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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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Software and services that access Planetary Data System (PDS) PDS4 data products need to parse product labels to retrieve, interpret, and process the referenced digital objects. Under PDS4 a driving principle is that the product label provide all of the information necessary for these functions to be performed accurately. However, significantly more information is available in the PDS4 Information Model (IM)[1], the controlling document used to define, create, and syntactically and semantically verify the product labels. This additional information in the IM is made available for use, by both software and services, to configure, promote resiliency, and improve interoperability.
The purpose of the Committee on Earth Observing Satellites (CEOS) Working Group on Information Systems and Services (WGISS) Integrated Catalog (CWIC) is to provide a consistent search interface to help users find and access satellite data made available by CWIC data partners through the use of the WGISS-supported standards.
The purpose of the Committee on Earth Observing Satellites (CEOS) Working Group on Information Systems and Services (WGISS) Integrated Catalog (CWIC) is to provide a consistent search interface to help users find and access satellite data made available by CWIC data partners through the use of the WGISS-supported standards. This presentation will cover the current status of CWIC, CWIC Data partners, and the CWIC evolution/transition activities.
This presentation describes experiments conducted with single board computers to investigate methods for creating trust for enabling the development of cyber-resilient air transportation systems. Methods included secure communication to prevent unauthorized access to data, consistency of data obtained via sensors and by processing, and built-in safeguards to prevent mission failure. The motivation for this work are the following. The future air transportation system needs to ensure availability, integrity, confidentiality and safety of operations. Safety of vehicles and operations is paramount for successful integration of Urban Air Mobility (UAM), Unmanned Aerial Systems (UAS), supersonic aircraft and launch vehicles with conventional aviation operations in the National Airspace System. Security is becoming critical because the sensors, networks and computers are far more vulnerable to bad actors than their mechanical or human predecessors. The goal therefore is to design and develop cyber-resilient systems that continue to function even in degraded states. The main findings are (1) off-the-shelf hardware can support development of cyber-resilient onboard flight computers and (2) trust in system design and implementation can be accomplished by integrating layers in depth (detail) and in breadth (scope).
Increased space and ground sensing is enabling dramatic new measurements of a wide range of Earth Science and Applied Earth Science phenomena, including: volcanism, flooding, wildfires, weather, and many other phenomena. New Space ventures have produced significantly greater access to data and miniaturization of sensing has enabled cubesats and smallsats to deliver data of outstanding resolution. The advent of the internet of things has produced incredible amounts of relevant terrestrial data as well. Artificial Intelligence offers the potential to automate both data interpretation and resource allocation to best allocate sensing assets. We describe efforts to build and experiment with such “sensorweb” systems and offer some direction for the future sensorweb observation systems.
NASA is the U.S. space agency that provides end-to-end research about our home planet. NASA has more than two dozen satellites and instruments in orbit, including several on the International Space Station. NASA develops technologies that can help mitigate or adapt to climate change, like sustainable aviation technologies. NASA provides information that aides in disaster response and informs planning. Working to make climate change data more accessible for researchers, planners and individuals in vulnerable communities. NASA facilities are also impacted by climate change.
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Small RNA sequencing (small RNAseq) is a powerful tool for studying the regulation of gene expression in various organisms. Small RNAseq has been leveraged in space biology research to study how expression of small RNAs, e.g. micro RNAs (miRNAs), small interfering RNAs (siRNAs), and piwi-interacting RNAs (piRNAs), change upon exposure to the space environment. NASA GeneLab currently hosts small RNAseq raw data derived from space-relevant experiments on the Open Science Data Repository (OSDR). To maximize the accessibility of these data to the scientific community, in addition to hosting raw data, which is only interpretable by bioinformaticians, GeneLab plans to process all small RNAseq datasets and make those processed data available to the scientific community via the OSDR. In this study, we present the development of the GeneLab standardized pipeline for processing small RNAseq datasets. Using human, plant, and synthetic small RNAseq datasets, we interrogate various open-source software and publicly available databases to evaluate their accuracy and reproducibility in each step of the pipeline. For quality control and adapter detection and trimming, we evaluated TrimGalore!, FASTX, SeqKit, and DNApi methods to optimize alignment to reference genomes. We compared BWA, Bowtie, and Bowtie2 to determine the optimal alignment tool. For each alignment tool we also assessed various reference databases, including Ensembl reference genomes and different types of small RNA reference databases, including genome, hairpin, and miRNA references from the miRbase and MirGeneDB databases. To quantify the aligned data, we compared SAMtools, HTSeq, and RSEM for counting alignment events from each alignment tool used. Finally, we evaluated various tools, including DESeq2 and EdgeR, for data normalization and subsequent differential expression analysis. We will present the results from our comparative analyses for each pipeline step and propose a consensus pipeline for processing small RNAseq data derived from various organisms exposed to the space environment.
