Application of Machine Learning to Investigation of Arcing on Geosynchronous Satellites

Plis, Sergey M.; Ferguson, Dale C.; Plis, E.

doi:10.1007/s40295-022-00314-2

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2023

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Cited by 2 publications

(2 citation statements)

References 34 publications

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“…Electrical arcing on satellite solar panels is a new and important field in radio remote sensing. Arcing has been studied on geostationary satellites via passive radio observations at Arecibo, and required the high sensitivity of the legacy 305-m dish (Ferguson et al, 2022;Plis et al, 2022). Since the arcing is highly correlated with the 10 to 30 keV magnetospheric electron flux, it amounts to a method for remotely sensing the satellite environment.…”

Section: Scientific Support and Applied Sciencementioning

confidence: 99%

Science Goals for a New Arecibo Observatory

Isham,

Brum,

Kooi

2023

Bulletin of the AAS

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Section: Scientific Support and Applied Sciencementioning

confidence: 99%

Science Goals for a New Arecibo Observatory

Isham,

Brum,

Kooi

2023

Bulletin of the AAS

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“…The emergence of Mega Constellations and Intelligent Spacecraft marks the beginning of a new era in space exploration, presenting a significant challenge for the future development of intelligent satellite control and management systems (ISCM) [1][2][3]. Recognizing and predicting satellite states through telemetry data is an essential aspect of ISCM.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Telemetry-Based Recognition and Prediction of Satellite State Using TS-GCN Network

Liu,

Qiu,

et al. 2023

Electronics

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With the continuous proliferation of satellites, accurately determining their operational status is crucial for satellite design and on-orbit anomaly detection. However, existing research overlooks this crucial aspect, falling short in its analysis. Through an analysis of real-time satellite telemetry data, this paper pioneers the introduction of four distinct operational states within satellite attitude control systems and explores the challenges associated with their classification and prediction. Considering skewed data and dimensionality, we propose the Two-Step Graph Convolutional Neural Network (TS-GCN) framework, integrating resampling and a streamlined architecture as the benchmark of the proposed problem. Applying TS-GCN to a specific satellite model yields 98.93% state recognition and 99.13% prediction accuracy. Compared to the Standard GCN, Standard CNN, and ResNet-18, the state recognition accuracy increased by 37.36–75.65%. With fewer parameters, TS-GCN suits on-orbit deployment, enhancing assessment and anomaly detection.

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Application of Machine Learning to Investigation of Arcing on Geosynchronous Satellites

Cited by 2 publications

References 34 publications

Science Goals for a New Arecibo Observatory

Science Goals for a New Arecibo Observatory

Real-Time Telemetry-Based Recognition and Prediction of Satellite State Using TS-GCN Network

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