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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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NASA NTRS2016

Share Data with OPeNDAP Hyrax: New Features and Improvements

During the upcoming Summer 2016 meeting of the ESIP Federation (July 19-22), OpenDAP will hold a Developers and Users Workshop. While a broad set of topics will be covered, a key focus is capitalizing on recent EOSDIS-sponsored advances in Hyrax, OPeNDAPs own software for server-side realization of the DAP2 and DAP4 protocols. These Hyrax advances are as important to data users as to data providers, and the workshop will include hands-on experiences of value to both. Specifically, a balanced set of presentations and hands-on tutorials will address advances in 1. server installation, 2. server configuration, 3. Hyrax aggregation capabilities, 4. support for data-access from clients that are HTTP-based, JSON-based or OGC-compliant (especially WCS and WMS), 5. support for DAP4,6.use and extension of server-side computational capabilities, and7.several performance-affecting matters. Topics 2 through 7 will be relevant to data consumers, data providers and notably, due to the open-source nature of all OPeNDAP software to developers wishing to extend Hyrax, to build compatible clients and servers, and/or to employ Hyrax as middleware that enables interoperability across a variety of end-user and source-data contexts. A session for contributed talks will elaborate the topics listed above and embrace additional ones.

OPeNDAP↗
NASA NTRS1994

CLIPS, AppleEvents, and AppleScript: Integrating CLIPS with commercial software

Many of today's intelligent systems are comprised of several modules, perhaps written in different tools and languages, that together help solve the user's problem. These systems often employ a knowledge-based component that is not accessed directly by the user, but instead operates 'in the background' offering assistance to the user as necessary. In these types of modular systems, an efficient, flexible, and eady-to-use mechanism for sharing data between programs is crucial. To help permit transparent integration of CLIPS with other Macintosh applications, the AI Research Branch at NASA Ames Research Center has extended CLIPS to allow it to communicate transparently with other applications through two popular data-sharing mechanisms provided by the Macintosh operating system: Apple Events (a 'high-level' event mechanism for program-to-program communication), and AppleScript, a recently-released scripting language for the Macintosh. This capability permits other applications (running on either the same or a remote machine) to send a command to CLIPS, which then responds as if the command were typed into the CLIPS dialog window. Any result returned by the command is then automatically returned to the program that sent it. Likewise, CLIPS can send several types of Apple Events directly to other local or remote applications. This CLIPS system has been successfully integrated with a variety of commercial applications, including data collection programs, electronics forms packages, DBMS's, and email programs. These mechanisms can permit transparent user access to the knowledge base from within a commercial application, and allow a single copy of the knowledge base to service multiple users in a networked environment.

Compton, Michael M.↗
NASA NTRS2003

Jell-Molds and Cookie-cutters: Shrinkwrap Isn't Just for Leftovers Anymore

So what is Shrinkwrap all about? For those of you who may not know about it, Shrinkwrap is a type of data structure that can manifest itself as a feature or model. It is cleverly covered up, almost hidden, and doesn't get the press or widespread use of a solid or surface. The shrinkwrap feature is located under the data sharing submenu of the feature menu. The shrinkwrap feature, as described by PTC, is a collection of surfaces and datum features of a model that represents the exterior of the model . The advantages and applications of the shrinkwrap feature are in the creation of minimal memory guzzling representations of assemblies. These can be used to represent subassemblies in parent assemblies, and can handle control of dependency issues, geometry represented, and additional references through the use of the shrinkwrap feature options. The shrinkwrap model is an option available under the save as umbrella. Its function, as described by PTC, is to share data with internal and external design groups and improve performance in large assembly design . Some of the benefits of the shrinkwrap model include being able to represent complex assemblies with a single, lightweight part that protects design intent and parametric data, and the ability to improve performance of large assembly modeling in the area of less load time. The proper-scale models can be saved as IGES, STEP, and VRML (for fly-throughs).

