{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import MultipleLocator\n",
    "import seaborn as sns\n",
    "from scipy.stats import binned_statistic\n",
    "\n",
    "# Enable LaTeX\n",
    "import os\n",
    "os.environ[\"PATH\"] += os.pathsep + '/Library/TeX/texbin'\n",
    "plt.rcParams['text.usetex'] = True\n",
    "plt.rcParams['text.latex.preamble'] = r'\\usepackage{amsmath}\\usepackage{amsfonts}' "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Figure 1\n",
    "Slice plots of the magnetic micromirror field. Data are available to interested researchers upon reasonable request."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Figure 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load saved data\n",
    "data = np.load(\"data_figure_2.npz\", allow_pickle=True)\n",
    "\n",
    "# Recreate the plot using the pre-computed data\n",
    "fig, ax1 = plt.subplots(figsize=(5,4))\n",
    "\n",
    "# Compact scatter plots\n",
    "ax1.scatter(data['E_field_0_23'], data['kappa_field_0_23'], color='dodgerblue', marker='v', label='$\\kappa$ (mm field: $\\delta B_\\mathrm{mm}/B\\\\approx 0.23$)')\n",
    "ax1.scatter(data['E_field_0_3'], data['kappa_parallel_field_0_3'], color='k', marker='*', label='$\\kappa$ (mm field: $\\delta B_\\mathrm{mm}/B\\\\approx 0.3$)', zorder=5)\n",
    "ax1.scatter(data['E_field_0_3'], data['kappa_perp_field_0_3'], color='k', marker='p', label='$\\kappa_\\perp$ (mm field: $\\delta B_\\mathrm{mm}/B\\\\approx 0.3$)', zorder=5)\n",
    "ax1.scatter(data['E_excluded_wavenumbers'], data['kappa_excluded_wavenumbers'], color='grey', marker='d', label='$\\kappa$ (mm wavenumbers excl.)')\n",
    "\n",
    "# Plot theoretical lines\n",
    "ax1.plot(data['r_g_theory'], data['kappa_theory'], color='grey', ls='--', label='theory for $\\kappa$')\n",
    "ax1.plot(data['r_g_theory'], np.ones(3) * 1e21 / 2, color='grey', ls='-.', label='theory for $\\kappa_\\perp$')\n",
    "\n",
    "# Add vertical lines and labels using the loaded data\n",
    "vertical_lines = data['vertical_lines'].item()  # Extract the dictionary\n",
    "\n",
    "for i, x_pos in enumerate(vertical_lines['positions']):\n",
    "    ax1.axvline(x=x_pos, zorder=-2, lw=2 if i == 0 else 1, c=vertical_lines['colors'][i], ls=vertical_lines['line_styles'][i])\n",
    "    # Adjust label position for 'spacing (x)' and 'box size (x)' to the left\n",
    "    if vertical_lines['labels'][i] in [r'spacing ($x$)', r'box size ($x$)']:\n",
    "        plt.text(x_pos * 0.8, vertical_lines['label_positions_y'][i], \n",
    "                 vertical_lines['labels'][i], fontsize=10, color=vertical_lines['label_color'][i], rotation=90)\n",
    "    else:\n",
    "        plt.text(x_pos * 1.01, vertical_lines['label_positions_y'][i], \n",
    "                 vertical_lines['labels'][i], fontsize=10, color=vertical_lines['label_color'][i], rotation=90)\n",
    "\n",
    "# Annotations and vertical lines\n",
    "ax1.set_xlabel('$r_\\mathrm{g}$ [npc]')\n",
    "ax1.set_ylabel('$\\kappa_\\mathrm{mm}$ [cm$^2$/s]')\n",
    "ax1.loglog()\n",
    "ax1.legend(loc=4, ncol=2, fontsize=8.3)\n",
    "\n",
    "# Create secondary x-axis\n",
    "ax2 = ax1.twiny()\n",
    "ax2.set_xlabel('$E$ [eV]')\n",
    "ax2.set_xscale('log')\n",
    "\n",
    "ax1.set_xlim(3, 2.5e3)\n",
    "plt.ylim(3e16, 3e28)\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Figure 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MtK31qVSqbsvirq6uRqFQUFVVhbu7e8s7rQmGhjPgFAQzW2h/lfMShJ7NYFdnQuisLrezp9FqtWRnZ9t5eW3RmSYkF2OTSqVq8ToldbBsO3g6gov83PbEAMhoe6mYvyTCR1kWT/ueIRYxB4u4NxX2Oh2otbBoNJ3uTNZT72dHj2vrvewLmM1mzGZzj3goAj3H4cOHMZvNzT53O3fupLGxEQcHB0aOHNlj9hgMBnJzc4mKimLvXkvv4KFDh1JTU9NsZvTx48fx9/e3VR80NDSwY8cOxo0bh0wmuyg7OqRDZ+lxjzotLc0mvkuXLiU5Odm2/rxq1SoWLVpEcnIy6enptqSxPom1jhpono50aeLo6NhhUYGeGV3n6OjY6eu0J9Jg6V5mDYd/efACDOsAPfV+dvS4C3kvu5u6ujqys7NJTk5GJBL1artSga5Hq9Vy4sQJrr/++t42xQ6TyYREImHYsGGcOHECBwcHdu7cyahRo+yir25ubuzatYu4uDiCgoJYt24d2dnZ+Pr69miyY48LdUpKCikpKbZOY02JjIy0bW+t3rrPcAnWUQuco+kYywRfe3GfFAGbL42Ku0sas9nMjz/+yLFjx/Dy8urW8haB3mHdunUt5mb0JgUFBUgkEsRiMQ4ODri4uPDTTz8hk8maLZH6+/vj5OSEVqtl//79ZGdnExcXZyux7Cn6zBr1JcclWEct0By5xNIT/EDJuTXtMEXbxwh0DXv37uXYsWMMGjRIEOnLlIaGhj7RRMdKQ0MDGRkZ+Pn5ERYWhqOjI5WVlRQWFtpaUzetkTabzbi4uGAwGPjll19wd3fv0qlYHUUQ6gvFKtLQJAQucCnh7gDXx4CPC8weYFnAMGMRbYHupbS0lN9++w0PDw9hKtZlTEBAAJmZmT03LKkdTp8+zbhx4/D09LTNWI+KiiIoKAixWIzJZLLLkbAux3z//ffo9XpuueWWLp2K1VEEoW4PbSnseRACpoB/CsjOLvoXb7b869/1DdgFLh65BBybfLqbhrqbbvvqEIwJab8pikDX0bQU66abbupTHpdA15KSkkJlZSXFxcW9XktdVFSETCbD19eX2tpaXF1dAZDL5bi6uraayNh0KlZvTf8ShLo9ZG4QPR+K0+D4/yxhbs9h4D8Fak8IQt0HmRZjCWc36JtncbeEINI9i1qtpq6ujvHjxwulWFcAfaELnkajYc+ePVx77bWApU+/n58f/v7+VFRUoFAocHFpXtZhnYoVGBjYLVOxOoog1O0hcbKEuT2HwoDHLdvU+yzCHTq3d20TaBGFA7y3t+19OlqeJdD1+Pr68tBDDwlNTQR6jGPHjpGbm4tKpSIuLo7i4mJMJhO+vr5IpVL27NnTLOO76VSsm266qVumYnUUQagvBKtwC/RJiutAJga9qfV9eqI8S8Ceuro6ampq8Pf375V1PoErl+HDh+Pj48OaNWs4dOgQkydPtnUV8/X1ZdOmTRgMBjuh/vnnn6mqquKGG27otqlYHUUQaoHLDn8X+GsyaA3QaGzZQ266Zt20vai7Q8vr2QIXh7UU68SJEzz44IO9/sUncOURERHBzTffzK5du9BoNLY2wMePH2fQoEG4uLjYZXwnJibi7OzM0KG975QJQi1w2ZF6uHP7Wwd6TI6wrG2X1MIvud1j25VKenq6rRRLEOkrj7y8PLtErLS0NBobG1tcF+5OfH19CQsLIz09HaVSaesI1tKQmoiIiF5LHjsfoVefwGWHzmhZp07wBV+XtgdyNCXGEz7OAqWwdNqllJaWsmHDBqEU6wrmxIkTpKWlAbBjxw4SExOpr6/vcTukUimJiYmEhoaycuVKNm3aRHBwsO15k8lEamoqp0+30Da6FxE86s5SvAkcvMBjSG9bItAKA32hnyeU1lkGdew5A9llre8vAkaHQEHfH4J2ySGUYgkABAYGEhUVRWVlJWq1Gg8PD9u89J7EGtoeOXIkxcXFKJVKOzsKCws5cuQISqXSTsB7G0GoO4v/ZMh6AmpVEHJTb1sj0AIJvudmU28rgLsHty3UaSro52WZwCWXwIEOjLkU6Bi5ubmUlJQwceJEoRTrCqaystJW5lRZWYlGo6GhocFWy9xTiEQi2/CX4cOHExQUBJwT8JCQEBYsWICnp2eP2tUeglBfCAMWw575glD3Uc5v7tdet7/SetA0nmuQkl/VLWZdkcTGxnLvvff2Ke9EoOcZPXo0Wq3WNhhm+/btKBQ906u3oaHBzmu2dhtrKtJarRaDwYCbm1uzCVp9AWGN+kIo2STUUPdh8qssgzWiPWBmLByvaHv/aTFw7xC4KRZmxVn+Fbg4GhoabC0aQ0NDhdGVVyDbt2/niy++sD1uWjc/evRo2+jI7uTIkSOUlLTfE3jdunW89957VFX1zbt0waPuLLoqKNoAw//X25YItMK2Aoj2hDAlHFfDoXZC2QoH+F9Gj5h2RWA2m1mzZg1qtZr7779faGxyheLo6MicOXN65drmsx2MDAaDnedeU1ODs7OzXfOSrKwssrOzGTBgQLtzoXsL4Ta3s+yZD0OX97YVAu2Qq4ZNee2LNJxrkCJwDusX3YVgnYoVGBgoiPQVjIeHBytXrmTHjh1oNJoeuab1c2sNb7u5uaHRaDAajZSUlLB3716MxnNlIBUVFfz666+9NhWrowgedWfRVZ4bzCHQZ7iYlqD+LvBQMjS20SDlSsP6hZWRkUFMTAxubm4d+hITpmIJWFGpVNx+++2oVCo2btxIY2Mjt99+e7des+nnVq1Wc/LkSSorK4mJicHd3Z3w8HDkcjkARqPRbipWb2ShdxRBqDuL5zDI/QCi7+9tSwSacDEtQTvbIOVKIT09nS+++IKYmBiSk5MJCwtrcwKSUIol0JQxY8ZQVFREfHw8iYmJPXbdsrIySkpKmDp1KjqdjiNHjuDq6mrrRGbl999/58yZM4wZM6bPNDZpDSHg11mGvGgp0arM6m1LBJrQtO1ngq9FtK0/Y0LbPrbpvtafKxVr6PD06dPk5OTw8ssvM2HCBJYvX87333/f5rG7du2ipKREmIolAFjWqCMiInp8+aO4uJiYmBjAMsIyICDA9pz1833y5Em2bdtGYGAgEydO7FH7LgTBo74QXPv23deVjFxiaQN6oORcWVZYO1Ug7g6QHGj5Fyzh71+Od6+dfRGTyYRYLMZkMvH9999z9OhRjh49yv33389LL71EWFhYm8ePGDECqVTK8OHDe8hiAYHmWBurgEWYy8vLba1KRSKR3VSsWbNm9epUrI4ieNQClw3uDjA7ziLMswdYfp8VZxHttpgWAyV1Fk/64BXc7MRaQvXVV18xZcoU3nnnHYYMGcJdd92Fo6Njq19o1uQcqVTKiBEjhFIsgV7FyckJrVZLUVERR48excHBgdBQS1jNbDazbt06qqurue666/Dy8uplazuG4FELXDZUN1rWqseEWEq0Okqj4Vx2eGmdpUvZlYpKpeKVV14hMTGRlStXcu211xIaGkpgYGCL+5vNZr799lvc3NyYNm2aINICvY5IJCIqKgqj0YhYXIleX0FRkQa53A1X11AqKysZMGBAn5iK1VEEoRa47DBjCYHrjJZ/kwNhexvC3Xh2v5JaGBt6LgR+pdB0tF9kZCS7d+/m448/ZvTo0Tz++OPcdFPrHfisU7EGDx4siLRAn0KjUfG//9lPxXr44WPcd999GAyGPluK1RKCUF8oxZvBf9K5x1lPgP/V9tsEeoVQxTlh1hlB0U4uy89n16PL6uFU1ZXXQtT6hfXqq6/i4+PDxIkTWbBgAQqFgoSEhFaPazoVa+rUqT1lroBAh9DpagCYOfMLTCYTP/54JzpdDRKJ5JJYl26KINQXSvEGKN4IQ5bC4ZchegEUpwGCUPc2598nK9rxkGfGwpojlt870iDlUqWp53z+try8PAYOHEhdXR2pqalUV1fzwAMP2GXMNqVpKdasWbOEUiyBPkNFxXF0uhrKyix1lz4+cWRkWFoPZmVtBEAud8PLK6bXbOwsglBfKJ7JEDAFavOg5oQlE9w1sv3jBLqdoxUW8c2vsnjXBe14yGV1EO/T9oStywGRSGTL7G66raSkhE8//RRnZ2cWLlzIr7/+yrZt21oVaYDNmzfbpmIJAzcE+goVFcd5++1+dtvkcjeGDBlORgbs2fMEe/Y8AVjC4JeKWAtCfaHUqiB7KXgmQe0JqD1pEW2BXiejyJJY5udiyeLOVbe9f6gCkgIt69OXY2eyw4cPs2LFCl566SVkMhlg711nZGQwcuRIDh48yD//+U9uueUWnn/++TbPOWTIEBobGxk7dmy32y8g0FGahrt9fOKQyVxtYvzww8dsnvaaNbfb9r0UuGSEOjU1FaVSycaNG1mwYIGty4xGo2Hp0qVMmTIFT09Phg0b1jMGDXjcMqBDroDQWZDzksXDFugTHFdbfjrCV4e615beJj8/H7VazYoVK7jvvvtwdHS0ifSbb77JiRMneOONN7jmmmt48sknUavV7SaG+fr6Mn369J4wX0DggjCbzWzcuIHw8ApGjBjR2+ZcFJeEUGs0GtLT01m2bBkajYZly5axYsUKAObMmcPGjZZ1h+XLl3evUOe8ZFmHnvSb5bG8SSeNAY9333UF2uVien0nBli8cCuTImDzZRQcmTx5Mv379+fzzz/n1KlTxMZa5njq9XqCgoJYt24dzzzzDLfddhsLFy5EqVS2eB6z2cwPP/xAbGwscXFxPfgKBAQ6hlxuGZ25Zs25nuJG4+vExHjxzjv9W9z3UqBX6ikyMzNb7P2qUqlYvnw5qampLF++3DZxRalUsmzZMgCbRw2QlpZGZGQkKpUKjUbDwoULu9dwuRLGrureawhcEOf3+v7qkOXH+ntrWDuZycSW3+WS9juZXSoUFBRw8OBBpFIp4eHhREZGcuutt7Jq1bnP8KxZs9iwYQMSiYT77ruvzTF/6enp7N+/nyNHjvSE+QICncbLK4aHHz7G3LmbkEgsIzZHj05Gr68FLCHx+fMzLqn1aegFjzo1NZXIyEgyMzObPTdnzhxbdp5KpeKBBx6w+1JJS0tDqVTa7vhVKhUqlcr2nFqtZv78+d1nvGsk/JoIMQsgZDa4hnfftQQ6hdZgEdnO4O4A18eAj4ulk5kISw12e53MLhWqqqr46KOPmDdvHiNGjOC2224jIiKCF154AYPBgIODA+PHj8fLy4tnn32WBQsWtBryFkqxBC4VlMpIVq/egtHoAUBNTR41Z5ejfXziCAjooeXRLqTHhXr27NktbrcKrpXIyEjS0tLstqWkpODp6cmCBQts4e5hw4YRGRlJZGQkHh4e3SvUxRth0gZL+DtroWWN2hoGF+hVZsdZRFaEfc9uRylotPB1C171hXYyu1QYOHAgixcv5uDBc+PERo0axT333MOxY8eoqanh6quvtj3X2mQsg8FAamqqUIolcEmwZcsWCgsLSUwcSWbmSrsw+KUU7m5Kn1mjTktLw9PT026bp6cnmZmZ7N271xbaViqVNlFPSUmxeeAajabZ8V3OgMWgzoDQuRD9QPdeS6BTNA1vT46ATXn2j9vichRps9mM2WxGqVSSk5NDaWkpt9xyC2BZs46OjsbT0xNXV9cW66ubsnHjRkpLS5k0aZJQiiXQpzhzpoyAAC9bJCgvL4/t27cTGBjI1Kl3MGrUaFt296VWO92UPiPU1vXo81Gr1cydO5e0tDTS0tLYuHGjLRweGRlJYmIiqampqFQquzB5UxobG2lsPDcHsbq6+sKMlCuEzmOXACHubT++XDEajUgkEqqqqlAoFIhEIuRyOQ899BCrVq3i9OnTBAcH2y0fAe22UvT29iYqKooxY8Z08ysQEOgYBoOR5175lle+2U5SiIKPFiVjLsxgz8k6ZDIf21SsS1WYz6fPCHVraDQalEqlLWSekpJi93xHQt1Lly7lueee6xb7BPoeZfUwNQbyNRCmtDy+3DGbzbz88stotVqioqK4/fZz4T6JREJ0dDQbNmzg3nvv7fS5k5OTSUpKuqR6IwtcvuQXqbnnqU/ZtS+XScYzZBTo+eSTb7kpohiZMYTrrr/zkpmK1VH6TBd9pVKJWm1f+KpWq1stFekMixcvpqqqyvZTUHAZxjoFbPx83NKVzM/V0rv75ytgtnROTg6TJ09m8uTJbN++ndOnT1NQUIBerwcgKSkJiUTC//73P8xms200ZWuYzWa2bNliiz4JIi3QF/hpy36umreUvLxCFur38fozt7PU5RhKkeVzXlIjZkD8wF62suvpM0J9vqdsJSkp6aLP7eDggLu7u92PwOWLu4OlK5nWAMcqYHRI+8f4OFvqqS/VMHlhYSFyuZwxY8Ywfvx4ZDIZubm57Nu3D/PZovK77rqLkBDLm9HeUII9e/bwxx9/sHXr1m63XUCgI2gNBu7616dI3KX8XbcHjygflkgkHJs4liRxIQCG2ko+37S3ly3tenpVqJuuS1s7jVlRqVQkJSV1iUctcGUxOQK25lumZumM4OvS9v4DfSztQ52kkBJp6ft9qeHh4YFUalnJGj58OL///jtPPPEEJpMJkUhkE2upVMoTTzzBiRMnWj1XaWkpGzduxMPDo9UbaAGBnkZnNKL1EGOsqMSjWs3GcePIU5fj5F2Ne6ilEmF8cDX+1X9gMup72dqupcfXqK0JYWBZO05OTratP69atYpFixaRnJxMenp6q8lhAgJt4Sg91/wELALcFgl+58q3thXA3YMvrQEdGzZsICUlxZb56ufnx8svv8ztt9/OiBEj7LK6r7nmGry8vGhoaGjxXEIplkBfxd3BgeuGhvLr2mO43nQrqffezIGPH0WiLbfbL/DEeo58cJiouS/g4BHUS9Z2LT0u1CkpKaSkpNg6jTUlMjLStr21emsBgfYoqLIkkykcziaVtTM96/zV10tpOfbIkSO8/vrrnDp1innz5uHu7o5cLue+++7jL3/5i92+VsGOiopCoWi5/ZpQiiXQF7GOrrzHq4i9zhV82BDDDS5emM1G9CJHGofezjWLd/Lf2AJSrpJgNhmQuXn3ttldRp/P+hYQ6CzbCiDa09IKtKSu/RnT+dWW/t75Got3fbyiR8y8aIxGI9nZ2Xz66acoFAqblyyTyWwi3XSspdWr9vDwaPF8JpMJjUZDaGioUIol0Gc4f3TlfVfDp5vhjqdd8ZeNpcboxIZX9yExmxl77TwGzJ2OvqYcsfTyiQYJQi1w2TEzFtYcaX+8pZVt+WeFXWmZuNWesPcV0tLSiIqKwsfHh4KCAluimFqtxtPTs9ns6fYQi8XcfPPN6HS6Th0nINCdNB1dCZaBG2FUsu9ILuEBAdQ11uFcUcYIhYmkO25BJBYjdbpEs0JbQfhrFLjsKKvrXELYzFiLqG/Ku3REes2aNfz4448MGTIEgHXr1lFQUIDRaCQnJwej0dhhsTWbzWRnZ9tC48K6tEBfoaLiOGVlhwFLn26XqjoSJQO4L6aQm42/8e4zExjtU8JT2gzeeGshosv0BvPyfFUCVzShCpgSaRl9ec8QS3JYW3RW2Hsbg8HArl27WL9+PVu2bKG0tJT9+/eTnZ2NRCKhpqaGFStWYDAYOnS+PXv2sGrVKnbs2NHNlgsIdBxryNvaq1sud6Oh9DSBYh/kYXUwfB+ffTCY0B3rMEyciP+gQb1scfchhL4FLjvaGmvZEqEKSAq0lGg1GtufX93bSKVSli1bxujRo5k3bx5z5sxh2bJltjXqsLAwvvnmG9s42LYoKSmxlWJ1Rc8CAYGuomnIOyhoOEplJLvzy/AHoqJmkr91J3X+OzDJzcy6zDtPCkItcFmSGGCpoy6pbb/UqrPC3le44YYbWLlyJT///DP5+fkkJCSg1WrJyMhgwYIFSCSSNgduGAwGVq9eLZRiCVwSbNmyBXNdGYjB0dTIsFmz2bp9Bw433ojbZdYy9HyE0LfAZcfMWEvGd2kdhCthanTb+48OOTfLWi7pWCezvsL48eMJCgpiyZIlmM1m8vPziY6OZtSoUUDbrT+tpVgTJkwQSrEE+hzWkZRr1tzOO2/3p3Tn+wSILTXTjfkHKd75OiJEzLzj/t40s0cQhFrgskPpCN8fsSSG/Xzc0vO7LUIV5xqk6IwWT/xSwcPDg6effhpvb298fX3ZvHkzI0eObPc4rVZLTk4OYWFhQimWQJ/EyyuGhx8+xrSJrzNemkispMTu+VhJBDN95uEqdu4lC3sOIfQtcNlxqqrtx+dzvs+puIQiwCaTCalUyjvvvENGRgajR4/u0HGOjo785S9/6VR2uIBAT+PpGc36k0ZCENFo1nHImEuhuYx7rvkcza7ViEXiy6qxSWsIQi1w2eHvYsn4tiISQT9Py79mM/wvw37/oxWWcHl+lcW7LmhH2PsSYrEYo9GITqfjvffeIyEhoc36abPZTEVFBd7e3ri4tNMEXUCgl9m3bx/HVdXIQ6czcuIEwh1dkMvd8PKKIXDwDZddY5PWEIRa4LKjs8lhGUVQ3WiZuHWwtOONUnoSa1JYSUkJjo6Odi1AJRIJTk5ODB06FKBND3nPnj1s2LCBm2++mZiYmG63W0DgYvDy8iI0NJTrb7kFJycnu+ekTu6XXWOT1hBiXgICWDqSbSvomyINlqSwxsZGPv30U9skrM5iLcVSKBSEhoZ2sYUCAl1PWFgY99xzTzORvtIQPGqBKx53B0sdtXVtutEIvxzvXZtaor6+Hn9/f7vRrydPniQ8PLzdY/V6va0U66abbhJKsQT6NDt37iQ4OJiQkJA2KxeuFASPWuCKZ1qMpZRLZ7SEvvsSJpPJ9nttbS01NTUcPXqUPXv28OGHH7J169YOnSctLU0oxRLok+gbtCyasYBNn1rGGtfW1rJ582Y2bNhwwdGjyw3Boxa47JBL7OdRt0ej4VyP79I66NdHeieYzWbEYjEnT57k5ZdfJjc3F7lczjvvvMP1119PZGQkt956a7vnKS8vZ8+ePUIplkCfo7SynhffXM9hjYz3Xt/CcpGCBXdM4YEHHkAqlQre9FkEoRa47LgtwTLe8mAJFFS3v3+j0SLuJbWWNqLufSQqbP2SWr16NTfffDNjxozBZDKxZMkSHn/8cdu6XVvdxwC8vb25/fbb8fb2FkqxBPoMf+7L56XPd+LYUM/Lo3z4ZfcJ/vHaT/y+/xTvPnMrngqhKsGKINQClx0fZ4GPMwzygzGhlvKrQ6Wte9k/n12PLqu31Fzn96HyrLKyMuRyOQkJCYAlozs8PByNRmMT6tZE2mw2YzAYkMlkREVF9ZjNAgKtUVFxnMbGap59/2d+3niMaLEDdzkfx+f+h5imWEXs2h18skPCoFnPs+adh0juLyQ9giDUApcpZfWwNR8SfGGwH/i7gtZg8bLL6u33lUsg1B0cpYDZ8vuhdvqD9xQ+Pj5MmjQJrVaLm5sbeXl56HQ6AgIC2j12z5497Nmzh1tvvRWvy7wXskDfxzoNCyAYWDAFQn+J4t1BE9m7di1iuZwl7npmmI/yVUU0b27ezueCUANCMpnAZUi0J9wUC7PiLOvPH2dZsrg351k87POZFQf9vS2tR5WOfa+FqFKpJD09nczMTPbs2cOMGTMA2ky0sZZimc1mXF3b6aEqINADNJ2Gta/2FgAMUaE8d8stvObnx+NiMRMWLqSuVoJEbGbxzMm9aW6fQvCoBS47+nlZvOnzPWew1EufT6PhXPi7LxIUFISfnx9SqZT4+Ph2Q95NS7GEqVgCfYGKiuOUlR0GwMcnjplBRzlZBXuCPYnL+pXqai1Txs5gyLhx7PjvzwxBwwB/3162uu8gCLXAZYejpGWRhnPZ3U05VQVRHvbHVDd2j20XilRq+VPtSOMH61SsyZMnExQU1N2mCQi0SdOQN1imYk1KSWHD9ytIdD9MSe1WakSlDBr0V5auWkuZwYH7hip7z+A+iCDUApcdpXUQ79P+HGor/q4wPAga9Of6gX+yv3tt7C7y8vJspVgdHdAhINCdNA15BwUNx8srBi+vGAwfDCfQt4aDxX7gU8rIm5+jUO2JHBN/e+HxXra6byEItcBlR6jC0mlsbKil9Ko94VU4wHt7e86+7iQkJISxY8eSlJQklGIJ9FnMZjMKpRwAL3EjRcC4YX6I12czdO50PISwtx2CUAtcdnR2KEdxHcjEoDe1v2938vbbb3PmzBmWLl3a6WOttdRSqZTJk4UkHIG+g1zuBsCaNbcD8PDDx8jOzsZNegTwIchXQ5ERYvdvxS3Qm7889XAvWts3EYRa4LLEx9niWZfUwel2mp74u8BDyZakso544F1J05GU9957LzU1NRd0nj179lBSUsK1116LXC7vShMFBC4KL68YHn74GGfO7GHNmtvR1qupyvyRILEPAAFiHxKPDUVTpGXgimcRCZGgZghCLXDZMdDXMn+6tA6mRMKeM22vV6ce7jnbmmJtEQqwY8cO+vXrZ5sRrdfreeGFF3j22WfbPY+1FMvd3V3ojSzQJ/HyikGnq8EZR0rWLCWoXmP3fGCsO+4RMuLiwnrHwD7OJXPrkpqaSlpaGosWLUKlUjV7fs6cOb1glUBfJMEXvj9iGVv5cRYkB7a9v85o+XGUnvu9J7CWVy1ZsoTbb7+dxx9/nK+//ppjx46h1Wptmd5tIZRiCVwqyOVuaNFRW1NIo1lHhiGH4sH3oR8wD7PcFQ8/H2Ru3r1tZp/kkvCoNRoN6enpLFu2DI1Gw7Jly1ixYoXt+bS0NDIzM3vRQoG+xPnVxR3t6z8mtOfGWxqNRiQSCYWFhdTW1pKRkcHvv//O9u3byc3N5f777+epp54C7MPj5yOUYglcKlRXSzHxVxr6hxCVNJK63EKkUl9MDg4MvuY29DXliKXCjWZL9IpHnZmZSWJiYrPtKpWK5cuXk5qayvLly9FoNIClM9OyZcsAyxfTggULbMdY94mMjOx2uwUuDfKrYFIERHvAzFg4XtGx43pqTo/ZbEYikWAymXjttdc4deoUMpmMmTNncscddyCRSHB0PNcerTWRPnbsGHv27CE8PFwoxRLodUxGI1v//ndUa9a0+Lyvry/x8eNInnQfQeFjcHEJsT0ndXLHyVf4Dm+NHveoU1NTiYyMbNEDnjNnDhkZGYBFtB944AFWrVplez4tLQ2lUolSqbTbNnv2bJuQCwhsK7C0EQ1TWjqRtdTkpCW0hm41y4Y15L148WIKCgrIzs7m9ttvZ/ny5QwePJj4+HikUmm7U7E8PT2JiIhgxowZQimWQK+T+913bE3bie+23fiPHo2zr32JlYuLC7Nnz+4l6y5telyoW/uPOn/dOTIykrS0NLttKSkpeHp6smDBAjZu3EhmZiYpKSndZqvApYm7A4QpLMJ7rAJGh8D2graP8XEGjRZC3Ds2GvNi2bJlC2VlZXzzzTcAPPLII4waNYpPP/2UadOmAa23CLXi7e3NXXfd1e22CgicT0XFcXS6Gmp1OhrNciR6d558ZTXrZcm4GfWU/d8Sbn/7eQBKTpygsaGBUaNGCfOlL5A+s0adlpaGp6en3TZPT08yMzPZu3cvGo2GhQsXolQq7UTdKuYqlYqVK1cyf/78HrVboO8xOcLSuzsl0pIY5tvOWNuBPpb+4KV1lmPayxLvCo4fP84nn3zCwIEDeeyxx3jrrbeYPHkyQ4cObffYjIwMnJycGDBgQPcaKSDQAue3BAX4ZsscCoy+iGPkiEsM/OeQiddveZLSJB9GS8XMcnMjMTHRbklHoOP0mXiZda35fNRqNXPnzrV52CtWrLCFw4cNG8bs2bPtQuEt0djYSHV1td2PwOWLNXvbilM7t6MJfp3LEu8K5s+fzx9//MHHH3/M7NmzqaqqYsaMGQQGBmIytd55paSkhF9++YW0tDQMhh6K1QsINKFpS9CDWCI6zmYtz98QwfcvPMTHK//BrV7V1BWBwx+VeNcZuOmmmwSRvgj6jFC3hkajQalUMnv2bFJSUli2bBnDhg2z2yclJYUTJ0606k0vXboUhUJh+wkJCWlxP4HLg4IqmBpjaQ06NcaSXNYWF5ol3hms9c0rV67kmmuu4dVXX8XX15ddu3bh4eHBM888Y9u3tfVmvV5PamoqZrOZWbNmdah8S0CgKzl/ClZNvjsAd4UUMPeBCSR5mLmuf39efvtJ/mHYj6jaSIx7KGFh9vXRjY2N1NTUoNfre/w1XIr0mb90pVKJWm0/g1CtVrfrLXeExYsX89hjj9keV1dXC2J9GWNLJjvbmay9ZLL8akuWeL7G4l13NEu8o1jLq8rKytiwYQNz5szh8OHDfPTRRwwfPpx33nkHiUQC0GYC2caNGykrKxNKsQR6hZamYM0OMRAgGUBJv0I++HA4YGkRuqGykl1egVAFf7t3JnV1dVRXVxMQEABAUVERp0+fxtvbW5iX3gH6jFCnpKTY1UZbSUpKuuhzOzg4CI0grjDyqyxrzmBJLmtrbOW2/AvLEu8ITbuP/fjjj0ycOJH777+f0tJSvv32W9atW0diYiLh4eFA6wlkQimWQG9jDXmPHfs0ISEj8fKKYXj/IIx5PpxRK3BLmk7N8Q+prCzm3z//hnuVlP6uZkICvDhy5AiFhYXo9Xo0Gg3V1dWIRCIhHN5BelWorWFtaF4HrVKpSEpK6hKPWuDKYlqMpc93R8dWzoyFNUcgV936PheKVXiff/55Vq9eTUFBAYGBgcycOZNHHnmEw4cP20S6LbRaLe7u7sycOVMoxRLoFazDNbZuXQJYPGcXNxHVgIdHCcXHdwKw9PNvGXyoiPVmX56cM5GysjJqa2vR6XTk5uYikUiIiIjocPc9gV4Q6rS0NDZu3AhY1o6Tk5NtJVurVq1i0aJFJCcnk56ebldDLSDQUTo7trKsk/OrO4o1jJ2dnU1hYSFZWVl89tlnPPPMM/z888+8+eabxMXFdehcgwYNYsCAAcIXm0CPoK+rQ+LggLjJ5806XCMvbys//3wfR48eQJR/BAegRhPKT/viqXP2plgDyloFepGEyFAXsrOzaWhooL6+npiYGEJDQ5HJZJw5c4bGxjZCXQI2evyvPiUlxZYUdj6RkZG27ZdLYXxOTg7btm0TysZ6kM6Orezs/OqOYvWmX375ZQ4ePAjAnXfeydixY7nnnnvIz88nNja2zXMcPnyYoKAg3N3dBZEW6Db+PHWKYxVnkzMaG3F88klMoaHoHn2UhoYGDEYj1w0YQKx3DMeOHUSEiNoDP+KtyQdgqJeZub463i+X0F+vRiEzMf2aZCKCvDEajRgMBnx9fXF3d0cmk/XiK700Ef7yuxkPDw9bAoVA93HPEIvAAjjJYJCvpeFJR4S3s/OrO8uyZcuYO3cuMTExfPLJJ4wePZrff/8daLuPd0lJCampqQQEBHDfffcJzSIEuoWKiuM8/v375FZrUePF7IMHub60DGlpKe+sWEFWoKVecV9FBZ/NnIm/uxvjpYm4qfPtzjNlkJaxVYc583sVUYueJWT8eE6ePIlWq8XT05O4uDicnJx64yVe8ghC3c2oVCqioqJ624zLni8PXvixo0MgvdBSey2XWOqo2+tk1hl8fX35/fffWblyJTfccAPLli3j/vvvBzpWinXdddcJIi3Q5ZjNZgoLD/HBB4OYenZbqO9y8lcncJIE1o0o4a+qfOR33sldaWncf7YZT2jMWGoUERgb66jwHYwydhwRThJOb3gbcW0ljkNG4D18ODk5OQBEREQQGhoqfIYvAkGoO0lGRgZqtRqNRsOYMWMICAggJyeHyspKBgwYgIeHh93+KpVKGMHZAzRtcJIYABlF5x5PioDNea0fG6o4J8w6Iyi6KRF1/vz5TJgwoUMJkkIplkB3UFNTQ1VVle2nosISTgoMuY3Cgi/59Yc/iCfZsq+zkoVlJoa//AkTZ4whUiolMzOToKAgYua+iMzNh9MlFVRVVfHjngxuvPVt9MWnkOsdUKlUODs7ExcXh5ubW2++5MsCQag7QWVlJR4eHrbJXytXriQ+Pp6AgIBWS2YqKyuFEoQeRC6xCO+BknONS8IUbR9z/n2+ohsr+fr1s9ShtlUvLZRiCVwo63Nz2VdkuUs1m800aLWIm5RBnT5zhhhHR/rJa3FwAAcHS8lVoPcQxKe2EBTXgKi0mNNZ/mx472/c8eAL7DhhYugGFYf8g3BxdkCn0+EUYqnSCQ93Y+vWrZSUlFCmqaOsxozRWEdQUBCRkZG2/gACF4cg1J2gqKjIrr/y9OnT+eyzz1i0aFGr+wvr0z2HuwNcHwM+LjB7gEWAzVhEuy2OVlhKtPKrLCJf0E4ns66grTDgkSNHcHR0FEqxLgPK6urIKi5uc58IDw+iz5tzsDIjg/W5uW0eNyY0lMdGjrQ9LirK5pX1X5FZUYPE2dLQyam6GpNUSqOzM2agvL6eGcFOVKmehhcs8855UsbeG+uIu3YS0WMLyPWvBeDj/iuYhBeTgBclZew8Ws0zf72xmfiOHTuWYcOGcerUKSQSCfHx8c3mNghcHIJQd4KAgAA78VWpVAwbNoy1a9cyffr0ZvurVCoiIyNt/4IlpBkVFUV2dnaLxwhcONWNlsSwMSGW7mQdJaPIcqyfCxws7Z566s4wffp0xo4di0LRTihAoMfIq6xkU14b6ydAmELBlPPyUR5dv56vD7WdrRihVKJ69FG7bVvz81lz5AhTIiORnSeMJrOZ9bm51Dc2cnNYGFVVVRQWHmLbtrmMAcvP1D8ZH5HIj9OmIXd1Zdr33/PbyZNM/eor7hoUy/4mwwrnzl3Ndy80Hzt8PuMS+9uJ9KlTp3B2dsbLywsXFxf69euH2WwWsrq7AUGoO4GHh4dtPbqyspLIyEgCAgLIyMhg7dq1xMfH2zVuiYyM5NChQ7Z16+3bt9tCQuc3eBHoOjoj0laOqy0/vUlhYSH+/v6IxeJmuQ4Cvcsbu3fzxu7dbe7j5eRE+cKFdttuTUjg60OH+OTGG5kYEWH3XGldHSM//JDbEhKanWvR6NF8ceAA8+LjuW/YMMxmM1VVVVRXV7M6J4f1ublMdXfn2LFjABiN9QAMGfIoWVlv8O6uP/BOy0RfWYm2tJLn5S8AMP61AK7yjWa/Tm671ndf3MJVP7yFcd/3gAcung0A/P4CuDt6I/90Cwp5AOOvOldKWF5ebuskuWDBAsLCwoTywW5EeGc7SUujBRMTE23r1k0JCAgQQt99lL8kwkdZluSxpqVdHelk1h2UlJTw4YcfMmjQIG688caevfgVxE9Hj/LC1q1t7uPn6sr3c+ciabLscP+wYbyxezcfTJ/OfecNBSqvryf89deZ38J3wLSYGBIDAvhw3z7uHDzYbsnj7T17cJJK+UeT8DVYyqUUjZXMCXHh+T//5M7BgxED+/fvx2gy8c6hQwz39mZaQgJmczlyOWg0lq/ykJCBZGVB2fGdZOw8hvvs2ThUOoAlAZuJ/yhi5T9WAY/brid67f9Q7/2T2CmWm8OA+HJiJpxkWW0oX54+zGNiJdNThiGVWrxpo9HI6tWrkcvljBs3Dl9f3zbfT4GLRxDqHmT06NG2rmzQsugL9AxfHTqXKX4xpV1dQdNSrK7obX8lcKa6mm+zs21TyVrC08mJOwYPRtpEcMvq6th95gwpkZGEt7C08P2RIxTW1DTbPtDXlzkDBrBk61buHDzYLhz9yo4diEQiu/ViKyKRiH9PmMD0r79my8mTTDrrVZfW1fFOejqPjRiBh6MjDQ0NVFVVIZFoePddS7e6eGATj/DZ/v1cW1OD4swZDvbrx4m6Oj6eMwelq4633x5hdz03t0DQyZi8bDgFDIfhy/l2zzzmEXa+aQA4ezYw/I4DuPnW222PnZLHdw1FfLtPRrE5jNnXDcdsNlNQUMCmTZsoKipi3LhxTJo0qZV3X6ArEYS6h5kyZUpvmyBA8yEdoe6WOdZWDnVxO9G2uFxKsRr0eoxtCKdEJMLpvPXL6sZGfjhyBGMbM7jdHByYERtrJ7hrjx3j/zZswEkqbbaGC1Cv12Mym7kxNhbPJk027hg8mP/8+SdeTk68f8MNdsfklJXx4b59vDBpkp03beWZ8eNJeO89Ptu/3+ZVl9fX89aePfztqqvwdnZu0X6rV/3v339n4tm+7i/8/jti4DoPD3bu3IlOpwPAGoAbM+Yptm37L9eFBfDG+vW4rVmDSa/nw3nzmBwRwdiwMIqKLOvK06/5jLUjLYvOYf9MYWLMm2zhbAblywuZ1+o7C9pqOSIvNY1mMYcaT/Lp0dmEa0u4d0g5GI3sNoUxemgUIweHkZWVxeHDh9mxYwdJSUmMHz++jTMLdCWCUAtc8cyKswh3ldbyuHWp6Xoul1KsP0+dYuKnn2JqQ6ilYjF77r+foU2Wg9YdO8ZdP/zQ7vmPPfwwMV5etsc3DxzI4k2buHvwYF679lq7fRsNBqLefJMJ4eF2Ig0gl0h4auxY/rJuHf8aN474JmHb5//8k2B3d+4529jjfAb6+jL7PK+6LW/aislk4v8SE7l13To+WLsWH62WFUePMic4GF1VFa6urjg41OHoKKK+/gwAHh7hANwarcB77RfUuelxMCm5Ki2Na9/6H8+JnrOc/En7G58tX77Lyf3rCR4Sweks/7bfVMBkkBBxwxLECkf6ufnxgFcMud99x96l/0HmImXnh6+j9/Jif9Y+6urqOHDgAGFhYcyZM0covepBBKFugz2aZKhTwp49be7n6elJdHQ0ALm5uajVaoYPt8xmVavV5LZTZmElOjraVtawZ8+eFs/bEc6/9vnn7QiXwmvCwXJe0+k96DrwSRY7eSL1sVzbUJaLqUGNPHQ4jQZYm6XGUNH8NZkMIDfAoX1wSt61ryk5OZnNmzfj4OBAZGQkGo2m1/+fzGYzX2/ejIOLi21me0FBAdXV1bZ9pSIR/dzd7dZb6xobkYlEjAkP5/5hw6itraW4uBh/f39cXV15c/dusktKqFSp2FNwLtsvyGBAKZMxzs+PJwYOtLwmDw+io6MxA4Pefhsnkcgm0k1f05zgYN5LT2eKszPeTXoVpJ46RVFNDQ8PGmTb1vT/6e4hQ3hu82b+/sMP/HfIEABUNTV8e+gQC+PjycrIsHtvmr6fMz09Sc3J4bP9+7kxNpY3du1iblgYqkOHsCZSGwwGGhoacHFxQSwW09DQgLy8nH4uLmheew1pdTWu113Hs9dcQ5ivL/n5+/j886vtrllYaAm/p//6dzy+f4p84OBf87hpSzphJ5qkbOtkrF39INY156o1GSSP1dpKrM7nnQGlTJsYw1vP3M8rfq9Y/n81zsSGxNo+exWhoTj2i0cWGsq23Fxqs7IQi8Xk5ORQUVHBwIEDyc3NvejPXn5+Pn5+fm3uI2BBEGqBK55TVRDtI6Ow4VwmbHW9rtuvKxKJuPPOO1GpVNTX17d/QA+QXljIbdu2tbvfK4mJjGnijXo5ODArLIx1hYWsmjMHc0MDuWIx0dHRaGUy7v7hB+6OjMT1vNC3k1TK7ZGRvHfsGPdGRxPg5IRELEYiFvPT0aMcrqzk3bNf/uczLzycb06e5PO8PP5xdgqZzmjkkxMnuDowkH6t1PLKJRIeHDiQZ/bs4b6oKCLd3PjoxAl8HR2ZHhzc5uuOdnPjhuholmzdyuHyckTArMBAampqbB24ampqKC8vJzAwEBcXFxwcHAgMDOS506cxlZbSKJHw+MmTRJ3toa3TWUR10KDncHUNRyp1xsUllLGjvqHgjTc4efbamY5OzE4YRsZLrwNn16Zfts8ybw+tRMSIqyJx9XXlWfOzLQqqSCxG+eCDlJSUYKitxdXVFbVaTXFxMZGRkcTExHTqmgIXj8jcVjbGZUp1dTUKhYKqqirc3d1b3mlNMDScAacgmHm6Zw0U6BAldbBsO3g6gou8/f1bo6351XU6UGth0WhLnXVXUV9fj3Mra5pdQZVWyz9++43qNsYISsRi/jVuHAObCK7OaKTfW28RolDwztSpzY7515YtbMvPJ+/RR3F3sG/hVlJbS8Qbb/D4qFE8N3Gibfujv/7K5wcOkPfooyha6NJXp9MR/sYb3BQby4qzvQXMZjOJK1fi7uDA73ff3epr+M8ff7B02zZUf/sbAW5uvJueziO//kr2Qw8R6+3d6nE6o5GYt95iZHAwz4wfz8B33+W9adNY0E4yn8lkYqdKxb/+8x+GFhVRdsst3Hu21HL06NHIZDJbYpinpydyueWDqauqYu20aWT5+nLAzY15GRlc8803eA0cSFFRJitXJjJz5hf4+MQhl7vh5hjG3o9fIefzLzmzp+MtiJNuPUhAfDlF2d7s/SqBB049jFQs5r2QNwH4ZGw9uVuWIpG03EjHaDSSl5fH6dOnkUgkxMTEIJFIWLlyJc7Ozjz44INd1mlx586dNDY24uDgwMg2lg4uVzqkQ2cRPGqBK57Ozq++WIqLi/noo4+49tprGXZeqU9X0Wg08sWBA3g7OzPYv/la5SmNhsPl5dw1eLCdUFvXcBesW4dYJLJ77kx1Netzc3l2/PhmIg2WsqaHkpN5ffdu/j5