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DMSS: Decision Management System for Safer Spacecrafts

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Ad-Hoc, Mobile, and Wireless Networks (ADHOC-NOW 2019)

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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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References

  1. Biswas, G., Khorasgani, H., Stanje, G., Dubey, A., Deb, S., Ghoshal, S.: An approach to mode and anomaly detection with spacecraft telemetry data. Int. J. Progn. Health Manag. 7 (2016)

    Google Scholar 

  2. Bostock, M., Ogievetsky, V., Heer, J.: D\(^3\) data-driven documents. IEEE Trans. Vis. Comput. Graph. 17(12), 2301–2309 (2011)

    Article  Google Scholar 

  3. Díaz, M., Martín, C., Rubio, B.: State-of-the-art, challenges, and open issues in the integration of Internet of Things and cloud computing. J. Netw. Comput. Appl. 67(C), 99–117 (2016)

    Article  Google Scholar 

  4. Douglas, D.H., Peucker, T.K.: Algorithms for the reduction of the number of points required to represent a digitized line or its caricature. Cartographica Int. J. Geogr. Inf. Geovis. 10(2), 112–122 (1973)

    Google Scholar 

  5. Folk, M., Cheng, A., Yates, K.: HDF5: a file format and I/O library for high performance computing applications. In: Proceedings of Supercomputing, vol. 99 (1999)

    Google Scholar 

  6. Hundman, K., Constantinou, V., Laporte, C., Colwell, I., Soderstrom, T.: Detecting spacecraft anomalies using LSTMs and nonparametric dynamic thresholding. In: Proceedings of the 24th ACM SIGKDD, pp. 387–395, New York, USA (2018)

    Google Scholar 

  7. Ibrahim, S.K., Ahmed, A., Eldin Zeidan, M.A., Ziedan, I.: Machine learning methods for spacecraft telemetry mining. IEEE Trans. Aerosp. Electr. 55, 1816–1827 (2018)

    Article  Google Scholar 

  8. Keim, D., Andrienko, G., Fekete, J.-D., Görg, C., Kohlhammer, J., Melançon, G.: Visual analytics: definition, process, and challenges. In: Kerren, A., Stasko, J.T., Fekete, J.-D., North, C. (eds.) Information Visualization. LNCS, vol. 4950, pp. 154–175. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-70956-5_7

    Chapter  Google Scholar 

  9. Martiínez-Heras, J., Baumgartner, A., Donati, A.: MUST: mission utility & support tools. In: DASIA 2005-Data Systems in Aerospace, vol. 602 (2005)

    Google Scholar 

  10. Martinez, J., Lucas, L., Donati, A.: Dependency finder: surprising relationships in telemetry. In: 2018 SpaceOps Conference, p. 2696 (2018)

    Google Scholar 

  11. Oliveira, H., Lais, A., Francisco, T., Donati, A.: Enabling visualization of large telemetry datasets. In: SpaceOps 2012, Stockholm, pp. 11–15 (2012)

    Google Scholar 

  12. Royer, P., et al.: Data mining spacecraft telemetry: towards generic solutions to automatic health monitoring and status characterisation. In: Observatory Operations: Strategies, Processes, and Systems VI (2016)

    Google Scholar 

  13. SparkJava: Spark: A micro framework for creating web applications in Kotlin and Java 8 with minimal effort (2019). http://sparkjava.com. Accessed 6 June 2019

  14. Tilkov, S., Vinoski, S.: Node. js: using JavaScript to build high-performance network programs. IEEE Internet Comput. 14(6), 80–83 (2010)

    Article  Google Scholar 

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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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Correspondence to Olivier Parisot .

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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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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-31830-7

  • Online ISBN: 978-3-030-31831-4

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