Machine Learning Detects Anomalies in OPS-SAT Telemetry

Author:

Ruszczak BogdanORCID,Kotowski KrzysztofORCID,Andrzejewski Jacek,Musiał Alicja,Evans David,Zelenevskiy Vladimir,Bammens Sam,Laurinovics Rodrigo,Nalepa JakubORCID

Publisher

Springer Nature Switzerland

Reference25 articles.

1. Benecki, P., Piechaczek, S., Kostrzewa, D., Nalepa, J.: Detecting anomalies in spacecraft telemetry using evolutionary thresholding and LSTMs. In: Proceedings of the GECCO, GECCO 2021, ACM, New York, NY, USA, pp. 143–144 (2021)

2. Castellani, A., Schmitt, S., Squartini, S.: Real-world anomaly detection by using digital twin systems and weakly-supervised learning. CoRR abs/2011.06296 (2020). https://arxiv.org/abs/2011.06296

3. Chicco, D., Jurman, G.: The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genomics 21(1), 6 (2020)

4. ESA: WebMUST - web client for OPS-SAT directory (2021). https://opssat1.esoc.esa.int/webclient-must

5. Evans, D., et al.: Implementing the New CCSDS Housekeeping Data Compression Standard 124.0-B-1 (Based on POCKET+) on OPS-SAT-1. Small Satellite Conference (2022). https://digitalcommons.usu.edu/smallsat/2022/all2022/133

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