# European Space Agency Benchmark for Anomaly Detection in Satellite Telemetry

arXiv: https://arxiv.org/abs/2406.17826 · HTML: https://mlsanity.com/paper/2406.17826/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jun 2024.

## Built on by (most cited first, 12 of 12)

- [The OPS-SAT benchmark for detecting anomalies in satellite telemetry](https://mlsanity.com/paper/2407.04730/) (2024)
- [Trojan Horse Hunt in Time Series Forecasting for Space Operations](https://mlsanity.com/paper/2506.01849/) (2025)
- [Deep Learning-Based Anomaly Detection in Spacecraft Telemetry on Edge Devices](https://mlsanity.com/paper/2603.29375/) (2026)
- [A Radon-Nikod'ym Perspective on Anomaly Detection: Theory and Implications](https://mlsanity.com/paper/2502.18002/) (2025)
- [CAPMix: Robust KPI Anomaly Detection for AIOps in Noisy and Dynamic Environments](https://mlsanity.com/paper/2509.06419/) (2025)
- [An Improved Time Series Anomaly Detection by Applying Structural Similarity](https://mlsanity.com/paper/2509.20184/) (2025)
- [Trojan horse hunt in deep forecasting models: Insights from the European Space Agency competition](https://mlsanity.com/paper/2603.20108/) (2026)
- [A Hierarchical Ensemble Pipeline for Anomaly Detection in ESA Satellite Telemetry](https://mlsanity.com/paper/2605.06681/) (2026)
- [PHOENIX: Fine-Tuned SLM-Powered Autonomous Satellite Lifetime Extension via Predictive Self-Healing and Multi-Agent AI Recovery](https://mlsanity.com/paper/2608.07126/) (2026)
- [Structure-Aware Unsupervised Anomaly Detection for Spacecraft Telemetry with Adaptive EVT Thresholding](https://mlsanity.com/paper/2609.10017/) (2026)
- [Temporal and Multimodal Deep Learning for Cyberattack Detection in LEO Satellite Systems](https://mlsanity.com/paper/2609.10746/) (2026)
- [MORA: Modeling Observed Changes for Drift-Robust Time-Series Anomaly Detection](https://mlsanity.com/paper/2610.09473/) (2026)

## Abstract (arXiv)

Machine learning has vast potential to improve anomaly detection in satellite telemetry which is a crucial task for spacecraft operations. This potential is currently hampered by a lack of comprehensible benchmarks for multivariate time series anomaly detection, especially for the challenging case of satellite telemetry. The European Space Agency Benchmark for Anomaly Detection in Satellite Telemetry (ESA-ADB) aims to address this challenge and establish a new standard in the domain. It is a result of close cooperation between spacecraft operations engineers from the European Space Agency (ESA) and machine learning experts. The newly introduced ESA Anomalies Dataset contains annotated real-life telemetry from three different ESA missions, out of which two are included in ESA-ADB. Results of typical anomaly detection algorithms assessed in our novel hierarchical evaluation pipeline show that new approaches are necessary to address operators' needs. All elements of ESA-ADB are publicly available to ensure its full reproducibility.