Bibliography

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) on board the Vega flight 16, it produces its first images in October 2020. The aim of this paper is to describe the design of the payload especially the optics and the proximity electronics, to describe the use of the payload for space weather purpose. A preliminary analysis of a first image showing the relevance of such an instrument for auroral monitoring is performed. This analysis allowed to reconstruct from one of the first images the local electron input flux at the top of the atmosphere during the exposure time.

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 features. We create a database containing high-rate scintillation events from two geomagnetic storm cases and several stations in the high-latitude region of the Northern hemisphere. By clustering high-rate signatures in signal phase and power according to their major signal characteristics with an agglomerative hierarchical clustering, it is possible to extract different groups of similar types of scintillation signatures. As a result of this study, the database of scintillation signatures in various locations in the auroral oval and polar cap evolves and will be further expanded beyond the storm cases studied in this paper. These can then be linked to the geomagnetic conditions and dynamics in the ionosphere through additional datasets from other instruments, therefore potentially helping us to get a further insight into the ionospheric irregularity physics.

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