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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 NTRSDate not supplied

Usage-Based Discovery of Earth Observations

Most providers of Earth Observation data enable search via dataset characteristics, (e.g., quantity being measured, instrument, location, and time). However, the Earth Science Information Partners Federation (ESIP) is attempting to improve dataset discovery by capturing information on how datasets are used. At a usage-based discovery hackfest at the July 2020 ESIP Summer Meeting, participants pooled their collective skills to implement a prototype based on the connections between datasets and data usage, which can reveal unanticipated patterns in dataset connectivity that are not apparent through traditional approaches. A survey of natural hazards websites, focusing on hydrology-related hazards such as floods and hydrology datasets such as rainfall, illustrated the difficulty of gleaning which dataset was actually used and how it was accessed. This difficulty highlights the need for greater transparency on dataset usage in website design. An application wireframe for a usage-based discovery database was conceptualized as a tool to help applications data specialists rapidly assemble a fit-for-purpose website as part of a disaster response, where customer feedback was integral to the manner in which the website would be developed and delivered. Capturing connections among datasets and instances of usage, either for research or applications, could help users identify datasets with a proven track record of utility or those that have been supplanted by more accurate and(or) precise representations. It may also help Earth Observation providers maximize returns on investments in data services. Social discovery, i.e, following discovery paths laid down by previous users of the data, could be a critical tool for improving the timeliness and applicability of dataset delivery at times and in circumstances where practitioners don’t have the capacity to evaluate the deluge of datasets that are returned by today’s search tools.

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NASA NTRSDate not supplied

Creating Benchmark Data for Artificial Intelligence and Machine Learning Space Biology Research

To identify an appropriate AI/ML approach for a specific problem, the best practice is to measure algorithm performance through the benchmarking process. A scientific benchmark consists of an AI-ready dataset and a reference implementation on a specific scientific question. The NASA Science Mission Directorate (SMD) has started the “Benchmark Initiative for AI/ML to create scientific benchmark datasets in three applications: 1) scientific benchmarking, which finds the best algorithm for a specific problem; 2) application benchmarking, which measures algorithm performance against a set of parameters; and 3) system benchmarking, which evaluates performance of hardware and software architecture. Currently, there are no standardized datasets available to benchmark AI/ML algorithms in the domain of space biology. In this work, we constructed two AI/ML-ready biological datasets from experiments in space-flown mice: cellular imaging and RNA-seq. First, radiation-exposed immune cells harbor DNA damage foci that can be fluorescently marked to visualize the amount of damage following exposure to ionizing radiation. However, such large datasets are difficult to analyze visually, due to imaging inconsistencies and human bias, and classical image processing approaches can fail on imaging artifacts. AI/ML are therefore exciting alternative, providing the speed of machines and the accuracy of humans. We have made this dataset available at https://registry.opendata.aws/bps_microscopy/. Second, high-throughput nucleic acid sequencing (DNA-seq, RNA-seq) has become widespread in biomedical research due to the growing availability and affordability of these assays. However, most sequencing datasets suffer from high dimensionality and low sample count. In this work, we used a generative adversarial network to synthesize a standardized, AI-ready, publicly available benchmark dataset for space biology RNA-seq data with sufficient space-flown and ground control mouse liver samples from NASA GeneLab. This dataset is available at https://registry.opendata.aws/bps_rnaseq/. These datasets are now fully open the Space Biology community to test their favorite AI/ML approaches.

James Casaletto↗
NASA NTRS2017

Security Vulnerability Profiles of NASA Mission Software: Empirical Analysis of Security Related Bug Reports

