CCSDS link layer protocol suite
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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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A standard data system interface for space data systems will have a major impact on the cost of building spacecraft and the ground systems built to support their operation. Properly designed, a standard interface will provide a 'plug-and-play' environment for the construction of spacecraft and ground systems. Using the standard, commercial vendors of spacecraft devices, spacecraft instrumentation and ground support systems could build and market products that would be installed without the traditional software development effort.
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Deep Space Telecommunications Requirements: 1) Automated file transfer across inter-planetary distances; 2) Limited communication periods; 3) Reliable transport; 4) Delay and Disruption Tolerant; and 5) Asymmetric Data Channels.
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The color of the ocean (apparent optical properties or AOPs) is determined by the spectral scattering and absorption of light by its dissolved and particulate constituents.The absorption and scattering properties of the water column are the so-called inherent optical properties.
In 2018, a working group sponsored by the NASA Plankton, Aerosol, Cloud, and ocean Ecosystem (PACE) project, in conjunction with the International Ocean Colour Coordinating Group (IOCCG), European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), and Japan Aerospace Exploration Agency (JAXA), was assembled with the aim to develop community consensus on multiple methods for measuring aquatic primary productivity used for satellite validation and model synthesis. A workshop to commence the working group efforts was held December 5–7, 2018, at the University Space Research Association headquarters in Columbia, MD, USA, bringing together 26 active researchers from 16 institutions. In this document, we discuss and develop the workshop findings as they pertain to primary productivity measurements, including the essential issues, nuances, definitions, scales, uncertainties, and ultimately best practices for data collection across multiple methodologies.
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Mass spectrometry imaging (MSI) represents an exceptional tool for exploring complex biological systems spatially at the molecular level. However, due to its multidimensional nature and large-scale data output, it presents considerable challenges when it comes to extracting meaningful biological insights. Recent advancements, such as the METASPACE platform, have enabled researchers to efficiently process, annotate, and interpret MSI datasets by leveraging machine learning and cloud-based infrastructure. In this tutorial, we present a detailed and user-friendly R-pipeline designed to help METASPACE users navigate untargeted metabolomic annotations and transform them into practical insights about their biological systems. By combining METASPACE annotations with rapid R-based screening, this workflow not only streamlined the analytical process but also enhanced the understanding of spatial molecular distribution, especially for complex systems. Here, this easy-to-follow approach has the potential for applications in diagnostics, drug discovery, environmental and ecological processes, and more. We envision this pipeline to be particularly useful for newcomers to the field of MSI and
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