With the increasingly prominent role of high-resolution remote sensing data in supporting the digital economy and intelligent industries, on-orbit assembly of large space optical systems has become a critical pathway to overcome launch vehicle constraints and achieve large-aperture detection. However, during on-orbit assembly, the manipulator is required to simultaneously satisfy multiple objectives, including replicating standardized paths, avoiding non-cooperative obstacles, and precisely passing through key waypoints. Existing global methods are limited by the real-time bottleneck of high-dimensional search, while local methods lack global consistency guarantees. Although Dynamic Movement Primitives (DMPs) possess parametric trajectory representation capabilities, they struggle to reconcile the inherent conflict between path shape accuracy and safe obstacle avoidance under multi-objective constraints. Therefore, how to synergistically optimize path passing accuracy and obstacle avoidance safety for manipulators in dynamic, unstructured space environments has become a core challenge constraining the effectiveness of on-orbit autonomous assembly for large-aperture optical systems.

Researchers from the Institute of Coal Chemistry, Chinese Academy of Sciences, have identified the nickel dimer (Ni₂) play vital role inCO₂ methanation. Encapsulated in MFI zeolite with MnOₓ promotion at only ~0.86% Ni loading, the catalyst achieves ~76% CO₂ conversion, ~98% CH₄ selectivity and CH₄ space time yield of ~450 mol·mol Ni^-1·h^-1 at 400°C.

With the widespread application of radar in space target detection and identification, acquiring radar images of non-cooperative targets at different attitudes has become the data foundation for supporting deep learning-based automatic target recognition algorithms. However, the uncontrollable motion trajectories of non-cooperative targets and limited illumination time render the acquisition of measured images extremely difficult; although anechoic chamber measurements and electromagnetic computations can obtain multi-attitude images, they are respectively constrained by high costs and computational time, making it difficult to meet the demand for large-scale data. Existing image generation methods based on Generative Adversarial Networks (GANs) can augment data scales, but most can only control the azimuth angle without precisely constraining the elevation angle, resulting in generated images whose projection morphology does not match reality. Therefore, how to achieve radar image generation with precise dual-dimensional control over both azimuth and elevation angles has become a key technical challenge in data augmentation for radar recognition of non-cooperative targets.