The lunar polar regions are key areas for future exploration due to the long-term continuous illumination and persistently shadowed regions that can cold trap abundant water and other volatiles. However, the complex terrain, dynamic lighting, and solar wind-induced electric-field environment present multiple challenges for polar investigation and sampling missions. China’s Chang’E-7 (CE-7) will explore the Moon’s south polar region in 2026. One of the scientific goals is to drill samples in a wide area with a rover for in situ analysis. This study analyzes the engineering constraints of the polar illumination condition, slopes, and electric field for landing and sampling-site selection. Then, we create a 3D model of CE-7’s lunar rover in three operating environments by employing the Spacecraft Plasma Interaction Software, with the rover sampling (i) on a flat surface, (ii) in a shadow, and (iii) near a meter-scale crater under different solar altitude angles. The results show that the rover can be charged to different potentials under the combined effects of solar wind incident angles and surrounding terrains. We find that a favorable traversing and/or sampling site of the rover for future polar exploration is in the upwind direction of a bulge (positively elevated terrains, such as the lander or boulders) or crater, which will cause a minimum charging effect on the rover. Our results have important implications for minimizing the risk of charging effects and guiding the lunar polar region exploration.

A space floating potential detector (FPD) is an instrument being developed to study the charging of spacecraft structures. It measures the floating potential that arises as the spacecraft interacts with the low Earth orbit (LEO) plasma as well as the Earth's magnetic field. The FPD belongs to the active potential control system and consists of three functional modules. Leakage current-eliminating and zero-clearing techniques are used in the detector design to ensure the measurement of the floating potential over the long term within the range from -150 to 0 V and at a voltage resolution of less than 1 V. In order to investigate the performance of the FPD, a typical LEO space plasma environment is simulated in the laboratory. The calibrations of the FPD show that the output voltage is basically linear with the input bias voltage, and the maximum nonlinear error is less than 0.9%.

Characterizing the microscopic morphology of cosmic dust particles is essential for understanding planetary formation and evolution; however, the shallow depth of field inherent in miniature imaging systems precludes single-shot all-in-focus acquisition. Multi-focus image fusion (MFIF) computationally extends the depth of field, yet existing methods degrade markedly under spatial misalignment and produce hard-boundary stitching artifacts at focus transition regions. We present FIIF-Net, an end-to-end framework designed for misaligned multi-focus microscopic dust images. In the alignment stage, a residual feature downsampling pyramid equipped with a learnable repair factor recovers fine morphological textures lost during conventional downsampling of low-contrast microscopic features, and a dynamic lookup operator adaptively modulates the correlation search radius to accommodate vibration-induced, spatially non-uniform displacements. In the fusion stage, a dual-path interactive fusion strategy incorporating STA bidirectionally couples the image-content path with the focus-guidance path, enabling content features and continuous focus cues to mutually modulate each other and thereby replacing binary decision-map stitching with physically consistent soft transitions. Zero-shot super-resolution is further integrated to compensate for the edge attenuation inherent in defocus-aware blending. A dedicated multi-focus misaligned cosmic dust particle dataset, combining real focal stacks with physics-based rendered data, is constructed for training and evaluation. Experiments on both synthetic and real data demonstrate that FIIF-Net surpasses state-of-the-art MFIF methods quantitatively and visually, particularly in preserving edge details within focal transition regions.