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 lunar surface becomes charged under the influence of solar wind and sunlight, and the potential differences on the surface form an electric field environment of various scales, which is the main mechanism for material transfer on the lunar surface. The electric field probe of Chang’E-7 will, for the first time, conduct in-situ electric field detection on the lunar surface. This paper introduces the design and results of the physical verification test of the probe in plasma environment based on the qualification model of the electric field probe. The basic principle of the electric field probe is the plasma electric probe principle. By clamping the probe current at a specific value, the potential of the probe can be determined according to the plasma V-I characteristic curve. The potential difference between different probes is the potential difference formed by the electric field environment on the plasma environment. Whether the probe can measure the plasma V-I characteristic curve is the key to the success of the probe design. With the help of a ground low-energy plasma simulation device, this test was carried out, and fixed current drive and scanning current measurement were conducted. The test results show that the electric field probe of Chang’E-7 can correctly reflect the plasma environment inside the simulation device and obtain a stable V-I characteristic curve. This proves that the electric field probe can achieve the function of obtaining the plasma potential by driving the probe current when working on the lunar surface, and the physical characteristics of the probe have been fully verified through the test.

Accurate observation of sea surface wind fields is essential for tropical cyclone forecasting and meteorological hazard mitigation. The HY-2 series microwave scatterometer continuously measures Ku-band ocean surface winds. However, its current wind speed retrieval algorithm struggles in high wind conditions and systematically underestimates speeds during extreme events such as typhoons. To address this bias, this study utilized the HY-2 wind speed data of nine tropical cyclones between 2021 and 2022 as the data source. The Stepped Frequency Microwave Radiometer (SFMR) wind speed measurements served as the ground truth. A modeling dataset was constructed by resampling the SFMR reference data to match the 25 km spatial resolution of the HY-2 scatterometer, followed by spatiotemporal matching within a two-hour time window. The matched dataset was then randomly divided into a training set and a testing set at a 7∶3 ratio. Subsequently, the Broad Learning System (BLS) was employed to conduct the regression analysis and develop a high-wind-speed correction model. BLS employs a shallow, flat architecture in which input features are expanded into “enhanced nodes”, avoiding the deep stacks typical of conventional neural networks. This structure reduces computational cost and accelerates convergence while maintaining predictive performance. Validation results demonstrate that the corrected HY-2 wind speeds achieved a Root Mean Square Error (RMSE) of 4.47 m·s^–1, representing a 35% improvement compared to the uncorrected data. For wind speeds exceeding 25 m·s^–1, the corrected RMSE reached 6.76 m·s^–1, marking significant enhancements over the original values of 13.27 m·s^–1. Additionally, a comparative analysis using Typhoon Chanthu (in 2021) as a case study revealed that the corrected HY-2C maximum wind speed increased from 22.09 m·s^–1 to 32.73 m·s^–1, closely matching wind fields retrieved by Synthetic Aperture Radar (SAR). Further validation through wind speed profile comparisons confirmed the effectiveness of the proposed model. These results demonstrate that our correction framework markedly improves extreme-wind retrieval accuracy, yielding bias-corrected HY-2 products that are more reliable for applications, such as storm surge simulation and typhoon track forecasting.

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.

Currently, there are relatively few spaceborne methods for detecting near-space atmospheric wind fields, and the Fabry-Perot Interferometer (FPI) is one of the more important and widely used detection techniques. To address the gap in China’s space-based FPI wind sensing capabilities, the National Space Science Center developed a spaceborne FPI wind interferometer. This paper mainly introduces this instrument’s optical design, structural design, thermal control design, optical simulation, and result analysis. First, the optical design is discussed based on the wideband detection requirements, and the imaging system’s image quality is evaluated. Then, based on optical simulation data, wind speed inversion and accuracy analysis of the spaceborne FPI instrument are conducted. The wind speed errors at the 557.7 nm and 762.0 nm bands are –1.722 m·s^–1 and –2.3672 m·s^–1, respectively, indicating that the spaceborne instrument design meets the wind measurement requirements. Then, the key points of the instrument’s structural design and the thermal control solution for the imaging part are presented, along with a translational filter switching device driven by a trapezoidal lead screw and a micro gear stepping motor or micro linear motor. The paper also explores the relationship between the temperature control accuracy of the instrument’s core components (the etalon) and wind measurement errors. A combined active and passive design is adopted to minimize the impact of temperature fluctuations on the results, which is verified with simulation results.

