{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Problem 12.6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Fundamentals of Solar Cells and Photovoltaic Systems Engineering**\n",
    "\n",
    "**Solutions Manual - Chapter 12**\n",
    "\n",
    "**Problem 12.6**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**We will now compare the results of the solar irradiance components on a  35⁰-tilted plane with those obtained at the normal plane (measured using a 2-axis tracking system).**\n",
    "\n",
    "**(a) Using the tabulated data for the solar irradiance components at the normal plane provided in the online repository of this book,  calculate the diffuse irradiance from the global and direct components and calculate the RMSE with respect to the experimentally measured diffuse irradiance.**\n",
    "\n",
    "**(b) Calculate the percentage increase in the cumulative day irradiation of every component (global, direct and diffuse) when the irradiance values are acquired at the normal plane (using a 2-axis tracking system) with respect to a fixed plane at 35⁰.**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use the package [pandas](https://pandas.pydata.org/) to handle the data and [matplotlib.pyplot](https://matplotlib.org/stable/index.html) to plot the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We start by importing the measured solar irradiance components."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>G(n)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time(UTC+1)</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>00:00</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01:00</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>02:00</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>03:00</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>04:00</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05:00</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>06:00</th>\n",
       "      <td>7.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>07:00</th>\n",
       "      <td>501.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08:00</th>\n",
       "      <td>710.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>09:00</th>\n",
       "      <td>824.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10:00</th>\n",
       "      <td>878.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11:00</th>\n",
       "      <td>917.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12:00</th>\n",
       "      <td>925.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13:00</th>\n",
       "      <td>925.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14:00</th>\n",
       "      <td>902.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15:00</th>\n",
       "      <td>882.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16:00</th>\n",
       "      <td>851.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17:00</th>\n",
       "      <td>786.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18:00</th>\n",
       "      <td>674.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19:00</th>\n",
       "      <td>500.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20:00</th>\n",
       "      <td>212.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21:00</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22:00</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23:00</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               G(n)\n",
       "time(UTC+1)        \n",
       "00:00          0.00\n",
       "01:00          0.00\n",
       "02:00          0.00\n",
       "03:00          0.00\n",
       "04:00          0.00\n",
       "05:00          0.00\n",
       "06:00          7.32\n",
       "07:00        501.73\n",
       "08:00        710.20\n",
       "09:00        824.01\n",
       "10:00        878.76\n",
       "11:00        917.63\n",
       "12:00        925.71\n",
       "13:00        925.23\n",
       "14:00        902.60\n",
       "15:00        882.13\n",
       "16:00        851.88\n",
       "17:00        786.07\n",
       "18:00        674.93\n",
       "19:00        500.10\n",
       "20:00        212.07\n",
       "21:00          0.00\n",
       "22:00          0.00\n",
       "23:00          0.00"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "G_normal = pd.read_csv('data/Global_2-axis.csv', index_col=0, header=0) \n",
    "B_normal = pd.read_csv('data/Direct_2-axis.csv', index_col=0, header=0) \n",
    "D_normal = pd.read_csv('data/Diffuse_2-axis.csv', index_col=0, header=0) \n",
    "\n",
    "G_normal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can estimated the diffuse irradiance $D$ by substracting the direct irradiance $B$ from the global irradiance $G$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "time(UTC+1)\n",
       "00:00      0.00\n",
       "01:00      0.00\n",
       "02:00      0.00\n",
       "03:00      0.00\n",
       "04:00      0.00\n",
       "05:00      0.00\n",
       "06:00      7.32\n",
       "07:00    138.64\n",
       "08:00    190.80\n",
       "09:00    212.49\n",
       "10:00    222.97\n",
       "11:00    223.29\n",
       "12:00    234.33\n",
       "13:00    239.43\n",
       "14:00    244.45\n",
       "15:00    248.33\n",
       "16:00    246.44\n",
       "17:00    228.76\n",
       "18:00    198.15\n",
       "19:00    145.92\n",
       "20:00     56.51\n",
       "21:00      0.00\n",
       "22:00      0.00\n",
       "23:00      0.00\n",
       "dtype: float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D_normal_estimated = G_normal['G(n)']-B_normal['Gb(n)']\n",
    "D_normal_estimated"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot the measured and estimated irradiance components. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1f913256e88>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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y+IwZMxgxYoTh8XvvvceCBQsM+4KCgggNDeWrr77iiSee4OeffyYuLo5Dhw7h5qZ8OgwODi4M3dERS0tLfHx8TBK/qJ5SMnMZ+91BQqNSAWVG5FUTuxEoQ5trBJVKxYCW3vRu5slP+6+w+J+LJGfmkpOn44udl/m/w1d5YUBTHukUgKVaOgZEzSHJTnmkx0LaDXNHUSF6vf62E7h16tTJsB0XF8fVq1d5+umnmThxomF/Xl6eoYXm+PHjtG/f3pDoiLonNTuXccsOcOp6CgAejjb8PLFbnV7Is6ayUlswvmcQD7avz5IdF1mxN4JcrZ74dA2vrz3Nij0RvH5fC3o38zJ3qEKUiiQ75eFopv/gJrzv2bNnCQoKuuVxB4fCT+IFI7e++eYbunbtanSeWq0MHbazk1WW67K07FyeWHaQE9cKEh1rVk3sSrCXJDo1mYu9Fa/f15Ix3Royf/M5Np6KBuBibDrjlx/i7iYevH5fC5r73HrmWiGqA0l2ysNEXUnmsn37dk6dOsULL7xQqvO9vb3x9/cnLCyM0aNHl3hOmzZt+Pbbb0lMTCyxdcfa2hqtVlvCM0VNl56Tx/jlhzgWmQwo8+j8NKEbTbxlVuTaoqG7A1+M7sjhiETe23DWMErrv4vxDPnkPx7p3IA3h7bA3lreUkT1JJ2utVxOTg7R0dFcv36do0ePMm/ePIYPH87QoUMZN25cqa8zZ84cPvjgAz755BMuXLjAqVOnWL58OQsXLgTgsccew8fHhwceeIA9e/YQFhbG77//zr59+wAIDAwkPDyc48ePEx8fT05OTqW8XlG1MnLyeHL5QY7kL0VQz96KnyZ0leUfaqlOgW6sfa4HnzzaDn9XpTVXp4dVByOZsfo4Op3MzSOqJ0l2arnNmzfj6+tLYGAggwcPZseOHXz66aesW7fO0AVVGhMmTODbb79lxYoVtG7dml69erFixQpDV5i1tTVbtmzBy8uLIUOG0Lp1az788EPDPUaOHMngwYPp06cPnp6erFq1qlJer6g6mZo8nlxxiEMRSqLjam/FygldaeErXRq1mYWFiuHt/PlnZi9eHtwMe2vl//iW0BgWb7tg5uiEKJmseo6sei7qrvL+fmdptDy14hD7whIAcLa15OeJ3QjxlykF6pqd52N5asUhChp1ljzenqFt/MwblKgzSrvqubTsCCHKJDtXy8QfDhsSHSdbS1ZO6CqJTh3Vu5kXrw1pYXg869cTnM4fkSdEdSHJjhCi1AoSnd2X4gFwsrHkx6e70qa+q3kDE2b19F1BjOxQH4DsXB3P/HCYuDSpyxPVhyQ7QohSycnT8uzKI/x3UUl0HKzVrHiqC+0CXM0bmDA7lUrF+w+G0L6BKwA3UrJ5duURcvJkBKaoHiTZEULcUU6eludWHmXn+TgA7K3VfP9UFzo2rGfmyER1YWul5qsxHfFxVmq/jlxJ4s0/Tsvq6aJakGRHCHFbmjwdU346xvZzyhIpdlZqVjzZhU6BMlu2MOblbMvX4zpiY6m8tfzf4Wss3xNh3qCEQJIdIcRt5Gp1TF11lG1nYwCwtbJg2fjOdAmSREeUrE19Vz56qI3h8dwNofx7Ic6MEQkhyY4Q4hbytDqmrTrG32eURMfG0oJlT3Sme2N3M0cmqrvh7fyZ3LsxoEw6+PzPRwnPX0VdCHOQZEcIUaLleyLYdFpZC8na0oJvn+hEj2APM0claopZA5vRv4Wynl9qdh4Tvj9EanaumaMSdZUkO0KIYrJztXz1bxgAKhV8PbYjdzfxNHNUoiaxsFCx6JF2NPVWFoO9HJfB9FXH0MqSEsIMJNkRQhTz65FrxKcr86QMCfGldzMvM0ckaiInWyu+GdcJV3srAHacj+Ojv8+ZOSpRF0myI1CpVPzxxx+Gx+fOnaNbt27Y2trSrl27W+4zl7FjxzJv3jyzxlDdrFixAldXV8PjJUuWMGzYsHJdK0+r46tdlw2Pn8uvvRCiPBq6O/DF4x1QW6gA+GpXGGuPXTNzVKKukWSnlho/fjwqlQqVSoWVlRXe3t4MGDCAZcuWodPpjM6Niori3nvvNTx+++23cXBw4Pz58/zzzz+33GcOJ0+eZMOGDUydOtVsMdQEEydO5NChQ+zevbvMz/3r5A2uJWUB0KuppywDISqsR7AHb9/f0vD4ld9PcfxqsvkCEnWOJDu12ODBg4mKiiIiIoJNmzbRp08fpk+fztChQ8nLyzOc5+Pjg42NjeHx5cuXueuuu2jYsCHu7u633GcOS5YsYdSoUTg5OZkthsqi1WqLJaLlZWNjw+OPP85nn31WpufpdHqW7ixs1ZksrTrCRMZ2a8hjXRoAytxNz/xwmJjUbDNHJeoKsyY7eXl5vPHGGwQFBWFnZ0ejRo149913jf7g6/V65syZg5+fH3Z2dvTu3ZszZ84YXScnJ4epU6fi4eGBg4MDw4YN49o1aSa1sbHBx8cHf39/OnTowGuvvca6devYtGkTK1asMJxXtBtLpVJx5MgR3n33XVQqFXPmzClx386dO1GpVCQnJxuuc/z4cVQqFREREQBcuXKF+++/n3r16uHg4ECrVq3YuHGj4fzQ0FCGDBmCo6Mj3t7ejB07lvj4+Fu+Hp1Ox6+//lqseyYwMJC5c+cybtw4HB0dadiwIevWrSMuLo7hw4fj6OhI69atOXz4sNHz9u7dyz333IOdnR0BAQFMmzaNjIzC4bErV66kU6dOODk54ePjw+OPP05sbKzheFJSEqNHj8bT0xM7OzuaNGnC8uXLAUr18ynoelq/fj0tW7bExsaGK1euoNFoePnll/H398fBwYGuXbuyc+dOo9hXrFhBgwYNsLe358EHHyQhIaHYz2vYsGH88ccfZGVl3fJnerNtZ2O4EJMOQKeG9WQ+HWEyKpWKd4a1MvxOxabl8MwPh8nOlSUlROWzNOfN58+fz5dffsn3339Pq1atOHz4ME8++SQuLi5Mnz4dgI8++oiFCxeyYsUKmjZtyty5cxkwYADnz583fLqfMWMGf/31F6tXr8bd3Z2ZM2cydOhQjhw5glqtNnncj6x/hPisW78pVxYPOw9+GfpLha7Rt29f2rZty5o1a5gwYUKx41FRUfTv35/Bgwcza9YsHB0defbZZ4vtuzlxKMmUKVPQaDT8+++/ODg4EBoaiqOjo+E+vXr1YuLEiSxcuJCsrCxeeeUVHn74YbZv317i9U6ePElycjKdOnUqdmzRokXMmzePN998k0WLFjF27Fh69uzJU089xf/+9z9eeeUVxo0bx5kzZ1CpVJw6dYpBgwbx3nvv8d133xEXF8fzzz/P888/b0hYNBoN7733Hs2aNSM2NpYXXniB8ePHGxK2N998k9DQUDZt2oSHhweXLl0qU2IBkJmZyQcffMC3336Lu7s7Xl5ePPnkk0RERLB69Wr8/PxYu3YtgwcP5tSpUzRp0oQDBw7w1FNPMW/ePEaMGMHmzZt5++23i127U6dO5ObmcvDgQXr16nXHWPR6PZ8XbdXp0xiVSlWm1yPE7VhbWrB0dAeGLdnD9eQsTlxLYfaaUyx8uK38rolKZdZkZ9++fQwfPpz77rsPUD6hr1q1yvBGqtfrWbx4Ma+//jojRowA4Pvvv8fb25uff/6ZSZMmkZKSwnfffcePP/5I//79AeUTeUBAANu2bWPQoEEmjzs+K57YzNg7n1hNNW/enJMnT5Z4zMfHB0tLSxwdHfHx8QHA0dGx2L7SiIyMZOTIkbRu3RqARo0aGY4tXbqUDh06GBUaL1u2jICAAC5cuEDTpk2LXS8iIgK1Wo2XV/GRQUOGDGHSpEkAvPXWWyxdupTOnTszatQoAF555RW6d+9OTEwMPj4+/O9//+Pxxx9nxowZADRp0oRPP/2UXr16sXTpUmxtbXnqqacM12/UqBGffvopXbp0IT09HUdHRyIjI2nfvr0h+QoMDCz1z6ZAbm4uX3zxBW3btgWU7sJVq1Zx7do1/Pz8AJg1axabN29m+fLlzJs3j08++YRBgwbx6quvAtC0aVP27t3L5s2bja7t4OCAq6srERERpUp29l1O4ER+HUVzHyf6yAgsUQncHW349olOjFy6l0yNlrXHrtPcx4lJvaTLVFQesyY7d911F19++aXhze3EiRPs3r2bxYsXAxAeHk50dDQDBw40PMfGxoZevXqxd+9eJk2axJEjR8jNzTU6x8/Pj5CQEPbu3VtispOTk0NOTo7hcWpqapni9rAzz8RqprqvXq+vkk9R06ZN47nnnmPLli3079+fkSNH0qaNMo38kSNH2LFjh6Glp6jLly+XmOxkZWVhY2NTYuwF1wXw9vYGMCRZRffFxsbi4+PDkSNHuHTpEj/99JPhHL1ej06nIzw8nBYtWnDs2DHmzJnD8ePHSUxMNHSvRkZG0rJlS5577jlGjhzJ0aNHGThwIA888AA9evQo08/I2traKPajR4+i1+uLvf6cnBxDrdTZs2d58MEHjY537969WLIDYGdnR2ZmZqli+cKoVSdYPmmLStPC15mFD7fl2ZVHAfhw8zmaeDvSt7m3mSMTtZVZk51XXnmFlJQUmjdvjlqtRqvV8v777/PYY48BEB2tzN5a8EZVwNvbmytXrhjOsba2pl69esXOKXj+zT744APeeeedcsdd0a4kczt79ixBQUEVuoaFhVLuVXRF49xc49lRJ0yYwKBBg9iwYQNbtmzhgw8+YMGCBUydOhWdTsf999/P/Pnzi13b19e3xHt6eHiQmZmJRqPB2tra6JiVlZVhu+BNuqR9BQmLTqdj0qRJTJs2rdh9GjRoQEZGBgMHDmTgwIGsXLkST09PIiMjGTRoEBqNBoB7772XK1eusGHDBrZt20a/fv2YMmUKH3/8cal+PqAkI0WTCp1Oh1qtLrELtiAxLMsq0omJiXh63nkywHNRqey+pHTNNnS3Z0hI6VvwhCiPwSG+vNC/KYu2XUCvh2mrjvPHlB4Ee9W+wQfC/MxaoPzLL7+wcuVKfv75Z44ePcr333/Pxx9/zPfff2903s2fMEvTMnG7c2bPnk1KSorh6+rVqxV7ITXI9u3bOXXqFCNHjqzQdQreQKOiogz7jh8/Xuy8gIAAnn32WdasWcPMmTP55ptvAOjQoQNnzpwhMDCQ4OBgoy8HB4cS71kwv09oaGiFYi96/5vvHRwcjLW1NefOnSM+Pp4PP/yQu+++m+bNmxsVJxfw9PRk/PjxrFy5ksWLF/P1118b9sOdfz43a9++PVqtltjY2GJxFXQhtmzZkv379xs97+bHoLSQZWdn0759+zve9+eDkYbtZ3s1xlItAzVF5ZvaN5ghrZXf6/ScPCZ8f5jkTI2ZoxK1kVn/or300ku8+uqrPProo7Ru3ZqxY8fywgsv8MEHHwAY/rjf3EITGxtraO3x8fFBo9GQlJR0y3NuZmNjg7Ozs9FXbZSTk0N0dDTXr1/n6NGjzJs3j+HDhzN06FDGjRtXoWsHBwcTEBDAnDlzuHDhAhs2bGDBggVG58yYMYO///6b8PBwjh49yvbt22nRogWgFC8nJiby2GOPcfDgQcLCwtiyZQtPPfUUWm3JozM8PT3p0KFDueaOudkrr7zCvn37mDJlCsePH+fixYv8+eefhvl7GjRogLW1NZ999hlhYWH8+eefvPfee0bXeOutt1i3bh2XLl3izJkzrF+/3vD6SvPzKUnTpk0ZPXo048aNY82aNYSHh3Po0CHmz59vKIyeNm0amzdv5qOPPuLChQssWbKkxC6s//77j0aNGtG48e1rIXK1Ovbkt+p4O9swooP/nX+AQpiAhYWKj0e1paWv8jc4IiGT538+Rp7WNFMwCFHArMlOZmamobm/gFqtNnQ1BAUF4ePjw9atWw3HNRoNu3btMtRGdOzYESsrK6NzoqKiOH36dJnrJ2qbzZs34+vrS2BgIIMHD2bHjh18+umnrFu3rsKj1KysrFi1ahXnzp2jbdu2zJ8/n7lz5xqdo9VqmTJlCi1atGDw4ME0a9aML774AlDqqvbs2YNWq2XQoEGEhIQwffp0XFxciv1OFPXMM88Y1dmUV5s2bdi1axcXL17k7rvvpn379rz55puGLjRPT09WrFjBr7/+SsuWLfnwww/5+OOPja5hbW3N7NmzadOmDffccw9qtZrVq1eX+udzK8uXL2fcuHHMnDmTZs2aMWzYMA4cOEBAQAAA3bp149tvv+Wzzz6jXbt2bNmyhTfeeKPYdVatWsXEiRPveL+07MI5lybe3QgbS9OPYBTiVuytLfnmiU64Oyhd07svxfP+xrNmjkrUOnozeuKJJ/T+/v769evX68PDw/Vr1qzRe3h46F9++WXDOR9++KHexcVFv2bNGv2pU6f0jz32mN7X11efmppqOOfZZ5/V169fX79t2zb90aNH9X379tW3bdtWn5eXV6o4UlJS9IA+JSWl2LGsrCx9aGioPisrq+IvWFRYVlaWvkGDBvq9e/eaO5Rq7dSpU3ovLy99cnLybc9LSUvXb91zRN9j7iZ923f+1qdn51ZRhEIYOxieoA9+bYO+4Svr9Q1fWa8/de32v7tC6PW3f/8uyqwFyp999hlvvvkmkydPJjY2Fj8/PyZNmsRbb71lOOfll18mKyuLyZMnk5SURNeuXdmyZYvRDLqLFi3C0tKShx9+mKysLPr168eKFSsqZY4dYV62trb88MMPt518UMCNGzf44YcfcHG5/VIPSZm5FJQ7j+8RiIONWf8kiDqsc6AbrwxuztwNSqvOku2X+HJsRzNHJWoLlV5fhqEdtVRqaiouLi6kpKQUq9/Jzs4mPDycoKAgbG1tzRShEKaXq9Vx9loCMdev8uHuBH5/vheu9tZ3fqIQlSQ7V8s9H+0gNk2ZGmTLC/fQ1FtGZ4lbu937d1Ey5EKIOio+PccwjP3+Nr6S6Aizs7VS88w9hZOPfr7jkhmjEbWJJDtC1EF5Oh2J6coQX5UKHuoUYOaIhFA83rUBbvnFyn+duEF4fMYdniHEnUmyI0QdlJiuQZvfqmNvrcbD0eYOzxCiathbWzLhbmXSU50evpDWHWECkuwIUcfodHriC1p1ACcpShbVzNhuDXGxU2ZAX3vsOlcTS7fkiRC3IsmOEHVMYqaGvPy5rBxtrGS2ZFHtONla8WTPQADydHq+3HX59k8Q4g7kr5wQdYhOrycurXAR3ILaCCGqm/E9AnHMb3X89fA1olOyzRyRqMkk2RGiDknOzCU3fyp+Z1srbK1lLipRPbnaWzO2e0MANFodX/0rrTui/CTZEahUKv744w/D43PnztGtWzdsbW0Ni2+WtM9cxo4dy7x58yr1Hr1792bGjBmVeg9Tuznmzp07s2bNGsNj/U2tOp5OUpQsqrcJdwVha6W8Ta06GGn0+ytEWUiyU0uNHz8elUqFSqXCysoKb29vBgwYwLJlywxrjxWIiori3nvvNTx+++23cXBw4Pz58/zzzz+33GcOJ0+eZMOGDYYFOytq586dqFQqkpOTjfavWbOm2MKflaEyk6o333yTV1991fDvnZqVS06essiqg42lzJYsqj13RxtGd1Vad7JzdXy7O8zMEYmaSpKdWmzw4MFERUURERHBpk2b6NOnD9OnT2fo0KHk5RUu/ujj44ONTeGn/MuXL3PXXXfRsGFD3N3db7nPHJYsWcKoUaOMlgupDG5ubpV+j8p23333kZKSwt9//41erzfMSgvgJa06ooZ45p5GWFsqb1Ur910hKUNj5ohETSTJTi1mY2ODj48P/v7+dOjQgddee41169axadMmVqxYYTivaDeWSqXiyJEjvPvuu6hUKubMmVPivpJaRI4fP45KpSIiIgKAK1eucP/991OvXj0cHBxo1aoVGzduNJwfGhrKkCFDcHR0xNvbm7Fjx952zSudTsevv/7KsGHDjPZrNBpefvll/P39cXBwoGvXruzcudNw/FZxRERE0KdPHwDq1auHSqVi/PjxQPEWl8DAQObOncu4ceNwdHSkYcOGrFu3jri4OIYPH46joyOtW7fm8OHDhuckJCTw2GOPUb9+fezt7WndujWrVq0yHB8/fjy7du3ik08+MbTCFfzs7vSzycjIMMTi6+vLggULiv281Go1Q4YMYdWqVaTn5JGVq7Tq2FmpDYWfQlR33s62PJI/6WWGRsvyPeFmjkjURJLslFPC8hVc7NX7jl9Xn5tc7LlXn5tcqucmLF9h8rj79u1L27ZtjWo5ioqKiqJVq1bMnDmTqKgoZs2aVeK+0pgyZQo5OTn8+++/nDp1ivnz5+Po6Gi4T69evWjXrh2HDx9m8+bNxMTE8PDDD9/yeidPniQ5OZlOnToZ7X/yySfZs2cPq1ev5uTJk4waNYrBgwdz8eLF28YREBDA77//DsD58+eJiorik08+ueX9Fy1aRM+ePTl27Bj33XcfY8eOZdy4cYwZM4ajR48SHBzMuHHjDEswZGdn07FjR9avX8/p06d55plnGDt2LAcOHADgk08+oXv37kycOJGoqCiioqIICAgo1c/mpZdeYseOHaxdu5YtW7awc+dOjhw5UizmLl268N9//xVr1VGpVLf9txOiOpnUqxGWFsrv7PK9EaRm55o5IlHTyMe7ctKlp5MXE3PH87Q+PsX3JSaW6rm69PRyxXYnzZs35+TJkyUe8/HxwdLSEkdHR3zyY3d0dCy2rzQiIyMZOXIkrVu3BqBRo8I1b5YuXUqHDh2MCo2XLVtGQEAAFy5coGnTpsWuFxERgVqtxsvLy7Dv8uXLrFq1imvXruHn5wfArFmz2Lx5M8uXL2fevHm3jcPNzQ0ALy8vXF1db/t6hgwZwqRJkwB46623WLp0KZ07d2bUqFEAvPLKK3Tv3p2YmBhDi1rRxHDq1Kls3ryZX3/9la5du+Li4oK1tTX29vZGP9c7/Wz8/Pz47rvv+OGHHxgwYAAA33//PfXr1y8Ws7+/P5GRkaRlabCwsMDGUo1z/mRtQtQU9evZM7JDfX45fJW07Dx+3HeFKX2CzR2WqEEk2SknC0dHLL2973ieOv/N9OZ9pXmuRX4riKnp9foq+WQ/bdo0nnvuObZs2UL//v0ZOXIkbdq0AeDIkSPs2LHD0NJT1OXLl0tMdrKysrCxMW6VOHr0KHq9vtj5OTk5htqi28VRFkWf453/71eQQBXdFxsbi4+PD1qtlg8//JBffvmF69evk5OTQ05ODg4ODre9z51+NllZWWg0Grp3727Y7+bmRrNmzYqdb2dnh06nQ5OTg62dHZ7SqiNqqOd6N+bXI1fR6eHb/8IY3yNQiuxFqclvSjm5Pzke9yfHl+u5AUu/MG0wZXT27FmCgoIqdA0LC6UHtKDLBiA317hpecKECQwaNIgNGzawZcsWPvjgAxYsWMDUqVPR6XTcf//9zJ8/v9i1fX19S7ynh4cHmZmZaDQarK2VyfB0Oh1qtZojR46gVhvPGVOQLNwujrKwsipsESlIGEraVzD6acGCBSxatIjFixfTunVrHBwcmDFjBhrN7Qss7/SzKeieK43o2Hhs7eyxtbPDSm2Bq7206oiaKdDDgeHt/Fl77DpJmbn8fCCSiUVWSBfidqRmp47Zvn07p06dYuTIkRW6jqenJ6DU3hQ4fvx4sfMCAgJ49tlnWbNmDTNnzuSbb74BoEOHDpw5c4bAwECCg4ONvm7V8lEwv09oaKhhX/v27dFqtcTGxha7TtGuoVvFUZA0abXa8v8wbuG///5j+PDhjBkzhrZt29KoUaNiiYq1tXWxe9/pZxMcHIyVlRX79+83PCcpKYkLFy4Ui+HQsRO0CFFapDydbLCQVh1Rg03p05iCX+Gv/g0jO9f0/29F7STJTi2Wk5NDdHQ0169f5+jRo8ybN4/hw4czdOhQxo0bV6FrBwcHExAQwJw5c7hw4QIbNmwoNiJoxowZ/P3334SHh3P06FG2b99OixYtAKVoODExkccee4yDBw8SFhbGli1beOqpp26ZeHh6etKhQwd2795t2Ne0aVNGjx7NuHHjWLNmDeHh4Rw6dIj58+cbRn7dLo6GDRuiUqlYv349cXFxpJuwTio4OJitW7eyd+9ezp49y6RJk4iOjjY6JzAwkAMHDhAREUF8fDw6ne6OPxtHR0eefvppXnrpJf755x9Onz7N+PHjDa1tBXLytOzbs5vu9/TF0sICN3tZGkLUbMFeTgwJUVp+49Nz+OXQVTNHJGoKSXZqsc2bN+Pr60tgYCCDBw9mx44dfPrpp6xbt65Yl09ZWVlZsWrVKs6dO0fbtm2ZP38+c+fONTpHq9UyZcoUWrRoweDBg2nWrBlffKF04fn5+bFnzx60Wi2DBg0iJCSE6dOn4+LiUuxNu6hnnnmGn376yWjf8uXLGTduHDNnzqRZs2YMGzaMAwcOEBAQcMc4/P39eeedd3j11Vfx9vbm+eefr9DPpag333yTDh06MGjQIHr37o2Pjw8PPPCA0TmzZs1CrVbTsmVLPD09iYyMLNXP5n//+x/33HMPw4YNo3///tx111107NjR6Nonz4dz/MhBHnjkcTwcrbGwkFYdUfMVLUz+ctdlw0SZQtyOSl+06KKOSk1NxcXFhZSUFJydnY2OZWdnEx4eTlBQELa2tmaKUBTIzs6mWbNmrF692qhAVxjL1eqYOGUG6WmpvPPRJzTzdcKyhCRSfr9FTTTh+0NsOxsLwAcjWvNYlwZmjkiYy+3ev4uSlh1Ro9ja2vLDDz/cdvJBAfFpObh5eDBl1mu4OVqXmOgIUVM937eJYfuLnZfI0+puc7YQMhpL1EC9evUydwjVWp5WR0KGhvHPTsNCpcLDUZaGELVLuwBX7m7iwX8X47mamMW64zcY2bH4PFNCFJCPe0LUMgkZGnT5vdP17K2xUst/c1H7TOtX2Lrz+c5LaHV1viJD3Ib8FSwlKW0SNYFWpyc+XVkaQoUKT6fbj8CS32tRU3UOdKNrkDJpa1hcBhtPRd3hGaIuk2TnDgomjcvMzDRzJELcWWKGxvAJ19XeCmvL24+6K/i9Ljo5ohA1RdHWnSXbL6GT1h1xC1KzcwdqtRpXV1diY5XKf3t7e5luX1RLOr2e2KQM9PkzODtbWZGdnV3iuXq9nszMTGJjY3F1da3wVARCmEOPxu60b+DKschkzsekse1sDANblX79PlF3SLJTCgUz8RYkPEJUR5o8nWF1czsrC25k3bkw2dXVtUyLuwpRnahUKqb2DeapFYcB+Gz7JQa09JYPpKIYSXZKQaVS4evri5eXV7H1n4SoLlbui2D5XiUhnzmwKZ2C/G57vpWVlbToiBqvTzMvWvk5c+ZGKqeup7DrQhy9m3mZOyxRzUiyUwZqtVreHES1te1iEtfTlNlkOzf2kUkCRZ1Q0Lrz7MqjgNK606upp7TuCCNSoCxELZCdq+XolWQAAtzsCHCzN29AQlShgS19aOrtCMCRK0nsC0swc0SiupGWHSFqgSNXktDkzyLbo5GHmaOpItcOQ/RJsHYCm5u/nJXvlrL4aV1gYaFiSp9gpq8+DsBn/1yiR+M68v9AlIokO0LUAnsuFS6f0SPY3YyRVAFNBmx+FY7+cOdz1TYlJ0G3So4cPMHZF5x8wa4eSFdIjTG0jR+Lt10kPD6DfWEJHI5IpFOgm7nDEtWEJDtC1AJ7Lxc223dvXIuTnetHYc1ESLhUuvO1OZCZA5nlWEtNbQNOPuDsp3x38rvpcX5SZC1dhtWB2kLF5N6Neem3kwAs2XGJFU92MXNUorqQZEeIGi41O5eT15IBaOLliJdTLSxM1mlh76ewfS7o8pR9Vg5w94tKi0xOKuSkQXb+d6Ov1MLvBc8tDW0OJF9Rvm7H1qUw8XHyLWwZqhcIQb2kK60KPdDen0/+uci1pCx2no/j5LVk2tR3NXdYohqQZEeIGu5gWCIFE8f2DK6FdQop12HtJIj4r3CfX3sY+R24Ny79dfR6yMu5KQEq8pWdAukxkBYNaTeU76k3IDv59tfNTlG+4s4VP+baEHq9Am0eAbX8ua1sVmoLnuvdmNfXngaUWZW/HtfJzFGJ6kD+9wlRw9XqLqzQdfDntCIJhwruegH6vAbqMi5xoVKBla3y5ehZ+uflZuUnQFHKV2pU4XZBQpQWBXklzFadfAXWTYb/FkDvVyFkJFjI9BWV6aGO9fnsn0tEp2azJTSGs1GptPB1NndYwswk2RGihtt7WalHsVBBt6BakuzkpCtFyMd+LNzn7A8jvobAu6o2Fis7cAtSvm5Fr1cSsoKkKPUGnF4Dl/9RjideVmqN/lsAvWdDi2FgITN/VAYbSzWTejXinb9CAfh8xyWWPN7BzFEJc5P/bULUYPHpOZyLTgMgxN8FF/tasKDn9aPw1T3GiU7LB+C5PVWf6JSWSqWM3vJqAY37QvsxMHYNPLkZAu8uPC/uHPz6hPL6zm1UkiRhco92boCHo1IrteFUFJfj0s0ckTA3SXaEqMH2h9WiLiydFv5bCN8NUFpCQClCHv45jFqhJBM1TcPuMH49jPsTAroW7o85Basfg2/6wsVtkvSYmJ21mgl3NwKUH+03/4aZOSJhbpLsCFGDFa3XqdGTqKVcgx+Gwz/vFI6Y8usAz/6ntJLU9PluGvWCp/6G0b8rxdUFbhyFn0bCskEQtst88dVCY7o1xNFGqdTYfCaavPxJN0XdJMmOEDXY3vzJBK3UKjoH1sCWD4Azf8DSnkVGW6ng7pnw9Jayjbaq7lQqaNIfJu6AR1eBd+vCY1cPwA/DYMVQuLLPfDHWIo42lvRqphSiJ2fmcvhKkpkjEuYkyY4QNdT15CwiEjIBaB9QD3vrGjbeICcd1k1RalgKRls511e6ffq9VfbRVjWFSgXNh8Ckf2HU9+DZvPBYxH+wfDD8+CBcO2K+GGuJgS29DdtbzsSYMRJhbpLsCFFD7avJQ86vH4Gv7oZjKwv3tXoQnttdfYuQTc3CAlo9AM/thRHfgluRVqzL2+HbvvDzIxB1wmwh1nS9m3lhaaF0gW49G41eaqPqLEl2hKih9hZZD6vGTCao0yrDr78bCIn5RaNWDjD8C3hoec0sQq4oCzW0GQVTDio/B9eGhccubFZGbv0yBmLOmC/GGsrFzoqujZT1sa4mZnE+Js3MEQlzkWRHiBpIr9cbipNtrSxoF+Bq3oBKI+U6fD8M/nm3sAjZv2N+EfLoml+EXFFqS+Xn8PxhGLpYmVeowNm/YGkPWPkQhO2U0VtlMKBFYVfWVunKqrMk2RGiBgqPzyA6VZmxt3OgG9aW1fy/8pV9SgvFld35O1Rw9yxlhFJtKkI2BUtr6PQkTD0K934EjoVv1lzaqoxa++puOPELaHPNF2cN0b9I3c7Ws5Ls1FXV/C+kEKIke4rU61T7Lqwj38P39xeuPO5cH8ZvgH5v1t4iZFOwsoWuk2DacRj8Ibg0KDwWfQrWPgOftIU9nyhrc4kS1a9nT8v85SJOXkshOqWEZT1ErSfJjhA10L7LhfU6PaprcbI2Dza+DH9NA11+C0RQL6XbKrCneWOrSaztodtzMO2YUtfkV2Tpg9TrsPUtWNgSNr8GyZHmi7MaGyCtO3WeJDtC1DA6nd4wEsvZ1pJWfi5mjqgEmYmwcgQc/KpwX9dnYcwasHczX1w1mdoSQkbAxO0wfiM0GwLk1zlp0mH/5/BJO/j1SWXJDWFglOyESrJTF9WwiTmEEGejU0nKVFpKujVyR21RzQp7Y8/CqscgKVx5bGEFQxdCh3Hmjau2UKmUlrHAnhB/EfZ9DidWKauu67VwZo3y1bAn9JgKTQbV+UVHW/k54+9qx/XkLPZdjictOxcnW+lCrUvq9v8AIWqgfUZLRFSzLqzzm+DbAYWJjoOnMkmgJDqVw6MJ3L8YXjgDvV8D+yL1W1f2wKpH4fMucHgZ5GaZLUxzU6lUhtadXK2eXRfizByRqGqS7AhRwxith1VdipP1emURz1WPgSZ/LhOfNsrSCA26mTe2usDBA3q/Ai+chvs/AfcmhccSLsL6F2BRK9jxAaTXzTd66cqq2yTZEaIGydXqOJC/0rmHow1NvBzNHBGgyYTfJyiLeJI//0urB5Vh5a4BZg2tzrGyg47jlQkKH/sFGhaZjTozAXZ9qCQ9f02HhMtmC9McugS54WyrVG5sPxdLriwMWqdIsiNEDXLyWgoZGi2gdGGpzD0RX8p1WH4vnP6tcF/fN5RRQ9b25ourrrOwgGaD4ckNSutayEhQqZVj2hw4skLp3vprBqTeMGekVcZKbUGf5l4ApGXncSAs0cwRiaokyY4QNUi1GnJ+9SB83RuijiuPrRzgkZ/gnpdkNuTqxL8DPLQMph+H7s+DtZOyX5cHR5bDp+1hyxuQkXDby9QGxl1Z0WaMRFQ1SXaEqEH2XKomkwke+wlW3AcZscpj14YwYSu0GGq+mMTtuTaAQe/Di2eg9+zCpCcvG/Z+pkxQuPNDyKm960f1auqJlTp/YdDQGFkYtA4pc7Jz/vx55syZQ79+/WjcuDG+vr60adOGJ554gp9//pmcnJzKiFOIOi87V8uRyCQA6tezI8DNDN1E2jxl8rp1k0GrUfYF3q10lXi3qvp4RNnZukDvV2H6CaWlR22j7Nekwc4PlKRn7xLIrX0zDTvZWtG9sfIh4UZKNmdupJo5IlFVSp3sHDt2jAEDBtC2bVv+/fdfOnfuzIwZM3jvvfcYM2YMer2e119/HT8/P+bPny9JjxAmdvRKEpo8pajSLF1YWUnw8yhl8roCnSfA2LXgUM2GwIs7c3BXWnqmHVOKmgtqejITYMvr8FkHpbanlq2/JaOy6iaVvpTteA0bNuSll17i8ccfx83t1jOg7tu3j0WLFtGuXTtee+01kwVamVJTU3FxcSElJQVnZ2dzhyNEif739zk+36GMoPnk0XYMb+d/h2eYUNwFZc6WxPwRPBaWMOR/0OmpqotBVK6Ey7BjnnGxOYBbY+jzGrQaUSsmJ4xOyabbB/8A0NLXmY3T7zZzRKIiSvv+XepkR6PRYG1tXeoAynq+OUmyI2qCB7/Yw7HIZAAOvtYPL2fbqrnxxa3w21OQk9/kb+8OD/8o61vVVtGnYPtcuLDZeL93a2Xx1iYDa3wB+rAluzl5TVk8dfcrfahfT0YO1lSlff8udZpe1sSlpiQ6QtQEadm5hj/OwV6OVZPo6PWw51P4aVRhouMdotTnSKJTe/m0hsd/gae2GM/TE3MKfn4Ylg2GiD3mi88EBrQo7MraJl1ZdUK52iSvXbtGenp6sf25ubn8+++/FQ5KCGHsYHgiWp3SCNuzqup1jiyHrW9imCiwxf3KRIH1GlbN/YV5NeiqLPUxZg34tivcf3U/rBgCP46AG8fMFl5FDGzlY9iWVdDrhjIlO1FRUXTp0oWGDRvi6urKE088YZT0JCYm0qdPH5MHKURdV3SJiILRJJUqIwG2vVP4uNerMOoHsKkGMzaLqqNSQXA/eGYnPPwDeDQtPHb5H2Wepf8bB3HnzRVhuTT1dqRB/mjGA2GJpGTWriJsUVyZkp1XX30VtVrNgQMH2Lx5M6GhofTu3ZukpCTDOWWdt+D69euMGTMGd3d37O3tadeuHUeOHDG63pw5c/Dz88POzo7evXtz5swZo2vk5OQwdepUPDw8cHBwYNiwYVy7dq1McQhRnRUkOyoVdGt06wECJrNjLmQnK9ttHoE+s2tFcaooJ5UKWg6HyfvhgaXg0qDwWOg6+KK7sthoDVF0YdA8nZ4d52PNHJGobGX667Vt2zY++eQTOnXqRP/+/dm9ezf169enb9++JCYqU2+XZfr6pKQkevbsiZWVFZs2bSI0NJQFCxbg6upqOOejjz5i4cKFLFmyhEOHDuHj48OAAQNISyuc+GrGjBmsXbuW1atXs3v3btLT0xk6dCharbYsL0+IaikhPYezUUrNTIifC672lVwPF3UCDi9Xtq0dof87tz9f1B0Wamj3OEw9DPf+DxyU5RfQa5XFRrfPVWq9agAZgl63lCnZSUlJoV69eobHNjY2/PbbbwQGBtKnTx9iY8uWHc+fP5+AgACWL19Oly5dCAwMNExWCEqrzuLFi3n99dcZMWIEISEhfP/992RmZvLzzz8bYvruu+9YsGAB/fv3p3379qxcuZJTp06xbdu2Eu+bk5NDamqq0ZcQ1dX+Imv4VPr8Ono9bHoFQ53OPS+Bs2/l3lPUPJY20PUZZQmKbpML9//7P1j3fI2Ym6dTw3q42lsBsPN8LDl58uG4NitTstOoUSNOnjxptM/S0pJff/2VRo0aMXRo2aaK//PPP+nUqROjRo3Cy8uL9u3b88033xiOh4eHEx0dzcCBAw37bGxs6NWrF3v37gXgyJEj5ObmGp3j5+dHSEiI4ZybffDBB7i4uBi+AgJkZWZRfe0tsh5W98pOdk7/DpH7lG23xtDtucq9n6jZrB1g8AcweD6Q36p/fCWsegxyig9iqU4s1Rb0zV8YNEOjZd/l2r82WF1WpmTn3nvv5euvvy62vyDhadeuXZluHhYWxtKlS2nSpAl///03zz77LNOmTeOHH34AIDpaWajN29vb6Hne3t6GY9HR0VhbWxu1ON18zs1mz55NSkqK4evq1atliluIqlRQr2NpoaJLUCXW6+Skw5Y3Cx8P/lD5BC/EnXR7FkYtB3V+F+ulrfD9UEiPM29cdzBQurLqDMuynPz++++TmZlZ8oUsLVmzZk2ZCoN1Oh2dOnVi3rx5ALRv354zZ86wdOlSxo0bZzjv5jogvV5/x9qg251jY2ODjY38ERfV343kLMLjMwBo38AVe+sy/Zctm90LIe2Gst1kEDQdePvzhSiq1YPg4AmrH4fsFGVY+ncDYMzv4N7Y3NGV6O4mnlhbWqDJ07HtbAzvDQ/BwqJmT5goSlamlh1LS8vbzlCoVqtp2LD0c3D4+vrSsmVLo30tWrQgMjISAB8fZS6Em1toYmNjDa09Pj4+aDQaoxFhN58jRE21r6qGnCeGKStfA1hYKV0TQpRV4F3KXEzO+UuZJIXDdwPh2pHbP89MHGwsuStY+X8Vk5rDqespZo5IVJZyjSXV6/X8+uuvTJ48mYceeogRI0YYfZVWz549OX/eeH6GCxcuGBKmoKAgfHx82Lp1q+G4RqNh165d9OjRA4COHTtiZWVldE5UVBSnT582nCNETbWnSL1OpU4m+PfrhauYd59SbT+JixrAqwU8vRW88j/IZsYrXVoX/jZvXLcgo7LqhnIlO9OnT2fs2LGEh4fj6OhoVOzr4uJS6uu88MIL7N+/n3nz5nHp0iV+/vlnvv76a6ZMmQIo3VczZsxg3rx5rF27ltOnTzN+/Hjs7e15/PHHAXBxceHpp59m5syZ/PPPPxw7dowxY8bQunVr+vfvX56XJ0S1oNfrDS07tlYWtGvgWjk3urgNzm9Uth194J5ZlXMfUXe4+MOTmwqXm8jNVIqWj/5g3rhK0K+Fl2GpL0l2aq9yFQCsXLmSNWvWMGTIkArdvHPnzqxdu5bZs2fz7rvvEhQUxOLFixk9erThnJdffpmsrCwmT55MUlISXbt2ZcuWLTg5ORnOWbRoEZaWljz88MNkZWXRr18/VqxYgVqtrlB8QphTREImUSnZAHQOdMPGshJ+n/M0sPnVwscD3gUbp1ufL0Rp2bnC2DWwdhKcWavMxfPnVEiNgl4vV5vFRL2cbGkf4MrRyGTOx6QRmZBJA3dZGLS2KfWq50UFBQWxadMmmjdvXhkxVTlZ9VxURyv3X+GNP04D8Mrg5jzXuxK6lvZ+BlveULYDuir1FtXkTUjUEjodbHkd9n9RuK/DE3DfQlBXYsF9GSzdeZn5m88B8MZ9LZhwdyMzRyRKy+Srnhc1Z84c3nnnHbKyssodoBDi9ooWJ1fKZIJpMbBzfv4DFdz7kSQ6wvQsLJSC94FzC/cd/R5+GQOakkf3VrWidTtbpCurVipXsjNq1CiSkpLw8vKidevWdOjQwehLCFExOp2efWFKsuNka0mIf+lr4Urtn3dAk7/sSscnwK+d6e8hRIEeU2Hkd8poP4ALm+D7+5VFZ80s2MuRRh4OAByOSCQxQ2PmiISplasNcfz48Rw5coQxY8bg7e1dpvWwhBB3di46zfAHt1sjd9Smnvvj6iE4/pOybesCfd+8/flCmELrh5S5eH4ZAzmpcP1w4Vw8bkFmDW1AS2+++jcMnR62n4vloY71zRqPMK1yJTsbNmzg77//5q677jJ1PEIIjJeIMHkXlk4Hm14qfNzndXCoxDl8hCiqUS94ciOsfAjSoyHxsjIXz+hfzdq6WJDsAGwNjZZkp5YpVzdWQECAFPIKUYmM63VMnIgc/0mZ3RbAswV0etq01xfiTnxaw4St4NFMeZwRCyvug0v/mC2k9g3q4e6gLHfx74V4snNlYdDapFzJzoIFC3j55ZeJiIgwcThCiDytjgPhykrnHo7WNPV2NN3Fs5Jh25zCx/fOrzYjYkQd49oAntoMAd2Ux5p0+PlhOL7KLOGoLVT0a6EsDJqVq2XPpfg7PEPUJOVKdsaMGcOOHTto3LgxTk5OuLm5GX0JIcrv5PUU0nPyAGWJCJPWxO36SJnRFqDlcKVLQQhzsXeDcX9Ai/uVx7o8+ONZ2L3YLOEMaOlj2JYJBmuXcn2kW7RokRQlC1FJKm3Ieew5OPiVsm1pazwUWAhzsbKDUd/Dplfg0DfKvm1vK11dwf2qNJS7gj2wtbIgO1fHtrOx6HR6WRi0lihTsrNlyxb69OnD+PHjKykcIUTR5vOepqrX0eth8yvKJ2eAu15QuhGEqA4s1DDkf2DvDrs+VPb9OQ0m71VGC1YRO2s1dzfxZGtoDPHpORy7mkzHhvWq7P6i8pSpG+vZZ5/F09OTRx55hJ9//pnk5ORKCkuIuik7V8vhK0kA+LvaEeBmZ5oLn9sAYTuVbZcG0HO6aa4rhKmoVNDrFQi6R3mcek1ZoLaKDZSFQWulMiU7YWFh/Pvvv7Ru3ZrFixfj4+NDv379+PTTT6VYWQgTOBqZhCZPByhdWCbpLs7Ngr9nFz4e9L7SdSBEdWNhAcOWgHV+Uf6xH5WFaqtQvxbeFPRcbQmNrtJ7i8pT5gLlNm3a8MYbb3Dw4EHCwsIYNWoUmzdvpkWLFrRt25a33nqLw4cPV0asQtR6ey8V1uv0DDZRF9beJZAcqWwH9SosBhWiOqrX0Lie7M+pyijCKuLmYE2nhspAm7C4DC7HpVfZvUXlKddorAJ+fn48++yzbNy4kfj4eN566y0iIiIYPHgw8+bNM1WMQtQZRScT7G6K4uSUa/DfAmVbpVaGmsvgAlHddRwPjfoo22k34O/XqvT2A6Qrq9YpU7Jz6dKlWx5zcHBg5MiR/PDDD8TGxjJx4sQKBydEXZKek8eJaykANPZ0wNvZtuIX3fIm5OUv2NvlGfBqUfFrClHZVCoY9hlYOymPj/8EF/6usttLslP7lCnZadq0KQEBAYwbN47ly5ffsk7HwsICT09PU8QnRJ1xMDwBrU4PmKgLK2