iBB5OThTW1LAiI4Onx41rUaQBXORyFo4axZObN/Pk2LGEKZWsPXaMfcXFbLnrrjZf59+uuorXdu1i+fbtvJiSwgtbt3LLwIFtinTT1/mXdevIr6pqdW1ar9fb9cauqanBoFZz1/79yAwG3AoLCRk/HoVCYesi5+Tk1GxK1P633sKo03Hn8uWo5XIqHn2UvUuWcPVXXyGXWzzxNWtuP3fAv/999peOibRIYiJ6bD4B8eXAuRIrSUk+ZrOe6AHf8ZzfVKZOHNqqSAPU1dVx+vRpFAoFsbGxODk5cebMGVxdXZkxY4bQDrmXEPoTClzxWOdX9wR6vZ7Vq1djMBi6dX3O18WF+4YORW8ysWrOHH697Ta7nwA3Nwb7+XHt2TXGptw1ZAihCgXP//mn3fYXt23DVS7n4VZC0QCPjxqF3mjk9V27AFi2bRvOMhmPtHEMwEPJySgdHXlh61bMZjP//v13xoeFMeFslnRrKB0d+ceIEfwvI4Mlf/5JUW0tT48b1+YxVu4eMoQQhYKdp0/z1NixyCUSGhoaKCs7l/JfUlLCoUOHKCgooL6+Hg8PDwzr1+Pi7o5nSgradesIVCjw9vZuNblKnZND7rffkvDQQ8RER3NVaChJTz9NxcGDnPj+e7y8Ynj44WPMn5/BzJlfdMh2K/8s+SePHL+V8Q+nEzvFvnNa7JQ80lcv4tZF7/CC+1gKKhuYMXlIs3OYzWa0Wksmpbu7O0OGDGHIkCG2m42goCAefvhhwsJaLvES6H4Ej1rgisffBR5KhsYOzq++GDZs2EBZWRkpKSkdKsWq0mqJfecdKtpYwxaJRHx0ww3c1iR5CmDx2LF8uG8f76ans7BJRvmfp06xOS+P7+fORdxCz/GmXrU1NH6mupqVmZmtetNW/FxdeTApidd372ZufHy73rSVpl71QF/fDnnTVqxe9ZKtW7ktIaFdb9qKVCRiiasrJ3//nbvO9uvPy8ujtLTUFsb28vJCJBKhUChwcXGhZNcuDm3bxshlywgcM4a1U6eS9eqrjHzhhRavYTabSV+yBPfISPrfdhs1dVpq6xsJSEwk4oYb2P/aa4SkpODqGkpZWRnl5YcB0Dy+B6VsI6fSRhO2ZwJgEeXqUyd4+q4nCT1smXAlc5EhcfBHJDbTWCsjavY/cY2bwMdvvcrgxt+Ri/Q4aaoZNekqbgoLYuLw/s3sO3jwIHV1dSQnJyOVSluc7iZkePcuglALXPGkHu7+a5jMZmLfeIPTGg1iiYS3duyAHTua7ffi5Mk80KTDlYtcjqtcjlwiYVEL5Vt/nDrFd9nZhLTQvCNUoeC+oUN5accOHkpOxvXseulzf/zBYD8/boyNbXaMlbuGDOG/W7fy/J9/8u3s2R3ypq08Pno07+3dy6TPPuuQN23loeRklu/Ywd/Wr++QN21F6ejIYyNG8Nwff7TpTRsMBqqrq21hbE1JCbIPPySquhrVV18x4N57CQwMxNvb2y6Mbb2hMup07P3vf/FNSiJ82jREIhFD/v539jz3HNGzZ+PTwjKG2Wik8vBhoufOZceBU9z91CdUVtXz2hNzGDt6NHk//YTm5EmOVFVhNpvR6y03TkqXXwCYEKkg72wBgNRJwsHXl3FtmJycJp9ZR4UbyU+8g8zNB6mTJYz+14WLMTT8lYPvvMryvwYz4Oz88/MRiUR4e3vj5OQkzIvuwwhCLXDF03SWdXdh0Oupra5GJBKxcNSoZmt9B0pK+PLgwWaep/RswtddP/zAyOBguxpks9nM+5mZTIqIYFwrYcnzver2vGkrTb3q+4YO7ZA3bcX/rFf96q5dPD9xYrvetBWrV70wLY1/T5jQoWOsLFAouKqigmiXcxl/jY2NduvLtbXnSpbEYjHaDRugsRH/yZM59O67hE+dirKF9XwrRz/7jJr8fMa89ppN1CJnzSJ39WrSlyzh2u++Q3xev2uxVEq/e+9j+Ye/sTa1kJGDIxmXGM2Cf3/JSHkVj40ci+/gwdTl5eHq6oqX11iSkpIpKSvm6vkrWXjjdPjGoson1/1MeVYWjY/8mxeL/yBn7b+Rn82idPJtPjtA6uTO0H/+u9l2vV7PiRMnCA8Px9HRkcCz2ecCfRdhjVpAoAeQy+X8a9w46oEJ0dEsHD3a7qeioYEBPj7MbqGt7K0JCUR7evLcH3/Ybf/x6FGyiot5to0OUU296lqdrkPetBXrWvWN33zTYW/aypNjx7J4zBgeveqqDh8D8NjIkWQtWNBhbxosnu6ef/2L8rQ0Mt5+27ZdpVKRk5PDmTNn0Ol0eHt7ExUVxbBhwxjo60vlxo0Meughxvz3v0idncl8+eU2r3P4448Jv/56lE3Kk8QSCUP+/nc0R49SvHNns2MKSzU8ntHIT6JQbvbT8euKR1j53O38PUFOVqML/1fgRUZOPpGRkfj6+iKRSPDyimFA7FgGxiazIbeUaUukxCX/zKF3Xyds2jQ259UwuH8wEcEdC/E3Ra1Wk56eTnFxMSUl7cx/FegzCEItINDNWCsgF4wfz2A/v2aCu+v0aVs2dUtertWr/vHoUfYVFdnO+dwff7TpTVtZPHYsVVotd65Zw+a8vFavcz5Wr1prMPB/I0d2yJu24uXszAuTJ+PWiWPAUjLWUpb6+RiNRjQaDTU1NRz94gtqTp3CJTkZ1dnfwTIUJzY2lquuuoqRI0cycOBAQkJCcHNzI+OFF3ALDaX/nXcid3Nj6OOPk//rrxSfTYJrCd/hwynavh19rX0zkfzffkPm6orH2VpuwDbt6rWPfmbH/jz+c10ok09to/j3LTScOcPgg1v4ck4MjUYTL678tcXrzZg8mN/Tj7E/ciQ7xP78Ue/KkcFTWL8tu8WksPber+PHj3PgwAGMRiOxsbGEhoZ26hwCvYcg1AIC3UhxcTHvvfcehYWFiEUinh0/ns15efx5VkzAsmbcmjdt5XyvuiPetBWrV73myJEOe9NW7h4yhC9mzuQfI0a0v3MXYKivpyAtrdmwDb1eT3l5OSdOnCAzM5Nt27aRlZWFav9+Dr37Lv1uuYXh//kPDl5eZCxditlsRqlU4u/v32z99eTPP1OWkUHSU08hObtuH3799fgkJrL3v//FqGu52c2whQvR19Vx4J13MBpN1NRpqTh4kNzUVAb97W84enlRW1vLiRMn2LVrF/v27SMqwJFrQisZHpRPyDUJZLz4IulLluDk7U3yA/egrqpn0oiW/z9mTB6Ki5Och5d/z4rGUN43RfHwyz9iMJqYc03zSV2tUVNTQ0ZGBmfOnEGhUJCUlIS/v7+wJn0JIaxRCwh0E9ZSrIqKCtu2G2NjbV71pjvvtHnT386e3aaX23StOrOoqMPetJXFY8fy8/HjLJ08uUPetBWZRNIsm7w72ffqqxz/+msS//1vFGPH2taXm3Zuk0qleHh4oFAoyHv1VaTOziQ8/DByNzeSn3ySrY8+ypktWwhuZbJT9ooVBIwdi3+TJhsikYhhCxfy27x5FKSlEd5CsxeXgAAGLljAxrfe56/pjaiKq3jArYyR0dE4jBxJenq6zU4HBwdCQ0NJTEykePf3uFZloxx7HWe2fEN9cTFj33yTDXty0RuM3NiKd+zv7U7h78ubDSwRi0S2kZNtYZ12lZdnKduKiIggNDRUEOhLEEGoBQS6iaalWNaEHatXfdN33/HnqVMs3batXW/ayq0JCTz/55/c9O23nKqq4o82OnadT6hCwcm//71TIt1TmM1mampqqDl+nNxvv8XJx4esV1/Fx8UFsbMzjo6O+Pn5oVAoUCgUODs7IxKJKN61i8K0NEYuXYr8bPvO4MmTCRgzhowXX8R/1CikLSSyeQ4cSNGff9Ko0eDQpBSpaNs2xFIpHm1EHLJ843lOmoRHQREjIv149Ugj1yTEsOC4CndXS4a4r68v7k16oUeH+oLpDDI3NxIXL0Zz9CjBkyax5rH3GZ4QTpCvstXrSSTiNhuUtIZWq+Xw4cNUVVXh7OxMXFycrcWpwKWHEPoWEOgGjh49Snp6OhEREc2mYlm96vt++qnNtenzsXrVp6qqOuVNW+krIm0wGFCr1bYmGwaDgYy9e231xld/9RXo9ch27mTkyJGMGDGCuLg4W+9sqwAeeu89POPjCT/bdhQsnnHiE09Qd+YMeT/+2OL1hz72GCaDgf1vvmnbVnvmDNkrV9L/zjtRRDbPoK6tb+S+pz/j/n9/xbgBfjxVu51bctbwRIIjW49X8n9vbcXZM4SYmBgUCoWd19rPx/LvH39s48mdGpar/Zj72Pts3JHDjJQhF/t2tsixY8eoqqoiKCiIxMREQaQvcQSPWkCgk2w8cYKTGk2rz2sbGzn95594Ojkxc+bMZqHGpl51R71pK7cmJLC3sJAHuqn1aFdhNpupPHIEj9hYdDpdi2VS0dHRBAcHI5PJcDt5kqJjx5j88ce4BAYy6K9/JevVVxl42204tOLhKqKjObluHdrycpx8fGzbyzIzbc+3hJOPDwkPP0zmsmUwchLHGqQEbfwSuVLJwL/8xW5fo9FIRUUFn69N56uf9/C/Z29jRJwnucvzqc/OZuGrT3BrnZFpf3mbx5alsuWTx2zHmox6SnZ+jbRkHwADnAopNR5kZ10sJiRMHZfArdM6nknfHgaDAYlEgkgkIiYmhoaGBttELIFLm0tGqFNTU1EqlWzcuJEFCxYQefauNzU1FYD09HSmTJlCSkpKb5opcAVw2/ffU9bOtKsb/f15YezYVpvt3xgby4NJScyLj++UpysVi3nzuus6ZW9PYjabqaurI+ezz8h95x187rsPWZObCrlcjo+PDwqFwiYiuqoqTn38MWHTpuF3tgSs/+23o1qzhr3//S8pn33W4rrqoEceIX/9eva98gqjXnwRgEaNhqxXXyV8+nR8E1tPuIq5+WZWfrqehxZ9TaNZRKypkhVPPoLMxQWTyURlZSWlpaWUl5djNBoJ9LCsCft4utKvXz+i33sPXVUVTt7eRCiN1Gt1jBkWZTt/Y+UZTnz3JNqKU3bXneB6kGvDqoma+wIOHu13pusoNTU1HDx4kMjISFsC3fn9xgUuXS4JodZoNKSnp7Ns2TI0Gg3Lli1jxYoVpKWloVKpWLhwIcOGDWPOnDlknDdLVkCgq5mfmMhru3aR9+ij+LrYj9T6eN8+7v3pJ5678UYGtFFmJBaJeHfatO42tdsxmUxUV1fj5OSEg4MDBoOB3Zs3U/rRR4jkctSrVpEwYQKegYEoFAocHR2bie7B997DqNUy9J//tG0Ty2QkPfUUm+69l1M//0z49dc3u7aDUsmQxx5j9zPPED17Nr5JSex/801MBgND/+//WrVZU1PPw0u+YXWFB2ONhSTLqvlE1p831qzlnqL1uIYmoPW0zMZ2d3fH19eXkT4+vPh5Oj9symLquAQkcrnNi9+akUuFpo6ZKeeGeshcvTGbDEidPQie8jAeAyZRmbOJ0xvfwWwyIHPrfA10Wzg6OiKVSoVEscuUXlmjzszMJLGFu12VSsXy5ctJTU1l+fLlaM6GF5VKJcuWLQOwedQAKSkpLFy40HZsUjsj6gQEuoJ/jBiBRCTi5fNagBacOcOT69czo1+/DtUCX4rodDrKy8sxnc1E1mg0ZGVl2QZZyGQyzJs2IZXJSPn8c0R6PQ2//dZimZSVhvJyHJRK5OdFH5wDAhBLpTSUl7dqT+TMmXgNGsTe//6X8gMHyP3uOxIeftguFN6UE/llXDXvRdJ2HObzZffyr2uiGKgv5b1HxjAs0Ihn/VFyDhwiIiKCq666imHDhhEcHIyDgwMzJg9m3e8H0evtW9n9sCmLsEAvhsaF2LaJZQ5EznqeAQs+wzN+MiKRCM/4FAYs+JTIWc8jlnauvrwlampqKDpbVy+TyUhOTu7WQS8CvUePe9SpqalERkaSeXYdqSlNPWKVSsUDDzzAqlWrbM+npaWhVCpbbBq/YsUKm5gLCHQnXs7OtkEQ/xw1Cl8XF/R6PYu/+YZinY6HBg7sbRO7BLPZTK1Gw5FvvsFlxAhq9Xpb+dHQoUNRKBS4u7sTERGBh4cHYFkfLk1LY/izz+IzcCADH3yQ/W+8QeRNN+HRv3+L1xn017/yy4wZHP7oIxIeesh27YylS3H08SFm3rxWbRSJxSQ//TTr581jywMPoIyJod8tt7S6/7FTJeQXqfl1xSNMGN6fojgfGpOScA0LI9I5Ft3RU5RXNbY4KWrG5KEs/3ADdzzxEZ6Kc5GUHzZlcccNI5rdhLTW1lPq1Pbs4fZoWnZl7dUtk8kEb/oypsc96tmzZ7c4g1elUtk9joyMJC0tzW5bSkoK8+bNs3nUVpYvX87ixYtt69YCAt3N+V71L+vX80tNDeN9fJiSkNDL1l0Y1jB2QUEBhw4dYseOHfz+4oscf/ttjr//PiaTCT8/P/r162db/5RKpYSFheHi4oLJYCB9yRI8Bw4kctYsAPrfcQduYWHsXbKkWRMTK+4REcTefTfZ779PbUEBAGe2bKHwzz9JfOIJpO2stXoMGEBO8nRe0EbjfNdDzfptNyXSzxFHuYSte48B4BsYSPK113LVVVfhYLQkuSUEyVo8dkhsMDdPTeZMqYaDx8/YfmIj/bl7xsgWj+lqtFqtpdGLSoWTkxNDhw5FJmvZXoHLhz6zRp2WltYsQ9HT05PMzEz27t2LRqNh4cKFKJVKO1FPS0sjJSWFYcOGkZqayuzZs3vadIErkKZe9QxfXz7KyKBSJOK1GTN627QWMTQ0sO+VV4g9K5xwbpqUUqlELBZTVVXF/v3n5ns6NDRQu2EDzqGh1G3dSv9HHrFrk3k+x7/9Fs2xY1zz9deIz45FlMjlJD35JJvvv5+T69YR0aSUqikDFyzg5Lp1ZLz4IqNfeYWMF18kYMwYgidPbvN1qavqePA/X/FTVg3+XsHc+MLPLG2Q8eDN4zEYDJSXl1NRUUF8fDwikQh/Px/GJUay7o+D/Ouh6UgkEtxcnSne/jk1udsB8NMdo2j75/iNuBmx5JwIikQiPv5vx0ZvdjVms5mSkhKOHz+O0WgkKCiIyMhIYfzkFUKfqaPWtFLuolarmTt3rs3DXrFihS0crlKpmDNnDg888ABRUVFs3LixxXM0NjZSXV1t9yMgcLFYvepFa9eyTSxmenQ0Q/voJKLslSs5/vXXbH/6aZtne+bMGQ4cOEBNTQ1gSZwKCwtj8ODBjBkzBt3PP+Pk6cnUb79FERFB+pIlmM/rktWUysOHcfTyQtmvn912r4QEZK6uqLOzWz1W6uzMsIULOfP77/zx17/SUFpK4pNPthnO3ZaRy/B5S9mWcZxvX32Aw78u4b5Zo/i/5alcc9/L/LphC0ePHqWiooK6ujoAFAoFd84cy4FjheSdLqex8gxHPrifoj8/sjt30R8fcuSD+2msPNP2G9sD6PV6cnJyOHLkCBKJhEGDBhETEyOI9BVEn/GoW0Oj0aBUKm2ectPyq8jISCorK9s9x9KlS3nuuee6zUaBKxMvZ2fmDx7MK+npADzfjvfXk1jLpKqqqig9coTDH32EQ1wc6sxMTv7yCxHTpuHl5YVEIrGN3JRIJERERABw5o8/OLNlC2NefRW5uztJTz/NpnvuIe+nn4hsJWoQd++9nFy7lpyPPiLhwQdt2w+88w4mo5HYu9r2RkOuvhr/UaMo3LGDhPnzcW+joUtG9imumf8Go4ZE8dGSO3CWQ54ql5mjAvBzHcWrX+9l4dsatnz0MD4+PkibhMOvGR2Pk6OMITctwVkGK8cX4yyT8kZWAIlT5vLIRKduy87uLGq1miNHjqDT6fDx8aFfv35CqPsKpM8ItVKpRK1W221Tq9UtJo51lsWLF/PYY+caEVRXVxMSEtLGEQICHePJiRNZuX8/U6KieiTT26jTkf6f/xAzbx5eTdbCjUYjNTU1tqYi1dXVGAwGzGYz6v/9D6lSyYBnnuH0O++Q9fLLBE+YgKurK66urs2v0dhIxtKl+I8cScjVVwPgN3w4YVOnsu+VVwieNKlZhjaAIjKS2LvuIuf994mYPh3X4GA0x49z7MsvGfTII7g0maXdEo06A2v8R/GFkyP/9YpnkNncqkft7eGKyWTm+jHR5KuOoNfrAUtU4LYbx7H7aBWV1Q0EtHBNV2cHVr02n8OqYgDKdCNolLhzXZwL865LwlPhgnvkcPQ15V2SnX2h1NTUcODAASQSCbGxsfj5+QkJY1cofUaoU1JSWLFiRbPtXVFy5eDggEMnx+0JCLRFcXGxbe1z34IF+LcgeN3BkU8+QbVmDeX79zPyo4/wOluGdPr0advwBZFIhLu7O+7u7jQeOEBRTg7j3nqL4EGDCH32WdZNn86h996zq1tuytEvv6S+qIjx775rJwxDH3+cddOmkb1yZavHxi9YwMmffyZz2TLGvvkme5cswTUkpF1v+oiqmDuf+Jhjp0qYMjaB/3vlB7bsPcH//n0bXkrLe2s2m6mtrUUmk9nKoX7+8xCjBk4mODgYX19fnJycqKnTsnn3UZ57uOX1cIDJI+KYPKL19fauyM6+UMxnb1Dc3NyIiIjAz8/PFvUQuDLplFBnZWV1aD+lUkl4Bwa/W8PaQLOMbWtddFd41AICXUnTqVhBQUFEXWCbxpLdu1H064fj2dKm1jCbzTQ0NFBdUMChFSsInjSJ01u2sPPNN7nu3/9GIpHg5eWFSCRCoVDg5uaGWCzG0NDAugULCBw3jqCJEwFwCQwkfv58Dr77LpEzZrTYZtNsMIBY3Cx7WiyRIJJIMJ31XltC5uLCsIUL2fbYY+z+178o3buXiStX2sZJtvTaPlmzk/97aRVhAV5s/fxxEvoF8fMfB1nw7y8YPu9FPlpyJ+OT+1FbW0tGRgYhISFERUUxc/IQXvzgN+IHDsbZ6dz5f916iEadgRmTh7Z4zb5Mfn4+FRUVDB48GLFY3GKZmMCVR6eEetKkSUyZMqXVMgsrVVVV/Pbbby0+l5aWZkv6Wrp0KcnJybb151WrVrFo0SKSk5NJT0+3q6EWEOgrNJ2KdaE3kuUHDrDpvvsIGDWKCStW2HmuJpOJ2tpau/7Yer2e6o8/Ru7uzsgXX2T3smWc+eUXGh56CNeAgBbD2LnffUdDWRmTP/zQ7vxx99yDas0aDrz9NmNff72Zbf1vv53j331HxosvMuHdd23bs159FZFYTPx5/bDPJ+Tqq/EfORLVmjWEXH01AecNJWnKwWNneOj5r7h5ajLvPH2LTXBTRvRjzWt3seA/33Hjw+9SseNVXF1dCQsLw9vbsm48I2UIz7y9ltc/28Sg/ufacX68ZgfDBoQSFnjp9bnW6/Xo9Xp0Op3gRQvY6JRQz58/nxfP9tRtiyeeeKLV51JSUkhJSWmxOUlkZKRtu1BmJdAXaWsqVkcxGY3sXbIER09PirZv59SGDbgnJ9utL5uaZFe7urricPo0hXv3Muqll5C5uDD8n/9k3e+/s//VVxn90kstXsfJzw+zwUDt6dO2kiyw9MPWVlTg1EoXK6mzM4mLFrH173/n9JYtBE+cSNm+fah++IHkZ59tNwIAkDN8Oq9lO/Cv5KmMbWO/fuF+uDo70D/cD5lURGFhIaWlpbYqEIWLnPgof9uoR2uyG0BMmB+J8WE8/7+fm513+T9ntWtjX8BsNlNWVoanpydSqZSIiAjCw8OFjG4BOzol1B0R6c7sJyBwKVFTU8OPP/6IUytTsTrK0W+/RZ2dzZTPPyf7/fdJf+EFvJ56CrFcjkQisc1dtoaxRSYTP//rX5aErrMDOeTu7gx57DF2PfUU0bNn43fVVc2uE3rNNRz/5hv2vvACU9essYWf9738MhJHRwad7QLWEsEpKQSMHk3G0qX4DR/O3iVL8IyPJ2pW2wKorqrjof98xY+b9zMsLpQHX/qRbYeLeO2Jubi5NPcQZVIxE5Oj+XrdTkb2c8JsNiMWi/H29sbFTcm+Yz+0uda8+eN/oKlpsNsmFonwUrq0ckTfQa/Xc+zYMcrKyggODiY6OhqxuM9UzAr0IS7qU/H99993lR0CAn2e4uJi9Ho9N9xwg91ULNWPP1J55EiLx1gToKytN7WVlex//XXcRo/GZ9gwEhcvxlhdjWzPHhITExkzZgyDBw8mPDwcDw8PpFIpJ9eupTY/v1ldccQNN+A5cCAH3n67xWuLRCKSnnqK2oICjnz6KQAle/Zw6uefGfLYY8gVilZfq0gkIvGpp2goLSXtzjupPHqU5H/9y9bIpCW2ZeZy1c0v8ufe43zzygNs/2oRHz5/Jz9u3s+oW5eRmZNvt//x48fZsWMHCeFuHMuvoE4nJjY2llGjRjFw4EDSDxefXWse0uo15TIpvp5udj/eHq59PjtarVaTnp5OWVkZPj4+wlq0QJt02KNOSkrCy8vL9thsNpOXl8dNN93ULYYJCPQ0jvWnMUqc0Dt4tfh8TEwMjz76qN1acNm+fex68klcQ0OZ9uOPIJHYlUlVVVVhNBoJCAigf//+HHjjDUTA4H/8AwC30FAG3HsvOR9+yOA77kDk5tbsutawdcWhQyhjYmzbG6uqqM3PJ7iN0a7KmBj63347h1asIOzaa9n73//iPWQIkTfe2O774R4WRtw995C9ciVRs2fblYOdT2Gphmvnv0nywHA+XXo3oQGW9eFbrx/OVYMiuH3RR0y6+1X2fvsY0RGhgOU7xMXFhXnXj+K1bzL4cF02u4+e64uwccdhhsWFEBbY8v/HpYjRaESlUnHmzBmh7Eqgw3RYqBcvXsys88JemzZt6nKDBAR6iozCgxTXlgKgFBmYLa+iARHfNXqgx/LFGaaIQtboQnWVHD8XhZ1Im4xG0p9/HpfQUOrOnGHTf/+Lw6RJtmTLpmVSXl5eqHNyyE1NJXHxYkKbtOIccP/95K1dS+ayZXbJW1Z8k5IImzaNrFdfJWTSJJsnvP+11zADQ/7+9zZfZ8JDD3Hy55/ZcPvtNKrVXPPdd4g6GGKNnz8fqbMzMXPntrmfj4cb7i6OjEuKtom02WzGaDQSFerD8IRwikrVFJ/JJyo8BJFIRExMjE2gFswbx5q0LLJzi+zO+6+/TO2QnZcCNTU1HD58mPr6ehQKBXFxcULCmECH6LBQny/SAJP7UCcmAYHOsuXkdopqSvFwUvConydqgwR3iYTgxnxWq6up1FYxxK8aj4Nn+Px4A0/9828YjZYRh87OzuSe7W3t/fjjkJlJxY8/0m/8eHyio+3KpKyc3rcPzGYUUVF2dkgcHXENCqK+yF6kmjL0n/9k3fXXs/+tt0h++mnKDxzgxOrVJD39NI5ebXucMldXhj3+ODsWLSLmllvwbKNf9/kYJVIOhyUSJnOk5QKrs9eQSbh+QgI/bNrPwnsmU1ZWRmlpKc7OzgwYMICftuznxkmDGTJkiE2cm3qRS/8xk6X/mNlhuy4lzGYz+fn5nDx5ErAkzYaEhAhetECHETIXBK5YxoWNwIyZ/xswkVgnBwicSINHAikKN+ZEjQIguKIeXU05yVdZHu/Zs4eCggK0FRXsf/NNAq69loFTpjD+qadw9PCg7scfCQ0NRaFQNEsMCho/Hu8hQ8hYutSuFrlgwwZK9uxhcBuesbOvLwkPPUTu2US0vUuW4BEXR3Q7nq6VsGnTGPfWWwxt0qGvPY6dLGH8na9wx6KPGH3bcrKOFLS6b2NjI2MGB3PsZAmpP6Vx8uRJ9Ho9Tk5O7NqfR3F5NfOmXdViJ7TLnSNHjpCXl4eTkxPDhg0jNDRUEGmBTnFBQi0kkQlcDiQGDCLQ2RPf2sM0OAWhlftQJfFBL5ITXHeEfnI3vMuP4xsQSPKIUTg4OBAWFoaPjw9Zr72GSCxm5FNPERgYiNLXl2ELF3I6LY3CrVtbvJ5ILCbp6aepVqk4+uWXAOjr6shcvpygCRMIGj++TXv733Yb7pGRbL7/ftTZ2SQ9/XSbyV121xaJCJ40Camzc7v7ms1mPvtxJyNvWUa9Vsc3rzyAs6OM8Xe+wttfbbGF9vV6PYWFhWRlZbFz50783U04O0j5/s889p9qJKfQxJZ9RbzyyUb8vd0ZMSiinStfngQGBhIcHExiYiJuLeQgCAi0xwW1ED2/J7eAwKWI2KjjL8EROFDLQU0dnlXbMAO7tA2Mc3fkaiQUGeUEJt1g84AiIiKoPHIE1Zo1zWqKQ6+9ltxVq8hctozAsS1XD3vGxRE9bx4H33mHsKlTOfbllzRWVpK4eHH79spkluEYd99N5E034TNkSFe8DXYYjSbuf+ZzvvklnbtuHMnLC2fj6uzAtWMG8K83f+Lxl1azedcRXnlsGvmn8mzlVD4+Pvj6+nLHjeW8n7qNtD0n7M678N5rrpjSI2vZVVBQEEql0lZqJyBwofSZXt8CPUNpaSn19fWUlpYyfPjw3janxzCbzZh1dYgkckRSOWaDDuOp7fST1bO2ooqshkrujBqHycGNbw/ux1PsybAgb/YFTUGqOG/YhjVs2cLIR7PJdO75Vhj8yCPk//YbO594grKMDOIXLMA1OLhDr8MvOZkpn3/e5lzoi0Gr05O6IYMHbx7Pq4vm2LY31Ndx7/XxKFwdWbLiV178+3SUSiV+fn54e3vbplO9vngery+e1y22XSro9XoqKipwcHAQWiALdAlXxi3uFcb5LV5ra2s5evQoJpOJY8eOERAQwKlTpygvL+8lC7sXd81BXDXZmBo0tvfCVFeBrmAvpjrLaxZJ5UgVgSASEeUdQXZtKfrDWZzO2Ul5g4YAZSByR2d8w2Kbnd+jf3+iZs1i/xtvoG0SXTr1yy+UpqczbNGiNu2TKxQMeewxSnbvxjkggAH33dep1+czbBhSJ6dOHdNRXJwcmDi8P9m5hdTW1tq219TUUFxcTNbhfIbFhdAvMpjBgwfj7+9vN0LySsVoNNrm3Ds7O5OcnEzUeUmDAgIXiiDUlwnWu/hDhw41S1Q5ceIEoaGhiMViQkNDKS8vx9XV9bL6gjUbdBhry5CU7MOt+jDuVdmICtMx6+oAEDspkHqEInJsskboE0etWwxxohpGGySEfbmUgV+9xE0+EXgZKqjxGgailv9EBv/975iB/Wd7Zetra9n30kuETJlC4Jgx7dobeeONxN1zDyOXLkXSA5Pdfv7jIINm/IeXP95o1560KWazmZqaGkYPCmJbRi5pv29Hp9MB4O/vT8LgoWzec7zNBiRXIjU1NWRkZHDgwAEaGxsBcHJyEhLGBLqMC/qm9uhAr1+BnuXEiRM4OTlRVVXF7t27GThwIC4uljaKYrGYmpoanJycCA0NxWAw4O3tzZEjRxg2bBjyViYb9VWs06TKSqqQqqswmapoNDUAZvykarQiGWKRGB8HHWViy0dcJJEh9Ypsdq4aRTzOdfnMd3Oi1MUFhV7PDLmBRgcfGpxDoZVBUY6engx59FHSn3+eqFmzyP/tN3S1tQxbuLBDr0EkFrc6KrIr0TbqefL1H3jvmz8YGhfCM2/9xJbdR/jg+TsJ8LGsm1qXQkpKSmhoaKB/kMVbP3qmAWsVs0wmY9u+k9RrdcxIGdLtdl8KnF92FRERccn9LQlcGlyQULdUUy3QexQWFmIymQgLC8NgMHD69GnbF4ZWq8XV1RVvb29MJhMajQaFQsGgQYP4888/KSsrIygoqJ0r9C7WaVJubm6IRCLUajUHDx6kVgeSOsDJCYmbPwpRDXJtGaV+ExGbjfiUbsGlsZB6WXOBtmIWy6gvM+IWHsKuKdfRHzMDXZ2paPRod605as4cclevZueTT1JbUMCgRx7BJTCwi1/9hdN0xvNrT8xhwdxxbNl9lPv+9RnJc19g6d+uIy7E1RbilslkBAUFMdTXl7GJR3jp0z/54IcM2/mqaxuIjw4gJqzlYR5XEg0NDRw+fJjq6mqcnZ2Ji4sTMroFuo0uiX2ePHmyQ/OnBbqHwMBAAs8KRGNjI+7u7shkMsASEi8qKiIiIgK9Xk9paSmFhYVIpVJ0Ol2fFGmDwUB9fb2tn/apU6c4deqUrbzF3d2d4OBg9DIFm40KXFzkOEgaUBSup841CoODpTNWvXMY7poDNDgFYZa0HF4W1dcg/+w19LfdxtXR0Tg7yDBmH8H5wDoan/gcaF2sxRIJyU8/zYZbb8UtPJzYu+7q8vfiQjGbzUy46xX8vN1tM54BJo2I5c9P/8Gt/1zB/P+ksuKJaxg+OAZfX188PDxs4dpl/5zFj5uymp13yqgBPfky+hxms5ni4mJyc3MxGo0EBwcTEREhTLsS6FYuWKitM6P/8pe/kJaWRlJSEt98801X2iZwFutgh4aGBnx9fTGbzYhEIkwmE0ePHuXo0aPMmDEDvV6Po6MjAwYMoLi4GH9/f8Risa1Hu0wmIzY2FrVajYODA/369evlV2Z5bY2NjXa9sevq6hCLxYwZM8Zmv0gkst18yGQyoqOjKakDzn4/KioPYBaJqVae60dd5TEIv8JC3KsOUeWZ2OL1XX58G5G2ni3HK0gZpcQkdqQy6mo8vv8Ox50/UZfUdk9s78GDGbVsGcr+/W3TqfoCIpGIqBBvwoO9iQr24ODBg/j5+eHr60twgDeTrurHgdwybrhuEkr35pOmBvcPZnD/jmWiXyno9XqOHj1KeXk5crmc+Ph4PD0vvZnXApceF+1Rr1q1ioqKCpKSkrrCnisKq+C2hE6nQ61W4+vrS2ZmJj4+PmRkZHDdddfhdDbjVywW4+zsjE6nw2g0kp2dzalTp8jIyKBfv374+/tTUFCAj4+P3bl788vFbDZTV1dnJ8zWBBwAuVyOj48PCoXClrFt7ZfdGlJjHc71p9B4DMEsPieWJokTNe79ca86RI0iHpPEvq+ypKwA501fcXzoFArqdJw0h+HmG43OwRvt8OtwXfUKFUOn0d6fSfj111/AO9F9mEwm1Go1109I4OWP09AZTFRWVtpCsyKRiK1Zp0kZGdeiSAs0p6GhgX379qHT6fDx8aFfv362G0cBge7mgoXabDazePFiod/3BWAV6LayQgsLC3F2dsZoNCISifD390cul2MwGOz2k0gkhIaGotVqKSwsxMPDg8DAQEJDz00oamhoaOkSPYLRaKS+vt4mEqdOnbIl3wC4uLjg5eWFQqHA3d0dR0fHTmfLGsWOGCQuODYUU+cac25t2WzGUVuMQeqOSdzc2zWLJSAW4+cfSnz8WBzCE9GdfU7U2ACSvvdFfERVzBOvfs+Nk4Zw98yRtvfKbDZTVVVFSUkJZWVlGAwGRiUEUq/V8cfeE0wbN8qW5V9UVsWu/Xms+PdtvflSLikcHR1xc3PD19cXX19fIaNboEe5YKF+//33+fbbb/nggw+oqqpiwYIFXWnXZcX5nrP19xMnTrB582buvPNOHM4r0SksLGTw4MHIZDISExMpKChAJpOhVqvtklbKysowmUy4uLgwdWrzSUMBAQE9Woal0+nQ6/W2jPPs7Gw0Go0tjO3p6YnJZLIJc1d4JWaRhCqPoXiVb8Ox4QxaZ0vI1qnuFA6N5ZT5jm+xzMqg9KP+mntw3/Ap4WNvwnh2u3z/7zjs/x3Ng6+CRNpq5ndPYjab+XjNDv75UiruLo78tj2HtF2HWfb36WjrqykrK7NFJlxcXAgNDcXX15f46ADe+WoLpRXVtnNlZOcjkYiYNr71sZUClrKr0tJSIiMjEYlEDBw4UBBogV7hgr/Bhw4dytChQ22PH3jggS4x6HKk6R+3yWSiuLgYDw8PoqKi2L9/PyUlJYSGhtqNRwwMDLSrdw0JCUEul5OVlYXBYLA1U3BzcyMnJ4dRo0ZhNBoRi8W26+n1+m7tjGT11puGsRsaGnB3d2fYsGGApf5WqVR2OIx9oWidA9E6BqCozKLR0R8wodDsp945BJ1j8yxlo0HPrp8+IDhsEInunrh98yKav70L+kbcvn6RxgGjaEy8uk+IdGV1PX99/mvWpO3jvlmj+fdD15H6626efmc92/YeZdEdwxkWF0xoaCh+fn62mySAe2aO5onXvmdHlsrunDelDMVTIYS926KgoICysjL8/f1xcXERRFqg17h8Ol70UawhaatQ1dbWcvjwYQYOHMjAgQMJDAzk9OnTNqEWi8WUlZVRVVVFeHg49fX1nDhxwpaRazKZiIiIsHnpXl5eNkG3Zp4ePHgQNzc3Dh06xNVXX91ltZ3WMqmmwqxvMgXK0dHRJsxWfH19u+TaHUHjMRS/ovW4Vh9GbDIgMhupUg5ucd8ju36jRl2CKWoQNfMWoXzvH8iztiDLP4xEXYTm7++1W57VU1x9/+ucLq7ky+X3cdOUoWRkZBAb5MD7i6ey/Ms9LHznTzZ//A8iI5sPvfjrrRP4660Tet7oSxStVouDg4NtXnZoaKjdjY+AQG/QKaH+4IMPSEpKYsiQIezbt4/KykomTZrUXbZdFuzatYucnBymTp2Kn58fTk5OREdHo9FoAAgKCiIjI8PuGFdXV9tatJOTE+Hh4RQVFSGXy4mNjbUbbmANzeXm5hIaGorJZKKqqoqQkBCuP5vk1FbSWlsYDAZ0Oh3OZycuZWdnU1FRAVi8fldXV/z8/Gxh7PPD9z2NUeZKjXssbtVHADPVygRM0ubTokpOHqHgyF68AiIIHzSKRqAxfhTuXy5BXFNJ/TV3Y/TvG5OezGYzIrOR+EgfbppiiWBFRFhs8/DwwOzozW0LP8RLceWNj+xKmpZdhYaGEhYWhkwmExLGBPoEHRbqJ554Ai8vL/73v/+RlJTE/fffbyvREmidqKgojh8/bqszLy0txdfX1+b9BgcHs3nzZqqrq20h4dLSUsLCwgCLILq5uTVrpmAVX0dHRwwGA2VlZURFRXHs2DFMJpPtC6axsbFDAmotkwKLZ2w2m9m9e7dthi5YvGM3NzebMPeV2tGs4gMU154GQIKZ253NGIGvTp/AhCXkG+UZxhD/eLR1NWRv+wmZgxMJE2babmBqbn0Kr2duxOTuTe31vZdvYTQaKS8vRyQS2ZKWUq6K4u1vd1FVU4/Czdkua/+HTVkkxAQRFerTxlkF2qJp2ZWDg0O3LM0ICFwMHRbq5ORkW0eyTZs2sXr1aqGG8CxteaweHh5UVlZSVFSESqXCzc0NHx8fHB0tpUIVFRUYDAZOnDhBQkICUqkUtVrdarjt/Izx0NBQPDw8kMvllpnDwcGYTCZ2795NUFAQ7u7uBAQEtHielsqkgoODiY6ORiQSERISYpeI5ufXsx2pcnJyaGxstMuFaIms4v2cqFQR6OaHCBFfnazAIBKRp/DEZDZTVFtCdWMNg/0GcOjPH9Bp6xmaMg9Hl3NfyEb/cDR/fQOT0hcc2p/ZfLHsO1zAw0u+ZnxyP555cCq1NdWUlpZSXl5uSw60LhssuOUaXvtyB+u35TDvunNlkNpGPb9uPcRjd6V0u72XKxUVFRw9elQouxLo03Qq9L1582YmTZrE5MmT2bdvHydOnGj/oMuMpglfVtoKK6vVaoKDg2lsbEQqleLt7Y1IJEKr1SKTyfDy8uK+86YnNV13Pp/zryUSiew8ABcXF+Li4oiOjrb7wrFO97GKcnV1NUaj0e44Ly8vu5sva4lXb1BZWUl2djZms5mAgAD8/f1b3Xd0yEhOVKq4vl8KcY7eeD85FbNUTsV/15FVlc+n+1cxIdxSyhQ5ZCwKnyD8wpuPidQNntCNr8iCyWTi7a9+5+k3fiQ0wIO3vtjMus0ZLLpjOEE+lmiFr6+vXe17WJAXwwaE8tmPO/FUnLuJOHDsDLX1jcKQjAvAaDRy4sQJCgsLkUgkxMXFCWVXAn2WDgv1rFmzWL16te3x0KFD2bhxY7cY1Zc5/w9Zr9dTWFiIo6Oj3R+61fOtra3Fx8eH4OBg/P39kUgk1NbWUlRURExMjO08Tb3ykJCQi/7CsIayHRwcbGFs6yQksVhsC2F3ZZlUV2E2m8nMzMTNzQ0HBwcyMzO55pprWg21R3tGEaYI5rfcP0g+WgAmE+KGWpx/fJffQtzo5xVJhIflpsMzIBzPgPAus1WvN/Let38QHx3A5BFtz4guVddw31OfkLbrKLMnxXLHdXGcKqpm2Rd7+Nurm3ll4WzuntBy9GDedUkseuV7Nu8+ard9UL8g4qKaR0wEWqempobDhw9TX1+PUqkkNjbWFuESEOiLdMqjbjqMo7q6mn379rFp0ybbtvvvv7/rLOuDWDs+5ebm4uXlRUxMDOXl5VRUVNiJblPEYjGlpaVIpVJbGLm8vJzy8nK7YzrqobeEtUxKLpcjlUppaGiwhb5jYmIQiUQEBQUhEolQKBS4ubnZJaT1NU6ePElFRQUTJkzAwcGBDRs2cOzYMeLiWhZCkUjENdHjSdv4Fk5bN1Fzy2JE2npcfngT6ZQppEx6mIzfviJq6DiUvl3XFvPkmXLuWvwJew6eBODROybxn0duQC6z/7PSarU4Ojpy09/+h6qglP/MH83YoRH4+fkxeqQvN990HX9f+h0P/udrvJRuTJ84qNm1HrltIrOuHobJZD9r3FspZCR3htOnT9sigZGRkV1yUywg0N1ccHnW5MmTiYiIIDLSMpmouz/sqampKJVKNm7cyIIFC2zXXblyJZGRkWRmZjJ79mzb9u6grKzMJtLl5eVs376duLg4rrrqqlaPCQwM5KeffqKuro6ysjLUajWHDx9mwoQJF2yHyWSipqbGLoyt1+uJi4vDz88PR0dHgoKC7MaRWpPT+jo6nY4DBw7YGnYAxMTEkJOT02apTH+PCCL3H6TI0xvx+LmYTSYa0j7hwYM5qAKyKSs4hqd/WJcJ9arfMnh4ydd4uLuw+ePH2HMwj3+9+RNb9x7n06X3EB1msV2lUpGfn8+IESMIC/TEaDSy4LZptklgVuZem8hXP+8hPMirxeuJRCKCfJVdYvuVjFwut027cnUVMuUFLg0uWKg9PDz47rvvutKWVtFoNKSnp7Ns2TI0Gg3Lli1jxYoVqFQqTpw4wfz580lJSWHOnDmsWrWq2+wwm80MGDDAJoBubm5s2LCB/v37N2ssIhKJMBgM7N69G29vb4qKivD09MTf39+WRd1R9Hq93fpyTU2NbQ27aZmUtYzKWgN6KXLw4EGMRiODB5+rf46Pjyc/P5+srCxGjx7d4nHO277HvayEf0+axETNKRoNOjKHDOKJP/8kf/savBLGEz5oVJfY+MxbP/HSRxuYffUw3n76FhRuTowcEsnIwRHctfgThs97gZ/e+StjEmNQKpXodDrMZjMzJg/hzic+Rl2jw93d/sb2h01ZRIX4MDCm74zJvBywll0pFAqcnZ3x9fXF29u7T0eUBATO54KFesqUKaxZs8YuI7ejoy4zMzN54IEHmtUPq1QqUlNTiYyMRKVSMX/+fJRKJUqlkmXLlgHYPGqAtLQ0W4cu6/HdSWVlpV34tbGxkZEjR/LHH3/g4+ODt7c3UVFRtrVUqVTKxIkTW2zh2VqmuHVt2Roq12q17Nq1y/a8RCKxrS33tTKpi0Wn06FSqYiLi7MNHgHLtKz4+HgyMjKoqalpVqomaqjBdfVr1I+agS4qkvW5v6Mz6nGNvYr8w6dIOnWA8gUvthn1qdJo+OHLr3FXKpk2+ybkbZS0HVYVMzAmkM9evAeTyURJSQmlpaU0VKp58s4k5r+4gRP5JYxJjMHT09OWoHftmHgc5FLWbMriH3eey9Q2GIys/f0Ad88YJYRhu5j6+nqOHj2Kt7c3AwcOBBBEWuCS44KFOj09naVLl+Ll5WUTnePHj7d7nFWIMzMzmz03Z84cm3irVCoeeOABOw85LS3NJtxg8bSberLWJiLdRWhoKKdOnUImk1FVVYWTkxOJiYlUV1dTUlKCn59fM9GUSqVtZopby6SsLQrLysrIycmxhbEdHBwICAjA1dUVhUJxWbcylEqlKBQKioqKGDBggO0L1Ww2c+bMGZydne0E3IpYp0WkrcPkG8w10eNZmfElACnGAKpkjgSbjDg6OGJudqSFnP0HWP35F0ilUlTHjpN3PJeb77ubwJCQFvefPiGBBf/+kj937EVkbMBkMiESifDw8GDHkSpkUgkzUpqP1XRzcWTKyDi+XLsbrybtO08VqqnQ1DEjZUjn3jCBVjEYDEilUlxcXOyiYAIClyIXLNQqlQq1Wt3p42bPnt3q+ZoSGRlJWlqa3baUlBQ8PT1ZsGABGzduRKlUdrs4N8XFxYWGhgYaGhrw9PS0/fG317+6qbC2ViY1fPhwnJ2dcXd3JzAw0CZIIpGI/v37d+8L6yOIxWKGDRvG5s2bycvLs0VLCgsLKS4uZvTo0S1GJ4wKH+pT7sDllw+IG3kD0Z7hyMUyEtQQX5xL/fV/wezc/P9Hr9fz6/dr2PX7n8QNSuCmO26jvraObz78mPeWv8K1M25k8JgJgOX/r7GxkZMnT+Lj1IBELOKnLfu5fVqirZxKLpfz2OubmDwiFoVb8xsKgNtvGMGtj3/Agn9/abc9ISaIxAG9Vw53uWAtu1Kr1SQlJSGVSnu0ja2AQHdwwUKdnJzcYhjyQklLS2vWQMXT05PMzEz27t2LRqNh4cKFKJVKm6gnJSXx7bff2vbv7NrvheDt7d3i9tZC2Tqdzq6pSG1trc3DblomZfUeHR0d6devX/e9gD6Ot7c34eHhHDx4kODgYCQSCfv27cPf35/AwNbXb+umP4jj7p9x/3Y58x98FZEZvF7/CyavAOquu7fFY3748msOZmRyw81zuWrcWEQiES6urjy48P/47Yef+Dl1NY3aBmJGTQYckEgkFBcX4+PpzshBYexXVfNsTCwAWp2RM6Xl7MhS8d4zt7Zq542TBlO9541m28XitseeCrTP+WVXRqOxRyfHCQh0FxcV+lYoFHh4eNgGRnQk9N0arXnGarWauXPnkpaWRlpaGhs3brSFw4cNG8bevXtJS0tDpVLx/vvvt3iOxsZGW3tMsJSWdTUikchWJuXk5IRIJKK8vJxDhw7Z9rE2OLGuLff1MqneYtCgQZw5c4YDBw7g5OSEVqtl/PjxbQqZ2cmFmrmPo1z5OOo17xIYFIlDzg4q//YOyFuukXVXKpA7OJA8ZrTduSVSKSMnjGf75i24miqQ1BYB4UilUkaMGIGDgwO3Ttfx0PNf4Tf2cbtzSqVirp/Q9vhIiUT4P+9KzGYz+fn5tjnnUVFRBAcHCzc+ApcNFyzUTeunuxPrOrQ1ZJ6SYt8ucf78+e2eY+nSpTz33HNdbpvJZEKr1dqyrfPy8sjPz7eFsd3c3PD397clflkFXKBtHB0dGThwIPv27UMsFtO/f/8ORW4ah19H5U/vEbr5CxxlchoHT2iz21j80CH88dtGTh7PJTIqDGNtGabaEqReUWRnZSGVyYgenEy907koirVv+m3Th+Pl4YJOb7Q7Z7CfEi+lUPbTUzQ0NHD48GGqq6ttXfmEsiuBy40LFuoXX3yRxYsX4+7uTlVVFS+++CJLly69YEOUSmWzNW+1Wt0l85QXL17MY489ZntcXV1NSCuJQm3RUpmUWCxm9OjRtmQik8lk85IdHByIjY29aPuvRKKiosjLy0On07Xa6OR8Sk4dQeUXww0lJxEZ9ZTfsrjN/QODAlAqFezftpEgqSUjWCSWYjY0cigzi5gBcTj4x1KnbX6sXCblhoktj9AU6H6aTrsyGo0EBwcTGRkpRKgELksuWKjT0tJswqxQKC66NColJYUVK1Y0256UlNTC3p3DwcGh0yMYzWZoNDpS1eBJ1bFjVFVVUVdXZ3teIpGgVCpRKBSYTCYkEoltGUDg4hGLxUyYMAGTydShdUZtXTWHtv6E0d2Hj2PnIXN0ZJLCl/Mbo5pNRkx15ZhqSjDVV9I/ypdDh08SHh6MyFGBWO6AvjiPgpMnmXP3nd3z4gQuivOnXQ0cOFD4uxO4rLlgobYmRFm5EKFuWl51fkcxlUpFUlJSl3jUF8LJugBOaWJA6gaFhTg4OODr62sLY1/OZVJ9Bblc3qH9zGYzB//4gcrKGvJKoLa2HrPJxNHlLzPvvnvwOzs9TF96FFNNCWazCREixM4eDB09nvR9H5G62r7CwMnZmdiEgbQ8GkWgNxGLxdTV1eHr60tMTEyf6lMvINAdXLBQz5s3j2uuuYYpU6awceNGJk+e3KHjrAlhYFk7Tk5Otq0/r1q1ikWLFtnmXHdnl7H2UMpr0TtWoXBxQDFihNC0vw+j09Zz7PhpDh+rwi8wiLsfeQSDwcC3H3zAu0uXMW3ObJLHjAaTEZGjG1JXX8QuPoikckID4dnXX8FktJdkiVSCVCqlTtdLL0rADqPRiFqtxsfHB4lEwrBhwwSBFrhiEJnPd43boLq62q5eODU1lb1799rNqm5pv75GdXU1CoWCqqqq1u1cEwwNZ8ApCGae7lkDBTpESR0s2w4HNq1l+4bfGDlhHNfOuBHZ2Ylhdbnb+G1zJhlZx7lmxg2Mu3pKp6MgdTpQa2HRaPAT5l90KUajEb1e36F9T5w4QUVFBQMGDBCSxS4T9u3bh16vRyaTtTtz/lJHJpM1a4bVIR06S6c86smTJ5Oenm57PHv27BYbmMyZM4fffvutM6cWuIwpKSmhurq6y/uP63Q6KvJPIBGZkctlTEoKBo0K/OIQiUQ4B8Zz3S0DyDr0PFKpTFiq6EPU1tZy+vTpZktorWFN1iwrK6OsrKybrRPoCZycnHB0dEQkEpGXl9fb5nQrIpGI4ODgC77J7JRQq9XqNr9srU0/OhoGF7j80el07Nq1i8bGRjw8PFptGAPw8o4drDlyxPY4aedOnOrr2drk8zQuNJSlKSlotVrW/7SGhn1HCfOL5E+dnhMnTjMg8dxsZrGLJ8f2pGM0GIgfKmRo9xWMRiOnT5/G2dkZHx+fFm+grKWPMplMCHFfpjRNzm1tKt7lgNlspqysjNOnTxMTE3NBsxk6JdTWOa4CAh3l0KFDGI1GFAoFmZmZpKSktFpCk6tWs6OggLnx8fiXljJ8505EZjPGIUMojYnh8/37cRWJ2LdvH7m5ueTlHsVV4Ulw/HB8duRx9P/bO/e4Nsvz/78TzseGUKDQ0pZAz+cEaj1sTg12Ovt1KrSbzs05W6bb7L6ba8Y2p86f62DfuYO6Sbq5zUO3CrY6d1LS2Xq2QLC09EQJ0BOlLSFAOYfk9wc+zwgknCEB7vfrxavlOeW+k/Bcz3Xd13V9zrWz8sYkl2uWl37MnPnzUPXpeifwHl1dXTidTmJiYvr1bnc6nXR1dckh0cDAwGFXbAgmB3a7XXbupnoOUExMDNXV1XR1dY3IUIuiQ8G40dDQQGVlJcuWLSM1NRWbzTbgw57h6qvxVypZO2sWN7/zDlELFhC3di2f+s9/SAsLwwnc9okWeEVFBX6BwYSmfQVlzGKWa3UcLTtE5bHjVB4/QeXxE5w8eowT5UdYvmb1hM1ZMHT6etKSF93R0YFCoSAkJEQYacGUYLTLbqIRrmBccDqdmM1mIiMjWbBgAUqlEo1Gw+HDh0lMTHT7BJ0UFcU9q1ax//nniS8rY+H/+3+krFjBv+64g9KXXuJT69bx+bVreeONN+js7GT9/3yB3ZdmALBCp2Xfv9/gD79+yuWaSqWS5VM8UWUqYLfbaW9vx+l04u/vT1BQ0LRtXmKxWNDpdDQ0NHh7KG6x2WzodDoRYZ1AhKEWjAvV1dXU19dz3XXXyTfcFStWcObMGcrKyli7dq3L8U6nk9bWVr63ahX/efRRbGvWcDkqCmVsLM6bbuKaf/6Trz34IAqFgrNnz5KamsqCRYvhUs/5s2Yn8P2fPUFXh2s9VWBQEOGRYyMcIxh7JP11Kfs7ODh42q9JazSaSZVcZbFY+vXBEIwtY2KoS0tLp3x6vWDoOJ1OysrKmDt3LjExMfJ2qYuU2Wxm0aJFREZG0tzczIULF7hw4QJ2u52gt94iFPjJggV8pNUSFh5OTlwc3woJwf7nPxP361/z9a9/nfDwcBr6VPZE+HBJoKA/3d3dtLS04HQ68fPzIzg4eNp60X3xVqOnkZCZmcnevXsn1ZgnGyP+q1i/fr38/zVr1vD73/9+TAYkmBoEBgbS1tbWr/ymvb0dpVJJbW0tBw4cwGw2c+bMGfz8/IiPiqIyP5/5d91FjVLJnw4f5uXycsqamki85x7OmEw0VVejVquH3LVM4Lt0d3fjdDoJCgoiJCRkWhpps9mMTqeTf6AntCxpsUth5qysLHQ6HZmZmeTm5pKeno5Op/OoOtgXnU6HwWAgPT0dAIPBgE6nIz09HYvFMug4oKf/ft8OlAaDAbPZLI/L3XUEo2fEfxmSatWOHTtITU2lpKRkzAYlmNwoFArWrFnDxYsXOX36NNBjoE+cOMGRI0cIDg7m/PnzspiCVqtl7dq1JC9dStSSJbR8+CFfWb6c//fCG3z3mT3crEnG9uab2MPCaBX6wpOatrY2OcwdEBBAaGgogYGB07bGfdeuXej1ekpKSjzeQ81mMzk5OZSUlGAymdBoNBQWFqLRaDCZTG7PcXeN5ORkCgsLKSgowGKxUFJSQn5+Pjk5OUMahztycnLQarXk5+ezbdu2EV9HMDAjNtTp6ens3buXxsZGiouL+d3vfjeW4xJMcmbNmsWcOXP4+OOPaWho4IMPPuDIkSP4+fmRnJzMqlWruPLKK0lJSSEyMhKFQoHSz4+0H/2I00dOUP1iMe0f1mP96ALn/lLGpRPVKD77WeJmz/b21AQjpK2tjeLiYnn9VaFQjKhUZSqRnZ2NxWIhKiqKrKwst8eoVCo5rKxWq9FqtUDPWnZfxUFPqFQq2bkqLCzEYrGQmZnJ5s2bUalUQxrHWM1HMHxG7J7s2rWLxsZGkUQg6Mfly5exWCzMnz+f2tpaampqCA8P5+LFi1x55ZUDSowes4fx/yI/Taulntu/kIrN2YV5VymPBK3jd1d+btp6XlOB4OBg4uPjiYiIGLKBmQ5ImgZSaFndp+a/7+8jofc1dDodVqtVfl0pfO5uHL0/p6F+Zn2vI2zE6BmxoVar1ZSUlPDss8+Sl5dHZmYm991331iOTTBJcDgcWK1WZsyYIfe0bWhoICoqiqVLl3L48GGCgoJkL9sTf/lnEfc9/Dzrls8l8/jrrL4cQ0VtLbfYS9i9KoMv/+AFzl26zNa7Ree7yUJ9fT21tbUsW7YMhUJBSkoK7e3twlB/wssvvyzL+2q1WjQazZDXnXuTnJxMSUnJkBK6tmzZQmVlJTqdTvbQk5OT+40DemSGMzMz0Wg0Hg3upk2b0Ol06PV6dDqd2+sIRsewRDk8IYW/J0vrUCHKMXqcTic2m426ujouXbqE3W5n4cKFJCQkAD11sf7+/nR3d/Pmm2/S0tLC+vXriYjwXCr1iz8V8sjTr3NuXw51f3+Noscfx6lQEHT99dz2y1+ReP33+dKGK8h9qEcARhLlUAdD2DjllglRjpHR3d1NZWUl586dw9/fn9WrV8t9jtvb26mqqiIpKWnKd6QSeOby5ctyZ7KpLrTi7js/HFGOMUmznDFjxqQx0u4oLS3lWK8e02fPnuXApaW0dfe0N+zq6uLAgQPU1NTIx5w8eZIDBw7Ivzc1NXHgwAHq6urkbYcOHeLQoUPy73V1dRw4cICmpiZ524EDBzh58qT8e01NDQcOHJATbtra2jhw4ABnz56Vjzl27BilpaXy71arlQMHDrh4KW7ndOAAbW1tI56T0+nkwIEDmEwmPvjgAw4ePMjJkyc5f/48s2bNIjo6Wp5TdXU1AH5+fiQlJRETEyN/QT3NaeGsQLq7Hez98BjqG24gaP58AqKjueWJJ/joUBUNTa2sSYn67/tZe5bA8wdwdvXMydndReepA9it/52T/eJJOk/9d06O9iY6Tx2gu/m/n1NX7SG6av/7OXU319F56gCO9v9+TodLB/6cBP+lqamJ4uJizp07h0qlIjU1dcrfiAWC8USk0AoGpa2tjaqqKurq6jh16hQAERERzJs3D6VSSV1dHQkJCR7bPYaEhPTr6eyOOXGRrFw4m92FpXxqdSKzt21j/pw5BEdE8KrpTWbOCGXVwvhBryPwDk6nk5qaGvnhLzk5mTlz5oi8AoFglIxJ6HuyIULfgyOFpCQvuq2tjcDAQGJjY4mLiyM8PHxcbsDbjf8i9w//5gdfXMwdt99GcHAwTuC6r/yCmz+9nF9lb5KPFaFv36GtrY2jR4/S1NREWFgYS5Ys8ehFi9C3AEToe9z0qAVw5MgR2traWLZsmVyLePfdd3t7WGOGw+Hg4MGDBAYGyglAycnJ+Pn5oVKpxt07WqmJpL2zmx//uZwf/7ncZd/t6aL7na/hdDqpra2lsrKS7u5uEhMTSUpKmpbNSwSC8UIY6mFSWVmJWq3m3LlzLF26lKVLl3p7SKPCbrdz6dIl/P39mTlzJkqlEn9/f/z8/OSn3YE0pMeSpqYmDpe8y+b0OK5Pv8klXB4WEsTV2uQBzhZ4g66uLiwWC/7+/ixfvpyoqKjBTxIIBMNCGOphUlVVRXJyMrW1tTQ0NHhsk9fe3k5hYSEbNmyY4BEOjsPhoL6+ngsXLlBfX4/D4SAyMlI2yMuXL5/wdUWn08mePXtoa2vjG1/7IosWLZrQ1xcMj/b2doKDgwkMDGTFihWEhoZOezENgWC8EIZ6GLS3t8u1wQBGoxGdTkdtbS3Qs07X3t4OQFRUFLW1tRw5csQnvG535VTQk7EfFxfnIp7hyUhfunSJixcvsmTJkmG99uWzZ2m7cIGYAYRbDhw4QFVVFampqcJI+zjV1dWcOnWK1NRUQkNDmTFjhreHNCVJT09Ho9GQmZmJXq932VdQUIBer0elUmE0GtFoNJjNZrZt2+al0QrGE2Goh4EU7paIj4+npKQEnU7HkSNHKC8vR6fTkZCQQHBwsItR9xaXL1+mtraWixcv0tnZIwEZHh4uG+ehJvPY7XY+/PBDWltbmTFjhlwv7Q6n00n7Jw8Cjq4u3rr/flrOnEG/ezdhn7QADfTzw6/XOubKlSux2Wxcf/31I52qYIKIioqisbFx2rf/HG8MBkM/Ay1htVrl5ibSMVJHMMH4Y7PZJlQtTGR8DAONRuMS6t6wYYP8e3x8PBs2bKChoYHyctckKMnjnigkrx56OkOdPXsWPz8/5s2bx9q1a0lNTSUxMXFYGbdHjx6lvb0dtVpNaWmp7JG748F//YvQn/6U0J/+lMz77qPRYqEB+OnmzfJ2rdEIIKtrhYSEsH79ehE+9UGcTifV1dVyx6wZM2awcuVKj+V4k4G+KlBDOX40vastFouLEtVQsNlsmEwmjJ/8rUiYTCYXA65Wq2XRjYEwGAzy/3urcfW9ft9xj2TeBQUF5ObmYjKZyMzMHPJ5Q/1czGbzsMeVmZlJenq6y3gMBgOZmZku17LZbGRlZcnfd6PRSHp6uqxYlpmZiUqlmtBe5sJQjxFHjhzBYrEQEhIie5sJCQkcOXJkQhNsTpw4wUcffSQ34oiPj0en07F27VqSkpJGJA/Z3NzM8ePHWbx4MVdccQVtbW0cP37c4/HLY2MBeHTlSu46fpzuG/QEfPU+Us+d449z5xKgVMrH/OMf/2D//v04HI4RzFYw3rS2tlJaWkp1dbWshAael0fGg86WTh5TPMZjisfobOkck2tOtGCEpxackiHu/WOz2TCbzWi1WvR6fT9PuXf/bJPJhNVqJSMjg8LCQo+vL0ljQo8RBWT1rI0bN47VNPvhbvwDMV6fi9FoJDs7m8LCQtRqNUajUVYey8/PR6fTUVBQgM1mY/v27S6qZFu2bKGwsJDCwkI2bdpETk4OgCztOREIQz1GXH311XIWeHx8vMu28aoV7ezs5OzZsy4dyKKiopg1a5Zs+AIDA2URhBMnTvDWW29x4cKFIb+G0+mktLSU4OBgFi9eTEREBAsXLuTYsWNcvnzZ7TlfXbOGxMhIQl9+mYtBan51Ss2PC6qpXHQVfs8/j6Kri4c//WmOHDlCcXExp06dEk0xfAyn08m5c+coLi6mqamJxMREli1b5u1hjQm5ubkUFxeTlZUlazFLBkLyBCVPMj09Xb5pm0wmsrKySE5Oxmw297uuZAjdYTKZSE9Pl71k6Xy9Xs+WLVtcflQqFVqtFovF0s8j7StyIUldGo3GAT3XwsJC2QtXqVQUFxfLY5BCuO7eh4HmnZmZSWZmpuypS9rZmZmZcr/vgTzfu+++my9/+cs8/PDDQP/PRcJms8ne8EgN45YtW2TVMUnHu7CwkLS0NKCnp3lhYSEqlYqcnBy3YW1pTNL7r9fr2bVr14jGM1yEoZ5k2O12zp8/T1lZGR988AEVFRVcuHCBjo4OAGJiYli0aBFBQUE4HA5OnjxJd3c358+fJy4ujsTERMrLy4fc+vLcuXOcP3+eNWvW4P+JFvTSpUsJCgri448/dntOoJ8fD0dHc/xwPT/uXIx/gD9Xr0nmp1VBvFSv4ruXrCQEBvL6668TGhrK5z//eWGofYjOzk4OHz7MiRMnCAgIYPXq1SQnJ094bXRnSyedLZ10tfz3u9rV0iVvHynbtm1Do9GQl5c3oGiEJJghGTitVkteXh6FhYVs37693/EDebSSfrR0sx9KiFev18uGXKJv2Fuj0bgY+aGg1+vJzs5m8+bNREVFyR62J9zNW/LQeytwGY1GsrKyyM/PHzTcnZuby3XXXcfzzz8vn+/pc1GpVOTn51NYWDgmhjE/P5+MjAyXdWaVSjWoUExWVtaQ3+OxRiSTTQLclVMpFArUajWxsbHMnDnTbWJPfX09gYGBNDc3ExAQwIwZM2hubgYY8lrwwYMHmTVrlkvymL+/P6tWreKDDz7g4sWLLhnjANbGFl54dh9F/gvRrI7n7acfIjgogC//4iV27/yAkx+0EPErIx2ONr74xS8OKNQhmFguXbrE8ePH6erqIi4ujgULFsgPaBPN9vD+xvD/4v5P/v8jzkfG9fX1er1bQy5lWA8Hs9ksr316ShAbCiORvLRYLP08xIyMDNlYJSUlkZGRMeh1es+7qKgI6AmfS1RWVg45dF1UVITdbvf4sN8Xs9lMcXExFotlROpiEgaDgZycHDQaDSqVSr6WO3nR3thsNpcEPgm1Wj0hiWWTxlBLT31FRUWkp6fLX/aCggJUKhUWi4XU1FQ5vDEVaG9vp7q6mosXL9Ld3Q30PPnFxsYSExMjG9uamhoUCgVz587F4XDIno9CoeDixYvMnTtX/iJ1dXWRlJREa2sroaGhg44hKCiItrY2ufmJRGtrKwqFwu2a93O736OoLZSsFDu/SeyiurkRjX8U+/wucuc1Efzt7U527q9h+/+uF6VYPkJ3dzcnT56ktrYWf39/li5dSuwneQTTAcmb6u3perpxj1RjOS8vD51OR0lJyYivMRSD2pfenjz0GD3JUKlUKpd5unsfJHqPOS0tDZVK5eJh6nQ6iouL0Wq1gxrTtLQ0goOD+epXvzpoC1Gj0UhlZSXZ2dlySH0kGAwGsrKy5Dmkp6dTWFhIRkYGZrPZY08M6IlkpKam9tvuzniPB5Mi9G0ymbBYLGRkZJCVleWyJmKxWOTw0EStF4wXTqeTpqYmuYxKoVBw/vx5QkJCSE5O5sorr2T16tUkJCTIRrq9vZ2Kigref/99+RyJ8PBwZs2a5fIadrudoKAgTp48OWDmtsSaNWtobGyksrJS3tbW1kZ5eTnJyclua2iv0aYAoDpaxlVtbTz+9ts8V1pKw6VLfLr4Pdr9Alm5dA7r168f5jskGC9OnTpFbW0tKpWKtLQ0nzDS2Zezyb6czUN1D8nbHqp7SN4+GjQaDenp6XLSlsViwWAwUFRU5PHG23vdWkoo6r0u7WmN2mKxyPv0ej1Go3FEnvFYYbVa5TXfvvrR7t4Hd/Petm0bJSUlZGVlyRnSGzduJC8vj8zMzAHfR+n8jz/+mK1bt/Lggw/Khr335yKRmpqK2WzGYDC4fQAYymeQm5tLQUGBPA+j0Yher5fXvysrK+WHDmmNfPPmzfI4rFar26z6ifocvSLKYTab2bx5s0vYBHq+EAUFBfIToJRY0RuTyUR+fr785UpOTpafkjQazZA8al8V5bh06RKHDx8mJSWFOXPmAP/tAOWO3l7uX/7yF2699VZCQ0Pl7YcPH0alUsnXOnbsmJz1mJSUxLp164a07lhcXMzp06e56aabCA4O5sMPP6Suro6bbrrJrUftcDhI+ezD6Lpq+Z+Q89yTlkZ0eDjfPnqUhoNneNqxkMOvPULy3Bg3rzZ0hCjH6HA6nTidTpRKJd3d3dTV1REfHz/u+QLDFeXobOmUw+DZl7MJHK8PewpjMBjYtGmTT0Ucx0OUQwptTwSSAzmUdWuf0KMeDlII290aT2ZmJtu2bZPXTzZv3tzvmLy8PJd6wJycHAoLCzEYDBNagD5a2tvbOXXqFEVFRfK6cVRUFImJiS7lXMHBwXh6lup9Q42OjpZLpqSM75iYGFpbW+VjYmNjue2227jrrru46qqrhpwctGLFChQKBWVlZVy4cIFTp06xcuVKj6VeSqWSW69fhVkZg7KmhjvOnSPs7FnmHTjAkXlaVi6cPWojLRgdnZ2dlJaWyiFOPz8/EhISRFLfFCUnJ2dUYePJQnb26CItw6GwsHDCkssmfI3a0xpL3zURqeygN7m5uWRnZ/erISwsLJRLCAbKvPQ2nZ2dXLx4kbq6OpqamoCexKy2tjYiIiLw8/NzG15xd/N0Op2cOHGCS5cucfXVV7Ns2TKKi4tZs2aNLKjR3Nwsl4pBT5hGCtX0XssejKCgIFasWEFJSQl1dXVER0czf/78Ac+5Tb+aZ3e9zUvz19NVcpakoHb+ELwc85lWDPddO6TXFYwfAQEBKBQKWcrUlw10YFjguCeOTQemg6GeKGfNZrNNmOcOPrRGbTKZ+sX7pY470n69Xo9Wq5W9cimBDJD3+RruyqkuX75MbGwsK1as4KqrrhpwPdBut3P69GmXNWLoMdRtbW1yEwqVSsXJkyc5cOAAdrsdhULB5cuXXTzq3gy31CYpKQm1Wk17eztarXbQG/vVa1K4Tb+Gy6o4agmhrhXOhsWgWzaPO2+5YlivLRgbOjs7OX36tGyYV61aRXJysk8baYHAF5no6K3PZH17yhK0Wq1YLBYyMzPRaDTYbDb0ej0ZGRls2bJFbk4AsGnTJrfX6OjokOuMAdmbHU+cTidHjx7l0qVLQy6n6n1uc3MzkZGRHD16lLCwME6fPo3NZpONpFKpJCEhgdbWVjo6OlAqlWRmZpKYmCjfePsmko0GpVLJVVddRWNj45A6rfn5Kdn586/hcDh48dFHaaio4LOPflNkeXuJ3mVXERERqFQqoRktEEwSfMZQe8Jms6HRaGhoaHC7fyhqMdu3b+exxx4b66G54HQ6aWhowOl0Eh0djUKhoLu7m8jIyH7lVNLxnsKOVquV5uZmGhoaCAwMRKPRcPHiRfz9/V3OaWpqor29Xe65PHfuXJfrjKWhBggNDR1SSVdv3n//fSxKJWlf+pIw0l7AXdnVZMrlEAgEPhT6dtcZZqxq1LKzs2lsbJR/evcsHg1Op1Oub3Y6nXK/b4nly5f3K6eSUCgUsqfdl1OnThEZGUliYiIpKT2lToGBgbKRlM6Ji4tz6bktJZF1dHRw6NAhLl68KI/NW3R3dxMbG8uNN97otTFMV5qamiguLqa2tpaoqCifKbsSCATDw2cMtaduPe6KzIdLUFAQkZGRLj+joaWlBYvFwkcffSQbZqVSyZIlS1z6Ifc1wr0N5sWLF/nzn//MkSNH5H3S/sTERJqamlAqlfj5+WG321GpVCxYsIBTp07Jx0VERBAeHi6rcymVSiorKzl06BDh4eGEhIS4HcdEcu2117JlyxahijWBOBwOqqurKS0tpaOjg5SUlEmvdjUdSU9PJysrq19SLSALSACywMRECUQIJh6vhr57t17r26VHShTzlTBde3s7Fy5coK6ujpaWFqDnAaB3iVJ0dLTLOZJBlQyl9G9LSwsxMTGkpaVRXV0ta1ZLXnZtba2LjnV1dTVlZWXs27ePpUuXMmfOHBQKBW1tbcybN4/6+noUCgWzZs3CarUSGxvLvHnzxu/NGAKlpaUsWbKE4OBgr7WgnI60trZy9OhRmpubCQsLY+nSpYSFTbEC8GmC0KP2bSZSk3rCPWqTySTXQW/fvt2lIXx+fj4Gg4GCggLy8vK8/sXrdARwtjUGs9nMhx9+iMVioaOjg/j4eFavXs26desGNIhS+Qv0dPM6deoUe/fu5e9//zvQI25RX19PZ2envPasVCrp7Ox0WQY4e/YskZGR3HLLLVxxxRVyEpBSqSQ4OJja2lpZg7qlpQWr1UptbS0nT570qHA1nhw5coTXXnuNN954Y8Jfezpjt9sxm800NzeTmJiITqcTRtoD012PuvexNpuN9PR0j++JOwWskehBDxVf0KR2d1zfbROpST3hro6kCOOuBk2j0cjbR9LTdiypuTyLalsKTv9IlJ+UU8XFxREVFTXkbNmuri5KSkpoampi3rx5qNVqPv3pT1NVVUVbWxshISHMnDmTmpoaFixYIHvgarVaDltDT/jYHUFBQaxevZrm5ma5tO2aa67BYrHQ2tpKYmLimIY7HXY7nY2NBPeJHPSmsbFRVsW6/vrrx+y1BZ6Rkgv9/f3RaDSEhob6TCTKV5nongsD6VH3NUwbN27EYrGg1Wrllpq9G2tIbZOl8zUaDRkZGaSnp3tMru2tR90bqYXmYEpiE8l4fTaSJrVWqyUrKwuj0SjPOT8/H6PRSEFBAVartd9xQL9tW7ZskTWph5LUPBp8Zo3a1wjzb0MdWM9SVZWsKx0dHT3skpby8nJCQ0NZtGgRMTExKBQKkpKS5OskJyfz7rvvcunSJbmV44ULF/q11HM4HG6TwgIDA+Usc6fTib+/PwsXLiQ5Odmjkbbb7SNSoPnwRz/i9Vtuoe3SJbf7HQ4He/bsoa2tjVtvvVWoYk0A9fX1FBcXy/3hExISpqSRrj/0Jpbdj1B/6M1RX2u661FL2Gw2brjhBhcj7UmTWnq/kpOT+z1Y9NWlNpvN8vq6u+N78+Uvf5m7775bPtcXNKndHeduG0ycJrUw1B6YGdzIiojDxAY3DFjz7Amn00lAQABLlizh/PnzABw/fpwzZ84QEBAgG1F/f3/i4uLk1/D39ycoKKhfrbdSqRw0KWyoSWPFxcUUFhZ6LHmT2HnoEOt+/3vW/f733PHoo1S//jqtly/zxP33y9u/+Mor8vHvv/8+1dXVpKWliVKsCcJut9PR0eGxsc1Uoe1CJbZj+2m7UDn4wYMw3fWo4b/CHGq1esihZql9c+/kNne61NL13R3fm1/+8pdcd911vPDCC/K5vqZJLR032LbxRmT5jBOS0Zw9ezbFxcUcPXqUiooK+emto6ODgIAAtyGxRYsWDUmsYCRcvHiRU6dO4e/vT2lpKdddd51HA3/+8mU+OnuWz2k0XL3vPX4RsZYawsk6Wso1n2rF2NLC5U88OYfDwYkTJ4iJiRGlWONMU1MTAQEBhISEEBcXh1qtFln148BU1qO22Wzk5eWhVqtl6c2BIjHS+9DXsLvTpR7o+N5I7+HHH388pO/vRGtS9z5uoG0ToUktDPUoGaxPckdHB8nJyWg0GqKjowkNDaWlpYULFy6QlJTkEs6WrtN7fXoscTgcmM1moqOjWb58Ofv376empsZj3+4snY7t775L7L6PybXNxy9yBmsWzOZJs5PP5RfR8pkkHt6wAejx+L/yla/Q0tIijMY44XA4qKmp4dSpU8yYMYPVq1cDTIv3u6PhnMu/Y8100qOWtkmvn5OTw+bNm2WveDjvhTtd6oGO741Wq2XGjBnce++9g6pnTbQmdd/jPG2DidGkFqHvYdK73hlwyerue5yEpPscGxtLeHg4/v7+1NTUyOf3zg4fT06ePElTUxNarZa4uDgSExM5ePCgvL7Zl0CFH2tO+/HXknaSYsIofuVh3tixlR9+YR3/bo4g7q161kTEcOZMjwyon5/fqGvUBe5pbW2ltLSUmpoawsLCWLBggbeHNCE4uruoffd5Gk+8A0DjiXeofe8FHN1do7rudNaj7otk/AsKCob8Xki406UeKv/7v//Lxx9/zIMPPuhyrrc1qd0d526bxER8ll7Ro/Y2Y6lH3dLSwsGDB4mLi2P+/Pn91rNbW1vZsWMHW7dupaOjg7q6Oj788EMWL14sy0dOBG1tbfzrX/9i/vz5clJEa2sr//73v1229eZPe97n/p/s5GbFKeK23clvv/AFAD46c4btX9vKRxeiSUxK4OaVsH79eq688soJmYvEdNCjdjqdcqmdw+Fg7ty5zJ8/f1L36R6qHnVHw1kqX/4B7fU1/fYFR88jeeNPCYqaPZ5DnTJMFz1q8E1N6kmnRz3ZaWxs5IMPPuCtt97i9OnTNDU1ER8fT3Jycj8j7XQ6CQ0NpbW1lbfffpszZ87g5+eHXq9n5cqVE9oxrKysDD8/P5YvXy5vCw0NZenSpVRWVrp9Sl2ztKd3+PzkOHZUVFDzyTGP7d9Pu24RCqeDiPZLhIaGulxXMDZ0dnZy+PBhTpw4QUBAAKtXr0aj0UxqIz0cAsJn4nTY8Q+NYv6tD7Mm+y3m3/oj/EOjcDrsBETM9PYQJw3TRY8apqYmtVijHgadnZ2Ul5czc+ZM4uLieO211+js7OTOO+90e7xCoaCiooJ169axfPlyr4a7bDYbM2bMcOmkBj3heKfTSWNjY78w18qFs5kdGcjhE7UsWRDDT995h3vXrOHfFRU8VnqS/YpY5s2P4POf/7woxRpjeqtdxcXFsWDBgmnX4U0ZEITmjscJiIjBP6Tn+6VepidSs5au5kso/UVL1OEwXQz1VNSknl5/+aPE6XQyZ84cWaXqrrvu4uWXX+aNN94gOTmZ2NhYQkNDXcIYCxYs8In1xGXLlvH+++9z7tw5EhISgJ75fPzxx7IASF8UCgUbb72aP7zUxhPlR7g/OJiP6+rYaLVScradsGAnn7/pahYuXDjR05nSnD17loqKClntajoLaYTE9k+48g+JxD9E5EIIvMtE9iuYHjG0MUIqqZJoaGhg8eLFXHPNNbS0tFBVVeXW6+mbgOYNZs+eTVxcHKWlpXJyW01NDZcuXUKr1XoMp952o44mhx8FVQo0B85T9sZRut8/w4cBCaxIjuKmz66fyGlMC2bOnCn3gp/ORlogEPQgPOphEBERQVdXFydPnkSpVHL+/HmWLVvGjBkziImJ8XieN9Wreo9Bq9XyxhtvcPz4cRYsWMDBgwdJTEwc0BikLpvHDesWU1YK3ec6ifRr5f3uaGbEqcm+/45pURo03khlV4GBgcyePZugoCAXFTaBQDC9EYZ6GCgUCqKjo+ns7CQ4OLhf9u1gNdXeJiIigoULF3L06FEaGxvp7u5m1apVA56jUCj4++++SUttLX/fsIHu1jbWfO97LLnnnokZ9DTA4XBQV1dHYGAgCQkJPv0dEggEE48w1CMgPj7e7fbJcINdunQpp06d4syZM6xcuZLQ0NAhnRcWH0/83XdT8frrBFx11TiPcurjdDppbm4mMjISf39/Vq1aRVBQ0KT4DnmD+voKOjubAQgMjCA62vt5HwLBRCHWqKcZ/v7+pKWlMXfu3GElgTU2NvJBezv1N95InIcHFcHQkMquzGYzjY2NQE83uulSdjVc6usrePrphRiNOoxGHU8/vZD6+opRXXMiZC6NRiNZWVkuUr5jhUKh6Hfd3NxcoqKigJ6MZOn/Er0FRhQKhdykJDMz02M/bnf0FQoRjD/Co56GxMXFERcXN+Tje6ti3XnnnaIUaxT0LbsSetGDI3nSt932IgB79nxJ3jZSJkLmMi8vr18P7LFCo9Gwa9cul5aikvDHUM/vXa6l0+nIz88f0vmSWIhg4hCP8IJB6a2KJUqxRkZ3dzfHjx/n8OHDOJ1Oli5dypIlS6ZdbfRwqK+voLbWzMWLRwGIiVlCTMwSAC5ePEptrXlEnvV4yVz2fQ2pFae7a0mSjZmZmdhsNhdpSJ1OR25urvx/d6hUKhfBC0lGc6RkZ2d7rAmWrp2enk5BQYH8fg13zIKRIwy1YECsViv/+c9/hCrWKGhsbKS4uJja2lqioqJE2dUQ6B3u3rPnS0DP2nRgYE80Z8+eL404DD5eMpfuXkMyfr2vJUlDFhYWsmnTJvlakjRkVlYWRUVF5OXlkZqa6jEsnZWVJXvFeXl5/TpkST2spZ/i4mKP49VqtR735+fnyxGIvqIgwx2zYGSIx3nBgERFRXHrrbcSFxcnSrGGiVR2VVNTg1KpJCUlhdmzZ4uEsSHQO9wdE7PEJYHsm988QWdnMxcvHh2TMLgnxlLmsve1ioqK5DaXer2e7du3s2nTJhdpSEkOd6CmGlqt1kWkou+xkoazxEAet8ViITU11e0+g8FAZmYmmzdvZu/evS77hjtmwcgQHrXAI1K52apVq5g1a5a3hzOp6O7ultWuwsPD0el0zJkzRxhpH2W8ZS57X0uj0biE0yUDOZIWw+np6WzevJlNmzYN+9zeSBKO7tBoNJSUlJCfn98vkuBLKmBTGeFRT2KsVisnTpwgNTV1zNc6jxw5wgcffEBGRgYzZswY02tPB/z8/IiIiCAqKmrSq115g94hbolvfvMEAE8/vdDtscNBklLMyclxkXbsLUnZF2mt2WQyyZ6qFMIeDjk5OfIaNfSEloebhS6xZcuWfkllQ0Gar81mw2q1yu+DO3JzcykqKgIYdub7SN4fQX+EzOUoZS69hcPh4M0336SpqYklS5awYsUKj8c2dXTwds1/pQK7SkroevNNQh56CMUn4ezYsDDWzu6RDGxsbOR3v/sdfn5+3H///WMqQTeW+JrMZWdnJzU1NSQnJ6NUKn2+AY63GKrMpVQ7LYW4t2zpyaA2GnVuQ+KCycV4yVz6IqOVuRQe9SSloqKC5uZm5syZw/Hjx5k/f77Hsqlfffghj+zbB0BIVxdf+udHVNsj8Pvx4/x7ZU8WrQJo/eEPCVQq2bNnD+3t7dx5551T/g9oLDl//jxnz54lMjKSuLg4YaRHSV8DLGV/Q08GeHy872grCwTjiYjHTULa2tooLy9Ho9Gwdu1aQkJCKC0t9Sj88cXly1EqFGSvu5r15R284FzEgYAEzJZI/rh4HXFhYWxctoxgf3/ee+89qqurWbt2rSjFGgJ2u10WOUlMTGTlypXDqlEXDE7vMHjvDHCBYLogPOpJyMGDB/Hz82PFihX4+/uzevVq3nvvPc6dO8fsT8LXvVkQHc3nYpN45vG/weUuHr5+Hl/+9j3ccYeB+3/2OvaUEL7/xbs4d+4cb731FrGxsaOqyZwuNDY2cvToUSIiIli6dCkKhUIk14wD0dEL5ExvEC1EBdMP4VFPMi5cuMCpU6dYsWIFgYE9C7MJCQnEx8e7SFj25sChavY9V8yM9hZ+OKMKw8++w5y5s3jx4Qw22k8SaGnjxz/djVqtZsWKFdxxh1DFGgiHw0FVVRWlpaV0dnaKZLsJIDp6AfHxWuLjtcJIC6YdwlBPMo4cOYJKpSIpKUneplAoWL16Na2trVRVVbk9r6urm6+0H+OfuqV0fZKB/FZCPHNmdrDUr4X29h5FsNtuu02EbgegtbVVlF0JBIIJZdKEvqUG9EVFRaSnp8vdgmw2G9u3byc9PR21Wu2xxGCqEBkZSXV1Ne3t7YSEhMjb6+vrAdx6d6nL5qL2s1M+cznvqFTsKCkhKzWVJ959l8987laOvniCe6jH4XCIMiIPOJ1Ozp49R2VlJQ6Hg7lz54qyK4FgElFQUIDVasVms7Ft2zZvD2dYTIq7jMlkwmKxkJGRQVZWFgaDQd6XmZlJTk4Oer1+WrStW758OX5+fhw8eFDe1tnZycGDB5k7d67b1pRKpZJrE8P48JKTezQatr/7Ls8WF3O6sZG4inq6FUpmqrppa2ubyKlMHro7qTh6iIqKCgIDA1m9ejUajUYYacGkRupoNh0wm81oNBq2bNkyKeu6vXKnMZvNbhu3WywWcnNz5cb40hdJr9fLT0C9W92ZTCY0Gg0Wi2VSPiWNhMDAQFauXMmpU6e4cOECAIcPH6a7u5tVq1YBUFJSwl133UV3d7d83tcevJMGAkkoLOXCWRvf2fV3NgRG8Z+PLMwL6uDOrK8KJSc3ONsaCDxfRGODlVmzZpGamipaJE4AB8+f53M7d/LZF1/0+JOZn4/Vhx4uezsQEmazmdzcXLf7JhKbzYbBYHBpfapSqQZtYCL1CR+MkciGPvjgg8M6p+/5ycnJQz5eq9XKjW2G21XOF5jw0HdBQYHHXrmZmZmyLJzFYmHz5s0uvWqhp/m81OjeYrG4qMdYrdZ+jemnIvPnz5dVf9auXUtlZSUrV66UQ+FPP/00O3fu5P777+eaa64B4DOfXkNM+Ev8X1kHAXQA8CYNoIjiq9cuEKVYnlAGgEJB8qJlLJ4X4+3RTBsutLTwz4oKVsXFsSA6ut/+j86c4UJLC7+9+WYvjK4/ktBGX7Zv386OHTu8/nBXXFzs1oPOzMwkNzfXrZMjqWNBz317oO5nEyEb2huNRuPW4ErR195s3LgRlUqFxWLBZDJ5VAnzZSbcUHv6sPu+ub374Urk5uaSnZ3t8gFptVr5Q4uKipoWhlqhUKDVajGZTOzfv5+IiAgWLOjJhO3q6uK1114D4JVXXpENtZ+fkg8LHmb3fffjADquvoauF1+gY92VfP/R4bUFnOo42hrB6UAZGoUiKJzO+HWoZ4ow90Si12hIS0ggyN+flzMyXJL1mjo6mP+rX7FFpyNmBFEgs9mMwWBAo9FQXFzMpk2bqKyspLi4mJKSkkH3u6OwsLCfsTObzZjNZkwm07BbfI41er3erSHV6/UYDAaP0ciSkhIsFks/h6k3vWVDpbakeXl55OXlUVBQIC9b5uTkyK1LNRoN+/btY+vWrezbt4+CgoJ++UUDtR+VpDdtNhvFxcVyfpInnWyLxUJmZiYajYb8/PxJF/72mWQyk8nUrwZVrVZjNptlo6TX69FqtfLTnV6vl/9wbDbbtKphjYqKIjk5mZMnTxIREUFiYiKdnZ10d3fT0NDA1VdfzdNPP82LL74IwOrVq3tk9R7/PoV33YXy7Ak64qNZ//++T0iI5zaO0w2no5uu84dB4UfgvCsABSiEkZ5oFAoFj37mM3xu507+U1XFDb0ezp/66CNau7owXH31iK9vtVopLCzEaDRSWFgoSzlK96GB9nsyBn2RnIixNtI2mw2j0ejxNYc6vsGwWq0YjUYaGhpQqVRy+FyS1pTYtm0bu3btkrd7UhZ7+eWXKSkpkZcrV61axa9//Wuqq6v5yU9+0u9hYCBjWlhYSFpamuzgWSyWAROJJWGRyYrPGGpPiQ1Wq9Xlachms6HX68nIyECj0aDT6eSnNk9PfR0dHXR0dMi/NzU1jccUJpxVq1Yxb948/P39WbRoEW+99RZxcXE8++yz3HLLLTz++OPk5eXh7+/Phg0bAIhZvRrN7bdT9be/8T/PPEOUUMUCegy0QumHQumHf+xiFAHBouTKy9yUkkJaQgKP7t/P9UlJKBQKGtvb+cUHH7BFp2P2IP2RB2IwecbhyDdaLBa3+yQnY6xRqVRjmo+jVqux2WwuczCbzeTl5bFt2zaMRiPbtm3DYDCMaq3dk2xoUlLSsGVDzWYzNpuNrKysMXsw8WV8xlB7wmazodFoaGhocLt/KKHu7du389hjj4310LyOn58f0dHRlJeXM2/ePB577DEeeeQRAOLj4ykpKSEhIYHdu3dzxRVXAHDixAnivvIVln7ta0TOn+/F0fsGTqcTR9M57NZqAmavQRkYil9Y/zVRwcTjzqt+6sCBUXvTMLg843Cic5KH2JfhJjwNlbH2qK1Wa78HjV27drFp0yYyMjLkhLL09PQhJ2INRza0qqpqRAleeXl56HQ6OTw/GZPEhorPGGqVSiV/uBLuvkAjITs7m+985zvy701NTSQmJo76ur7C008/zZ/+9Cd+/etfA2A0GvnBD36ASqUiNDSUtWvXAj0tL3fv3o2/vz9bt2715pB9Aqe9E/uFY3S3WlH4B4Oje/CTBBNKb686NSGBJ8fAm54oioqK5KxqKSq4Y8cOdu3a5eKl6/X6Qff3ZiQetclkcvFae3v67gyotN68a9cu+Zj8/Hz5p+99eSSyodXV1S5r1OC6Lu1pjbr3NfV6PUajkY0bNw7j3Zh8eE3mUqFQuIhISF/U3usIUVFRVFVVjXnG5FSQuZTo7u5m9uzZ1NXVsWrVKg4ePEhYWBhXXXWV/CX/+OOPWbFiBX/+85+pqanhzjvvnBJZ3qORuey+fBH7xRM4u7vwi5iFf0wKCmX/59bhyFwKhsZQZS4l/llRwed27mR9cjL7qqupfPBBnzPUBoOBTZs2yUbKZDJRWVnpkmGcmZlJfn4+ubm58tKdwWCQjxls/3ggZUl7IwlXyFwOXebSq1kyvdel+4YtpHppb5c1+CLl5eUsXLiQefPmMW/ePOrq6li7di1lZWUEBAQQGRmJUqnkD3/4A6GhoRw5coT33nuPmpqaaa+K5XTY6bpwjK7z5eB0EjBrGQFxi90aaYFvIHnVb1RW+qw3nZOT45JkZbPZXDxJqZTUZrNRVFQkZ4NLXu5g+8eLwsLCaVEpM9mZcI/aZDJRWFgo1+6lpaXJWZEWi4W8vDzS0tIoKioiOzt7XAz1ZPeoGxsb2bBhA++88w4xMTFkZ2eTkZHBo48+ynPPPYdCoeDxxx/nhz/8Ia2trVitVp577jlmzpzJ5s2bp4zgxnA9akdbI10XjuLsakcZqiYgdhEK/6ABzxEe9dgzXI8a4K2qKu7/xz/Y++Uv+6Shnoz0TSCbaIRHPXSPesLdCL1ej16vdxvO0Wg08nZv1x36MhEREfzpT3/iz3/6Ez95/HECAwNJTEzkxIkTxMTE8Ne//pXrr78egNDQUMrLy/Hz85u2qlhOp5NuazXdDTWgUBIwcwHKGQkiq3sScV1SEke/8Q3xmY0hIlo5eRAFoj7ApUuXMJlMtLa2Dun4559/nsWLFxN9psfT/2NeHgkJCVy4cIHw8HCuu+46l+PT0tL49re/Pa1VsRwdzSiCwglI1OGnmi1u+JMQ8ZkJpivCUHsZh8NBSUkJVqvVRWjDHQ1tbbxUVsaTO3bQ1dWF8eWXATh4+DCR8fGcOHGCqqoqzKWlAJw6dYr29naAadfH2+l00n35ohxaC4hbQsAcLcrA6fU+CASCyY8w1F6moqKCpqYmFi5cyOnTp6mrq+t3TGdnJ3v27OG7v/gFX3r0UQ59+CFKhYITbe0oAsNIj4knXK2GW24Bf3/e+egjGhsb2blzJy+88AJeSuz3Kt2NZ+k6X46j+TwACr8AFKLDmEAgmISIVFcv0tbWRnl5OcnJyaxatQqr1YrZbObGG2/Ez89PPu7o0aPcfvvt8u8BgYHMVy+k4vxh6GzhXxdaiHi7gRWP/ZjZt97KN++5hxdeeIH29nauvfbaaRUylDxov8hZYO9AGS6ENAQCweRGuBhe5ODBg/j5+bF8+XJZaOPy5ctUVFS4HLdq1Spef/11oqOjCQoPJ2rejVxIuJYv3vsgCoWC2AQNHckZdP3tGN+/9no++OCDaVeKJZVdddf3dEJSKP3xn5ksyq6mCLaKCt576CE6m5u9PRTBJCU9PV3u196XgoICuVzYaDRiMpnIzc2d4BF6RhhqL3HhwgVOnTrFypUrCQzsqS1SqVSkpKRw5MgRl8SyoqIiHnvsMX73+xfpuHyZzsZqdMvbqbWUsXTpUmosR1i7LJy61mC+8eAO3nrrLWJjYz12BJpqONsb6TxdTHfTeZydLdMy1D+VcTocfPTII9T8618cevppbw9nyuBJX2GqIgmKuGux2rsLptSTvLKycoJH6BlhqL3EiRMniIiIYH6fftvLli3D4XC4fEl+//vfU1xczD9f3w1A44Vy/vP8H3jnnXc4ceIEzxcVcXx+GMscVuwtl1EqldOiFMvhcODXaMFZWwr2TgJmLsA/fsW0CvVPByyvvkr9wYPMXb+eEzt30nDsmLeH5BZ3ghVms5nc3NxRiVmMBZLyVe8GKiqVSm5x6gmpz/dguOt1PtjxDz744LDO6Xv+cPuo22w2TCZTvz7pfRXRJNXG8ejTPlKEofYSsbGxNDc39xMbOXfuHA6HgxkzZtDS0kJzczN79uwBwPTmvwBQhcey/MYbCY+Joauri0d27ODBikoO+UXz5S+kc88990z5UqzW1laOHSrFv+kUBIqyq6lKh83Gx08+yfwNG7jyZz8jYv58ip94wueiJrm5uW4jWNu3b2fLli3j2gZ0KBQXF7v1oDMzMz2GeM1mM+np6aSnp8u9uD0xmMEfazQajVsRDskQ9/6x2Wyykpler++nsthb0MNkMmG1WsnIyPApzWphqL1ESkoKM2bMwGw2yzedzs5ODh48SFdXF8nJyYSHhxMZGcnFixf5/Oc/z5lP6qYbLtdxdt8+FF/5Ctd+4QuEXb6M7WQ9HfiRcfMVzJkzx5tTG1ecTidnz56luLiY1pZm7JFzUSSIsqupStlTT9Hd2cma734Xv8BAUn/4Qy6azVS//vqIrykZoKysLHQ6Hbm5ufL/h7LfHYWFhf1CqmazWW4F6m30er3bBid6vV4W3nBHSUkJ+fn5Aypy5ebmUlxcTFZWFhaLBbPZLBvugoICcnNzsVgsZGVlkZ6eLr8f+/btY+vWraxcudJtq9SBlu5MJhPp6emylyydr9fr2bJli8uPSqVy6cHeO0LQV3VLo9HIxn4okYSJQmTaeAmlUolWq+Wtt96SZd4OHz5Md3c3GzZsICsri2eeeQa1Ws1TTz3FrbfeSk5ODk888QQOlCydtYQZh2p4c/58vm02sz90PolR4SycP3U96Y6ODo4fP47VaiU4OJhFC5aw99AMhBM9NbEeOULFrl1ov/c9QmJ6svdnrVvH3M9+ltL/+z9mX3cdgRERI7u21UphYSFGo5HCwkLy8/PlRCO1Wj3g/qHKSGq1WjQazZh3WRxrmUtPWK1WjEYjDQ0NqFQqOXzeu6c5wLZt29i1a5e83VN/8pdffpmSkhJZFnTVqlX8+te/prq6mp/85Cf9PN2BPNrCwkLS0tLkkLvFYhlU+9vd+2IymVx6nWs0Gp/sfS4MtReJiYlh3rx5lJWVER4eTmVlJStWrCAoKIibb74ZvV7PbbfdRlVVFa2trbz77rvEx8czc+VGSmo74Rz4n2vmaRYA8M3rV3h5RuPL5cuXsVqtzJo1i5SUFOo7xNd3KnP42WeJmDuXhXfe6bJd+73v8dr69VT89a8s27x5RNeWvCi1Wu0iKTnU/b2xWCxu90nh1rFmJDKXA6FWq/v1/TabzeTl5bFt2zaMRiPbtm3DYDCMaq1dStLqS1JS0rDFR8xmMzabjaysrFE9mAxHd9ybiDudl1m5ciXnzp3j7bffJiIigoULF5KXl8cDDzzAH//4RwB27NjBj3/8YxISErh8+TIHX3qUS/XN7HvgflrPnsOWNJ95X/gCX/ni7YO82uTDbrfT1dVFSEgI0dHRpKam/reBf4d3xyYYX2YkJ1P77ru01tUR3ms5p768HKfdzoxRJPsMdoMezg1c8hD7MpKEp6Ew1h5174xniV27drFp0yYyMjLkhLL09HS3htbTNcE1yczTeypFFIdLXl4eOp2OkpKSfiHsoTJZNCWEofYyISEhLF++nNLSUtasWYNSqZQTN5599lmgx5PMysrCaDTS3d3NgQ/f46abbsL+nQd4/xe/IEh/DV/+wufx85taKQdSe1WlUolOp0OpVE55lR3Bf1m2eTNVf/sbJT/7Gdd+UpZlb2ujZPt24j/1KWb36WnvaxQVFclrtRaLhczMTHbs2MGuXbtcvHS9Xj/o/t6MxKPuK5nZ29N3Z0CzsrIwGAzy+rVarSY/P1/+6WvYNRoN6enp5OTkyOvBBoMBi8Xica25