The permanently shadowed regions (PSRs) of the Moon are located at the Moon's polar regions that are permanently in shadow due to their inability to receive direct sunlight. Images of these areas are usually dark and significantly affected by noise, obscuring the lunar terrain information. Although image denoising has made considerable progress, there is still limited study on images denoising of lunar PSRs. The main challenge lies in the fact that images of PSRs are characterized by low contrast, complex noise type, and uneven illumination. The existing deep learning-based methods exhibit poor physical interpretability and cannot effectively remove complex noise. Therefore, this study introduces a novel denoising method by using combination of physical noise models and deep Learning. Specially, the physical noise model is used to simulate the noise of lunar PSRs according to the imaging principles of the lunar reconnaissance orbiter camera narrow angle camera. The improved deep learning model, which incorporates full-scale skip connections and Transformer is used to denoise the images. The proposed method is tested in 297 PRSs images with latitudes below -70 degrees and compared with state-of-the-art methods. Experimental results demonstrate that the proposed method outperforms existing methods in restoring terrain details and provides better quantitative and visual outcomes. This approach has the potential to improve the clarity of lunar PSR images and support future lunar exploration.

Obtaining high-visibility images of the lunar polar permanently shadowed region (PSR) is quite important for internal landforms and material existence exploration. However, PSR images usually have poor quality due to a lack of sufficient illumination. Existing researches, that attempt to address this problem, face challenges caused by relying on virtual assumptions, manual processing, and paired data. To solve these problems, we aim to avoid using paired datasets and directly optimize PSR images, and accordingly propose a zero-shot parameter learning model (ZSPL-PSR) for PSR image enhancement. Our ZSPL-PSR, which enhances PSR images by estimating parameters to adjust image properties, consists of a parameter learning network and a parameter weight learning structure. Particularly, first, a parameter learning network that integrates robust information is constructed to separately estimate the midtone brightness parameters, shadow brightness parameters, and contrast parameters. Where these parameters are beneficial for iteratively improve the overall brightness, shadow brightness, and contrast of the image. Second, a parameter weight learning structure is exploited to coordinate the priority of different parameter maps. In addition, to highlight the terrain details in the enhanced PSR image, we use USM sharpening for postprocessing. The experimental results display the fully interpretable enhanced PSR maps of the lunar north and south poles and their sharpened versions, showcasing rich landforms in PSR. To validate the model performance, a benchmark PSR testing set has been constructed, and extensive comparisons conducted on it demonstrated that ZSPL-PSR exceeds other zero-shot learning methods significantly in image quality. Our code is available at https://github.com/dl-zfq/ZSPL-PSR.

BACKGROUND: Maintaining psychologically adaptive relationships among team members operating in an isolated, confined, and extreme (ice) environment for an extended period continues to be a challenge, with relevance for long-duration missions to the Moon and beyond. METHODS: two male architects were studied who lived and worked over a 60-d period in a polar ice environment in a lunar analog habitat they designed and helped construct. Psychological measures were completed at different points of the mission, including a post-mission debriefing interview. RESULTS: team members were highly different from each other on a number of personality traits, personal values, and stress and coping factors. Marked differences were noted on NeO-Pi-3 agreeableness and extraversion personality traits, and Portrait Values Questionnaire (PVQ) stimulation, Power, and achievement values. team effectiveness Questionnaire (teQ) findings showed consistency between team members with high ratings on the Passion and commitment and Purpose and Goals scales, and low ratings on the Roles scale. the leveling influence of decision authority and its deleterious effect on interpersonal interactions and work performance was evident. the interior design with attention to materials that made it more earth-like and the circadian lighting system were associated with ease of work performance and promotion of relaxation and privacy. DISCUSSION: the study findings demonstrated the impact of incompatibility in personality traits and values on team performance, challenges regarding decision authority in a long-term dyadic relationship, and highlighted the human factors components of the habitat that facilitated effective individual and team functioning. IP: 14.98.160.66 On: Thu, 03 Feb 2022 15:55:45 Copyright: Aerospace Medical Association

Permanently shadowed regions (PSRs) at the lunar poles pique scientific interest on account of their cold trapping of volatiles that is highly relevant in the current scope of lunar exploration. Interiors of PSRs are largely unknown due to the challenging illumination conditions. In this letter, we describe a method for synthesizing images at PSRs based on the knowledge of incident solar illumination geometry and local topography that reflects light into PSRs.

Direct sampling has never been performed in the permanently shadowed regions (PSRs) of lunar poles up to now. In the Chinese Chang'e-7 (CE-7) mission, a mini-flyer will fly from the lander in a solar illuminated region at the lunar south polar region to the nearby PSRs to collect samples for analysis. In this letter, four potential craters of the lunar south pole, including Shackleton, Shoemaker, de Gerlache, and Slater are discussed for this proposal. Design principles of the landing site, sampling site, and flight route are presented. The local surface slopes are calculated using a digital elevation model (DEM) to select a flat area as a potential landing site, which should allow ample time for solar illumination to support the rover from the lander and allow the flyer to reach the neighboring PSRs. Mini-RF data are applied for further validation of the flat landing and sampling sites, particularly for some rocky rough surfaces that are not identified in DEM and optical images of PSRs. The craters de Gerlache and Slater are found to be suitable for further analysis when high-resolution synthetic aperture radar (SAR) data are acquired by the new polarimetric SAR carried by CE-7.