With the continuous advancement of deep-space exploration missions, the potential threat of microorganisms in the closed environment of space stations to astronauts' health has attracted increasing attention. Fusobacterium nucleatum, as an opportunistic pathogen colonizing the oral cavity and mucosal sites, is closely associated with various diseases including periodontal disease and colorectal cancer. Under extreme space conditions such as microgravity and radiation, the growth, virulence, drug resistance, and metabolic characteristics of microorganisms may undergo significant alterations, thereby increasing the risk of infection for astronauts. However, current research on the effects of microgravity on anaerobic bacteria remains extremely limited, and the phenotypic changes and molecular regulatory mechanisms of F. nucleatum under simulated microgravity conditions have not yet been elucidated, making it difficult to provide a scientific basis for the prevention and control of anaerobic bacterial contamination and infection risk assessment in space stations.

With the exponential increase in the number of objects in low Earth orbit (LEO), collision risks have risen correspondingly, making high-precision orbit prediction a core requirement for ensuring satellite safety and space environment management. Two-Line Elements (TLEs) are the only publicly available orbital data source and are widely used in conjunction with the Simplified General Perturbations 4 (SGP4) model for LEO object orbit prediction. However, LEO objects are significantly affected by complex time-varying perturbations such as atmospheric drag, causing model prediction errors to grow rapidly over time—short-term errors can reach hundreds to thousands of meters, while medium- and long-term prediction errors may exceed 10 km. Although traditional dynamical models can achieve relatively high accuracy through numerical integration, they incur substantial computational costs and suffer from limited atmospheric density modeling accuracy (only about 5%–15%). Analytical methods, while computationally efficient, struggle to handle complex perturbations. In recent years, bidirectional long short-term memory (BiLSTM) networks have demonstrated strong capabilities in time-series prediction; nevertheless, an effective approach for systematically modeling and compensating for TLE-based prediction errors using solely publicly available TLE data has remained lacking.

With the growing demand for in-situ exploration of complex extreme terrains such as polar craters in lunar exploration missions, quadruped robots have demonstrated unique advantages owing to their efficient jumping and locomotion capabilities in weak gravitational fields. However, lunar gravity is only one-sixth of Earth's, which substantially prolongs the flight phase of underactuated jumping. Incorrect takeoff actions on rugged terrain generate rotational torques that cause the robot to continuously rotate in mid-air, with the angular momentum accumulating over time and leading to catastrophic consequences, thus imposing significant uncertainty on landing control. Conventional optimal control methods rely on offline preplanned trajectories and are unable to cope with the variability of unknown rugged terrains; existing deep reinforcement learning models lack effective utilization of temporal causal relationships, relying solely on current states for decision-making and struggling to predict the long-term consequences of actions. Therefore, how to leverage historical sequence information to model temporal dependencies between actions, enabling the robot to possess forward-looking prediction and adaptive adjustment capabilities during the jumping process, has become a key technical challenge for jumping control on complex lunar terrains.

With the continuous growth of global communication demands, fiber-optic networks are undergoing rapid expansion, and the most widely used silica-based optical fibers are now approaching their intrinsic loss limits. Heavy-metal fluoride ZBLAN fibers theoretically exhibit losses in the mid-infrared band that can be approximately 20 times lower than those of silica fibers. However, this theoretical advantage has long been constrained by the inevitable crystallization problem during ground-based fabrication—ZBLAN glass possesses a critical cooling rate far higher than that of silica, rendering it highly susceptible to crystallization during the fiber drawing process on Earth, which leads to a significant increase in scattering loss. Although microgravity environments may reduce crystallization rates by suppressing buoyancy-driven convection, and theoretical models support this mechanism, the lack of space-qualified automated fiber drawing equipment has previously prevented the acquisition of fiber samples of sufficient length for systematic characterization. Therefore, validating the practical feasibility and performance enhancement effects of ZBLAN fiber drawing under microgravity conditions has become a key scientific issue in overcoming the bottleneck to realizing the ultra-low-loss potential of this material.

With the increasing threat posed by space debris to the safe operation of on-orbit spacecraft, the Whipple shield, as a critical means of defending spacecraft against hypervelocity impacts, has evolved from conventional single-layer configurations to advanced architectures employing high-performance materials such as fiber-reinforced composites. Advanced shields such as carbon fiber–reinforced composite laminates involve additional energy dissipation through failure modes including fiber fracture, delamination, and melting, resulting in shielding mechanisms that differ significantly from those of traditional Whipple shields. However, the currently predominant ballistic limit equations lack accuracy and generalizability for different advanced configurations, making them unsuitable for direct comparison of shielding performance. Hypervelocity impact experiments are costly and cannot be performed for every configuration, while existing numerical studies have mostly focused on the macroscopic morphology of debris clouds, with insufficient quantitative analysis of the hazardous fragments within them. Therefore, how to establish a method capable of quantitatively evaluating and comparing the shielding performance of different advanced Whipple shields has become a critical technical issue urgently to be addressed in the field of spacecraft space debris protection.

SKKU InfoLab develops a 4D fMRI AI model that analyzes brain activity across space and time while providing interpretable information for trustworthy computer-aided diagnosis