Recently, learning-based approaches have achieved impressive results in the field of low-light image denoising. Some state of the art approaches employ a rich physical model to generate realistic training data. However, the performance of these approaches ultimately depends on the realism of the physical model, and many works only concentrate on everyday photography. In this work we present a denoising approach for extremely low-light images of permanently shadowed regions (PSRs) on the lunar surface, taken by the Narrow Angle Camera on board the Lunar Reconnaissance Orbiter satellite. Our approach extends existing learning-based approaches by combining a physical noise model of the camera with real noise samples and training image scene selection based on 3D ray tracing to generate realistic training data. We also condition our denoising model on the camera's environmental metadata at the time of image capture (such as the camera's temperature and age), showing that this improves performance. Our quantitative and qualitative results show that our method strongly outperforms the existing calibration routine for the camera and other baselines. Our results could significantly impact lunar science and exploration, for example by aiding the identification of surface water-ice and reducing uncertainty in rover and human traverse planning into PSRs.

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The early conceptual design (CD) phase of space access vehicles (SAVs) is the most abstract, innovative and technologically challenging phase of the entire aerospace design life cycle. Although the design decision-making during this phase influences around 80 percent of the overall life cycle cost, it is the most abstract and thus least understood phase of the entire design life cycle. The history of SAV design provides numerous examples of project failures that could have been avoided if the decision-maker had had the capability to forecast the potential risks and threats correctly ahead of time during the conceptual design phase. The present study addresses this crucial phase and demonstrates a best-practice synthesis methodology prototype to advance the current state of the art of CD as applied to SAV design. Developed by the Aerospace Vehicle Design (AVD) Laboratory at the University of Texas at Arlington (UTA), the Aerospace Vehicle Design Synthesis process and software (AVDS) is a prototype solution for a flight vehicle configuration–flexible (generic) design synthesis capability that can be applied to the primary categories of SAVs. This study focusses on introducing AVDS, followed by the demonstration and verification of the system’s capability through a sizing case study based on the data-rich Boeing X-20 Dyna-Soar spaceplane.

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In India, waste material disposal is a problem because lack of land and disposal techniques. Rajasthan is a big supplier of kota stone due to this large amount of kota stone slurry powder is produced. Kota stone slurry powder is a good inert filler material and a good stabilizing material, which improves the pore structure and enhance the resistance of the concrete to various harmful actions and mortars with pozzolanic and non-pozzolanic material. Kota stone slurry powder enhances durability properties. In this study we discuss the effect of adding kota stone slurry powder as a stabilizing agent and filler material into expansive soil. In this investigation kota stone slurry powder is mixed with expensive soil in different proportions and the effect of changes in parameters of expansive soil are recorded. In the present study, we discuss the effect of adding the kota stone slurry powder as a stabilizing agent and filler material into expansive soil. In this investigation kota stone slurry powder is mixed with expansive soil in different proportions and the effect of changes in parameters of expansive soil are recorded in the form of tables and graphs.