Beam power of ground-based coherently combined beams for the momentum coupling with space debris

Deep learning-based low-SNR signal detection for passive space debris detection

Managing space debris: Risks, mitigation measures, and sustainability challenges

Abstract The rapid development of low‐Earth orbit (LEO) satellites brings increased attention to spacecraft collisions, space debris, orbital decay, and satellite reentry. Neutral density and associated drag force on the satellite orbits elevate space risks, significantly determined by space weather disturbances, particularly geomagnetic storms. On 3 September 2024, the Australian Binar‐2, 3, and 4 satellites were deployed, but their actual lifetimes were only around 2 months, much shorter than designed, only 20% that of the nearly identical Binar 1 satellite launched in 2022. For the first time, we analyze the premature reentries of the Binar‐2, 3, and 4 satellites to unveil the severe space weather impact on their lifetimes, especially the influence of accuracy of medium‐ and long‐term space weather prediction on satellite lifetime designs. Our findings reveal that the premature reentries of Binar‐2, 3, and 4 satellites were caused by enhanced neutral density due to much higher solar and geomagnetic activities than predicted. The actual satellite lifetimes align well with estimations based on observed space parameters, whereas large deviations occur when using predicted parameters. The widely used predictions for medium‐ and long‐term space weather underestimated the F10.7 index and sunspot numbers during Solar Cycle 25 (SC25), especially during the peak time, leading to discrepancies in the designed satellite lifetimes. Our results illustrate the importance of medium‐to long‐term space weather forecasting for the lifespan of LEO satellites.

This report presents Frigate, a novel astronomical dataset comprising still-frame images of a section of Low Earth Orbit (LEO), collected by ExoAnalytic Solutions Inc., a commercial space situational awareness (SSA) provider using ground-based optical systems. Datasets of this kind are rare and typically inaccessible to most stakeholders. Through agreement with the company, the images were collected and prepared for use in machine learning and computer vision tasks related to SSA. The raw images contain lens glare artefacts, vignetting, and outer-region degradation. To address this, we additionally release a lightly processed version of each image featuring artefact removal, background subtraction, and sharpening. Usage notes describe optional downstream techniques including stacking, streak detection, and automated annotation. Given the lack of publicly available space surveillance data, this dataset represents a valuable resource for satellite tracking, orbital debris detection, and astronomical computer vision. The dataset described in this paper is wholly owned by ExoAnalytic Solutions Inc. and was released to the research team through mutual agreement of the benefits of its processing and application to the domain. All data and code are openly available.

The rapid accumulation of space debris poses a serious threat to operational spacecraft, with the capture and removal of rapidly tumbling non-cooperative targets being a primary challenge. Non-contact electromagnetic de-tumbling technology is a promising solution due to its enhanced safety. This paper addresses the issue of torque modeling and validation in the electromagnetic de-tumbling process for a specific configuration involving a magnetic dipole and a spherical shell under a symmetrically distributed magnetic field. Based on the principles of electromagnetic induction, an approximate analytical expression for the electromagnetic eddy current torque on a rotating spherical shell within a dipole magnetic field is first derived. A high-fidelity finite element model is then established, which reveals a systematic discrepancy between the initial theoretical model and numerical simulation results. A distance-dependent power-law correction factor is introduced to calibrate the theoretical model, significantly improving its accuracy and reducing the average error to 1.5 percent. Finally, a ground-based experimental platform is designed and implemented. The experimental results demonstrate that the corrected approximate analytical model agrees well with the empirical data, verifying its validity and accuracy under the given conditions and providing a reliable theoretical basis for the design of future space debris de-tumbling controllers.

The rapid expansion of satellite constellations and the growing density of orbital debris have intensified the need for advanced computational tools capable of optimizing orbital architectures, collision avoidance, and long-term space sustainability. Traditional optimization methods are increasingly constrained by the combinatorial complexity of constellation design variables—including orbital planes, phasing, revisit requirements, and communication coverage—and the nonlinear dynamics of debris propagation. This study introduces a hybrid quantum–classical optimization framework that integrates quantum approximate optimization algorithms (QAOA), quantum annealing, and classical multi-physics orbital simulation to improve decision-making in constellation deployment and debris mitigation. Quantum subroutines accelerate the search for globally efficient orbital configurations, while classical solvers handle high-fidelity astrodynamics, propagation models, and mission constraints. Preliminary simulation results indicate that the hybrid framework yields more efficient constellation geometries, reduces collision risk, and enhances debris avoidance planning compared to classical baselines. These findings highlight the potential of quantum-assisted optimization to support safer, more resilient, and sustainably managed space systems.