I3nFmjbNu7Q+9XK35NIaqKawAMLtJD8Oc0yEqqklsHejjQxEupGzoamURcWk6V3FdUnjIlO7t27WLSpEncuHGD559/nsaNGxMUFMTTTz/NypUruX79emXFKUStV7Rep8Lz62jzlHlLCvR7G+xcK3ZNIapa+7EQ3F/ZTo+GTVWXsBe07uj1sP2ctO7UdGVKdu6++27eeOMNtm3bRnJyMjt27ODJJ58kPDycZ555hgYNGtCsWbPKilWIWm1v/mSCKpWy0nmFHFkOMaeVbd920H5Mxa4nhDmoVHD/p2CTP9fOydVwbmOV3Fq6smqXchcoW1lZcc899/DSSy8xe/ZsJk+ejKOj423reoQQJUvM0BAalQpAKz9nXO2ty3+xjATYXmQ0y5D/KZO2CVETufjD4A8KH6+fAZmJlX7btvVd8XKyAeC/i/FkavIq/Z6i8pQ52cnOzmb79u28+eab3H333dSrV49p06aRnp7O0qVLiYyMrIw4hajV9oeZcJXzHXMhO1nZbvsYBHSp2PWEMLd2j0OTQcp2egxsernSb2lhoaJfC6V1JydPx38XZWHQmqxMy0X06tWLQ4cO0bhxY+655x6mTp1Kr1698Pb2vvOThRC3ZLIh51En4PByZdvaEfrPqVhgQlQHKhXc/wl80RWyU+DUr8pCtpU8Z9TAlt6sOqh8gN8aGsOgVj53eIaorsrUsrN37148PDzo06cP/fr1o2/fvpLoCGECBcXJlhYqugS6le8ien1+AacyooteL4OT/HEWtYSzL9z7UeHj9S8oXbaVqHtjdxyslS7gf87GkKfVVer9ROUpU7KTnJzM119/jb29PfPnz8ff35/WrVvz/PPP89tvvxEXF1dZcQpRa0WlZBEWnwFAuwBXHGzK1OBa5ELHIXKvsu0eDF2fM02AQlQXbR6BZkOU7Yw42DirUm9na6WmVzNlGpWkzFyOXKmaoe/C9MqU7Dg4ODB48GA+/PBDDhw4QHx8PB999BH29vZ89NFH1K9fn5CQkMqKVYhaad9lEw05P/Vb4Xb358GyAkXOQlRHKhUMXQx2+SuRn1kDZ9ZW6i1lVFbtUKHlIhwcHHBzc8PNzY169ephaWnJ2bNnTRWbEHXCnqLz65R3MkGdrvCPvoWlUs8gRG3k5A1DPi58vGEmpFder0KfZl6o81cG3Xo2Br1eX2n3EpWnTMmOTqfj4MGDfPTRR9x77724urrSo0cPvvjiC3x8fPj8888JCwurrFiFqHX0ej378ouTbSwtaN/AtXwXurofUvMn9WzcD+zLWfcjRE0QMhKaD1W2MxNgw4tKzVolcLW3NtTRXUnI5GKsLAxaE5WpOMDV1ZWMjAx8fX3p3bs3CxcupE+fPjRu3Liy4hOiVruSkMmNlGwAOge6YWNZzvlwTv9euB0y0gSRCVGNqVQwdBFc2QtZiXD2T6VLq5J+9we09GZf/vQQW0NjaOrtVCn3EZWnTC07//vf/zh79izXrl1j5cqVTJgwQRIdISpgT5Eh5z2Cy1mvo82DM38o25a20HxIxQMTorpz9IL7FhQ+3jAL0mMr5VZF63a2SN1OjVSmZGfSpEk0bdq0smIRos45EFY4E2y5JxMM3wWZ+UlT00FgI586RR0RMgJaPqBsZyUqw9EroTsrwM2e5j7K/6sTV5OJSc02+T1E5Sp1svPss89y9erVUp37yy+/8NNPP5U7KCHqiiuJmYDSKt/S17l8FzHqwnrIBFEJUYPctwDs8z8onFtvPCrRhAYWad0pOgmoqBlKXbPj6elJSEgIPXr0YNiwYXTq1Ak/Pz9sbW1JSkoiNDSU3bt3s3r1avz9/fn6668rM24haoXolCwAPB1tsLYsx+DIvBw4+5eybe0ETQaYMDohagAHDxi6EP5vnPJ44ywIutvkE2p2aFjPsH02Ko0H25v08qKSlfqv63vvvcfFixe55557+PLLL+nWrRsNGjTAy8uLZs2aMW7cOMLCwvj222/Zt28frVu3rsy4hajxcrU6YtNyAPB1sS3fRS5tgxxlAVGa3wdWdiaKTogapOXwwuLk7GT4a4bJu7Na+hW2vIbeSDXptUXlK9NoLC8vL2bPns3s2bNJTk7mypUrZGVl4eHhQePGjVGpVJUVpxC1TlxajuHvsU95k52iTfatpQtL1GFDPobw/yAjFi5sgpO/QNtHTXZ5LydbPBxtiE/PITQqFb1eL+95NUi5JxV0dXWlbdu2dOvWjeDgYPlHF6KMolIKixx9XcrRIqPJgAublW07N2jU2zSBCVET2bspw9ELbHoZUm+Y9BYFrTuJGRpiUnNMem1RuSo0g7IQovyiiyQ75WrZOb8JcpUCZ1oOB7WViSITooZqMRRaP6xsZ6fAX9NN2p1VdBBBaFSKya4rKp8kO0KYSVR+cTKUs2ZHJhIUorh754Nj/sipi1vg+M8mu7TU7dRckuwIYSZGLTvOZUx2spLg4lZl28kXGvYwYWRC1GD2bnD/J4WPN78KKddNcmnjlh1JdmoSSXaEMJOo1ArU7JxdD7pcZbvVg2BRzmUmhKiNmt0LbR9TtnNSYeubJrlskIcDtlbK26a07NQs5U528vLy2LZtG1999RVpaWkA3Lhxg/R0WSRNiNIo2rLj5WxTtiefLjIKSyYSFKK4wR8UTjZ45g9ILt2kuLejtlDR3Edp3YlIyCQ9J6/C1xRVo1zJzpUrV2jdujXDhw9nypQpxMXFAfDRRx8xa9YskwYoRG1VkOy4O1hja1WGlpn0WAj/V9muFwj+HUwfnBA1nV096DJR2dZr4eBXJrls0bqdc9KVVWOUK9mZPn06nTp1IikpCTu7wub3Bx98kH/++cdkwQlRW2l1esP6OmUeiXXmD9DrlO2QkcpaE0KI4jo9Der8VtMjP0BOxXsepG6nZipXsrN7927eeOMNrK2tjfY3bNiQ69dNUwgmRG2WkJ5Dnk4ZElvmkVgyCkuI0nH0hDajlO2cFDhe8TUbZURWzVSuZEen06HVaovtv3btGk5OsuKyEHcSVd45dpKvwtX9yrZnC/BuZeLIhKhluk0u3N6/FHS6Cl2uuY+ToTFVWnZqjnIlOwMGDGDx4sWGxyqVivT0dN5++22GDBliqtiEqLXKPXvymTWF29KqI8SdebcqnF08Kbxw1vFysre2JMjDAYBz0WnkaSuWPImqUa5kZ9GiRezatYuWLVuSnZ3N448/TmBgINevX2f+/PmmjlGIWie6yISC3mWZY6foWlghI0wYkRC1mFHrzhcVvlxB3Y4mT0dYfEaFrycqX5kWAi3g5+fH8ePHWb16NUeOHEGn0/H0008zevRoo4JlIUTJjOfYKWWyE38Rok8q237twb1xJUQmRC0UPADcm0DCRYj4D6JOgm+bcl+upZ8z609GAUrdTlNvKd+o7sqV7ADY2dnx5JNP8uSTT5oyHiHqhHKti3VaurCEKBcLC+j2LGyYqTze/wU8+GW5L3fziKwH2vtXNEJRycrVjfXBBx+wbNmyYvuXLVsm3VhClEJUWZeK0OuNJxJsJV1YQpRJ28fA1lXZPvUbpMWU+1IyIqvmKVey89VXX9G8efNi+1u1asWXX5Y/Wxairiho2XG2tcTBphQNrDGnIf6Cst2gB7jIJ0khysTaATqOV7Z1uXDo23JfysvJFg9HZf6e0KhU9CZcWV1UjnIlO9HR0fj6+hbb7+npSVRUVIWDEqI20+v1hmSn1COxihYmt5YuLCHKpcszYJH/4eLwd5Cbdfvzb6OgdScxQ0NMao4pohOVqFzJTkBAAHv27Cm2f8+ePfj5+VU4KCFqs8QMDZr84aqlqtfR6wvrdVRqaPlA5QUnRG3m4l/4/yczAU7+X7kvZVy3k1LBwERlK1eyM2HCBGbMmMHy5cu5cuUKV65cYdmyZbzwwgtMnDjR1DEKUasYz7FTimTn2iFIiVS2G/UGB4/KCUyIuuDmSQbL2QUldTs1S7lGY7388sskJiYyefJkNBoNALa2trzyyivMnj3bpAEKUduUeSSWLA8hhOnU7wgBXeHqAYg7C2E7oHHfMl9G1siqWcrVsqNSqZg/fz5xcXHs37+fEydOkJiYyFtvvWXq+ISodco0x45OC2fWKttqa2gxtBIjE6KOKNq6s698kwwGeThga6W8hUrLTvVXrmSngKOjI507dyYkJAQbGxtTxSRErRZj1LJzhwLliN2Qnj9EtslAsHWpxMiEqCOaDwWXBsr2pa0Qd77Ml1BbqGjuo7TuRCRkkp6TZ8oIhYmVK9nJyMjgzTffpEePHgQHB9OoUSOjLyHErZWpZqfo3DrShSWEaagtoeukwsf7l5brMkXrds5JV1a1Vq6anQkTJrBr1y7Gjh2Lr68vqoIlYIUQdxSdWjjc9bY1O3kaCP1T2bZygKaDKzkyIeqQDmNh5wegSYcTq6HfW2DvVqZL3Fy30ymwbM8XVadcyc6mTZvYsGEDPXv2NHU8QtR6BS07DtZqnG43oeDl7ZCdrGw3HwLW9pUfnBB1ha0LtB8DB76EvCw4shzunlmmS8iIrJqjXN1Y9erVw81NMlghyqrohII+Lra3bxWVUVhCVK6uk4D8/4MHv1FaU8uguY8TBf+FZURW9VauZOe9997jrbfeIjMz09TxCFGrpWbnkanRAneYPVmTCec3Ktu2LtC4XxVEJ0Qd49YImt+nbKdFQegfZXq6vbUlQR4OAJyLTiMvf7JQUf2UqxtrwYIFXL58GW9vbwIDA7GysjI6fvToUZMEJ0RtU+o5di7+rdQSALQYBpbWlRyZEHVUt8lwbr2yve9zaD0KylCH2tLXmbC4DDR5OsLiM2jq7VRJgYqKKFey88ADD5g4DCHqhqiUwuLk247Eki4sIapGwx7g0waiT0LUcYjcDw27l/rpLf2cWX9SWRMy9EaqJDvVVLmSnbffftvUcfDBBx/w2muvMX36dBYvXgwo9Q3vvPMOX3/9NUlJSXTt2pXPP/+cVq1aGZ6Xk5PDrFmzWLVqFVlZWfTr148vvviC+vXrmzxGISqqVC072SlwYYuy7eAFQfdUQWRC1FEqFXSfAmvzh6Lv/7xsyc5NI7IeaO9v6giFCVRoUkFTOXToEF9//TVt2rQx2v/RRx+xcOFClixZwqFDh/Dx8WHAgAGkpaUZzpkxYwZr165l9erV7N69m/T0dIYOHYpWq63qlyHEHZVqjp1zG0Gbv4pyqwfAQl35gQlRl7UaAY4+yva5DZAUUeqnyoismqFcyY5Wq+Xjjz+mS5cu+Pj44ObmZvRVFunp6YwePZpvvvmGevXqGfbr9XoWL17M66+/zogRIwgJCeH7778nMzOTn3/+GYCUlBS+++47FixYQP/+/Wnfvj0rV67k1KlTbNu2rTwvTYhKZdSy43yLAmWjiQQfquSIhBBYWkOXCcq2XgcHvir1U72cbPFwVFYQCI1KRV/OhUVF5SpXsvPOO++wcOFCHn74YVJSUnjxxRcZMWIEFhYWzJkzp0zXmjJlCvfddx/9+/c32h8eHk50dDQDBw407LOxsaFXr17s3bsXgCNHjpCbm2t0jp+fHyEhIYZzSpKTk0NqaqrRlxBV4Y7rYmUkwOUdyrZLANTvXEWRCVHHdXwKLPP/Tx79EbJL/75Q0LqTmKEhJjWnMqITFVSuZOenn37im2++YdasWVhaWvLYY4/x7bff8tZbb7F///5SX2f16tUcPXqUDz74oNix6OhoALy9vY32e3t7G45FR0djbW1t1CJ08zkl+eCDD3BxcTF8BQQElDpmISoiOr9A2cbSAld7q+InhP4B+vwu2JARYFEtepqFqP0c3KHNI8q2Jg2OrSz1U43rdlJMHZkwgXL9JY2OjqZ169aAshhoSoryjzt06FA2bNhQqmtcvXqV6dOns3LlSmxtbz0q5eZJ1/R6/R2Xp7jTObNnzyYlJcXwdfXq1VLFLERFFdTs+N5qQsHTawq3ZRSWEFWr6GroB74EXelqP6Vup/orV7JTv359oqKUoXbBwcFs2aKMHDl06FCpVz8/cuQIsbGxdOzYEUtLSywtLdm1axeffvoplpaWhhadm1toYmNjDcd8fHzQaDQkJSXd8pyS2NjY4OzsbPQlRGVLz8kjLVtZGbnEkVipN+DKHmXbvYkyHFYIUXW8mhdO4Jl8RSlWLoWbR2SJ6qdcyc6DDz7IP//8A8D06dN58803adKkCePGjeOpp54q1TX69evHqVOnOH78uOGrU6dOjB49muPHj9OoUSN8fHzYunWr4TkajYZdu3bRo0cPADp27IiVlZXROVFRUZw+fdpwjhDVRbTRSKwSipPPrAXyixtDRpZpYjMhhIl0L9K6s/+LUj0lyMMBWyvl7VRadqqncs2z8+GHHxq2H3roIerXr8/evXsJDg5m2LBhpbqGk5MTISEhRvscHBxwd3c37J8xYwbz5s2jSZMmNGnShHnz5mFvb8/jjz8OgIuLC08//TQzZ87E3d0dNzc3Zs2aRevWrYsVPAthbnecY+dU0VFY0oUlhFk07gcezSD+PETug+tHwb/DbZ+itlDR3MeZ41eTiUjIJD0nD8fbLfIrqpxJ/jW6detGt27dTHEpIy+//DJZWVlMnjzZMKngli1bcHIqnKFy0aJFWFpa8vDDDxsmFVyxYgVqtcxNIqqX286enBgGN/KXWfFpDZ5NqzAyIYSBSgXdnoP1M5TH+5fCyG/u+LSWfkqyA3AuKpVOgbJYdnVS6mTnzz//5N5778XKyoo///zztueWtnXnZjt37jR6rFKpmDNnzm2Hs9va2vLZZ5/x2WefleueQlQV4zl2bkp2jJaHkLl1hDCrto/CP+9CViKcWQMD3gFnv9s+5ea6HUl2qpdSJzsPPPAA0dHReHl53XZtLJVKJbMXC1EC4zl2bqrZMRqFNaKKIhJClMjKDjo9Bf99DLo8OPgN9L/9MkkyIqt6K3WBsk6nw8vLy7B9qy9JdIQo2S1rdmJCITZU2Q7oCq4NqjgyIUQxnSeARf5cWEeWgybztqc393EyjCmQEVnVj8xYJkQVKZhjx0qtwt3BuvCArHAuRPXj7FvYypqVBCdX3/Z0e2tLgjwcADgXnUaeVlfZEYoyKHU31qefflrqi06bNq1cwQhRmxXMnuzlZIuFRf5HQL2+MNlRWUDLB8wTnBCiuG6T4eQvyvb+pdBh/G1nNW/p60xYXAaaPB1h8Rk09Xa65bmiapU62Vm0aJHR47i4ODIzM3F1dQUgOTkZe3t7vLy8JNkR4ibZuVqSMnOBm0Zi3TgKSeHKduDd4HTryTCFEFXMrx007KlM9hl/AS7/A00G3PL0ln7OrD+pTLgbeiNVkp1qpNTdWOHh4Yav999/n3bt2nH27FkSExNJTEzk7NmzdOjQgffee68y4xWiRrplvY4sDyFE9VZ0CYl9n9/2VJlJufoqV83Om2++yWeffUazZs0M+5o1a8aiRYt44403TBacELVFVMotVju/dqhwu8X9VRiREKJUmt0L9QKV7bAdEHv2lqfKiKzqq1zJTlRUFLm5ucX2a7VaYmJiKhyUELVNdGrhhII+RYedJ1xSvjv7g73MyyFEtWOhhq7PFj6+zRISXk62eDgq60OGRqWi1+srOzpRSuVKdvr168fEiRM5fPiw4R/z8OHDTJo0SZZpEKIEJbbsZCZCZoKy7d7YDFEJIUql/RiwyW+1OfELZMTf8tSC1p3EDA0xqTlVEZ0ohXIlO8uWLcPf358uXbpga2uLjY0NXbt2xdfXl2+//dbUMQpR45VYs5MYVniCe3AVRySEKDUbJ+gwTtnW5sDZv255qnHdTkplRyZKqVxrY3l6erJx40YuXLjAuXPn0Ov1tGjRgqZNZT0fIUpSYstOQRcWSLIjRHXX6kHYt0TZDtsBnZ4s8bSb63b6NpcRltVBhRYCbdq0qSQ4QpRCQcuOhQo88/v0JdkRogbxaw+2LpCdAmG7QKdV6nluIiOyqqdyJzvXrl3jzz//JDIyEo1GY3Rs4cKFFQ5MiNqkoGXHy8kWS3V+77EkO0LUHBZqaNQbQtdBdjLcOA71OxY7LcjDAVsrC7JzdTIiqxopV7Lzzz//MGzYMIKCgjh//jwhISFERESg1+vp0KGDqWMUokbT5OlIyFAKFY3m2ClIdiwsZT0sIWqCRn2UZAcgbHuJyY7aQkVzH2eOX00mIiGT9Jw8HG0q1IkiTKBcBcqzZ89m5syZnD59GltbW37//XeuXr1Kr169GDVqlKljFKJGi03LpmAEqqFeR6eDhMvKdr1AUFuZJTYhRBk07lO4fXnHLU8rWrdzTrqyqoVyJTtnz57liSeeAMDS0pKsrCwcHR159913mT9/vkkDFKKmK3EkVloU5OavoixdWELUDPUCwS1/moirByAnrcTTpG6n+ilXsuPg4EBOjtIs7+fnx+XLlw3H4uNvPf+AEHWRjMQSohYpaN3R5UHEnhJPkZmUq59yJTvdunVjzx7lH/m+++5j5syZvP/++zz11FN069bNpAEKUdMZt+zkz55slOzIhIJC1BiN+xZuX95e4inNfZxQqZRtadmpHspVNbVw4ULS09MBmDNnDunp6fzyyy8EBwcXWx1diLqu5JadwtZQadkRogYJvAtUatBrb5ns2FtbEuThQFhcBuei08jT6gpHYQqzKHOyo9VquXr1Km3atAHA3t6eL7649VohQtR1RutiOUs3lhA1mq0L1O8MV/dDwkVIvgquAcVOa+nrTFhcBpo8HWHxGTT1djJDsKJAmVNNtVrNoEGDSE5OroRwhKh9irbseN+c7Fg5gJOvGaISQpRb0a6ssJJHZUndTvVSrna11q1bExYWducThRCGmh0PRxusLS1AmwtJEcpB98YYOveFEDVDKYagy4is6qVcyc7777/PrFmzWL9+PVFRUaSmphp9CSEUeVodsWnKyEVDvU7SFaW/H6QLS4iayK8D2Lgo22E7lKUjbiItO9VLuQqUBw8eDMCwYcNQFflUqtfrUalUaLXF/+GFqIvi0zVodcqMgj4y7FyI2kFtCUF3w7n1kJUEUSfA33j1AC8nWzwcbYhPzyE0KtXw/ijMo1zJzo4dt545UghRKCqlsDhZ5tgRohZp3FdJdkBp3fEvvlRSSz9n/r0QR2KGhpjUHOPlYkSVKnOyk5uby5w5c/jqq69kxXMh7qDE2ZMTLhaeIMmOEDWT0Xw7O+DumcVOaemrJDsAoVEpkuyYUZlrdqysrDh9+rQ0xwlRCneeY6dRFUckhDAJtyBl+QiAyP2Qk17sFKnbqT7KVaA8btw4vvvuO1PHIkStE51apGXH+abZk+09wK6eGaISQphEQeuOLheu7C12WEZkVR/lqtnRaDR8++23bN26lU6dOuHg4GB0fOHChSYJToiarljLTk66sggoSBeWEDVd475weJmyfXk7NB1odDjIwwFbKwuyc3XSsmNm5Up2Tp8+TYcOSjHWhQsXjI5J95YQhaKLFCj7uNhC/OnCg5LsCFGzBd4NKgvQ60qcXFBtoaK5jzPHryYTkZBJek4ejjbletsVFSSjsYSoRAUtO/XsrbC1UssCoELUJnau4N8Jrh2EuHOQch1c/I1OaeWnJDsA56JS6RToVvVxivLV7Agh7kyn0xOTX7NTuNq5LAAqRK1SdDblElp3jIqUpW7HbMrUsjNixIhSnbdmzZpyBSNEbZKQoSFXmz+hoLNN/s4iLTseTcwQlRDCpBr3hV3zle3LO6D9GKPDRkXKUrdjNmVKdlxcXCorDiFqHeM5dm4aiYUK6gVVfVBCCNPy7wg2zpCTmr90hA4sCjtNmvs4Y6ECnV5adsypTMnO8uXLKysOIWqdYrMn6/WFyY5rAFjJBGNC1HhqK6VQ+fwGyEyA6JPg185w2M5aTZCHA5fjMjgXnUaeVoelWipIqpr8xIWoJEZz7LjYKn8Is1OUHVKvI0Ttcce6HaVXRJOnIyw+o6qiEkVIsiNEJSk2x46siSVE7WS0dMT2Yoelbsf8JNkRopJE35zsxMuaWELUSm6NwLWBsh25HzSZRodlRJb5SbIjRCWJMppQ0E7m2BGitlKpClt3tJpiS0dIy475SbIjRCUpaNlxsrFUZk2Vbiwhaq9GRep2burK8nSywdNJmX4iNCoVvV5flZEJJNkRolLo9XpDzY7Pzaudq63BJcBMkQkhKkXQPcrSEVBykXJ+605ihoaY1JyqjEwgyY4QlSI5M5ecPB2Qn+zotJAYphx0awQWajNGJ4QwOXs38FPWjCQ2FFKjjA4b1+2kVGVkAkl2hKgUxUZipVwDbf6nOenCEqJ2us0QdKnbMS9JdoSoBNGpUpwsRJ1jNAT9pmRHRmSZlSQ7QlSC4nPsFF0AVNbEEqJWqt8ZrB2V7YKlI/IFujtgZ6V0X0vLTtWTZEeISmC8LpZMKChEnVCwdARARhzEnik8ZKGiua8TABEJmaTn5JkjwjqrTGtjCSFKp9iEgpLsiCqk1+vRZ2WhTUlRvpKT0SanYNehPVZeXobz8hISSPnrLyxsbVHZ2GJha1P43dYWlY2N4ZiVrw8qtRTW31HjvnBhk7J9eTv4tDYcaunrzLHIZADORaXSKdDNDAHWTZLsCFEJiq6L5etcpGbHxgUcPMwUlaiJ9Hl55CUkok1OQpucgtrVFdtmTQuP6/VcnzYdbVIS2hQlqdGmpKDXaIpdq/4Xn2PVt7CuJPf6dWI/nF+qOJrs3YOlW+Gbc8LyFSR88w0qWxssPT1x6tMXp4EDsWkUVIFXWws0vmm+nZ7TDQ9vrtuRZKfqSLIjRCUoqNmxs1LjbKWF5EjlgHtjZbZVUafp9XpUN/0epG7ZQtbxE+TFx6GNjycvPoG8+Hi0SUlQZBI6lxEj8Jv3vuGxSqUi4+BBdCl3Hs6sTTY+R5edfYszi7OwsTG+VmoK2sREAPJuRJF94iRxixdj06QJToMG4TxoINbBwcVeZ63nHqzMo5VyFa7sg9wssLIDZESWOUmyI0QlKOjG8nWxRZUUDuS/WUkXVq2my8kh98aN/GQlnry4/O8JyndtXDx5CQmg19Pk311Gz03fvoOUP/644z1ykuJJ06ShVqmxtLDEQmWB2sUFXUoKKltb1C4uqF1db/qubNu2amV0LZvGjfFb8DH67Bx0Odnos3PQ52Sjy85Bn51duE+Tg8rW1ui5akdHrPz90WVno01IKIzv4kVyLl4kfskSrBs1ot7jj+M2ZnT5f6g1jUoFjXrDsR+V6Sau7IXgfgA093HGQgU6vYzIqmqS7AhhYmnZuYbiQylOrvn0Gg15iYn5iUuckrTEx5MXF0e9MWONum3Sd+3i+rTpt7la/jVVKv48v5ZUbQZpmjRSNak0yg4lpMg5uZaQ7KAiyUFPioOKVHtIt4MIp93sWdXD6HpOD+rRWFuhs1ZhaZGJhSobtSquMBlSqZWvM7+jDlVjpbbC18GXBk4NaNC4gfLduQG+Dr5YWpTubcH96adxf/ppADTXrpO2ZQtpW7aQdfy44RxNWBh5sbHFX38JLVu1SuO+SrIDSldWfrJjZ60myMOBy3EZnItOI0+rw1It44SqgiQ7QpjY7UdiyRw71c3Nb7x6nY6rEyaSFxdHXlwc2uTkWz7XoUcPtAHeXE27ytW0qySlHjRKWG6mVUGKAyQ76Jm3400y7Arv69tAj+toNckOkOwAWTaUusszzV4F6EGXS64ut1TPuZh0sdg+SwtL6jvWJ8ApgIbODQ3fGzg1wNfx1omQdX1/3J96EvenniQ3Opq0LVtJ3fI3WUeO4jRooNG5mmvXiBz3BE4DB+I0aCB2bduisqhlb/iNegP5/yZhO40OtfRz4XJcBpo8HWHxGTT1djJDgHWPJDtCmFjxOXakZae60GVnk332LNmnz5B9+hRZp8/g1LcPXjNnGs5RWViQffasUitzBx9tms0f1wv/veul6RnTUkWyo9Iqo3wv3E63U1p1ShLlriLKXdm2VdviZe2Mk7UTzjbKdysLK7R6LVqdVvl+h22dXkeeLq/E4zl5OWh0xQuY83R5RKRGEJEawX/X/zM6ZqmyxN/Jv8REyM/Rz5AIWfn44DZuLG7jxpIXF4faw7ggP+3vLeTeuEHiihUkrliBpbc3TgMH4jxoIHbt29eOEV/2buDXDm4cg5jTkBYDTt6AUrfz14kbgFK3I8lO1ZBkRwgTM27ZsYPrRScUlJadqqLXaMi+cDE/qTlN9ukz5Fy8CFqt0XlZ+W/G0RnRhKeEczXtKoEOYJdmQZqTmgR7HYkOOpIcINlRpSQv+clMdL0slE/wiiQnFZ8NV96s7S3tcbVxxcnaCR8bZ5paO+NckMBYOxuSGOf8/c42zoZta7V15f5s9HpiM2OJTIskMjXS6PvVtKtk5WUVe06ePo8rqVe4knqF3dd3Gx1zsHJgSNAQHmr6EC3dWxr2W3p6Fr9ObCxYWkKe0tWbFxND0o8/kvTjj6g9PHAa0B/XkQ9hF9Kq2HNrlMZ9lWQHlNadto8AxUdkPdDe3wzB1T2S7AhhYkYtO85FWnYcfcBGPsVVBn1eHqjVRt1RsYs/IXHZsts/z1LN1bRIpv4xjPCUcMN+60f1aCxVoNKjJDMltzZ42nnSzCmA+k5K108DpwYEOAUQ4BSAi41Lta1LUalUeDt44+3gTWefzkbH9Ho9cVlxxZKggu8lJUIZuRn8euFXfr3wKy3dW/JQ04cYEjQEByuHYud6z34Vj+eeJW37DtL+/puMvXvR5ypdb9r4eJJXrSZ51WrqjR2Lz+uvVc4PoCo07gv/LVC2L28vTHZkRJZZSLIjhIkVXRfLzzZHmUkV6nQXlj4vj5ywMDThEejzcpXhKOhBp0Ov1yuP9XqsGwRg39n4zTf5t9/QZecow6/1OtDr0eefnxsdRfap02SfPUuj9euxrl/4KblYy4CFBVaNG5EU5M4Zz2y22UdwxjWdPMs4SIkzOlVjpSQplipLfB19DQlM0a/6TvWxs7SrlJ+XOalUKrzsvfCy96KTTyejY3q9nviseK6kXuFq2lVDS8/eG3vJzMsEIDQhlHf3vcv/Dv2PIUFDGNV0FK08jP8t1K6uuI54ENcRD6JNSyN9505S//6bjH//M8wPZB3YsGpecGWp3wWsHCA3Q1k6Qq8HlQpPJxs8nWyIS8shNCq19hdrVxOS7AhhYkVbdvy0NwoPeNSNZEev1Raru7gydhxZx47d8bkuw4cXS3Zi/vdxqeaQyT592jjZadsW52H3o2kSwEn3TP62vsC+pKPk6SKKPEt5k7FQWdDWsy1tPNrQwLmBoaWmLKOT6gKVSoWnvSee9p5GiVBGbgYbwzfy24XfCE0IBSArL4vfL/7O7xd/p4VbC0Nrj2PB2lH51E5OuNx/Py733482PYOUtWtJ/Xsz9R55xOg8XWamMqtzTSlmtrSGwLvg4t+QHgOxoeCtJH0tfZ3ZlRZHYoaGmNQcZSCDqFTyv1gIEyuo2bFWW+CSeaXwQC1s2dFlZJB9/jzZZ0KVwt/QUPRZWTT+e7PReTbBwaVKdgzzERVRmk+9VgEB6HOVFgGdXsfp+NPsjNnJzrsucTFpE6QVf469pT09/XvSO6A3d/vfTT3beqWIT5TEwcqBUU1HMarpKM4knOH3C7+zIWyDobXnbOJZ3tv/Hh8f/thQ29PKvVWxf1u1owNuY8dQb8zoYsei580jJ/Qs3rNfLZYQV1uN+yjJDihdWQXJjp8zuy4orYmhUSmS7FQBSXaEMLGClh0fF1tUiUXe4Gt4sqPLzCTrxAmyQ5WkJjs0FE1EhNHsvgW0ycmoXV0Njx26d0ObnoZts2ZY2NsDKrCwAJUy+gmVClQWWDcs3nXh8/ZbSk2HquC8wueonZ2xbdmSHAdr9kftZ+eet/j32r8kZCcUuw6An4MfvQJ60bt+bzr5dKr0QuC6qJV7K1p1b8WsTrPYFL6J3y78xumE04Bxa09zt+Y81OQhhjQagpO1cS3bzYlO9tmzpPy+BvR6rowdh9OAAXi9NAvrBg2q7HWVS+PCpTm4vB16TAWK1+30be5d1ZHVOZLsCGFCmZo8UrKUYsvaNqFgzqVLRD751O1PsrDApnFj8uLjjZId5yFDcB4ypFz3db733mL7MnIziM6I5kjMEXYeXsmBqAMlDqUGaOPRhl4BvehVvxdN6zWV+ogqYm9lz8imIxnZdCRnE87y+8XfWR+2nozcDADOJZ5j7oG5LDiygHuD7uWhJg8R4hFS4r+PPk+LTbNm5Jw7B0Da1q2k79xJvbFj8XjuWdRO1bTw36MpOPtD6nVlJuXcbLCyLTYiS1Q+SXaEMKFbrnauUoNr9S+41Ot0ZOzZQ9Ivv+A6ciROfQoXNbRp2hTUasPQbZWVFTbNmmHbogW2rVpi27IlNk2bYmFbsSb5zNxMYjJjiM6IVr4yo4nJiDF8j8mIIS23hH6pfHaWdnTz7UbvgN7cU/8ePOxk4VVza+Hegjfc3+DFji/yd8Tf/HrhV07FnwKU1p41F9ew5uIamtVrxkNNH2Joo6FGtT12rUMI+v03UtauJXbxJ2jj49Hn5pK4bBkpa9fiOW0qrqNGobKsZm9pKhU06gPHV0JeNkTug8Z9CHR3wM5KTVaultPXJdmpCtXsN0OIms1ojh1nGwjLn2OnXkOlYLGayo2NJWXNWpJ//ZXc69cB0OfmGiU7Fra2eE6diqWXF7YtW2DTuDEqK6sy3Sc7L9sokSlpO1VT9j/+XnZeSvdUQG+6+HTB1lJqIKojeyt7HmzyIA82eZBzief47cJvbAjbQHpuOgDnk87z/oH3WXJ8Cc+2eZZHmj2ClVr5HVOp1bg+9BBOg+8l4ZtvSFy+HL1GgzYpieh33iXp55/xfv0NHLp1NedLLK5xfrIDyqisxn1QW6gI8XfmUEQSkYmZxKRm4+0sv7OVyazJzgcffMCaNWs4d+4cdnZ29OjRg/nz59OsWTPDOXq9nnfeeYevv/6apKQkunbtyueff06rIgva5eTkMGvWLFatWkVWVhb9+vXjiy++oH79+uZ4WaIOKzoSq5FNGmiUP+LVsQtLr9ORsXcfyb/8QtqOHYZJ3grknL+ALjMzv8ZG4fHspDLdIzM3kyMxRzgQdYAD0Qc4n3gefQlFyKVlbWGNt4M3Pg4++Nj70MC5AXfXv5uWbi2le6qGae7WnDe6Fbb2/HbxN07GnQQgJSeF+Yfms+rcKl7o+AL9GvQz/PuqHR3wemEG9R4eReyChaRu3AhAzsVL5EZFme313FKj3oXbl7fDgHcB6NbInUMRyizd+y4nyOSClcysyc6uXbuYMmUKnTt3Ji8vj9dff52BAwcSGhqKg4MyGdVHH33EwoULWbFiBU2bNmXu3LkMGDCA8+fP45TfTztjxgz++usvVq9ejbu7OzNnzmTo0KEcOXIEdW2YelzUGNGphclOkKrIH95qlOzkJSWR/OtvSivO1avGB1UqHO66i3qPPIxj795l7hbI1eZyIu4EB6IPcCDqAKfiTpGnz7vzE1HWZfK298bbPj+Zyf8q+rieTT1JamqZoq095xPPs+LMCtaHrQcgMi2SF3a+QAevDszqNIvWnq0Nz7Py98d/4QLqjRlDzIcfQl4eLsOHmetl3JqDB/i2hagTEH0K0mPB0Yvujdz5bLvSzS3JTuVT6fUlDKUwk7i4OLy8vNi1axf33HMPer0ePz8/ZsyYwSuvvAIorTje3t7Mnz+fSZMmkZKSgqenJz/++COP5M/LcOPGDQICAti4cSODBg0qdp+cnBxycnIMj1NTUwkICCAlJQVnZ+di5wtRWm/8cYqV+yMB+K/fFQL2zFYO3LcAOk8wY2SFci5eJOx+4zcFtacHriNG4jrqIazL0CKq1Wk5l3SOA1EHOBh1kKOxR0ucYRdAhYpmbs0Icg4yap3xcfDB28EbN1s3LFQ1ZA4VUanOJJzh40MfczjmsNH+ewPvZXrH6fg7GicGep0ObVISlu7uRvuj5szBOjAQt9Gjy9zlalLb5sDuRcr2iG+hzSiyc7W0mbMFjVZHAzd7/n25z20vIUqWmpqKi4vLHd+/q1XNTkr+xGFubm4AhIeHEx0dzcCBhavm2tjY0KtXL/bu3cukSZM4cuQIubm5Ruf4+fkREhLC3r17S0x2PvjgA955551KfjWiLipas+OWHVl4wEwtO3kJCeTeuIFd68JPxDZNmmDXoQNZR4/i0LMnro88jFOfPqV6M9Dr9YSnhnMw6qCS4EQfvG2NTaBzIF19u9LVtyudvTvjautqipclarlW7q1YNmgZO6/uZOGRhUSkRgCwKWIT2yK3MabFGCa0mYCztfLmprKwKJboZB49RvLqXwDI2L2H+ks+q3DxfLk16lOY7FzeDm1GYWulpn0DVw6EJxKZmMn15Cz8XWvfjNzVRbVJdvR6PS+++CJ33XUXISEhAERHRwPg7W08B4G3tzdXrlwxnGNtbU29evWKnVPw/JvNnj2bF1980fC4oGVHiIoqqNlRW6iwTytca6kqkx29Xk/mgQMk/fILadv+wbp+fRpt3GDU/eP9+muonZ2xLsXvfXRGtFJzk193E5sZe8tzvey96ObbjS4+Xejq2xUfBx+TvCZR96hUKvo06MNd9e/itwu/sfT4UpJyksjV5bL8zHLWXlrLs22f5eFmD2NlUTxRzzxyWBkNpdeTsXs3Vyc9S8AXn2PhUHy9rkrXoBtY2kFelpLs5C8d0aOxBwfCEwGlK+uhjlJnWlmqTbLz/PPPc/LkSXbv3l3s2M199KVZS+R259jY2GBjY1P+YIW4hYKWHW8nG1QJ+SOxLO3Aya9K7p/8xx8kLP0SzZXCmZs14eFkHjyEQ9cuhn12rW6/onR0RjR/Xf6L9WHrCUsJu+V5ztbOhsSmq29XAp0DpaZGmJSVhRWPNX+MoY2G8u2pb1kZuhKNTkNyTjIfHvzQUMTcN6Cv0e+ex8SJ2LVty7XnJqPLyCDzwAEiJz5DwNdfoXZ0vM0dK4GljbJ0xKWtkB4NcefAqwXdG7uzaJtyyt7L8ZLsVKJqkexMnTqVP//8k3///ddoBJWPj/KpMDo6Gl9fX8P+2NhYQ2uPj48PGo2GpKQko9ad2NhYevToUUWvQAjIztWSkKFMbOfnbAUJ+S077sHKbMGVLH7pUuI++dRon9rNDdcRD2IdcOc/oll5WWyP3M66S+vYH7W/xFFTdpZ2dPDqYEhumtVrhtpCBgGIyudk7cQLHV/gkWaP8OmxT9kQtgGAK6lXmLFjBh29OzKr0yxCPEIMz3Ho0oUGy5cROWEiutRUso4eJfKpp2nwzdeoXVyq9gU07qMkO6C07ni1oG2AC7ZWFmTn6th/OUEWBa1EZq0G1Ov1PP/886xZs4bt27cTFBRkdDwoKAgfHx+2bt1q2KfRaNi1a5chkenYsSNWVlZG50RFRXH69GlJdkSVik0tLHoPsU8BXf4oJPfGlXpfvV5P7OLFRomOfdeu+C9aSJOdO/CaNQsrv5JblvR6PcdjjzNn7xz6/l9fXv3vVfZF7TNKdNp5tuPZts+yfNBy9jy6hy8HfMmTIU/S0r2lJDqiyvk5+vHh3R+y6r5VdPDqYNh/JOYIj214jFf+fYUb6YUL8Nq1aUPDFcsNM3pnnzzJlfFPkpeUVLWBGy0dsQMAG0s1nRoqNao3UrKJTMys2pjqELO27EyZMoWff/6ZdevW4eTkZKixcXFxwc7ODpVKxYwZM5g3bx5NmjShSZMmzJs3D3t7ex5//HHDuU8//TQzZ87E3d0dNzc3Zs2aRevWrenfv785X56oY6JSCkchNbOKKTxQifU6er2e2I/+R+Ly5YZ9Xi+9hPvTt1/WITojmvVh61l3aZ2h+LMof0d/hgcPZ1jjYcVGvghRHYR4hLBi8Aq2X93OoiOLuJKqdN1uDN/ItivbGNNyDBNaT8DJ2gnbli1p8MP3RD71NNr4eHLOniVy3DgaLFuGpadn1QTs2RycfCEtCiJ2Q14OWNrQvbE7uy/FA0rdTkN3M9QU1QFmTXaWLl0KQO/evY32L1++nPHjxwPw8ssvk5WVxeTJkw2TCm7ZssUwxw7AokWLsLS05OGHHzZMKrhixQqZY0dUqaJz7ARW0Rw7OefPk/jjj4bH3m+8gduY0SWem52XrXRTXV7Hvhv7inVT2VnaMShwEMMbD6eDdwcZBi6qPZVKRb8G/bin/j383/n/48sTX5Kck4xGp2HZ6WWsvbiW59o9x6imo7Bt2pSGP/xA5Pjx5MXGkhsbR15iUtUlOwVLR5z4WSlUjtwPjXrRrVHhKLJ9YQk82qWaL25aQ1WreXbMpbTj9IW4nS93XebDTcpChTtbrCMwXBn2ytPbIKBzpd03dfNmrs96CZ+33qTeww8bHdPr9ZyIO8G6y+vYHL7ZMC1/UV18ujA8eDj9G/TH3sq+2HEhaopUTSrfnvyWlWdXkqvLNezv5tuNBb0X4GztjCYykmtTp+H7/vvYhdy+UN/kTv4Ka/Ln27rrBeg/h1ytjnbvbCFDo8XTyYaDr/WTup0yqJHz7AhRkxnPsVNkZuJKrtlxHjwY25DWWNcv7G6SbipRFzlbO/Nipxd5pPkjfHLkEzZFbAJgf9R+xm0cx5J+S6jfoAFBa9egqoJBA8XcvHRE/zlYqS3oHOTGzvNxxKXlcDkug2CvKh4tVgdIO7UQJlK0Zsc+LULZsHMDezeT3UOv0SjrWN2kINHZd2Mfz259lkG/D+KTo58YJTp2lnYMbzycZYOWsXHERp5r+5wkOqJW8nf056NeH7Fs0DLq2SijdC+nXGb0xtGcjDtZLNHR63TELlhAzuXLlRuYoyf45E/wGXUCMpRane43dWUJ05NkRwgTKWjZsVdlY1kwGsSE9Tq6nByuTZ3Gtecmk5Q/M2wBvV7PNye/4Zmtz7Dnxh50ep3hWGefzsztOZedD+9k7l1z6ezTWepxRJ3Q2aczPw35iUDnQAASsxN56u+n2BKxxXCOXq8nZu77JHzzLVfGjiP7/PnKDapRkWUhwnYC0L1xYbKz/7IkO5VB/uIJYSIFsye3dygypNVEyY4uK4trz00mfdcuAGLmzycvLg5QCo9f/e9VPj1WOPTc39Gf59o+x6YRm1g2aBnDg4dLPY6okwKcA1g5ZCWdfZS6uRxtDjN3zeTbU9+i1+vRZ2aSdfw4ANrERCLHPUHW6TOVF1AJQ9Bb+bngZKtUlewPS0Cnq/OltCYnyY4QJpCr1RGXrsyz08YurvCACep1tOkZXJ34DBl79wKgsrcn4MsvsfT0JC4zjqf+foqN4RsN509rP42NIzYyud1k6jvJjKxCuNi48FX/rxjeeLhh3ydHP2HOvjlo7axpsGI5tm3bAKBNSSHyySfJPHascoJp0B0s89foCtsBej1qCxVdg5TWnYQMDRdi0yrn3nWYJDtCmEBcWg4F4xqbWRVZO6qCLTva1FSuPv00mYeV1Z8tHB1p8O23OHTtQmhCKI9teIxT8acApSZncZ/FTGwzUbqphLiJldqK93q+x7T20wz71lxcw3NbnyPDFhp8twy7Th0B0KWlcfXpCWQeOlQJgdhCw/wJb1OvQ/wFwLgra590ZZmc/EUUwgSiiozECsQ0c+zkJSUROf5Jsk6cAMDCxYUGy5dj36E9W69s5YlNTxCTqUxe6OPgw4/3/ki/Bv3KfT8hajuVSsXENhP53z3/w9rCGoAD0QcYs3EMN/SJNPj6axx6dAdAl5lJ5MRnSN+zx/SBGHVlbQduKlKWZMfkJNkRwgSKDjv3ybtWeKCc3Vh58fFEPjGe7NBQQFnjquH3K7ANacXSE0t5ceeLZGuVe7b1bMuq+1bRzK1Z+V+AEHXI4KDBfDfoO8NIrfCUcMZsHMPJ9AvUX7oUh173AKDPzubac5NLHAFZISXU7TT3caKevbJ6+4HwRKnbMTFJdoQwgaLDzt2yIpUNlwCwsivztfR6PdemzyDngtK8benpScMfvofgQF7+92W+OP6F4dxhjYexbNAyPOw8KvYChKhj2nm146f7fiLIRVmTMTE7kaf/fpotN3ZQ/7PPcOyvtJLqNRriPvkUvVZrupt7tQRHZTFrwv+FnHQsitTtpGTlEhqVarr7CUl2hDCFgpYdV9Kwzk1RdpazVUelUuH92mwsnJyw9PWl4Y8/kOLnzPjN49kcsVk5BxUvdHyBuT3nYq22NslrEKKuCXAK4Md7f6SrT1cANDoNL/37Et+d+x7/hQtxHjIEq4YNCPj6K1SmXH5IpYJmQ5TtvCw4rwww6BEsXVmVRZIdIUwgKn9drEYmWhPLrlUrGnz3LQ1//JGLjhk8tv4xziQow2HtLe35pM8nPBXylEwrL0QFudi4sLT/Uh4MftCw79Njn/LWwXfx/PB9An/+GSsvL9PfuPVDhdunfwdkcsHKJMmOECZQ0LLTyKJ8yU5uTAx6nc5on12bNmzPPcUTm58gNksZ4eXv6M+PQ36kT4M+JV1GCFEOVmor3unxDtM7TDfsW3d5Hc/tmEKGY/FVlbSpJuhiatADnPyU7Uv/QGYiwV6OeDjaAHAwPJE8re42FxBlIcmOECZQkOy0tC77sPPs8xcIHzGS6Pfeo2BdXp1ex+fHP+elf18iR6vM39PBqwM/3/czTes1NW3wQghUKhUTWk/g414fY6POTziiDzJm4xiupipr3eVev87V558n4vHH0efm3u5yd2ZhASEjlG1dLpz9E5VKRbdGyvIy6Tl5nL4hdTumIsmOEBWk1emJye/GamoZU3igFDU72ecvEDluHNqEBJJXrSZxxfdk5WUxa9csvjzxpeG8B4If4JuB3+Bma7p1toQQxQ0KHMR3g74z/F+LSI1g9MbRHIs9xo3Zr5G+7R80ly6T+MOPFb9ZyMjC7VO/ATLfTmWRZEeICkpIzyEvf5ioYY4dCytwaXDH58a89x7aFKWg2bZNG3IG9+SJTU+w9cpWQClEntVpFu/2eFcKkYWoIm092/LTkJ9o5NIIgKScJCb8PYGLj/dQiouBuM8/Jzc6umI38msPbso9iNgNqVH0aFw4snLv5fiKXV8YSLIjRAUVTCioQod33nVlp1sQqIv39ReVfeGCYWZkq4YNSP/oRUb/9wxnE88C4GDlwJJ+S3ii1RNSiCxEFavvVJ8fh/xIV9/CkVovxCzhxgBl1XJ9ZiYxH86v2E1UKggpKFTWw5m1BLrb4+OsLCdxOCIJTZ7U7ZiCJDtCVFBBsuNLIlZ6jbKzFPU6yUVWLo++twNP7p5CfJbySc7f0Z+V967knvr3mD5gIUSpOFs7s7T/UkY2Kexuer3lGfKcHQBI27y54jMsG43K+g2VSmXoysrK1XLyWnLFri8ASXaEqLDo/AkFg4xGYt2+XkeXkUHKunUAaG0sedHuL0Mhckfvjqy6bxXB9UyzYroQovysLKx4u/vbzOgwA4AMOxU/9C6c3TjmvbnoNJry38CzGXgrrUVcPwKJYbJ0RCWQZEeICiqYYydIVaT//g4tOyl/rUeXkQHAjhZasmyVbqoRTUbwzYBvqGdbr3KCFUKUmUql4unWT3N/o/sB+LtlDjeCnAHQRESQuHxFxW5QMCoL4PTvRkXKeyXZMQlJdoSoIMMcO0YTCja55fl6vZ6k1asNj7e0t8BCZcHLnV9mTvc5WKmtKi1WIUT5vdLlFbzsvNCrVCzqk4HeQvmQEr90KbnXr5f/wkVHZZ1eQ4CbPfXrKUvNHIlMIjvXhEtV1FGS7AhRQQU1O0GlnD056/hxcs6dA+CCH0T4qBjRZARjW46VQmQhqjEXGxfe6fkOAFe8VWztpHww0Wdnk7J+Q/kvXK8h1O+ibMeGQkyooStLk6fjWGRyRcIWSLIjRIUVtOw0Vud3Y1k7geOtp5e3bdkS7w/nEdbAmi0dlP+CY1uOrfQ4hRAVd5f/XYaC5Z96arkR6Ij/55/hMemZil34pkJlo/l2ZOmICpNkR4gK0Ov1RKdkY0Ue/sQpO90bG+biKImFjQ3729jw6mgd/4ao6FW/l2E+DyFE9Ter0yx8HXzJslUx49Es/vKLufOT7qTVg6DKf0s+/TvdGxVOILpf6nYqTJIdISogMUODRqujgSoGNfnzYdyhOFmv1/N96PfKA5WKJ1o9UclRCiFMydHakfd6vqc8UKlYfGQxV1KvVPCiXhCUP9VEUgS+6aEEutsDcOxqElkaqdupCEl2hKiAqBKLk2+f7ByOOUxoQigALdxa0Mm7U6XFJ4SoHF19u/Jos0cByNZm88buN8jT5pG6dSuaq1fLd9GQIl1Zpwq7snK1eg5fSaxoyHWaJDtCVEB0GYqT0/fs4doLL7Bt7WLIX/BTZkcWouZ6oeMLBDgFABARfoxDo4dxfeo0Yua+b1jUt0xa3A8Fy8KcWUP3RoVTUMh8OxUjyY4QFVDyHDslTyiY9NPPpG3azAOfHKVdmB5ve28GBg6sijCFEJXA3sqeuT3nokJFthXoLkcAkL5rF+k7dpT9gnauEDxA2U6P4W6rc4ZDUqRcMZLsCFEBBbMnN7rD7Mm5N26QvnMnAAlOcDJIxegWo7GykDl1hKjJOnh3YGzLsWTbqPi+X2Erbczc99FlZZX9gkUmGKx3+U+CvRwBOHkthfScvArHW1dJsiNEBRTOsZPfsuPgBbYuxc5L+r//A51SwLytnQW21g6MbDqy2HlCiJpnavupBLkEsa+5ipOBSsKTe+MG8V99VfaLNbsXrJTCZEL/5K5AZaZmrU7PoXCp2ykvSXaEqIDolGwcycRLlazsKKFeR6/RkPzb7wBoVbC9rTKJoLO1cxVGKoSoLLaWtsztORcLCzX/396ZB0RVtm38PjPsiyiiqIAKIqCiJIqAuGtaFmku2aeppUWYlW9mqS2v5qtiZmr6mpnlHm+5pBZZappZuQNmLmiYCLLvMOzMXN8f04yMoHMGDnCC+/ePnufMubiemXOe5z7P+vlIBVX+XbPmfL6Fym7dMk3MwpbIe7T2/6V59LjdNf0p7sqqPRzsMEwdSMsvpc5GxusUHjtG6iztbubnvQTKb6GkZ7o/01AWGYZpAHq16UUzfGdQamuBvg3Utu6goqJ2g5WrLDDYM/dH/f95kHLt4WCHYWoJAErNLyWPqsGOU/U9sXIj/6f//2F/gR7u9DC52Lk0hEWGYRqQWX6zqGurrvR1fwVl/t1wW/Tbb1R4+IhpQl2GE1m1JCIiy/gfyM9ZO7bvSko+5ZdUSOi4+cDBDsPUkoKSSiqpUD9w2nlZfDwVnz9PRETJjkRXOgk0vTsvIsgwTRELpQUtC1lGaktz2jbibvWaHhFBmqIi8UJmFkTdn9D+v6KIpjpqu7I0IDrH43ZqBQc7DFNLUgu0My3cFfcPdnK//Er//6O9FdTb2Z96tunZIP4Yhml4urXuRmF+YXTeS6BYD213lmWvnqQpKzNNqMoCg4PKftb//9TNLEl8Njc42GGYWlJtJpagIGrVWX8eAJVdv05ERGVmRD/35FYdhmkOPN/zeeru1IO2jFTQskkK2jvdncwcHY1fWJXOA4js2hERUZv0X6iFoG0Z4nE7tYODHYapJdrVk3F3q4iWHYnMLPXnBUGgkrVv0dtTlbRlpIIc23akIW5DGsUrwzANh7nCnJaFLKOc1hb0u4eCtl3ZRhczLpomolBqNwclIkFdTjMdLxMRUVxaIeUUlUvsuOnDwQ7D1JLU/FJqQ/lkL/y9cFgN0853XttJf7oK9JOfgqZ2n0pKhbKBXTIM0xh4tvKk2Q/NJiIiEOid396hksoS02Zm+d5di+sJ5Sn9/8/yFHST4WCHYWpJWn7JAwcnZxRn0KFbh4iIqIVFCxrTZUxD2mMYppF5tsez1KtNLyIiup2fQHs3zKGECRNJrVKJE3DtS9SyExERdS6MJifKJyJeb6c2cLDDMLUkraCM3BVV19i5G+yU30mmyGuRVKnRLu8+yXsS2ehWRWUYplmgVChpachSslRa0sRfNNR34y9UeuUKZa3/rzgBQdC37gjQ0ONmZ4mI6BSP2zEZDnYYppZUb9nRLihYmZtLN0ePJu95n1HIFQ2ZKczoaZ+nG8klwzCNibuDO83xn0MneimozEyblrNrF5VevyFOoMoCg5OstMFOfIaKMgpLpbbapOFgh2FqiXZBwerdWPlf7ycqL6fOqWrqkgoa7T6a2tq0bSSXDMM0NlO6TaGO3n3p6/5/V7lqNaUtWSJu/I5zD6I23YiIqFvlNXKhTCIiOvMXr7djChzsMEwtUJVVUmFp5d1p50pLohauBI2Gcr/8Uv+5o70VNK37tEZyyTCMHFAICvpPyH/oxxAbSmmlTSuJjqb8gwfFCfS8O1A5VHmaiHgKuqlwsMMwtSAtv5QUpKFOumCndRcihYKKTp2miqQkIiK61Fmgzr79ydvRuxGdMgwjB9zs3WhO4DzaMrLKysorV5K6oMD4xVVmZY0x0wY7Z3iQsklwsMMwtSAtv5RchEyyENTahL+7sHK/vLsP1hF/gab34EUEGYbR8pT3U2QT0p9O+2hXVtbk5FLGB6uMd2c5ehC59CEiom7CbeoiJNOtrCJKzS+pb8tNBg52GKYWpOaXGG4A2tqTKtLSqPD4cSIiyrEjyuvblfp36N9IDhmGkRuCINCS/kto7yh7KtXu7Ul5e/ZQ1saNxi+usn2Ebs0d7soSDwc7DFML0vJLq62xk7d7Nwka7RvasYcEeqbndBIEoZEcMgwjR9rbtacXRiygrQ/frX4zDkcZ3zurx5NEpC1PnlCcIiJwsGMCHOwwTC1ILTCciQWHzpS9W7vpp1ogig5sTY95PNZY9hiGkTFjPccSHh9G24crKM6FaPYTWRST+8eDL2rRXrtfFhG5K9LJV7jFiwuaAAc7DFML7m3ZKfwjlZClnQp6wUug0f2eIQulRWPZYxhGxgiCQCsHraTsMf3pvSlKyjYroZeOvURnU88++EKD7SNO053cEkrKKa5nt00DDnYYphak5pfqV0+GVUsqd3Ol8z5mpBaITvSxpEnekxrZIcMwcsbazJr+O/y/FNxxIBERlVSW0Oxjs+nU9aOk+u23mi/qPoZIoV2Z8HHlaRJIw11ZIuFgh2FqQW5eHrkKWUREJLT2pG8srtIHTxK9NFtJniPGUUurlo1rkGEY2WOptKSPhn5EQ9yGEBGRRWEp5YfNocSwMCo8caL6BTaORF2GExFRByGHAoTr3JUlEg52GMZESivU5FB6R39c4ehBX1z7goiI8uwV9EyPqY1ljWGYfxgWSgtaPXg1PdzpYXrkgoY6p4MEtYaSXnmFis6cqX5BT8NZWadvZpu2k3ozhYMdhjER7Xidu9POD1uZUUZxBhERDXYbTJ0dOjeSM4Zh/omYK81p5aCVVPTMY/RbN+2MK6GikhLCX6TimFjDD3uPJjKzJiKi0cqzlFWgooRsHrdjDA52GMZEqu6JVZhiSTGnL5FZpfbNanp3XkSQYRjTMVOY0fLBK+jWq0/Q+a7agEdRWk5/PT+DSi5fuftBSzsi70eIiMhRUNEAxWU6