+vD19ZXP9EdERESZY+4kIiJSpvJZf6tVq4a7d++qulkiIqJvFnMnERGRIpUPVJcvX45du3Zh06ZNCmukERERUfqYO4mIiBSpfHmaPn36QENDA4MGDcLIkSNha2sLfX19hToSiQR///23qrsmIiL6KjF3EhERKVL5QNXCwgKWlpZwcXFRddNERETfJOZOIiIiRSofqF68eFHVTRIREX3TmDuJiIgUqfweVSIiIiIiIqK8yJeBamxsLObPn48WLVqgSpUqCAoKAgC8f/8eS5cuxZMnT/KjWyIioq8WcycREdH/qPzS35CQEDRs2BCvXr1C6dKl8fDhQ8TFxQH4dA+Or68vXrx4gRUrVqi6ayIioq8ScycREZEilQ9Ux40bhw8fPuDOnTsoWrQoihYtqrC9Xbt2OHr0qKq7JSIi+moxdxIRESlS+aW/p0+fxsiRI+Hq6gqJRKK03dHREa9evVJ1t0RERF8t5k4iIiJFKh+oJiYmwsrKKsPtHz58UHWXREREXzXmTiIiIkUqH6i6urri0qVLGW7//fffUaVKFVV3S0RE9NVi7iQiIlKk8oHq6NGjsWvXLixYsAAxMTEAAKlUiidPnqBnz564evUqfvnlF1V3S0RE9NVi7iQiIlKk8smUevTogRcvXmDKlCmYPHkyAKBly5YQQkBDQwNz585Fu3btVN0tERHRV4u5k4iISJHKB6oAMHnyZPTs2RP79+/HkydPIJVK4eTkhA4dOsDR0TE/uiQiIvqqMXcSERH9T74MVAGgVKlSKr9M6eXLl3j27BmioqIghFDa3qFDB5X2R0REVJCYO4mIiD7Jt4EqAMTFxWWYGEuVKpXtdl6+fIl+/frhwoULAJBuexKJBGlpabkPloiIqBBg7iSighAfHw8jIyMAnz53DA0N1RwRkSKVD1STkpLg4+ODzZs34927dxnWy0li7N27N65evYqJEyeiVq1aMDU1VUWoREREhQJzJxERkSKVD1SHDh2KrVu3ol27dqhfvz7Mzc3z3Oa1a9cwYcIE+Pj4qCBCIiKiwoW5k4iISJHKB6oHDhzAgAED4Ovrq7I2S5QooZKkTUREVBgxdxIRESlS+TqqEokEVatWVWmbY8eOxebNm5GQkKDSdomIiAoD5k4iIiJFKj+j2rZtW5w9exaDBg1SWZuDBg1CWloaSpcujU6dOqFEiRLQ1NRUqCORSLgYOhERfZWYO4lIpZpLsq7z+S3vnkaAZoY1/+e08qRs9G368OEDjI2N1RqDRKQ3DWAOvH//XuF5VFQUfvrpJ1SrVg2DBg1CqVKllBIjAFhYWGS7j7t378LDwwMvX77MsM73MHOhp6cnDh8+rO4wiIjU5lv5HGTuLDjfyjFDlCPZGKjGpwFG5z/9P84dMORA9ZuV08/By5cvw9PTM9PJ/QpCns+oFilSBBKJ4i+DEAK3b9/G5s2bM3xdThLjwIEDERMTA19fX85cSEREXz3mTiIiKqyWL1+O2NhYPHnyBM7OzmqLI88D1WnTpiklW1W7c+cOfHx88PPPP+drP0RERAWBuZOIiAqjjx8/4tq1a0hNTcXy5cuxevVqtcWS54HqjBkzVBBG5hwcHPK9DyIiooLC3ElERIXRoUOH5LenHDt2DCtXroSGhsrn380Wlffar18/XL9+PcPtQUFB6NevX47a9PHxwZo1a/Dq1au8hkdERFToMHcSEVFhsHz5cvls8e/fv8f58+fVFovKZ/318/ND06ZNUatWrXS3P3/+HFu3bsVvv/2W7TYvXboEMzMzuLi4oGnTpihZsmS6MxeuWLEiT7ETERGpA3MnERU0Q01ANFN3FFSYREREIDg4WP48NjYWixYtQtOmTdUSj8oHqll58+YN9PX1c/Saz6+NPnr0aLp1mGyJiOhbxdxJRET5zdfXF2FhYQpl9+7dQ2xsLExMTAo8HpUMVA8dOoRDhw7Jn2/YsAFnz55VqhcdHY2zZ8+iRo0aOWpfKpXmOUYiIqLChLmTiIgKk+3btyvljoiICPj7+2PYsGEFHo9KBqr379/H3r17AXz6dvb69eu4deuWQh2JRAJDQ0M0aNAAS5cuzbLNffv2oXnz5moZvRMREeU35k4iIios7ty5o7TGNwCkpKRgw4YNahmoqmQyJW9vb3z48AEfPnyAEAKbN2+WP5c9YmNjERoaiqNHj6JMmTJZttm1a1dYWVnB3d0dS5YswYMHD1QRKhERUaHA3ElERIXFokWLEBkZme628PBwPHnypIAjyodZf6VSKbp165bndt6+fYutW7eiZMmSWLhwIcqXLw9HR0eMGDECp06dQnJysgqiBeLi4jB9+nS0bNkSFhYWkEgk8PPzS7euVCrFunXrULlyZejr68PS0hLu7u74+++/leotXLgQDg4O0NPTQ8WKFbFz506VxEtERN8e5k7mTiIidfn48SP+/PPPDLeHh4dj+fLlBRfQ/yvwyZSyy8zMDF5eXvDy8oIQAkFBQTh+/DiOHz+OtWvXQl9fH40bN4aHhwd+/PFHlCxZMlf9REZGYubMmShVqhQqVaqEixcvZli3X79+CAgIQK9evTB8+HDEx8fj9u3biIiIUKg3efJkzJ8/Hz///DNq1KiBQ4cOoVu3bpBIJPDy8spVnERERFlh7iQiopx6+/YtbG1tYWVlBQBIS0vDy5cvFdbj/nLW+AIh8sHx48dF06ZNhYWFhdDU1BQaGhpKj7wICwsTv/32m+jUqZMwNTUVGhoaonz58mLixIni4cOHOWorKSlJhIaGCiGEuHHjhgAgtmzZolRv9+7dAoA4cOBApu2FhIQIbW1tMWzYMHmZVCoV9evXFyVKlBCpqak5iu9zbdq0yfVriYi+Bd/y5yBzJ3Mnkco0Q/486KuU08/B6Oho0aNHj3yKJvtUfunv/v374eHhgfDwcHh5eUEqlaJr167w8vKCvr4+KlasiGnTpuWpD2tra/Tt2xd79+5FZGQkzp49i5YtW+Lw4cPYs2dPjtrS1dVFsWLFsqy3dOlS1KxZE+3bt4dUKkV8fHy69Q4dOoSUlBQMHTpUXiaRSDBkyBCEhITg6tWrOYqPiIi+fcydzJ1ERKRI5QPVefPmoWbNmrh9+zZ8fHwA/O+yn7t37yI0NFThNHJGxo4dq3T/Snq0tLTQuHFjLFq0CPfu3cPEiRPzvA9fio2NRVBQEGrUqIFJkybB1NQURkZGcHR0VErut2/fhqGhIcqVK6dQXrNmTfl2IiKizzF3MncSEZEild+jev/+fcybNw+amprQ0vrUfEpKCgDA3t4eQ4cOxYIFC9CrV69M21m6dCmWLVuGsmXLokePHujatSvs7e2z7F9bWzvP+/Clp0+fQgiBXbt2QUtLCwsXLoSpqSlWrFgBLy8vmJiYoGXLlgCA0NBQWFtbQyKRKLRhY2MD4NOi7RnZuXNnphNHXL16FZ6enirYIyIiKkyYO5k7iVQqv77b4e8SFSCVD1QNDAygo6MD4NOkDrq6uggNDZVvt7a2xvPnz7Ns59GjRwgICMCuXbswefJkTJkyBXXq1EGPHj3QuXNnWFpaqjr0DMXFxQEA3r17h2vXrqFWrVoAAE9PTzg4OGD27NnyZJuYmAhdXV2lNvT09OTbM9K1a1d07do1w+2enp44fPhwrveDiOhr960OOJg7mTup8IqPj4eRkRGAT8e1oaGhmiPKhuaSrOvkBn+Xvkpfa+5U+aW/Li4uuH//vvx55cqV4e/vj9TUVCQlJWHHjh0oVapUlu04Oztj+vTpePDgAW7evInRo0fjxYsXGDp0KIoXLw5PT0/s3r070+SlKvr6+gAABwcHeaIFACMjI7Rp0wZBQUFITU2V101v+v+kpCSFtoiIiGSYO5k7iYhIkcoHqu3bt8ehQ4fkCWfy5Mm4ePEizMzMYGVlhcDAwBzfC1O1alUsWbIEL1++xLlz59CrVy9cvnwZXbt2hbW1NXr16oVTp06pelfkihcvDuDTN9pfKlq0KFJSUuQTRNjY2CAsLAxCCIV6sm/GZW0RERHJMHcydxIRkSKVD1THjh2Lly9fyi/h8fDwwMWLF/Hzzz9j0KBBOHfuHPr06ZOrtiUSCRo3boyNGzciLCwMv//+O+rVq4ft27ejdevWKtwLRcWLF0exYsXw+vVrpW1v3ryBnp4ejI2NAXz6FjwhIQEPHjxQqHf9+nX5diIios8xdzJ3EhGRIpUPVNNTv359LFu2DIsXL0bjxo3z3N7Hjx9x5MgRbNu2Tb7IeHrf2KpSly5d8OrVK5w5c0ZeFhkZiUOHDsHd3R0aGp/eyrZt20JbWxtr166V1xNCYP369bC1tUXdunXzNU4iIvo2MHcydxIRfc9UPpmSzOvXr3Hp0iVERESgY8eOKFGiBKRSKaKjo2FqagpNTc0ctSeEwLlz57Bjxw4cPHgQMTExMDY2RpcuXdCjRw+4u7vnOtbVq1cjOjpaPqvgkSNHEBISAgAYMWIETE1N4e3tjT179qBjx44YM2YMTE1NsX79eqSkpGDu3LnytkqUKIHRo0dj0aJFSElJQY0aNfD7778jMDAQAQEBOd5vIiL6fjB3MneSGmQ18VDaZ//3NAKyczieFlnXScdXOXETUT5R+UBVCIFff/0Vq1evRmpqKiQSCSpUqIASJUrgw4cPsLe3x8yZMzF69OhstRcUFIQdO3Zgz549CA8Ph5aWFlq0aIEePXrA09NTPiNgXixevBgvXryQPz9w4AAOHDgAAOjRowdMTU1hbW2NP//8E2PHjsWyZcuQkpKCOnXqYPv27ahUqZJCe/Pnz4e5uTl8fX3h5+eH0qVLY/v27ejWrVueYyUiom8PcydzJxERKVL5QHXRokVYsWIFJkyYgCZNmqBZs2bybaampujQoQP279+fZbKdNm0adu7ciWfPnkEIgbp162Lq1Kno0qULLCwsVBpzcHBwtuo5OjrKk3BmNDQ04O3tDW9v7zxGRkRE3wPmTuZOIiJSpPKB6saNG9GrVy/MnTsX7969U9pesWJFnDhxIst2Zs+ejbJly2LmzJno3r17thYsJyIi+hoxdxIRESlS+UD11atXmU56YGhoiNjY2CzbuXXrFqpUqaLK0IiIiAol5k4iIiJFKh+oFi1aFK9evcpw+61bt7K1aDkTLRERfS+YO4m+A1lN2gQU6MRNRIWdypen6dChA9avX49nz57JyySST7+Yp0+fhp+fHzp37qzqbomIiL5azJ1EhZehJiCafXoYcgJqogKj8oGqj48PbGxsULlyZfTq1QsSiQQLFizADz/8gFatWqFixYqYNGmSqrslIiL6ajF3EhERKVL5QNXU1BTXrl3D+PHj8fr1a+jp6eGPP/5AdHQ0pk+fjsDAQBgYGKi6WyIioq8WcycREZEild+jCgD6+vqYMmUKpkyZkh/NExERfXOYO4mIiP5H5WdUT506hbi4OFU3S0RE9M1i7iQigPfDEn1O5WdUW7VqBU1NTVSqVAn169eXP6ysrFTdlVy/fv1QvHhxjBo1Kl/7ISIiyg/MnURERIpUfkb12rVrmDdvHkqUKAF/f3906tQJxYoVQ7ly5TBw4ED4+/sjODhYpX36+flh7ty5cHJywtSpU1XaNhERUX5j7iQiIlKk8jOqNWvWRM2aNTF27FgAwP379xEYGIjAwECcPHkSmzdvhkQiQWpqqsr6fP78OeLi4vDHH3/g4sWLKmuXiIioIDB3EhERKcqXyZRkkpKSEBERgYiICISHhyMqKgpCCDg5Oam0Hzs7OwCAm5sbhg4dqtK2iYiIChJzJxERUT5c+nv06FFMmDABdevWhampKZo2bYr9+/ejXLly+O233xAaGopHjx7lqM2ZM2fi7t27GW6/d+8eZs6cmdfQiYiI1IK5k4iISJHKz6h6enpCU1MTHTt2xJQpU1CvXj2Ymprmqc0ZM2bA2dkZ5cuXT3f73bt34ePjg2nTpuWpHyIiInVg7iQiIlKk8oFq69atceXKFezZswdXr15F/fr10aBBA9SvXx/lypVTdXcAgPfv30NHRydf2iYiIspvzJ1E9C2Lj4+HkZERACAuLg6GhoZqjoi+BiofqB45cgTAp29qZRNBzJo1C2/evIGFhQXq1auH+vXr49dff820nUuXLilM7nDgwAE8efJEqV50dDR2796NChUqqHQ/iIiICgpzJxERkaJ8m0ypfPnyKF++PIYMGYLk5GTs3LkTCxYswOHDh3HkyJEsk+2FCxfg4+MDAJBIJDhw4AAOHDiQbl1XV1esWrVK5ftARERUkJg7iYiIPsmXgWpcXBwuX76MS5cuITAwEDdu3MDHjx+hpaWF2rVro379+lm2MX78eAwfPhxCCBQtWhTr169Hx44dFepIJBIYGBhAT08vP3aDiIiowDB3EhER/Y/KB6rVqlXDP//8g7S0NBgZGaFOnTqYNGkS6tevj1q1akFfXz9b7ejr68vrPn/+HFZWVjAwMFB1uERERGrH3ElERKRI5QNVe3t79OzZE/Xr10eVKlWgoZH3FXBka70RERF9i5g7ieir1VySdZ20z/7vaQRoZqPd0yK3EdE3QuUD1f3796u6SQghsGHDBmzevBnPnj1DVFSUUh2JRILU1FSV901ERJTfmDuJiIgU5dtkSjJRUVHo2LEjlixZgipVquSqjfHjx2Pp0qWoXLkyevToAXNzcxVHSfR94PTwRF8H5k4iIvre5ftA9ePHj7h48WK63+Rm19atW9GxY0fs2bNHhZEREREVTsydRET0vcv7TTAFIDExEU2bNlV3GERERF8N5k4iIvqafRUD1SZNmuDGjRvqDoOICqn4+HhIJBJIJBLEx8erOxyiQoG5k4gKC0NNQDT79DDMzkRKRFDxQDUxMRFjxozBkSNH5GX6+vro3bs3ihcvnut2165di2vXrmHu3Ll49+6dKkIlIiIqFJg7iYiIlKn0HlV9fX34+vrC1dVVXmZiYoItW7bkqV0XFxdIpVJMnToVU6dOhZ6eHjQ1Fb+OkUgkiImJyVM/REREBY25k4iIvgXv3r3D3bt3YWtrC2dn5zy3p/LJlKpVq4a7d++qtM2OHTtCIsnGGk1E37us1jLjOmb0nYmMjMS8efOwZMkSdYeSKeZOopzhLPZEhc/ly5fRvn17SCQS/Pzzz5gzZw4sLCxy3Z7KB6rLly/Hjz/+iPLly6NPnz7Q0sp7F35+fnkPjIiIvju//fYbVq1ahfnz50NbW1vd4WSIuZOIiL4VK1aswMqVK1GmTBn4+PhgyJAh0NDI+R2nKh+o9unTBxoaGhg0aBBGjhwJW1tb6OvrK9SRSCT4+++/Vd01EX2LsjpLDPBMMWVo69atEELg6NGjaN++vbrDyRBzJxERfQuEEPDw8MCgQYOwevVqTJ06FRs3bsTKlSvRoEGDHLWl8ll/LSws4OLiggYNGqBWrVooUaIELC0tFR65OQX88uVLDB48GC4uLjA3N8elS5cAfLqsa+TIkbh9+7aqd4WIiL5iT548QWRkJFJTU7F06VJ1h5Mp5k4iIvqWaGlpYfTo0Xjy5Anq1auHpk2bwsvLC69fv85+G6oO6uLFi6puEvfv30f9+vUhlUpRq1YtPHnyBKmpqQCAIkWK4M8//0R8fDw2b96s8r6JiOjrtHz5ckRERAAAnj9/jsjISBQpUkTNUaWPuZOIiL4VQvzvqjULCwusWbMGQ4cOxejRo+Hi4gJvb29Mnjw5y3ZUPlDND+PHj4eZmRmuXbsGiUSCokWLKmxv3bo1du/eraboiL4esnXMiL51UqkUx44dkz8PDQ3Fxo0b4e3trcaoChZzJ33VODkg0VejcePGAD5drQMAXbt2hZ6eXrp1NTQ0MHXq1IIZqMouI5Jdcyx7npWcXKN86dIlTJs2DVZWVumuBVeqVKkcnUYmIqJv27lz5/D+/Xv5c6lUim3bthWagSpzJxERfSvev38PIQTi4uIAANHR0RlOYGhvb5/tdvM8UG3UqBEkEgkSExOho6Mjf54RIQQkEgnS0tIyrPMlqVQKAwODDLe/ffsWurq6OYpbJi4uDosWLcL169cRFBSEqKgobNmyBX369MnwNSkpKahUqRIePHiARYsWYezYsUrxLl68GOvWrUNoaCjKlCkDb29vdO3aNVcxElHmeKaYvrRo0SLExsYqlEVGRuKff/5BxYoV1RTV/zB3MncSEX0rZBP9HTlyBO3atcPJkydhZ2eX53bzPFC9cOECAEBHR0fhuSpVrVoVx44dw9ChQ5W2paamYteuXahdu3au2o6MjMTMmTNRqlQpVKpUKVv3Ca1atQovX77McPvkyZMxf/58/Pzzz6hRowYOHTqEbt26QSKRwMvLK1dxEhFR9sTGxuL+/ftK5ZGRkVi0aBH8/f3VEJUi5k5lzJ1ERF83R0dHjBo1CiYmJippL88D1YYNG2b6XBW8vb3h4eGBIUOGyJNVeHg4zp49i7lz5+LBgwdYvXp1rtq2sbFBaGgoihUrhps3b6JGjRqZ1o+IiMDMmTMxYcIETJs2TWn769evsWTJEgwbNkwe04ABA9CwYUOMGzcOnTt3hqZmdm6kIPq6cTF2Updt27bJJ1H60qVLl5CSkqL2NVWZOxUxdxIRfb3++OMPODg4wM3NLdNZ9t+9e4ewsDA4OjoqLcGWHpUvT5MfWrVqBT8/P+zevRvu7u4AgB49eqB58+b466+/sG3bthyvyyOjq6uLYsWKZbv+xIkT4eLigh49eqS7/dChQ0hJSVH4BlsikWDIkCEICQnB1atXcxUnERFlz4YNG5CSkpLutnfv3uHIkSMFHJF6MHfSt0x2y4do9un/RKQ+7u7uqFixYpbzLURERKBixYr47bffstVuvsz6m5SUhP379+Ovv/5CTEwMpFKpwnaJRJLj6fB79uyJDh064PTp03jy5AmkUimcnJzQokULGBsbqzL8DAUFBWHr1q34888/M7yX6Pbt2zA0NES5cuUUymvWrCnf/sMPP+R7rERE36MXL17gxYsX8jVHhRBISEiQn9FPS0vD6tWr0aFDB3WGmS7mTuZOIqKvVWpqKlq2bImdO3eibdu26dYpV64cmjZtisOHD2PYsGFZtqnygeqLFy/QuHFjBAcHw8zMDDExMbCwsEB0dDTS0tJQpEgR+eWAOWVoaIj27durOOLsEUJgxIgR6NKlC+rUqYPg4OB064WGhsLa2lopGdvY2AAA3rx5k2EfO3fuxM6dOzPcfvXqVXh6euY8ePp+3M6HNnN5zMnWawSAzp07Q0srlx83+bFPQK73iwq34sWL4/z58/Ln0dHRWL58OWbMmCEvs7S0VENkmWPuZO6kLxRkPiuovgoyn32rfVGhtWjRIhw4cACdOnXCxo0bM5xcr3nz5liyZEm22lT5QHXcuHGIiYnBtWvX4OjoiKJFi2L37t2oV68eVq5cidWrV+PUqVOZtiGbbKFUqVIKz7Miq58f/Pz88O+//2Lfvn2Z1ktMTEx3FkXZWkKJiYkZvrZr166Zzm7o6emJw4cPZzNi+i5lte5cbuTymPv8HtW9e/fm/h7V/NgnINf7ReqV1YBDW1sb1apVkz9/9+4dihQpolBWGDF3MnfSFwoynxVUXwWZz77VvihXCuLLOhMTExw/fhw9evRA//798fbtW4wbNy7derJlbLKi8oHq+fPnMXToUNSsWVO+hp0QArq6uhg3bhwePHiA0aNHKyzE/iV7e3uFaftlz7OSk2n7cyI2Nhbe3t4YN24cSpYsmWldfX19JCcnK5UnJSXJtxN99bKTlLgYO1G2MXcydxIRfe20tbWxe/duDB8+HBMmTMDjx4+xZs0ahQkMT506BWdn52y1p/KBakJCgnwhVxMTE0gkEsTExMi316lTR2nttC/99ttvkEgk8p2SPVeXxYsX4+PH7mYzfwAAteZJREFUj+jSpYv8sqWQkBAAQFRUFIKDg1G8eHHo6OjAxsYGFy5ckK95JxMaGgrg02VpREREn2PuZO4kIvpWrF69Gra2tpgyZQouX76M0aNHw8bGBgcPHsSBAwewdu3abLWj8oFqqVKl5IlIS0sLtra2uHbtmnziivv378sv5cnIl9c0Z7aAeEF4+fIloqKi4ObmprRt7ty5mDt3Lm7fvo3KlSujcuXK2LRpEx48eABXV1d5vevXrwMAKleuXFBhExHRV4K5k7mTiOhb4u3tjapVq2L48OEYPHiwvLx3794YOHBgttpQ+UDV3d0dhw4dwvTp0wF8SpTz5s1DVFQUpFIp/P390atXL1V3m69GjhyJdu3aKZRFRERg0KBB6NOnD9q2bQsHBwcAQNu2bfHLL79g7dq18rXghBBYv349bG1tUbdu3YIOn4iICjnmTuZOIqKv1fTp01GxYkWl8hYtWuC///7DjRs3EBkZiXLlysHR0THb7ap8oDpx4kTcuHEDycnJ0NXVxaRJk/DmzRvs27cPmpqa6NatW6YLwQLAzJkzc9yvRCLB1KlTcxXz6tWrER0dLZ9V8MiRI/JvtkeMGIGqVauiatWqCq+RXcbk5uamkIhLlCiB0aNHY9GiRUhJSUGNGjXw+++/IzAwEAEBAVywnIiIlDB3MncSEX2tpk2bluE2DQ0N1KpVK1ft5nmgevjwYVSvXl1+/0ipUqUUZhDU09PDpk2bsGnTpmy3+fkyAjKye1aEEErlsntacptsFy9ejBcvXsifHzhwAAcOHADwaXF0U1PTHLU3f/58mJubw9fXF35+fihdujS2b9+Obt265So+oq+RbDF2IlLG3KmMuZOIiD6nkdcG2rdvj4sXL8qfOzo65nkaeKlUqvB49eoVKlSogK5duyIoKAgxMTGIiYnB9evX4eXlhUqVKuHVq1e57i84OBhCiHQfssktvmRvbw8hRLqTW2hoaMDb2xvBwcFITk7G3bt30b1791zHR0RE3xbmTuZOIqLvwd27d9GsWTOYmZnBxsYGAwYMUJgsMDN5HqgaGxsjOjpa/jw4ODjba+Nk17Bhw+TfrFavXh3GxsYwNjZGjRo1EBAQACcnJwwbNkylfRIREeUX5k76VsXHx0MikUAikSA+Pl7d4RCRGn38+BGtW7dGWloaAgICsHjxYpw6dQpDhw7N1uvzfOlvzZo1MWfOHISHh8sv8zl+/DjCwsIyfI1EIsEvv/yS7T7Onz+PBQsWZLi9SZMmmDBhQvaDJiIiUiPmTiIi+lbcvXsX5cuXT7f81atXOHfunHzt1Ojo6GzfcpLngeratWvRq1cvzJo1C8CnRLpjxw7s2LEjw9fkNNnq6enh6tWrGDJkSLrbr1y5kuW0/URERIUFcycREX0ratSogX79+mH27NkwNzeXl5uZmUEikeDp06fygerjx48V6mQmzwNVZ2dnXLlyBUlJSYiIiIC9vT2WL1+Otm3b5rVpue7du2PlypUwMzPDiBEj4OTkBAB4+vQpVq5ciR07dmDkyJEq64+oIMXHx8PIyAgAEBcXB0NDQzVHRET5jbmTiIi+FUFBQRg1ahRKly6NGTNmYOjQodDQ0ICjoyM6deqEDh06oGXLlnj//j0CAwOxYcOGbLWrsuVp9PT0UKpUKUyfPh3u7u6ws7NTVdNYsGABIiMjsXr1aqxZswYaGp9urZVKpRBCoGvXrple3kRERFQYMXcSEdHXrkKFCjh//jwOHDiAsWPHYsOGDVi+fDnc3d2xe/du7Nq1C9evX0fZsmUxb9481K5dO1vtqnwdVdli5aqko6MDf39/jBs3DsePH5dPh29nZ4dWrVqhUqVKKu+TiCg9PANO+YG5k74KzSVZ10n77P+eRkBWS+CeFllUIKKvRYcOHeDh4YElS5agXbt2aNq0KZYsWQIvLy94eXnluD2VD1TzU8WKFVGxYkV1h0FERPTVYO4kIqKCoqOjA29vb/Tt2xcTJ05E+fLlMXr0aEyePBkGBgY5auurGqgSfZWy+gY6p98+A/wGmoiIiIgKjbt372L//v0IDw9H8eLF0blzZ/j5+WHYsGEYOXIktm3bhnnz5qFHjx7ZbjPP66gWlBMnTqBZs2awtLSElpYWNDU1lR5ERET0P8ydRESU3/z9/VGpUiUcOXIEsbGxOH/+PCpWrIgDBw6gRo0auHr1KubPnw9vb2/Uq1cPN2/ezFa7X8VAdf/+/fDw8EB4eDi8vLwglUrRtWtXeHl5QV9fHxUrVsS0adPUHSYREVGhwdxJREQFYcaMGRg0aBBu3ryJ7du34/z589i4cSNmzpwpr9O9e3f8999/cHd3R8OGDbPV7ldx6e+8efNQs2ZN/Pnnn4iKisK6devQr18/uLu7Izg4GLVr14aDg4O6wySir11+TBQC8FJtUgvmTlIHQ01ANFN3FERUkD58+IBSpUoplNnZ2eHDhw8KZQYGBpg1axYGDBiQrXbzZaCalpaGU6dO4dmzZ4iKioIQin+kSSQSTJ06Ndvt3b9/H/PmzYOmpia0tD6FnJKSAgCwt7fH0KFDsWDBAvTq1Ut1O0FUQJjUiQhg7iQioq9Tnz59MHPmTISFhcHV1RVv3ryBr68vhg4dmm797C7FpvKB6s2bN9GxY0eEhIQoJVmZnCZbAwMD6OjoAADMzMygq6uL0NBQ+XZra2s8f/48b4ETERGpCXMnERF9rRYsWABnZ2fs3r0bZ86cga2tLebPn4/evXvnqV2V36M6dOhQJCYm4vfff8f79+8hlUqVHmlpaVk39BkXFxfcv39f/rxy5crw9/dHamoqkpKSsGPHDqXTzURERF8L5k4iItWIj4+HRCKBRCJBfHy8usP5LkgkEgwcOBDnzp3DvXv3cPr06TwPUoF8GKj+888/mDBhAtq0aQMzMzOVtNm+fXscOnQIycnJAIDJkyfj4sWLMDMzg5WVFQIDAzFx4kSV9EVERFTQmDuJiIgUqfzS3xIlSmR42VJujR07FmPHjpU/9/DwwMWLF3HgwAFoamqidevWaNy4sUr7JCJKD+8p/vbt2bMHu3btgq+vL6ysrDKte+LECdjZ2cHV1TVPfTJ3EhERKVL5QHXChAlYvHgxBg4cCBMTkzy3l5ycjFOnTsHe3h4VK1aUl9evXx/169fPc/tERESf27x5M9LS0rIcpALAqlWrYGRkhD179uSpT+ZOIiIiRSofqH748AFGRkZwdnaGl5cXSpYsqbSguEQiwS+//JKt9nR0dNC5c2esWLFCIdkSERHlh3///ReDBw9WKn/06BGWLVuGJUuWwMDAAABQp04d+Pn55blP5k4iIiJFKh+ofn6Z0erVq9Otk5NkK5FIULp0aURGRqokPiIiosy8e/cOtra2SuUhISHw9fXF7Nmz5QPV4sWLIywsLM99MncSEWVDfqx3zrXOCy2VD1TzY6r7SZMmYcyYMejcuTNcXFxU3j4REZGMsbFxuoPPN2/eAABiYmJgaWkJAEhISJAPWvOCuZOIiEiRygeq2V3ANSeuXbsGS0tLlC9fHo0aNYK9vT309fUV6kgkEqxYsULlfRMR0felWrVqCAgIwIQJE6Cl9b80uWfPHmhoaODQoUP45ZdfIITA/v378zyREsDcSURE9CWVD1Tzw+eXQZ07dy7dOky2pGrx8fEwMjICAMTFxcHQ0FDNERFRQRg5ciQ8PT1Rq1Yt9O7dG/r6+ti3bx+MjY3RsWNHjB8/HhcuXMCrV6/w999/4/Dhw+oOOV3MnURE9DXLl4HqP//8g1WrVuGvv/5CTEwMpFKpwnaJRIKnT59mu70vX09ERJRfWrdujaVLl2Ly5Mnye0Jr1KgBf39/6OrqQldXF2fPnoWZmRk2bNgADw8PlfTL3ElERPQ/Kh+oXrx4ES1btoS5uTmqV6+O27dvw93dHUlJSbh69Src3NxQrVo1VXdLRESkMqNGjcLPP/+M+/fvw9TUFKVLl5Zv27Ztm8r7Y+4kIlINrnf+7dBQdYPTpk2Do6Mj/vvvP2zZsgXApwkd/vzzT1y5cgUhISH46aefVN0tERGRShkYGKB69eowMjLCrVu38Oeff+LOnTv48OGDyvti7qT8FB8fD4lEAolEgvj4eHWHQ0SULSofqP7111/o378/TExM5GvApaV9mie6Vq1aGDRoEKZOnZqzIDU0oKmpmenD0NAQLi4uGDx4cI4ujSIiIvpSREQEfvnlFxQvXhy2traoUaMGGjRogKpVq8LMzAx16tTBnj17VNYfcycREZEilQ9UtbS0YGxsDAAwMzODtrY2IiIi5NsdHR1x//79HLU5bdo0VKxYEZqamvDw8MDo0aMxevRotG7dGpqamqhcuTKGDh0KV1dXbNmyBVWrVsXff/+t0v0iIqLvQ3BwMKpUqYKVK1dCR0cHderUgaOjIyQSCSwtLTFs2DAAgJeXF/r06QMh8r4GH3MnERGRIpXfo+rs7IzHjx8D+DTxQ9myZXHw4EF0794dAHDs2DEUK1YsR20WL14ckZGRePjwIRwdHRW2PXnyBI0aNYKrqysWLVqEx48fo06dOpg0aRKOHTummp2ibw8XjCaiDIwdOxbx8fE4ffo0mjRpIi+/efMmPD09YWxsjKtXr+Lw4cPo3LkzatWqhSFDhuSpT+ZOIiIiRSo/o/rjjz9i586dSE1NBQCMGTMGBw4cQOnSpVG6dGkcPnwYgwYNylGbixYtwrBhw5QSLfApuQ8bNgzz5s0DAJQuXRqDBw/GlStX8r4zRET03Tl37hx++eUXhUEqAFSvXh0zZszAqlWrIISAp6cnevXqhQ0bNuS5T+ZOIiIiRSo/ozp16lSMGjVKfo9N7969oampif3790NTUxOTJ09Gnz59ctRmSEiIwqLrX9LS0sKrV6/kz+3t7ZGcnJyr+ImI6PuWkpICExOTdLeZmJggLi4OiYmJMDAwQJ06dRAQEJDnPpk7Kdfy4wohgFcJEZHaqXygqq2tDUtLS4WyHj16oEePHrlu083NDevWrUPPnj1hbW2tsC0sLAzr1q2Dm5ubvOzZs2c5vkSKiIgIAOrWrQtfX1/07NkTRYoUkZfHx8djxYoVcHBwgIGBgbxM9v+8YO4kIiJSpPKBqkxycjL++usvREREoF69egrJPqcWL16MVq1awdnZGe3atYOzszOAT/fY/P7770hJScFvv/0GAEhKSoKfnx9atWqlkv0gIqLvy/z589GgQQM4OzvD09MTtra2iIiIwJEjR/Du3Tvs2rVLXvf27duoVKmSyvpm7iQiIvokXwaqK1euxIwZMxATEwMAOHPmDNzd3REZGYmyZcti4cKF6NevX7bba9SoEa5cuYLp06fjwIEDSExMBADo6emhadOmmDFjBqpWrSove/Pmjep3ir47XDCa1C0+Ph5GRkYAgLi4OBj+H3v3HRbF8bgB/F1AuoAoCKIUeyHYscSKDUUxokQQrLGCBY29i8ZObETFEjGoiAUFS6LGntijxhiNHRUFKyBdyv7+4Md9PY/OwgG+n+fhkdudm5m9O+9l7nZndHSU3KMvQ5MmTXDp0iXMnDkT+/fvR1JSEsqVKwdbW1vMnj0bXbt2lZV1c3ODsbGxJO0yO4mIiP5H8oHqtm3b4OXlBRcXF3Tt2lUuVCtVqgQ7Ozvs3r07X2ELAI0bN0ZoaCjS09NlU/YbGxtDRUXy+aCIiOgLZ21tjdDQUABAbGwsdHR0ssybzydcKihmJxERkTzJk8rHxwe9e/fGrl270KtXL4X9TZs2xb///lvg+lVUVGBiYgITExNJgjYuLg7z5s2Dvb09DA0NIQgC/P395cqkp6fD398fjo6OqFatGnR0dGBtbY1FixYhKSkpy3q3bt2KevXqQVNTE7Vq1cK6desK3VciIip+5cuXL/KBHbMzA7OzaGSeISR2yfidiKg0kDx5Hz58mOM1LoaGhnj37p3UzRbY27dv4e3tjbt372Z7nVFCQgKGDh2KN2/eYPTo0Vi9ejVsbW0xb948dO/eXWGxdz8/PwwfPhwNGjTAunXr0KpVK4wfPx7Lli0rjkMiIqIicvv2bXTp0gUGBgYwNTXF8OHDZafqFgazk9lJRETyJD/118DAAG/fvs12/507d0rUrIKmpqaIiIiAiYkJrl27hubNmyuUUVdXx59//onWrVvLto0YMQKWlpaYN28eTp48ic6dOwMAEhMTMWvWLDg4OGDfvn2ysunp6Vi4cCFGjhyJChUqFM/BERGRZD5+/AgHBwfUqFEDO3fuRHR0NKZPn47ExMRCL1HD7GR2EhGRPMm/Ue3Rowc2bdqE6OhohX3//vsvNm/eDEdHR6mbLTANDY1cw19dXV0uaDP16dMHAHD37l3ZttOnT+Pdu3fw8PCQK+vp6Yn4+HgcOXJEgl4TUanXVcj9x1H3f+UddXMvT5K4fft2ttufP3+OTZs2wcHBAW5ubpg+fTp+/fXXQrfJ7GR2EhGRPMkHqosWLUJaWhqsra0xe/ZsCIKA7du3w93dHc2aNYOxsTHmzp0rdbNKERkZCQByywfcuHEDANCsWTO5sk2bNoWKiopsPxERlUzNmzeHp6cnoqKi5LYbGBhAEAQ8evRItu3BgweSfNPH7GR2ElHpEx8fD0EQIAgC4uPjld2dMkfyU3+rVKmCv/76CzNnzkRQUBBEUURAQADKly8PV1dXLF26tFDrwpUky5cvh56entx1RREREVBVVVVYrkBdXR0VK1bMcfr/wMBABAYGZrv/4sWLJeoT9VKtKP7mye65KYttFdXfjGxLTuonl/A5/w2o5falKd8fJHHlyhVMmDABtWrVwvz58+Hh4QEVFRVUr14d/fr1g5OTE+zt7fH+/XucP38emzZtKnSbzE5mZ4GVsPctSdopq22V9ueqrLZViPeH1NRU2e/Ozs5QUyuSlT+RmpoqO3une/fuRdZOSVMkR2lsbIwtW7Zgy5YtePPmDdLT02FkZJTnmQbPnTtXoHbbtWtXoPsVxOLFi/H7779j/fr1MDAwkG1PTEyEurp6lvfR1NSUrWOXFVdXV7i6uma739HRUbZcAhVSUZwmmd1zUxbbKqrTTNmWnPg0QPdUxu97G+Zhts4v4P2hOAYcX331FU6dOoXg4GBMnjwZmzZtwurVq2FnZ4egoCDs3r0bly9fRt26dbFkyRK0bNlSknaZnczOAilh71uStFNW2yrtz1VZbasQ7w+frne+d+/eIlvvvLDtlNYP64p8OG5kZJTv+3To0AGCkPcXoiiKEAQBaWlp+W6rIIKCgjB79mx89913GDNmjNw+LS0tfPz4Mcv7JSUlQUtLqzi6SERlQOaSEqQcTk5O6NmzJ3x8fPDNN9+gc+fO8PHxgYuLC1xcXIq0bWbn/zA7iYi+TEUyUI2Pj8f+/fvx+PFjREVFKUxBLwgC1qxZk+39T58+XRTdksSJEycwaNAgODg4YOPGjQr7TU1NkZaWhtevX8udwvTx40e8e/cOVapUKc7uEhFRIairq2PGjBkYOnQopk+fDmtra3h5eWHWrFnQ1taWtC1mJ7OTiIj+R/KB6smTJ+Hs7JzlzIWZcgvb9u3bS90tSVy+fBl9+vRBs2bNsGfPnizPD2/UqBEA4Nq1a+jRo4ds+7Vr15Ceni7bT0REJdft27exf/9+vHr1ClWqVIGzszP8/f3h6emJ8ePH45dffsGSJUvg7u4uSXvMzkYAmJ1EVILk5TTjT09IcdQFcrtE57iY9fbc2spvOzm1VYpIPuuvp6cndHR0cOzYMURHRyM9PV3hp7hOM5LS3bt34eDgAEtLSxw+fDjb05Ds7OxgaGiIDRs2yG3fsGEDtLW14eDgUBzdLbM4uxoRFbWAgAA0bNgQhw4dwocPH3Dq1CnY2NggODgYzZs3x8WLF7F06VLMmDEDX3/9Na5du1boNpmdzE4iIpIn+Teqz549w7Jly9Cli7QXViUlJWH//v24fv06YmJikJ6eLrdfEARs3bq1QHX7+voiOjpaNqvgoUOHEB4eDgAYN24cVFRU0K1bN0RFRWHKlCkK67nVqFEDrVq1ApBxnc3ChQvh6ekJZ2dndOvWDefPn8eOHTvwww8/wNDQsEB9JCKi4jF//nyMGjUK69evl2375Zdf4O3tDScnJwCAm5sb+vTpgyVLlqB9+/aF/uCM2cnsJCIieZIPVG1sbBATEyNpnU+fPkXHjh0RFhYGAwMDxMTEwNDQENHR0UhLS0OlSpVkM2EVxMqVK/H06VPZ7eDgYAQHBwOA7LSu58+fAwCmT5+ucP/BgwfLwhYAPDw8UK5cOfj4+CA0NBTVqlXDqlWrMGHChAL3kYiIikdsbCzMzc3ltllYWCA2NlZum7a2NhYuXIjhw4cXuk1mJ7OTiIjkST5QXbZsGVxdXWFvb6+wcHdBTZkyBTExMbh06RKqV68OY2NjBAUF4euvv8batWvh6+uLY8eOFbj+sLCwXMt8PqlFbkaMGIERI0YUsEdERKQsQ4YMgbe3NyIjI1G/fn28fPkSfn5+8PDwyLK8hYVFodtkdmZgdhIRUSbJB6rt27fH6tWr0apVK9SrVw/VqlWDqqr8Fb+CICAkJCTPdZ46dQoeHh6wtbXF+/fvAWSEn4aGBqZMmYK7d+/Cy8tL4bQiIiKi/Fq2bBlq1qyJoKAgnDhxAmZmZli6dCkGDx5cZG0yO4mISp/iWkbuS12uTvKB6v79++Hu7o60tDSEh4crnCoFIF/rvAFAQkICLC0tAQB6enoQBEHuFKlWrVph8uTJheo3lQBFMbsaUCZmPSOi4iMIAkaOHImRI0cWW5vMTiIiInmSD1SnT5+OOnXqYP/+/ahdu7YkdZqbm8smaFBTU4OZmRkuXbokm9Tizp070NTUlKQtIqIvUXx8vOx6xbi4OOjo6Ci5R18WZicRESlLeHg4evbsKbtcIz09HeHh4WjYsKGsTIsWLbBp06Zi7ZfkA9WXL19ixYoVkgUtkDFtfUhICObNmwcg4/qhJUuWICoqCunp6QgICMCgQYMka4+IiKg4MTuJiEhZTExMEB0dLTdBHgC5tb07depUzL0qgoFq8+bN8ezZM0nrnD59Oq5evYrk5GRoaGhg5syZePnyJfbt2wdVVVUMGDAAP/74o6RtEhERFRdmJxERKYuamho6duwIf3//LPebmJgoZQZ2yQeq69atQ69evdCkSRN8++23ktRpbm4ut1SApqYmtmzZgi1btkhSPxERkTIxO4mISJkmT56MI0eO4M2bNwr7TExMJJnhPr8kH6i6ubkhNTUVrq6uGDFiBKpWrZrlzIV///231E3TF+BLnfWMiMo2ZicRESlTgwYNUKlSJYWBqoaGBjw9PZXSJ8kHqoaGhqhYsSJq1apV4Dq8vb0hCAJmzZoFFRUVeHt753ofQRAwZ86cArdJRESkLMxOIiJStiFDhmDmzJlIS/vfMhtGRkZwdXVVSn8kH6ieOXOm0HXMnz8fgiBg2rRpUFdXx/z583O9D8OWiCgbRbH0E5d9khSzs2Tg7NdE9CX77rvvsHbtWrx48UK2rWHDhkp7L5R8oCqF9PT0HG8TEVHpxIFA0WF2EhFRYVSsWBE1atSQDVQNDAwwZcoUpfVHRWktExEREX2h4uPjIQgCBEFAfHy8srtDRAQAmDRpkuwDZUNDQ7Rr105pfSkVA1VVVVXs2rUr2/1BQUEKk04QERF9yZidRESUXw4ODjAwMAAA9O7dG4KQh8uHikiJPPX3c6KY87VQaWlpSn0QiYiIShpm52eK4lptgNdrE1GZoqamhrZt22Lv3r1KWTv1U6XiG1UA2Ybphw8fcOzYMVSqVKmYe0RERFSyMTuJiCi/Jk6cCENDQ6WsnfqpEjtQXbBgAVRVVaGqqgpBEODu7i67/elPhQoVEBAQABcXF2V3mYio1Mpco1jskvE7lU7MTiIiKqzmzZvLzfyrLJKf+luvXj0MHDgQbm5uhRqF29rawsPDA6IoYv369ejSpQtq164tV0YQBOjo6KBp06ZwcnIqbNeJiKiwcju9kqdWZonZWcbwNGMiKuXU1JR/hajkPahWrRrmzZuHuXPnonXr1hg0aBCcnZ2hr6+fr3q6d++O7t27A8iYGW/06NFo0aKF1N0lIiJSOmZnyZB5ZgERESmf5Kf+Hj9+HOHh4VixYgUSExMxcuRImJiYoF+/fggJCUFKSkq+69y2bRuDloiIyixmJxERkbwi+U63cuXKmDhxIiZOnIh79+5hx44dCAwMxIEDB2BgYID+/fvD3d0drVu3znOdaWlpOHbsGB4/foyoqCiF2QwFQcCcOXOkPhQiIqJiwewkIiL6nyI/+bhOnTpYuHAhPD09MWHCBOzduxcbN26En58fqlevDi8vL4wZMwYqKtl/uXvt2jX07dsX4eHh2U63z7AlIqKygtlZ9vE0YyKinBXprL/x8fHYsWMH7O3tYW5ujgMHDqBnz57Ys2cPDhw4gDp16mD8+PEYM2ZMjvV4eHggMTERBw8exPv375Genq7wk5aWlmMdRESkfJxdOHfMTiIioiL4RjXzNKMdO3YgNDQUCQkJaNq0KXx8fODq6iq3ZpujoyNmzpyJn376CX5+ftnWeevWLfzwww/o1auX1N0lIiJSOmYnERGRPMkHqiYmJnj//j3MzMwwbtw4DBo0CPXq1cu2vI2NDWJjY3Oss2rVqtmetkRERFTaMTuJiIjkST5QdXBwwMCBA2FnZwdByH0dMRcXl1wXHJ82bRpWrlyJkSNHQk9PT6quEhFRGRMbG4u9e/fKbsfFxeH+/fv4+eefZdssLCzQqVMnZXQvW8xOIiIieZIPVP39/aWuErGxsdDV1UXNmjXh4uKCatWqQVVV/uImQRAwceJEydsmIqLSIzExERMnTsSHDx/ktl+4cEH2e48ePUrcQJXZSUREJK/QA9Vnz54V6H7m5uZ5Ljt58mTZ776+vlmWYdgSEZGxsTHq16+PS5cuZbnfwMAAU6dOLeZeKWJ2EhER5azQA1VLS8s8nab0ufzMNPjkyZN8109ERF8mLy8vDBs2DAkJCQr7DA0N0a5dOyX0Sh6zk4iIKGeFHqj+/PPPBQrb/LCwsCjS+omIqOzo06cPpk6dmuW3lr179y7yzMoLZicREVHOCj1QHTJkiATdyJsXL17g3LlzeP36Nfr27YuqVasiLS0NMTEx0NfXV7j2hoiIvjzq6upo1aqVwkDVxMQEEyZMUFKv5DE7iYiIcqai7A7khSiKmDRpEqysrODm5oZJkybh/v37ADJmdLS0tMS6deuU3EsiIioppkyZgooVK8ptMzEx+aK+ZWR2EhFRaSb5rL8AEBUVhcDAQDx+/BhRUVEK67gJgoCtW7fmub4VK1ZgzZo1mDZtGjp16oQuXbrI9unr68PJyQn79++Hl5eXVIdARESlWJMmTVCxYkW8e/cOAKChoQFPT08l9ypnzE4iIqL/kXygeuzYMfTr1w/x8fHQ09NDhQoVFMrk97qczZs3Y9CgQVi8eLHsj45P2djY4Ndffy1wn8ui+Ph46OrqAsj45FxHR0fJPSIiKj6CIMDV1RULFy5Eeno6jIyM4OrqquxuZYvZSUREJE/yger3338PExMTBAcH46uvvpKkzufPn6N169bZ7tfR0VFYM4+KDwfFRFQSjRkzBn5+foiMjETDhg1L9HsTs5OIiEie5NeoPnz4EOPHj5csaIGMdfGeP3+e7f6//vorX2vLERFR2Ve5cmVUrVoV5cqVw5QpU5TdnRwxO4mIiORJPlCtVasWYmNjJa3TyckJGzduxOPHj2XbMk+BOn78OPz9/eHs7FyguuPi4jBv3jzY29vD0NAQgiDA398/y7J3796Fvb09dHV1YWhoiIEDB+LNmzcK5dLT07F8+XJYWVlBU1MTNjY2CAwMLFD/iIio4EaNGgUVFZUSsXZqTpidzE4iIpIn+UB10aJFWL9+PcLCwiSrc8GCBTA1NUWjRo0waNAgCIKAZcuWoU2bNujevTtsbGwwc+bMAtX99u1beHt74+7du2jYsGG25cLDw9GuXTs8fPgQixcvxuTJk3HkyBF06dIFHz9+lCs7a9YsTJs2DV26dMG6detgbm6OAQMGYPfu3QXqIxERFYybmxs2bNhQItZOzQmzk9lJRETyCn2N6vjx4xW2GRkZoV69eujSpQuqVaumsEabIAhYs2ZNntvQ19fHpUuX4OPjg3379kFTUxNnz55FjRo1MG/ePEyZMgVaWloF6r+pqSkiIiJgYmKCa9euoXnz5lmWW7x4MeLj4+VOlbK1tUWXLl3g7++PkSNHAshYr87Hxweenp7w9fUFAAwfPhzt27fHlClT4OzsXPg167rm4Q+utE9+d9QF8tLkcVFxW3G2RUQkMS0tLQwdOlTZ3VDA7FRCdhIRUalS6IFqZqBk5fDhw1luz2/YAhl/bMyePRuzZ8/O1/1yo6GhARMTk1zL7d+/Hz179pS7nqdz586oXbs29uzZIwvbkJAQpKSkwMPDQ1ZOEASMGTMGAwYMwMWLF9GmTRtJj4GIiEoXZiezk4iIclboU3/T09Pz/ZOWlpZ7xZ+ws7PDyZMns91/+vRp2NnZFfZQsvXixQu8fv0azZo1U9hna2uLGzduyG7fuHEDOjo6qFevnkK5zP1ERPRlY3YyO4mIKGeSL0/z7NkzGBkZZXs6UWJiIt68eZOvmQbPnDmD4cOHZ7v/9evXOHv2bL77mlcREREAMk51+pypqSnev3+P5ORkaGhoICIiApUrV1a4Hirzvi9fvsy2ncDAwBwnjrh48SIcHR2BPOR16idn1jr/Dajl5fIsR0fFbSWsrQIprrayaqestlXan6uy2payXxfF3VYZwuwsGdlZIGXxvaQ42yoJ7yXMzi+7LWW/Loq7rVJE8oGqlZUVAgICMGDAgCz3h4aGYsCAAfn+ZDiniTAePnyI8uXL56u+/EhMTASQcarT5zQ1NWVlNDQ0ZP/mVC47rq6uOS5I7+joiNDQ0LxdN1oQoaGK29hW4dopq22V9ueqrLal7NdFMbTlWAaCNyvMTmbnF9vWF/C+VaTtsK2iaaeMtVVas1Pygaoo5jxJTkpKClRUcj/jePv27di+fbvs9qJFi7B582aFctHR0bh16xZ69OiR/87mUeYn3MnJyQr7kpKS5MpoaWnlqRwREVEmZiezk4iI5EkyUP3w4QOio6Nlt9+9e4dnz54plIuOjsbu3buzPA3ocwkJCXLrrMXGxiqEtCAI0NHRwejRozF37tyCH0AuMvubeRrTpyIiImBoaCj7JNjU1BSnT5+GKIpyn2Rn3rdKlSpF1k8iIio9mJ3MTiIiyp4kA9VVq1bB29sbQEYAenl5wcvLK8uyoihi0aJFudY5ZswYjBkzBkDGKVFr1qxR2tfWZmZmMDIywrVr1xT2XblyBY0aNZLdbtSoEbZs2YK7d++ifv36su2XL1+W7SciImJ2NpLdZnYSEdHnJBmodu3aFbq6uhBFEVOnToWrqyuaNGkiVybzE9ymTZtmOQNgTp48eSJFNwulb9++2L59O54/f45q1aoBAE6ePIn79+9j4sSJsnK9e/fGxIkTsX79etnyA6IoYuPGjTAzM0Pr1q2V0n8iIipZmJ3MTiIiyp4kA9VWrVqhVatWAID4+Hj07dsX1tbWUlQt5+zZszhy5AiePn0KALCwsICDgwPat29fqHp9fX0RHR0tm1Xw0KFDCA8PBwCMGzcO+vr6mDlzJvbu3YuOHTtiwoQJiIuLw4oVK/DVV1/JLSZftWpVeHl5YcWKFUhJSUHz5s1x8OBBnD9/Hjt37uSC5UREBIDZyewkIqKcSD6Z0rx586SuEh8/foSrqysOHjwIURRhYGAAIOO6HR8fH/Tp0weBgYEoV65cgepfuXKlLMABIDg4GMHBwQAAd3d36Ovro1q1ajh79iwmTZqE6dOnQ11dHQ4ODvDx8VGYqXDp0qWoUKEC/Pz84O/vj1q1amHHjh3ZzuZIRERfNmYns5OIiORJPlAFgKioKAQGBuLx48eIiopSmM1QEARs3bo1z/UtWLAABw4cwOTJk/H999+jcuXKADLWgPPx8cGKFSvg7e2NhQsXFqi/YWFheSrXoEEDHDt2LNdyKioqmDFjBmbMmFGg/hAR0ZeH2cnsJCKi/5F8oHrs2DH069cP8fHx0NPTQ4UKFRTK5LSuW1Z27dqFwYMHY/ny5XLbjY2NsWzZMrx69QoBAQEFDlsiIiJlYnYSERHJk3yg+v3338PExATBwcH46quvJKkzIiICLVq0yHZ/ixYtsHv3bknaIiIiKm7MTiIiInm5rx6eTw8fPsT48eMlC1ogY5KFM2fOZLv/7NmzqFq1qmTtERERFSdmJxERkTzJB6q1atVCbGyspHUOHjwYe/bswejRo3Hv3j2kpaUhPT0d9+7dw5gxY7B3714MGTJE0jaJiIiKC7OTiIhInuSn/i5atAienp4YMGAALC0tJalz5syZePToETZt2oTNmzdDRSVjfJ2eng5RFDF48GDMnDlTkraIiIiKG7OTiIhInuQD1ZMnT8LIyAj16tVDly5dUK1aNYX1zwRBwJo1a/Jcp6qqKvz9/TFp0iQcOXIEz549A5CxFlyPHj1gY2Mj6TEQEREVJ2YnERGRPMkHqr6+vrLfDx8+nGWZ/IZtJhsbGwYrERGVOcxOIiIieZIPVNPT06WuUub27ds4evSobO02Kysr2NvbSzr5BBERUXFjdhIREcmTfKBaFJKTkzFq1CgEBARAFEW562ymT58ONzc3bNmyBerq6kruKRERUcnA7CQiotKsyAaqT548wa+//oqnT58CyLgmpnv37rCyssp3XdOmTcMvv/wCDw8PjBs3DjVq1IAgCHj48CHWrl2LDRs2wNDQEKtXr5b4KIiIiIoPs5OIiChDkQxUv//+e6xZs0bhVCYVFRV4eXlh5cqV+apvx44dGDhwoNw1PABQp04d/PTTT/jw4QN27NjBsCUiolKL2UlERPQ/kq+j6uPjg1WrVsHJyQkXL15EdHQ0oqOjcfHiRfTr1w+rVq3CqlWr8lVnSkoKWrZsme3+1q1bIzU1tbBdJyIiUgpmJxERkTzJB6qbN2+Go6Mj9uzZgxYtWkBPTw96enpo0aIFdu/ejV69esHPzy9fdXbr1g3Hjh3Ldv9vv/2Grl27FrbrRERESsHsJCIikif5QDUsLAzdunXLdn+3bt1kMw/m1cKFC/HkyRM4OTnh5MmTePr0KZ4+fYrff/8dffr0wdOnT7Fw4UK8f/9e7oeIiKg0YHYSERHJk/waVWNjY/z999/Z7v/7779hZGSUrzrr1asHAPjnn38QEhIit08URQBA/fr1Fe6XlpaWr3aIiIiUgdlJREQkT/KBqrOzM9asWQNLS0uMGzcOOjo6AID4+Hj4+vpiy5Yt8PLyyledc+fOhSAIUneViIioRGB2EhERyZN8oLpw4ULcvHkTM2fOxNy5c1GlShUAwMuXL5GamoqOHTvC29s7X3XOnz9f6m4SERGVGMxOIiIieZIPVLW1tXHy5EmEhITIrQVnb2+PHj16oFevXvyEl4iI6BPMTiIiInlFso4qAPTu3Ru9e/cuquqJiIjKHGYnERFRBsln/SUiIiIiIiIqDEm+UXV0dMxXeUEQFGYgJCIi+pIwO4mIiLInyUD18OHD0NTUhImJiWzK+5zwOhsiIvrSMTuJiIiyJ8lA1czMDC9evEClSpUwYMAAuLi4wMTERIqqiYiIyiRmJxERUfYkuUb1+fPnOH36NBo3boyFCxeiWrVq6Ny5M7Zt24bY2FgpmiAiIipTmJ1ERETZk2wypfbt28PPzw+RkZHYt28fKlasiLFjx8LY2BhOTk7Yt28fkpOTpWqOiIio1GN2EhERZU3yWX/LlSuH3r17IygoCK9evZIFcP/+/bF8+XKpmyMiIir1mJ1ERETyimx5muTkZBw7dgwhISG4ceMGNDU1YWlpWVTNERERlXrMTiIiogySDlTT09Nx7NgxDBkyBJUrV4arqysSExOxefNmvH79GgMHDpSyOSIiolKP2UlERKRIkll/L1y4gF27dmHv3r149+4dWrZsicWLF+Pbb79FpUqVpGiCiIioTGF2EhERZU+SgWqbNm2gpaWFHj16wNXVVXaa0rNnz/Ds2bMs79OkSRMpmiYiIiqVmJ1ERETZk2SgCgCJiYnYv38/goODcywniiIEQUBaWppUTRMREZVKzE4iIqKsSTJQ3bZtmxTVEBERfTGYnURERNmTZKA6ePBgKaohIiL6YjA7iYiIsldky9MQERERERERFQQHqvnw4MEDuLi4oGrVqtDW1kbdunXh7e2NhIQEuXIXLlxAmzZtoK2tDRMTE4wfPx5xcXFK6jUREZHyMDuJiKggJJtMqax7/vw5bG1toa+vj7Fjx8LQ0BAXL17EvHnz8NdffyEkJAQAcPPmTXTq1An16tXDjz/+iPDwcKxcuRIPHjzAr7/+quSjICIiKj7MTiIiKigOVPMoICAA0dHR+OOPP9CgQQMAwMiRI5Geno5ffvkFUVFRqFChAmbOnIkKFSrgzJkz0NPTAwBYWlpixIgROH78OLp27arMwyAiIio2zE4iIioonvqbRx8+fAAAVK5cWW67qakpVFRUoK6ujg8fPuDEiRNwd3eXBS0ADBo0CLq6utizZ0+x9pmIiEiZmJ1ERFRQHKjmUYcOHQAA3333HW7evInnz58jKCgIGzZswPjx46Gjo4N//vkHqampaNasmdx91dXV0ahRI9y4cUMJPSciIlIOZicRERWUIIqiqOxOlBaLFi3C4sWLkZiYKNs2a9YsLFq0CACwb98+ODs749y5c2jbtq3cfb/99lucP38eERER2dYfGBiIwMDAbPdfv34dTZo0KeRREBGVXo8ePcK///6r7G5QPjA7iYiUq7RmJ69RzQdLS0u0a9cOffv2RcWKFXHkyBEsXrwYJiYmGDt2rCyENTQ0FO6rqakpF9JZcXV1haura5H0nYiISBmYnUREVBAcqObR7t27MXLkSNy/fx9Vq1YFADg5OSE9PR3Tpk2Dq6srtLS0AADJyckK909KSpLtJyIi+hIwO4mIqKB4jWoerV+/Ho0bN5YFbSZHR0ckJCTgxo0bMDU1BYAsT1GKiIhAlSpViqWvREREJQGzk4iICooD1Tx69eoV0tLSFLanpKQAAFJTU2FtbQ01NTVcu3ZNrszHjx9x8+ZNNGrUqDi6SkREVCIwO4mIqKA4UM2j2rVr48aNG7h//77c9sDAQKioqMDGxgb6+vro3LkzduzYgdjYWFmZgIAAxMXFwdnZubi7TUREpDTMTiIiKijO+ptH586dg52dHSpWrIixY8eiYsWKOHz4MH799VcMHz4cmzdvBpAxu2Dr1q1Rv359jBw5EuHh4fDx8UG7du1w7NgxJR8FERFR8WF2EhFRgYmUZ5cvXxa7d+8umpiYiOXKlRNr164t/vDDD2JKSopcufPnz4utW7cWNTU1RSMjI9HT01P88OFDsfd327ZtIgDx6tWrRd5GVj/Tpk2TtP7z588r7E9PTxerVq0qAhAdHBwK3d6nfvrpJxGAaGtrK2m9oqjc4yqO10Vxt1WUz9Wnbt26Jfbt21c0NzcXNTQ0xCpVqoidO3cW165dK3lbt2/fFt3c3MQqVaqI6urqoqmpqThgwADx9u3bkrWR+fxoaGiI4eHhCvvbt28vNmjQQNK2Mn80NDREU1NTsWvXruKaNWuU8h5JxYPZmX0bzM78YXZKi9lZMMzO4sNZf/PB1tYWR48ezbVcmzZt8OeffxZDj0oOb29vWFlZyW2ztraWrH5NTU3s2rULbdq0kdt+9uxZhIeHZ7msQWHt3LkTlpaWuHLlCh4+fIiaNWtK3oYyjqssKo7n6sKFC+jYsSPMzc0xYsQImJiY4Pnz57h06RLWrFmDcePGSdZWcHAwXF1dYWhoiO+++w5WVlYICwvD1q1bsW/fPuzevRt9+vSRrL3k5GQsXboU69atk6zO7GS+V6SkpCAyMhJnzpyBl5cXfvzxR4SGhsLGxqbI+0DFi9mZPWZnwTA7pcHsLBxmZzFQ9kiZik5xfipcVG1k1u/k5CRWqlRJ4RP4ESNGiE2bNhUtLCwk/fT08ePHIgAxODhYNDIyEufPny9Z3aKovOP6tO2y8qlwUT9XmXr06CEaGRmJUVFRCvtevXolWTsPHz4UtbW1xbp164qvX7+W2/fmzRuxbt26oo6Ojvjo0aNCt5X5/DRq1EjU0NAQX7x4Ibe/KD4Vzuq1cPLkSVFLS0u0sLAQExISJGmPqKCYnQXH7Cw9bTE7C47ZWXw4mRKVCq6urnj37h1OnDgh2/bx40fs27cPAwYMkLy9nTt3okKFCnBwcEC/fv2wc+dOydsAiv+4yqLieq4ePXqEBg0awMDAQGGfsbGxZO2sWLECCQkJ2LRpE4yMjOT2VapUCX5+foiPj8fy5csla3PmzJlIS0vD0qVLJaszP+zs7DBnzhw8ffoUO3bsUEofiMoiZidlh9lZeMzOoseBKkkiJiYGb9++lfuRkqWlJVq1aoXAwEDZtl9//RUxMTFwcXGRtC0g4w3cyckJ6urqcHV1xYMHD3D16lXJ2ynu4yqLiuu5srCwwF9//YXbt29LXvenDh06BEtLS7Rt2zbL/e3atYOlpSWOHDkiWZtWVlYYNGgQNm/ejJcvX0pWb34MHDgQAHD8+HGltE+kDMzOgmF2Fh6zs/CYnUWPA1WSROfOnWFkZCT3I7UBAwbg4MGDSExMBJDxJtu+fXvJF4P/66+/8N9//8nCrk2bNqhatWqRfdpYXMdVFhXnczV58mQkJCSgUaNGaN26NaZNm4bjx4/L1oOUQkxMDF6+fImGDRvmWM7Gxgbh4eFyS3kU1qxZs5Camoply5ZJVmd+VK1aFfr6+nj06JFS2idSBmZnwTE7C47ZyewsLThQJUn89NNPOHHihNyP1L799lskJibi8OHDiI2NxeHDh4vs1KXKlSujY8eOAABBENC/f3/s3r07y4XrC6u4jqssKs7nqkuXLrh48SIcHR3x999/Y/ny5ejWrRvMzMwQGhoqSRuZ4Vm+fPkcy2Xu//DhgyTtAkD16tUxcOBAbNq0CREREZLVmx+6urqS/gFBVNIxOwuO2VlwzE5mZ2nBgSpJwtbWFp07d5b7kZqRkRE6d+6MXbt2ITg4GGlpaejXr5+kbaSlpWH37t3o2LEjnjx5gocPH+Lhw4do0aIFXr16hZMnT0raHlA8x1UWKeO5at68OYKDgxEVFYUrV65gxowZiI2NRb9+/XDnzp1C158ZorkFTl5DOb9mz56N1NRUpV1vExcXJ/kxEZVkzM6CY3YWDLOT2VmacHkaKlUGDBiAESNGIDIyEt27d8/y4vzCOHXqFCIiIrB7927s3r1bYf/OnTvRtWtXSdsEiv64yiJlPVcAoK6ujubNm6N58+aoXbs2hg4dir1792LevHmFqldfXx+mpqa4detWjuVu3boFMzMz6OnpFaq9z1WvXh3u7u7YtGkTpk+fLmnduQkPD0dMTEyRLI9A9KVjdlImZiezszThQJVKlT59+mDUqFG4dOkSgoKCJK9/586dMDY2xk8//aSwLzg4GAcOHMDGjRuhpaUlabtFfVxlkbKeq881a9YMACQ75adnz57YvHkz/vjjD4U1AgHg/PnzCAsLw6hRoyRp73OzZ8/Gjh07iv16m4CAAABAt27dirVdoi8Bs5MyMTuZnaUJB6pUqujq6mLDhg0ICwtDr169JK07MTERwcHBcHZ2zvL0oSpVqiAwMBChoaHo37+/pG0X5XGVRcp4rk6fPo0OHTpAEAS57UePHgUA1KlTR5J2pkyZgh07dmDUqFE4d+4cKlasKNv3/v17jB49Gtra2pgyZYok7X2uRo0acHd3h5+fHywsLKCmVvQxcerUKSxcuBBWVlZwc3Mr8vaIvjTMTgKYnczO0ocDVSp1Bg8eXCT1hoaGIjY2Fo6Ojlnub9myJYyMjLBz507JwxYouuMqi5TxXI0bNw4JCQno06cP6tati48fP+LChQsICgqCpaUlhg4dKkk7tWrVwvbt2+Hm5oavvvoK3333HaysrBAWFoatW7fi7du3CAwMRI0aNSRpLyuzZs1CQEAA7t27hwYNGkha96+//or//vsPqampePXqFU6dOoUTJ07AwsICoaGh0NTUlLQ9IsrA7CRmJ7OztOFAtQwTRREAoKqqquSelA47d+6EpqYmunTpkuV+FRUVODg4YOfOnXj37p3cp3WlSXG+LoqqLWU8VytXrsTevXtx9OhRbNq0CR8/foS5uTk8PDwwe/ZsSa+NcnZ2Rt26dbFkyRJZwFasWBEdO3bEzJkzYW1tLVlbWalZsybc3d2xfft2yeueO3cugIxrlQwNDfHVV19h9erVGDp0aJmdDIJKF2Zn/jA7S09bzE5mZ2kjiJn/G6jMWbt2LSZMmICHDx8W6SdIVLoU5+uCr0EiKm34vkVZYXYSFT8uT1OGXb16FTo6OrCwsFB2V6gEKc7XBV+DRFTa8H2LssLsJCp+PPW3DNq/fz/OnDmDnTt3Yvjw4cVyQTeVfMX5uuBrkIhKG75vUVaYnUTKw1N/yyArKyvExsaiT58+WL16NXR0dJTdJSoBivN1wdcgEZU2fN+irDA7iZSHA1UiIiIiIiIqUXiNKhEREREREZUoHKgSERERERFRicKBKhEREREREZUoHKgSERERERFRicKBKhEREREREZUoHKgSERERERFRicKBKhEREREREZUoHKgSERERERFRicKBKhEREREREZUoHKgSERERERFRicKBKhEREREREZUoHKgSERERERFRicKBKhEREREREZUoHKgSERERERFRicKBKhEREREREZUoHKgSEREVgw0bNqBp06bQ0dGBIAgQBAGWlpbK7hYREVGJpKbsDhAREZV127Ztg4eHh7K7QUREVGpwoEpERFTEAgICZL8bGhrC09MTenp60NfXV2KviIiISi4OVImIiIpYWFiY7PcePXrA29tbeZ0hIiIqBQp8jerly5cxaNAgVK9eHVpaWtDR0YGNjQ3mzp2L9+/fS9lHIiKiUmnIkCEQBAFPnjyRbduxY4fsGtUhQ4Yor3NEREQlWIG+UfX29sb8+fMhiqLc9n/++Qf//PMPtm/fjuPHj6NOnTqSdJKIiIiIiIi+HIL4+WgzF/v27YOzs7Ps9tdff40uXbogPj4eAQEBiIyMBADUq1cP//zzD1RVVaXtMRERUSnx22+/4fbt21i8eDGioqIAAM2aNUP//v0BANbW1rC3t1dmF4mIiEqkfA9UmzVrhr/++gtAxnU2hw8fhiAIAIC7d++ifv36srIHDhzAN998I11viYiISiFLS0s8ffoUADB48GD4+/srt0NEREQlXL6uUU1ISMD169dlt48ePQoVFRXZtTafDlIB4I8//pCml0RERERERPTFyNdANSoqSuG61Jy8efMm3x2issnS0hI9e/ZUdjcKxNLSkhOeEBFRsWN2EtGXLF8D1QoVKshO8wUAOzs7rFixItufzGtwSBqOjo7Q1tZGbGxstmXc3Nygrq6Od+/eSdbuhQsXMH/+fERHR0tWp5QSEhIwf/58nDlzpsB1lPRjzPTu3TusWLEC7dq1g5GREQwMDNCyZUsEBQVlWT45ORnTpk1DlSpVoKWlhRYtWuDEiRNZlr1w4QLatGkDbW1tmJiYYPz48YiLi8uy7PXr1+Ho6AhDQ0Noa2vD2toaa9euzdexnDx5EsOGDUPt2rWhra2N6tWrY/jw4YiIiChw/65evYqxY8eiQYMG0NHRgbm5Ob799lvcv38/yzrv3r0Le3t76OrqwtDQEAMHDizUB2z37t3DxIkT0bp1a2hqakIQBLllST5laWkpOxvl05/Ro0fnu90NGzbA2dkZ5ubmeZpJ9vfff4ednR309fVRvnx5NG3aNNvX0Od9zu6P5mvXrkEQBPj7+yMsLCzLY8vqJ/PxefXqFSZPnoy6detCW1sbOjo6aNq0KRYtWlTi/19SycbszBqzk9mZSdnZGRwcjP79+6N69erQ1tZGnTp18P3332f5ugoKCoK7uztq1aoFQRDQoUOHArWZn9dEfh+fzw0ZMgS6urrZ7tfV1ZXldnZ/G3z+k3n5SFJSElatWoUWLVpAX18fmpqaqF27NsaOHZvn/pVk+Zr1V1tbG40bN5ad/hsZGYnRo0crPPgpKSk4dOgQWrVqJV1PCW5ubjh06BAOHDiAQYMGKexPSEhASEgI7O3tUbFiRcnavXDhAhYsWIAhQ4bAwMBAsnqlkpCQgAULFgBAgd+wcjrGe/fuQUWlwCs5SerixYuYNWsWevTogdmzZ0NNTQ379++Hi4sL7ty5I3scMg0ZMgT79u2Dl5cXatWqBX9/f/To0QOnT59GmzZtZOVu3ryJTp06oV69evjxxx8RHh6OlStX4sGDB/j111/l6jx+/Dh69eqFxo0bY86cOdDV1cWjR48QHh6er2OZNm0a3r9/D2dnZ9SqVQuPHz+Gr68vDh8+jJs3b8LExCTf/Vu2bBn+/PNPODs7w8bGBpGRkfD19UWTJk1w6dIlWFtby8qGh4ejXbt20NfXx+LFixEXF4eVK1fin3/+wZUrV6Curp6v4wEynp+1a9eifv36qFevHm7evJlj+UaNGuH777+X21a7du18t7ts2TLExsbC1tY22z9WMm3btg3fffcdunTpgsWLF0NVVRX37t3D8+fP891udoyMjBAQECC3zcfHB+Hh4Vi1apVC2atXr6JHjx6Ii4uDu7s7mjZtCiBj8Lt06VKcO3cOx48fl6x/9GVhdmaN2cnszKTs7Bw5ciSqVKkCd3d3mJub459//oGvry+OHj2K69evQ0tLS1Z2w4YN+Ouvv9C8efNCfbCUn9dEfh6fwlq9erXcBwlHjx5FYGAgVq1ahUqVKsm2t27dGm/fvoW9vT3++usv9OzZEwMGDICuri7u3buH3bt3Y9OmTfj48aNkfVMKMZ+CgoJEALIfc3NzccKECeKSJUvEGTNmiI6OjqKenp4IQHzy5El+q6ccJCQkiOXLlxe7deuW5f5du3aJAMTdu3dL2u6KFSsK/XxaWFiIDg4O0nXqE2/evBEBiPPmzStwHVIcY3F4/PixGBYWJrctPT1dtLOzEzU0NMS4uDjZ9suXL4sAxBUrVsi2JSYmijVq1BBbtWolV0f37t1FU1NTMSYmRrZt8+bNIgDx2LFjsm0xMTFi5cqVxT59+ohpaWmFOpazZ88q1HH27FkRgDhr1qwC9e/PP/8Uk5OT5e57//59UUNDQ3Rzc5PbPmbMGFFLS0t8+vSpbNuJEydEAKKfn1+Bjundu3fihw8fRFHM/TUl5f+JsLAwMT09XRRFUdTR0REHDx6cZbknT56IWlpa4vjx4wvUTk59vnr1qghA3LZtW5b7HRwcRAsLC4XtUVFRopmZmVi5cmXx7t27CvsjIyPFhQsXFqi/JYmFhYUsN7N7fqhoMDuzxuxkdmZSdnaePn1aYdv27dtFAOLmzZvltj979kx2/A0aNBDbt29foDbz85rIz+OTlcGDB4s6OjrZ7s8pt3P6P+bg4CCqqKiI+/btU9iXlJQkfv/997n2raTL90BVFEVx/vz5oiAIcgPWrH5K+htXaTR48GBRTU1NfPXqlcK+nj17iuXLlxcTEhJEURTFR48eif369RMrVKggamlpiS1atBAPHz6scL+1a9eK9evXF7W0tEQDAwOxadOm4s6dO0VRFMV58+bl+NympKSI3t7eYvXq1UV1dXXRwsJCnDFjhpiUlCTXRlZh6+/vL6qqqoqTJ0/O8ZivXr0qdu3aVaxYsaKoqakpWlpaikOHDhVFMeMP76z6lxm8f//9tzh48GDRyspK1NDQECtXriwOHTpUfPv2raz+3I7RwsJC4Q0kL4/t6dOnRQBiUFCQuGjRItHMzEzU0NAQ7ezsxAcPHuR4zPm1du1aEYB469Yt2bYpU6aIqqqqcgEliqK4ePFiEYD47NkzURQzAlRNTU2cMmWKXLnk5GRRV1dX/O6772TbNmzYIAIQ79y5I4qiKMbFxRU6dD9naGgoOjk5yW7np3/ZadKkidikSRO5bcbGxqKzs7NC2dq1a4udOnUqYO//J68D1eTkZLlALKycAm/atGmiurq6GB0dLYqiKMbGxsoGuHlRFAPVpUuXigBk7zllFQeqysXsZHZmhdmZM2VkZ6YPHz6IAMRJkyZlW6YwA9XsZPWayE5Wj09WimKgeunSJRGAOGLEiFzbL80KdE7GvHnzcOXKFQwbNgy1atWClpYW1NTUYGRkhDZt2mD69Om4ePEiLC0tC1I95cDNzQ2pqanYs2eP3Pb379/j2LFj6NOnD7S0tPDq1Su0bt0ax44dg4eHB3744QckJSXB0dERBw4ckN1v8+bNGD9+POrXr4/Vq1djwYIFaNSoES5fvgwAcHJygqurKwBg1apVCAgIQEBAAIyMjAAAw4cPx9y5c9GkSROsWrUK7du3x5IlS+Di4pLjcWzatAlDhw7F9OnTsWLFimzLvX79Gl27dkVYWBimT5+OdevWwc3NDZcuXQKQcdrghg0bAAB9+vSR9c/JyQkAcOLECTx+/BhDhw7FunXr4OLigt27d6NHjx6yicFyO8bP5fWxzbR06VIcOHAAkydPxowZM3Dp0iW4ubnl+PjkV+b6xZ+eFnLjxg3Url0benp6cmVtbW0BQHZa6j///IPU1FQ0a9ZMrpy6ujoaNWqEGzduyLb9/vvv0NPTw4sXL1CnTh3o6upCT08PY8aMQVJSUqGPIy4uDnFxcXLHkZ/+ZUUURbx69UquzhcvXuD169cKdQIZj09udUrl1KlT0NbWhq6uLiwtLbFmzZoibe/3339H3bp1cfToUVStWhXly5dHxYoVMWfOHKSnpxdp29kJDQ2FlpYW+vXrp5T26cvA7GR2ZoXZmT1lZ2dWz01xyGu7WT0+xSk0NBQAMHDgQKW0X2yUOkymfEtNTRVNTU0VTj/ZuHGj3KkcXl5eIgDx/PnzsjKxsbGilZWVaGlpKfskr3fv3mKDBg1ybDO7T3Nu3rwpAhCHDx8ut33y5MkiAPHUqVOybZ9+KrxmzRpREIQ8nc534MABEYB49erVbMvkdPpS5ifknwoMDBQBiOfOncv1GDP7/uknXXl9bDM/Fa5Xr57cKSNr1qwRAYj//PNPToeeZ+/evRONjY3Ftm3bym1v0KCBaGdnp1D+33//FQGIGzduFEVRFPfu3avweGRydnYWTUxMZLdtbGxEbW1tUVtbWxw3bpy4f/9+cdy4cSIA0cXFpdDHsnDhQhGAePLkSdm2/PQvKwEBASIAcevWrbJtmd/+/fLLLwrlp0yZIgJQ+GYjv3L7RrVXr17ismXLxIMHD4pbt24V27ZtKwIQp06dWqh2c/pkVk9PT6xQoYKooaEhzpkzR9y3b584YMAAEYA4ffr0XOsuim9UK1SoIDZs2DDXtokKg9mpiNnJ7MyJsrIz03fffSeqqqqK9+/fz7aM1N+oZveayEpWj092iuIb1T59+ogAxKioqFzbL81KxlXulGeqqqpwcXHBxYsX5WYT3bVrFypXroxOnToByLj42tbWVu6if11dXYwcORJhYWG4c+cOAMDAwADh4eG4evVqvvty9OhRAMCkSZPktmdODnPkyBGF+yxfvhwTJkzAsmXLMHv27FzbyJyc4fDhw0hJScl3Hz+9AD8pKQlv375Fy5YtAUBuTeD8yOtjm2no0KFykwu0bdsWAPD48eMCtf+p9PR0uLm5ITo6GuvWrZPbl5iYCA0NDYX7aGpqyvZ/+m92ZTP3Axmf2iYkJGDQoEFYu3YtnJycsHbtWowaNQq7d+/GgwcPCnws586dw4IFC/Dtt9/Czs5O7jjy2r/P/ffff/D09ESrVq0wePDgPNf5aZmiEhoaiqlTp6J3794YNmwYzp49i27duskmvCgKcXFxiIqKwoIFC+Dt7Y2+ffti586dsLe3x5o1a3KcFbWofPjwAeXLly/2dunLwuzMH2Yns1OZ2blr1y5s3boV33//PWrVqlXo+vIip9fE57J7fIrThw8fAKDM5ycHqqVQ5qkvu3btApAxA9v58+fh4uICVVVVAMDTp09Rp04dhfvWq1dPth/ImD1OV1cXtra2qFWrFjw9PfHnn3/mqR9Pnz6FiooKatasKbfdxMQEBgYGsjYynT17FtOmTcO0adMwZcqUPLXRvn179O3bFwsWLEClSpXQu3dvbNu2DcnJyXm6//v37zFhwgRUrlwZWlpaMDIygpWVFQAgJiYmT3V8Lq+PbSZzc3O52xUqVACQsS5xYY0bNw6//fYbtmzZgoYNG8rt09LSyvJxyjzNKPMPkcx/syv76R8smb9nnu6VacCAAQAyZtEDMh73yMjILH+y8t9//6FPnz6wtrbGli1bFI4jr/37VGRkJBwcHKCvr499+/bJ/m/kpc5PyxQXQRAwceJEpKamypaLSExMzPZxLMgfA9k9f66urkhMTJTktK1PlzDLCz09PaUMkOnLw+xkdmZidpbc7Dx//jy+++47dOvWDT/88EOB6vj48WO2j2N2Swfl9Jr4VE6PT2EUJDsBlPn85EC1FGratCnq1q2LwMBAAEBgYCBEUSzQtRv16tWTTWPdpk0b7N+/H23atMG8efPyXEde/3M1aNAAderUQUBAAJ48eZLnuvft24eLFy9i7NixePHiBYYNG4amTZtm+2bzqW+//RabN2/G6NGjERwcjOPHj+O3334DgGK7Ji+7NzHx/6/zKagFCxZg/fr1WLp0aZbXKJiamma5VEnmtipVqsjKfbr987KZ5T69T+XKleXKGRsbA/jfHxBOTk4wNTXN8udzz58/R9euXaGvr4+jR48qfDqYn/5liomJQffu3REdHY3ffvtNoUxudRoaGmb5iXFRq1atGoCMP1aAjPXisnsc87Lu6efy+vxlJ6dP4RMSEmRl8qNu3bq4f/9+6Z9Cn0o8ZiezE2B2ZtW/TMrOzr///huOjo6wtrbGvn37oKaWr1U0ZS5cuJDt47hy5UqF8rm9JjLl9vhkR1NTE8nJyVm+dkVRRFJSUoGyE8i4Frks40C1lHJzc8Pt27dx69Yt7Nq1C7Vq1ULz5s1l+y0sLHDv3j2F+/3333+y/Zl0dHTQv39/bNu2Dc+ePYODg4NskgMg+zC1sLBAenq6wikrr169QnR0tFwbQMaF6b///jvKlSuHTp064eXLl3k+3pYtW+KHH37AtWvXsHPnTvz777/YvXt3jv2LiorCyZMnMX36dCxYsAB9+vRBly5dUL16dYWy+fkkKz+PbVH56aefMH/+fHh5eWHatGlZlmnUqBHu378vOz0kU+ZkH40aNQIAWFtbQ01NDdeuXZMr9/HjR9y8eVNWDoBsfcsXL17Ilc18LjMn0fDx8cGJEyey/PnUu3fv0LVrVyQnJ+PYsWNZhnF++gdkfKrbq1cv3L9/H4cPH0b9+vUV6jQzM4ORkZFCnQBw5coVhTqLS+YpbZmPY7du3bJ9HLt165bv+vP6/GXHwsIi2wXEM/9P5Pf136tXLyQmJmL//v35uh9RQTA7mZ3MzpKZnY8ePYK9vT2MjY1x9OhR6OrqFriuhg0bZvs4fr6Wcl5eE0DeHp/sWFhYIDU1FY8ePVLY9/DhQ6SlpRUoOwFgx44d+bpfqaPMC2Sp4B4/fiwCEHv37i0CEOfPny+3P3PSggsXLsi2xcXFidWrV5ebtODTqeYzTZkyRVRRUZGtB5k5rfqNGzfkymVOCDFy5Ei57VOnTs1xQogHDx6IJiYmYv369bNs/1Pv379XWD4jc0IDX19fURQzJn0AIE6YMEGuXExMTJaPjYeHh8IEEtkdY2bfs5oQIrfHNnNCiL1798rVl7ksQHaTzuRm9+7dooqKiujm5pbj0iKZU5d/uhZcUlKSWLNmTbFFixZyZe3t7UVTU1PZcy6KorhlyxYRgPjrr7/Ktl2/fl0EIA4YMEDu/q6urqKampr44sWLPB9HXFycaGtrK5YvX168du1ajmXz2r/U1FTR0dFRVFNTE48cOZJjnaNHjxa1tLRkSw2Ioij+/vvvIgBxw4YNeT6O7OQ0yci7d+/E1NRUuW0fP34Uv/76a1FdXV2MiIgocLs5TcqQOcHKzJkzZdvS0tLENm3aiIaGhrlOgrF69WoRgHjgwAG57UlJSaKtra1obGyssNZcpuwmU3r//r1oamoqmpqaivfu3VPY/+rVqzKxjiqVDMxOZiezs+RlZ0REhFi9enWxSpUq+VrasrCTKeX1NZGfxycrN27cyPL/miiK4oQJE0QA4s2bN7O8b05/S9jb24sqKioKmSyKGcsQlYV1VAv2nTopnZWVFVq3bo2QkBAAUDh1afr06QgMDET37t0xfvx4GBoaYvv27Xjy5An2798PFZWML9O7du0KExMTfP3116hcuTLu3r0LX19fODg4yE4jyfwkcNasWXBxcUG5cuXQq1cvNGzYEIMHD8amTZsQHR2N9u3b48qVK9i+fTu++eYbdOzYMcu+16xZE8ePH0eHDh3QrVs3nDp1SmEa+Ezbt2/H+vXr0adPH9SoUQOxsbHYvHkz9PT00KNHDwAZ10PUr18fQUFBqF27NgwNDWFtbQ1ra2u0a9cOy5cvR0pKCszMzHD8+PEsT53K7hh1dHQUyub1sc2PM2fOoGPHjpg3bx7mz5+fbbkrV65g0KBBqFixIjp16oSdO3fK7W/durXsU+8WLVrA2dkZM2bMwOvXr1GzZk1s374dYWFh2Lp1q9z9fvjhB7Ru3Rrt27fHyJEjER4eDh8fH3Tt2hX29vayco0bN8awYcPw888/IzU1Fe3bt8eZM2ewd+9ezJgxI8+nwQAZr9nMZa7u3r2Lu3fvyvbp6urim2++yXf/vv/+e4SGhqJXr154//69wieN7u7ust9nzpyJvXv3omPHjpgwYQLi4uKwYsUKfPXVVxg6dKjc/TKX2vp0EpasxMTEyCZhyLxezdfXFwYGBjAwMMDYsWMBZEyktGjRIvTr1w9WVlZ4//49du3ahdu3b2Px4sUwMTHJ24P4/w4dOoS///4bAJCSkoJbt25h0aJFAABHR0fY2NgAAHr37o1OnTphyZIlePv2LRo2bIiDBw/ijz/+gJ+fX66nbI0cORI///wznJ2dMWzYMDRu3Bjv3r1DUFAQbt++jV9++UVu8pO8qFChAg4cOIAePXqgUaNGcHd3l/1/vH79OgIDA9GqVat81UmUHWYns5PZWfKy097eHo8fP8bUqVPxxx9/4I8//pDtq1y5Mrp06SK7fe7cOZw7dw4A8ObNG8THx8vyrl27dmjXrl2ObWXKz2siP49PVho1aoThw4djzZo1ePDggex4Tpw4gaNHj2L48OE5XhebnV9++QVdu3aFk5MTevXqhU6dOkFHRwcPHjzA7t27ERERkeWpzqWKskfKVHA//fSTCEC0tbXNcn/mwtoGBgaipqamaGtrq7Cwtp+fn9iuXTuxYsWKooaGhlijRg1xypQpCgtdL1y4UDQzMxNVVFTkPtlJSUkRFyxYIFpZWYnlypUTq1WrludFyy9fviyWL19ebNeuXZZT4YtixqeQrq6uorm5uaihoSEaGxuLPXv2VPgU8cKFC2LTpk1FdXV1uU98w8PDxT59+ogGBgaivr6+6OzsLL58+TLLKfmzO8acFi3P6bHNz6fChw4dkpv2Pjvbtm0TkcUC65k/n3/SnJiYKE6ePFk0MTERNTQ0xObNm4u//fZblnWfP39ebN26taipqSkaGRmJnp6ecp/CZvr48aM4f/580cLCQixXrpxYs2ZNcdWqVTn2OysWFhbZHkdW37zlpX/t27fP8fH53O3bt8WuXbuK2traooGBgejm5iZGRkYqlKtUqZLYsmXLXI8p87nN7ZiuXbsm9urVSzQzMxPV1dVFXV1dsU2bNuKePXtyf+CyMHjw4Dy/JmJjY8UJEyaIJiYmorq6uvjVV1+JO3bsyHNbUVFR4sSJE2X/5/X09MSOHTvKfTqfley+Uc308uVLceLEiWLt2rVFTU1NUVtbW2zatKn4ww8/KLwfERUGs/N/mJ3MTlFUfnbm1Pbn35jOmzcv27JZLbWUnfy8JvL7+GQlLS1NXLNmjdiwYUNRU1NT1NTUFBs2bCiuXbtWdjZBVnJb6i4hIUFcuXKl2Lx5c1FXV1dUV1cXa9WqJY4bN058+PBhnh+PkkoQxUJelU5EhTZ16lQEBgbi4cOHSpnEh7J3584dNGjQAIcPH4aDg4Oyu0NERP+P2VlyMTtJCpxMiagEOH36NObMmcOgLYFOnz6NVq1aMWiJiEoYZmfJxewkKXzRA9WrV69i7NixaNCgAXR0dGBubo5vv/02y1kt7969C3t7e+jq6sLQ0BADBw7EmzdvFMqlp6dj+fLlsLKygqamJmxsbGRT4RNl5+rVqxg5cqSyu0FZ8PT0xIULF5TdDaISg9lJJQWzs+RidpIUvuhTf/v164c///wTzs7OsLGxQWRkJHx9fREXF4dLly7B2toaQMai4I0bN4a+vj7Gjx+PuLg4rFy5Eubm5rhy5Yrc5CEzZszA0qVLMWLECDRv3hwhISE4cuQIAgMD4eLioqxDJSIikgSzk4iIioVyL5FVrj///FNhKYX79++LGhoaopubm2zbmDFjRC0tLfHp06eybSdOnBABiH5+frJt4eHhYrly5URPT0/ZtvT0dLFt27Zi1apVFZajICIiKm2YnUREVBy+6FN/W7durbCUQq1atdCgQQO56b7379+Pnj17wtzcXLatc+fOqF27Nvbs2SPbFhISgpSUFHh4eMi2CYKAMWPGIDw8HBcvXizCoyEiIip6zE4iIioOX/RANSuiKOLVq1eoVKkSAODFixd4/fo1mjVrplDW1tYWN27ckN2+ceMGdHR0UK9ePYVymfuJiIjKGmYnERFJTU3ZHShpdu7ciRcvXsDb2xsAEBERAQAwNTVVKGtqaor3798jOTkZGhoaiIiIQOXKlSEIgkI5AHj58mWObQcGBuY4ecSpU6egq6ubr+MhIipLKlasiH///VfZ3aDPMDuJiEqu0pqdHKh+4r///oOnpydatWqFwYMHAwASExMBIMupzzU1NWVlNDQ0ZP/mVC4nrq6ucHV1zXa/o6MjQkND83YwRERlkKOjo7K7QJ9hdhIRlWylNTt56u//i4yMhIODA/T19bFv3z6oqqoCALS0tAAAycnJCvdJSkqSK6OlpZWnckRERGUBs5OIiIoKB6oAYmJi0L17d0RHR+O3335DlSpVZPsyTz3KPI3pUxERETA0NJR9EmxqaorIyEiIn634k3nfT+slIiIqzZidRERUlL74gWpSUhJ69eqF+/fv4/Dhw6hfv77cfjMzMxgZGeHatWsK971y5QoaNWoku92oUSMkJCTIzXoIAJcvX5btJyIiKu2YnUREVNS+6IFqWloa+vfvj4sXL2Lv3r1o1apVluX69u2Lw4cP4/nz57JtJ0+exP379+Hs7Czb1rt3b5QrVw7r16+XbRNFERs3boSZmRlat25ddAdDRERUDJidRERUHL7oyZS+//57hIaGolevXnj//j127Nght9/d3R0AMHPmTOzduxcdO3bEhAkTEBcXhxUrVuCrr77C0KFDZeWrVq0KLy8vrFixAikpKWjevDkOHjyI8+fPY+fOnbJrd4iIiEorZicRERUL8QvWvn17EUC2P5+6ffu22LVrV1FbW1s0MDAQ3dzcxMjISIU609LSxMWLF4sWFhaiurq62KBBA3HHjh2S9LdXr16S1ENEVFrxfVD5mJ1ERKVLaX0fFETxs9kLqMTiFPtE9KXj+yDlF18zRPSlK63vg5Jeo5qYmIhJkybh0KFDUlZLRERUZjE7iYiIFEk6UNXS0oKfnx9evXolZbVERERlFrOTiIhIkeSz/jZt2hS3b9+WuloiIqIyi9lJREQkT/KB6urVq7F7925s2bIFqampUldPRERU5jA7iYiI5Em+PM2QIUOgoqKCUaNGYfz48TAzM4OWlpZcGUEQ8Pfff0vdNBERUanE7CQiIpIn+UDV0NAQFStWRJ06daSumoiIqExidhIREcmTfKB65swZqaskIiIq05idRERE8iS/RpWIiIiIiIioMIpkoPrhwwcsXboU3bp1Q+PGjXHlyhUAwPv37/Hjjz/i4cOHRdEsERFRqcXsJCIi+h/JT/0NDw9H+/bt8fz5c9SqVQv//fcf4uLiAGRcg+Pn54enT59izZo1UjdNRERUKjE7iYiI5Ek+UJ0yZQpiY2Nx8+ZNGBsbw9jYWG7/N998g8OHD0vdLBERUanF7CQiIpIn+am/x48fx/jx41G/fn0IgqCwv3r16nj+/LnUzRIREZVazE4iIiJ5kg9UExMTYWRklO3+2NhYqZskIiIq1ZidRERE8iQfqNavXx/nzp3Ldv/BgwfRuHFjqZslIiIqtZidRERE8iQfqHp5eWH37t1YtmwZYmJiAADp6el4+PAhBg4ciIsXL2LixIlSN0tERFRqMTuJiIjkST6Zkru7O54+fYrZs2dj1qxZAAB7e3uIoggVFRUsXrwY33zzjdTNEhERlVrMTiIiInmSD1QBYNasWRg4cCD279+Phw8fIj09HTVq1ICTkxOqV69eFE0SERGVasxOIiKi/ymSgSoAmJubS36a0rNnz/D48WNERUVBFEWF/U5OTpK2R0REVJyYnURUXOLj46GrqwsAiIuLg46OjpJ7RCSvyAaqQMaLPrtgNDc3z3M9z549w7Bhw3D69GkAyLI+QRCQlpZW8M4SERGVAMxOIiKiIhioJiUlYcGCBdi6dSvevXuXbbn8BOPgwYNx8eJFTJ8+HS1atIC+vr4UXSUiIioRmJ1ERETyJB+oenh4YPv27fjmm2/Qtm1bVKhQodB1Xrp0CdOmTcOCBQsk6CEREVHJwuwkIiKSJ/lANTg4GMOHD4efn59kdVatWlWS0CYiIiqJmJ1ERETyJF9HVRAENGnSRNI6J0+ejK1btyIhIUHSeomIiEoCZicREZE8yb9R7d27N37//XeMGjVKsjpHjRqFtLQ01KpVC/369UPVqlWhqqoqV0YQBC6GTkREpRKzk4gk1VXIvcynl7w76gKq2Zb8n+OKk7JR2ZSSkoJy5coptQ+CmNU0gPnw/v17udtRUVH49ttv0bRpU4waNQrm5uYKwQgAhoaGeW7j9u3b6NmzJ549e5ZtmS9h5kJHR0eEhoYquxtEREpTVt4HmZ3Fp6y8ZojyJQ8D1fg0QPdUxu9xdoAOB6plVn7fB69evYqePXvi1atXRdir3BX6G9VKlSpBEOT/M4iiiBs3bmDr1q3Z3i8/wThy5EjExMTAz8+PMxcSEVGpx+wkIqKSatWqVXj//j3CwsJgaWmptH4UeqA6d+5chbCV2s2bN7FgwQKMGDGiSNshIiIqDsxOIiIqiVJTU/HHH38gNTUVa9aswapVq5TWl0IPVOfPny9BN3JmZWVV5G0QEREVF2YnERGVREeOHEFUVBQAICQkBD4+PlBRkXz+3TyRvNVhw4bh8uXL2e6/cuUKhg0blq86FyxYgJ9++gnPnz8vbPeIiIhKHGYnERGVBD/++CPi4uIAZMyfcO7cOaX1RfJZf/39/dG5c2e0aNEiy/1PnjzB9u3b8fPPP+e5znPnzsHAwAB16tRB586dUa1atSxnLlyzZk2h+k5ERKQMzE4iIlK2d+/e4dGjR7Lb0dHRWLFiBTp06KCU/kg+UM3Ny5cvoaWlla/7+Pr6yn4/fPhwlmUYtkREVFYxO4lIajqqgNhF2b2gkmTr1q2IjIyU23br1i3ExcVBV1e32PsjyUA1JCQEISEhstubNm3C77//rlAuOjoav//+O5o3b56v+tPT0wvdRyIiopKE2UlERCWJv7+/wuzyr1+/xq5duzBy5Mhi748kA9U7d+5g7969ADI+nb18+TL++usvuTKCIEBHRwft2rXDjz/+mGud+/btQ9euXaGnpydFF4mIiEoUZicREZUUt2/fxtu3bxW2f/z4ERs2bFDKQFWSyZRmzJiB2NhYxMbGQhRFbN26VXY78+fDhw+IiIjA4cOHUbt27VzrdHV1hZGREezs7ODj44O7d+9K0VUiIqISgdlJREQlxcqVK/HmzZss90VGRiIsLKx4O4QimPU3PT0dAwYMKHQ9b968wfbt21GtWjUsX74c1tbWqF69OsaNG4djx44hOTlZgt4CcXFxmDdvHuzt7WFoaAhBEODv759l2T179qBly5YwMDBAxYoV0b59exw5ckShXHp6OpYvXw4rKytoamrCxsYGgYGBkvSXiIjKHmYns5OISFlSU1Nx5syZbPdHRkZi7dq1xdeh/6ecRXHywMDAAC4uLti+fTsiIyNx4cIFDBw4EJcuXUKPHj1QsWJF9OrVC35+foWaev/t27fw9vbG3bt30bBhw2zLrVu3Dv3790elSpWwdOlSzJkzBzExMejZsyeCg4Plys6aNQvTpk1Dly5dsG7dOpibm2PAgAHYvXt3gftJRESUG2YnERHlV2RkJAwNDdGwYUM0bNgQ1tbWqFChguy2jY0N4uPji71fgiiKotSV/vrrr/jxxx9x/fp1xMTEIKsmPr9QNz9evXqFo0eP4ujRozhx4gRiY2NRv3599OzZE0OGDEGdOnXyXFdycjKioqJgYmKCa9euoXnz5ti2bRuGDBkiV6527dowMDDA5cuXIQgCAODDhw8wMzODnZ2dbEKMFy9ewMrKCiNHjpTNuCiKItq3b48nT54gLCxMYXmAvHJ0dERoaGiB7ktEVBaU5fdBZiezk0gyXYWiqfe45MMGKgb5fR+MiYnB2LFjERAQUIS9yp3k36ju378fPXv2xKtXr+Di4oL09HS4urrCxcUFWlpasLGxwdy5cwvVRuXKlTF06FDs3bsXb9++xe+//w57e3uEhoZiz549+apLQ0MDJiYmuZb78OEDjI2NZUELAHp6etDV1ZVbMiAkJAQpKSnw8PCQbRMEAWPGjEF4eDguXryYr/4REVHZx+xkdhIRkTzJB6pLliyBra0tbty4gQULFgAAhg0bhp07d+L27duIiIiAlZVVrvVMnjwZf//9d67l1NTU0LFjR6xYsQL//vsvpk+fXuhjyEqHDh3w22+/Yd26dQgLC8N///0HT09PxMTEYMKECbJyN27cgI6ODurVqyd3f1tbW9l+IiKiTzE7mZ1ERCRPkuVpPnXnzh0sWbIEqqqqUFPLqD4lJQUAYGlpCQ8PDyxbtgyDBg3KsZ4ff/wRq1atQt26deHu7g5XV1dYWlrm2n65cuUKfQxZWbt2Ld6+fYvx48dj/PjxAIBKlSrh5MmTaNWqlaxcREQEKleuLPfpMQCYmpoCyFi0PTuBgYE5Thxx8eJFODo6FuYwiIioBGJ2MjuJJFVUn+3w/xIVI8kHqtra2lBXVweQMamDhoYGIiIiZPsrV66MJ0+e5FrP/fv3sXPnTuzevRuzZs3C7Nmz0apVK7i7u8PZ2RkVK1aUuus50tbWRp06dVC1alX07NkTsbGxWLVqFZycnHD+/HnUrFkTAJCYmAgNDQ2F+2tqasr2Z8fV1RWurq7Z7ud1NkT0pSurAw5mJ7OTSFJFdY0q/y+VSqU1OyU/9bdOnTq4c+eO7HajRo0QEBCA1NRUJCUlYdeuXTA3N8+1npo1a2LevHm4e/curl27Bi8vLzx9+hQeHh6oUqUKHB0dERQUlGN4ScnZ2RnPnj2Dv78/+vXrh6FDh+LMmTP4+PEjZs2aJSunpaWV5fT/SUlJsv1ERESfYnYyO4mISJ7kA9U+ffogJCREFjizZs3CmTNnYGBgACMjI5w/fz7f18I0adIEPj4+ePbsGU6ePIlBgwbhzz//hKurKypXroxBgwbh2LFjUh+KzOPHj/Hbb78pfBphaGiINm3a4M8//5RtMzU1RWRkpMJsjZmfjFepUqXI+klERKUTs5PZSURE8iQfqE6ePBnPnj2TncLTs2dPnDlzBiNGjMCoUaNw8uRJhenr80oQBHTs2BGbN29GZGQkDh48iK+//ho7duyAg4ODhEch79WrVwCyXhYgJSUFqampstuNGjVCQkIC7t69K1fu8uXLsv1ERESfYnYyO4mISJ7kA9WstG3bFqtWrcLKlSvRsWPHQtf38eNHHDp0CL/88gvOnDkDIOP6naJSs2ZNqKioICgoSO7T3vDwcJw/fx6NGzeWbevduzfKlSuH9evXy7aJooiNGzfCzMwMrVu3LrJ+EhFR2cHsZHYSEX3JJJ9MKdOLFy9w7tw5vH79Gn379kXVqlWRnp6O6Oho6Ovr53vhblEUcfLkSezatQsHDhxATEwMypcvj/79+8Pd3R12dnYF7quvry+io6NlswoeOnQI4eHhAIBx48bByMgIw4YNw5YtW9CpUyc4OTkhNjYW69evR2JiImbMmCGrq2rVqvDy8sKKFSuQkpKC5s2b4+DBgzh//jx27txZ4AXLiYio7GN2MjvpyxYfHw9dXV0AQFxcHHR0dJTcI2mU1eOiIiZKLD09XZw4caJYrlw5URAEUUVFRTx58qQoiqIYHR0tli9fXly1alWe67t8+bI4YcIE0dTUVFRRURHV1dXFXr16iUFBQWJiYqIkfbawsBABZPnz5MkTURRFMSUlRVy3bp3YqFEjUVdXV9TV1RU7duwonjp1SqG+tLQ0cfHixaKFhYWorq4uNmjQQNyxY0eh+9mrV69C10FEVJqV1fdBZiezk0gURTEuLk72/yguLq7gFXVB0fwo+7ioQPL7PhgdHS26u7sXUW/yTvJvVFesWIE1a9Zg2rRp6NSpE7p06SLbp6+vDycnJ+zfvx9eXl451jN37lwEBgbi8ePHEEURrVu3xpw5c9C/f38YGhpK2uewsLBcy6ipqWHs2LEYO3ZsrmVVVFQwY8YMuU+LiYiIssPsZHYSEZE8yQeqmzdvxqBBg7B48WK8e/dOYb+NjQ1+/fXXXOtZtGgR6tatC29vb7i5ueVpwXIiIqLSiNlJREQkT/KB6vPnz3Oc9EBHRwcfPnzItZ6//vpLbqIFIiKisorZSVRy8fpKIuWQfKBqbGyM58+fZ7v/r7/+ytOi5QxaIiL6UjA7iajU6irkXubTVaocdYG8zI92XMy9DJVpkg9UnZycsHHjRgwZMgT6+voAMtZwA4Djx4/D398fU6dOlbpZIiKiUovZSfQF4ICOKF8kX0d1wYIFMDU1RaNGjTBo0CAIgoBly5ahTZs26N69O2xsbDBz5kypmyUiIiq1mJ1ERETyJB+o6uvr49KlS5g6dSpevHgBTU1NnD17FtHR0Zg3bx7Onz8PbW1tqZslIiIqtZidRERE8iQ/9RcAtLS0MHv2bMyePbsoqiciIipzmJ1ERET/I/lA9dixY/j6669ls6MRERFRzpidREqU27WjvG600HRUAbFL7uWIPiX5QLV79+5QVVVFw4YN0bZtW9mPkZGR1E3JDBs2DFWqVMGECROKtB0iIqKiwOwkIoADOqJPSX6N6qVLl7BkyRJUrVoVAQEB6NevH0xMTFCvXj2MHDkSAQEBCAsLk7RNf39/LF68GDVq1MCcOXMkrZuIiKioMTuJiIjkSf6Nqq2tLWxtbTF58mQAwJ07d3D+/HmcP38ev/32G7Zu3QpBEJCamipZm0+ePEFcXBzOnj2LM2fOSFYvERFRcWB2EhERySuSyZQyJSUl4fXr13j9+jVevXqFqKgoiKKIGjVqSNqOhYUFAKBBgwbw8PCQtG4iIqLixOwkIiIqglN/Dx8+jGnTpqF169bQ19dH586dsX//ftSrVw8///wzIiIicP/+/XzV6e3tjdu3b2e7/99//4W3t3dhu05ERKQUzE4iIiJ5kn+j6ujoCFVVVfTt2xezZ8/G119/DX19/ULVOX/+fNSsWRPW1tZZ7r99+zYWLFiAuXPnFqodIiIiZWB2EpVcnOCISDkkH6g6ODjgwoUL2LNnDy5evIi2bduiXbt2aNu2LerVqyd1cwCA9+/fQ11dvUjqJiIiKmrMTiIiInmSD1QPHToEIOOT2syJIBYuXIiXL1/C0NAQX3/9Ndq2bYvvv/8+x3rOnTsnN7lDcHAwHj58qFAuOjoaQUFB+OqrryQ9DiIiouLC7CQiIpJXZJMpWVtbw9raGmPGjEFycjICAwOxbNkyhIaG4tChQ7mG7enTp7FgwQIAgCAICA4ORnBwcJZl69evj3Xr1kl+DERERMWJ2UlERJShSAaqcXFx+PPPP3Hu3DmcP38eV69excePH6GmpoaWLVuibdu2udYxdepUjB07FqIowtjYGBs3bkTfvn3lygiCAG1tbWhqahbFYRARERUbZicREdH/SD5Qbdq0KW7duoW0tDTo6uqiVatWmDlzJtq2bYsWLVpAS0srT/VoaWnJyj558gRGRkbQ1taWurtERERKx+wkIiKSJ/lA1dLSEgMHDkTbtm3RuHFjqKgUfgWczLXeiIiIyiJmJxERkTzJB6r79++XukqIoohNmzZh69atePz4MaKiohTKCIKA1NRUydsmIiIqasxOIiIieUU2mVKmqKgo9O3bFz4+PmjcuHGB6pg6dSp+/PFHNGrUCO7u7qhQoYLEvSQiIio5mJ1ERPSlK/KB6sePH3HmzJksP8nNq+3bt6Nv377Ys2ePhD0jIiIqmZidRET0pSv8RTDFIDExEZ07d1Z2N4iIiEoNZicREZVmpWKg2qlTJ1y9elXZ3SAiIio1mJ1ERFSaSTpQTUxMxKRJk3Do0CHZNi0tLQwePBhVqlQpcL3r16/HpUuXsHjxYrx7906KrhIREZUIzE4iIiJFkl6jqqWlBT8/P9SvX1+2TU9PD9u2bStUvXXq1EF6ejrmzJmDOXPmQFNTE6qqqnJlBEFATExModohIiIqbsxOIiIiRZJPptS0aVPcvn1b0jr79u0LQRAkrZOIiKikYHYSERHJk3ygunr1avTo0QPW1tYYMmQI1NQK34S/v3/hO0ZERFRCMTuJiIjkST5QHTJkCFRUVDBq1CiMHz8eZmZm0NLSkisjCAL+/vtvqZsmIiIqlZidRERE8iSf9dfQ0BB16tRBu3bt0KJFC1StWhUVK1aU+zE0NMx3vc+ePcPo0aNRp04dVKhQAefOnQMAvH37FuPHj8eNGzekPhQiIqJiwewkIiKSJ/k3qmfOnJG6Sty5cwdt27ZFeno6WrRogYcPHyI1NRUAUKlSJfzxxx+Ij4/H1q1bJW+biIioqDE7iYiI5Ek+UC0KU6dOhYGBAS5dugRBEGBsbCy338HBAUFBQUrqHRERUcnD7CQiotKs0APVzNOI2rVrJ3c7N5nl89rG3LlzYWRklOVacObm5njx4kWe6yMiIlImZicREVHOCj1Q7dChAwRBQGJiItTV1WW3syOKIgRBQFpaWp7bSE9Ph7a2drb737x5Aw0NjXz1O1NcXBxWrFiBy5cv48qVK4iKisK2bdswZMiQLPvh5+cHPz8/3Lt3D9ra2mjYsCFWrVqFhg0bypVbuXIlNmzYgIiICNSuXRszZsyAq6trgfpIRERlC7OT2UlERDkr9ED19OnTAAB1dXW521Jq0qQJjhw5Ag8PD4V9qamp2L17N1q2bFmgut++fQtvb2+Ym5ujYcOGOV4nNGzYMOzcuRODBg3C2LFjER8fjxs3buD169dy5WbNmoWlS5dixIgRaN68OUJCQjBgwAAIggAXF5cC9ZOIiMoOZiezk4iIclbogWr79u1zvC2FGTNmoGfPnhgzZowsrF69eoXff/8dixcvxt27d+Hr61uguk1NTREREQETExNcu3YNzZs3z7Lcnj17sH37dgQHB6NPnz7Z1vfixQv4+PjA09NT1qfhw4ejffv2mDJlCpydnaGqqlqgvhIRUdnA7JTH7CQiKv3evXuH27dvw8zMDDVr1ix0fZIvT1MUunfvDn9/fwQFBcHOzg4A4O7ujq5du+L69ev45Zdf8nXdzqc0NDRgYmKSa7kff/wRtra26NOnD9LT0xEfH59luZCQEKSkpMh9gi0IAsaMGYPw8HBcvHixQP0kIiLKD2YnEREVpz///BN2dnaoW7cuxowZg/fv3xeqviKZ9TcpKQn79+/H9evXERMTg/T0dLn9giDkezr8gQMHwsnJCcePH8fDhw+Rnp6OGjVqoFu3bihfvryU3Vfw4cMHXLlyBR4eHpg5cybWrVuHuLg4WFlZYenSpfj2229lZW/cuAEdHR3Uq1dPrg5bW1vZ/jZt2hRpf4mIqPRhdjI7iYjKgjVr1mDt2rWoXbs2FixYgDFjxkBFJf/fj0o+UH369Ck6duyIsLAwGBgYICYmBoaGhoiOjkZaWhoqVaoEXV3dAtWto6OT46lDReXRo0cQRRG7d++Gmpoali9fDn19faxZswYuLi7Q09ODvb09ACAiIgKVK1dWmBTD1NQUAPDy5cts2wkMDERgYGC2+y9evAhHR0cJjoiIiEoSZiezk5ToRhHUmdVrrija+dLayoPU1FT8+uuvADLOLFFTKxWrcZYZoiiiZ8+eGDVqFHx9fTFnzhxs3rwZa9euzfdZPJI/c1OmTEFMTAwuXbqE6tWrw9jYGEFBQfj666+xdu1a+Pr64tixYznW8ezZMwAZU+d/ejs3meWlFhcXByDjvOtLly6hRYsWAABHR0dYWVlh0aJFsrBNTEzMchZFTU1N2f7suLq65ji7oaOjI0JDQwt8HEREpV1ZHXAwO5mdpERds59xu8Cyes0VRTtfWlt5EB8fL/tgb+/evdDR0ZGyV6WSMrJTTU0NXl5eGDRoEObMmYPOnTvDyckJPj4+MDMzy1sdUnfq1KlT8PDwgK2trey8ZFEUoaGhgSlTpuDu3bvw8vLCkSNHsq3D0tJSbtr+zNu5yc+0/fmhpaUFALCyspIFLQDo6uqiV69e2LFjB1JTU6GmpgYtLS0kJycr1JGUlCRXFxERUSZmJ7OTiKisEEVR9ruhoSF++ukneHh4wMvLC3Xq1MGMGTMwa9asXOuRfKCakJAAS0tLAICenh4EQUBMTIxsf6tWrTB58uQc6/j5558hCALKlSsnd1tZqlSpAgCoXLmywj5jY2OkpKQgPj4e+vr6MDU1xenTp2Vr3mWKiIiQq4uIiCgTs5PZSfnz6bdmcXFx/NaMSIk6duwIIGPpMiDjTJfMM2I+p6Kigjlz5ihnoGpubo7w8PCMytXUYGZmhkuXLsHJyQkAcOfOnWw7nunzBcOzWkC8OFWpUgUmJiZ48eKFwr6XL19CU1NTNilFo0aNsGXLFty9exf169eXlbt8+bJsPxER0aeYncxOIqLS6v379xBFUXbJR3R0tOxD089lfiibF5IPVO3s7BASEoJ58+YByAjKJUuWICoqCunp6QgICMCgQYOkbrbI9e/fH2vWrMGJEyfQpUsXABmfGoSEhMDOzk42k1Xv3r0xceJErF+/XrYWnCiK2LhxI8zMzNC6dWulHQMREZVMzE5mJxHlQV6uh/30agZHXSC3JZiPi7kUoNz8/fffAIBDhw7hm2++wW+//QYLC4tC1yv5QHX69Om4evUqkpOToaGhgZkzZ+Lly5fYt28fVFVVMWDAAPz444851uHt7Z3vdgVBwJw5cwrUZ19fX0RHR8tmFTx06JDsk+1x48ZBX18fM2bMwJ49e9C3b19MmjQJ+vr62LhxI1JSUrB48WJZXVWrVoWXlxdWrFiBlJQUNG/eHAcPHsT58+exc+dOLlhOREQKmJ3MTiKi0q569eqYMGEC9PT0JKlPED+92rUAQkND0axZM0mvH8lqnZ3Ma1Y+764gCLJrWgo6IYSlpSWePn2a5b4nT57IvqJ+/PgxJk+ejJMnTyIlJQWtWrXC0qVL0bx5c7n7pKenY9myZfDz80NERARq1aqFGTNmwM3NrUD9y8SZC4noS1dW3geZncxOKhxJr1Etillrs/qWrqhmx2VbcuLTAN1TGb/H2QE6/EY13++DMTExGDt2LAICAvJU/uzZs7Cyssp1Fvl3794hMjIS1atXz9MkeYX+RrVPnz4ICAjAgAEDAGSMpFevXl2oaZA/X+T8xYsXcHBwgLW1tWy2KAD477//sHr1aty5cyfHmRBzExYWlqdy1atXR3BwcK7lVFRUMGPGDMyYMaPAfSIiorKL2amI2UlychuQ5Pf0TuCLGJAQKYOdnR3Kly+P0NDQHNdKff36NWxsbLB27Vp4enrmWq/ix6/5VL58eURHR8tuh4WFyS6klYqnpydq1aqFHTt2oFmzZihfvjzKly+P5s2bY+fOnahRo0aeDpaIiKgkYHZSWRUfHw9BECAIAuLj45XdHSIqJqmpqbC3t0dISEi2ZerVq4fOnTvn+dvdQn+jamtrix9++AGvXr2Cvr4+AODo0aOIjIzM9j6CIGDixIl5buPUqVNYtmxZtvs7deqEadOm5b3TRERESsTsJCIqGjqqgNhF2b348qxYsQLBwcHo168fNm/enO3M8127doWPj0+e6iz0QHX9+vUYNGgQFi5cCCAjSHft2oVdu3Zle5/8hq2mpiYuXryIMWPGZLn/woULuU7bT5RfXKONssLXBUmB2UlERGWJnp4ejh49Cnd3d3z33Xd48+YNpkyZkmW5vJ5BVOiBas2aNXHhwgUkJSXh9evXsLS0xOrVq9G7d+/CVi3j5uaGtWvXwsDAAOPGjUONGjUAAI8ePcLatWuxa9cujB8/XrL2iIiIihKzk4iIyppy5cohKCgIY8eOxbRp0/DgwQP89NNPcmuqHjt2DDVr1sxTfZItT6OpqQlzc3PMmzcPdnZ2kqydk2nZsmV4+/YtfH198dNPP8lmNkxPT4coinB1dc3x9CYiotKI396WfcxOooLh6Z1EJZevry/MzMwwe/Zs/Pnnn/Dy8oKpqSkOHDiA4OBgrF+/Pk/1SL6OauZi5VJSV1dHQEAApkyZgqNHj8qmw7ewsED37t3RsGFDydskKos48CEqmZidVCrkZRmS/M7Gy5l4icqkGTNmoEmTJhg7dixGjx4t2z548GCMHDkyT3VIPlAtSjY2NrCxsVF2N4iohOEAnCh7zE4iIipK8+bNyzJnunXrhnv37uHq1at4+/Yt6tWrh+rVq+e53lI1UCXigISKVFF8WwDwGwMlSkpKwoEDB+Dq6qrsrhAREZVJc+fOzXafiooKWrRoUaB6S81A9ddff8WPP/6I69evIyYmBqKo+IdfWlpaFvckygJPXyL6IgQGBsLDwwMuLi4QhDz8vy9jmJ1ERFRalYqB6v79+/Htt9+iQYMGcHFxwYYNGzBgwACIooiQkBDUqlUL33zzjbK7SVQg/Jb4C1XCPiwpq6/DjRs3Ii0tDRcuXMDXX3+t7O4UK2YnEREp2+3btzFx4kRcvXoVWlpacHBwgI+Pj2wN8ZyUioHqkiVLYGtriz/++ANRUVHYsGEDhg0bBjs7O4SFhaFly5awsrJSdjeJlC+3wQ9PW6UvyJs3b/Ds2TOkpKRg2bJlCA0NVXaXihWzk4iIlOnjx49wcHBAjRo1sHPnTkRHR2P69OlITEzEzp07c71/kQxU09LScOzYMTx+/BhRUVEKpxoJgoA5c+bkub47d+5gyZIlUFVVhZpaRpdTUlIAAJaWlvDw8MCyZcswaNAg6Q6CiEoGXjdKBeTn54fXr18DAG7evImEhARoa2sruVfZY3ZSWcBlY4i+PLdv34a1tXWW258/f46TJ0/K1k6Njo7Oc5ZJPlC9du0a+vbti/Dw8CyvhQHyH7ba2tpQV1cHABgYGEBDQwMRERGy/ZUrV8aTJ08K13EiojzgH2Glx44dO5Ceng4AeP36NYKCgjB06FAl9yprzE4iIiqtmjdvjmHDhmHRokWoUKGCbLuBgQEEQcCjR49kA9UHDx7IlcmJitQd9fDwQGJiIg4ePIj3798jPT1d4Se/EzfUqVMHd+7ckd1u1KgRAgICkJqaiqSkJOzatQvm5uZSHwoVt65C7j+Ouv8r76ibt/sQ0Rfn5s2bePfunex2cnIyfvrpJyX2KGfMTipK8fHxEAQBgiAgPj5e2d0hojLmypUruHv3LmrVqgVfX1/Zh8TVq1dHv3794OTkhL59+6Jjx47w9fXFrFmz8lSv5N+o3rp1Cz/88AN69eolWZ19+vTB2rVrsXLlSmhoaGDWrFno3bu3bJQeHx+Pn3/+WbL2iAAJvznjdaNUQPz2tuBWrlyJt2/fym17+fIlnj9/jmrVqknWTkpKCl6+fIlKlSoVagIqZicREZVWX331FU6dOoXg4GBMnjwZmzZtwurVq2FnZ4egoCDs3r0bly9fRt26dbFkyRK0bNkyT/VKPlCtWrVqtqctFdTkyZMxefJk2e2ePXvizJkzCA4OhqqqKhwcHNCxY0dJ2yQiKjNK2AzDRe3jx484f/68wvaIiAisW7cOy5cvz7UOKysrTJ48GZ6enjmWi4qKgpWVFebMmYMFCxYUuM/MTiIiKu2cnJzQs2dP+Pj44JtvvkHnzp3h4+MDFxcXuLi45Ls+yU/9nTZtGjZv3owPHz5IUl9ycjJCQ0Nx69Ytue1t27bFqlWrsHLlSgYtUR5lfkMndsn4nagsCg0NlTvt91PBwcF5GhA+e/YM0dHRuZYzNjaGhYUFrly5kt9uymF2EhFRWaCuro4ZM2bg/v370NPTg7W1NWbNmoWEhIR81yX5N6qxsbHQ1dVFzZo14eLigmrVqkFVVf4vYkEQMHHixDzVp66uDmdnZ6xZswY2NjZSd5eIygCeIkufWrVqVbbX4b1//z7Pa6q+ePECf/31V67lqlevXuiBKrOTiIhKs9u3b2P//v149eoVqlSpAmdnZ/j7+8PT0xPjx4/HL7/8giVLlsDd3T3PdUo+UP30NCNfX98sy+QnbAVBQK1atRSuNaIvEwckRJSTN2/e4N9//812f1RUFJYuXYpDhw7lWtfGjRuxcePGXMsJglDo03aZnUREVFoFBARgyJAhaNy4MerWrYv//vsPCxcuRGBgIJycnHDx4kXs3LkT06dPx4YNG7BmzRo0a9Ys13olH6gWxVT3M2fOxKRJk+Ds7Iw6depIXj+RMnHwTSQddXV1eHt7y27HxcUhJCQEbm5usm01atTIU11OTk5wdHSUvI9ZYXZSgXGtaSJSsvnz52PUqFFYv369bNsvv/wCb29vODk5AQDc3NzQp08fLFmyBO3bt8/TDOSSD1QtLCykrhKXLl1CxYoVYW1tjQ4dOsDS0hJaWlpyZQRBwJo1ayRvm4gKJj4+Hrq6GcsJxcXFFWpGVCp6ZeUDE319fYwfP152+927d3jw4IHctrxq3LgxBg0aJGX3ssXsJCKi0io2NlZhuTMLCwvExsbKbdPW1sbChQsxfPjwPNUr+UC1KHx6GtTJkyezLMOwJSIi+h9mJxERFYchQ4bA29sbkZGRqF+/Pl6+fAk/Pz94eHhkWT6vH84WyUD11q1bWLduHa5fv46YmBjZoq+ZBEHAo0eP8lzf5/cnIiIqSk+ePEGFChWKtU1mJxERlUbLli1DzZo1ERQUhBMnTsDMzAxLly7F4MGDC1Wv5APVM2fOwN7eHhUqVECzZs1w48YN2NnZISkpCRcvXkSDBg3QtGlTqZslIiKSjKmpKRISEpCWlqYw+25RYHYSEVFpJQgCRo4ciZEjR0par+TrqM6dOxfVq1fHvXv3sG3bNgAZEzr88ccfuHDhAsLDw/Htt99K3SwREZFkpk2bhqpVqypcX5MVT09PbNmypVDtMTupKHENbSIqjSQfqF6/fh3fffcd9PT0ZJ9Cp6VlTDfXokULjBo1CnPmzMlfJ1VUoKqqmuOPjo4O6tSpg9GjR+fr1CgiIqLPnT59Gv3794eBgUGuZRMTE7Fjx45CtcfsJCIikif5qb9qamooX748AMDAwADlypXD69evZfurV6+OO3fu5KvOuXPnIiQkBP/++y+6d++OmjVrAgAePHiA3377DV999RXs7Ozw8OFDbNu2DYGBgTh37hwaNmwo3YER0f9wOQQqoNIyG3RYWFiWM/6ePXsW9vb2ePHiBQwNDQEAX331FY4ePVqo9pidRERE8iQfqNasWRMPHjwAkHG+ct26dXHgwAHZGnZHjhyBiYlJvuqsUqUK3r59i//++w/Vq1eX2/fw4UN06NAB9evXx4oVK/DgwQO0atUKM2fOxJEjR6Q5KCIi+qIkJiZmOYhOSkpCcnKy7NtOAKhQoQKio6ML1R6zk4iISJ7kp/726NEDgYGBSE1NBQBMmjQJwcHBqFWrFmrVqoXQ0FCMGjUqX3WuWLECnp6eCkELZIS7p6cnlixZAgCoVasWRo8ejQsXLhT+YIiI6ItUpUoV3Lp1S2H73bt3AQBPnz6VbXvy5AmqVKlSqPaYnURERPIkH6jOmTMHf//9t+wam8GDB+OXX36BtbU1GjZsiJ9//hnTpk3LV53h4eFQU8v+y181NTU8f/5cdtvS0hLJyckFOwAiIvridenSBf7+/rhy5Yps2+PHj7Fjxw5UqlQJ3t7eePfuHa5fv45NmzahQ4cOhWqP2UlERCRP8lN/y5Urh4oVK8ptc3d3h7u7e4HrbNCgATZs2ICBAweicuXKcvsiIyOxYcMGNGjQQLbt8ePH+T5FioiIKNOsWbNw4MABfP311/jqq6+gpaWFGzduYP78+dDR0cH48eNhZGQEADAyMsK8efMK1R6zk4iISJ7kA9VMycnJuH79Ol6/fo2vv/4alSpVKnBdK1eulE0E8c0338gmhHj48CEOHjyIlJQU/PzzzwAyrh/y9/dH9+7dJTkOIiqYzOUQ6AuT20RbpWSSLXNzc1y8eBGLFi3C1atXoaWlhZUrV8LDwwMAULduXZw8eRIGBgYYNGiQZAM8ZicREVGGIhmorl27FvPnz0dMTAwA4MSJE7Czs8Pbt29Rt25dLF++HMOGDctzfR06dMCFCxcwb948BAcHIzExEQCgqamJzp07Y/78+WjSpIls28uXL6U/KCIi+qLUqFFDtqbp5zp16oROnTpJ2h6zk4iodCktM9mXVpJfo7pt2zZ4eXnB3t4eW7duhSj+75PwSpUqwc7ODrt37853vY0bN0ZoaChiY2Px8uVLvHz5EnFxcQgNDZUFLRERUWnE7CQiIpIn+UDVx8cHvXv3xq5du9CrVy+F/U2bNsW///5b4PpVVFRgYmICExMTqKgUvvtxcXGYN28e7O3tYWhoCEEQ4O/vn+N9UlJSUL9+fQiCgJUrVyrsT09Px/Lly2FlZQVNTU3Y2NggMDCw0H0lIiLle/HiBUaMGIFGjRqhVatWWLRoET5+/FioOpmdzE4iIpIn+UD14cOHOV7jYmhoiHfv3kndbIG9ffsW3t7euHv3bp4XOV+3bh2ePXuW7f5Zs2Zh2rRp6NKlC9atWwdzc3MMGDCgQJ+GExFRyZGWlobu3bvj8uXLGDFiBHr27AkfHx98//33haqX2cnsJCIieZJfo2pgYIC3b99mu//OnTslalZBU1NTREREwMTEBNeuXUPz5s1zLP/69Wt4e3tj2rRpmDt3rsL+Fy9ewMfHB56envD19QUADB8+HO3bt8eUKVPg7OwsW36AiOhLU1om2Xr9+jWMjY0Vtv/777+4ffs2bt26BWtrawAZ13cuW7YM69atK3B7zE5mJxGVMLlNDgjkf4JAJUwOWJpJ/o1qjx49sGnTJkRHRyvs+/fff7F582Y4OjpK3WyBaWho5Cv8p0+fjjp16mS7ZEBISAhSUlJkM0MCgCAIGDNmDMLDw3Hx4sVC95mIiIpW7dq1sWLFCqSkpMhtV1dXhyAIiI2NlW378OEDNDQ0CtUes5PZSURE8iQfqC5atAhpaWmwtrbG7NmzIQgCtm/fDnd3dzRr1gzGxsZZfppaGly5cgXbt2/H6tWrIQhZf8py48YN6OjooF69enLbbW1tZfuJiKhk27VrF7Zu3Qpra2scPnxYtr1u3bpo164d+vbti++//x5Dhw7FsmXLMHr06EK1x+xkdhIRkTzJT/2tUqUK/vrrL8ycORNBQUEQRREBAQEoX748XF1dsXTp0kKtC6csoihi3Lhx6N+/P1q1aoWwsLAsy0VERKBy5coKYWxqagoAOU7/HxgYmOPEERcvXixRn6hLrqj+DsnqMSuKtrJ7bspiW6X9uSqrbSn7dVHcbRWhHj16oGvXrli7di0GDhyIli1bYtWqVahbty6OHDmCtWvX4vLly9DW1sb27dvRv3//QrXH7GR2Fhjft0pPW6X9uSqrbRXidZH6yZm8zn8DarmdLVyW34uKQJGso2psbIwtW7Zgy5YtePPmDdLT02FkZJTnmQbPnTtXoHbbtWtXoPvlhb+/P/755x/s27cvx3KJiYlZngKmqakp258dV1dXuLq6Zrvf0dERoaGheexxKZSXawEKIqvHrCjayu65KYttlfbnqqy2pezXRTG0VZwDDjU1NUyaNAmDBw/GzJkz0bhxY4wZMwbz5s3D9OnTJW+P2cnsLBC+b5Wetkr7c1VW2yrE6yI+DdA9lfH73oYZ8zAUqK0iVlo/rCuSgeqnjIyM8n2fDh06ZHt6UFZEUYQgCEhLS8u9cAF8+PABM2bMwJQpU1CtWrUcy2ppaSE5OVlhe1JSkmw/ERGVHhUrVoSfnx88PDwwfvx41K5dG4sWLcLw4cPzlVX5wezMwOwkopKstEwQWFoVyUA1Pj4e+/fvx+PHjxEVFSW3cDmQMUHCmjVrsr3/6dOni6JbBbZy5Up8/PgR/fv3l522FB4eDgCIiopCWFgYqlSpAnV1dZiamuL06dOyPwAyRUREAMg4vausiY+Ph66uLoCMtfV0dHSU3CMi+lI9evQIzZo1Q/ny5QFkrM0ZFRWFkydPAgBSU1PRoEEDnDhxIte63rx5g6NHj+LVq1cwNTVFz549cfbsWezbtw9TpkyBn58fVq9ejTZt2kjSd2bnl5WdRESUM8kHqidPnoSzs3OWMxdmyi1s27dvL3W3CuXZs2eIiopCgwYNFPYtXrwYixcvxo0bN9CoUSM0atQIW7Zswd27d1G/fn1ZucuXLwMAGjVqVFzdJiL64lSvXh0VK1bEo0eP5LYnJCQAAMqVK5enU6COHz8OJycn6OrqombNmoiIiMC4ceNw+PBh9OvXD7169cLKlSvRo0cPODg4YMWKFahatWqB+83sZHYSEZE8yWf99fT0hI6ODo4dO4bo6Gikp6cr/BTVaUZFZfz48Thw4IDcj5+fHwBgyJAhOHDgAKysrAAAvXv3Rrly5bB+/XrZ/UVRxMaNG2FmZobWrVsr5RiIiL4EgiDA2dk52/2VK1fG4MGDc61n4sSJcHBwwMuXL/HHH3/g0aNHmDlzJiZPngwgY3mWWbNm4d69e1BTU1OYrTa/mJ3MTiIikif5N6rPnj3DsmXL0KWLtCdsJyUlYf/+/bh+/TpiYmKQnp4ut18QBGzdurVAdfv6+iI6Olo2q+ChQ4dkpyeNGzcOTZo0QZMmTeTuk3kaU4MGDfDNN9/ItletWhVeXl6y9feaN2+OgwcP4vz589i5cycXLCciKmJjx47F9u3bZaeNfqpevXrQ09PLtY7IyEiMGDFCbiKjDh06wNfXV66cqakpAgICCr3OJ7OT2UlERPIkH6ja2NggJiZG0jqfPn2Kjh07IiwsDAYGBoiJiYGhoSGio6ORlpaGSpUqya6RLIiVK1fi6dOnstvBwcEIDg4GALi7u0NfXz9f9S1duhQVKlSAn58f/P39UatWLezYsQMDBgwocB+JiChvzMzMYGZmpjBQ1dfXl30jmpvevXtj0aJFAID69evj5cuXWL58Ofr06ZNl+VatWhWqz8xOZicREcmTfKC6bNkyuLq6wt7eHs2aNZOkzilTpiAmJgaXLl1C9erVYWxsjKCgIHz99ddYu3YtfH19cezYsQLXn926bjmxtLRUmOgik4qKCmbMmIEZM2YUuE8lRl6mAf/0bDRHXSAvH3wfz/qxIyKSwrhx4zBq1CjZrLEAUKFCBXTu3DlP91+/fj1MTEywbt06REZGwszMDG5ubpg1a1aR9JfZWcayk4iICk3ygWr79u2xevVqtGrVCvXq1UO1atUUTtkRBAEhISF5rvPUqVPw8PCAra0t3r9/DyDj2hUNDQ1MmTIFd+/ehZeXF44cOSLpsRARUenk7OyM2bNn4/nz57JtPXr0yPOapJqamrIJf4oDs5OIiEie5APV/fv3w93dHWlpaQgPD0dsbKxCmfyuO5eQkABLS0sAgJ6eHgRBkDtFqlWrVnk+nYuIiMo+LS0tNG3aVDZQrVy5MiZOnKjkXmWP2UlERCRP8oHq9OnTUadOHezfvx+1a9eWpE5zc3PZBA1qamowMzPDpUuX4OTkBAC4c+cONDU1JWmrrODapkT0pZs6dSrOnDmD6OhoGBsbo2bNmsruUraYnURERPIkH6i+fPkSK1askCxoAcDOzg4hISGYN28egIxp7ZcsWYKoqCikp6cjICAAgwYNkqw9IiIq/Vq2bCmbRGjEiBHK7k6OmJ1ERETyJB+oNm/eHM+ePZO0zunTp+Pq1atITk6GhoYGZs6ciZcvX2Lfvn1QVVXFgAED8OOPP0raJhERlW6CIKB379746aef8rR2qjIxO4mIKDtf6pmSkg9U161bh169eqFJkyb49ttvJanT3Nwc5ubmstuamprYsmULtmzZIkn9VDg6qoAo7dJ/RESSGDt2LG7cuJGntVOVidlJREQkT/KBqpubG1JTU+Hq6ooRI0agatWqWc5c+Pfff0vd9JeDS8YQEeVJzZo1cfbsWWV3I1fMTiIiInmSD1QNDQ1RsWJF1KpVq8B1eHt7QxAEzJo1CyoqKvD29s71PoIgYM6cOQVuk4iISFmYnURERPIkH6ieOXOm0HXMnz8fgiBg2rRpUFdXx/z583O9D8OWiIhKK2YnEdEXLLezJb/QMyUlH6hKIT09PcfbREREJI/ZSUREZYmKsjtARERERERE9KlSMVBVVVXFrl27st0fFBSkMOnEly5zJl6xS8bvRET0ZWF2EhFRaVYiT/39nCjmfI51WloaBCEPM+ESERF9IZidRERlw5e6FGSp+EYVQLZh+uHDBxw7dgyVKlUq5h4RERGVbMzOkis+Ph6CIEAQBMTHxyu7O0REJU6JHaguWLAAqqqqUFVVhSAIcHd3l93+9KdChQoICAiAi4uLsrtMRESkVMxOIiIqKyQ/9bdevXoYOHAg3NzcYGFhUeB6bG1t4eHhAVEUsX79enTp0gW1a9eWKyMIAnR0dNC0aVM4OTkVtutERERKwewkIiKSJ/lAtVq1apg3bx7mzp2L1q1bY9CgQXB2doa+vn6+6unevTu6d+8OIOP0mNGjR6NFixZSd5eIiEjpmJ1ERETyJD/19/jx4wgPD8eKFSuQmJiIkSNHwsTEBP369UNISAhSUlLyXee2bdsYtEREVGYxO4mIiOQVyay/lStXxsSJEzFx4kTcu3cPO3bsQGBgIA4cOAADAwP0798f7u7uaN26dZ7rTEtLw7Fjx/D48WNERUUpzGYoCALmzJkj9aEQEREVC2YnERHR/xT58jR16tTBwoUL4enpiQkTJmDv3r3YuHEj/Pz8UL16dXh5eWHMmDFQUcn+y91r166hb9++CA8Pz3a6fYYtERGVFczOUq5rHpb9Sfvkd0ddIC9L2h7PeckhIqKypEhn/Y2Pj8eOHTtgb28Pc3NzHDhwAD179sSePXtw4MAB1KlTB+PHj8eYMWNyrMfDwwOJiYk4ePAg3r9/j/T0dIWftLS0HOsgIiIqDZidRERERfCNauZpRjt27EBoaCgSEhLQtGlT+Pj4wNXVVW7NNkdHR8ycORM//fQT/Pz8sq3z1q1b+OGHH9CrVy+pu0tERKR0zE4iIlKWuLg47NmzR3Y7ISEBDx8+xM8//yzbZm5ujs6dOxdrvyQfqJqYmOD9+/cwMzPDuHHjMGjQINSrVy/b8jY2NoiNjc2xzqpVq2Z72hIREVFpx+wkIiJlSUpKwqRJkxATEyO3/dKlS7Lfu3btWuwDVclP/XVwcMDx48fx9OlTLFmyJMegBQAXFxekp6fnWGbatGnYvHkzPnz4IGVXiYiISgRmJxERKUulSpVgbW2d7X59fX1MnTq1GHuUQfJvVP39/aWuErGxsdDV1UXNmjXh4uKCatWqQVVVftYBQRAwceJEydsmIiIqaszOL4+OKiB2UXYviIgyTJo0CTdv3kR8fLzCPkNDQ3Ts2LHY+1TogeqzZ88KdD9zc/M8l508ebLsd19f3yzLMGyJiKi0YHYSEVFJ0qtXL1SsWDHLgaqDg0OOs8wXlUIPVC0tLSEIeZiG/TP5mWnwyZMn+a6fiIiopGJ2EhFRSVKuXDm0a9cOO3b8X3v3Hldznv8B/HVYXUQRpwtRZxozDGLH3cNvbLaERh5FY8r94c6ih8sirWbKtqO0RK6NxSrl1pAwmFzGLsZlLeuxxmwoRUkpUlHq+/ujR2ccp6jO55zTOb2ej8d5zPb9fPq8P9/5njmv/Zy+l1iV7ba2tggICNDLnDReqP7tb3+rV9jWhaOjo1bHJyIi0iVmJxERNTSLFy/G999/j9zcXOU2W1tbODs762U+Gi9UJ0+eLGAatfPw4UP8+OOPyMnJwejRo+Hg4IDy8nI8e/YMVlZWatfeEBERNUTMTiIiamh69OiBNm3aKBeqJiYmmDVrlt7mo/uTjetBkiQsXLgQCoUC48aNw8KFC/HLL78AqHzuj5OTEzZs2KDnWRIRETUczE4iIqqrCRMmKK9HtbGxwfjx4/U2F+F3/QWA/Px8xMfH4969e8jPz1d7jptMJsP27dtrPV5ERASioqKwdOlS/P73v4e7+6+3ybOysoKPjw8OHjyot/OniYiINMXs1L+ioiK0aNECQOVi3sLCQs8zIiLSrRkzZiA6OhrZ2dn45JNP0LJlS73NRfhC9cSJExgzZgyKiopgaWmJ1q1bq/Wp63U5MTExmDhxIsLCwpCXl6fW7uLiguPHj9d7zkRERPrE7CQiooZALpfD0dERubm5enl26puEL1QXLVoEOzs7JCYmonv37kLGzMjIwMCBA2tst7Cw4APNiYjIYDE7iYiooZg7dy6mTZuml2envkn4QjU1NRURERHCghaoPD86IyOjxvZr167V6dlyREREDQmzk4iIGgpfX1+Ulpbq5dmpbxJevVOnTigsLBQ6po+PD7Zs2YJ79+4pt1WdAnXy5Ens3LkTvr6+9Rr7xYsXCA4OxrBhw2BtbQ2ZTIadO3eq9KmoqMDOnTvh5eWFDh06wMLCAt26dcOqVavw8uXLasfdvn07unTpAjMzM3Tq1Ik3rCAiohoxOytpNTuHyt7/8mrxa3+vFrX7HSIiI2NmZoapU6fqexriF6qrVq3Cpk2bkJaWJmzMr7/+Gvb29ujZsycmTpwImUyG1atXY9CgQRg+fDhcXFwQGBhYr7Fzc3MREhKC27dvo0ePHtX2KS4uxpQpU/DkyRPMmjUL69atQ9++fREcHIzhw4er3fBi69atmDZtGrp27YoNGzZgwIABmD9/PlavXl2vORIRkXFjdjI7iYhIlcan/s6fP19tm1wuR5cuXeDu7o4OHTqoPaNNJpMhKiqq1jWsrKxw6dIlREZG4sCBAzAzM8O5c+fg7OyM4OBgLFmyBObm5vWav729PbKysmBnZ4erV6+iT58+an1MTEzwz3/+U+Van+nTp8PJyQnBwcFISUmBm5sbAKCkpAQrVqyAp6cnDhw4oOxbUVGB0NBQzJgxo9qbZBARUePB7GR2EhHRu2m8UI2Ojq6xLTk5udrtdQ1bADA3N0dQUBCCgoLq9HvvY2pqCjs7u3f2MTExqfaGFN7e3ggODsbt27eVYXvmzBnk5eVhzpw5Kn3nzp2LuLg4HD16VK/PIyIiIv1jdjI7iYjo3TQ+9beioqLOr/Ly8jrVGDJkCFJSUmpsP3PmDIYMGaLprtRZdnY2AKBt27bKbdevXwcA9O7dW6Vvr1690KRJE2U7ERE1XszOhpmdFk0Byb3yZdH0/f2JiEh7hN/198GDB5DL5TWeTlRSUoInT57U6U6DZ8+exbRp02psz8nJwblz5+o8V02Fh4fD0tISw4cPV27LyspC06ZNYWNjo9LXxMQEbdq0waNHj2ocLz4+HvHx8TW2X7x4EV5eXoC28trLS30ba2lWx1hrGfqxMtZa+n5f6LqWEWF2Mjsbba2G8FnC7GzctfT9vtB1LQMifKGqUCiwe/du+Pv7V9uelJQEf3//On8z/K4HnaempqJly5Z1Gk9TYWFh+OGHH7Bp0ya0atVKub2kpAQmJibV/o6ZmRlKSkpqHNPPzw9+fn41tnt5eSEpKUl7dxlMSlLfxlqa1THWWoZ+rIy1lr7fFzqo5WUEwVsdZiezs9HWagSfW1qtw1raqWNktQw1O4UvVN++i9/bysrKavVMnl27dmHXrl3Kn1etWoWYmBi1fgUFBbh58yZGjBhR98nW0969exEUFISpU6di9uzZKm3m5uYoLS2t9vdevnxZ7xtXEBGR8WJ2MjuJiEiVkIXq8+fPUVBQoPw5Ly8PDx48UOtXUFCAhIQE2Nvbv3fM4uJiPHnyRPlzYWGhWkjLZDJYWFhg1qxZWLlyZf13oA5OnTqFiRMnwtPTE1u2bFFrt7e3R3l5OXJyclROYSotLUVeXh7atWunk3kSEVHDxuz8FbOTiIjeJmShunbtWoSEhACoDMCAgAAEBARU21eSJKxateq9Y86ePVv5jatCoUBUVJTe/2z9008/wdvbG71798a+ffvwm9+o/+vr2bMnAODq1asq31RfvXoVFRUVynYiImrcmJ2/YnYSEdHbhCxUhw4dihYtWkCSJPzxj3+En58fPv30U5U+Vd/g9urVS+2ufu9z//59EdPUyO3bt+Hp6QknJyckJyfXeBrSkCFDYG1tjc2bN6uE7ebNm9G8eXN4enrqaspERNSAMTt/xewkIqK3CVmoDhgwAAMGDAAAFBUVYfTo0ejWrZuIoVWcO3cOR48eRXp6OgDA0dERnp6eGDx4sEbjRkdHo6CgQHlXwSNHjiAzMxMAMG/ePDRp0gQeHh7Iz8/HkiVLcPToUZXfd3Z2Vu6/ubk5QkNDMXfuXPj6+sLDwwPnz59HbGws/vznP8Pa2lqjuRIRkXFgdjI7iYioZsJvphQcHCx6SJSWlsLPzw+HDh2CJEnKOwUWFBQgMjIS3t7eiI+PR7Nmzeo1/po1a5QBDgCJiYlITEwEAOUDxjMyMgAAy5YtU/v9SZMmKcMWAObMmYNmzZohMjISSUlJ6NChA9auXYsFCxbUa35ERGTcmJ3MTiIiUiV8oQoA+fn5iI+Px71795Cfn692N0OZTIbt27fXeryvv/4a3333HRYvXoxFixbB1tYWQOUz4CIjIxEREYGQkBCEhobWa75paWnv7fO+OzK+bfr06Zg+fXq95kNERI0Ps5PZSUREvxK+UD1x4gTGjBmDoqIiWFpaonXr1mp93vVct+rs2bMHkyZNQnh4uMp2GxsbrF69Go8fP8bu3bvrHbZERET6xOwkIiJSJXyhumjRItjZ2SExMRHdu3cXMmZWVhb69etXY3u/fv2QkJAgpBYREZGuMTuJiIhUvf/p4XWUmpqK+fPnCwtaAHBwcMDZs2drbD937hwcHByE1SMiItIlZicREZEq4QvVTp06obCwUOiYkyZNwr59+zBr1izcuXMH5eXlqKiowJ07dzB79mzs378fkydPFlqTiIhIV5idREREqoSf+rtq1SrMnTsX/v7+cHJyEjJmYGAg7t69i23btiEmJgZNmlSurysqKiBJEiZNmoTAwEAhtYiIiHSN2UlERKRK+EI1JSUFcrkcXbp0gbu7Ozp06ICmTZuq9JHJZIiKiqr1mE2bNsXOnTuxcOFCHD16FA8ePABQ+Sy4ESNGwMXFReg+EBER6RKzk4iISJXwhWp0dLTyfycnJ1fbp65hW8XFxYXBSkRERofZSUREpEr4QrWiokL0kEq3bt3CsWPHlM9uUygUGDZsmNCbTxAREekas5OIiEiV8IWqNrx69QozZ87E7t27IUmSynU2y5Ytw7hx4/Dtt9/CxMREzzMlIiJqGJidRERkyLS2UL1//z6OHz+O9PR0AJXXxAwfPhwKhaLOYy1duhR///vfMWfOHMybNw/Ozs6QyWRITU3F+vXrsXnzZlhbW2PdunWC94KIiEh3mJ1ERESVtLJQXbRoEaKiotROZWrSpAkCAgKwZs2aOo0XGxuLCRMmqFzDAwAff/wxNm7ciOfPnyM2NpZhS0REBovZSURE9Cvhz1GNjIzE2rVr4ePjg4sXL6KgoAAFBQW4ePEixowZg7Vr12Lt2rV1GrOsrAz9+/evsX3gwIF4/fq1plMnIiLSC2YnERGRKuEL1ZiYGHh5eWHfvn3o168fLC0tYWlpiX79+iEhIQEjR47E1q1b6zSmh4cHTpw4UWP7999/j6FDh2o6dSIiIr1gdhIREakSvlBNS0uDh4dHje0eHh7KOw/WVmhoKO7fvw8fHx+kpKQgPT0d6enp+OGHH+Dt7Y309HSEhobi6dOnKi8iIiJDwOwkIiJSJfwaVRsbG9y4caPG9hs3bkAul9dpzC5dugAA/vOf/+Dw4cMqbZIkAQA++eQTtd8rLy+vUx0iIiJ9YHYSERGpEr5Q9fX1RVRUFJycnDBv3jxYWFgAAIqKihAdHY1vv/0WAQEBdRpz5cqVkMlkoqdKRETUIDA7iYiIVAlfqIaGhuLf//43AgMDsXLlSrRr1w4A8OjRI7x+/Rqurq4ICQmp05hfffWV6GkSERE1GMxOIiIiVcIXqs2bN0dKSgoOHz6s8iy4YcOGYcSIERg5ciS/4SUiInoDs5OIiEiVVp6jCgCjRo3CqFGjtDU8ERGR0WF2EhERVRJ+118iIiIiIiIiTQj5i6qXl1ed+stkMrU7EBIRETUmzE4iIqKaCVmoJicnw8zMDHZ2dspb3r8Lr7MhIqLGjtlJRERUMyEL1fbt2+Phw4do27Yt/P398eWXX8LOzk7E0EREREaJ2UlERFQzIdeoZmRk4MyZM/jtb3+L0NBQdOjQAW5ubtixYwcKCwtFlCAiIjIqzE4iIqKaCbuZ0uDBg7F161ZkZ2fjwIEDaNOmDf7whz/AxsYGPj4+OHDgAF69eiWqHBERkcFjdhIREVVP+F1/mzVrhlGjRmHv3r14/PixMoDHjh2L8PBw0eWIiIgMHrOTiIhIldYeT/Pq1SucOHEChw8fxvXr12FmZgYnJydtlSMiIjJ4zE4iIqJKQheqFRUVOHHiBCZPngxbW1v4+fmhpKQEMTExyMnJwYQJE0SWIyIiMnjMTiIiInVC7vp74cIF7NmzB/v370deXh769++PsLAwfPHFF2jbtq2IEkREREaF2UlERFQzIQvVQYMGwdzcHCNGjICfn5/yNKUHDx7gwYMH1f7Op59+KqI0ERGRQWJ2EhER1UzIQhUASkpKcPDgQSQmJr6znyRJkMlkKC8vF1WaiIjIIDE7iYiIqidkobpjxw4RwxARETUazE4iIqKaCVmoTpo0ScQwREREjQazk4iIqGZaezwNERERERERUX00+oXqixcvEBwcjGHDhsHa2hoymQw7d+6stu/t27cxbNgwtGjRAtbW1pgwYQKePHmi1q+iogLh4eFQKBQwMzODi4sL4uPjtbwnREREusHsJCIibWv0C9Xc3FyEhITg9u3b6NGjR439MjMz8dlnnyE1NRVhYWFYvHgxjh49Cnd3d5SWlqr0XbFiBZYuXQp3d3ds2LABHTt2hL+/PxISErS9O0RERFrH7CQiIm0TdtdfQ2Vvb4+srCzY2dnh6tWr6NOnT7X9wsLCUFRUhGvXrqFjx44AgL59+8Ld3R07d+7EjBkzAAAPHz5EZGQk5s6di+joaADAtGnTMHjwYCxZsgS+vr5o2rSpbnaOiIhIC5idRESkbY3+L6qmpqaws7N7b7+DBw/i888/VwYtALi5ueGjjz7Cvn37lNsOHz6MsrIyzJkzR7lNJpNh9uzZyMzMxMWLF8XuABERkY4xO4mISNsa/UK1Nh4+fIicnBz07t1bra1v3764fv268ufr16/DwsICXbp0UetX1U5ERGTsmJ1ERKSJRn/qb21kZWUBqDzV6W329vZ4+vQpXr16BVNTU2RlZcHW1hYymUytHwA8evSoxjrx8fHvvHHEv/71L3h5eQFmI+uzG+/n5aW+jbU0q2OstQz9WBlrLX2/L3RQ6+7du+LHJ61gdrJWvesYay1DP1bGWkvf7wsd1DLU7ORCtRZKSkoAVJ7q9DYzMzNlH1NTU+U/39WvJn5+fvDz8xMxZSIiIr1idhIRkSZ46m8tmJubAwBevXql1vby5UuVPubm5rXqR0REZMyYnUREpAkuVGuh6tSjqtOY3pSVlQVra2vlN8H29vbIzs6GJElq/QCgXbt2Wp4tERGR/jE7iYhIE1yo1kL79u0hl8tx9epVtbbLly+jZ8+eyp979uyJ4uJi3L59W6XfTz/9pGwnIiIydsxOIiLSBBeqtTR69GgkJycjIyNDuS0lJQW//PILfH19ldtGjRqFZs2aYdOmTcptkiRhy5YtaN++PQYOHKjTeRMREekLs5OIiOqLN1MCEB0djYKCAuVdBY8cOYLMzEwAwLx582BlZYXAwEDs378frq6uWLBgAV68eIGIiAh0794dU6ZMUY7l4OCAgIAAREREoKysDH369MGhQ4dw/vx5xMXF8YHlRERkFJidRESkVRJJjo6OEoBqX/fv31f2u3XrljR06FCpefPmUqtWraRx48ZJ2dnZauOVl5dLYWFhkqOjo2RiYiJ17dpVio2N1eEeVdqxY4cEQLpy5YrWa1T3Wrp0qdDxz58/r9ZeUVEhOTg4SAAkT09Pjeu9aePGjRIAqW/fvkLHlST97pcu3he6rqXNY/WmmzdvSqNHj5Y6duwomZqaSu3atZPc3Nyk9evXC69169Ytady4cVK7du0kExMTyd7eXvL395du3bolrEbV8TE1NZUyMzPV2gcPHix17dpVaK2ql6mpqWRvby8NHTpUioqKkp4/fy6kDukOs1PzGszOumF2isXsrB9mp+7wL6oA0tLSatWva9euOHHixHv7NWnSBMuXL8fy5cs1nJnhCAkJgUKhUNnWrVs3YeObmZlhz549GDRokMr2c+fOITMzs9rHGmgqLi4OTk5OuHz5MlJTU/Hhhx8Kr6GP/TJGujhWFy5cgKurKzp27Ijp06fDzs4OGRkZuHTpEqKiojBv3jxhtRITE+Hn5wdra2tMnToVCoUCaWlp2L59Ow4cOICEhAR4e3sLq/fq1St888032LBhg7Axa1L1WVFWVobs7GycPXsWAQEB+Otf/4qkpCS4uLhofQ4kBrNTc8zO+mF2isHs1AyzUwf0vVIm7dHlt8LaqlE1vo+Pj9S2bVuprKxMpX369OlSr169JEdHR6Hfnt67d08CICUmJkpyuVz66quvhI0tSfrbrzdrG8u3wto+VlVGjBghyeVyKT8/X63t8ePHwuqkpqZKzZs3lzp37izl5OSotD158kTq3LmzZGFhId29e1fjWlXHp2fPnpKpqan08OFDlXZtfCtc3XshJSVFMjc3lxwdHaXi4mIh9Yjqi9lZf8xOw6nF7Kw/Zqfu8GZKZBD8/PyQl5eHU6dOKbeVlpbiwIED8Pf3F14vLi4OrVu3hqenJ8aMGYO4uDjhNQDd75cx0tWxunv3Lrp27YpWrVqptdnY2AirExERgeLiYmzbtg1yuVylrW3btti6dSuKiooQHh4urGZgYCDKy8vxzTffCBuzLoYMGYI//elPSE9PR2xsrF7mQGSMmJ1UE2an5pid2seFKgnx7Nkz5ObmqrxEcnJywoABAxAfH6/cdvz4cTx79gxffvml0FpA5Qe4j48PTExM4Ofnh//973+4cuWK8Dq63i9jpKtj5ejoiGvXruHWrVvCx37TkSNH4OTkhP/7v/+rtv2zzz6Dk5MTjh49KqymQqHAxIkTERMTo7wxjq5NmDABAHDy5Em91CfSB2Zn/TA7Ncfs1ByzU/u4UCUh3NzcIJfLVV6i+fv749ChQygpKQFQ+SE7ePBg4Q+Cv3btGn7++Wdl2A0aNAgODg5a+7