Abstract A search for second-timescale optical transients is one of the frontiers of time-domain astronomy. However, it has been pointed out that reflections of sunlight from Earth-orbiting objects can also produce second-timescale “glints.” We conducted wide-field observations at 2 frames per second using Tomo-e Gozen on the 1.05 m Kiso Schmidt telescope. We identified 1554 point-source glints that appeared in only one frame (0.5 s). Their brightness ranges from 11 to 16 mag, with fainter glints being more numerous. These glints are likely caused by satellites and space debris in high-altitude orbits such as the geosynchronous Earth orbit or highly elliptical orbits. Many glints brighter than 14 mag are associated with known satellites or debris with large apogees (>30,000 km). In contrast, most fainter glints are not associated with cataloged objects and may be due to debris with sizes of 0.3–1 m. The event rate of second-timescale glints is estimated to be 4.7 ± 0.2 deg −2 hr −1 (average) and 9.0 ± 0.3 deg −2 hr −1 (near the equator) at 15.5 mag. Our results demonstrate that high-altitude debris can represent a significant foreground in searches for second-timescale optical transients. They also imply that deep surveys such as Rubin/LSST will detect many of these glints in single-exposure images.

Abstract The use of Very Low Earth Orbit (VLEO) has often been cited as a possible solution to the growing problem of space debris, especially as we enter the era of mega-constellations of thousands of satellites. By definition, interaction with the residual atmosphere in VLEO induces drag which rapidly pulls debris and failed satellites from orbit, minimising any long-term impact on the debris population. As such, it provides a relatively consequence free environment, at least in terms of debris considerations, for rapid and high-risk technological innovation. There are many benefits achieved from operating satellites at lower altitudes, such as improved resolution for Earth observation and reduced latency and power requirements for communications constellations. These benefits, combined with the self-cleaning nature of VLEO and rapidly reducing launch costs, continue to drive growing use and interest in VLEO. However, the sustainable use of space also demands the consideration of other impacts of the use of low Earth orbit generally, including VLEO, on the Earth and orbital environments. These go beyond debris considerations to include light and radio pollution for optical and radio astronomy, and the carbon footprint of the space industry. Meanwhile, the impact on the atmosphere of satellites re-entering the atmosphere at end-of-life is known to have impacts on ozone depletion and the Earth’s radiative balance, the significance of which is still being assessed. One can imagine that in the future, with the drive for a circular space economy, moving end-of-life satellites to a reprocessing facility in-orbit becomes mandated; but this is realistically still a long-term goal. In the near term, what are the opportunities and benefits that VLEO can provide to space sustainability, and how do these need to develop to continue to be part of the solution in the long term? This paper reviews factors driving the growing interest in the use of VLEO, including benefits for sustainability, alongside the longer-term technology challenges that need to be addressed to ensure that VLEO remains a sustainable solution in a future circular economy for space.

With the increasing complexity of space missions, the accuracy and efficiency of orbital maneuver planning have become crucial. This paper proposes an analytical derivation-based method for generating orbital maneuver solution sets to address the maneuver planning problem for spacecraft on-orbit services under J2 perturbation. By establishing an analytical relative motion model corrected for J2 perturbation, this method enables the rapid generation of maneuver solution sets that satisfy multiple constraints, providing diverse options for the initial mission planning phase. Simulation validation demonstrates that the method maintains good applicability across mission scenarios at different orbital altitudes. The generated solution sets not only enhance the flexibility of orbital maneuver planning but also provide a quantitative basis for optimizing the selection of mission timing windows, holding certain application value in scenarios such as space debris removal and on-orbit maintenance services.

The automatic, accurate perception of targets in space is a crucial prerequisite for many on-orbit aerospace missions. Therefore, research on perception technologies within spaceborne images is meaningful. The development of deep learning has revealed its potential for application to space target perception. However, implementing deep learning models requires large-scale labelled datasets. Therefore, we build a multitask synthetic benchmark space target dataset, NCSTP, to address the limitations of current datasets. First, we collect and modify various space target models for satellites, space debris, and space rocks. By importing them into a realistic space environment simulated by Blender, 200,000 images are generated with different target sizes, poses, lighting conditions, and backgrounds. Then, the data are annotated to ensure the dataset supports simultaneous space target detection, recognition and component segmentation. All data can be used for training space target detection and recognition models. We further annotate the components of each satellite for component segmentation. Finally, we test a series of state-of-the-art object detection and semantic segmentation models on the dataset to establish a benchmark.