The Muon Telescope at Siziwang Station in Inner Mongolia is used to detect the secondary cosmic ray muons reaching the ground. The Muon Telescope began construction in November 2019, was completed in April 2023, and produced scientific data. The muon telescope consists of a scintillator observation stack, an electronics recorder, a monitoring platform, and a power supply. The scintillator observation stack is composed of 48 detector units, divided into upper and lower layers with 24 units in each layer, arranged in a 6×4 array. In each detector unit, the plastic scintillator has dimensions of 50 cm × 50 cm × 5 cm. The distance between the upper and lower layers is 89 cm. A 5cm-thick layer of lead bricks is laid between the upper and lower layers to filter out low-energy cosmic rays and low-energy particles in the surrounding environment. The detectors have a total area of 6 m². Muon signals generated by the 48 detector units of the Muon Telescope are processed through front-end circuits for amplification, discrimination, and shaping, then sent to the FPGA logic circuit for directional coincidence calculation. This produces raw muon counts in 15 directions. After undergoing barometric correction calculation, a dataset of corrected muon counts in 15 directions is formed, with a temporal resolution of 1 hour. The count rate in the vertical direction is the highest, with the 1-hour count rate being around 600000 counts and the relative statistical error approximately 0.13%. The muon data can sensitively reflect diurnal variations, long-term variations of cosmic rays, and short-term Forbush decrease perturbations induced by coronal mass ejections. Spanning from May 2023 to April 2025, this dataset covers the high-activity phase of the 25th solar activity cycle. It provides valuable data resources for research on solar eruptions, their interplanetary disturbance propagation, and geomagnetic response processes, while also supporting space weather early warning efforts.

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.

Equatorial Plasma Bubbles (EPBs) are large-scale depletion structures characterized by significantly reduced electron density, which frequently emerge in the low-latitude ionosphere during post-sunset hours. These dynamic plasma irregularities play a crucial role in space weather phenomena, as their evolution can induce severe amplitude and phase scintillations in radio signals, leading to disruptions in satellite communications, global navigation systems, and radar operations. Given their substantial impact on technological systems, accurate prediction of EPB evolution has become a critical challenge in both space physics research and operational space weather forecasting. To address this challenge, this study introduces a novel data-driven approach for EPB evolution prediction by leveraging the SimVP (Simpler yet Better Video Prediction) framework, an advanced deep learning architecture designed for spatiotemporal sequence forecasting. The proposed model learns the complex nonlinear dynamics of EPB structures from historical airglow image sequences, capturing both their morphological transformations and drift patterns. Through extensive experimentation, we systematically evaluate the influence of key parameters—including time resolution, input/output sequence length, and environmental noise—on prediction performance. Our findings demonstrate that an optimal configuration with a 3 min temporal resolution and a 6-frame input/output structure achieves superior predictive accuracy, as evidenced by high Structural Similarity (SSIM=0.989) and Peak Signal-to-Noise Ratio (PSNR=34.704) metrics. Further analysis reveals that the spatial complexity of EPB structures, such as bifurcation events and irregular boundary deformations, significantly affects prediction fidelity, whereas the impact of light pollution—a common issue in ground-based airglow observations—is comparatively minor. The model proposed in this paper demonstrates robust cross-station applicability. Beyond forecasting, the model also exhibits potential for reconstructing corrupted airglow data, offering a computational solution to enhance observational datasets affected by atmospheric or instrumental noise. This work not only establishes a robust, machine learning-based tool for EPB evolution analysis but also contributes to the broader development of Artificial Intelligence (AI) applications in space weather modeling and ionospheric research.

In order to solve the problem of frequency stability and transmission accuracy decrease caused by temperature fluctuation during on-orbit operation of an extravehicular load equipment, a highly efficient thermal control scheme is proposed which bases on passive thermal control as the main approach and active thermal control as the auxiliary method. The scheme employs a single-phase liquid cold plate as the main heat dissipation surface, and 10-unit multi-layer insulation components for comprehensive encapsulation. High thermal conductivity materials are utilized to achieve efficient heat conduction. Simultaneously, heating sheets and TEC semiconductor ceramic sheets are used for precise temperature control. Through finite element simulation analysis of temperature distribution under both high and low temperature conditions and optimization design, the temperature variation of key components is controlled within ±0.5 K. The ground constant-temperature thermal balance experiment and the in-orbit data results indicate that this scheme effectively suppresses the interference of temperature fluctuations on the load equipment, significantly improves the overall temperature uniformity of the equipment. It enables photodiodes and other sensitive devices to operate within the optimal temperature range (25-50°C). The temperature change rate of temperature-sensitive devices on the extravehicular load equipment is better than 0.1 K·min^–1, meeting the on-orbit stability requirements of high-precision time-frequency transmission systems. It can provide an important reference for the thermal control design of similar space load equipment.

To compare the propagation characteristics of Large-Scale Traveling Atmospheric Disturbances (LSTAD) and Large-Scale Traveling Ionospheric Disturbances (LSTID) that propagate in tandem in the thermosphere-ionosphere, this paper takes advantage of the CHAMP satellite’s ability to simultaneously observe atmospheric mass density and electron density across latitudes, studying a pair of LSTAD and LSTID events that propagated in tandem over long distances on 19 March 2002. Around 04:00－06:00 UT on 19 March, with a sudden and significant increase in the AE index, the CHAMP satellite observed the LSTAD and LSTID propagating in tandem in the Northern Hemisphere. Over the next approximately 6 h after 04:00 UT, these disturbances in atmospheric mass density and electron density propagated southward, crossed the equator, and entered the Southern Hemisphere, eventually dissipating there. On the other hand, the ground-based GNSS chain observations also confirmed the existence of the LSTID observed by the satellite. Through comparative analysis, it was found that due to the highly controlled movement of electrons by the Lorentz force while neutral particles are not constrained by it, the horizontal propagation speeds of LSTAD and LSTID along the meridian direction show significant differences. Therefore, at the same time and position on the same orbit, their phases are not the same and may even differ significantly.