ZRV/tljHR5rBYvXozi4mL07NkTAwcOxNKlS3Hy5EmUlZUJq/Hs2TM8evQIPXr0eGc/FxcXZGZmorCwUFjtFStW4PXr11i9erWwMevCwcEBVlZWuHv3rl7qE+kDs7P+mJ31x+xkdhoKLlRJiI0bN+LUqVMqL9G++OILlJSUIDk5GYWFhUhOTtbaqUu2trZwdXUFAMhkMowdOxYJCQkoLy8XXk9X+2WMdHms3N3dcfHiRXh5eeHGjRsIDw+Hh4cH2rdvj6SkJCE1qsKzZcuW7+xX1f78+XMhdQHggw8+wIQJE7Bt2zZkZWUJG7cuWrRoIfT/QBA1dMzO+mN21h+zk9lpKLhQJSH69u0LNzc3lZdocrkcbm5u2LNnDxITE1FeXo4xY8YIrVFeXo6EhAS4urri/v37SE1NRWpqKvr164fHjx8jJSVFaD1AN/tljPRxrPr06YPExETk5+fj8uXLWL58OQoLCzFmzBj897//1Xj8qhB9X+DUNpTrKigoCK9fv9bb9TYvXrwQvk9EDRmzs/6YnfXD7GR2GhI+noYMir+/P6ZPn47s7GwMHz682ovzNXH69GlkZWUhISEBCQkJau1xcXEYOnSo0JqA9vfLGOnrWAGAiYkJ+vTpgz59+uCjjz7ClClTsH//fgQHB2s0rpWVFezt7XHz5s139rt58ybat28PS0tLjeq97YMPPsD48eOxbds2LFu2TOjY75OZmYlnz55p5fEIRI0ds5OqMDuZnYaEC1UyKN7e3pg5cyYuXbqEvXv3Ch8/Li4ONjY22Lhxo1pbYmIivvvuO2zZsgXm5uZC62p7v4yRvo7V23r37g0Awk75+fzzzxETE4N//OMfas8IBIDz588jLS0NM2fOFFLvbUFBQYiNjdX59Ta7d+8GAHh4eOi0LlFjwOykKsxOZqch4UKVDEqLFi2wefNmpKWlYeTIkULHLikpQWJiInx9fas9fahdu3aIj49HUlISxo4dK7S2NvfLGOnjWJ05cwa/+93vIJPJVLYfO3YMAPDxxx8LqbNkyRLExsZi5syZ+PHHH9GmTRtl29OnTzFr1iw0b94cS5YsEVLvbc7Ozhg/fjy2bt0KR0dH/OY32o+J06dPIzQ0FAqFAuPGjdN6PaLGhtlJALOT2Wl4uFAlgzNp0iStjJuUlITCwkJ4eXlV296/f3/I5XLExcUJD1tAe/tljPRxrObNm4fi4mJ4e3ujc+fOKC0txYULF7B37144OTlhypQpQup06tQJu3btwrhx49C9e3dMnToVCoUCaWlp2L59O3JzcxEfHw9nZ2ch9aqzYsUK7N69G3fu3EHXrl2Fjn38+HH8/PPPeP36NR4/fozTp0/j1KlTcHR0RFJSEszMzITWI6JKzE5idjI7DQ0XqkZMkiQAQNOmTfU8E8MQFxcHMzMzuLu7V9vepEkTeHp6Ii4uDnl5eSrf1hkSXb4vtFVLH8dqzZo12L9/P44dO4Zt27ahtLQUHTt2xJw5cxAUFCT02ihfX1907twZf/nLX5QB26ZNG7i6uiIwMBDdunUTVqs6H374IcaPH49du3YJH3vlypUAKq9Vsra2Rvfu3bFu3TpMmTLFaG8GQYaF2Vk3zE7DqcXsZHYaGplU9V8DGZ3169djwYIFSE1N1eo3SGRYdPm+4HuQiAwNP7eoOsxOIt3j42mM2JUrV2BhYQFHR0d9T4UaEF2+L/geJCJDw88tqg6zk0j3eOqvETp48CDOnj2LuLg4TJs2TScXdFPDp8v3Bd+DRGRo+LlF1WF2EukPT/01QgqFAoWFhfD29sa6detgYWGh7ylRA6DL9wXfg0RkaPi5RdVhdhLpDxeqRERERERE1KDwGlUiIiIiIiJqULhQJSIiIiIiogaFC1UiIiIiIiJqULhQJSIiIiIiogaFC1UiIiIiIiJqULhQJSIiIiIiogaFC1UiIiIiIiJqULhQJSIiIiIiogbl/wGSyFoytqXUMQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x1100 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_width = 10\n",
    "fig_height = 11\n",
    "\n",
    "height = 12\n",
    "heightC = 1\n",
    "heightB = 12\n",
    "hsep1 = 3.5\n",
    "hsep2 = 5\n",
    "hsep3 = 3.5\n",
    "hsp = 3\n",
    "tot_height = height + heightC + 2 * heightB + hsep1 + hsep2 + hsep3 + 2 * hsp\n",
    "\n",
    "width = 12\n",
    "widthM = heightC/tot_height * fig_height/fig_width\n",
    "widthB = 12\n",
    "wsep1 = 1\n",
    "wsep2 = 3.5\n",
    "wsp = 4\n",
    "wshift = 1.5\n",
    "tot_width = (width + widthB + wsep1 + wsep2 + 2 * wsp) / (1 - widthM)\n",
    "widthM *= tot_width\n",
    "\n",
    "fig = plt.figure(figsize=(fig_width, fig_height), constrained_layout=False)\n",
    "\n",
    "ax = fig.add_axes(\n",
    "    (\n",
    "        (wsp - wshift) / tot_width,\n",
    "        (hsp + 2 * heightB + hsep2 + hsep3 + heightC + hsep1) / tot_height,\n",
    "        width / tot_width,\n",
    "        height / tot_height\n",
    "    )\n",
    ")\n",
    "axM = fig.add_axes(\n",
    "    (\n",
    "        (wsp - wshift + width + wsep1) / tot_width,\n",
    "        (hsp + 2 * heightB + hsep2 + hsep3 + heightC + hsep1) / tot_height,\n",
    "        widthM / tot_width,\n",
    "        height / tot_height\n",
    "    )\n",
    ")\n",
    "\n",
    "axB = [None for _ in range(5)]\n",
    "\n",
    "axB[0] = fig.add_axes(\n",
    "    (\n",
    "        (wsp + width + wsep1 + widthM + wsep2) / tot_width,\n",
    "        (hsp + heightB + hsep2 + hsep3 + heightC + hsep1 + height) / tot_height,\n",
    "        widthB / tot_width,\n",
    "        heightB / tot_height\n",
    "    )\n",
    ")\n",
    "for n in range(2):\n",
    "    axB[3 - 2*n] = fig.add_axes(\n",
    "        (\n",
    "            wsp / tot_width,\n",
    "            (hsp + n * (heightB + hsep3)) / tot_height,\n",
    "            widthB / tot_width,\n",
    "            heightB / tot_height\n",
    "        )\n",
    "    )\n",
    "    axB[4 - 2*n] = fig.add_axes(\n",
    "        (\n",
    "            (wsp + width + wsep1 + widthM + wsep2) / tot_width,\n",
    "            (hsp + n * (heightB + hsep3)) / tot_height,\n",
    "            widthB / tot_width,\n",
    "            heightB / tot_height\n",
    "        )\n",
    "    )\n",
    "\n",
    "caption = 'Pot. gradient, Vostok station, 2006–2020'\n",
    "\n",
    "for axis in ['top', 'bottom', 'left', 'right']:\n",
    "    ax.spines[axis].set_linewidth(0.5)\n",
    "ax.tick_params(length=6, width=0.5)\n",
    "ax.tick_params(length=0, width=0.5, which='minor')\n",
    "ax.grid(False)\n",
    "\n",
    "ax.set_xlim((0, 24))\n",
    "ax.set_xticks(np.arange(0, 25, 2))\n",
    "ax.set_xticklabels(np.arange(0, 25, 2), fontsize='large')\n",
    "ax.set_xlabel('UTC, hours', fontsize='large')\n",
    "\n",
    "ax.set_ylim((0, 12))\n",
    "ax.set_yticks(np.arange(0, 12.5, 1))\n",
    "ax.set_yticks(np.arange(0.5, 12, 1), minor=True)\n",
    "ax.set_yticklabels([])\n",
    "ax.set_yticklabels(month_name, fontsize='large', ha='center',\n",
    "                   minor=True)\n",
    "tr = []\n",
    "for label in ax.yaxis.get_majorticklabels():\n",
    "    tr.append(label.get_transform())\n",
    "for label in ax.yaxis.get_minorticklabels():\n",
    "    label.set_transform(\n",
    "        tr[0] + tf.ScaledTranslation(\n",
    "            -0.05, 0, fig.dpi_scale_trans\n",
    "        )\n",
    "    )\n",
    "\n",
    "for axis in ['top', 'bottom', 'left', 'right']:\n",
    "    axM.spines[axis].set_linewidth(0.5)\n",
    "axM.tick_params(length=0, width=0.5)\n",
    "axM.tick_params(length=6, width=0.5, which='minor')\n",
    "axM.grid(False)\n",
    "\n",
    "axM.set_xlim((0, 1))\n",
    "axM.set_xticks([])\n",
    "axM.set_xticks([0.5], minor=True)\n",
    "axM.set_xticklabels([])\n",
    "axM.set_xticklabels(['mean'], fontsize='large', minor=True)\n",
    "\n",
    "axM.set_ylim((0, 12))\n",
    "axM.set_yticks([])\n",
    "axM.set_yticklabels([])\n",
    "\n",
    "lbl = ax.set_title(caption, fontsize='large')\n",
    "pos = (width + wsep1 + widthM) / 2 / width\n",
    "lbl.set_position((pos, 1.))\n",
    "\n",
    "# the bounds of the colour bars (in V/m)\n",
    "pg_bound = np.arange(90, 191, 10)\n",
    "\n",
    "# the colours\n",
    "colour_hour = np.array([[163, 163, 255], [71, 71, 255], [1, 14, 236],\n",
    "                        [4, 79, 149], [6, 143, 63], [49, 192, 13],\n",
    "                        [142, 220, 7], [234, 249, 1], [255, 184, 0],\n",
    "                        [255, 92, 0], [255, 0, 0]]) / 255\n",
    "\n",
    "pg_cmap = colors.ListedColormap(colour_hour[1:])\n",
    "pg_norm = colors.BoundaryNorm(pg_bound, pg_cmap.N)\n",
    "\n",
    "pg_cax = fig.add_axes(\n",
    "    (\n",
    "        (wsp - wshift) / tot_width,\n",
    "        (hsp + 2 * heightB + hsep2 + hsep3) / tot_height,\n",
    "        (width + wsep1 + widthM)  / tot_width,\n",
    "        heightC / tot_height\n",
    "    )\n",
    ")\n",
    "pg_cbar = fig.colorbar(cm.ScalarMappable(norm=pg_norm,\n",
    "                                         cmap=pg_cmap),\n",
    "                       cax=pg_cax, orientation='horizontal')\n",
    "pg_cbar.outline.set_linewidth(0.5)\n",
    "pg_cbar.ax.tick_params(length=6, width=0.5)\n",
    "pg_cbar.set_ticks(pg_bound)\n",
    "pg_cbar.ax.set_xticklabels(list(map(str,\n",
    "                                    pg_bound.astype(int))),\n",
    "                           fontsize='large')\n",
    "pg_cbar.set_label('Potential gradient, V/m',\n",
    "                  fontsize='large')\n",
    "\n",
    "ax.imshow(vostok_pg_data_to_plot,\n",
    "          cmap=pg_cmap, norm=pg_norm,\n",
    "          extent=[0, 24, 12, 0], aspect='auto', interpolation='none',\n",
    "          rasterized=True)\n",
    "\n",
    "axM.imshow(vostok_mean_pg_data,\n",
    "           cmap=pg_cmap, norm=pg_norm,\n",
    "           extent=[0, 1, 12, 0], aspect='auto', interpolation='none',\n",
    "           rasterized=True)\n",
    "\n",
    "low = [100] + [80] * 2 + [100] * 2\n",
    "high = [180] * 3 + [200] * 2\n",
    "step = [20] * 5\n",
    "coeff = [1] * 5\n",
    "captionB = ['Vostok station, 2006–2020, diurnal mean',\n",
    "            'Vostok station, 2006–2020, 3–4 UTC',\n",
    "            'Vostok station, 2006–2020, 9–10 UTC',\n",
    "            'Vostok station, 2006–2020, 15–16 UTC',\n",
    "            'Vostok station, 2006–2020, 21–22 UTC']\n",
    "\n",
    "col = ['orangered'] * 5\n",
    "\n",
    "for n in range(5):\n",
    "    for axis in ['top', 'bottom', 'left', 'right']:\n",
    "        axB[n].spines[axis].set_linewidth(0.5)\n",
    "    axB[n].tick_params(length=6, width=0.5, axis='y')\n",
    "    axB[n].tick_params(length=0, width=0.5, axis='x')\n",
    "    axB[n].grid(color='0.', linewidth=0.5, axis='y')\n",
    "\n",
    "    axB[n].set_xlim((-0.5, 11.5))\n",
    "    axB[n].set_xticks(np.arange(12))\n",
    "    axB[n].set_xticklabels(month_name, fontsize='large', va='top')\n",
    "\n",
    "    axB[n].set_ylim((low[n], high[n]))\n",
    "    axB[n].set_yticks(np.arange(low[n], high[n] + step[n] / 2, step[n]))\n",
    "    axB[n].set_yticklabels((np.arange(low[n], high[n] + step[n] / 2,\n",
    "                                      step[n]) / coeff[n]).astype(int),\n",
    "                           fontsize='large')\n",
    "    axB[n].set_ylabel('Monthly mean fair-weather\\npotential gradient, V/m',\n",
    "                      fontsize='large')\n",
    "\n",
    "    axB[n].set_title(captionB[n], fontsize='large')\n",
    "\n",
    "    axB[n].annotate('', xy=(12, np.min(data[n])), xycoords='data',\n",
    "                    xytext=(12, np.max(data[n])), textcoords='data',\n",
    "                    annotation_clip=False,\n",
    "                    arrowprops=dict(\n",
    "                        arrowstyle='<|-|>,head_length=0.8,head_width=0.3',\n",
    "                        patchA=None, patchB=None, shrinkA=0., shrinkB=0.,\n",
    "                        connectionstyle='arc3,rad=0.', fc='black',\n",
    "                        linewidth=0.5\n",
    "                    ))\n",
    "\n",
    "    ampl = (np.max(data[n]) - np.min(data[n])) / \\\n",
    "        np.sum(data[n] * data_counter[n]) * np.sum(data_counter[n])\n",
    "    axB[n].text(12.2, (np.min(data[n]) + np.max(data[n])) / 2,\n",
    "                f'{ampl * 100:.0f}%',\n",
    "                fontsize='large', ha='left', va='center', rotation=270)\n",
    "\n",
    "for n in range(5):\n",
    "    axB[n].bar(np.arange(12), data[n],\n",
    "               yerr=std_error(data[n],\n",
    "                              data_sqr[n],\n",
    "                              data_counter[n]),\n",
    "               width=0.8, color=col[n])\n",
    "\n",
    "ax.text(-(0.3 * widthB - wshift)/width, 1 + 0.05 * heightB/height, 'a',\n",
    "        fontsize='x-large',\n",
    "        fontweight='semibold', ha='left', va='bottom',\n",
    "        transform=ax.transAxes)\n",
    "for n in range(5):\n",
    "    axB[n].text(-0.3, 1.05, chr(ord('b') + n),\n",
    "                fontsize='x-large',\n",
    "                fontweight='semibold', ha='left', va='bottom',\n",
    "                transform=axB[n].transAxes)\n",
    "\n",
    "fig.savefig('figures_two_parts/pg_diurnal_seasonal.eps', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b9bf03b-5a1b-4ccf-aaf7-c47337cd05a1",
   "metadata": {},
   "source": [
