Analyzing Data Quality and Decay in Mega-Constellations: A Physics-Informed Machine Learning Approach

• URL: http://arxiv.org/abs/2510.11242v1
• Date: Mon, 13 Oct 2025 10:28:23 GMT
• Title: Analyzing Data Quality and Decay in Mega-Constellations: A Physics-Informed Machine Learning Approach
• Authors: Katarina Dyreby, Francisco Caldas, Cláudia Soares,
• Abstract summary: This study critically evaluates the accuracy and reliability of publicly available ephemeris data for a mega-constellation - Starlink.<n>By analyzing two months of real orbital data for approximately 1500 Starlink satellites, we identify discrepancies between high precision numerical algorithms and the published ephemerides.<n>We extract the acceleration profile of satellites during deorbiting and provide insights relating to the effects of non-conservative forces during reentry.
• Score: 1.3509194648045753
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In the era of mega-constellations, the need for accurate and publicly available information has become fundamental for satellite operators to guarantee the safety of spacecrafts and the Low Earth Orbit (LEO) space environment. This study critically evaluates the accuracy and reliability of publicly available ephemeris data for a LEO mega-constellation - Starlink. The goal of this work is twofold: (i) compare and analyze the quality of the data against high-precision numerical propagation. (ii) Leverage Physics-Informed Machine Learning to extract relevant satellite quantities, such as non-conservative forces, during the decay process. By analyzing two months of real orbital data for approximately 1500 Starlink satellites, we identify discrepancies between high precision numerical algorithms and the published ephemerides, recognizing the use of simplified dynamics at fixed thresholds, planned maneuvers, and limitations in uncertainty propagations. Furthermore, we compare data obtained from multiple sources to track and analyze deorbiting satellites over the same period. Empirically, we extract the acceleration profile of satellites during deorbiting and provide insights relating to the effects of non-conservative forces during reentry. For non-deorbiting satellites, the position Root Mean Square Error (RMSE) was approximately 300 m, while for deorbiting satellites it increased to about 600 m. Through this in-depth analysis, we highlight potential limitations in publicly available data for accurate and robust Space Situational Awareness (SSA), and importantly, we propose a data-driven model of satellite decay in mega-constellations.

