Optical properties are required for the correct understanding and modelling of protoplanetary and debris discs. By assuming that comets are the most pristine bodies in the solar system, our goal is to derive optical constants of real protoplanetary material. We determine the complex index of refraction of the near-surface material of comet 67P/Churyumov-Gerasimenko by fitting the sub-millimetre/millimetre observations of the thermal emission of the comet’s sub-surface made by the Microwave Instrument for the Rosetta Orbiter (MIRO) with synthetic temperatures derived from a thermophysical model and radiative-transfer models. According to the two major formation scenarios of comets, we model the sub-surface layers to consist of pebbles as well as of homogeneously packed dust grains. In the case of a homogeneous dusty surface material, we find a solution for the length-absorption coefficient of α ≈ 0.22 cm−1 for a wavelength of 1.594 mm and α ≥ 3.84 cm−1 for a wavelength of 0.533 mm and a constant thermal conductivity of 0.006 Wm−1K−1. For the pebble scenario, we find for the pebbles and a wavelength of 1.594 mm a complex refractive index of n = (1.074 − 1.256) + i (2.580 − 7.431) · 10−3 for pebble radii between 1 mm and 6 mm. Taking into account other constraints, our results point towards a pebble makeup of the cometary sub-surface with pebble radii between 3 mm and 6 mm. The derived real part of the refractive index is used to constrain the composition of the pebbles and their volume filling factor. The optical and physical properties are discussed in the context of protoplanetary and debris disc observations.
<p><strong>Introduction</strong></p> <p>Optical properties are required information for correct models of the early solar system and protoplanetary disks. To this day the properties used in the models are mostly provided by laboratory studies of primitive analogs. Comets provide us with a closer analog to the protoplanetary material but have been, until recently, difficult to observe. Rosetta has given us the unique opportunity to observe with unprecedented accuracy.</p> <p>The Microwave Instrument for the Rosetta Orbiter (MIRO) (Gulkis et al. 2007) measured the thermal radiation emitted from the subsurface of comet 67P/Churyumov-Gerasimenko (hereafter 67P). MIRO operated at millimetre (hereafter MM) and sub-millimetre (hereafter SMM) wavelengths with corresponding frequencies of 188.2 GHz (1.594 mm) and 562.8 GHz (0.533 mm), respectively.</p> <p>The thermal radiation received by MIRO is strongly dependant on the sub-surface material properties, mainly its temperature and optical constants. Our aim is to derive from these observations the complex refractive index of the material of comet 67P's subsurface. To achieve this, we match the brightness temperatures measured by MIRO with synthetic values derived from a one-dimensional thermal model of the subsurface combined with radiative transfer models.</p> <p><strong>MIRO data selection</strong></p> <p>MIRO data used in this study are taken from the ESA Planetary Science Archive. We selected only those measurements where the orbiter was close to the surface of the nucleus, to minimise the MIRO beam size on the nucleus and, thus, to be able to focus on specific areas of the comet. Additionally, we set our focus on flat areas, which allows us to use a one-dimensional thermal model.</p> <p>Just before equinox in March 2016, the Rosetta orbiter was 11 km from the surface of 67P, observing the relatively flat region Imhotep (Auger et al. 2015). This region is located on the bottom of the big lobe and consists of four sub-regions, a,b,c and d (Thomas et al. 2018). We analysed MIRO measurements in sub-region 'a', as it is the smoothest and closest to the equator (Thomas et al. 2018). Fig. 1 illustrates the location of sub-region 'a' on the nucleus.</p> <p><img src="data:image/png;base64, iVBORw0KGgoAAAANSUhEUgAAAZAAAAEyCAYAAADUa4YpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAFFwAABRcBpcZNXQAAABl0RVh0U29mdHdhcmUAd3d3Lmlua3NjYXBlLm9yZ5vuPBoAACAASURBVHic7L1pjBzXeTb6nKreu6d7OKRIrTRl7bIkK5KgxUscW3EWGfBFoOTLjy+fjcAxchH/CGwgMBw4iPPjwsFN7ADOYiRIvsRxNsOObfmzbyg5XqREkuVYkiVZDklJoSSbtKghZ6Z7eu+uOvdH9XP6qTM1lOhIJIesFyBnpruWU6eq3ud9n3c5xlprkUsuZ7nwNdDXIX81Tr0EQQBjzKkexlkl5gQmvPBqDiSXXE4HsdZmAkQuueTy35McQHLZsuKDQg4OueRyciUHkFxOC/GVv3oNueSSy+kpOYDk8qpKFgjk4JBLLmeG5ACSy48tmwWcc3DI5ZUSa20eRD+NJQeQXDZInpGUy+ki+XN3eksOIGeZ5BlJueSSyyslOYCcQZKDw+kho9EI//iP/4hHH30UhUIBu3fvxnve8x7UajUEQXCqh5dLLq+YmLyQ8PSSzYLOm32Xy+kjcRzjC1/4Aj7zmc9gOBwiCAIEQYAwDFEqlXDNNdfgXe96F5aWllAoFBCGIabTqftnjEEQBLDW5mAjkhcTnlw5kULCHEBOomSlqmZ9nsvWEWstDh48iJWVFTzxxBP48pe/jCiKMJ1OneIjMBSLRRSLRbRaLYzHYywtLWEwGKT+BUGAXbt24cYbb8Qv//Ivo1arnepLPOWSA8jJlRxAToFkpabmU3vmibUWURTh6NGjWFtbw3333YdvfOMbmEwmsNYijmNEUZTKHuLvBJEoihDHMYIgwHQ6dX8DQKFQQLlcRrFYxJ49e3DjjTdiz5492L59OxYWFrC4uJjyXOI4Rr1ed/ufiV5LDiAnV3IAeQUlr3Y+u8Rai+FwiF6vh8ceewyTyQSLi4s4cuQIVlZWcOTIESwvL+PFF1/EdDrFeDwGAIRhCCChsay1DlCo7OM4RqFQQKFQcEBDAOE+3B5IgKRYLKJSqQBIlGij0UC1WsWOHTvQ7XbR7XYxHo/RarUQBAF27tyJO+64A5dccskZBSQ5gJxcyQHkZUoODrmoHDhwAPfffz/+7d/+DcPh0Fn19Cj4N4AUADCeweeHn/PvOI4xHo9RLBZTQAPAeSD0UEqlUur7QqGAarWKIAjQarUwHA5hrUWhUEC/30cYhhiPx07BFotFXHjhhbj00ktx3XXX4ZprrkGxWDwJs/fqSQ4gJ1fOOgDJq53PLlHAt9ZieXnZKclWq+WUtL4HmxkJ6+vraLfb2Lt3L/bu3Ys4jhGGofMUjDGpZ8kYkwISAA48/M+BBCAAOI+kUCi4Y3AfHpv0lTEG4/HYnb9YLKJcLiMIAlSrVZRKJUwmE3fcwWAAYwzK5TKstQ68giDAhRdeiDvuuAO33nqrO/dWkxxATq6c0QDCl3mLDfsVl+l0iueeew69Xg+vfe1rUa/XU5y7tRa9Xg8//OEPsby8jOXlZbRaLVx88cXYuXMnGo3GlnwpoyjCXXfdhQMHDmB5eRlHjx5FHMcwxiAMQ1SrVVx++eV4xzvegXq9jkqlgslkgna7jX//93/H4cOHcfToUURRhKWlJRw6dAi9Xg8AUiDAudG/1XvQz0kX0VMJgsB9TyBQikpBiN4HgaJYLMIYg9Fo5PYpl8toNpuYTqcIw9Bld1WrVXQ6HQwGAwBwx1aKjMd+zWteg9tvvx233XZbyhPaCqJznMurL2c8gGRZemeDjMdj7N27F48++iiOHTuG1dVVGGNw/vnn413veheuuOIKDIdDPPLII7jnnntw5MgRtNttlEqllBW3uLiIt771rXjb296GhYWFU3xVL1+63S4+97nPYe/evU4BqrehgWtSS0EQODpKLXDf+ucxVAH7lfgKIKSboihywMH9uB0D7nxeCS78HID7Sa+H90mD8sViEc1mE2EYIooiF4wvlUrodrsYDAZurAQYXj/v73A4xGg0wtLSEq655hrceOONuOiii7C0tOS8t9PZoNhKgLfVJQeQM1Amkwm+8IUv4Atf+IJTXpPJxFEthUIB5557Lvr9Pg4dOpSiXHwLjtb07t27ce211+K6665DpVLBysoKAOD6669HoXBqakwnkwkOHjyIYrGIc845B2EYolgs4tvf/jb++q//Gt1u110TlT0Ap5j5j4qb184ANr0HnR8qdm7LuITv7dJzCIIA5XIZrVYL0+kU6+vrqX186ozPK8dD0OFnrBPROIrGXYIgQLPZRLlcTsVO4jjGaDRCEASYTCZu/DzPwsICwjBEq9VCv9/HysqKAylrLYrFImq1Gq666iosLCzgkksuwete9zq0Wq3TDkxyADl5kgPIGST9fh9PP/007rrrLjz77LNOccZx7CxrWqVhGG5QkCq0ciuVCiqVirNK+bxQAe3evRu//du/7QK6J0uGwyH+4i/+Ag8++CAAoFQqYceOHfiJn/gJ7N27F5PJxFnYwMZnQS39KIoQhqH7ByQxBqWUaMmTElKrn8fjXCpYhWGIHTt2YDAYoFarYW1tDZPJxCl9joHHUJqKMQqlu0qlkot/ZB3HGIN6vZ4K1nMe9NwcG8GT4FupVDCdTjEajVLPgJ6L42g0GviFX/gFvOlNbzrp9/94kgPIyZMzGkCAudt/JstoNMK9996LL37xi+j1ejDGoFAoOM9jPB6nlCGF1iWAlKKk9UrgUJCh9a3W85vf/Ga8973vdSmp+/btwwUXXIDpdIpjx47hP/7jP/DOd74T27Zte0Ws1WeffRZ/+Id/iCNHjqQ+X1hYQKFQSNE0vDYgDRq+YUFArFQqbi4mk4kLbBtj0Gg0UCwW3Xzycx5Pj8lzlstl7NixA6PRCN1uN1XL4QMIiwqp9OM4dn9r7KNUKqFUKjnPwo+9hGGIWq3mPIhSqeTiXN1uF8PhMBVboRcCIJU+PJ1OUSwWUa/XUS6XMRwOMZlMUKlUnNc5HA5x3nnn4cMf/jAajcZ/676+UpIDyMmTHEC2sEwmE3z961/HV77yFTz//POw1qJSqTiLk8rNV1gMoAJzy5kWKGsFAGAwGLi2GWoZA0j9rFQqeN/73of77rsPDz/8cMrKJWVWKBTwlre8BXfeeafj6H2ZTqc4cOAA9u/fj9FohEsvvRTNZhNra2s4dOgQDh48iH379qHT6Til6mcmLS4uYjweb1Cuul2xWHTAwPmgwrzgggswGo3QbredsudcMk5QKBSwtrYGIHm+qMy5Lc/FQD1Ta1dXV50VTw9GwU3/ce50fFEUOQDhZwQvjm9paQnT6dRtQxCJogij0QiDwQDtdtuBk4oG7nlvuX+5XEa5XMb6+jpqtRqMMahWqw6MrrvuOtxyyy245JJLnKFwqqitHEBOnuQAsgWl3+/jsccew2c/+1kcPHgQQRBgNBo52oI0BzC3ulWhk8IC4GIjhUIBk8kEk8kExWIxpZjU+6D4cQQem0ooK7uH/Pwtt9yCX/qlX0q13ojjGJ/85CfxzW9+E+VyGYVCIaXgsqg2f1zT6RSNRgNLS0sYj8cYDAYuQ4nXMh6PUSqVHIgQ4KgcmRpL2o9tRqy1zqpvNpvo9/sYjUaO6uE812o1RFGEXq+HarWKxcVFB2aMyURRhMlksoFSIwjp9dIYIIBQmTMDSz0Unq9UKmE4HKYoOWstBoMB4jh2P+lJqQfiJw4QUGgoxHGMcrnsDAt6O3EcOyB9zWteg5//+Z/HZZdd5sDmZIJJDiAnT04EQPJuvKdY4jjGU089hb//+7/Hvn37MBgMXHGYKprxeOy4a9I5YRg6cKC3US6XsbKy4ixbAE5x+1Ywvyco+ODBgDJ/p+Imrx6GIer1Oqy1eOihh3D06FG8733vc9XTP/rRj3DvvfemKCEFL4rSOQAcWPJ8g8EAa2trLi0XSECSVjhTWpvNJtbX1zEajTCZTBynT8+AilWt/GKx6ICFljcBgWOJogiVSgXdbhdRFGE4HKJer2M0GqFYLLoANpDOBtPkBb12rS1hgN/fj1ljCwsLiKIIg8HAnTMIAgwGA2dQ0KMgEDCeo/da76ufNabJAUBCn47HYwe8o9EI+/btw/79+7Fz5040m03s3r0bN910Ey677DIHfLmcfbIlPZDNgsSnWqbTKX74wx/isccew/LyMowxWFxcxNraGuI4xg033IAoinDs2DH0ej0UCgU88sgjOHDggHupNa5RKpVSNQ60SMfjMaIoQr1ex3A4BADs3LnTBcSn0yk6nY5ThJp+qplCWjCntA4wV+pUDsqfLywsoFarYTQaORqF9+Nnf/Znceedd6Lb7eJDH/oQjhw54hQclSVBSZWab5XzfFqRXa/X0Wg0nAehWUgLCwsoFouuvUe323XpywBcDIgeBjOhmEnFax2NRqnrr9frblvesx07djhvZzgcot/vu1iHxi50vpXK0jgVPTMKM8Wm0ykqlQp27NgBAE6hD4dDB/y831EUuWdCQUC9IY5Df3KcfBZKpVIKPElxEcx0X362Z88eXH/99bj55ptx4YUXvmpAknsgJ0/OeArrdASQp59+Gn/0R3+EY8eOueweVdAA3EvOF5EvLQOZVJq0aEmjUPFSCft1BbR0eT5jDPr9vqNCeC7N0AHg9uHnejx+z4AtFSJ5eHoJy8vLmE6n2LZtG4AkAPvBD34Qd911F+6//34AcCBE61jngJw8MG/roZaynyFWq9VcbQOzhIbDoQPbfr+PcrnsigMZuymVSqhWq25e+I/B6Gq1ilqthl6v5wCFoLKwsIB2u+0AfmFhwV0/u+gSnHwKS/8mxaXeVq1W26Ac+XxXq1Vs377dKXXWfdDzJHjy3ozHY/d8aDxECx2ZRKDeDp8PNRb4GXtyqTdCYNQ06UajgQ984AO44oor/juv0aaSV6OfPMkB5CTLww8/jI9+9KPO0iwUCjjvvPMcIDDDptvtolAoON7cWovRaOSUGQOxtCSpbKlkaXmqBUkQUM9BFbDy7uptqBeiBWpUCEpjEbyAeSowx8seT81mE71ezynYQ4cOAYDzAqy1ztpWK59KbjPOXjOYOOZKpYKFhQWXTVar1VAsFlEoFNBut9FoNFxr9F6v52IdWutCmpAemtKDURQ5IB6Px9i2bZs7V7lcRqVScddAKg2YJyjwevReGGNSylvjHkofct8wDFEul1Gr1VyLkjAM0e/3UavVMB6Psba25pIsyuUyxuMx+v2+u2/0iDTOZW3SLFKfMY1raR0