    "### Data adjustment using meteoparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d16e0930-f81f-4288-a7a9-2f3942194f81",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load and filter meteorological data from CSV files into separate dataframes, \n",
    "# then join them with the daily FW PG dataframe to create a combined dataframe\n",
    "# without missing values\n",
    "\n",
    "temp_df = pd.read_csv('./data/Vostok/vostok_daily_temp.csv', parse_dates=['UTC']).set_index('UTC')\n",
    "temp_df = temp_df[temp_df[\"COUNT\"] == 4][['T']]\n",
    "\n",
    "wind_df = pd.read_csv('./data/Vostok/vostok_daily_wind.csv', parse_dates=['UTC']).set_index('UTC')\n",
    "wind_df = wind_df[wind_df[\"COUNT\"] == 4][['Ff']]\n",
    "\n",
    "pressure_df = pd.read_csv('./data/Vostok/vostok_daily_pressure_mm_hg.csv', parse_dates=['UTC']).set_index('UTC')\n",
    "pressure_df = pressure_df[pressure_df[\"COUNT\"] == 4][['Po']]\n",
    "\n",
    "meteo_df = temp_df.join([wind_df, pressure_df, daily_df]).dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c45bdf6b-d725-4d87-b033-d90fa4f3f9a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate anomalies for potential gradient (pg_anom), temperature\n",
    "# (temp_anom), wind speed (wind_anom), and pressure (pres_anom)\n",
    "# based on a moving average window of +/-10 days.\n",
    "\n",
    "pg_anom = np.zeros((len(meteo_df.index)))\n",
    "temp_anom = np.zeros((len(meteo_df.index)))\n",
    "wind_anom = np.zeros((len(meteo_df.index)))\n",
    "pres_anom = np.zeros((len(meteo_df.index)))\n",
    "\n",
    "halfwidth = 10\n",
    "for i in range(len(meteo_df.index)):\n",
    "    sum_pg = 0\n",
    "    sum_temp = 0\n",
    "    sum_pres = 0\n",
    "    sum_wind = 0\n",
    "    counter = 0\n",
    "    for j in range(max(i - halfwidth, 0),\n",
    "                   min(i + halfwidth + 1, len(meteo_df.index))):\n",
    "        if abs((meteo_df.index[j]-meteo_df.index[i]).days) <= halfwidth:\n",
    "            sum_pg += meteo_df[\"Field\"].iloc[j]\n",
    "            sum_temp += meteo_df[\"T\"].iloc[j]\n",
    "            sum_pres += meteo_df[\"Po\"].iloc[j]\n",
    "            sum_wind += meteo_df[\"Ff\"].iloc[j]\n",
    "            counter += 1\n",
    "    pg_anom[i] =  meteo_df[\"Field\"].iloc[i] - (sum_pg / counter)\n",
    "    temp_anom[i] =  meteo_df[\"T\"].iloc[i] - (sum_temp / counter)\n",
    "    wind_anom[i] =  meteo_df[\"Ff\"].iloc[i] - (sum_wind / counter)\n",
    "    pres_anom[i] =  meteo_df[\"Po\"].iloc[i] - (sum_pres / counter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "326311ea-74ce-4de1-ac7a-e30e173fc8d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Temp: a=-0.4294158980334601\tb=0.0037844298864231447\n",
      "Temp: Correlation coefficient: −0.08\n",
      "Temp: Significance level: 0.01.\n",
      "Temp: Number of points: 2160.\n",
      "Temp: Critical value (((P − 2)r^2/(1 − r^2))^(1/2)): 2.5781094908361655.\n",
      "Temp: Threshold correlation coefficient: 0.05541251361022274.\n",
      "\n",
      "\n",
      "Wind: a=2.765059809437386\tb=-0.0018943407537914312\n",
      "Wind: Correlation coefficient: 0.12\n",
      "Wind: Significance level: 0.01.\n",
      "Wind: Number of points: 2160.\n",
      "Wind: Critical value (((P − 2)r^2/(1 − r^2))^(1/2)): 2.5781094908361655.\n",
      "Wind: Threshold correlation coefficient: 0.05541251361022274.\n",
      "\n",
      "\n",
      "Pres: a=0.05633213858322216\tb=-0.006489771491659805\n",
      "Pres: Correlation coefficient: 0.01\n",
      "Pres: Significance level: 0.01.\n",
      "Pres: Number of points: 2160.\n",
      "Pres: Critical value (((P − 2)r^2/(1 − r^2))^(1/2)): 2.5781094908361655.\n",
      "Pres: Threshold correlation coefficient: 0.05541251361022274.\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# calculate linear regression coefficients for temperature, wind speed \n",
    "# and pressure anomalies against potential gradient anomalies.\n",
    "\n",
    "temp_coeffs = np.polyfit(temp_anom, pg_anom, deg=1)\n",
    "wind_coeffs = np.polyfit(wind_anom, pg_anom, deg=1)\n",
    "pres_coeffs = np.polyfit(pres_anom, pg_anom, deg=1)\n",
    "\n",
    "\n",
    "for title, anom, coeffs in zip(\n",
    "    [\"Temp\", \"Wind\", \"Pres\"],\n",
    "    [temp_anom, wind_anom, pres_anom],\n",
    "    [temp_coeffs, wind_coeffs, pres_coeffs],\n",
    "):\n",
    "    P = len(meteo_df.index)  # the number of points\n",
    "    a = 0.01  # significance level\n",
    "\n",
    "    q = st.t.ppf(1 - a / 2, P - 2) # critical value\n",
    "    r = q / np.sqrt(q**2 + P - 2) # threshold correlation coefficient\n",
    "\n",
    "    corr = np.corrcoef(anom, pg_anom)[0, 1]\n",
    "    print(f\"{title}: a={coeffs[0]}\\tb={coeffs[1]}\")\n",
    "    print(\n",
    "        f\"{title}: Correlation coefficient: −{-corr:.2f}\"\n",
    "        if corr < 0\n",
    "        else f\"{title}: Correlation coefficient: {corr:.2f}\"\n",
    "    )\n",
    "    print(f\"{title}: Significance level: {a}.\")\n",
    "    print(f\"{title}: Number of points: {P}.\")\n",
    "    print(f\"{title}: Critical value (((P − 2)r^2/(1 − r^2))^(1/2)): {q}.\")\n",
    "    print(f\"{title}: Threshold correlation coefficient: {r}.\")\n",
    "    print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "dafd0f56-b6fa-4883-9640-01fdcb4a3870",
   "metadata": {},
   "outputs": [],
   "source": [
    "# update the \"CorrectedField\" column (copy of \"Field\" column) in the `meteo_df`\n",
    "# by subtracting the linear regression adjustments \n",
    "# based on temperature and wind anomalies.\n",
    "\n",
    "meteo_df[\"CorrectedField\"] = meteo_df[\"Field\"]\n",
    "meteo_df[\"CorrectedField\"] -= temp_coeffs[0] * (meteo_df[\"T\"] - meteo_df[\"T\"].mean())\n",
    "meteo_df[\"CorrectedField\"] -= wind_coeffs[0] * (meteo_df[\"Ff\"] - meteo_df[\"Ff\"].mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0590fff-4817-440f-9550-d4438b742769",
   "metadata": {},
   "source": [
    "### Figure: source vs adjustment PG for new and earlier Vostok datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9a209c25-af4a-4c67-80cc-2ce657f9cb3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate seasonal variation parameters for specific years: \n",
    "# average FW PG, count of FW days, and the sum of squares of FW PG hourly values\n",
    "\n",
    "data, data_counter, data_sqr = np.array(\n",
    "    [\n",
    "        calculate_seasonal_var_params(daily_edf[daily_edf.index.year <= 2001]),\n",
    "        [vostok_old_data, np.zeros(12).astype(int), np.zeros(12)],\n",
    "        calculate_seasonal_var_params(daily_df),\n",
    "        calculate_seasonal_var_params(meteo_df[[\"CorrectedField\"]], key=\"CorrectedField\")\n",
    "    ]\n",
    ").swapaxes(0,1)\n",
    "\n",
    "data_counter = data_counter.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "84050d4a-259f-4513-8d91-0ffc9840f542",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1400 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 14), constrained_layout=False)\n",
    "ax = [None for _ in range(4)]\n",
    "\n",
    "for n in range(4):\n",
    "    ax[n] = fig.add_subplot(4, 4, (2*n + 1, 2*n + 2))\n",
    "\n",
    "low = [140] * 2 + [100] * 2\n",
    "high = [220] * 2 + [180] * 2\n",
    "step = [20] * 4\n",
    "coeff = [1] * 4\n",
    "\n",
    "caption = ['Vostok station, 1998–2001',\n",
    "           'Vostok station, 1998–2001 (adjusted)',\n",
    "           'Vostok station, 2006–2020',\n",
    "           'Vostok station, 2006–2020 (adjusted)']\n",
    "col = ['orangered'] * 4\n",
    "\n",
    "for n in range(4):\n",
    "    for axis in ['top', 'bottom', 'left', 'right']:\n",
    "        ax[n].spines[axis].set_linewidth(0.5)\n",
    "    ax[n].tick_params(length=6, width=0.5, axis='y')\n",
    "    ax[n].tick_params(length=0, width=0.5, axis='x')\n",
    "    ax[n].grid(color='0.', linewidth=0.5, axis='y')\n",
    "\n",
    "    ax[n].set_xlim((-0.5, 11.5))\n",
    "    ax[n].set_xticks(np.arange(12))\n",
    "    ax[n].set_xticklabels(month_name, fontsize='large', va='top')\n",
    "\n",
    "    ax[n].set_ylim((low[n], high[n]))\n",
    "    ax[n].set_yticks(np.arange(low[n], high[n] + step[n] / 2, step[n]))\n",
    "    ax[n].set_yticklabels((np.arange(low[n], high[n] + step[n] / 2,\n",
    "                                     step[n]) / coeff[n]).astype(int),\n",
    "                          fontsize='large')\n",
    "    ax[n].set_ylabel('Monthly mean fair-weather\\npotential gradient, V/m',\n",
    "                     fontsize='large')\n",
    "\n",
    "    ax[n].set_title(caption[n], fontsize='large')\n",
    "    if np.sum(data_counter[n]) == 0:\n",
    "        ax[n].text(0.5,\n",
    "                   1 - 0.01\n",
    "                   * (ax[n].get_position().x1 - ax[n].get_position().x0)\n",
    "                   / (ax[n].get_position().y1 - ax[n].get_position().y0)\n",
    "                   * fig.get_size_inches()[0] / fig.get_size_inches()[1],\n",
    "                   '(after $\\it{Burns~et~al.}$, 2012)',\n",
    "                   fontsize='large', ha='center', va='top',\n",
    "                   transform=ax[n].transAxes)\n",
    "\n",
    "    ax[n].annotate('', xy=(12, np.min(data[n])), xycoords='data',\n",
    "                   xytext=(12, np.max(data[n])), textcoords='data',\n",
    "                   annotation_clip=False,\n",
    "                   arrowprops=dict(\n",
    "                       arrowstyle='<|-|>,head_length=0.8,head_width=0.3',\n",
    "                       patchA=None, patchB=None, shrinkA=0., shrinkB=0.,\n",
    "                       connectionstyle='arc3,rad=0.', fc='black',\n",
    "                       linewidth=0.5\n",
    "                   ))\n",
    "    if np.sum(data_counter[n]) == 0:\n",
    "        ampl = (np.max(data[n]) - np.min(data[n])) / np.mean(data[n])\n",
    "    else:\n",
    "        ampl = (np.max(data[n]) - np.min(data[n])) / \\\n",
    "            np.sum(data[n] * data_counter[n]) * np.sum(data_counter[n])\n",
    "    ax[n].text(12.2, (np.min(data[n]) + np.max(data[n])) / 2,\n",
    "               f'{ampl * 100:.0f}%',\n",
    "               fontsize='large', ha='left', va='center', rotation=270)\n",
    "\n",
    "fig.align_ylabels([ax[0], ax[2]])\n",
    "fig.align_ylabels([ax[1], ax[3]])\n",
    "\n",
    "for n in range(4):\n",
    "    if np.sum(data_counter[n]) == 0:\n",
    "        ax[n].bar(np.arange(12), data[n],\n",
    "          width=0.8, color=col[n])\n",
    "    else:\n",
    "        ax[n].bar(np.arange(12), data[n],\n",
    "                  yerr=std_error(data[n],\n",
    "                                 data_sqr[n],\n",
    "                                 data_counter[n]),\n",
    "                  width=0.8, color=col[n])\n",
    "\n",
    "for n in range(4):\n",
    "    ax[n].text(-0.3, 1.05, chr(ord('a') + n), fontsize='x-large',\n",
    "               fontweight='semibold', ha='left', va='bottom',\n",
    "               transform=ax[n].transAxes)\n",
    "\n",
    "fig.subplots_adjust(hspace=0.3, wspace=1.6)\n",
    "\n",
    "for n in range(4):\n",
    "    if np.sum(data_counter[n]) > 0:\n",
    "        for m in range(12):\n",
    "            ax[n].annotate(f'{data_counter[n, m]}',\n",
    "                           xy=(m-0.15, ax[n].get_ylim()[0] + 3),\n",
    "                           rotation=270, ha='center', va='bottom',\n",
    "                           fontsize='large', color='0.')\n",
    "\n",
    "fig.savefig('figures_two_parts/pg_corrected.eps', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "83a15933-44ff-4abc-baf6-7a99a069f8ca",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}