Advanced Materials Development for Extreme Environments at NASA Glenn Research Center
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Experimental efforts to understand chemical weathering of Venus surface rocks and minerals have been undertaken using a range of methods and conditions. A challenge with many of these experiments is measuring and maintaining the gas composition at the desired values because of the lack of instruments and sensors that can monitor gas compositions within vessels at Venus conditions. Thus, the oxygen fugacity, f O2 , and sulfur fugacity, f S2 , for Venus experiments are often not well known or constrained. Oxygen and sulfur fugacities dictate the stability of many minerals such as iron oxides and sulfides, and thus play a crucial role in weathering reactions.
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In recent years, unsupervised deep learning ap-proaches have received a significant attention to estimate depthand visual odometry (VO) from unlabelled monocular imagesequences. However, their performance is limited in challengingenvironments due to perceptual degradation, occlusions andrapid motions. Moreover, the existing unsupervised methodssuffer from the lack of scale-consistency constraints acrossframes, which causes that the VO estimators fail to providepersistent trajectories over long sequences. In this study, wepropose a unsupervised monocular deep VO framework thatpredicts 6 degrees-of-freedom pose camera motion and depthmap of the scene from unlabelled RGB image sequences.We provide detailed quantitative and qualitative evaluationsof the proposed framework on a) a challenging dataset col-lected during the DARPA Subterranean challenge1; and b)the benchmark KITTI and Cityscapes datasets. The proposedapproach outperforms both traditional and state-of-the-artunsupervised deep VO methods providing better results for bothpose estimation and depth recovery. The presented approach ispart of the solution used by the COSTAR team participatingat the DARPA Subterranean Challenge
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Evaluate risk factors, biomarkers, and countermeasures for adaptation and resilience in ICE environments. Identify how meaningful work influences the relationship between risk factors, the valence and social process domains, and operational and performance outcomes. Develop an operationally acceptable and valid measure of meaningful work in ICE. Examine meaningful work as a countermeasure.
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Effective thermal management is crucial for maintaining spacecraft and onboard systems at optimal temperatures, ensuring their longevity in space missions. Passive thermal management is particularly promising due to its lower power consumption compared to active systems. This study focuses on developing materials with low solar absorptance and large thermal emittance to facilitate self-cooling under the harsh thermal conditions of space. We employ electrospinning, a nano/micro-manufacturing technique, to create a lightweight, fibrous thermal control material from silica (SiO 2 ). Using scanning electron microscopy (SEM), we examine the nanoporous structure of the electrospun material. Its optical properties, including reflectance, transmittance, and absorptance across ultraviolet, visible, and infrared wavelengths, are assessed using spectrometers interfaced with integrating spheres. To test durability in space, we conduct high temperature endurance and ultraviolet resistance tests to observe changes in optical properties of the materials and evaluate their performance in low Earth orbit (LEO). We also compare the material's solar reflectance and thermal emittance to existing spacecraft materials. The findings suggest that electrospun silica nanofibers presents a new paradigm for passive thermal control in space applications.
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