Randazzo, John↗
NASA NTRS2021

Perspectives on flood forecast-based early action and opportunities for Earth observations

This paper seeks to identify opportunities to integrate Earth observations (EO) into flood forecast-based early action and propose future directions for research and collaboration between EO and humanitarian communities. Forecast-based early action (FbA) is an approach to shift disaster response toward anticipation to mitigate impacts to at-risk communities; however, timely and accurate information is needed in the development of data-based triggers and thresholds for action. Therefore, this paper considers the readiness of a wide range of EO for flood monitoring and forecasting in the design, operations, and evaluation phases of FbA. The most significant opportunities for EO to inform FbA efforts lie in the design and evaluation phases, as EO can aid in the development of impact-based triggers. The EO products most readily applicable include precipitation, streamflow estimates, and exposure mapping, and those requiring the greatest amount of further research include vulnerability and impact assessments. This paper identifies collaboration opportunities for the EO and humanitarian communities to create tailored products, such as overlays combining flood extents with exposure maps. Such collaboration opportunities can be fostered by open data sharing, data verification efforts, and incentives for supporting boundary organizations capable of enabling the use of EO for FbA.

disasters↗
NASA NTRS1995

Visualization of International Solar-Terrestrial Physics Program (ISTP) data

The International Solar-Terrestrial Physics Program (ISTP) is a multispacecraft, multinational program whose objective is to promote further understanding of the Earth's complex plasma environment. Extensive data sharing and data analysis will be needed to ensure the success of the overall ISTP program. For this reason, there has been a special emphasis on data standards throughout ISTP. One of the key tools will be the common data format (CDF), developed, maintained, and evolved at the National Space Science Data Center (NSSDC), with the set of ISTP implementation guidelines specially designed for space physics data sets by the Space Physics Data Facility (associated with the NSSDC). The ISTP guidelines were developed to facilitate searching, plotting, merging, and subsetting of data sets. We focus here on the plotting application. A prototype software package was developed to plot key parameter (KP) data from the ISTP program at the Science Planning and Operations Facility (SPOF). The ISTP Key Parameter Visualization Tool is based on the Interactive Data Language (IDL) and is keyed to the ISTP guidelines, reading data stored in CDF. With the combination of CDF, the ISTP guidelines, and the visualization software, we can look forward to easier and more effective data sharing and use among ISTP scientists.

Kessel, Ramona L.↗
NASA NTRSDate not supplied

Standard measures in Analogs of Spaceflight

Standard measures are defined as research measures (physiological, performance, or otherwise) that quantify the subject’s health and performance before, during, and after a spaceflight or a spaceflight analog mission. The Standard Measures Cross-Cutting Project (SMCCP) collects and analyzes standard measures that support assessment and quantification of health risks and efficacy of countermeasures and technologies required to prevent or mitigate adverse outcomes from exposure to the spaceflight environment. SMCCP uses the following spaceflight analogs: - Human Exploration Research Analog (HERA) in Houston, TX: 4-person isolation/confinement; nominal mission duration of 45 days; run by the Human Research Program (HRP). SMCCP uses weekly questionnaires to gather data on the crewmembers’ emotional and mental state and collects blood samples biweekly to measure crewmembers’ serum cortisol levels at the start, middle, and end of the mission. - Institute for Biomedical Problems (IBMP) Ground-Based Experimental Complex, called NEK, in Moscow, Russia: 6-person isolation/confinement; duration of 8 months; run by IBMP. Standard measures data collected during NEK are the same as for HERA above. - :envihab in Cologne, Germany: 12-person bed rest facility; closed environment; 30 to 60-day campaigns; run by the German Aerospace Center (DLR). For bedrest studies, SMCCP uses the international standard measures as documented in the International Academy of Astronautics publication titled Guidelines for Standardization of Bed Rest Studies in the Spaceflight Context (June 2014). These international standard measures evaluate potential changes in bone, muscle, cardiovascular, and visual systems, as well as changes in psychology and nutrition. The international standard measures complement those collected during studies by individual investigators and allow comparison of responses across different bed rest and countermeasures durations. In this presentation, we will review available SMCCP data collected during the recent isolation studies in HERA (C6) and NEK (SIRIUS-21), and during the SANS (SM1, SM2) bed rest studies in :envihab. These standard measures datasets are made available to concurrent investigators in the given mission or campaign via data sharing agreements and/or data sharing plans and to other investigators that have been granted retrospective access to the data (researchers with IRB approved protocols, risk custodians) to advance understanding of physiological and behavioral risks associated with these spaceflight analogs.

