Polar Mesospheric Clouds (PMCs), as ice crystal clouds formed in the middle and upper atmosphere (approximately 83 km high), have a seasonal onset that serves as an important parameter for studying the coupling processes between thermodynamics and dynamics in the polar mesosphere. Based on multi-source observational data from 1979 to 2023, the long-term evolution characteristics of the onset of PMCs in both hemispheres are systematically analyzed, and their correlations with the reversal time of stratospheric zonal mean wind and solar activity are examined. Results show that there are significant differences in the onset of PMCs between the two hemispheres: the interannual variation (with a standard deviation of 22 d) in the southern hemisphere is about twice that in the northern hemisphere (11 d), which may be related to differences in thermal and dynamic processes such as inter-hemispheric circulation modes and the intensity of gravity wave activity. In the southern hemisphere, the onset of PMCs season exhibits a very strong positive correlation with the reversal time of the stratospheric zonal mean wind, while in the northern hemisphere, although a negative correlation is observed, the approximately 60-day difference does not directly indicate a causal relationship between the two. The regulation of the onset by solar activity (Lyman-α radiation) also shows hemispheric asymmetry. In the northern hemisphere, there was a certain negative correlation with solar activity before 2011 that later weakened due to changes in the stratospheric dynamic background, whereas the southern hemisphere exhibited only a weak response. This indicates that both solar radiation effects and dynamic processes may jointly contribute. In addition, the discrepancies among multi-source data suggest that differences in detection systems and data types can introduce uncertainties in studies of the long-term variation characteristics of PMCs.

The Chang’E-7 lunar mission, scheduled for launch in 2026, has the primary scientific objective of detecting water-ice deposits within the Permanently Shadowed Regions (PSRs) at the lunar south pole. Understanding the distribution and concentration of lunar water ice is crucial for both fundamental science and future In-situ Resource Utilization (ISRU). In this study, we developed a high-fidelity model of the Chang’E-7 Lunar Neutron and Gamma-ray Spectrometer (LNGS) payload using the Geant4 toolkit (Version 10.07.p02) and established a quantitative inversion relationship between lunar surface water content and epithermal neutron count rates. The LNGS model, constructed by importing a detailed CAD model into Geant4, was rigorously validated against neutron beam calibration experiments conducted at the China Spallation Neutron Source (CSNS) Back-n facility. The results are as follows. The detector model shows excellent agreement with experimental data across the 0.4 eV to 1000 eV energy range, with a relative error of less than 6%, confirming the accuracy of the mass modeling and simulation setup. LNGS exhibits significant capability in discriminating soils with varying water content, as evidenced by both simulation and ground-based validation experiments using layered soil and water samples. Within the water-ice content range of 0.01% to 20%, simulations of Galactic Cosmic Ray (GCR) bombardment and subsequent neutron transport show that the epithermal neutron (400～700 keV) count rate decreases significantly with increasing hydrogen abundance. This relationship follows a modified Lawrence model with an exceptional coefficient of determination (R² = 0.9993). The slight parameter differences compared to the original Lawrence model are attributed to the different simulation tools, lunar regolith composition models, and distinct detector designs and energy responses between LNGS and the Lunar Prospector neutron spectrometer. This study provides a robust theoretical framework and a specific, validated inversion model for interpreting Chang’E-7 orbital neutron data, directly enabling the mapping of hydrogen concentrations from measured count rates. It establishes fundamental technical support for the development of in-situ resource utilization technologies on the Moon and paves the way for high-precision assessment of water ice resources in the lunar polar regions.

The responses of thermospheric winds at middle latitudes to the moderate geomagnetic storm of 18－19 March 2018, are examined using two ground-based Fabry-Perot Interferometer (FPI) observations from the Xinglong (XLON, 40.2°N, 117.6°E; magnetic latitude 35°N) and the Sutherland Astronomical Observatory (SAAO, 32.2°S, 20.48°E; magnetic latitude 40.7°S), combined with simulations from the Thermosphere-Ionosphere-Electrodynamics General Circulation Model (TIEGCM). The storm reached a maximum Kp index of 6, classifying it as a moderate storm. Ground-based FPI measurements provided high-resolution wind data at both stations, capturing the temporal evolution of zonal (east-west) and meridional (north-south) wind components. Meanwhile, the TIEGCM simulations offered a theoretical framework to interpret the observed disturbances and assess the model’s capability in reproducing storm-induced thermospheric dynamics. The results reveal that the response of thermospheric winds to the geomagnetic storm is more pronounced in the southern hemisphere than that in the northern hemisphere. Significant enhancements in equatorward and westward winds are observed at the SAAO station, with maximum meridional wind speeds reaching 128.4 m·s^–1 (equatorward) and maximum zonal wind speeds reaching –165.6 m·s^–1 (westward). Comparative analysis with TIEGCM simulations indicates that the model can reasonably reproduce the disturbance trends in observations, particularly in the variations of meridional winds at SAAO and zonal winds at XLON. The model successfully captured the transition from quiet-time wind patterns to storm-driven disturbances, including the shift toward westward and equatorward. However, certain quantitative discrepancies remain in the model’s predictions: the model underestimates the eastward zonal winds at SAAO and overestimates the equatorward meridional winds at XLON. Future studies could consider using multiple ground-based stations and a variety of observations, such as temperature, density, chemical composition for the study. Furthermore, investigating the role of seasonal and local time effects in modulating hemispheric asymmetries could provide deeper insights into thermospheric storm responses. Overall, this study contributes to a better understanding of the storm impacts on thermospheric winds and hemispheric differences, as well as their potential physical causes.