urqarVu3sm/fPvmel56eLoe8e/+/N72vqdfrMRqNbNy4cRjvxuRjwmUufQFfk7lsbm4mJyeHrq4uHA4HTz75JDNmzKCjo4O4uDg++9nPsnTpUrRaLV/96lf50Y9+xJe+9CWMRiMXL15k8+bNUzbL+/Tp0yiVShIS+qtdDVfmciQImcuxZzgyl6feeIN3v/Mdrv3tb5l97bUc/M1vOPrcc3zutdeImDdvgkY8OAaDgU2bNrkkLVVWVrpke2dmZpKfn09ubi4ZGRloNBoMBoN8zGD7xwOTyYTFYvHKuqyQufRhmUtBf2w2G0899RRNTU2EhYWxZs0aHn30UbZt28aJEyf43e9+x7JlyygrK+P48ePyeRs3bqS2ttanjLT0hzdSWlpasFgsLF68mICAABITE8dwdILJRuKNNzLryisp2b6dsIQEjj73HEvuvdenjDRATk4OWVlZckKVzWZz8SQtFgsWiwWbzUZRUZEcLpe83MH2jxeFhYVeLx0TDI7wqH3Ao4aeP9Q777yT4uJidu3axR133MEPfvADtm/fzt13380zzzxDxCcZrt3d3S69wH2FlpYW/va3v5GZmYm///CeAZ1OJ+fOnaOyshKHw8HSpUuJjY0d8BzhUU9OhuNRAzRaLPzrttvwCwkhMCKCz/3tb/iHhEzASKc2fRPIJhrhUQ/do55ai5qTlPfff59169bx+9//nu7ubv7+97+zdetWPvzwQxYsWMDzzz8vG2mbzcZvfvMbysvLvTxqV2w2GzU1NVxxxRWcP39+WOd2dHRw6NAhKioqCAwMZM2aNYMaacH0YYZGw+J77qGruRlddrYw0mOEN420YHiI0LcP8Pzzz3Px4kV+/vOfA/CnP/0JpVKJw+EA4OTJk6SkpOBwONizZw+NjY1yf3Bv43Q6OX78OCEhISxZsgSFQoHdbh9yCPzixYscP34cu90ul10N1xsXTH1WfOMbzL72WmauWePtoQgEE47wqL3EqVOnOHbsGEePHpVbhP7zn/8E4N5776W0tJRbbrkFgH/84x8AvPvuu7LXumDBAu8MvBcOh4NXXnmFkydPMm/ePBQKBRaLBaVSiUKhGLDNo91u59ixY5SXl6NQKFi2bBmLFy8WRlrgFr/AQGK0WtEiVjAtEXdFL1BZWUlKSorLti9t2MCLn7RFfO6558jPz8dqtZKfn09qaipnzpxh3759PqWKpVQqWbRoEfv376ejo4Pa2losFguJiYl0d3fT1NREdHR0v/NsNhvHjh2jvb0dtVrNokWLCAoK8sIMBAKBwPcRHrUX0Gg0/PKXvyQgIIDIyEj+78tfZv3Jk/zw3nvx9/dHrVbz4osv4u/vzxe/+EWSkpLYvXs3SqWSjIwMn/I6V6xYQWJiIn/5y19wOByo1Wq6urpob293yVCXcDqdnDx5ks7OThYsWCB3YhMIBsNut3t7CIIpjq9qVvvOHX8acfTSJd5NSOCqxx7j7R/+kD3/NNE1cxnNr+zBPzyc1J/8hJftdq5ubSU6NBR/f3+uu+46Ojs7fSrJSlqHXr16NaWlpcTExJCUlCSHJ5ubm2lpaSEsLIzOzk4CAwNRKBTyWnZoaKiXZyCYLFRVVWE2m7n++uuJiory9nCmDd7ODJ9oDAaDx85ufTWrgX79yccL4VF7gaKyMl556CEqT5/G6XTyXkM9B44VcuxyK52NTRyy2dhVXk5Hd7d8zooVKwZU7/EGkkGOiopCoVBw9uxZFAoFbW1tFBUVERcXR1hYGJcuXeLDDz+kvr4egLCwMGGkBUOmo6ODgwcP0t3d7aI252v4sh51bw/Qk3b0SDSrh6pXDSPTrB6NfOZU0qwWhtoLHC0shNOnubSrJ0ksIiyUhx95DD8FOJwOWgre4UtLlhHa3c2LL74o9831VSIjI0lISODtt9+mtbWVM2fOsGzZMlavXg1AREQEYWFhPhWyF0weDh06hNPp5Morr6S+vp7q6mpvD6kfvqxHbbFYqKysRK/Xy+Ia7hiuZvVw9KpBaFaPBmGoJwin04nT6cThcPByfs+XuvOyjZVzU6ipruSxRx7myKGPmaOKpbOhm+KXDlPwym5Onjwpe6K+gFQy1pe0tDTa2tp4++230Wg0tLS0yPXUQUFBaLVaZsyYMZFDFUwBrFYrFouF5cuXk5iYyNy5cykrK6Ozs3PE15xuetQmk8nF8/Pk2Y5Es3ooetXgXrP6wQcfBODVV1/1qFltMpnIysoiOTl5WmtWCxdnAjh9+jSLFy+mtbVV3qaMmIuj+RRlp5pQq9XExsZyoqqKeV+9i5UmC6V19Zw+1c26det8ohQLoK6ujoaGhn61zg6Hg9bWVq655hpWrlxJRUUF58+fJzg4mNjYWLlcSyAYDk6nE7PZjEqlkg3NqlWr+Ne//sXhw4dHJSE5nfSo+64zu/OaR8JQ9aph5JrVWq2WvLw8WeRjumpWTxpDLYVWioqKSE9P7/eGSg3tfZHZs2fzta99jaeeeoqIiAhycnLY8ZcyLlR+wNnzh1AqFHzve9/jDwcPciIggADlTJYEXSYubpFXS7F6Ny1xOp3U1taSmJjYL4QtqVolJydTUlIil10tXrwYpVIEbQQjw2KxYLVauf766+XvUUhIiNz3PikpacSJZdNJj1oyoqOhr2b1eOhVg2fNao1GM601qyeFoZYUXrZt24ZWqyUzM5OSkhKX/ePdvH40KJVKfvnLX5Kens5tt91GVVUVt2+4mkefeIMgf38WPfQQD2zdStKvf83dR0/zW4UazdJIr5diKRQKurq6aGtrw263ExQU5FIXbbfb8ff3x+FwUF1dzalTp1AqlSxYsMCt2pVAMBzOnDnDjBkz+tXiJyUlUVZWxpkzZ0ZsqKeTHnVqaqpL6HokDw99NatHq1cN0NDQAPRk9Eslmp7e95HqTU8VzWqvWAGz2czmzZtdjC30fBgFBQXyF19aO9Dr9fKXz2KxkJqaKp8jPSmO5AOYKIqKirjuuut49dVX6e7uZs+ePVRW/h8ERGDvsnPmQhP/Y3yB6HILR6rbiAhRcM9dG7xeimWz2di3bx9RUVEcP36c1tZWrFYr0dHRdHd3o1KpUKlUHD16lMuXLxMREcGSJUtERrdgTEhOTub999/n3LlzzJ49W95+7NgxFAoFSUlJXhzd4PiKHrVWq6W4uFh2eHbs2CHv0+l07N27F5VKNSzN6uHqVUN/zerq6mp+/OMfU11dzU033eR27NK6tclkkiOm01GzesLVsyRDrNPp+pVZSE8+gMc1iczMTHJycmTDXFBQQEZGhscPzB0TrZ61detWfvOb33DjjTfy5ptvEhYWxqZNm3juj38Cp4OAeTfiF/Xfdeiv3XE1T/3wC171SO12OyUlJcTHxzN37lwcDgcfffQRDocDpVKJWq0mLCwMi8WCw+Fg3rx5zJs3b0JD3UI9a3IyVPUsp9PJO++8Q3NzM+vXr8ff35+mpibefPNNlixZwrJlyyZw1AMzWfWoh8J4aVYL9Swf1qP2FEboGzqSMut6k5ubS3Z2tmykzWbzqNYexpOKigqam5sB2L17N9Az3qVLl/L444+j1Wq5feMXue/uL7Gy/TizVcHMvVTPFY//hE/deLXXw8b+/v7Ex8fLXr1SqSQsLIzk5GTCwsK4ePEi5eXlBAcHs2TJEpHRLRhzFAoFa9as4Y033uDYsWMsW7aM0tJSQkJCWLRokbeH58Jk1aMeCkKz2vv4zBq1lG3ZG6moXKvVytmWWq1W9qKl86Dni240Gic8G88dtbW1LFy40GXbt7/9bX71q19x6dIl7rjjDgIDA2lqauLs+XP8Z/NmLnz4IdaFC1myWkN4qO+01Gxvb8fhcNDS0kJ7e7u8ljRz5kxSUlKYNWuWqI8WjBsREREsWrSIY8eO4e/vT11dHddcc41PfuckI63RaPqFpjUajRwt7B0llByNwfZ7C5vNJoy0D+AzKbmeshKlOsrMzEw2b95McnKyHOLWarVkZGQM2uKuo6ODpqYml5/xJD4+nj//+c+EhYURFhZGfn4+Tz75JHl5eQQGBhIdHU1BQQFBQUEolUqUN9+MTaNB8+UvM3/+/HEd23CYM2cO0JPU09bWRnh4OOXl5XK4as6cOT55wxRMLZYsWUJwcDBlZWXEx8eTkJDg7SFNG6ZT+1BfxufvsjabDY1GI2cIukOv11NZWelx//bt23nsscfGY3huaWho4MCBA7z77rtcccUV7N+/n/j4ePbu3UtkZCQHDx6UbzZnzpzh3fJyYm6+mRs3bJiwMQ4FpVKJSqUiMjISpVIp60Z3d3cLAy2YMPz9/dFqtZjNZtYIPWrBNMRn7rYqlapfq8y+JQEjJTs7m+985zvy701NTSQmJo76up546aWXeOaZZ4iLi6Ozs5Pnn3+ep59+msjISBdvvqOjg1deeQWlUskdd9zhc8bP4XBw9uxZOeN2wYIFKBQKr6+fC6YfCQkJxMfHi++eYFriM6FvT2sxvUuxRkpQUBCRkZEuP+PJK6+8AsCLL74I9Ky1P/XUU3K23549ewBko33jjTd6vRSrLy0tLZjNZiorKzl37hyA6DAm8CriuyeYrnjVhevd6aZvHbRULz0Z1kgaGxvZsGGDbHjLyspYsWIF5eXlqNVqwsPD2b17NwcOHGDbtm1yhnRMTAz3338/AQEB3hy+C06nk7Nnz7qUXYk1QYFAIPAeE+5Rm0wmuc3c9u3bXVRX8vPzMRgMFBQUkJeX57MtQfsSEhKCUqnk4MGDVFRU8OCDD/L666/z9a9/nZaWFg4fPoxKpWLOnDns2rWLW265hZqaGgBZo9kX6OjooKysjJMnTxIUFMSaNWtISkoSbUAFAsGkp7fUZ18KCgrkhOaCggKMRqPb47zFhHvUUpcxdyn/Go1G3u4LbduGSmlpKW+//Tbf+ta3+O1vf0tAQADz5s3DbrfjdDp5+umneeCBB1AoFDgcDnbv3s2ZM2f45je/6RN9ZAEuXLjAiRMnsNvtxMfHk5KSgp+fn7eHJRAIxom+Yh1THWl51Z0DKOVDmc1mWUglPT19WB3gxhPhKo0Bf/3rX2UZS4VCwfvvv8/ChQv5+OOP8ff3595775W95nfeeYdTp06xdu1anzDSdrudo0ePcuTIERQKBcuXL2fRokXCSAt8in/sP0Tm/xopP3nO20PxiDtBCrPZTG5u7qjFKkaLpGzVu4GKSqUaVCNa6uM9GJ6kMwc6XpK5HAkj6aMu9eXoe15vRTStVit3l/OlttTCUI+Aixcvcs011/D888/z+uuv88orr+Dn58df/vIX7HY777//PgAHDhygtbWVN954A+iRu9y/fz9xcXFeb2QAPevRZWVl1NXVySpBM2fO9PawBAKZ9o4uvpOTT8a383jPfJJrvvRzjC+/06/9sLfJzc11q3S3fft2tmzZ4vWmIcXFxW57VWRmZnoM8Uq63Onp6S5LlO4YzOCPNRqNxq0hlXSie/9IetRWq5WMjIx+rab7GmWpBay3P7PeCEPthubmZu76pZVzn5Rut7W1kZ2dTWNjIwB//vOfee+99/jKV77C//zP/3D69GmeeOIJ6uvr5WtUVVXx7LPPEh4ezgcffEBHRwe7d+/2iVIs6SanUCiYP38+CxcuZMWKFQQGjlPDbIFgBByznOfTd/8fz+1+j19+P5PKN/4fX/n8lWzdvotN392BtbFlRNeVDFBWVhY6nY7c3Fz5/0PZ747CwsJ+D99msxmz2dyvFbI30Ov1bsPcer3eRVmrLyUlJeTn5w/oWOTm5lJcXExWVpbc9lQy3AUFBeTm5sriGunp6fL7sW/fPrZu3crKlSvdtkodSOLXZDKRnp4uG2HpfL1ez5YtW1x+VCqV3JLaaDS6RAjcGenMzEwKCwuHFEmYKHyrcHeCqa2tdVuq9dprr7HznTZWJMD3N/X8/rOf/Yy5c+dy//33889//hPoSQTz8/PjhRde4I477iA2Npb777+f2NhYdu7cyTXXXMPtt99OcHAwVVVVNDY28tnPftarpVgtLS0cO3aMhQsXEhER4RPhd4GgL1VnLnH1XbnMjVfzzgvfY8XCnlr+X31/IzdcsZivP/YiV92Zy5HXHxlRsqPVaqWwsBCj0UhhYSH5+fmySpNarR5w/1CjYVqtVl7vHEuGK3M5UqxWK0ajkYaGBlnT2mAwyK1SJbZt28auXbvk7Z76k7/88suUlJTIfcxXrVrFr3/9a6qrq/nJT37Sb+14IJGlwsJC0tLS5JC7xWIZUL5To9G4bS9tMplctvdu5epLTGtD/fzzz/PEE08APdJ5ixYtQqFQyHXQrxT1GGrpiXPr1q08/PDD1NfXc8011/Duu+8CcNddd/HAAw9www03EBQUxMGDB2Wd3JiYGAAWL17M17/+dfl3byH17W5qaiIiIsKrYxEIPBEWGkR7Zxff+tJ1spGW2HDdSv759ireKTk54ooJyYuSlnzAtV3mYPt7Y7FY3O6TdArGmuHKXA6GWq3ul1hmNpvJy8tj27ZtGI1Gtm3bhsFgGNVau16vdxuuTkpKGrb4iNlsxmazkZWVNaoHk8niqExrQ/23v/2NJ554ArPZjE6nY+bMmYSGhnLmzBlUYQqKLU6CN56lw34WpVJJV1cXVquVxMREQkJCuO+++/j9739PR0cHFy5c4JVXXqGzs5Nz587Jhrq5uRl/f39CQkK85kl3dHTQ0dFBZGQkERERrFu3ToS5BT5NrDqCq9ck89reg9x7+9Uu++z2bl7fV8Y9n79qxIZ6sBv0cG7gkofYl5EkPA2Fsfao3XWA3LVrF5s2bSIjI0NOKEtPTx9ygpXUZbL3++LpPa2qqhpR4lZeXp4sjTzS5K/JUl00rQ314cOHWblyJefPnwfg0qVLBAQEsHXrVkrffIZ95Z102HuOvf7663E6nezdu5fTp08ze/Zs+Slw7ty5/OpXv+Lhhx+mvLyc3bt3s2zZMhwOB/n5+TQ2NvLAAw/IylMTiVR2pVQqueKKK/Dz8xNGWjAp+PwNq/n+k3uwNbeiigiVt79rPkm9rYXP61d7b3BDoKioSF6rldY+d+zYwa5du1y8dL1eP+j+3ozEo+69jgu4ePruDGhWVhYGg0GOJqrVavLz8+WfvoZdo9GQnp5OTk6OnDVtMBiwWCwe15qrq6vZunUr+/btk5PV0tPT5ZB37//3pvc19Xo9RqORjRs3DuPdmHwonL6WPjkBSILder2e9957j7a2NpRKpdwhbNWqVTx41Qny/mXjnePwqU99iv379/PQQw/x5JNPEhMTQ2trK2q1mtOnT1NTU8PcuXNpa2vj0UcfJS0tjYyMDPbv389bb73FFVdcwU033TShc7Tb7VRUVFBXV0dAQACLFi2achnddS2Q8x6ogyFsnJ49WjrB2g6GqyEubHxeY7rR3t5OVVUVSUlJcltdd5y9YCNl/Y/48f2f41O6BfJ2Y/47fHjQwvF//sRnmgUZDAY2bdokGymTyURlZaVL5nBmZib5+fnk5uaSkZGBRqPBYDDIxwy2fzwwmUxYLBavyANfvnxZLmkNDw+f8NefSNx95yU71NjYOGhb62ntUUvZh0qlkqeffpqHHnqI1tZWDhw4wJcOwPJPdDva2tpIS0vj8uXLQI/nvWHDBv7+978D8Oabb3LfffcREhIi/1H1LsUaKHtxPLDZbBw7doz29naio6NZtGiR8KIFk47ZsSqu0abwk9/9o9++79yj9xkjDZCTk0NWVpacUGWz2Vz+7i0WCxaLBZvNRlFRkRwul7zcwfaPF4WFhT5VhiRwz7T2qMPCwmhpaSE3N5fvfe97PPbYYzz66KMApCYHUFzZJZ+TkpLCyZMngR41rp/+9Ke888473HnnnWRmZvLkk0/Kx3Z0dPDss8/S3NzMli1bJmxt2uFwUFVVxenTp1EqlaSkpExpxSHhUU9OhupRA7S0dXDugs1lmwIF82dH4+8vmvKMBm93JhMetfCoh0RLS08d5vPPP09wcLDsIf/xj3/knhk/4os/P8tfP4Arr7yS9957jz/84Q9s3bqVS5cuAT0hcXdJJB999BENDQ3cfPPNE2akW1paOHr0KJcvXyYiIoIlS5YQGho6+IkCgQ8TFhLEgnlx3h7GlGQ6tQ+d7ExrQ/3MM8/wjW98g8OHD7u0s5s1axa0wTP3wKNfjCXxayYUCgX33XcfN910k4valTvlq2uuuQaVSsWKFSsmYhrYbDbKyspwOp3MmzePefPmCSENgUAgmCJM67u51Gls1apVvPvuu3LC16uvvgqAOhwWzQlw8Uxnz57t0Uvu6OjA6XSiVCpZuXLlhIWcIyMjUavVQu1KIBAIpiDT2qPWarU88cQTPPTQQwQGBvL3v/+dHTt2EB0dDfx9WNdyOBy89NJLREREcMcdd4y7sbxw4QLNzc0kJyejVCpZvnz5uL6eQCAQCLzDtHa9rrzySn7wgx/IGdFKpZKsrKwRFcFLqlgRERHjbqSdTie1tbWcP3+ezs7OcX0tgcAXcDqdVFdXe3sYUwp3Ih0C32RaG+qxYqJUsXpnSS5evJi0tDRRdiWYFuTn55OSkkJlZaW3h+KRqSZzaTAYyMzMlAU1PNFbhENCEuPoi9FoJCsra1A1rpGgUCjIysqSu6hJc7XZbHKnSAlJICQzM3NIyl8mk8mrIh3TOvQ9FkyEKlbvsqsFCxYwe/Zsr3Q5Ewi8xV//+le6u7t55ZVXxrTP9VgxkMzljh07vJ5hPZjMpbv31GQyyQIVfYU4RkpeXp58TakvxVih0WjkcUqd3twJbEgGejhz0uv1XpUmFh71KHnzzTdpaGjgxhtvHJdSrJaWFsxmM6dPnyYiIqLfk6FAMBWx2+28+uqr7Ny5k5deeol///vfBAQE8MILL7Bz50527tzJvn37RnRtIXPput2dzGVvaUrJ6ElIvb/7RglsNhs6nc6jEZSuKbUWffDBB7n11lt566235OtKRtRms43ocxgMqbHMYJ3YJBlNSYtbihqMx5iGgvCoR8mnPvUpwsLC5N68Y4XT6eTs2bNYLBacTifz589n7ty5IqNbMC2orq7m9ttvl7XTQ0JC+PnPf87//u//ctdddwEQGxtLbW2tkLns85pj4fn1lq7s3StCihxs2bIFg8Hg4qVLYe0tW7ZgNBr7efDSNXNycrBYLOzZs4f9+/ej0WhcrltQUMD27dvZtGnTsD4H6cHCarVis9nYsWNHv3kNJocpIb2O9Nn1XjIYi+/GcBGGeoRIa8UqlYrrr79+TK/d0dHBsWPHaGhoICQkhCVLlgzauUYgmEqkpKRQWFjI3XffTVtbG6+++irXXnstgYGBfOtb32L16tX85S9/GfGDq5C5/C/uZC49UVRUBOA2pFxZWTmk9V6Jz3zmMyQlJcnXzc7OBnq8fMlQD+dzUKlUg4azPSmd9UVan9+8eTN79+7td42hjmmsEIZ6BDgcDnbu3MmyZctYs2bNmF5bUruy2+3Ex8eTkpKCn59olSiYftxwww189NFHzJ07l9dee43Kykpef/115s2bx/vvvz+qREohc/lf3MlceiItLQ2VSuU2dKzT6SguLkar1Q4po7z3Mp5Go8FkMqHVajGZTKSmpgJjrxet0WhQq9UYjUZ5Du4eUjQaDSUlJZhMJrZv3y4/RIzHmIaCMNQj4O233+bkyZNjrkZ1/PhxamtrCQgIYPny5VNO7UogGC6lpaUA/OY3v6G7uxs/Pz8cDgdWq7Wng6APM5llLj2xbds2srKy5Hn1FvTYuHEjN9xwg4tM5VDJycmR16ihJ/Q8FM93JBQWFspZ35KBzs/PdzkmNzdXjh4MJ0oAnuU5R8O0FuUYsBn6njnQdhZCZsNtZ+TNp0+f5rnnniM2NpbNmzePaZb32bNnsVqtQu1qiAhRjsnJcEQ57r33Xv74xz+yfv16vvvd7/Ktb32L48eP8+yzzw77BjqeCJnL4SNEOYQox7gglWL5+fmRkZExaiPtcDioqakhISGBoKAgEhISSEhImLJqVwLBcPn0pz+NVqvlgQceQKlUUlJSwuOPP+5znfiEzKVgPBEe9TA86t27d1NWVsbnPve5McnytlqtlJWVMWfOHFJSUkZ9vemG8KgnJ8PxqAXjh5C5nDiERz2BLFiwAEBOdBgJTqeTrq4uAgMDUavVLF++/JPe4gKBQDBxeLsJi2DoTBpDLbWcKyoqIj09XU6w8LR9PFixYsWopCulsqvOzk50Oh1KpVIkjAkEAoFgQCaFoZYSHrZt24ZWq5Vbw3naPpY4HE7+9Y9/sG7dulF5vn3LrqbhioNA4EJTU5Mc+hRMPzo6OuT/j0frZV/B6XTS3NwMMOLvulfeHbPZzObNm/sZVYvFQkFBgZxIsWXLFrk8QfKULRaLHHr2tH0seftIJ0XdRQQGBg6r3EDCbrdTUVFBXV2dKLsSCICgoCAiIyOpr6+nvr7e28MReImOjg75QW06aBdERkaOuJpnwg21ZIjdZTP29ogtFgubN2/uV9+Wl5fnNkvR0/bRcLoe9pd3EHdtHNddd92wz7fZbBw9epSOjg6io6NF2ZVAQI9XMXv2bOLi4rDb7d4ejsBLlJaW0tXVRUBAAIsXL/b2cMYVf3//UUUNJtxQe+p727e4XepU05vc3Fyys7PlFm6DbR8NHV1OXjkAfkqGXYrVW+1KqVSycOFC4uPjRYhPIOjFaG9egsmPw+EAENn/g+AzCg9SM/zeqNVq2fOWGp5rtVoXLVNP20fLP0rasbXC+tXBxMTEDPm8trY2SkpKOH36NJGRkaSmporaaIFAIBCMGJ95nPXUG9Zqtcrt9TQaDTabDb1eT0ZGhsftfeno6HBJXGhsbAR6klnc0d3dTXunk8RoWJjg5/E4d3R1ddHY2EhcXBxz587FbrcP63zB0GlugY4WaOiA9nH6JrfZocMOzU0Q0j0+ryEQTEdaWlro6OiYtvdIac5DSSz2GUPtCZvNhkajoaGhod8+T9v7sn37dh577