JADcvtVoNjUYDKysrjmYtl8suSysIAvT7fbz73e/Grbfe+opXjisdmMurK2d8DCSLRz8VMhwO8Td/8zf45je/6RQRFfTq6ioWFhbQ7XadNxKGITqdjsvLJyevFjGVpFqSPpDwp/LXVErcni+b9nTic6F0EbfzeXAKrV8FKqWXqNh6vR46nQ6m06lLO6ZiLpVKjjpS8FBPSs+j39PaVSqO/D+vd21tzSlbUk3aCoTJBPxb4xMEJAbo1bqmd7KysuLGxzliVhg9NDY0VAXMOVWrXn/340L0Igi2BPb19XW3hggpSs6j3kteq1JM9Kb0ftKz9e+tGg0caxzHmEwmzqtj7Gt9fR1xHKPZbLp7ViwW8elPfxrPPfcc3vnOd7pgey5nrmxJD0SVz6mSyWSCj33sY3j44YcBpLuzAnCpmQBSipoKl4FQ8uh+Sq6fpUOpVCopukmVhyovpaWo2LRgTuMbmiWklp6CjnorVLIKegQUBrlprXMOer3eBsDL8jr0nFozAcxpDI6xUqm4IDcVr56XoMCgP61zAiFBudlsYjwepwLPzP6iF0MPotVqOUVPWqlUKrnEBs280tbtvJeksehR8j7wGjmnvL7t27cjDEOXgTWZTFCtVtHtdp1HR6+13++nKC29t+rVcb50OxoknGc+pxxftVrFwsKC67tFY4HJC8Vi0SUixHGMPXv24K1vfStuuummV2xRrDwOcnLkjKewgFObyjuZTPCnf/qn+MY3vpGiVpTaIQgwHZJKWy1bFgQyEA7MgUhpKf2OCpreC5WIggWPQYXAbCPGGmjRa+qvenUal+D26uXwmPyc/wiavNZWq+WUplY9+56WZiDx/JrG6xsMSqc1Gg0Xi6CXQeudypqKmjQL54uAUa/XsbCw4L4vlUpYW1tz6b0MzGt1Oz0B/s2UawU93gOCrdKOSh/pMang6b3V63W0Wi0X82ASgc7VZDJBp9NJxcz8OIV/f2no8FiMc/neip0lcwBwRZcE1E6nAwBoNptuXz7LzCK88sor8cEPfvAVoZ9yADk5csZTWKdaDhw4gHvuuSdl1dHaVC6Zqa8MPKslqgoUmFv/WkzmAxMVP61XPxhNUToCmNNTCi56DvWOuL0WG+p63X5WkQblNWgcxzHW19c3HBtIUyT6u4qCLzBvBqjeSBzHTnEq4DFjiUqyUqlgfX3dXRfvF68njuMU7cY5YJxB5w9Aqu+Ugqt6keqVKfWlBaE8Hj0h9Rp5jDiO3QJX6imSGqRxos+Vxl+UyuR9JMjSY7bWusWqlE5khh2fp3a7jZ07d7pxLi0t4ejRoy4OVC6XAcAlKwDAU089hX/913/F29/+9pzOOgMlB5ATlNXVVXz84x9PxSPUkgbmFMl5552H9fV194KTb1cLHEAKdNRSZO8ndlZVz4HZWwBSx1IQo/KikqbiU+9NrXu1agkcSpVxe+6j9QQMBgNJ4JxBXV+pKU3mg4gCWBzHLuOoUqlg27ZtLr6iSk1Bi5lC9HZ4HAKZMUkm2Gg0cu1NCETT6RSLi4uujqRaraY6+lL50svwYzg6ls3+5jz6z4saERR6JvTe+N36+rq7f8yc4vGZHeevQcLf9bh8buld8HdmASqdp/fx2LFjrsaoXC47WmtxcdEBJIGex/jnf/5n7Ny5E9dff/2Jv3C5nNaSA8jLlDiOsW/fPnzsYx/D8vJyShHoNhRmzPT7/ZRypxJXBQ/AUUxMzcyiozQ2QE4cyI4bMHDPvwlM/Fw9EB5DFaJSHz4Fwu2ViuFn3IYFcKyB4dj842jcx/+OIEbQIBe/uLjoMoE07gDA0YOcU36vQXctrKTlzhoSAM57oaU9nU5T4LQZLcR76c8tf9Jr0GtX0NbjsmWKtdYVOCo1pfExKm7ScgQ6NmckCPAe8B+pM2utixWRhuKxFUjiOHZxFsbwhsMhKpWKGwMz2dQIGY1G2Lt3L6699tqchjrDJAeQlyHWWnz+85/HP/3TP6Ua7vkuuVra9BCoxBjk1cAqFSstSVqcwJxy0HMxpZaitJfGPPzvODbN0uJxfW7aBxX/c71GUke8Js186vV6TvH5Y/A7ACvAqgdF74s0DamSMAzRbDYdIGu6KucZQCo12b8Wzpk2IWTgGoBLr9aAsnoQPgge729j5otFqdflx/F8QOJcq4emAMiqeDZCrFQq7jpZX8LMP44LmK+SyGeQz0C323Vzos+HelF6fTyO3udqtQoAqcxBADh8+DCWl5dx7rnnIpczR3IAeRnS7XZx9913pyxEiioKYG5Jjsdj12eJRW3MmOF+VCBsB8L1rqkw2GaDf/MYBAq+oKS2fGXtB2j5ua+kVJn71+MrDc3qIXiQslpZWXEKnTEHbqvzpsFj34OhgmYgmVlWSrORUmFGGhWY0ko8D9uWK23I/RRQG41GKotI54XiZzRlzZF+x2MzqK/3MSv+o/QeDQUaHRob4dzR8yKI0HDZtm0b1tfXMR6PXa8r9gqjZ0zjhs8jMA98+wkTfKZ9b4tj5xiU5iQdRmDr9Xr49re/jXe+8534cSXrnuRyamXLAkiWsnu15JlnnsGRI0c2pJFSlIri56RvyC1rEaBy6I1GwxUZ0mpTK1c9C1VA/Fv5c+2ppFaiBm6zlCP/9r0BPRbPkeU5UJFxvQ3/OiisBidY6jzouKg8GQPSeebY6OVozQO3UU9Cg/08vsaCCK5MM97smcqipCh6r1QIHDpXvpehCltpPAIHixbVO+PvPO50OsVgMHCpyzQyWBOj96lUKrmFpmh48Jr8bsf+nPM4nDdW5LfbbVdA2uv1UqnK9JTK5TLuv/9+3H777ajX65lz/FKSA8jpJ3lp53GE1NNXvvKVDdSKAgGVnnYz1TgHAMcnqzKmBT8ej1GtVlNBTSBde5FFmel2PJdm/vA7XouvHHRfVUoUzSDyuXMqLnLcDO6qR8TxUBk2m000m03XPl2TEFS0Yy95faV72HGWdTRK4fE+tVotNBqNVCIBr5M1H0qx6LwoUGicRJW2zh1/V5BR8NC55k/NyNJ4mq5XXqvV3DXyOjhn3F89G6aFHz16FO1228Uo6Nk2m03UajUHVhwnRWMsnHcFO+1IQK+53++7OpsoilCtVl1MBIAr0uz1ejh69Cj+7u/+LuX15LK1JfdANhFrLT73uc/hy1/+MlZWVjZNN9XUSgCualdfPOXlVckwTZUZTFTKvjWtPa0UrGjdMVbgW/w8P8/Lnwpyqhx1O78NBj/Xc1BpqRKkRc3vGSdZWFhw1BNTSPm9r5h17FnzxvERAKgEde7Ys8lvcOjfY/85yqIo/aC578X5xoLGqXyPUD29LKNEr/XYsWOp81Oo/Env6fOhCQVKQSnosN1/GIYueO4bFzweW+X71Fa5XMZgMEC/34e11rVc4eJUSsmqd/Otb30Lu3fvxs/93M/l3sQZIFsaQF5Nueuuu/DpT386RXP4WUSqgPid0iVKa/h0AQD38mZZuHo+YJ6lxBRWpdHUkvbFT78lAKkSU+Wg52VWjlI7WUFkVSz0LHheViHzOIPBwCkleik+/cffqRz9ueY1EzT7/b67BmYfkd5S5cpxZIGGTw2q0uczwPtM8bfRMev9IcBq3U4Yhq7TL9uDrK6uppQ/7xWrxnmNpAG5rV/xroYFAFdoGEURtm/f7qguUk2a4sv5UA92MBikClYBpOjD6XTqKFgArv+bZg8y+QQAPvOZz6DdbuNnfuZn0Gq1XnZm1qtpMOby48mWBZBXU+I4xoMPPph6mTWoSeWgwWwqEE0PpbLSAjEFFqV4uJ/WXqgCUYqGwU4AKYDh2H2+2k9XJRdOpUMvwR8bRfnyLC7eD+BzuVQWopXLZVSrVYRh6OiM4XDoUmOV1tM4DwvSFKD8eAMBiJ/rOiHqffDe+Z6kXo/vXanC0qpu/V6vX7fVe0IhRamKm/+2b9+OVqvlmj3y2anX6yn6kM+YPlt6v5jRp94Yr3c4HLoKe/+5VE9avQbGU9gCn9fZ6XRcXM+YJK507Nix1HotNBRYPc9MtCiKsHfvXvzLv/wLbrnlFrztbW/DFVdckXskW1ByAMmQTqeD/fv3uxdPayq09sFPzQWQstIUaFQhqeKhIlFLXAPDpKtIO+j5lb/3vSEqFs1g0tRKejJhGDpFToXvW+N+Fb0qLj/GMplMMBwOXRopl1xlJ2Jeux+D0LFzbuM4dutRaMsNHYN6TZwLbdhIUYpHPTzOFUU9TrWgNYVYAU3nmdehljnre+h1UGkzTRiAa6ypxZj8jveJ51DDgTETZvcFQeB6VdVqNWf46Jx3Oh0H7ur1ct45J7wfWkOkzSNpyOh9YLsVHh9IUnx1OxbSslvyvn37cPDgQbz73e/G6173uhxEtpjkACJircWTTz6JP/7jP3ZWP+MSqtTr9Trq9XqKcqjVao4PVuWkAVKtCldKCUjTBxwLqQUCBsFKKShNT+U/Ao2eQ4HMz+oyxqSaDQJzRapUiR6DwWuOhUqdylYL2ggsDAIzZuMrdE3hZYopx+EDCRW41sqoJ5PlZfB8qlR5fgVX9XD0GPrT/92nfbgfu/bSEFGLn0Fqgh5pH6bAxnHsgtQEC46b6dPaG00VNeeSbdiHw6HLlmIMg4Cj1857