OpsSatNet: A Lightweight Sensor-Aware Attention Network for Satellite Telemetry Anomaly Detection

Satellite housekeeping systems combine heterogeneous sensor channels that require continuous fault monitoring. Existing methods rely mainly on hand-crafted statistics or generic raw-signal models, which can obscure localized morphology and do not explicitly adapt shared features to sensor type. They also rarely assess accuracy together with resource cost and temporal evidence, limiting deployment relevance. To address these limitations, we present OpsSatNet, a lightweight sensor-aware attention network for segment-level anomaly detection from raw, resampled telemetry. Specifically, a dilated residual convolutional backbone captures multi-scale patterns, the feature-wise linear modulation adapts shared features by sensor type, and additive attention pooling aggregates temporal features with learned weights. On OPSSAT-AD, the base model achieves an F1-score of 0.960±0.009 over five runs, compared with 0.946 for the strongest published baseline and 0.949±0.004 for the strongest retrained raw-signal baseline. It contains 258K parameters, while a 16K parameter variant retains an F1-score of 0.952±0.010 at 8 MFLOPs and 1.157 ms single-thread CPU latency per segment. Ablations show positive mean F1 differences for sensor conditioning and attention pooling, although neither individual effect is statistically significant. These results support lightweight, sensor-aware monitoring, while cross-mission and flight-hardware validation remain necessary.