# The OPS-SAT benchmark for detecting anomalies in satellite telemetry

arXiv: https://arxiv.org/abs/2407.04730 · HTML: https://mlsanity.com/paper/2407.04730/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 56 references that are in the map.
- First posted on arXiv 29 Jun 2024.

## Builds on (most cited first, 15 of 15)

- [Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery](https://mlsanity.com/paper/1703.05921/) (2017)
- [Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding](https://mlsanity.com/paper/1802.04431/) (2018)
- [PyOD: A Python Toolbox for Scalable Outlier Detection](https://mlsanity.com/paper/1901.01588/) (2019)
- [Deep Learning for Anomaly Detection: A Review](https://mlsanity.com/paper/2007.02500/) (2020)
- [ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions](https://mlsanity.com/paper/2201.00382/) (2022)
- [Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of Progress](https://mlsanity.com/paper/2009.13807/) (2020)
- [COPOD: Copula-Based Outlier Detection](https://mlsanity.com/paper/2009.09463/) (2020)
- [Adversarially Learned Anomaly Detection](https://mlsanity.com/paper/1812.02288/) (2018)
- [catch22: CAnonical Time-series CHaracteristics](https://mlsanity.com/paper/1901.10200/) (2019)
- [Deep Isolation Forest for Anomaly Detection](https://mlsanity.com/paper/2206.06602/) (2022)
- [Generative Adversarial Active Learning for Unsupervised Outlier Detection](https://mlsanity.com/paper/1809.10816/) (2018)
- [XGBOD: Improving Supervised Outlier Detection with Unsupervised Representation Learning](https://mlsanity.com/paper/1912.00290/) (2019)
- [LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networks](https://mlsanity.com/paper/2112.05355/) (2021)
- [European Space Agency Benchmark for Anomaly Detection in Satellite Telemetry](https://mlsanity.com/paper/2406.17826/) (2024)
- [An Anomaly Detection Method for Satellites Using Monte Carlo Dropout](https://mlsanity.com/paper/2211.14938/) (2022)

## Built on by (most cited first, 7 of 7)

- [On Revealing the Hidden Problem Structure in Real-World and Theoretical Problems Using Walsh Coefficient Influence](https://mlsanity.com/paper/2504.13949/) (2025)
- [Convolutional Autoencoders for Data Compression and Anomaly Detection in Small Satellite Technologies](https://mlsanity.com/paper/2505.00040/) (2025)
- [Towards Resilient Intrusion Detection in CubeSats: Challenges, TinyML Solutions, and Future Directions](https://mlsanity.com/paper/2604.06411/) (2026)
- [On-Orbit Space AI: Federated, Multi-Agent, and Collaborative Algorithms for Satellite Constellations](https://mlsanity.com/paper/2604.16518/) (2026)
- [A Hierarchical Ensemble Pipeline for Anomaly Detection in ESA Satellite Telemetry](https://mlsanity.com/paper/2605.06681/) (2026)
- [Syn2Logic: End-to-End Neuromorphic Design Automation](https://mlsanity.com/paper/2608.25536/) (2026)
- [Temporal and Multimodal Deep Learning for Cyberattack Detection in LEO Satellite Systems](https://mlsanity.com/paper/2609.10746/) (2026)

## Abstract (arXiv)

Detecting anomalous events in satellite telemetry is a critical task in space operations. This task, however, is extremely time-consuming, error-prone and human dependent, thus automated data-driven anomaly detection algorithms have been emerging at a steady pace. However, there are no publicly available datasets of real satellite telemetry accompanied with the ground-truth annotations that could be used to train and verify anomaly detection supervised models. In this article, we address this research gap and introduce the AI-ready benchmark dataset (OPSSAT-AD) containing the telemetry data acquired on board OPS-SAT -- a CubeSat mission which has been operated by the European Space Agency which has come to an end during the night of 22--23 May 2024 (CEST). The dataset is accompanied with the baseline results obtained using 30 supervised and unsupervised classic and deep machine learning algorithms for anomaly detection. They were trained and validated using the training-test dataset split introduced in this work, and we present a suggested set of quality metrics which should be always calculated to confront the new algorithms for anomaly detection while exploiting OPSSAT-AD. We believe that this work may become an important step toward building a fair, reproducible and objective validation procedure that can be used to quantify the capabilities of the emerging anomaly detection techniques in an unbiased and fully transparent way.