dTOrMSz/o+Bgh2FMJK3g7m7nt6840MS9ObRxg5oCzb2oj3OfRnbHMMw/FaVCSUsGL6Nb88bR7521AY+yuJRuPjeVSm9U2SW9ygKDoX93ZTEPhoMdhjER3UyskmxzUmZrZ0ZktSAaH/w8LyLIMEydUCqU9O6gJXRr4VN0zfXvtMISujFtCpUnJGgTPEcQLO2JiGiU4gLF3kzlcTtG4GCHYUxEt8ZO4l92+rSzwa3o4U4PN6IrhmGaCgpBQQsGLaJb706m+PbaNPM8FV17ZhJVpGcQmVuR0O0JIiKyE0qpZ8k5is9QNaJj+cPBDsOYSH5OBrUsV1F5ghUREamsiLwmziCzv9e/YBiGqSuCINDcQe9QwqKpdLuNNu18m0L6X1qU9sBggcFTPAXdCBzsMIyJmOX+RSm3bclMre2yOuVnQWN9eRFBhmGkRRAEennwQkpYMp32hgi07gkFfXBxDX32x2dE7oOp0tqJiIiGK2Ip5kZiI7uVNxzsMIyJ2BXeooybd7uwzMc9TvYW9o3oiGGYpoogCBQ+dAG1efVVgkL7gvVRzEe08fJmEnqMJSIiS6GC7BIOk0bD43buBwc7DGMC5ZUa6pJ8mWzytY/O5U4CPTn85UZ2xTBMUyfcL5zm+M/RH+8/toF++SKWKiu0AdCIyl8oLq2wsezJHg52GMYE0gtKqV1igv44Y3h3am/XvvEMMQzTbHi+5/M0r+886pgBem+XmpxjU+nUmfakriQaoPiDYuP+bGyLsqXJjKj8+OOP6YMPPqDU1FTq0aMHrV27lgYOHNjYtph/KAU5+ZR0+QZl3/iLbikt6YK9FSUVpFBmzh0K6lVJzycQ5dgRDXlmYWNbZRimGTG9x3SyT8whM82nRETUJpnol3PONDAwnXDlINEwXuurJppEsPPVV1/Rv/71L/r4448pJCSENm3aRI8++ihdvXqVOnbs2Nj2GBmi0YDSbqfQzYsxlH7jKpUm3iSz9BSyz86h1nmF5FBSSQoiakNEMf4CnRilJFJqE460UdDTRzR0xVegsA5csDAM07CMe/Q1itIIpFi4iWzKiZwTlfSTmTN59/2B1Jr3SKng9b7uRUATWIkoMDCQ/P39aePGjfq0bt260dixYykiIsLo9QUFBeTg4ED5+fnUokULyXyt/uolskvIohbpRUY/W9rCktK9HA3SOlzJJPOSSqPX5rq2oIJ2tvpjZbmaXC9liPKY0sOJKqzN9cd2mcXU+na+0evU5kq649fWIK3NzVyyyS01eq3KyYayOzsYpLnFppNCrTF6baZHSyp2tNYfW6rKqV2c8SmXFQLRWV9byqxUUS6KqEAooecPFVHINeN/86K7QMufVhqkPXFGTY/37UTBM783ej3DMEx98MOB1dTunc1k+Xc1cdtDTdldPUipVFJhW1vK6WhYn3WKTiMSUeVneLaikpZW+mOrgjJyvpEjytPtPu2Iqiyu2iqpgFqkF1GWe0uy6exFM0IXidIRi9j6+x/fslNeXk7R0dG0YMECg/SRI0fSqVOnarymrKyMysrK9McFBQX14m138c807gpo4DnjN9fvnQXa5W5Yoa76uZI6itjyZMcwBUW1vjv8qpUKtOmQWpTHHR1uU5LZ3Rtz6G0NzTpkPADIsSNa0f2WQdpr0WryizOe1+O9BNrlYpjXbUcqyabsPhdUYc1YBZ22v5vXrlmgZSLyqhaIPuyhJLXF3bymt6rZa7Y9UXpLoiwHomIHULGTmkILS6mtupKcK9XkrFaTt0s5ubg+adwwwzBMPfHI2Ll0rJKozaLNZK4m6vSXkjr9dZuIiL7vI9Cu9obl7P8OVZJSRPNGxEQFxdreLWd90zX0bxH1AhFRRM/bpKnSsjTlqpoGngWtC1VQjuVVmkHSBjti+ccHO1lZWaRWq8nZ2dkg3dnZmdLS0mq8JiIigt57772GsMfIBCWInAqI0ltpj+3VGsptr6bYh0Aaew2Z2anJxraCWllXUFtSU0+1mlpoNKR/ZGsKOj2HN5B7hmGYmhk+YS59n51Frh/tJzNx8Uiz5B/fjZWSkkIuLi506tQpCg4O1qcvW7aMdu7cSXFxcdWuqallx83NTfJurPV7XiObpGyyE7GMd5m9JWV5tjZIa3stk8xLK4xem9+hBamc7677oixXU7sr6aI8pvu0ocoq3Vg2WcXUKinP6HVqcyWl+RoGmI63csk6r8TotUWtbSivY0uDtA6X0kgQ0Y2V07kVlbS6241lUVRObW4Yb/5SKBRk7udB7s7O5GHbkuzNrIxe80CcvIjcAuqmwTAMIxExP+2l+CP/0++RpWpjS/muhsMFXC6miurGyvZwpFKHu2WkZUEZOYncbDTZrz1RlZadFskFZJ+hotxOLcm2kxdNHb3gAVebTrPpxnJyciKlUlmtFScjI6Naa48OS0tLsrS0rHdvr0xcU+9/o8nACxAzDMPUGv+hE8h/6IQHf6gZl7P/+HV2LCwsqE+fPnT06FGD9KNHj1L//v0byRXDMAzDMHLhH9+yQ0Q0d+5cmjp1KvXt25eCg4Pp008/pcTERAoPD29sawzDMAzDNDJNItiZNGkSZWdn05IlSyg1NZV8fX3p0KFD1KlTp8a2xjAMwzBMI/OPH6AsBfW1zg7DMAzDMPWH2Pr7Hz9mh2EYhmEY5kFwsMMwDMMwTJOGgx2GYRiGYZo0HOwwDMMwDNOk4WCHYRiGYZgmDQc7DMMwDMM0aTjYYRiGYRimScPBDsMwDMMwTRoOdhiGYRiGadI0ie0i6opuEemCgoJGdsIwDMMwjFh09baxzSA42CGiwsJCIiJyc3NrZCcMwzAMw5hKYWEhOTg43Pc8741FRBqNhlJSUsje3p4EQZBMt6CggNzc3CgpKUmSPbfkrMfe5KHH3uShJ2dvUuuxN3noNSdvVQFAhYWF1KFDB1Io7j8yh1t2iEihUJCrq2u96bdo0ULSH1jOeuxNHnrsTR56cvYmtR57k4dec/Km40EtOjp4gDLDMAzDME0aDnYYhmEYhmnScLBTj1haWtKiRYvI0tKyyeuxN3nosTd56MnZm9R67E0ees3JW23gAcoMwzAMwzRpuGWHYRiGYZgmDQc7DMMwDMM0aTjYYRiGYRimScPBDsMwDMMwTRoOdhiGYRiGadJwsNOASD3xTUo9OXuTWo+9yUePYRgtcn9W5VzOiYGDnQZEyn23pNaTszep9dibfPQ0Go1s9dibPPSaize5P6tyLudE/U1eZ6d+KS4uppiYGDp//jyZm5vTY489Ru7u7vrzAEz64aXUk7M3qfXYm3z0GIbRIvdnVc7lnMmAqTdUKhWmT58OS0tLhISEwN3dHYIgoGfPnti0aVOj6snZm9R67E0+egBw69YtREZGYu7cudi8eTPu3LljcF6j0TSaHnuTh15z8Cb3Z1XO5Vxt4GCnHomIiEDv3r1x9uxZpKWlISkpCSdPnkR4eDicnJwwYMAAxMXFNYqenL01p7zK2Vt96B04cABBQUFo1aoVRo0ahXbt2kGpVGLAgAH45ptvROvUhx57k4dec/Em92dVzuVcbeBgpx4JCgrC0qVLq6WXl5fj1KlT6NevHyZNmoTKysoG15OzN6n12Jt89Ly8vLB48WLcuXMHOTk5SE5OxnfffYennnoKrVq1Qnh4OIqKikRpSa3H3uSh11y8yf1ZlXM5Vxs42Kkn1Go1/vWvf2Hs2LH3/czBgwfh5+eHS5cuNaienL1Jrcfe5KOXkJCAVq1a4dq1a9XOFRYWIjIyEq1bt8bmzZuNakmtx97koddcvMn9WZVzOVdbONipR77//ntYW1tj5syZOHfuHEpLSw3OJyYmwtbWFomJiQ2uJ2dvUuuxN3nopaamIjg4GGvWrLnvZ958802MHz++2t+pbz32Jg+95uRNzs+q1HpSe6sNHOzUMzt37kTfvn0xZMgQvPPOO9i9ezeio6MRHR2NKVOmwN/fv9H05OytOeVVzt6k1ps/fz5atmyJiIgIXLt2rVqz9cqVKxtNj73JQ685eZPzsyq1ntTeTIWDnXqi6oj8s2fPYtasWfD29kbPnj3h4+MDQRAwceJE/Pbbbw2uJ2dvzSmvcvZWH3oAUFFRgTfffBO9evXCo48+iuXLl2P37t2IiYnBzp070aVLF6xbt65R9NibPPSagze5P6tyLudqC6+zU0/k5ORQUlIS+fj4kKWlJRERVVRU0IkTJ6iwsJC6d+9Orq6uZGdn1+B6cvbWnPIqZ2/1oacDAB05coS2b99O0dHRZGNjQ1lZWZSfn08LFy6k119/nSwsLBpFj73JQ6+pe5P7syrncq7W1Gso1UxZv349/Pz84ObmBktLSwQEBGDlypVIT09vdD05e5Naj73JRw8ALl++jAsXLhj01+fm5uLQoUM4c+YMkpOTG02PvclDrzl4k/uzKudyri5wsCMxkZGR8PLywuuvv47jx48jKioKL7zwAhwdHWFvb4933nkHKpWqUfTk7K055VXO3upD78qVK3j22Wfh7u6Oli1bwtraGoMHD8Znn31Wq6mmUuqxN3noNRdvcn9W5VzO1RUOdiRm8ODBeOedd6qlq1QqrFmzBp6enib1N0upJ2dvUuuxN/noPfzwwxg7diw++eQTXLx4Efv378eECRNgbW0NFxcX7Ny5E2q1Gmq1usH12Js89JqLN7k/q3Iu5+oKBzsSUl5ejkcffRQLFizQp1VWVqKyshIajQYajQZz585Fr169cPv27QbVk7O35pRXOXurD73c3FzY2Njgzz//rHbuzp07ePXVV+Hj44MLFy4Y1ZJaj73JQ6+5eJP7syrnck4KONiRmA0bNsDKygqHDh2qsYkzNTUVHTp0EL00tpR6cvYmtR57k4fe77//ju7du+PHH3/Up+kKPQBIT09HUFAQZs2ahfLy8gbVY2/y0GtO3uT8rEqtJ7W3usLBjsRUVlYiLCwM3t7eePHFF/Hll18avBWsXLkSHTp0aBQ9OXuTWo+9yUfv4Ycfhq+vL37//fcaz2/cuBH9+vVrFD32Jg+95uJN7s+qnMu5usJTz+uB1NRU2rZtG/3www9UVFRELVu2pMLCQsrLyyNra2sKDw+n8PDwRtGTs7fmlFc5e5NaLykpiZ577jlSqVQ0cOBAGjRoED300EPk5uZG8fHxNG3aNOrfvz+tWrWqwfXYmzz0mpM3OT+rUutJ7a0ucLBTj6SlpdGxY8fo6tWrVF5eToIg0LPPPkvdunUjQRAaVU/O3qTWY2+NpweABEGgixcv0rZt2+jMmTMEgMzNzSk7O5sKCgqoR48etGPHDmrXrl2D6rE3eeg1J29VkduzWp96UnurDRzsSIhGoyGFQkEnTpwgX19fcnJyqnausfTk7E1qPfYmH717uXnzJp04cYKSk5NJqVSSk5MTTZ06lWxsbBpdj73JQ6+pepP7syrnck4SGqq/rLlQVFQEHx8fjBs3DsXFxQZLZTe2npy9Sa0nR2+6a+TorT71iouLcfDgQVGbIza0HnuTh15z8Sb3Z1XOZXBdaYTwqmmCvxvISktL6fnnn6dx48aRtbU1CYJAKpWKPvroI4qNjTVZt6Kigl588UVJ9MrLyyXTIiIqKyuTVK+ueUWVRkopvzep9HTNtVJ7Ky0tpbCwsDrr1dc9fPjwYXruuefo008/Nfg7pqK7Tio9uXqTOp9y1tNoNLL1pkPK31TO9QORtHWE1PVDnWnY2Kr5UDWK/fXXX+Hu7o7XXntNEm0p9eTsTWo9U7VqWiSstr/riRMnJPUG4IFTXaX43up6D+uuv3z5MlauXImrV6/qz/38889YsWIFUlNTTfYVFxeHVatW1UlP5+3atWt11qpKXfWqfudS5LPqlF8p9KoSFxeHDz74QBI9KX9TALh69apkelLfv/d6lXOZKbWe1N5MgYMdCbhy5Qr27NmDzz77DDt27Ki2AFVRURG++eabGhemMoZuEaaqFBcX48CBA7hx44bR65OSknDmzBnk5+fXeF7XRCtGS6PR4OzZs9i1axcuXrxY42dMyWtCQgJOnz6N3Nzc+36muLhYlN6hQ4fw2WefPfAzpng7evQo/v3vfyMlJUWfdu9aEWL1Dhw4AFdXVxw6dOi+WmLzCQDZ2dnYtm0bXnrpJXz00UcoKSmp9hmVSmXSPRcXF4f9+/dj69at2L17NxISEmrt735oNBp9ALl8+XK0a9dOsp2OpdSTSkv33Eqhp7tfGtNbWVnZfV8ATP1d4+Li7juVW0dt8lpUVFQtTap7TrcYnqlacq4fAOnqCKnrB6nhYKeOREREwNfXF9bW1ujatSuCgoIQGBiI8PBwnDx5UvTy5joyMzMl69vcsWMHgoKCYGlpCUtLSyxduhTl5eW11tfl1dLSEoIg4Mcff0RZWRmSk5Nx4cIFlJWVmaQ3evRo9OnTB/PmzcPu3btx+fJlAMDJkyexc+dOk7T8/f2xdOlS/fH169dx4sQJHD58GFevXjU5z4MGDcIrr7yCiooKlJWVYfv27ViwYAGee+45bN++HYWFhaK1AgMD0apVK3Ts2BFr1qwxyUdNvPjii+jVqxf69OkDR0dHbN++HampqThw4AC2bdv2wOCxJlatWoWgoCA4OjqiVatWsLa2Ru/evTF79uz7FloPQq1WY9++ffj000+xevVqREZGVnsDTk9PR1RUlMnaQPVAEQDS0tLwzTffiLr+5MmT+O233+57T6Snp+Pbb78VpaVSqbBt2zZs3boVaWlp99UTk9effvoJsbGxNVbYpmoBwGuvvYbY2FgAeGBexerNmzcPO3bsMNjP6N5xLGlpaaL0xo0bh8DAQFy7dg2A9p6516Mp3i5duoTXXnsNzz//PA4cOFDjZ8R6A4D9+/djy5YtWLduHQ4cOFBt40qx3uRcPwDS1hFS1w9Sw8FOHcjKyoKdnR02bdoEQBsh79+/H2+++SaGDx+O4OBg/PTTTwBq7hK5l5ycHHTo0AHPPPMMoqKi7ltpnTx50qDFoSays7PRuXNnLFy4ENeuXcMnn3wCd3d3nDx5EsDdCiMzM1PUTZiVlYU2bdpg27ZtALQF37x58zBq1Ci4ubmhb9+++Pzzz43q6CgvL8fQoUPh4OAAf39/tG/fHoMGDcKqVavg5eWFadOmidbKzMyEIAj61ohNmzbB3d0dZmZmcHFxweDBg/X5FvM7ZGdnw9bWFn/99RcAYMaMGXB3d0dISAjGjx+Pvn37YtmyZaIKhMzMTCiVSn1hbGdnh7fffhvFxcWi/VQlKysLNjY2OHv2LADgiy++wIQJE9CjRw/07NkT/v7+mDZtGjIzM0XrtWjRAjt27ACgbW0LDg5GSEgIgoKCMGTIEP1bmJhNDwsKCjBx4kS0adMG7du3R0BAAAICAjBgwAC89dZb+spNLBUVFZLtkFxUVIS5c+eibdu2sLCwgLOzM77++msA9w8GHkRBQQGeeuopODs7w87ODi4uLsjNzUVOTg7OnTuHS5cuidbNzc2FtbU1xowZg7feegvffvut/k164cKFRltB7iUzMxMKhcJgJ+4jR45g//79iIqKQmJiokl6uvvk+PHjAICbN29i/vz5mDFjBmbMmIGoqCjRm2JmZWVBEAT07NkTfn5+OHr0qEle7iU3Nxf9+vVD7969MXz4cDg6OiI+Ph7nz5/H6tWrsW/fPtEVbUFBAaZNmwZ3d3d06tQJ9vb2sLGxwYABA/Dee++Z1G0l5/oBkLaOkLp+qA842KkDGzduREBAQI3nLl26hIkTJ8Le3h43b94Upbd+/XrY29tj0KBB+op6zpw5OHPmjL6rIjExEZ6enkYrjYiICPTv319/XF5ejhdeeAFBQUEGaYMHDxZV8C1fvhwDBgzQHx8+fBgKhQJvvPEGTp48iZdeegmCIJjUEhAdHY0ZM2YgOzsb586dQ3h4ODw8PKBUKjFkyBCsXbsWx48fN1qILl68GCEhIQCA06dPo1u3bliwYAFKSkrw888/45FHHkGLFi1EFQCAtgsrICAAxcXFOHXqFFxdXfVvyImJiVi3bp3+zcUYixcvNvjON23aBFdXV8yZM6dWszs2bNiAgQMH6o/Pnj0LQRDw5ZdfIj4+HpGRkbC1tdUXOsZYv349hgwZAkAbWADaAGrMmDG4cOEC+vfvj6FDh4quyJYuXYpevXrh/PnzALTdFZGRkZg9ezYCAwMRGhqKjIwM0flds2YNHBwc8PLLL+PkyZM1tnrk5+cjKirKaIH84Ycfol+/fti1axcyMjIwa9YsBAUFQaVSGQQlKSkpooKUFStWICAgAH/88QcqKysRGhqKJUuWoG3btvDw8MCwYcNw7tw5UfmMjY1Fy5Yt0bt3b3Tv3h1ubm54/PHHsXjxYgiCgO+//16Ujo7FixfD398fAPDXX38hPDwcVlZWsLW1RZcuXTB+/HjRATEAfPDBB/pVgqOjozFkyBB07twZU6ZMQWhoKIKCgkR7XLRoEYKCgnD79m0MGjQITk5OiIyM1Ff4un/FBopLly5FUFCQvox8+eWXMWXKFNja2mLYsGHw9/fH+++/L1rL398ff/zxBwDgu+++g6enJ8aMGQMvLy/MnDlT/5wYQ871AyBtHVEf9YPUcLBTB3bv3g13d3f88ssvAAz3TAG0TbxBQUHYsGGDKL2XXnoJL7zwAoqKipCcnIz3338fPXv21L8FrVy5Eu+88w4cHByMaoWGhmLFihUGabdu3ULnzp2xd+9evX8LCwtR3kaNGoWVK1fqj6dOnYoRI0boK+zS0lKMGDFC/xYjljlz5iA0NFR/vGLFCnTq1Anjx4+Ho6MjRo0aZVTD19cXDz/8MDIzMzFlyhSEhYUZFEhJSUnw9va+b/P2vRQVFaFHjx748ssvsWHDBkyaNKlawRsWFoaFCxca1XJ1dcXWrVsBaAvviooKfaE1atQog2Z8MWzYsAF9+vTRv+lPnz4djz76qMFn5syZg5kzZ4rSe//99zFixAjk5OTo0958802MHDkSgDZ4dHV1RXR0tCi9AQMGYPXq1dXSKysr8csvv8DLy0vUb6ojMDAQ/fv3R0BAABQKBXx8fLBo0SJ9gAEAH3/8MQIDA41q+fj44NNPP9Ufp6SkoFevXliyZIk+be/evRg7dqwobz4+PtiyZYv+eNy4cfDy8sL//vc/nDt3DkFBQXjooYdE7ZkEALt27dLvEv3NN9/gueee03crTp48GevXrxfdwuPm5obIyEgAwOzZszFs2DAcPHgQAPDtt9/CxcXF4Lkzxuuvv44XXngBgPaee+aZZ/RdufHx8Zg8eTLc3d2RnZ0typvumSgvL8fLL7+Mzp0769NMZcCAAfjkk0/0x5MnT0ZAQABiYmKQkpKC119/He3atRMVAAQFBel/U10ZMmXKFKxZswbHjx9Hu3btsGzZMlG+5Fw/ANLWEfVVP0gJBzt1oKioCEOGDMGTTz5pMFK/asUYEhJiUJjej/LycuzcuRNr1qypVrHGxsbitddeQ6dOnSAIglG94uJizJw5E5MnT9an6R6yV155RV/ZDB06FHPnzjXqTaVSYf78+fjyyy/1afPmzdM3aesK85EjR9ZqTMrIkSP1Prp27Yr169cD0DafGqtk8/PzMW7cOAwcOBCBgYGwt7fHvn379OcrKytRUVGBoUOHGhSIxli+fDmGDBmCDz74AAMGDKhWyQwfPhxvv/32AzUSEhKgVCprHPh35swZBAYGYvTo0aJbnIC7FfS4ceMwaNAg+Pn54aWXXjK4Z0aMGIG33npLlN7Vq1fh7OyMVatWISsrC3v37oW9vT32798PACgsLERwcLCoMVQVFRUIDw/HgAED9K0G9xbwx48fh6+vr6g3vIyMDAwcOFD/t69evYqFCxeiU6dOUCqVCAkJwcaNG+Hr64tXX331gVpJSUno1q0bLl26ZJC+ZcsWeHh46LvKhgwZghdffNGot4SEBHh5eeHKlSv6NHNzc4Nn5MSJEwgODq72Nx+U36CgIKxdu1af5unpiQkTJuCxxx6DUqk0Oggf0HYxCYKAlStX4vLly3BxcanWCrl582YEBASI3nH6+PHjcHFxwfXr1/HYY49h+/bt1bwHBwfj2LFjD9S5ePEirKysUFBQoA/w09LSEBYWBqVSiVdffVV/74hp2SkpKcGkSZMwefJkpKWloaSkBDY2NvrKGtB2/wQFBRmUCzWhUqkQGhpa7SXG0dFR/6Kk64K632Dequjqh/Hjx0tWP1S9N3SYWj8Ad+uIKVOm6NNqW0fo6oevvvpKnyZl/SAVHOzUEt0N++uvv6J3794wNzfHmDFj8O233yIxMRExMTFYv349WrZsiVu3bonSLC8v178Z6Srpqm/8MTExEAQBSUlJRrXS0tL0MwXKy8v1N9zNmzfh5uaG7777DhYWFqKbUEtLS/Vv/xUVFcjMzDSoxJKTk2FjY1NtFo8YLl68iNDQUOzcuROWlpa4fv26SWNZiouLcfr0aaxcuRJTp07FkSNHDM4nJyfD2tpadMEOaIOo6dOnQxAECIKAQYMGYcuWLfj666/x7rvvwtnZ+YF6uvuj6mBOtVqNsrIy/RvjkSNH0LVrV7Ru3VrUgGfdrJeoqChMnToVEREROHDgALp37464uDjk5ORgz549aNOmjajfQTe7ZO3atWjXrh0EQUCHDh0QFham/0xCQgKsra1F/66nT59Gly5d8NZbb9XYTZKYmAhbW1vcuXPHqFZKSgpWr16Nw4cPG6RXVlbi5MmTePbZZ+Hg4CDqmbh8+TKCg4MNWtl0Wr1798bq1auRl5cHMzMzUc9rbGws5s2bp+/uSEpKwiuvvKLvZlOr1YiPj0fHjh1NGudx6dIleHt748cff0R8fDwsLCyQl5cHQPvWLeY++fXXXzFs2DAMGzYMXl5e8PX1NRi7AwB//PEHunTpIno8lEqlwqRJkzB9+nTMmjUL48ePN/By+/Zt2Nraiuru0FWk9wYzn3/+Oby9vUW9gFVl79698PDwQGhoKAYPHgwPDw99sA5od9e2tbUV9fyvXbsW7du3x/fff68fZ+fk5KQ/f/bsWXh5eSErK+uBOrq8nThxAt26dYOlpSXGjx+P7777rtb1Q9XxazUFgqbUD4B2kPXPP/8MwHCAeG3qiLKyMn33tEajQW5urkEZXpf6QSo42Kkj+fn5yMjIwPfff4/JkyfDwcEB1tbW8PDwQNeuXWuMxGtCd6PFx8dXm9Whu2mWLFkCNzc30Vo3b96ssTCbPXs2BEFAcHCwyd5q0ktOTsb8+fNF6+m4ceOGvhDeunUrBEFAaGioPhgQ82an+27S09MNAgsdd+7cwbx580zaMRnQVrRqtRq3bt3Cf/7zH7i4uMDa2hqenp546KGHsGfPHqMaumDw+vXr961U7ty5g+XLl5vkLS8vz2C6+dNPPw1BENCtWzd4eHjgzTffNEkvOTkZv//+O65fv25QIaSmpmLBggWivztdN92mTZvQunVrtGzZEmFhYfjpp58QHx+PvXv3Ytq0aejbt69ob8XFxfrB3DXdD6+//jp69+4tSuv69ev6VjSdVwBYt24dBg8ejBUrVsDDw0O0N5VKpW+mr6ysREFBgcH5Dz/8ED179hStp7tfPvvsM8yePRvTpk3D4MGDDaYXmzLg+fDhw5g/fz4WLlxYLbhctWqVSd4AbUXv7++vfwGYNm0adu3ahdWrV2P8+PEYNGiQKJ17x6rp8l1SUoJPPvkEgiBg+vTporQ0Gg2Kiorw8ccfY/r06fj555/x7rvvYvz48VCpVIiNjcWcOXMMxqE8SCs/Px9hYWGwsrKCQqHAwIED8cUXX+g/8/7778PX11eUt6p88cUXGD58uH7clKenp0n1gxjE1g8PQvdMvPzyy6LriPvdk7r01NTUWtUPUsPBTi1IT0/HqlWr0KFDB/Tq1Qt9+/ZFaGgoPvnkE/z55584f/489u7de9+pqA/S69OnDwICAhAaGlptmmdUVJTRxenEaP3yyy8QBEFUX3FNeo8//riB3rFjxzB69GiDtyljei4uLnq9cePGYevWrVi3bh2uX79uVKMmb35+fujTpw+efPJJA29HjhzB4MGDRQUnVb35+fnB398fEyZMwJ49e1BaWoo///wT0dHRNQZVD9Ly9/ev8XfQBWpiZoro9Nq3b6+fdv74449j9+7dKCsrw9GjR/HGG2/g2LFjJuvp/E2aNAmRkZF6fzdu3MDatWvx66+/GtW7l9zcXKxZswYBAQEwNzdH69at4eLigjFjxuDMmTMm69VESUkJvL29q407eBD3FsxqtRp5eXno06cPBEHA5s2bJfF26tQp9O3bFxs3bjTJm0ajQV5eHsaNGwdBEAwq2tqSk5NTbRE7Pz8/0WNF7uXw4cN4+umnYW9vDxcXF7i7uyMsLMzkmXb3Iyoqymh32IO4cuUKXF1d4eDggPbt26N///7VWnuNkZKSggsXLhgMpo+JiUG/fv1EfW+6Z7vqrMvi4mKkpqbi2LFj2LNnj+j6oaqeLhi5t+Vbo9Hgu+++M1o/iNU7efKk6DriXq17JzMcPXpUdP1Qn/BGoLVg+vTpFBcXR6GhodS6dWvKzc2lixcv0tWrV6l9+/a0bNky6tevX530YmNj6erVq9ShQwd64403aOTIkZJozZs3j0aNGkW3b98mV1dXUiqVkngrKysjCwsLozvY1qQXHR1N8fHx5OzsTHPnzqVHHnmk1nmNiYmha9euGXgrLS0lS0vLWnu7cuUKubm50ZIlSyg4OLjW3mr7mz4or5cvXyY3NzdaunQpBQYG1krP0dGR8vLy6MKFCxQXF0cuLi4m+yspKSFra2uDNABUUlJCKpWK/vjjD7KzsxPtsSa9mj6ze/du+r//+z+ysLCok1ZERAS9/fbbVFRUJOrv1pRX3f2lVqtp06ZNdOzYMYqMjCRLS0uTvaWmptKhQ4dowoQJ5ODg8EA/YvR0/iorK+m///0v/fDDD3TgwAGysrKqlZ6O6Ohoat++PXXo0KHW3u71KJYH/Q7l5eW0e/duSk9Pp6lTp1Lbtm3r7O3UqVMUExND4eHhZG5uft/PxsXF0dq1a+n48eMUHBxMixYtIg8PD9H5Mqa3ePFicnd3N/n7ulfv2LFj1L9///vqiakjxHoTWz/UK40UZP1j0Wg0sLW11fd16tISExOxd+9ejB49Gp6enoiJiamTXlJSEvbt24fRo0fDx8dHlJ4YLS8vL1y4cEEyb97e3qJn6jxIb8+ePXo93TRvuXjbt28fHn30UXTt2lWy38HHx0dUPsXqeXp6Sv67itUDgLlz52Lfvn1ISEi475R63ZgvMV0xYvTELp4oRkulUokaR2SKnpgpymK0TJkiLtab2BliYvR041eM/a5itKqO+5DCm6671xRvNa1IDtxtpTFGv379MGzYMCxduhR9+/ZFp06dqo3LMWXZiZr07h37IrVeXfJ6r9b9vs+GhoMdE7l8+TJ8fX3v2xRfVlaGgIAALFiwoMH1xGrNnz+/wb1Jrcfe5KP3xRdfQBAEmJub6/e9OX78ONLS0vSVan5+PsaMGSNqZtL99NLT0/UBhG7mjG6AcF208vPzRWmJ1SssLKyTt7S0NH1XpEqlkvR7E+vNmD+dXkFBgSh/Yn9TKfOal5eHJ554QhJvBQUFor63bdu2wdfXVz/RRKVSYeDAgfoZfrqga8WKFaJmYcpZT2pv9Q0HOyZSXFyM4cOHIyQkBDdv3qxx1tD69evh5+fX4Hpy9ia1HnuTj97MmTMxa9YsxMfHY+nSpejcuTMEQYC/vz8iIiIQExODLVu2wMzMrMH1/sneoqOjG81bQ+dVam+mfHdSenviiSf0L5O6QP/w4cPw8vLSt3j89NNPEARBVD7lrCe1t/qGg51acPr0afj5+aF///7YuXMnUlJS9M1+xcXFmDBhgsH6BQ2pJ2dvzSmvcvYmpV5FRQWWLVtWbW2S33//HWFhYXBwcICdnR3Mzc3x3HPPNagee5OHXnPxVlpaivHjxyMiIkLfqqFrGRo2bJi+pXTy5MmYNGmS0XzKWU9qbw0BBzu15NKlS3jqqadgZWUFJycnjBkzBi+88AI6duyIfv36iV5IrD705OytOeVVzt6k1MvNzUVcXBwAbRfYveMjdu3aZdJS8VLqsTd56DUHbxqNBtHR0fjwww/1xzoOHjwIJycnJCYmwsHBQdQMRznrSe2tIeDZWHUkIyODoqKi6ODBg2RlZUW+vr40ceJE8vHxaXQ9OXuTWo+9yUePiEij0RAAUiqVtHnzZpozZw4VFxfLQo+9yUOvqXtDlRlJ5eXlNHbsWEpMTKSCggJKTEw02ZOc9aT2Vi80fHzVdDF1B+uG1JOzN6n12Jt89ADt4npV982Rkx57k4deU/ama/XQDYR+77336uRHznpSe5MSbtlhGKZeqaioIKVSSQqFQnZ67E0ees3BGwC6cOECeXh4UOvWrevsS856UnuTAg52GIZhGIZp0kgTRjMMwzAMw8gUDnYYhmEYhmnScLDDMAzDMEyThoMdhmEYhmGaNBzsMAzDMAzTpOFgh2EYhmGYJg0HOwzDMAzDNGk42GEYhmEYpknz/+EKJSxchd7ZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(G_normal['G(n)'], \n",
    "         linewidth=2, label='Global')\n",
    "plt.plot(B_normal['Gb(n)'], \n",
    "         linewidth=2, label='Direct')\n",
    "plt.plot(D_normal['Gd(n)'],\n",
    "         linewidth=2, label='Diffuse (measured)')\n",
    "plt.plot(D_normal_estimated,\n",
    "         linewidth=2, linestyle='--',label='Diffuse (estimated)')\n",
    "plt.ylabel('Irradiance (W/m2)')\n",
    "plt.xticks(rotation=70)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We calculate the root-mean square error (RMSE) between the estimated and measured diffuse irradiance. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The RMSE is 10.86 W/m2\n"
     ]
    }
   ],
   "source": [
    "RMSE=np.sqrt(((D_normal['Gd(n)']-D_normal_estimated)**2).mean())\n",
    "RMSE\n",
    "print('The RMSE is ' + str(RMSE.round(2)) + ' W/m2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now retrive the solar radiation components measured on a 35$^{\\circ}$ tilt plane."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "G = pd.read_csv('data/Global_35deg.csv', index_col=0, header=0) \n",
    "B = pd.read_csv('data/Direct_35deg.csv', index_col=0, header=0) \n",
    "D = pd.read_csv('data/Diffuse_35deg.csv', index_col=0, header=0) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we can calculate the deviation error for daily irradiation value in every component measured for the normal plane compared to the 35$^{\\circ}$ tilt plane. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Global component deviation error is 49.7%\n"
     ]
    }
   ],
   "source": [
    "G_diff = 100*(G_normal['G(n)'].sum()/G['G(i)'].sum()-1)\n",
    "print('Global component deviation error is ' + str(G_diff.round(1)) + '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Direct component deviation error is 61.6%\n"
     ]
    }
   ],
   "source": [
    "B_diff = 100*(B_normal['Gb(n)'].sum()/B['Gb(i)'].sum()-1)\n",
    "print('Direct component deviation error is ' + str(B_diff.round(1)) + '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Diffuse component deviation error is 24.3%\n"
     ]
    }
   ],
   "source": [
    "D_diff = 100*(D_normal['Gd(n)'].sum()/D['Gd(i)'].sum()-1)\n",
    "print('Diffuse component deviation error is ' + str(D_diff.round(1)) + '%')"
   ]
  }
 ],
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