Related papers

• Communication-Efficient Learning for Satellite Constellations [0.0]
  We focus on a federated approach, where satellites collect and locally process data, with the ground station aggregating local models.<n>We employ several mechanisms to reduce the number of communications with the ground station.<n>We then propose an error feedback mechanism that enhances accuracy, which yields, as a byproduct, an algorithm-agnostic error feedback scheme.
  arXiv  Detail & Related papers  (2025-11-25T11:47:47Z)
• ASTREA: Introducing Agentic Intelligence for Orbital Thermal Autonomy [51.56484100374058]
  ASTREA is the first agentic system executed on flight-heritage hardware for autonomous spacecraft operations.<n>We integrate a resource-constrained Large Language Model (LLM) agent with a reinforcement learning controller in an asynchronous architecture tailored for space-qualified platforms.
  arXiv  Detail & Related papers  (2025-09-16T08:52:13Z)
• STAR: A Benchmark for Astronomical Star Fields Super-Resolution [52.895107920663236]
  We propose STAR, a large-scale astronomical SR dataset containing 54,738 flux-consistent star field image pairs.<n>We propose a Flux-Invariant Super Resolution (FISR) model that could accurately infer the flux-consistent high-resolution images from input photometry.
  arXiv  Detail & Related papers  (2025-07-22T09:28:28Z)
• SpaceTrack-TimeSeries: Time Series Dataset towards Satellite Orbit Analysis [7.471495336112591]
  This study collects and curating a representative dataset of maneuvering behavior from Starlink satellites.<n>The dataset integrates Two-Line Element (TLE) catalog data with corresponding high-precision ephemeris data, thereby enabling a more realistic and multidimensional modeling of space object behavior.<n>It provides valuable insights into practical deployment of maneuver detection methods and the evaluation of collision risks in increasingly congested orbital environments.
  arXiv  Detail & Related papers  (2025-06-16T01:57:50Z)
• High Performance Space Debris Tracking in Complex Skylight Backgrounds with a Large-Scale Dataset [48.32788509877459]
  We propose a deep learning-based Space Debris Tracking Network(SDT-Net) to achieve highly accurate debris tracking.<n>SDT-Net effectively represents the feature of debris, enhancing the efficiency and stability of end-to-end model learning.<n>Our dataset and code will be released soon.
  arXiv  Detail & Related papers  (2025-06-03T08:30:25Z)
• Low-altitude UAV Friendly-Jamming for Satellite-Maritime Communications via Generative AI-enabled Deep Reinforcement Learning [72.23178920029957]
  This paper presents a satellite-maritime communication system assisted by low-altitude unmanned aerial vehicle (UAV) friendly-jamming.<n>We formulate a secure satellite-maritime communication multi-objective optimization problem (SSMCMOP)<n>In order to solve the dynamic and long-term optimization problem, we reformulate it into a Markov decision process.<n>We then propose a transformer-enhanced soft actor-critic (TransSAC) algorithm, which is a generative artificial intelligence-enabled deep reinforcement learning approach.
  arXiv  Detail & Related papers  (2025-01-26T10:13:51Z)
• Real-time gravitational-wave inference for binary neutron stars using machine learning [71.29593576787549]
  We present a machine learning framework that performs complete BNS inference in just one second without making any approximations. Our approach enhances multi-messenger observations by providing (i) accurate localization even before the merger; (ii) improved localization precision by $sim30%$ compared to approximate low-latency methods; and (iii) detailed information on luminosity distance, inclination, and masses.
  arXiv  Detail & Related papers  (2024-07-12T18:00:02Z)
• Stitching Satellites to the Edge: Pervasive and Efficient Federated LEO Satellite Learning [1.3121410433987561]
  This paper proposes a novel FL-SEC framework that empowers satellites to execute large-scale machine learning (ML) tasks onboard efficiently. Key components include personalized learning via divide-and-conquer, which identifies and eliminates redundant satellite images, and orbital model retraining, which generates an aggregated "orbital model" per orbit and retrains it before sending to the ground station. Our approach dramatically reduces FL convergence time by nearly 30 times, and satellite energy consumption down to as low as 1.38 watts, all while maintaining an exceptional accuracy of up to 96%.
  arXiv  Detail & Related papers  (2024-01-28T02:01:26Z)
• Secure and Efficient Federated Learning in LEO Constellations using Decentralized Key Generation and On-Orbit Model Aggregation [1.4952056744888915]
  This paper proposes FedSecure, a secure FL approach designed for LEO constellations. FedSecure preserves the privacy of each satellite's data against eavesdroppers, a curious server, or curious satellites. It also reduces convergence delay drastically from days to only a few hours, yet achieving high accuracy of up to 85.35%.
  arXiv  Detail & Related papers  (2023-09-04T21:36:46Z)
• SpectraNet: Learned Recognition of Artificial Satellites From High Contrast Spectroscopic Imagery [0.0]
  Current methods for extracting object identification from observed data require spatially resolved imagery which limits identification to objects in low earth orbits. Most artificial satellites, however, operate in geostationary orbits at distances which prohibit ground based observatories from resolving spatial information. This paper demonstrates an object identification solution leveraging modified residual convolutional neural networks to map distance-invariant data to object identity.
  arXiv  Detail & Related papers  (2022-01-10T19:51:00Z)
• Spacecraft Collision Risk Assessment with Probabilistic Programming [0.0]
  Over 34,000 objects bigger than 10 cm in length are known to orbit Earth. Among them, only a small percentage are active satellites, while the rest of the population is made of dead satellites, rocket bodies, and debris that pose a collision threat to operational spacecraft. We build a novel physics-based probabilistic generative model for synthetically generating conjunction data messages.
  arXiv  Detail & Related papers  (2020-12-18T14:26:08Z)
• Integrating LEO Satellite and UAV Relaying via Reinforcement Learning for Non-Terrestrial Networks [51.05735925326235]
  A mega-constellation of low-earth orbit (LEO) satellites has the potential to enable long-range communication with low latency. We study the problem of forwarding packets between two faraway ground terminals, through an LEO satellite selected from an orbiting constellation. To maximize the end-to-end data rate, the satellite association and HAP location should be optimized. We tackle this problem using deep reinforcement learning (DRL) with a novel action dimension reduction technique.
  arXiv  Detail & Related papers  (2020-05-26T05:39:27Z)

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.