Plasma bubbles in the ionosphere over equatorial and low-latitude regions are a common nighttime plasma density depletion structure that can cause severe scintillation interference on high-frequency communications, making them an important target of space weather monitoring. Traditional detection methods rely on statistical thresholds derived from temporal fluctuations in ion density; however, plasma bubbles exhibit significant variability in spatiotemporal scales (ranging from kilometers to hundreds of kilometers), and their occurrence frequency is strongly modulated by solar activity and seasonal factors. Consequently, fixed-threshold methods struggle to accommodate this diversity, with miss rates reaching up to 40% during periods of low solar activity. Although deep learning has demonstrated powerful capabilities in automatically extracting and recognizing complex waveform features in fields such as medical image segmentation and signal peak detection, its systematic application to ionospheric plasma bubble detection—and thus addressing the adaptability deficiencies of traditional threshold methods—remains underexplored.

With growing concerns over the impact threat of near-Earth small bodies and the utilization of space resources, the orbital evolution of binary asteroid systems has become an important subject in space science. When describing the full two-body dynamics of such systems, the gravitational potential of irregular bodies is determined by both their positions and attitudes, leading to coupling between translational and rotational motions, and the resulting model complexity significantly exceeds that of the classical two-body problem. Although existing numerical methods, such as explicit Runge–Kutta integrators, are widely used, they fail to preserve the symplectic structure and Lie group geometric properties of the system. In long-term simulations, key conserved quantities such as total energy and angular momentum are prone to numerical drift, severely undermining the long-term credibility of orbital predictions. While Lie group variational integrators can preserve geometric structure to a certain extent, their implicit equations are formulated on Lie group elements, which incurs substantial computational overhead. Therefore, how to construct a high-performance integration method that simultaneously achieves numerical accuracy, structure preservation, and computational efficiency has become a core challenge in the long-term evolutionary simulation of binary asteroid systems.

With the rapid development of 6G space–air–ground integrated communication networks, low Earth orbit (LEO) satellite constellations, leveraging their advantages of wide-area coverage and freedom from geographic constraints, have become key infrastructure for achieving global seamless connectivity. However, due to the relative motion among satellites in different orbits, inter-satellite links (ISLs) exhibit periodic dynamic on–off characteristics, among which a portion of the interruptions are predictable topological changes. Conventional Open Shortest Path First (OSPF) protocols compute shortest paths solely based on link distance, without considering link effective duration or bandwidth constraints, rendering them inadequate for adapting to the dynamic topological characteristics of satellite networks. Although fast reroute strategies can address sudden link failures, they consume substantial bandwidth resources and cannot provide protection for all nodes across the entire network. Therefore, how to proactively switch service paths before predictable link interruptions occur, so as to minimize service disruption and resource overhead, has become a core issue in ensuring service continuity in LEO satellite networks.

The near-Earth asteroid Apophis will make an extremely close flyby of Earth on April 13, 2029, at a distance of approximately 38,000 km—an event that occurs on average only once every 7,500 years, offering an unprecedented opportunity for planetary science and planetary defense. However, significant uncertainties remain regarding the physical parameters of Apophis, such as its shape, spin state, internal structure, and material composition, and predictions of tidal effects—including potential orbital changes, spin acceleration, surface material displacement, and dust ejection during the flyby—are highly dependent on model assumptions. Although multiple nations have planned exploration missions, how to coordinate global observational resources within the limited time window, optimize rapid-response mission architectures, and translate scientific understanding into effective planetary defense strategies remain critical challenges to be addressed.

With the long-term on-orbit operation of space stations, extravehicular status monitoring and surface inspection tasks have imposed higher demands on the motion planning capabilities of manipulators. Space redundant manipulators, when performing visual monitoring tasks, must simultaneously satisfy multiple complex constraints including kinematics, dynamics, obstacle avoidance, and end-effector line-of-sight, making this a typical nonlinear non-convex optimal control problem. Although sampling-based methods can rapidly generate collision-free paths, they struggle to guarantee dynamic feasibility in high-dimensional systems and often yield poor-quality paths. Optimization-based methods, while capable of directly generating high-quality trajectories, are highly sensitive to initial solutions and lack effective warm-start mechanisms. Therefore, how to merge the rapid search capability of sampling-based methods with the precise solving advantage of optimization-based methods to plan high-quality executable trajectories under complex constraints for space manipulators has become a key challenge in advancing the intelligent development of space robotics.