Multi-Tier Labeling and Physics-Informed Learning for Orbital Anomaly Detection at Scale

• URL: http://arxiv.org/abs/2605.09790v1
• Date: Sun, 10 May 2026 22:20:52 GMT
• Title: Multi-Tier Labeling and Physics-Informed Learning for Orbital Anomaly Detection at Scale
• Abstract summary: We present a multi-tier labeling cascade that composes three weak supervision sources of increasing fidelity.<n>applied to 232M Two-Line Element (TLE) records spanning 60 years, the cascade yields 8.6M labeled sequences of length 50 (430M timesteps) over 11 features.<n>On overlapping satellites, IMM-UKF surfaces 42.6x more anomalies than rule_v1 alone.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Detecting orbital anomalies, such as maneuvers, atmospheric decay, and attitude upsets, across the rapidly growing population of low-Earth-orbit (LEO) satellites is a prerequisite for collision avoidance, decay forecasting, and conjunction screening. The bottleneck is not modeling capacity but labels: there is no public ground-truth corpus of orbital anomalies, manual review does not scale to approximately 10^4 active satellites, and pure rule-based detectors trade recall for precision so aggressively that they are blind to most behavioral anomalies. We present a multi-tier labeling cascade that composes three weak supervision sources of increasing fidelity: a fast physics rule set (rule_v1), an Interacting Multiple Model Unscented Kalman Filter (IMM-UKF) bank, and a supplemental-element calibration step (supGP), to produce labels at a scale unavailable from any single source. Applied to 232M Two-Line Element (TLE) records spanning 60 years, the cascade yields 8.6M labeled sequences of length 50 (430M timesteps) over 11 features that include explicit time encoding and full mean-element state. On overlapping satellites, IMM-UKF surfaces 42.6x more anomalies than rule_v1 alone. We train a 6.5M-parameter Transformer in two stages, achieving a maneuver recall of 55.4% and decay recall of 62.8% on a held-out test set. An ablation on the time-delta feature alone yields a 107% relative improvement in decay recall. We frame the resulting model as a high-recall triage classifier whose role is to surface candidate events for downstream filtering, not to issue final attributions, and discuss the path toward a Neural-ODE-based orbital world model.