zvNR9BnkfPm0Ip8PLXwk4DIjjFlgvJ+rq6swxmDv3r246qqr8kLDLSY5gCBRlvfffz+++tWv4tlnn0W73XYvLYFDM1TieF7nwfRP33IH5iDAF54vlE8vaUUwwYcvOmkIDTorWPhBWeXKszKugI1rVVDx+OPyg+gaP1F+n6CQRX8obaRKisfSPk6qdHQuOH/+Nel1quWq4ECFpNY158IHGR4zi97Sjrl6nCxKjCsb6me0+LU1is4rQZbzymvTtFzOJ+eb5yLAcs6Hw2GqsSePycaQwHyFR/YJ82kwBUx9BtiHjd4IP9ekC86PFpnyeKurq+h2uy47i/f9qaeeQq/XQ7PZzHhDczld5awHkIMHD+JDH/qQaw2iL7laZFRSYThvre3n7VPBA+kXL8sb4cvIbCqemwp5MwWnIOUrMSDNu/uAwf013ZZ0kdYaqEejCld/155eVBoKkPSOeH5N/aVoai6381fFU26e1+mDiPap4jVqfMq/h36PLyphxmn6/b4LVOs6IqRt/Hvg3yf1aPQ54PYK1HqfqIj5PPAzzeyjh0Iaiasx8h6wErzf77umkuVyGa1Wy42Na56XSiWXjtvr9TYUFuraInpP2blXKS7OsQ+IcRy75Y95/awV0RRkjjeXrSVbGkDUfT4RsTZpu/GlL30Jn//851Ory1EJsbraWutWEKTFphkoFOWNCRRqQSs3rlQTlaRazKQ7lBoC0umhqsxVQfoBVz22DzKacusDlQ9gCphxHDvqQ3l2xi18oNTxcx33LKVLi5txDiC9BogqXJ9K4b3jcQgcvrcFzFOUaclT0ZGS4/XW63VXxc7WNFxISUFF54z3gjSUBvKpMDmXPLZSnqqYeUx6MwQ6Ywyq1WoqCM8ldpUSIwVmbdJtlwaOXjONJVasq5fNefeNEs61ehd+EgfnWeM2fAYIkPq+WGvx0EMP4Y477jhuHESfm1xOvWxpAPlxpd/v4+Mf/zgeffTRlIVLhcNsEQavoyhyRVij0Qj1et2lQfp1DGr9qotPRcjvqPSyaCFrretmqy+or+Qpek4NuGaBgp8yq1k72jWY+/pKXseitEW9Xk9x6txXj0Wvi9XVpHo495pBpZ6Fegu0kHV8VGrcX70iP+7EcWtDRT0/jQQqR9ZAjEajDXRkEASpYLjOE++rjoPjVeBllIYnrwAAIABJREFUAN0HSgVJ3l+NpTCO4QMg7w8NEy7pS8+FsReCi1KDvK/qTaj3xOfGvydKx/GZ0ueDbU4I2Iyt8Pp5zBdffPEl390cQE4vOesAxFqLBx98EA899JB7WdRSpNvOBYIAOEVAwCDNxapxxhB4fOXg1crzeWW+fL6SpcLRFxWYBzLVWlVA0hdLlYF/XJ6TlqCCAq1PXWBIq+xVAVLZU1GpVUlFofUvAFKUB2kjppbyGhXIOGcKBFmi9JgqOaXDOA/qZShNBswbCmorFP5kKiuQpCprGiy38c/LeaJx4lve2rZF77kek5lWBGouFUuA43OkfdL0eWdQm9QXxxFFkVPuukIh51DvuW9QqIeihknWc6v0H+edgFapVJxHSiYgl60jWxpAfhwKK4oifO5zn0tx5Kpc6HZba10jPo1LcDsFFfUwVCGpZ6CWqVqkFAUeVUyqrI/nFWR5J743ocFxACkAoCJSOozn0rgGAEeZsPp4MBhgfX3dKRzux8Cu3/eKP6msqeQI5lROfuBc58gHbH0edBsCro7L7xnGmAiQABy711arVUdlAnDWPrvVcnw+Vcd7rIFp9cqU7lFPhRlbPJdPvbGXmD4DpP1o1fNnrVbD+vr6hmeBBkStVnOeZxRFLouQ16jPJ/flsZXq0oxAXs9mqeMEEdJ7g8EAjUbDPSds55PL1pEtDyAnKsvLyzh06FDmsXyao91upxQVrTJahdpHSPliWqsUrbnQVE4/QM39C4VCitpRkFMvgH/znMq3awaZ0iBqoWrsYTqdOh7cr2ymAlALmuuWDIfDVEquziV7fOlaFFRgWpRYLBadBcxArm/h6vxyf5+mygIMNQ70mfGtap0bgggpnqWlJUcZDQYDRFHkFrvi/kqTafKEAoAaGBpP4nf0aLMKIAuFAhqNRgo4/GPyvsfxvEkn02QLhQJWV1dThhEBn/NHQGg0GlhdXXXH07laXFzE6upqao59ylS9kqxECU0aWV9fR61Ww3Q6xZNPPvmSFNWJGoy5vLqypQHkx5GHH354Q3xAPRl9eKkUjTGuOC6KkoZx5HOpPDToqV1IAbiMFAAbzg3AWWRUKEqV6AuoXola0rR2dTW6YrGIwWCwgdJQq5hjM2beoZYKXAFV+XWlOqbTqQNRn4ZTS1TjRAQebs85UHCm4lVPgQCclRygdSIKxPQudK59ikWz7XygZVzlhRde2GBI+O0/9Jr4TOg16rOk+2igX40BbmdtEivrdruoVqvOA1EhgNAjKpVKGAwG7jk9evRoapXFbreLRqPh7iWpMAI5MM9uo6Kn0DvjeRnD0DiQPme+0aLAwmtkfciLL76IlZUVbN++fcM15nJ6ylnXjVcVqCorilIgmmkyGo3Q6XSchT6dTl12TKvVcg0DNcCp60BkZUNxDBqT8Cu0lQ6j9c6iMaZj0qKt1+uuVXe9XseOHTtSXWGz6DD9W/PylUriuFkDQEuZY/UDrcAciJkezOP5lCEVMnl+jcX490W9EuX7mX7LuVZvRGMQ/Kfzqda3GhGa9KCFpNwm63icN/7zKUQdD/fxz8deXlrrwefv2LFjGAwGznvgmDh238Ph3CwtLTnKiJ7O+vr6hvGFYej6ZKmhwvsxHo9dvEJT3bkv58qfL/VM+MyrkcTvrLX4+te/fpy3N5fTTc46D+R73/ueC5T7LxAwz8ZSUYqJNAZ5Z74EtJr0pdHsJip+WtM8LhU3rUDNyOE2PheuY1IaiC/tdDpNLbtLWkyVHJUnx0NFpAsD0drXlfQ4Dlr8au37wKhKUQsttQ5FvQotmFMr3QcPXjsBTT0c3Ue9QKW2NAah6b9Umr7yVM9GAT3LW83yNnxvy99P76mvcNVDYdt8Bp8JBnxmtdaCn8Vx7NbpYJyJmXAsUqRnqZlimuKtMR4FUY37EfgHg0EKYHwPTY0HjodeLONAuWwdOesA5MCBAxviCfoSqwJhewnNEFpbW3P7xnHsGgcWCgWXMulTIrQK1aXnuXU7vjy+MvYtWB27Khe/ulmru7kPAQFIez+akaQ0j8ZGNqNldJw+ZabKTMGJikvTOP1r1yAtz6VFgEy3XlhYAJCkZ7NGR6+Fx6IwY4m1Nv41ca75U+k4Xp+fFabUFc+vBXd6H/3feV79TO8jt4+iCKurq1hYWEAQBG5tD46V56UXoGm7unpjs9lMtcD3vU+ej3Ok80kvT8en49eMvc3id3xm1diaTCZoNBr4xV/8ReSydeSsAxAqDSoGxg3UIwGSl5HrGlDZbd++Hb1ez3kg9C5IV+lntHKp9LUJII+vwMTzazU1xfc+lDrxqQ7dRy1HpTd4DC3oOh7VptlV7KHEJVEZD8ji83Vf0noEOcZC1KLX9huayUMPiBY2M8bYsFFBhQqK+6sVrPPDfXQu1dth4oNazH59hAaSOb/0bnmv1QvRefED/TyvD8iqpKnoWcxIyslai8FgsMFoUU+NtS3tdtt9VyqV0Ol0XNICn1W9Bg2083lRD5rzxnHzfWKdj47dp7I4TgL6b/7mb6YWVNtMssA2l1MjZx2ALC4u4vnnn3cPr7U2lXWiL6AWphUKBaysrKBcLmNpaQmrq6up75nG6lvmjBnwJQU2riOiXglfYn7nZ7FQqMSoSLXQTUEqS3FREehCVvS0SOvw3PSKfEucc6UUVLVaxXA4dP2WfGWg9TAKNAQHX9HyHlB50aqnMue183sGgdUSz1I2HDOvUT1BBVoGmalUec28Fo2h6b1Vik4VpKbo6vWpB8G51jESCDh3TCVeXl5GpVJxwMzaJdKLDPSzGJLPcb/fd0H1QqHgOgjwGpTW01YuWUWPKrxuLhmgxaBKj2V5XDfccANuuOGGHBi2mGx5APEpqJeSVqsFYG6NUnFp2qta3voicz9WLHe73RRN4lvUSrn4Vi6Dylph7v/0g63KNVNo/anHAcA1KvTjAFS86hkASI1Vz6tV1wQaYF5TonUytVrNVelr8ZrSeDy2ZoxpMFgVs4Ke3hNy5ZVKxYGVVtJr0gCVo86pUi18BujVcP79DDD13vSe6D3Va+GYSGPxGjT7SJM51Krnd0rtcTv+rfVHxWIxtXYKlTzvN+edPbHW1tbcPdG4m9J0Sj3Ri+Fc8Lr4vdJaSu0xvsFYh9J8vFYC/+23374h9pjL6S9bHkBOVPRlJjjQ4vK5ar5IfOD1RfApF1pUfGH5snB/Vcq6j9JWCg46Fv939T4YRFbO3s8Aygro+plTCp5UAD6Y0NLXOhFSWECywmKtVnON+xQktbsxs9c0mM1zqGLkZ6r89fPJZOKyzjg2AglTkVkYp54CFSuvW5sGAkglDWTFKvznSQFevSg/i4nC4DXBX69LPQ0/IYPXwnMx7qUZYVq0yWvR7DZ60Ay6a7Ghxjs4Fs0Q5HOjc6PPmAKpvhM0ZvxnnPN9zjnnnNBaIDmFdfrIlgeQE/FAut0uvvvd77oXQTl75fn12MDGDrjWWpe77u9DxaD8sdJTwMb8f4KIpoTyp3olvkKjYqb4WS+0nBUsfK+G4gMOP4uiyJ2DwEQFHUWRm4PpdIq1tTX0ej1UKhXUajVn/TPFGUCqdTgVDo+hdA7Pr3SIAgnrFqjwqKAYo9FYgi6fS6NAPTFtUcN7p1y/0i86d7wG3gs/iYHPlV9LoQqXx8+6vxwLgVAzo/Q47XYbw+HQZWZxHJPJxK0nwuwr0lBsicJ7QMpOaTl9DvWecMzlchmlUimV/eV7anwO/WJT3is++y8n9pHL6SdbHkBORP78z/98w6JJFH1hfFBQJaEcuCo03ZeZWaPRCO12ewM15HsL3E+Vp9IbHJ9vlVPB8cXn9r41qFSIWpAqqhw11qFWNYOinAufluL+pJmoZNg0kYFcKn21UpWa4Xj8Meu4lSIh+Cq9xW25L0GF18JteSxV1JxH/7p0rvQe+hSUJipoQD8rM49/q2JVT5DXS6+FRgPrMXjdjGtwPgjIzMLyvb0gCNz2nMd6vY52u+36mhGg1TtSg6VQKKDZbGJ5eTmVFq7vlVKgSoVp0sqxY8ewf//+PAayBeWsAZAXX3wRDzzwAIB5W3StnlXFpMqHn3EbKk6+iFQKrODmy1wqldBsNmGtdUVbAFzbBmDeOJBUj9JHPvetGUZUrqPRyNFBPiXlKzV+pgo7i6ahqGJUWkO5bD/7SwFWK7VZc0CvjYF50htKlykFyG1U6fJc9BwYMyG1pvSYn4DAOeL9p6ehAK/zrMrWBzwKLWhNMvAD4FT0ehytXVGvwb9efsbYhnoTumIlgYrZWOrR8rw0AJRO4rhYoKip3+r5+M8m77XW9+j1+l1+/Vgfx8J4yte+9jVcc801qfZAm8nLZRxyefXlrACQ6XSKT3ziE26ltiyqgC+/0g8+/aOpob6ioUVrbVLPceTIEUe16AvJF4cvGwPBWsjlKyo/mFoul1OZV35sQzl4pSh8T4k/9ZopPBavn3y7Um5KFfkWfBiGrlqe7S80/kHxlSi/Y2xClS7Hpd8pLaOeGC1ojpU9rNQ44P3Ua1dqywctNTLUY/AD4xrE1+dIr1PnmmvSq7epnoye358nHTfHpb3CCNL0zDiudrsNIGlNQmXvZ8L5zwl/Epzb7bZriKlt/DkWvhPqzeg7yXcxiiI8/fTT6PV6LwtAcjl95KwAkP379+O73/2uUxqkUZQ+YEUvMKcelAKiMlDrje68z2VrQN7nt2kh8rusIOpmCp3jazQabnlSFi9S9MX3A+pZMRVV4DyHb40zg8dfi0OVgh7D91DYQmMzr07nWpW8cv4KOjzWZDJxIMrPVeFrjQ95doKa3lO9B/xb50qTECgKQKr4Faz92JMCu39/1UjgnCrdx+tjJ2R/rn2wAeY1ODyGehLcT71BGjcMxPOaNS1ZfxIk+C5MJpMNLdvVy9d3jHPEd6VaraLRaCCXrSVbHkCy6BdfHn/8cfcC8cHVlEhg41oOVF5K2Rhj0Gg0UkV/Srmom6/Wvp6DLx2D7KqIVIlsZvUaY1zq6mAwcMfOshipxLKUi38eCufGP542buTfAJziULDJujeqfPkdFa5a5Eob+Z6ggp1SKD6w8brZKdgYk6ofUS/Jt7g1lkQl6HtrvBfq+SlQq0ebBRwsUNXnQAtIFfD1WeXP8XiciqcoMCj4KWVHIND5V/Chcg+CwFWtcy4VGHXu+WxlAZ6Csj7jmplH+jCOYzSbzRNK4/WpxFxOjWx5AHk5QgqFL4LSRUA6gwZA6gHnS0sLvN/vpzJLGChWC91P7VW6S18wVVxUKFp/QPGt78lkgk6nkwpqU5RGIIXDa/KVJBWIL1mfK22joMJ5pLKsVquo1+vo9/vo9/spCzrLOuc8aBqteke6r86nX5uhSkvvI3+y1kHHrZ4mFaM/5z6Npcqdx9eaCGMMplEEWJs6vnqnfmxAvS6el4qVVjrvu86jBsYVTNSL9IFDDRsdm2a1MYai4md/8fjT6dS1qOH6KaxC53UoiLCo1ve+BoMBRqMRarWa/zjmchrLWQEg55xzzgZeWpWJBiPVKgaQUvx8kdmyRHPvdQVD3wKksmCjORatkRILw3kXVFrMm9E9PnXgB89VwWnlM7/zPS1Kljem/7KOrUqA59DCwqz4hSpk/VsBhNek1r3SOzwez8GxqWfAOhC9bgVyKjSCEa9BKRqdex2/7wXZQgMvmAvwXOE6LKCN0uQYEE0QBRUAMWpxGwVMcG70XxuuUZ9FvWZ9BvjsqUFDa943QnQu/PEr4OoY/Llg4gNBjGPh7+rV6vPEZ5IgQWGhplK+vN+sou90OjmAbEE5IwDEt9hV4jjG448/jslksqFmYrOXTBvC8TM2FeQLREWl+2ZlUvFv5tkrJaGdR2u1WqrdCa8rK9NJA9vaFFCvQa15pWKoFDhenTsdm5+ey3EA6WpjDdRSyaj1mqXEfQXHufT7gPngoBQNLWUuPZsFTD4IEMh5TvU81FP099Hzq3K31iJGgMfNG/FC8UoAwErhYthwCsQTwFrAxgAMjAGuH9+N86ZPbaDnOE/8p16KBuj9eJPOk16vHkMBVwGU22s3Y62PYcZcFt3L50GpWwDueS6Xy27tEfWkeCyf1mN34TyAvvXkjACQ40kQBG79aooqM1pKGhDmy0Wrjy+GVhD79IEGUzWoqmtn8EX3aSzm6pdKJdcChIpNFT7PS2VNikqbIvrWK5UILUOlUzhG/UmhkvFpHgApi10tf46LKbZAduM8n2ZSOon785hUaJx7rYLXanO9Dt8rU8s5y9BQAPQVsU9h8ThjU8G/V38BvWAbYGaGSaEExDGMCYBiDTAhbDQFbAQY4HH7NpwzeQZmln7Me8qfeh4aOwqwagRwLOpRKdjx/viGhV6HPjP6bFYqFWf0cBv202LarlJmFI6BhhKfuTiOHVCRilOPU4PruWwtOSMA5HgeCADs3LnTVSP7HoMqWv0dwIYX1ae//O1Vifluv9Yc+LEQtf50fH5glv8IbPV6HcViEb1ez3kjSjlUKpVU+qqek+NXCcMQ1WrVZXbpdagHoABKRcPj03r1AVOpD6VSNiukVC9IqRUFFfWgFMA5bl+x8hq19oTb67zxunz6Ssf5o/BK9Mz5sxs1S2aIAIuZQo0NDBIwsQiAIEQUVPCt6v/ADd3PIojT68f7z7L/LPl0pe81Z32WlYqsz5MmRNAwGo1G6Ha7qVb0/C6LtuR94QJRvkdCD5nC+8f7RVp3z549mastbiYK9LmcOjkrViTcsWMHrLUpq86nolR8MNLt+KKodU96ihk1XCmQKaPcRj0UVVikA7Q1Cj0MABuUB61lrtsdx7FLg9Tt6H0xuGntvNeUKl39u1QqoVqtut5VHIvSPDpHqrR5PNYbcF+lhihKySil4YOxFl2qla5UoQIjExv4OYstuY/WiuizoECpVB3H6m9zzJwLwAAmAMIZ9TIdA3YKRGMgGsMihrUREI9h4xiwFp3CeThSuSY15/6zxvvtzxvvgxYKqsL2PTG/Y7ACpHq102myWiHXUxkMBuh2u+7Zmk6nWF9fx3A4xHA4dEYJj5XlvfGcfAd0/PTM6YWNx2M88cQT+M53vnNcQ1Dl5W6Xy6srZ4QH8lKyc+fOVKvzzXh4YGN6q3LfQPLCMU9eFYxauT6t5cc29Fz64lHhs92Kb0lTKTIeE0WRW5eDNJha5VEUoV6vO6Xkez16vcC80I/X+VLelm/d60+tvFbReaGXwfP5CpzUlY7d307H59dN+M0ulWLRWp2s+67xB/WYAABhFWvlXYANZvRUCBSqSdwDSOIe8QSAnf9tpkChjHCyjsb4kBsnlakCc5YnpJ8pbcT5Z1dbTekNgsB16dV5o6jX6CcZqNFTrVbdc6yGjt4nBs7p6TPwr5QlzwHMO13z2sbjMb7zne/gpptuQi5bR84KD+T6669Hq9VKWZ9qLWnwTpWU76GoglQumJ9lxUGy+HQg7dUo0Gzbti1Vra7nVCub/Z70fJoyqTESbXPOqucsz8Bai36/v2EdDQ0eq3AbHYd6ZFm0UNYxqQB9itGnzfT4CuAUbqdxKk0g4DVrM0gqUXqMDPwe794+U7wR62gBnA9j4WbGhEBQBIJC8s+EyT9rgXiCOKzgh9WbMAnqKcWqP/k7r9Ofe713el3aul9rlbSlvoofB9HPed30qBkH0V5bFH3m6OlzHZwsylBXgmQKPAAsLS0hl60lZ4UHMhgMUnUe5OJpYbKnkgZv1T1X65/7+55Dlgfj87Tq3mtwWumWyWSCxcVFdDqdFF0ThvO25Jqey+C1tUk1b7fb3TAWn2Lg6onqhfCfdpRVgPC3I8DxelToWYzHY5dEoICo9ImeR8/le3h+y3sqPx0r74N+nkUBqXfh15/omDgXOl4UqvhBeNUsu8oCMIAFbDydA0pYAqb9BDRgYMISLAww6cKaEC+UrwWsxbWDf0EYptO0dcwKrvocbgbKmqWl7UmYEeXPtXqZfEb4bmgartKFPg3sj13H4L8rFD6vfAZ5Tt0vl60hZ4QH8lIPnW+ZUSFpVpLfBZb/FGgYF6BS5L58YX1qzKe29AVWGofbRlGEXq+HQqHgUob1OH4MgPszoM58e4rSGcPh0LXP9rO51IL0s654Hq041kC+KiJ+p+LTd5x3nlvTf5XW4XVre3xVShpcz/L+1CPyPR8do2/hu1jXZIqJqWA9WEJsSohNEZE1+M/wJvSDJkxYBhAkQGKj5F8cA9EQmPST7+JpEv+YDoFpL/FE4gnsdATSW0pB+XUd+oypsaHPktJVvAZmwSlQqmfLZ0OBxH8GsuJWGmPxiyQ5t/q30rd+AoACOWV1dXWDl3Q8OZFtc3l15KzwQEqlkkuR9RWxvghAOtURmL8oyvf761aQzwXSGWE+PcSfuo2ei1W9XNOCQnoii0aiB0WFqZYj+275PDctWvLWpCj8gKuKggjHpwqIY1GqToFAvQYFZz/wqz85Hu7je19+CxCOgcCi86wpx+oxcsElpSkja/BI8e04WngNbGxRwASFaIA4CDEOW4CNYGMzq/OIkqB5UEyC6abAG5ME2K1NqKx4mvybST1ec9Xqet90ASf1pAgwQLr5Iq/Pr/fISm7g2iqanqvPFO+PtdZ5qUqj8XicQ46Bz6BSf8YYt2SuD3YcI6+X9/DKK69ELltLzhoA2bVrl1uClqJ0Cv8G0u63BlL5N5UX/+ZxdHulI/Tl88Wnh6IowurqqrMIK5UK+v1+Sln7FjObAzINkt9zvXAqD1qWWlVPRcF5oiLYbD4IoPxcPRoqLs4Nr4fiUzM+DZg1d2p5a+8xXntK8YuXojEin5ZkbMSfT55jGrSwWr0OsCEw7WEaFzBFcQYIMRBNgMAAYSH5V6gB0QgwmG2DWeDcJKBh7YzeMoAxqEbHcP7k+7A2RhimFawmDQBzClWvUzs/ZylnNQB4zO3bt8PaZGmBXq8HIO2dZRk1WunvP7N+vIZArMkO1lrX0FK9QwIjt+fzpl53LqdOrLUv+yacERQWcHwaq1Qq4d3vfndKAall7tMG6r6r5asxCFVq+iJQ8StdpePzKR9f9CWOoijVMNG3+HU7Y5IuqkphdTodDIdD91IzS4ZgwpRMVf4+JZJlefKcPs0FIGWx+nSIHpP3QpWeKnx/vhSsfc9OvQp6VAouPhVHSpNgqR6cMQYlM0I1WoOFhXtFCtXEwwgriZdB7yKeFQqGs8A5wuR3M9svKCbfwwIGKNgRbhz8H1SiNqy1GMUhflS43IEw55A/Few5X0wC8IFCnxGdO86LZm/xHugc6jPpJyQQuDi/fqIB77leh2Zu6bPC1F7N1CuXy7j44osz34lcTrrEL/ffWeGBAEkm1s6dO9Fut7G+vr6h6Mkv4tOgLSueVQHyBVZlqVyyBjy1ZTYpJP6dpQT0mGo5c1wcG7/nPvSM+ALreYBkTXcgbXlWq9VEkc3qVSg+wKl3oTSZT+Wpx6BgqbRHGIZulUKlzjQrScFKFZ+Oze8uwO2VNlElSkWmCo7zoRRkiCkadgXrtpUExBOXIvkRJF4EwsLs7wCYjgBrku8o4nEgqMziJElQvWRGyddhCQ9X/i90gnNwaVTE9vgwCkUA0RCVeN1dg3ojfB50zQ8fsLkfU3gVPBWws1KtfY+cgMDMPwKH0o/06BTUOEalzDRYr88uAFx11VU455xzcCKSRbXmcnLljAEQ34vIkquuugr33HPPBms6K86hFp3PwasLTpqG/LLyyv7L6f+t58j6XAPEuo1amEzVLZfLaDabroaESlmBTl9YvS7t+6VzwONEUeRakFMB8bgMzPqUFM/lt7pQAKYwOUEVkN4DTRbgsZWu4rE1acBPUtCqafZFU4tZ6xUMLKJZ9bgJq7BBAEwnACIkgXPAxNOZd2Fhg8I8mA4zq0I3QBDO4iWxqw2x8QTDKARsgO9VfxrtwnkAAjxV/Sk8FU+BQgUmGuDa4VexffKMuyZNeODf/nOh9Fa5XEYURahWq6nFtnwDyKcn9f5p8giAVPKIghU9In6mYOXTakqj8t4WCgW88Y1vzMFgC8oZAyAvRy699FLcfffdANJppcDcjff7O3EbDcqyEI2go0Fkiv+i8jxZlJkPDKo41TPiC0hRL4cvbLVaTdV90FpkdTiXl+VSosCcKuEx/THRi+L5eM3qOWi2mgKAnwXVaDTQ6/WcovNXqtOMI55fFZvv1egYgTlgKVjrvtofSqk3fta3NTxjrsNycBEQT2GjTkJbBRHAvlbWwsLCmABAAIMA1l134t3baAJEFghLMEEIG1nAhIhMBf9ZvR2xKaAdzlqhBCEwAyxEY9ighMfLP4NyuIJFexStYh8trGBX/Ky7fvUAeM8419Za11ONXQW42BPBhR4Fr117VenzzvnhOZjJp8koGqPxuxBovzau416v191nhUIBS0tLuPnmm08YQF7KYMzl1ZezCkD8tTZoLamostI0X81T54tDBaqBQT2GWsE+z+xbjlluvVqMSif4AVQFq2Kx6GgLrqHNrDFWCfsWKDPUeE4NgPN6dczqvVG4vT+n6lEEQeASArIy4TSzR+kmnUvNJvPn1gcDjXPp5zpmIFG807CB58xVeL5wLUbFc2DjWVxjOgTG60CxmtBZ1jpPw8YRzIzSMtYmtSDBrO9TACRtTaawxiRUlzWAjbEaXpjESIIwAaV4ksRSTDAvQLQxRsUdOBI1ccQUEIYBfn7y17DxPBuPc6VGD6+JnrLSr7xWjXFxDnXuNKNLKTT1MLkdnxl/7vm9n07MY+pzsmvXrryN+xaVswpAdu3ahUaj4Rq4+QqcDzVfKLZsKBaLjvvVXH3f2uUL5Vu5m3kY+plP2/hUDrejpUjrkYpTuWnSAgQNAiGVd6vVcv2MNC4BpL0RrpWiYKcBcfUUfPrPvy5SSDw2gS2rgMyYeYBXYyOb8fXqiSl4kXP3x+OD3Cho4EDxTThUvzXxLGJ6RBamWIWdjoF47iXBJPENEwQq2TFbAAAgAElEQVSwjG3wMxsDYSmp8ohjycyys6r0GUgAc9orKCOJSZoEsEw42y8ECmWgUEVkAkTTIgpmDurqffrPEj1kZnX5IKoGhSY++EaPxtiUguKx/OeWVKe/tK16KGzfzvN2u93cm9iiclZkYVGuvfZa3HjjjakFnXzFRb6XLx8AByTMHFIrDICLgWjGy2YBad/j2QxoNKCsfwMbi/WUclCuv1AopFaJ42f8R56cgMpWJ7q/AobvMRAQtG2GUmCqmJj9RIWjwORTUaqg/FYbaj37QM2fVIRaO6HzzXMEQYB1s4RvV/8HDhWugI0nMHGi1A2QgAEMgBgwAUwwy66axTSsnbUwMQYsDLQAYGOYsDRrZTKjuQgeYSnJ4gorSfpvSPCI5+BiZum/0TgBnnEP4bQLOx07a57Aqh6pArjvjbCmg0aQ0pxaXa7PqXrqfvNGzdAKgiTdnJQoA/xM7WW7Eu5L44VGQrPZ3PBM57I15Ky6a2EY4jd+4zdwyy23oFgsuiZxFCq7RqORUnRMOeRDr2tSUEEVi0W0Wi2nXNmRd7N4h/93Fp/vc/Xq5eiiTVSUvV7P0Vfkl5X79jNhGPxUK5/XolSRpuISoHyvSlt967gJHJxLUhlURlm1BlmJDDyeWsVUZAo6mhmk8aYsrw/G4GDtjegVz4EtLgDTEey0n8Q+7MwAsFGi8OMp7KQLA+tSdA1M0qJkVvdhTJAAio1gp4N0mq8xM+CJADtJ6kai4Sz9twgU67PtAiQZX1aKDy2K0XpqXvjsaexKgXezhA3/fvrxDJ0rfVb4mVJVvsGSVVsTRUnDT7Z15/k1pnbw4EG37HQuW0vOKgABkiDur/7qr+LCCy9MUSXKsw8Gg5Q3waI00gLKy/Ol7fV62L59OyqVSipV0Rflr3len2LRz5Wr5j96DlpRzvVASqUSarWao92osJWv7vf7KWVOoFFajtsqbeHTQxwvlYsWVWr+P4+lSi8rbqHegf7k91kJBf4YswBY+X9a78nnBfTCc5LYRDyCKZQSRV4oJx4DAlib0FCmVIMpNxPACEhFhTAwMMY6xW8BwIQwQQEmKMIU6zD0KoIQM24KYI1JPIuBwCTeR6EyL0IsVBIPpVDBxdiHYjBP2sjKbvITLPyVIaMo6bc2HA7dc+33vMpKptDmnbyX+l4oYCmQcK41K4uGBMGQxtgDDzyQ01hbUM46AAGS9UHe8573uBdRW3trLQPFGJP6XC1gfm+txeHDh9FoNFCv1507v1mQ3j++fyz9XQGG/DLHouu5d7tdBEHg2r3zc98i7PV6Gwr+VHT9DM4PvSuOy88m81Nnub+CiO9RcT+dBx5bV+zTYk1up+BHqxZIr52iHL0umsTPpwgxjsMkCF6ozjKpMItXBIBJqCoTT2BHSV2GCUswhmm7mNFXIZiRBZvUjFhjADuFjceY5WzBFCqzlQolWM7uvbAzamsGYq4QMUTBTHHu5EDqHrHjLZ8xgqNmsXGeOacKMn5cifSiehx6//Re829Suz7Q+8chHawZWuoZx3GM+++/3yVy5LJ15IwCkJcTB6FcffXV+Kmf+ilUKpXU4kmq4PnwK3fMnzwf4ybGGFf17aeZctssmkr/VgtQx8Lf9RrV8mS8otFoII5jDIdDpxBIu3GZUj2m0mIEJsYmqBjozSjn7SsLVVAKygoyWTSUgo9eE4/tN3vUdixK0ShIk+vX8WmcJjUGU8aosA2AAeIIJpomwXLeGovEEwkrcyXP2EYcw9pZijZmHhXiJPMqgYvEe7F2VgMyhY2GMCaECSszgCKVFiRZWgYw1sIEpcRbmdWZXDm8F6VpZ0Omk7U2RQsxnqHxPU1G8IFCEy3Um/PvBf+mB0n6U7tc+32wtD6IiRwaP/Gp3cOHD2P//v0n7IXkXsuplTMKQE5EwjDEr/3ar+Hqq692wT4Vdc3V86Cy5ktHL4C00srKyoaXT6mU472gWeK/cMrvayEj4y70lLiCXL/fx/r6uqv9UNAIgsA1RqxWq6hWq2g2m25lQ4JisVhMpVkq4GmQW70J/VytYk359BW7HsNPYVbOXQFKYzJUoEpLKvWlitUYgwgBJkFt3oZktm6HjWeLRM2oJxtPk5jFuJt01g0CmNICjCnAGMCYYO6BMONq1sbdBdKNgQmKsPEE1k4Aa2f0VwAwjsLK9TBMAuwzECmbEYJg3trd91A1/qOtR7I8TGOMS5TQ7f0+VEGQ7k2mc60ejiZQcFulFcvlsntX9Hv14o1J6ow+9alPpSi1XE5/OWsBBEhogF//9V/H7t27Uw85kFb6qoCUqlELmO43l/9UL0YtdYoqxqwYCK043Z5UGuMIVAQ6hk6nk2ohwYwbfzEtAG7/SqWCer2OSqXiCtDY/juKInS7XYzHY7fwj8YR9LqUHlJPRK+J22hMxvfUaL0q8Ogc+oqT94iZQhqw1fvn0y1FEyNk9Thm7UnCUtKqnR4Al6u1cZJia20SDA/MnIZi/CIIYILQeRyIIxibeBWMfySgU0iAw0azqIlN4iVBMTl2PAu6z445jObeFhd40vgWr0fpKk1OILByznjv6IVwn0ql4rb1qUhuy/eExyOAbJYdp0kafk2PxlCstVheXsbXvva1l/XuUnIP5NTKWQ0gAHDuuefiHe94BxqNBqrVKsrlMmq1Wuql9C0uNuHTl0cDmLSsN0u39UVfAr6EtKT5oup49Nh6Do5PFTxXhgOwQQmTl9aCO56/XC47TyQIAqytrbnzqQfjg64GeZVW0SQABQGl7TSwmgUY3EbnifPJ4+hxdU79lu0AcLSwG5EJZ8p9FtR2ADBfMMoUG0B5YdYXa5ZyS2qKsYs4mqX5FjGv9ygmxwuL8zoSMwumBwW4mEkcJZ4JbAJMPL+1MNEQraALY4xbR0XvE69PqT0Nsut2Kj79yG0VjDWWRKNEE0fUQ1eDRalJxhdZZKqrI/K4YRiiWq0iCAJ89rOfxX/9139tGG8up6ecUQCS9aK8HHnLW96CO+64w7nnGqAkd0vlw5cLQEpJUvFtluaq1BV/qmL0rXAqBD0fgFRbEoKYKmf/ReeaDFpQqOfidnzZwzB0XpS2Zee2Wemx/F4zeLKoJ1VWvlemCoVekB+z0LH43okmFWRlf/keYfI75oBhLWw0mqXk2iSWMVPiMEjiFoXqzDsI5yBB7wOAnfRhDebAYUxyXDuL0RBwOP8mSGiusJyk8EYT2Hg6Y7MSesuaAuLpvAmmpljrM8k6C86/JiLwmhlD4rz4z57eSz7HWuXOWiLOr08X6n0nmHA8/Ixj5rPKewbMVyr8q7/6K1eD9VKSeyCnVsKPfOQjHznVg3ilxA/Mnch+V199NS688EI89thj6PV6LpjMh1wDvUDa8iedpEHILL7XB5PNxsKfBDE/FkChMtHsFl/RatU1wYX9jCiTycSlHyv48dqZt0+QBNItT/Tceq1UHNxOFZt+7oNIFp2n8+Pf56ysq80AXH9vREcxMXWsml0AImAycIobxsziIMZ5DyYswoRFzNNwZz5KWADiyYyWimEs5oH4WVjDIJh5FxZ2VqgIY2BntSPJmOLZuRNgMgaAjbEw+gEW4uVU5pnGOABs8DL5O0GA88K/1YvR+6BAmxXs5jkJHnouBXR6fH6dET1XTXzgMxoEgTNelpaWXnZ7d9/Tz+XkyVnVyuR4YozBLbfcglqthk9+8pM4fPhwJiWiCtJX6AsLC1hbW3Oeg744vofhn9u3uNnsUJUjx3HnnXei0WjghRdewJEjR7B//36MRiMASCkZFheqBwHAeRb6Enc6HTSbTUeVEGx07ZMoSrq7GjPvpqvzoGDFa9FKaX6u4OJf+2bgqrQMt3+ptGqK0mZ6bmuBlWAXbwJMsQZrZrUaZtaexBSS3+NZAaBFQknBwK1GaMysT1acxDvmN3aGMzaVcTW/xhk40ZMxrDspuCze0IzRDHsIkE639ZMh/Ip0zpnvUfjAnwXYfI44h6Qg/Up0nXPOs8Y5/CJE1htleUdBkGT80fP44he/iGaziRtuuCEHiNNYcgARCYIA1113HX7nd34Hn/jEJ7B///5UN11VUJotBCTpo7VaLfViKfXlv6y6jf5OComei774LA78yZ/8SezZsyc19vX1dbz44ov44Q9/iMOHD+O5557Do48+6l5IVeLMv+fnHFe32021PmGTRVJKQRCg1+ulXnp//vTaKLwOKnJ/JTp/PhTcfI+Slm2xWHRekQ9O/tg2owqDwKAcxoANZop+1vCPip8pvSZIQAQFAAloWD/rSoGP+8+q2e0sUG9chtbs2kwIU6zPQChIxuEq15O4jDUhJkENdpr25DiXVMjD4RCVSmVD0oF6zOzC69cmUdgbi2CksTyN82m/ND9+wnHpcXTO+QxrJ+jxeOx6Y43HY1SrVQwGA/zt3/4tdu7cifPPP39DlmQup4cYe4aRiFpH8d+RwWCAe++9F/fddx+ee+45BEHg2i34oEDXm6LKS+sqlNLi72rB+1Ydq8rDMHTFffQC6vU6arUa3vCGN+C6667DpZde6rKk/Ovo9XpYX1/HgQMH8L3vfQ/33XefAy6lIorFoutpNBqN0Ov1UCwWUwH0er2OyWTiQEUVlZ9hpddCBUTFol4Ev6MXpYqKQiuYv5fLZXS73Q0ZcVlAreMgjcN79kz5Vnyv+FNJnQarwk1SGGidtzADFVgkHXUj2GicVJi71ibkrcwcTFKvlp1/Zmedew1gytuAaADXW4vUWRzBxmMYxLih98/YMX1ug6cBwM2ZtcmaMEy64Fwo1aXPmT8/URS5IL3G1OiF6v3T++YfM45jdxyudklDhIDHxA1ey2QyQa1WQ6vVcs8RwaRer+O2227Dr/zKr2x4tilZiSm5nBzJAeQ4olPz/PPP48Mf/jB6vR7q9TqKxSIGgwHa7bajffjSAHM6QAPRVFr68m0mfMFYp6GBYKUvqBCpYBncfPvb3443vOENuPLKK1PpwP51TadTPPzww3jooYfwrW99yxUeWps0vfPXtKaXpEChCpselG/xU4Fp/EM9O3ooekwNeivvr1lVOhf8XRWt3gs/ay6OY/zI7MFDtV9OxhnN2nXYKPnnWowUYYJAAuczb8d6lryZ/cd10Q1go0nSP4uFiBYA4lnPrRgoN2FMAkRJq/YASZYXYG0ME49xa/t/o2naAOZxLy1mpZfJtGzSXAqU3JbeIJCmBTWZgt+FYYhareaMEPVc9N4x7Zop4XEcpzpeaxwGSFbGpKfBOEmlUnFdHHg9g8EAxiRr2Pzu7/4uzjvvvE3flVxOjeQAcgLyl3/5l/jSl77kuosuLi6i0+ng6NGjiOMY27dvR7/fdy8aXX2fb+YYN4uLUNFScWZZjEpzAdiwdjVBjVYfrbw3velNeP3rX4/LLrsMzWbTUVa+9Pt9fP/738e3v/1t7Nu3D4PBAOvr6y4TR9NngXkTQ+3TpUIA9duR+HSK700QVLRoktea1coDSKejqnek23FuEZbxjfL/RNsuJQ0Q5yMGEM2aGc5AwRSSxaFc4SAzqgiWDKDTE8H8cxOyUxaMKcBOe8nvwazmBJgH15M/YG2MCyZP4trhXhg7V/DGmFSCA5Uw17tXGkufrSzPWcGfxyuVSiiXy+j1eq6bbrvd3pBFpUYCM7y4P3tuaTYVn7fRaIROp+PGy3haq9VCECRrxrRaLddRwVqLCy64AL//+7+fGSPbLFU5l1dfzjhi0Q+gvpJy55134siRI3j00UcxnU7R6XSc68+/a7XahjbifJmVztGgvBa6KbXEfXV/pvX6QX3m01PJsIBQi+pGoxG++tWv4u6773YK1lqLRqOB66+/HldccQV2796N888/H61WCzfddBNuuumm1Bysr6/jmWeewfe//308++yzWF1dxaFDhzAcDt15aQnTI1PviSDCFGQFfOX3VTmRRiE4cU0J7XasCoS/a3aan2Ls7sd0iEbwAtbCJpLYRDCjr0wSkwiDWfotAcEmdJcGw02SXUUHRCmspJ3JFNZYmKAA15wxKCfHMgAQJ8vexpPEUzFJrYixwDmTp1xhodKcqvR1dUw/ScGvR8qKKelzRxqUhYOdTsd5Fbrsr09pac8sjU3pvVxbW3MNQAuFggMXgt5oNHLPPQtzeRwWwm5WR5UDyKmRM84D0Qf21ZDBYIDf+q3fwvLyMgqFAkaj0YZ2HKrUgY0vrcZDFBxIzShNpMKXl2uf86UiYNEC5Dk1W8anwfzqbI6LL3CxWMTCwgJ27dqFCy+8EHv27MHFF1+MnTt3otlsbpiXTqeDF154AQcPHsQLL7yAF198Ec8//zy63a6jM7RPGFur+FlFCo60tH