Standard Measures↗
NASA NTRSDate not supplied

US-EUSST SSA Data Exchange for Improved Orbital Safety

Data sharing and exchange among different Space Situational Awareness (SSA) data collectors/providers is frequently discussed as a mechanism to improve the accuracy and precision of orbital safety products. Indeed, at the conceptual level this claim is quite sensible: objects with more tracking, and thus presumably better distribution of that tracking about their orbit, should produce updates with smaller epoch errors and more realistic covariances at epoch, which should then translate into better performance in prediction and thus improved close approach calculations. However, this thesis has not been verified with any large study involving two different data providers, through which the amount of realized improvement can be quantified and, perhaps more importantly, the logistics of effecting observation data sharing in a regularized way can be established and circulated to assist others. To create rubrics for helpful data sharing for conjunction assessment applications, and to collect empirical data on the improvements achieved through sharing of measurement data, the US Office of Space Commerce and the European Union Space Surveillance and Tracking (EUSST) Support Framework have embarked on a joint experiment to collect and exchange satellite metric observations on selected satellites of interest over an experimental period. Each entity will perform orbit determination with their own observations, the other entity’s observations, and with both observation data sets combined. The product results will be compared with each other, showing differences attributable both to the three different data groups and to the two entities’ different approaches to orbit determination. The exemplar satellites include both objects that have external precision reference ephemerides and those that do not, allowing the experiment to exercise both precision-comparison and assimilative-comparison techniques. The present reporting will give preliminary results and a set of lessons learned for data sharing; a second presentation later in 2023 will provide a full accounting of the experiment’s results. The hope is that, through this work, a pathway can be established for the expeditious and fruitful sharing among collection entities, both commercial and governmental.

space situational awareness↗
NASA NTRS1994

FAST User Guide

The Flow Analysis Software Toolkit, FAST, is a software environment for visualizing data. FAST is a collection of separate programs (modules) that run simultaneously and allow the user to examine the results of numerical and experimental simulations. The user can load data files, perform calculations on the data, visualize the results of these calculations, construct scenes of 3D graphical objects, and plot, animate and record the scenes. Computational Fluid Dynamics (CFD) visualization is the primary intended use of FAST, but FAST can also assist in the analysis of other types of data. FAST combines the capabilities of such programs as PLOT3D, RIP, SURF, and GAS into one environment with modules that share data. Sharing data between modules eliminates the drudgery of transferring data between programs. All the modules in the FAST environment have a consistent, highly interactive graphical user interface. Most commands are entered by pointing and'clicking. The modular construction of FAST makes it flexible and extensible. The environment can be custom configured and new modules can be developed and added as needed. The following modules have been developed for FAST: VIEWER, FILE IO, CALCULATOR, SURFER, TOPOLOGY, PLOTTER, TITLER, TRACER, ARCGRAPH, GQ, SURFERU, SHOTET, and ISOLEVU. A utility is also included to make the inclusion of user defined modules in the FAST environment easy. The VIEWER module is the central control for the FAST environment. From VIEWER, the user can-change object attributes, interactively position objects in three-dimensional space, define and save scenes, create animations, spawn new FAST modules, add additional view windows, and save and execute command scripts. The FAST User Guide uses text and FAST MAPS (graphical representations of the entire user interface) to guide the user through the use of FAST. Chapters include: Maps, Overview, Tips, Getting Started Tutorial, a separate chapter for each module, file formats, and system administration.

Walatka, Pamela P.↗
NASA NTRS1997

Visualization of IACG data

The common data format (CDF), adopted by the international solar terrestrial physics (ISTP) program and the Interagency Consultative Group (IACG), is considered. Extensive data sharing and data analysis are needed to ensure the success of the ISTP/IACG program. The CDF user guidelines and tools for creation, visualization and manipulation are underlined. Computer software applications based on CDF data sets and designed using ISTP/IACG guidelines are shown.

Kessel, Ramona L.↗
NASA NTRS1998

Open Architecture Data System for NASA Langley Combined Loads Test System

The Combined Loads Test System (COLTS) is a new structures test complex that is being developed at NASA Langley Research Center (LaRC) to test large curved panels and cylindrical shell structures. These structural components are representative of aircraft fuselage sections of subsonic and supersonic transport aircraft and cryogenic tank structures of reusable launch vehicles. Test structures are subjected to combined loading conditions that simulate realistic flight load conditions. The facility consists of two pressure-box test machines and one combined loads test machine. Each test machine possesses a unique set of requirements or research data acquisition and real-time data display. Given the complex nature of the mechanical and thermal loads to be applied to the various research test articles, each data system has been designed with connectivity attributes that support both data acquisition and data management functions. This paper addresses the research driven data acquisition requirements for each test machine and demonstrates how an open architecture data system design not only meets those needs but provides robust data sharing between data systems including the various control systems which apply spectra of mechanical and thermal loading profiles.

