The rapid increase in space debris poses a significant threat to the safety of in-orbit spacecraft and the utilization of valuable orbital resources. Active debris removal (ADR) has emerged as the most effective approach to mitigating debris growth. However, traditional rigid capture methods are limited by constraints such as capture distance, target adaptability, and the risk of generating secondary debris, making them inadequate for the increasingly complex space debris environment. Net-membrane capture systems integrate smart materials with controllable deployment mechanisms, offering advantages such as long-range capture, adaptability to non-cooperative targets, and reusability. However, the dynamic characteristics throughout the entire process—shooting, deployment, contact, and wrapping capture—remain insufficiently understood. The combined stretching, shearing, and bending deformations of the membrane, along with the complex contact mechanics introduced by debris spin, pose challenges for traditional modeling approaches such as the finite element method (FEM) and the absolute nodal coordinate formulation (ANCF).

In a recent study published in Space: Science & Technology, a research team from the Chinese Academy of Sciences and the University of Electronic Science and Technology of China proposed a dynamic modeling and simulation method for a net-membrane capture system that accounts for combined deformations. The study develops a dynamic model of the membrane using the multiparticle method (MPM), incorporating stretching, shearing, and bending stiffness to accurately describe combined deformations. A contact model based on continuous contact theory and Coulomb’s law is also established to simulate the interaction between the membrane and debris. Through multiple sets of numerical simulations, the study systematically analyzes the effects of shooting velocity, ejection angle, and bullet mass on membrane deployment behavior, leading to the identification of optimal shooting parameters. Simulation results demonstrate that the proposed model can effectively simulate the capture of both stationary and spinning debris with spherical or polyhedral shapes. During the capture of spinning debris, the tangential friction between the membrane and debris significantly reduces the debris’s spin rate, demonstrating excellent despinning capability. This research provides a theoretical foundation for parameter optimization and engineering design of net-membrane capture systems, contributing to the advancement of reusable and highly adaptable active debris removal technologies.

A new study published in Big Earth Data applies the INFORM Climate Change model to project future risks of humanitarian crises and disasters by integrating climate hazards, population dynamics, conflict, and socioeconomic development pathways. Incorporating forward-looking projections of vulnerability and coping capacity under different Shared Socioeconomic Pathways, the analysis shows that global risk may decline under moderate and rapid development scenarios but could rise sharply in the high-emission, fragmented SSP3 pathway. The findings provide evidence for prioritizing vulnerable regions and guiding targeted risk reduction and climate adaptation strategies.

Space missions are complex, multidisciplinary tasks that involve high risk and high cost. Systems engineering (SE) technology is an emerging discipline used to manage project complexity and ensure mission success . As technology advances and systems become more complex and volatile, SE needs to accommodate the constant reassessment, upgrading, and development of systems . Traditional SE relies on a large number of decentralized documents that cannot keep up with the changes in the system. Therefore, SE is transforming toward digitalization and has led to model-based systems engineering (MBSE), which provides a way to address SE challenges and is emerging as a paradigm and principle of SE. According to the International Council on Systems Engineering, MBSE is “the formalized application of modeling to support system requirements, design, analysis, verification and validation activities beginning in the conceptual design phase and continuing throughout development and later life cycle phase”.Space missions are complex, multidisciplinary tasks that involve high risk and high cost. Systems engineering (SE) technology is an emerging discipline used to manage project complexity and ensure mission success . As technology advances and systems become more complex and volatile, SE needs to accommodate the constant reassessment, upgrading, and development of systems . Traditional SE relies on a large number of decentralized documents that cannot keep up with the changes in the system. Therefore, SE is transforming toward digitalization and has led to model-based systems engineering (MBSE), which provides a way to address SE challenges and is emerging as a paradigm and principle of SE. According to the International Council on Systems Engineering, MBSE is “the formalized application of modeling to support system requirements, design, analysis, verification and validation activities beginning in the conceptual design phase and continuing throughout development and later life cycle phase”.

Local photothermal effect of AuNRs gives rise to high local temperatures. Two methods based on electron spin resonance (ESR) technique were developed to characterize the local temperature (T_local) around the excited rod. The obtained T_local is 20-30℃ higher than the global temperature (T_global) of the illuminated suspension measured using thermocouple. The local photothermal effects of gold nanorods (AuNRs) can promote the thermal decomposition of H₂O₂ to generate hydroxyl radicals. The AuNRs + H₂O₂ system can be used as a light-triggered hydroxyl radical source to regulate the generation of hydroxyl radical by time and space.

A new study published in Big Earth Data proposes an AI cube framework that integrates GeoAI models into geospatial data cube infrastructures to enhance large-scale Earth Observation data analytics. By introducing a model warehouse, intelligent model selection, and parallel inference pipelines on the Open Geospatial Engine platform, the approach significantly improves analytical capability and reduces inference time by over 80%. The framework advances the transition from traditional data cube processing toward AI-ready spatial data infrastructures.

Vehicle re-identification (Re-ID) stands as a cornerstone technology in intelligent transportation systems, enabling the tracking of individual vehicles across non-overlapping surveillance cameras in urban environments. Despite substantial progress in deep learning approaches, real-world deployment faces persistent obstacles from diverse vehicle poses caused by varying camera angles, viewpoints, and driving directions. These pose variations scatter feature representations of the same vehicle in the embedding space, leading to reduced discriminative power and lower identification accuracy. Traditional methods relying on deep metric learning struggle to bridge these gaps, as pose differences create discrete clusters even for identical vehicles, complicating reliable matching in practical traffic scenarios.

A recent study introduces an innovative strategy to mitigate this challenge by projecting vehicle images from diverse poses into a unified target pose, generating synthetic images that serve as pose-invariant auxiliary information to strengthen Re-ID models. Recognizing the high costs and logistical difficulties of acquiring paired images of the same vehicle from different cameras, researchers developed VehicleGAN, the first pair-flexible pose-guided image synthesis framework tailored for vehicle Re-ID. This end-to-end Generative Adversarial Network accepts a source vehicle image and a target pose as inputs, synthesizing the vehicle in the desired pose without depending on detailed 3D geometric models. VehicleGAN operates effectively in both supervised settings, using paired data when available, and unsupervised scenarios through a novel AutoReconstruction mechanism. In this self-supervised approach, the model transfers an image to the target pose and back to the original, reconstructing the input to learn robust transformations without requiring expensive paired annotations. This flexibility addresses key limitations of prior 3D-based methods, which demand precise camera parameters often unavailable in real surveillance setups, and supervised 2D methods burdened by labor-intensive labeling.