0PTmko3UYpPP5NEFWA1lqWIAjwSPFn8CwuB6kjAoixgJ2ta46ZFzGPdyBVz8EFoZLzzAoTDQBrYKNh4skEpSQNeBbjwKyHlgmLs9jIbKXDGdVVtW28cf1vULCJMuX1hmG4YU16xjk0kK0ZUTqnfjo1pVqtIgxD9Ho9jEYjNBoNdLtdd26lX/3EBAK0Zl6xal7fCXo3WkmvXSBYr8RnkI0/3/KWt+C9731v5jvpJ03kcvIk90BOUKrVKj70oQ/hS1/6Eh5//HEcOXLEWbpKrwDZWVYcI8W3GoH5Wh9ZgU5jTCrYrBYos5NINfB46p3oGNSC1IAraYW1tTWsrq7iiSeeSMU2tm3bhm3btmFxcRHXXHMNLrvsMuzZsweXX345Lr/88tSYe70e1tbWcOzYMRw+fBj/+Z//iQceeCBFo+ix1TvToDyBj3PJbZUW9DOT9BgKkuqVRFGEUaGUZFnZ2b3CDERmAfIEEJB4InQz7CzzysYz6snM4ucG4orM2ps0YCddIB7DGsZB7DymEvO4yefGxrCwKNohCna4Ye0XAgBb+JOO43WpsaDpsuoVE1g1LtTtdt2SBLwPVPY63wpK+qyzS4MG1P33keMn4Pj9tuid0lPh/r4nnMvpIWecB8KH+9U+RxRFuP/++/EP//APeP7551MvDK14VeS+m60vn6a3kttXi1qVLF9AVba0wvkdFYPWP2SlQfLlBZA6Js/PdEvtWUQvQr0B8tXAvM34LbfcgptvvhmXX345qtXqhvnjQl3D4RAHDhzAAw884PogqcfwUoF1jluzhii8NvXq/PTSOKzi3ur/QhfbMMu9nSGIVAMaCYzT+3ABcf09dsBibZzsPeuXZacjIJ7ChMVk0+k42a9Yn2Vhzc7HRakAvHbyH3jt+r/CWuviWRy/pkXT61Rjhc8Vnwf+zf21SpzPC6nBRqOR6jCtGYCcf1KkKToQ6XRe/V7HyvGqF1Uul1PxPj7L9Ir+7M/+7LipvHkg/dTIGQcgwKsbSPfloYcewkc/+lH3kgFIveyqCH2+XjORgHmbFHa+ZdO8SqWCarWK8XjsgthKyVAZ8IUNgvR61wBcozy++H72DYDUuDTmohQFt1Nvhtlig8HABUTr9XqKOqEVe9ttt+HNb34zrrzySjemrCDoaDTCI488ggceeAAPPvigA0RdC8VXGllxDhUqNu31FJsS/r/qb2AKLldLK11fC6bmGu9zXySobq3zToDQeS+mtOBau2PYTgoQw1kSg5nHWwp2jNtHf4Nw2nXXxQ4CmvqsXpmKPiOa2uuDrL9vEASuq8JmXgf3yWrQqTE6ttZXUFFvg0BkjEG1WkWlUsF0OnUBedYr3XTTTXj/+99/nHnPAeRUSQ4gr8C57rvvPnzxi1/E008/nVKuVHq0fH0L0e+ZRWuMVhc5ZABukSo2QFQLkkBFcMgKtNMzUA/Nt0T5k9Ytv+P4qQA4XnoBtFYJbgDcuXQMPJ7SbZQ3vOENeP3rX4+LL74Y27ZtQ71ed1XvKr1eD0888QSefPJJPPHEE+h2u1hbW3MV9+qZxXGcCjBr4Ja8e2RKuKf26xjZyiw2oT2YjFBL2IAp6S+RrvuYFQ3aOEnjtdEEmKwDhToMF5WaLVfrys6dco6xY7QfN4//T4ri8T0un+LzgZ2gzeQDUqzqRWpGIO+dn86rlr8f96AXoePRjDd9HtQYUS+EBgifK/Ww6vU6PvCBD+Cqq67C8SQHkFMjOYC8QjKZTPCpT30Kd999t1P6vsXmx0N8SovWOJsf+rwyA430aLTqly88lThXJNTt2BqFFBLbnQBz76NYLKLRaGA8HmM4HLp9/YI9HQswrxDnd2rBUsH5lePaeysrnbfRaOCKK67
<p>The ICAPS experiment (Interactions in Cosmic and Atmospheric Particle Systems) was part of the Texus-56 sounding rocket flight in November of 2019. ICAPS studies the agglomeration of 1.5 &#181;m-sized, monomeric silica grains under microgravity conditions, as would be present in the early stages of dust growth in protoplanetary disks, which our study aims at describing.</p><p>For this, a cloud of dust was injected into a vacuum chamber with ~7000 monomer grains per mm&#179;. A thermal trap was then utilized to stabilize the dust cloud against any external disturbances during the flight. Two overview cameras and a long-distance microscope with a high-speed camera were used for the in-situ observations of the particles (see Figs. 1 and 2).</p><p>This talk focuses on the data analysis and results of ICAPS, in particular with respect to Brownian motion and aggregate growth. From the total experiment time of six minutes of almost perfect weightlessness, we extracted the masses and translational friction times of 414 dust aggregates from their translational Brownian motion. For a subset of 69 of these particles, we were also able to derive their moments of inertia and rotational friction times from their Brownian rotation. With these data, we derived a fractal dimension close to 1.8 for the ensemble of dust aggregates. We compared this unambiguous physical method for the determination of the fractal dimension with an optical approach, in which the mass is derived through the particle extinction and the moment of inertia is derived from the microscopic images.</p><p>The combination of both methods then facilitates the growth analysis, for which the overview cameras were also used. We observed an initial rapid, charge-induced growth of aggregates, which was followed by a slower growth rate, which was dominated by ballistic cluster-cluster agglomeration. As a surprise, around 100 seconds into the flight, clear indication for runaway growth (or the onset of gelation) was observed.</p><p><img src="https://contentmanager.copernicus.org/fileStorageProxy.php?f=gnp.857ce136c38261213082561/sdaolpUECMynit/2202CSPE&app=m&a=0&c=7d2aa19a4c04fdb856b2dfee4d2634bb&ct=x&pn=gnp.elif&d=1" alt="" width="471" height="327"></p><p>Fig. 1: Examples of dust aggregates from the long-distance<br>microscope images.</p><p><img src="https://contentmanager.copernicus.org/fileStorageProxy.php?f=gnp.655e12c6c38266623082561/sdaolpUECMynit/2202CSPE&app=m&a=0&c=945438ed81e63b83298712bbcd057afe&ct=x&pn=gnp.elif&d=1" alt="" width="467" height="351"></p><p>Fig. 2: Image from one of the overview cameras after 100 s of experiment time (1024 x 768 pixels, about 12 x 9 mm&#178;).</p>
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