A Hierarchical Ensemble Pipeline for Anomaly Detection in ESA Satellite Telemetry

• URL: http://arxiv.org/abs/2605.06681v1
• Date: Wed, 22 Apr 2026 23:08:38 GMT
• Title: A Hierarchical Ensemble Pipeline for Anomaly Detection in ESA Satellite Telemetry
• Abstract summary: A hierarchical ensemble pipeline is introduced to address anomaly detection in telemetry data provided by European Space Agency (ESA)<n>The method integrates shapelet-based and statistical feature extraction, per-channel modeling, intra-channel stacking, and a final cross-channel aggregation.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A hierarchical ensemble pipeline is introduced to address anomaly detection in multivariate telemetry data provided by European Space Agency (ESA). The method integrates shapelet-based and statistical feature extraction, per-channel modeling, intra-channel stacking, and a final cross-channel aggregation. The pipeline is trained and validated using time-series cross-validation and two-level masking strategies to prevent information leakage. Results on the European Space Agency Anomaly Detection Benchmark (ESA-ADB) challenge demonstrate strong generalization, highlighting the effectiveness of hierarchical modeling in detecting subtle anomalies in realistic satellite telemetry.

Related papers

• STCAD: Scalable Trajectory Clustering and Anomaly Detection on Terabyte-Scale AIS Data [35.18016233072556]
  We present a scalable framework for unsupervised clustering of maritime trajectories derived from terabyte-scale Automatic Identification System (AIS) archives.<n> Variable-length trajectories are encoded with a custom BERT-based model trained via masked token modeling and clustered using CURE hierarchical clustering.<n>An intrinsic anomaly detection method based on reconstruction loss and clustering noise assignment identifies irregular navigation patterns.
  arXiv  Detail & Related papers  (2026-08-10T21:32:42Z)
• Air Quality Downscaling with Station-Guided Pseudo-Supervision [60.31045749983062]
  Super-resolving coarse atmospheric fields to local PM$_2.5$ variations is uniquely challenged by a mismatch in spatial support.<n>We present a station-guided framework for high-resolution PM$_2.5$ downscaling over Europe.
  arXiv  Detail & Related papers  (2026-07-06T16:32:25Z)
• Symmetric Divergence and Normalized Similarity: A Unified Topological Framework for Representation Analysis [17.24563134209321]
  We develop a unified topological toolkit serving fine-grained structural diagnosis and robust, standardized evaluation.<n>To enable reliable benchmarking across heterogeneous settings, we propose Normalized Topological Similarity (NTS)<n>NTS yields a scale-invariant metric bounded between -1 and 1, effectively overcoming the scale and sample-dependence of unnormalized divergences.
  arXiv  Detail & Related papers  (2026-06-04T16:14:13Z)
• Towards One-for-All Anomaly Detection for Tabular Data [87.63505963517512]
  Tabular anomaly detection (TAD) is critical in many real-world applications.<n>We propose OFA-TAD, a framework that only requires one-time training on multiple source datasets.<n>Experiments on 34 datasets from 14 domains demonstrate that OFA-TAD achieves superior anomaly detection performance.
  arXiv  Detail & Related papers  (2026-03-15T14:42:05Z)
• IoUCert: Robustness Verification for Anchor-based Object Detectors [58.35703549470485]
  We introduce IoUCert, a novel formal verification framework designed specifically to overcome these bottlenecks in anchor-based object detection architectures.<n>We show that our method enables the robustness verification of realistic, anchor-based models including SSD, YOLOv2, and YOLOv3 variants against various input perturbations.
  arXiv  Detail & Related papers  (2026-03-03T14:36:46Z)
• Unsupervised Anomaly Detection in NSL-KDD Using $β$-VAE: A Latent Space and Reconstruction Error Approach [39.146761527401424]
  This paper explores an unsupervised approach to anomaly detection in network traffic using $$-Variational Autoencoders on the NSL-KDD dataset.<n>We investigate two methods: leveraging the latent space structure by measuring distances from test samples to the training data projections, and using the reconstruction error as a conventional anomaly detection metric.
  arXiv  Detail & Related papers  (2026-02-23T12:42:00Z)
• Learning to Separate RF Signals Under Uncertainty: Detect-Then-Separate vs. Unified Joint Models [53.79667447811139]
  We show that a single deep neural architecture learns to jointly detect and separate when applied directly to the received signal.<n>These findings highlight UJM as a scalable and practical alternative to DTS, while opening new directions for unified separation under broader estimation.
  arXiv  Detail & Related papers  (2026-02-04T15:25:02Z)
• Detecting Silent Failures in Multi-Agentic AI Trajectories [7.001329254828447]
  Multi-Agentic AI systems are prone to silent failures such as drift, cycles, and missing details in outputs.<n>We introduce the task of anomaly detection in agentic trajectories to identify these failures and present a dataset curation pipeline.<n>This work provides the first systematic study of anomaly detection in Multi-Agentic AI systems, offering datasets, benchmarks, and insights to guide future research.
  arXiv  Detail & Related papers  (2025-11-06T04:00:54Z)
• Isolation-based Spherical Ensemble Representations for Anomaly Detection [60.989157958972356]
  Anomaly detection is a critical task in data mining and management with applications spanning fraud detection, network security, and log monitoring.<n>Existing unsupervised anomaly detection methods face fundamental challenges including conflicting distributional assumptions, computational inefficiency, and difficulty handling different anomaly types.<n>We propose ISER (Isolation-based Spherical Ensemble Representations) that extends existing isolation-based methods by using hypersphere radii as proxies for local density characteristics while maintaining linear time and constant space complexity.
  arXiv  Detail & Related papers  (2025-10-15T09:00:05Z)
• CLIP Meets Diffusion: A Synergistic Approach to Anomaly Detection [49.11819337853632]
  Anomaly detection is a complex problem due to the ambiguity in defining anomalies, the diversity of anomaly types, and the scarcity of training data.<n>We propose CLIPfusion, a method that leverages both discriminative and generative foundation models.<n>We believe that our method underscores the effectiveness of multi-modal and multi-model fusion in tackling the multifaceted challenges of anomaly detection.
  arXiv  Detail & Related papers  (2025-06-13T13:30:15Z)
• European Space Agency Benchmark for Anomaly Detection in Satellite Telemetry [2.0880207832785436]
  The European Space Agency Benchmark for Anomaly Detection in Satellite Telemetry (ESA-ADB) aims to establish a new standard in the domain.<n>The newly introduced ESA Anomalies dataset contains annotated real-life telemetry from three different ESA missions.<n>Results of typical anomaly detection algorithms assessed in our novel hierarchical evaluation pipeline show that new approaches are necessary to address operators' needs.
  arXiv  Detail & Related papers  (2024-06-25T13:23:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.