LF+2xMTEwc9974d54EZQxqrYOryW28PQCAQTEmam5uZMWNgG+MzhlqlUmG1Wl22DUfVZSCys7P5zne+I/8uNfWPjo72iZB0U1MTiYmJcrh8qiHmN7mZyvObynMDMT9fRirbSkhIGPRYnzHUer3erZboWJRcBQUF9Uv/98WuPJGRkZPuyzYcxPwmN1N5flN5biDm56sM5klLeDWZrPe6dN+Mbaku2hcNqkAgEAgEE8WEe9Qmk0nW6ty+fTtpaWlyAlh+fj4Gg4G0tDSKior61VALBAKBQDDdmHBDLXUTc9ecRKPRyNs91VtPRYKCgnjkkUembHceMb/JzVSe31SeG4j5TRWmpcylQCAQCASTBZ9peCIQCAQCgaA/wlD7MLm5uRQUFJCbm9uvxepkxWazYTAY+vV697R9MuFpDgUFBZhMJgwGw6T+HD3NT5qTzWbz2LhoMuBpfpmZmZN6XhKe5jcV7jMFBQUUFBRgMBhcWk9PhfsKCEPts0jtUDMyMtiyZQsGg8HLIxobiouL3d70PG2fTLibg81mo6ioCL1eT1pa2pgLx0wknj6jrKwsdDod27dvn/hBjSHu5mexWDCZTOh0OpKTk4mKipq031N385sK9xlJ7jgjI4OsrCyXOUyF+woIQz1hmM1mdDpdv+0Wi8XliVb6UlmtVlkCUKVS+eQT4XDnBD3JhO5K7jxt9xZjNTeVSiUb58LCQrKyssZz2ENmLD+7rKwsduzYQXZ2ts98hmM1P5vNRkNDA5WVleTn57N3716fmONYzc8X7zPDnZter2fbtm3yMb17b/jafWWk+EzDk6nMSKQ9N27cyObNm7HZbJhMpn5d27zNaOVKfZnxmJvJZEKlUvnETWOs52exWNDr9RiNRrRarawR7y3Gcn5arVb+f3FxMVu2bBn7AQ+TsZyfr91nxksGebIjDPUEMBJpT5VKRX5+PmazGa1WO6YSnmPBaORKfZ3xmJter0etVpOVlSX3EfAWYz0/yZvJyMhwuZl6i/H4/AwGA9nZ2aMe21gwlvPztfvMeMggTwVE6NuLDCTtabPZyM3NRavVYjKZfOYmMRiDyZVOZkYyN6PRSG5uLtBzU/TlZJ2RzM9kMmE0GuVjfZnRfDeliIgvM5L5TZb7zEhlkKcKwqP2IgNJe2q1WlQqlfzUOFkawAw0J+j5g+p945BCi562+xIjmdvGjRsxmUxyRz5fXgIYyfz0er3L/Hbs2DERQx0RI/1uThZGMj9pOcbX7zMjkUGGyf+ZSghD7YNIX0pfWA8bK3onfrhbw/S0fTIw0NxUKpV805iK85O29/53sjHY/ACvh/NHw2Dzm8z3mYFkkGFy31d6I0LfXmQ8pT29xVSck8RUnhuI+U12pvL8pvLchoIw1F7E05PeWEh7eoupOCeJqTw3EPOb7Ezl+U3luQ0FYagnmKko7TkV5yQxlecGYn5ifr7LVJ7bcBFr1BPAVJT2nIpzkpjKcwMxPzE/32Uqz200CPUsgUAgEAh8GBH6FggEAoHAhxGGWiAQCAQCH0YYaoFAIBAIfBhhqAUCgUAg8GGEoRYIBAKBwIcRhlogEAgEAh9GGGqBQCAQCHwY0fBEIJjmSOpDqamppKeny9sKCwvdamcnJyejUqnYu3dvv85QkopRTk6Oy3aDwUB0dDT19fUYjUb27t07aZWMBIKJRhhqgWCaI8kDGgwGl1aNA7Vn9KQmlZ2dzQ033NDPUBcUFFBSUuLzmtwCgS8iQt8CwTTHZrNhtVplIy3p945EHlCr1aJWq2V9Y+hpCylpHwsEguEjDLVAMM0xmUxoNBoKCgrIysqSPd6+QgjuMBgM6HQ60tPT5fOysrJcPOq8vDyys7PHZ/ACwTRAGGqBYJpTVFREVlYWGRkZ6HS6IXvSBQUFWCwWSkpKyM/Pl43zli1bMJlM2Gw2bDYbZrNZrEcLBKNArFELBNMck8nEpk2bANi4cSMqlQqz2YxKpRrQqy4sLJQT0eC/HrhKpSIjIwOj0Qj0eNgCgWDkCEMtEExzLBaL7PFK68i7du3qlxDWF51Oh9VqleUGe+sHZ2VlkZWVJWeHCwSCkSMMtUAwTZEyvdVqNQUFBQCy4R2KF7xlyxYqKyvR6XSo1Wq0Wq1s3KXwuUajEUlkAsEoEXrUAoFgWCQnJ1NZWTni8zMzM8nOzhbr1gLBEBHJZAKBQCAQ+DDCUAsEgmGh1+tJT093WZMeKgaDAZvNJsLhAsEwEKFvgUAgEAh8GOFRCwQCgUDgwwhDLRAIBAKBDyMMtUAgEAgEPoww1AKBQCAQ+DDCUAsEAoFA4MMIQy0QCAQCgQ8jDLVAIBAIBD6MMNQCgUAgEPgwwlALBAKBQODD/H/mfgznLMe3lwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load saved data\n",
    "data = np.load(\"data_figure_3.npz\", allow_pickle=True)\n",
    "\n",
    "# Extract vertical lines\n",
    "vertical_lines = data['vertical_lines'].item()\n",
    "\n",
    "# Create plot\n",
    "fig, ax = plt.subplots(figsize=(5,4))\n",
    "\n",
    "# Add Subedi and Kuhlen data\n",
    "ax.scatter(data['energies_subedi_2048'], data['kappas_subedi_2048'], marker='+', c='purple', zorder=120, label=r'turb.: $k^{-5/3}$ (Subedi et al., 2017)')\n",
    "ax.scatter(data['energies_kuhlen'], data['kappas_kuhlen'], marker='2', c='peru', zorder=214, label=r'turb.: $k^{-5/3}$ (Kuhlen et al., 2022)')\n",
    "\n",
    "# MHD simulation data\n",
    "ax.scatter(data['energies_mhd'], data['kappas_mhd'], facecolors='none', marker='P', edgecolors='olive', zorder=119, label='turb. from MHD sim.')\n",
    "\n",
    "# Plot scatter data for PW simulations\n",
    "ax.scatter(data['energies_PW_GS'], data['kappas_PW_GS'], marker='v', facecolors='none', edgecolors='teal', label=r'mm ($f_\\mathrm{mm} = 1$) \\& turb.: $k^{-2}$')\n",
    "ax.scatter(data['energies_PW_kol'], data['kappas_PW_kol'], marker='d', facecolors='none', edgecolors='brown', label=r'mm ($f_\\mathrm{mm} = 1$) \\& turb.: $k^{-5/3}$')\n",
    "plt.scatter(data['energies_f_01'], data['kappas_f_01'], marker='d', facecolors='none', edgecolors='darkgrey', label=r'mm ($f_\\mathrm{mm} = 0.1$) \\& turb.: $k^{-5/3}$')\n",
    "ax.scatter(data['energies_PW_kra'], data['kappas_PW_kra'], marker='p', facecolors='none', edgecolors='#08306b', label=r'mm ($f_\\mathrm{mm} = 1$) \\& turb.: $k^{-3/2}$')\n",
    "\n",
    "plt.scatter(data['energies_pic'], data['kappas_pic'], marker='*', facecolors='none', edgecolors='k', label=r'mm ($f_\\mathrm{mm} = 1$) field from PIC sim.')\n",
    "\n",
    "# Plot theoretical prediction lines\n",
    "b_turb = 3e-6 # [G]\n",
    "c = 3e8\n",
    "pc = 3.086*10**16\n",
    "\n",
    "def scatter_rate_mirror(energy, b):\n",
    "    r_g = 3.34*1e19 * (energy/1e18) * (1e-6/b)  # [m]\n",
    "    l_mm = 1e-7*pc\n",
    "    scatter_rate = l_mm * c / (r_g**2) * (1/3.)**2\n",
    "    \n",
    "    #scatter_rate = 5e17 / r_g**2 # [1/s]\n",
    "    kappa = c**2/scatter_rate # isotropic kappa = 3*kappa_xx\n",
    "    return scatter_rate\n",
    "\n",
    "def kappa_QBR(energy, b):\n",
    "    l_c = 5e5*pc/5\n",
    "    r_g = 3.34*1e19 * (energy/1e18)  # [m]\n",
    "    return r_g**2*c/(2*l_c) # isotropic kappa = 3*kappa_xx\n",
    "\n",
    "def bohm(energy, b):\n",
    "    r_g = 3.34*1e19 * (energy/1e18) * (1e-6/b)  # [m]\n",
    "    return 3*r_g*3e8/3 # isotropic kappa = 3*kappa_xx\n",
    "\n",
    "def powerlaw(energy, b, index):\n",
    "    if index == 1/2:\n",
    "        l_c = 5e5*pc/6\n",
    "    else:\n",
    "        l_c = 5e5*pc/5\n",
    "    r_g = 3.34*1e19 * (energy/1e18)/3  # [m] in 3muG b_field\n",
    "    return l_c*3e8*(r_g/l_c)**(index) #check factor 3\n",
    "\n",
    "x0 = 2.6e20\n",
    "lw = 1.2\n",
    "x = np.logspace(8, np.log10(x0), 10)\n",
    "plt.plot(x, 1e4 * c**2 / scatter_rate_mirror(x, b_turb), ls='--', alpha=0.5, lw=lw, c='k', zorder=-4)\n",
    "plt.text(2.4e13 / 1.8, 0.8e4 * 2e30 / 4, r'mm: $\\propto E^{2}/l_\\mathrm{mm}$', fontsize=8, color='k', rotation=55)\n",
    "\n",
    "plt.plot(x, 1e4 * powerlaw(x, b_turb, 0.0) / 7.0, ls='-.', alpha=0.5, lw=lw, c='grey', zorder=-4)\n",
    "plt.text(1.4e8, 2e33, r'$\\propto l_\\mathrm{c}$', fontsize=8, color='grey')\n",
    "\n",
    "plt.plot(x, 1e4 * powerlaw(x, b_turb, 1/3.), ls='-.', alpha=0.5, lw=lw, c='grey', zorder=-4)\n",
    "plt.text(1.2e8, 4e30 / 3, r'$\\propto E^{1/3}l_\\mathrm{c}^{2/3}$', fontsize=8, color='grey', rotation=12.9)\n",
    "\n",
    "plt.plot(x, 1e4 * powerlaw(x, b_turb, 0.5), ls='-.', alpha=0.5, lw=lw, c='grey', zorder=-4)\n",
    "plt.text(1.2e8, 5e3 * 2.3e23, r'$\\propto E^{1/2}l_\\mathrm{c}^{1/2}$', fontsize=8, color='grey', rotation=19)\n",
    "\n",
    "plt.plot(x, 1e4 * bohm(x, b_turb), ls='-.', alpha=0.5, lw=lw, c='grey', zorder=-4)\n",
    "plt.text(8e10 / 5, 2e25 / 20, r'$\\propto E$', fontsize=8, color='grey', rotation=38)\n",
    "\n",
    "x = np.logspace(np.log10(x0) - 0.8, 23, 10)\n",
    "plt.plot(x, 1e4 * kappa_QBR(x, b_turb), ls='-.', alpha=0.5, lw=lw, c='k', zorder=-4)\n",
    "plt.text(1.0e21, 5e36, r'$\\propto E^{2}/l_\\mathrm{c}$', fontsize=8, color='grey', rotation=56)\n",
    "\n",
    "# Add vertical lines and labels\n",
    "plt.text(1.0e20, 1e37, r'$r_\\mathrm{g} \\sim l_\\mathrm{c}$', fontsize=8, color='grey', zorder=-10, rotation=90)\n",
    "plt.axvline(x=x0, c='grey', alpha=0.5, lw=2, zorder=-10)\n",
    "\n",
    "plt.text(130 * 1e6, 1.7e36, r'$r_\\mathrm{g} \\sim l_\\mathrm{mm}$', fontsize=8, color='orange', zorder=-10, rotation=90)\n",
    "plt.axvline(x=100 * 1e6, c='orange', lw=2, zorder=-10)\n",
    "\n",
    "# Streaming theory\n",
    "x_st = np.logspace(7, 13, 10)\n",
    "plt.plot(x_st, np.ones(len(x_st)) * 1e29, ls=':', alpha=0.5, lw=lw * 1, c='grey', zorder=-4)\n",
    "plt.text(1.5e8, 4e28, r'$\\kappa_\\mathrm{st}$', fontsize=8, color='grey')\n",
    "\n",
    "# Micro-macro transition\n",
    "plt.text(0.6 * 1.1e12 * 1.2, 0.5 * 6.5e33, r'theory prediction for', fontsize=8, color='w', rotation=90)\n",
    "plt.text(0.6 * 2.8e12 * 1.2, 0.4 * 2e33, r'micro-macro transition', fontsize=8, color='w', rotation=90)\n",
    "plt.axvspan(0.6e12, 5e12, color='dodgerblue', alpha=0.5, zorder=-10)\n",
    "\n",
    "# Arrows for transitions\n",
    "arrow = plt.arrow(0.53 * 1e12 * 1.08, 3e38, -9.9e11 * 1.08 * 0.53, 0, head_width=1.3e38, head_length=0.3e10 * 0.53, color='grey', linewidth=2)\n",
    "plt.text(0.6e11, 2.5e38, r'$\\kappa(E,l_\\mathrm{mm})$', fontsize=8, color='grey', ha='center', va='top')\n",
    "\n",
    "arrow = plt.arrow(9.7e12 * 0.53, 3e38, 1.2e15 * 0.6, 0, head_width=1.3e38, head_length=3e14 * 0.53, color='grey', linewidth=2)\n",
    "plt.text(0.6 * 5.8e13 * 1.7, 2.5e38, r'$\\kappa(E,l_\\mathrm{c},\\delta)$', color='grey', fontsize=8, ha='center', va='top')\n",
    "\n",
    "# Add remaining plot elements\n",
    "plt.xlim([5e7, 5e22])\n",
    "plt.ylim([1e22, 1e39])\n",
    "plt.loglog()\n",
    "plt.xlabel(r'$E$ [eV]', fontsize=8)\n",
    "plt.ylabel(r'$\\kappa$ [cm$^2$s$^{-1}$]', fontsize=8)\n",
    "plt.legend(fontsize=7, title=r'sim. results', title_fontsize=8, loc=4).set_zorder(100)\n",
    "plt.tight_layout()\n",
    "\n",
    "# Show plot\n",
    "plt.show()\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Figure 4\n",
    "Example trajectories in 2-phase medium. Data are available to interested researchers upon reasonable request."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Figure 5\n",
    "1D data are available to interested researchers upon reasonable request."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load saved data\n",
    "data = np.load(\"data_figure_5.npz\")\n",
    "\n",
    "# Extract data\n",
    "f = data['f']\n",
    "y_sim = data['y_sim']\n",
    "y_sim_err = data['y_sim_err']\n",
    "\n",
    "# Constants\n",
    "kappa1 = 0.001\n",
    "kappa2 = kappa1 * 70\n",
    "\n",
    "def kappa_mixed(kappa1, kappa2, f):\n",
    "    s = f + ((1 - f) * kappa1 / kappa2)\n",
    "    return kappa1 / s\n",
    "\n",
    "def hex_to_rgb(hex_color):\n",
    "    hex_color = hex_color.lstrip('#')\n",
    "    return tuple(int(hex_color[i:i+2], 16) / 255 for i in (0, 2, 4))\n",
    "\n",
    "# Create a plot\n",
    "fig, ax = plt.subplots(figsize=(4, 3))\n",
    "\n",
    "# Plot theoretical curve\n",
    "f_detailed = np.logspace(-3, 0, 1000)\n",
    "kappa_mm = 0.001\n",
    "ax.plot(f_detailed, kappa_mixed(kappa1/kappa_mm, kappa2/kappa_mm, f_detailed), c='k', ls='-.', label=r'$(f_\\mathrm{mm}+(1-f_\\mathrm{mm})\\cdot\\kappa_\\mathrm{mm}/\\kappa_\\mathrm{res})^{-1}$')\n",
    "\n",
    "# Plot simulation data with error bars\n",
    "ax.errorbar(f, y_sim, yerr=y_sim_err, fmt='d', capsize=5, label=r'$\\langle r^2 \\rangle /2t$ (3D)', c='brown', markerfacecolor='none', markeredgewidth=1.4, zorder=13)\n",
    "\n",
    "# Customize plot\n",
    "ax.set_yscale(\"log\")\n",
    "ax.set_ylim(0.85, 90)\n",
    "ax.grid(True)\n",
    "ax.set_xlabel(r'$f_\\mathrm{mm}$')\n",
    "ax.set_ylabel(r'$\\kappa_\\mathrm{eff} / \\kappa_\\mathrm{mm}$')\n",
    "ax.legend(loc=1, fontsize=8)\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "# Show plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.9.6"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
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