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Life sciences · Preprint
arXiv · September 9, 2026
Early or partial results. Treat as a signal, not a conclusion.
This is a preprint describing an unsupervised machine learning framework for detecting anomalies in spacecraft telemetry, designed to operate without labeled historical anomalies or extended warm-up periods. The method achieves reported F₀.₅ scores of 0.700 and 0.698 on two ESA missions under chronological evaluation, but lacks comparison to existing anomaly detection approaches and has not undergone peer review.
Unsupervised algorithm development and retrospective evaluation on labeled telemetry dataset. Spacecraft telemetry from two ESA missions (Mission 1 and Mission 2) in the ESA Anomalies Dataset.. Intervention: Unsupervised anomaly detection framework with adaptive EVT thresholding, incremental monthly retraining, and statistical model selection.. ESA (European Space Agency) data; specific mission locations not stated..
Framework achieves F₀.₅ = 0.700 on Mission 1 under strict chronological evaluation Framework achieves F₀.₅ = 0.698 on Mission 2 under strict chronological evaluation Method produces predictions from second month of operation without labeled anomalies or mission-specific tuning
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This is an early-stage methodological study on an unsupervised machine learning framework for spacecraft anomaly detection, evaluated on a single dataset with no comparison to established baselines or clinical/operational gold standards.
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Quoted from the source exactly as published.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
Operational anomaly detection in spacecraft telemetry typically requires labeled historical anomalies or extended warm-up periods. These requirements are rarely met in practice. We propose an unsupervised, deployment-ready framework that produces predictions from the second month of operation without any labels, prior fault knowledge, or mission-specific tuning. The approach combines incremental monthly retraining, statistical model selection, and adaptive Extreme Value Theory (EVT) thresholding for false alarm control. On the ESA Anomalies Dataset (ESA-AD), it achieves $F_{0.5}=0.700$ on Mission~1 and $F_{0.5}=0.698$ on Mission~2 under strict chronological evaluation.
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