Abstract
The fast growing number of low earth orbit exploitation and deep space missions results in enormous volumes of telemetry data. In order to operate efficiently satellites constellations as well as spacecrafts, DMSS offers a self-learning visual platform for anomaly detection in telemetry data coming from embedded sensors. As use-case, the data of two space missions operated by the European Space Agency were analyzed: Mars Express and GAIA.
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Acknowledgments
We would like to thank the Institute of Astronomy of KU Leuven, and especially Bart Vandenbussche, Pierre Royer and Joris De Ridder for the excellent and fruitful collaboration. We also wish to thank David Evans and Jose Martinez-Heras from the European Space Agency.
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Parisot, O., Pinheiro, P., Hitzelberger, P. (2019). DMSS: Decision Management System for Safer Spacecrafts. In: Palattella, M., Scanzio, S., Coleri Ergen, S. (eds) Ad-Hoc, Mobile, and Wireless Networks. ADHOC-NOW 2019. Lecture Notes in Computer Science(), vol 11803. Springer, Cham. https://doi.org/10.1007/978-3-030-31831-4_46
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DOI: https://doi.org/10.1007/978-3-030-31831-4_46
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