Lightfoot, Michael C.↗
NASA NTRSDate not supplied

Upper E Traffic Management

This is a slide set as part of a meeting series with members of a working group aimed at the development of a concept that addresses needs and gaps in the management of high altitude airspace operations. This concept leverages elements developed through the UAS Traffic Management project with respect to a cooperative, service-based approach that provides services and capabilities in areas (e.g., Upper E airspace) that currently receive no or limited service from Air Traffic Control. This concept is meant to provide a safe, fair, and scalable approach to management of Upper E operations that reduces the burden on ATC while providing the flexibility and access desired by current and new users of the airspace. This set of slides includes partner feedback on the following concept elements: Upper E Traffic Management (ETM) planning processes, transit phase of operation, data sharing, time horizons for data sharing, and cooperative integration of military operations. An additional segment of the slides consists of an overview of the UAS Traffic Management (UTM) concept and its relationship to ETM.

Upper E↗
NASA NTRSDate not supplied

NASA GeneLab: Open Science for Life in Space

NASA’s GeneLab helps scientists understand how the fundamental building blocks of life – DNA, RNA, proteins, and metabolites – change from exposure to the space environment including microgravity and cosmic radiation exposure. GeneLab does so by providing fully coordinated epigenomics, genomics, transcriptomics, proteomics, and metabolomics data (collectively known as omics data) alongside essential metadata describing each spaceflight and space-relevant experiment. The open-access GeneLab repository currently consists of over 300 omics datasets generated by biological experiments, involving various model organisms, that are relevant to spaceflight. In order to maximize the intelligibility of these data, particularly for users with limited bioinformatics knowledge, GeneLab has started processing and analyzing these datasets to generate differential gene expression data and identify biological and physiological pathways that are dysregulated as a result of spaceflight. To aide GeneLab’s efforts to harmonize and democratize space-relevant omics data, over 130 scientists have joined one of four GeneLab Analysis Working Groups (Animal AWG, Plant AWG, Microbe AWG, Multi-Omics AWG) and together helped develop and adopted standard data analysis workflows for all data types available in GeneLab. Currently, the GeneLab Data System includes a data repository with federated search capability, an online controlled-access toolshed powered by "Galaxy" for users to process data with vetted standard workflows, a workspace for data sharing, a data submission portal, and the ability to browse and visualize transcriptomics processed data. The user interface was designed to be accessible to a broad variety of users, including high school and college students who can use it to learn about omics data analysis and space biology. The visualization portal enhances GeneLab’s ability to democratize omics data by removing the need for bioinformatics expertise to interpret transcriptomics data hosted on GeneLab. This presentation will provide an over-view of NASA’s GeneLab including how to navigate the GeneLab Data System and will conclude by providing resources for opportunities to work with GeneLab and NASA at large.

Amanda M Saravia-Butler↗
NASA NTRSDate not supplied

Developing Open-Source Training Materials for AI/ML and Space Biological Sciences Using NASA Cloud-Based Data

Artificial Intelligence (AI) and Machine Learning (ML) has gained significant traction in the biological and biomedical research fields in the last two decades, in part thanks to an increasing culture of open data sharing and reuse. Due to its capability for identifying complex relationships and patterns, AI/ML methodology is particularly well suited to recognize and predict biological patterns from high-dimensional next-generation sequencing data (e.g. whole genome sequencing, transcriptomic sequencing), as well as from biological or medical imaging data (e.g. microscopy, computed tomography, ultrasound, magnetic resonance imaging, radiography). These methodologies hold particular promise for space biosciences research and automated space health monitoring systems. However, there are many key considerations for properly training, validating, and testing a machine learning model in biological research or clinical application. Even with the positive culture of Open Science and data sharing, inexperienced researchers working quickly without proper checks can produce models that perform poorly outside of the immediate training dataset. Lessons learned from biological AI/ML research indicate that Open Science principles such as data sharing and open-source code must go hand-in-hand with publicly available, high-quality training curricula in best practices, with modules centered on real-life scientific use cases and data so future AI/ML practitioners gain experience on real problems. Here we present the development of open-source training materials for AI/ML and space biosciences, as part of the NASA Transform to Open Science Training (TOPST) initiative. We develop 4 independent training programs, focused on the following topics: 1) Fundamentals of Machine Learning and Space Biosciences Domain, 2) Open Science, Artificial Intelligence, and Ethical Best Practices for Data Sharing and Analysis, 3) Using AI/ML Classification to Identify Gene Networks Affected By Space Exposure in Mouse Liver, and 4) Using Neural Networks to Find DNA Damage Patterns in Immune Cells after Radiation. All programs leverage cloud-based NASA biological datasets. The curriculum we present will enable worldwide access to training in AI/ML and scientific analysis.

James Andrew Casaletto↗