Related papers

• Modeling Normal Is All You Need: Joint Latent Clustering for Anomaly Detection in Multimodal Cyber-Physical Systems [47.027290803102666]
  Normal behaviour is the union of many imbalanced, curved, thin-fringed operating regimes.<n>We model the normal law with a jointly learned latent representation plus explicit Gaussian-mixture mode clustering.<n>We evaluate under a deliberately fair protocol: raw point-wise metrics with no point adjustment, a trivial-detector difficulty split, prevalence-matched F1, and train-normal-only calibration.
  arXiv  Detail & Related papers  (2026-07-07T10:10:00Z)
• The Language of Elution: Autoregressive Prediction of the Next Feature in Untargeted LC-HRMS Lipidomics [0.0]
  Training on 15,242 features from four clinical lipidomics cohorts (342 plasma samples; SCIEX TripleTOF 6600+, Waters CSH C18)<n>LSTM reaches 98.4% top-1 accuracy (99.99% top-5; mean absolute error 3.6 Da) and the Transformer 98.0%.<n>These results establish that elution sequences are highly predictable and lay the groundwork for predictive MS/MS acquisition to improve annotation coverage in metabolomics.
  arXiv  Detail & Related papers  (2026-06-02T10:42:17Z)
• ATLAS: All-round Testing of Long-context Abilities across Scales [42.83200405769639]
  ATLAS is a benchmarking framework that redefines long-context evaluation as length-dependent capability profiling.<n> ATLAS contributes three methodological principles: (i) a layered taxonomy separating foundational operations from application workloads so failures can be attributed, (ii) length-aware AUC scoring that integrates score-length curves over a fixed 8K-1M grid, replacing single-point metrics with full degradation profiles, and (iii) ATLAScore, a harmonic-mean aggregate over taxonomy categories that penalizes imbalanced profiles.
  arXiv  Detail & Related papers  (2026-05-27T07:33:47Z)
• Distributional Energy-Based Models for Uncertainty-Aware Structured LLM Reasoning [40.342912574072024]
  Large Language Models produce structured outputs such as travel plans or code solutions.<n>Individual reasoning steps may appear correct while the output as a whole violates budgets, fails test cases, or contradicts earlier deductions.<n>We propose a learned quality scorer with deterministic analytical constraint penalties for verifying structured LLM outputs.
  arXiv  Detail & Related papers  (2026-05-15T17:08:27Z)
• Paired-CSLiDAR: Height-Stratified Registration for Cross-Source Aerial-Ground LiDAR Pose Refinement [46.13526676100776]
  We introduce Paired-CSLiDAR, a cross-source aerial-ground LiDAR benchmark for single-scan pose refinement.<n>The benchmark contains 12,683 ground-aerial pairs across 6 evaluation sites.<n>We propose Residual-Guided Stratified Registration (RGSR), a training-free, geometry-only refinement pipeline.
  arXiv  Detail & Related papers  (2026-05-01T13:14:20Z)
• ExoNet: Calibrated Multimodal Deep Learning for TESS Exoplanet Candidate Vetting using Phase-Folded Light Curves, Stellar Parameters, and Multi-Head Attention [0.0]
  ExoNet is a multimodal deep learning framework that processes phase-folded global and local light curve views alongside stellar parameter features.<n>It is trained on 7,585 labeled Kepler Objects of Interest and achieves Test AUC = 0.9549 and 86.3% accuracy.<n>ToI-5728.01 and TOI-6716.01 emerge as the most Earth-like unconfirmed candidates.
  arXiv  Detail & Related papers  (2026-04-16T22:26:29Z)
• Fragile Reasoning: A Mechanistic Analysis of LLM Sensitivity to Meaning-Preserving Perturbations [2.7946918847372277]
  Large language models demonstrate strong performance on mathematical reasoning benchmarks, yet remain surprisingly fragile to meaning-preserving surface perturbations.<n>We systematically evaluate three open-weight LLMs, Mistral-7B, Llama-3-8B, and Qwen2.5-7B, on 677 GSM8K problems paired with semantically equivalent variants.<n>All three models exhibit substantial answer-flip rates (28.8%-45.1%), with number paraphrasing consistently more disruptive than name swaps.
  arXiv  Detail & Related papers  (2026-04-02T05:30:20Z)
• Hierarchy-Guided Topology Latent Flow for Molecular Graph Generation [44.50339042016925]
  We propose a planner-executor model that generates bond graphs with 3D coordinates using a latent multi-scale plan for global context.<n>HLTF achieves 98.8% atom stability and 92.9% valid-and-unique, improving PoseBusters validity to 94.0% (+0.9 over the strongest reported baseline)<n>On GEOM-DRUGS, HLTF attains 85.5%/85.0% validity/valid-unique-novel without post-processing and 92.2%/91.2% after standardized relaxation.
  arXiv  Detail & Related papers  (2026-03-28T03:48:13Z)
• The Mirror Design Pattern: Strict Data Geometry over Model Scale for Prompt Injection Detection [0.0]
  We introduce Mirror, a data-curation design pattern that organizes prompt injection corpora into matched positive and negative cells.<n>We show that for L1 prompt injection screening, strict data geometry can matter more than model scale.
  arXiv  Detail & Related papers  (2026-03-12T12:50:57Z)
• Unsupervised LiDAR-Based Multi-UAV Detection and Tracking Under Extreme Sparsity [2.3646560222366695]
  Non-repetitive solid-state LiDAR scanning leads to an extremely sparse measurement regime for detecting airborne UAVs.<n>We introduce an unsupervised, LiDAR-only pipeline that addresses both detection and tracking without the need for labeled training data.
  arXiv  Detail & Related papers  (2026-03-12T06:19:51Z)
• MSign: An Optimizer Preventing Training Instability in Large Language Models via Stable Rank Restoration [48.446476072756276]
  Training instability remains a critical challenge in large language model pretraining.<n>We study training failures in a 5M NanoGPT model scaled via $$P.<n>We propose MSign, a new norm that periodically applies matrix sign operations to restore stable rank.
  arXiv  Detail & Related papers  (2026-02-02T07:18:45Z)
• Agentic World Modeling for 6G: Near-Real-Time Generative State-Space Reasoning [70.56067503630486]
  We argue that sixth-generation (6G) intelligence is not fluent token prediction but calibrated the capacity to imagine and choose.<n>We show that WM-MS3M cuts mean absolute error (MAE) by 1.69% versus MS3M with 32% fewer parameters and similar latency, and achieves 35-80% lower root mean squared error (RMSE) than attention/hybrid baselines with 2.3-4.1x faster inference.
  arXiv  Detail & Related papers  (2025-11-04T17:22:22Z)
• Deep Temporal Graph Networks for Real-Time Correction of GNSS Jamming-Induced Deviations [4.489167091323233]
  jamming is disrupting Global Navigation Satellite Systems (GNSS)<n>We introduce a receiver-centric deep temporal graph network that predicts, and thus corrects, the receivers horizontal deviation in real time.<n>A single layer Heterogeneous Graph ConvLSTM (HeteroGTM) aggregates one hop context and temporal dynamics over a short history.
  arXiv  Detail & Related papers  (2025-09-17T14:12:36Z)
• MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detection [53.03687787922032]
  Mamba-based models with superior long-range modeling and linear efficiency have garnered substantial attention.<n>This study pioneers the application of Mamba to multi-class unsupervised anomaly detection, presenting MambaAD.<n>The proposed LSS module, integrating parallel cascaded (Hybrid State Space) HSS blocks and multi- kernel convolutions operations, effectively captures both long-range and local information.
  arXiv  Detail & Related papers  (2024-04-09T18:28:55Z)

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.