Abstract We present Li lidar observations in the mesopause region above Kühlungsborn, Germany (54°N, 12°E). The lithium layer is mainly formed by meteoric ablation. But lithium has a much higher relative abundance in space debris compared with meteoroids, making it a good indicator of anthropogenic influence in the upper mesosphere. Our measurements reveal a strong seasonal increase in Li abundance between August and December 2024, growing from to . These values are in reasonable agreement with the first simulations based on the Whole Atmosphere Community Climate Model (WACCM6) with a newly developed Li chemistry network; however, WACCM‐Li does not explain high Li abundances of up to observed in January/February 2025. This may be due to anthropogenic effects or an unidentified natural mechanism. This study highlights the potential of lithium observations for monitoring the consequences of re‐entering space debris on the composition of the atmosphere.

Space Debris: Legal Challenges Toward a Sustainable Space Environment

Dynamic Modeling of a Net-Membrane Capture System with Combined Deformation for Space Debris Removal

Hypervelocity impacts (HVI) from micrometeoroids and orbital debris can produce dense plasmas that may interfere with spacecraft electronics via electromagnetic radiation. This work presents a computational framework to characterize plasma formation during the early stages of an HVI event. A solid-state shock model is used to calculate post-shock, pre-ionized thermodynamic properties for iron-on-iron impacts across a range of velocities (6–50 km/s), employing five different equations of state (EOS). These results serve as inputs to a 0D3V Monte Carlo collision model, which simulates the transient ionization of the shocked material. At low impact velocities (<15 km/s), all EOS produce similar results but diverge at higher velocities due to differences in how they capture phase transitions and quantum effects. The system enters a warm dense matter regime in the post-shock, pre-ionized phase, characterized by strongly coupled ions and moderately degenerate electrons. Ionization occurs on the order of femtoseconds, which is much faster than plasma expansion or electromagnetic propagation over an impactor's characteristic length, validating the assumption of instantaneous plasma formation. We find lower impact velocities will produce partially ionized plasmas, while higher impact velocities will produce fully ionized plasmas, with the threshold defining “low” and “high” velocities depending on the EOS used. Overall, this work provides estimates of temperature, density, and ionization levels immediately after impact, offering improved initial conditions for plasma expansion and radiation models. It also underscores the need for more accurate EOS in extreme regimes and lays the groundwork for future integration with experimental validation and electromagnetic diagnostics in the context of spacecraft missions.

Connecting laboratory and spectroscopic observations of aerospace materials to characterize the reflectivity of artificial space objects and debris in LEO regimes

Optimal placement and coordinated scheduling of distributed space-based lasers for orbital debris remediation

Review on orbital debris mitigation: Techniques for effective tracking, monitoring, and removal

Problems of Numerical Modeling of Space Debris Impacts Protection in three-dimensional multi-material statement

Accurate pose estimation of non-cooperative space objects is crucial for applications such as satellite maintenance, space debris removal, and on-orbit assembly. However, monocular pose estimation methods face significant challenges in environments with limited visibility. Different from the traditional pose estimation methods that use images from a single band as input, we propose a novel deep learning-based pose estimation framework for non-cooperative space objects by fusing visible and infrared images. First, we introduce an image fusion subnetwork that integrates multi-scale features from visible and infrared images into a unified embedding space, preserving the detailed features of visible images and the intensity information of infrared images. Subsequently, we design a robust pose estimation subnetwork that leverages the rich information from the fused images to achieve accurate pose estimation. By combining these two subnetworks, we construct the Visible and Infrared Fused Pose Estimation Framework (VIPE) for non-cooperative space objects. Additionally, we present a Bimodal-Vision Pose Estimation (BVPE) dataset, comprising 3,630 visible-infrared image pairs, to facilitate research in this domain. Extensive experiments on the BVPE dataset demonstrate that VIPE significantly outperforms existing monocular pose estimation methods, particularly in complex space environments, providing more reliable and accurate pose estimation results.