Mon Oct 13, F. Flechtner
• Sorted by Date • Sorted by Last Name of First Author •
Schefels, Clemens, Schlag, Leonard, and Helmsauer, Kathrin, 2025. Synthetic satellite telemetry data for machine learning. CEAS Space Journal, 17(5):863–875, doi:10.1007/s12567-024-00589-1.
• from the NASA Astrophysics Data System • by the DOI System •
@ARTICLE{2025CEAS...17..863S,
author = {{Schefels}, Clemens and {Schlag}, Leonard and {Helmsauer}, Kathrin},
title = "{Synthetic satellite telemetry data for machine learning}",
journal = {CEAS Space Journal},
keywords = {Satellite telemetry, Machine learning, Anomaly detection, Synthetic data, Labeled data, Software development, Information and Computing Sciences, Artificial Intelligence and Image Processing},
year = 2025,
month = sep,
volume = {17},
number = {5},
pages = {863-875},
abstract = "{For many machine learning tasks, labeled data are crucial. Even though
there are methods that can be trained with data with only few
labels, most of the tasks require many labels. In satellite
operations, a huge amount of data are generated by the telemetry
parameters of a satellite that keep track of its status. Modern
satellites collect telemetry data of thousands of parameters.
For example, the GRACE Follow-On satellites, operated by the
German Space Operations Center (GSOC) at the German Aerospace
Center (DLR), define about 80,000 unique housekeeping parameters
each. However, all these telemetry data lack a complete/holistic
set of labels. These data are usually unpredictable, hard to
reproduce, and very diverse. As a consequence, expert knowledge
is necessary to label these data, e.g., with anomalies.
Moreover, labeling data by hand can be very time-consuming and,
therefore, expensive. To overcome these obstacles, we
implemented a synthetic satellite telemetry data library that is
able to (a) generate a large variety of telemetry-like data, (b)
add a plethora of well-defined anomalies to these data, and (c)
deliver the labels for these injected anomalies. With these
data, we are now able to train, validate, and test our machine
learning models. Furthermore, we can compare different models
with reproducible data. Since satellite telemetry data are often
strictly confidential, we can share these synthetic data easily
with our research partners.}",
doi = {10.1007/s12567-024-00589-1},
adsurl = {https://ui.adsabs.harvard.edu/abs/2025CEAS...17..863S},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
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