NASA develops, runs, and maintains software systems for which security is of vital importance. Therefore, it is becoming an imperative to develop secure systems and extend the current software assurance capabilities to cover information assurance and cybersecurity concerns of NASA missions. The results presented in this report are based on the information provided in the issue tracking systems of one ground mission and one flight mission. The extracted data were used to create three datasets: Ground mission IVV issues, Flight mission IVV issues, and Flight mission Developers issues. In each dataset, we identified the software bugs that are security related and classified them in specific security classes. This information was then used to create the security vulnerability profiles (i.e., to determine how, why, where, and when the security vulnerabilities were introduced) and explore the existence of common trends. The main findings of our work include:- Code related security issues dominated both the Ground and Flight mission IVV security issues, with 95 and 92, respectively. Therefore, enforcing secure coding practices and verification and validation focused on coding errors would be cost effective ways to improve mission's security. (Flight mission Developers issues dataset did not contain data in the Issue Category.)- In both the Ground and Flight mission IVV issues datasets, the majority of security issues (i.e., 91 and 85, respectively) were introduced in the Implementation phase. In most cases, the phase in which the issues were found was the same as the phase in which they were introduced. The most security related issues of the Flight mission Developers issues dataset were found during Code Implementation, Build Integration, and Build Verification; the data on the phase in which these issues were introduced were not available for this dataset.- The location of security related issues, as the location of software issues in general, followed the Pareto principle. Specifically, for all three datasets, from 86 to 88 the security related issues were located in two to four subsystems.- The severity levels of most security issues were moderate, in all three datasets.- Out of 21 primary security classes, five dominated: Exception Management, Memory Access, Other, Risky Values, and Unused Entities. Together, these classes contributed from around 80 to 90 of all security issues in each dataset. This again proves the Pareto principle of uneven distribution of security issues, in this case across CWE classes, and supports the fact that addressing these dominant security classes provides the most cost efficient way to improve missions' security. The findings presented in this report uncovered the security vulnerability profiles and identified the common trends and dominant classes of security issues, which in turn can be used to select the most efficient secure design and coding best practices compiled by the part of the SARP project team associated with the NASA's Johnson Space Center. In addition, these findings provide valuable input to the NASA IVV initiative aimed at identification of the two 25 CWEs of ground and flight missions.

vulnerability↗
NASA NTRS1995

The NAS Computational Aerosciences Archive

In order to further the state-of-the-art in computational aerosciences (CAS) technology, researchers must be able to gather and understand existing work in the field. One aspect of this information gathering is studying published work available in scientific journals and conference proceedings. However, current scientific publications are very limited in the type and amount of information that they can disseminate. Information is typically restricted to text, a few images, and a bibliography list. Additional information that might be useful to the researcher, such as additional visual results, referenced papers, and datasets, are not available. New forms of electronic publication, such as the World Wide Web (WWW), limit publication size only by available disk space and data transmission bandwidth, both of which are improving rapidly. The Numerical Aerodynamic Simulation (NAS) Systems Division at NASA Ames Research Center is in the process of creating an archive of CAS information on the WWW. This archive will be based on the large amount of information produced by researchers associated with the NAS facility. The archive will contain technical summaries and reports of research performed on NAS supercomputers, visual results (images, animations, visualization system scripts), datasets, and any other supporting meta-information. This information will be available via the WWW through the NAS homepage, located at http://www.nas.nasa.gov/, fully indexed for searching. The main components of the archive are technical summaries and reports, visual results, and datasets. Technical summaries are gathered every year by researchers who have been allotted resources on NAS supercomputers. These summaries, together with supporting visual results and references, are browsable by interested researchers. Referenced papers made available by researchers can be accessed through hypertext links. Technical reports are in-depth accounts of tools and applications research projects performed by NAS staff members and collaborators. Visual results, which may be available in the form of images, animations, and/or visualization scripts, are generated by researchers with respect to a certain research project, depicting dataset features that were determined important by the investigating researcher. For example, script files for visualization systems (e.g. FAST, PLOT3D, AVS) are provided to create visualizations on the user's local workstation to elucidate the key points of the numerical study. Users can then interact with the data starting where the investigator left off. Datasets are intended to give researchers an opportunity to understand previous work, 'mine' solutions for new information (for example, have you ever read a paper thinking "I wonder what the helicity density looks like?"), compare new techniques with older results, collaborate with remote colleagues, and perform validation. Supporting meta-information associated with the research projects is also important to provide additional context for research projects. This may include information such as the software used in the simulation (e.g. grid generators, flow solvers, visualization). In addition to serving the CAS research community, the information archive will also be helpful to students, visualization system developers and researchers, and management. Students (of any age) can use the data to study fluid dynamics, compare results from different flow solvers, learn about meshing techniques, etc., leading to better informed individuals. For these users it is particularly important that visualization be integrated into dataset archives. Visualization researchers can use dataset archives to test algorithms and techniques, leading to better visualization systems, Management can use the data to figure what is really going on behind the viewgraphs. All users will benefit from fast, easy, and convenient access to CFD datasets. The CAS information archive hopes to serve as a useful resource to those interested in computational sciences. At present, only information that may be distributed internationally is made available via the archive. Studies are underway to determine security requirements and solutions to make additional information available. By providing access to the archive via the WWW, the process of information gathering can be more productive and fruitful due to ease of access and ability to manage many different types of information. As the archive grows, additional resources from outside NAS will be added, providing a dynamic source of research results.

Miceli, Kristina D.↗