The bidirectional Medium-Energy Proton Detector (MEPD) onboard the lunar surface exploration subsystem of the Chang’E-7 lander represents the first-ever implementation of dual-direction medium-energy proton measurements on the Moon. It is capable of providing spectral data of upward- and downward-directed medium-energy protons in the range of 0.03～30 MeV, offering crucial support for modeling the lunar particle radiation environment and for radiation protection in future crewed lunar missions. The unique challenges of ground calibration for the MEPD were addressed in this study. An electron accelerator was employed to achieve proton-equivalent energy calibration, while the full energy range was validated by analyzing the deposited energy of penetrating high-energy protons. In addition, the suppression capability against electron contamination was quantitatively evaluated through a combined approach of accelerator experiments and numerical simulations. The results show that the detector’s energy calibration deviation is better than 3%, its electron-rejection efficiency exceeds 94% for energies at or below 1.4 MeV, and the average geometric factors of the upward-and downward-facing detectors are 0.053 cm^–2·sr^–1 and 0.3041 cm^–2·sr^–1, respectively. These calibration results provide a reliable foundation for in-orbit data inversion. Furthermore, the established calibration and simulation framework offers valuable reference for the future calibration of lunar and deep-space charged-particle detectors.

The Chang’E-7 mission carries a Lunar Penetrating Radar (LPR) for investigating lunar shallow subsurface structures. To ensure the validity of the acquired data and improve the accuracy and consistency of its interpretation, this study presents a comprehensive calibration framework suitable for space-grade penetrating radar systems, incorporating full-system gain calibration and system transfer function calibration, among others. Applying this methodology, the lunar radar system was rigorously calibrated, clarifying the optimal parameter configuration for its in-orbit operation. Under this parameter setting, all performance metrics of the radar system meet the design requirements: the system gains of the Low-Frequency (LF) and High-Frequency (HF) channels are 171.02 dB and 169.70 dB, respectively, fulfilling the detection depth requirements of 400 m and 40 m. The acquired Time-Varying Gain (TVG) curve and system transfer function, validated through simulated lunar regolith experiments, can provide effective calibration baselines for scientific data obtained during lunar surface exploration. This calibration scheme can serve as a technical reference for the calibration of radar systems in future deep-space exploration missions.

To address the critical need for efficient image storage and transmission in aerospace applications, this study presents a CCSDS 122.0-B-1-compliant compression core implemented on FPGA. The design incorporates innovative encoding control logic and optimized data organization through co-optimization of algorithmic features and hardware constraints. A segment-based architecture with 256-pixel blocks achieves superior compression efficiency among existing solutions, while effectively containing error propagation through segmented compression. The architecture further enables continuous quality adaptation and progressive image transmission. To resolve performance bottlenecks in scanning and encoding processes, fully parallelized scanning with adaptive parallel encoding was developed, and a 50% efficiency improvement was demonstrated in validation tests. Supporting images up to 4096×4096 pixel with 16-bit depth, the core delivers 90.64×10⁶ sample·s^–1 throughput, meeting operational requirements for diverse space missions.

The Moon provides a unique and advantageous platform for astronomical observations, particularly in the visible and ultraviolet wavelength ranges, owing to its extremely tenuous exosphere, the absence of atmospheric turbulence, and a stable surface environment. These characteristics enable long-duration, continuous observations free from atmospheric interference. As one of the international payloads aboard the Chang’E-7 mission, the International Lunar Observatory Camera (ILO-C) project aims to exploit these advantages to observe the Milky Way and the broader universe from a distinctive lunar perspective. In addition to its scientific objectives, the project offers unique value for astronomy education and serves as a technology demonstration for future lunar-based astronomical observatories. The ILO-C camera will be mounted on the +y panel of the Chang’E-7 lander and will experience multiple mission phases, including cruise, lunar orbit, and surface operations. This paper systematically investigates the scientific calibration workflow for the ILO-C across these mission phases, with particular emphasis on fundamental calibration, color calibration, and flux calibration, and further presents observational and calibration pathways for cross-validation. The quality of the project’s scientific output will largely depend on the optimized implementation of these calibration schemes. Ideally, in-flight activation and observations are expected to be achieved, allowing coverage of a broader sky area and enabling cross-comparison with observations from other space-based and ground-based astronomical facilities.

