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AMICal Sat: A sparse RGB imager on board a 2U cubesat to study the aurora
AMICal sat, a dedicated 2U cubesat, has been developed, in order to monitor the auroral emissions, with a dedicated imager. It aims to help to reconstruct the low energy electrons fluxes up to 30 keV in Earth auroral regions. It includes an imager entirely designed in Grenoble University Space Center. The imager uses a 1.3 Mpixels sparse RGB CMOS detector and a wide field objective (f=22.5 mm). The satellite platform has been built by the polish company Satrevolution. Launched September, 3rd, 2020 from Kuru (French Guyana) o ...
Barthelemy, Mathieu; Robert, Elisa; Kalegaev, Vladimir; Grennerat, Vincent; Sequies, Thierry; Bourdarot, Guillaume; Le Coarer, Etienne; Correia, Jean-Jacques; Rabou, Patrick;
Published by: IEEE Journal on Miniaturization for Air and Space Systems Published on:
YEAR: 2022   DOI: 10.1109/JMASS.2022.3187147
Aerospace electronics; AURORA; cubesat; Detectors; imager; Instruments; Ion radiation effects; magnetosphere; Monitoring; Satellites
Creating a Database to Identify High-Latitude Scintillation Signatures With Unsupervised Machine Learning
In high latitudes, Global Navigation Satellite System (GNSS) signals experience scintillation due to moving irregularity structures in the ionosphere. These develop as a result of different physical mechanisms, which are as yet principally described on an elementary level for certain storm cases and events. Since there are years of GNSS data available from stations around the globe, we are investigating an unsupervised Machine Learning approach to extract a large variety of groups of scintillation events with similar feature ...
Bals, Anna-Marie; Thakrar, Chintan; Deshpande, Kshitija;
Published by: IEEE Journal of Radio Frequency Identification Published on:
YEAR: 2022   DOI: 10.1109/JRFID.2022.3163913
Databases; Feature extraction; Fluctuations; global navigation satellite system; GNSS data noise elimination; GNSS scintillation; Indexes; Instruments; ionospheric scintillation event detection; Radiofrequency identification; unsupervised machine learning