The Chang’E-7 orbiter is expected to carry the Wide-band InfraRed Imaging Spectrometer (WIRIS), which will acquire high spectral resolution images and thermal emission data of the lunar surface across a broad spectral range from the visible to longwave infrared (0.45～10 µm). These data will support scientific investigations into lunar surface mineral composition, thermal environment, and water/hydroxyl detection. Compared to previous lunar orbital hyperspectral instruments, WIRIS enhances quantitative retrieval capabilities for key spectral features such as the Christiansen Feature (CF) of silicate minerals and molecular water. Building upon the design of the Tianwen-1 Mars Mineralogical Spectrometer, WIRIS extends its spectral coverage into the mid- to long-wave infrared range (3.3～10 μm), and incorporates simultaneous temperature measurements to reduce thermal correction uncertainties in the 3 μm water/hydroxyl absorption region. This study addresses the quantitative calibration requirements of the newly extended spectral range by proposing spectral, radiometric, and geometric calibration methods tailored for the mid- to long-wave infrared bands. Based on calibration experiments, the sources of error and associated uncertainties are analyzed. The results provide essential methodological and technical support for accurate physical parameter retrieval and scientific application of WIRIS mid- to long-wave infrared data.

Single-Event Upsets (SEUs) in the space radiation environment pose a serious threat to the reliability of satellite-borne intelligent systems. Traditional fault-tolerance methods such as Triple Modular Redundancy (TMR) and periodic scrubbing face challenges including excessive resource overhead and high power consumption. This paper presents a lightweight fault-tolerance method based on Adaptive Boosting-based Fault-Tolerance Method (AB-FTM) to address SEU vulnerabilities in convolutional neural networks. The proposed approach constructs a heterogeneous ensemble architecture comprising three weak models (ResNet20, ResNet32, ResNet44) and integrated with a dynamic weight adjustment mechanism. By integrating a dynamic weight adjustment mechanism, the method not only significantly reduces the parameter scale (achieving an 18.2% reduction compared to ResNet110) but also enhances classification accuracy, robustness, and fault tolerance. Experimental validation on datasets including CIFAR-10, MNIST, EuroSAT, and Galaxy10 DECals demonstrates that when 0.032‰ of parameters are affected by single-event upsets, the proposed method improves classification accuracy by 53.25%, 63.49%, 57.67%, and 47.43% respectively compared to the TMR-based ResNet110, significantly outperforming traditional triple modular redundancy solutions. This approach provides a novel solution for future space science satellites employing satellite-borne intelligent systems, balancing reliability, lightweight design, and computational efficiency.

As a key indicator of global climate change and an essential freshwater resource, the accurate acquisition of multiple physical parameters of glaciers holds significant importance for global climate change research, ecological conservation, and water resource planning. In China, glaciers are predominantly mountain glaciers distributed in high-altitude regions. Constrained by harsh environments and complex terrain, traditional in-situ detection methods fail to achieve large-scale continuous monitoring of internal glacier parameters. Satellite-borne glacier remote sensing, meanwhile, faces limitations in resolution and interference from complex ground clutter in mountainous glacier regions, and thus has yet to be operationalized. Airborne radar, with its superior spatial resolution and flexible detection capabilities, has become a critical technical tool for glacier monitoring and research. However, airborne detection of mountain glaciers still confronts challenges posed by undulating ice surfaces and complex subglacial topography: scattering clutter from the uneven ice surface interferes with radar signal interpretation and precise inversion of key parameters, while the intricate subglacial structure and scattering losses caused by ice surface topography interact with dielectric losses within the ice, impeding accurate inversion of glacier dielectric constants. To address these challenges, this study integrates airborne ultra-wideband radar detection data from mountain glaciers with the Pseudo-Spectral Time Domain (PSTD) numerical simulation method. A coupled model of ice surface-subglacial dual interface topography and dielectric parameters is established. Through two-dimensional PSTD electromagnetic simulations, the interaction mechanism between topographic scattering and ice dielectric loss is elucidated. Furthermore, an inversion method for the imaginary part of the ice layer dielectric constant in measured regions is proposed based on dynamic range analysis. For the measured data from Laohugou Glacier No. 12, iterative optimization converges the estimated imaginary part value to 6.0×10^–4. The relative error between the estimated imaginary part and the theoretical mean is 21%. Cross-validation between simulation results and theoretical models demonstrates that this method effectively improves the inversion accuracy of glacier dielectric parameters in complex terrain by decoupling the synergistic interference between topographic relief and dielectric parameters, thereby offering a viable solution for studying internal dielectric properties of glaciers.