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Building sensors and controls systems, including building automation systems, consists of the sensor-based devices installed in buildings and the control and automation of those devices. Optimized sensors and controls systems could lead to 29% annual energy savings in commercial buildings and are integral to the growth of grid-interactive efficient buildings. Only 8% of small commercial buildings, however, have installed sensors and controls systems. This is largely due to cost barriers. This work seeks to increase the transparency of system costs and identify specific barriers and drivers for increased adoption. To gather industry insights, the team reached out to building owners, vendors, and contractors and conducted 20 interviews with the goal of collecting cost data and market characteristics regarding building sensors and controls. The qualitative interview data was analyzed using grounded theory to identify overarching concepts, such as barriers, drivers, and future directions on the field. From this analysis, primary barriers were found to be complexity, a lack of knowledge, and money. Primary drivers were found to be the knowledge of data, savings, and remote access. The qualitative cost data was collected in the form of invoices during the interviews. The cost values were used to develop a percentage-based cost stack which identifies the average fraction of the total cost attributed to each category (hardware, software, labor, fees, and taxes). This greater understanding of costs and barriers associated with commercial building sensors and controls systems lays the groundwork for future steps in increasing system adoption, reducing energy consumption, and market transformation.
The unprecedented scientific achievements in combating the COVID-19 pandemic reflect a global response informed by unprecedented access to data. We now have the ability to rapidly generate a diversity of information on an emerging pathogen and, by using high-performance computing and a systems biology approach, we can mine this wealth of information to understand the complexities of viral pathogenesis and contagion like never before. These efforts will aid in the development of vaccines, antiviral medications, and inform policymakers and clinicians. Here we detail computational protocols developed as SARS-CoV-2 began to spread across the globe. They include pathogen detection, comparative structural proteomics, evolutionary adaptation analysis via network and artificial intelligence methodologies, and multiomic integration. These protocols constitute a core framework on which to build a systems-level infrastructure that can be quickly brought to bear on future pathogens before they evolve into pandemic proportions.
Systems, apparatuses, and methods for implementing masked fault detection for reliable low voltage cache operation are disclosed. A processor includes a cache that can operate at a relatively low voltage level to conserve power. However, at low voltage levels, the cache is more likely to suffer from bit errors. To mitigate the bit errors occurring in cache lines at low voltage levels, the cache employs a strategy to uncover masked faults during runtime accesses to data by actual software applications. For example, on the first read of a given cache line, the data of the given cache line is inverted and written back to the same data array entry. Also, the error correction bits are regenerated for the inverted data. On a second read of the given cache line, if the fault population of the given cache line changes, then the given cache line's error protection level is updated.
A system for generating a machine learning system to generate guidance information based on locations of objects is provided. The system accesses training data that includes training time-of-arrival (“TOA”) information of looks and guidance information for each look. The guidance information is based on a training collection of object locations. The TOA of a look represents, for each object location of a training collection of object locations, times between signals transmitted by transmitters and return signals received by receivers. The return signals represent signals reflected from an object at the object location. The system trains a machine learning system using the training data wherein the machine learning system inputs TOA information and outputs guidance information.
dCache (https://dcache.org) is a highly scalable storage system providing location-independent access to data. The data are stored across multiple data servers as complete files presented to the end-user via a single-rooted namespace. From its inception, dCache has been designed as a caching disk buffer to a tertiary tape storage system with the assumption that the latter has virtually unlimited capacity. dCache can also be configured as a disk-only storage system with no tape backend. Owing to the idea that a tape resource is infinite, or purely physically limited by budget considerations, the system has never provided for any restrictions on how much data can be stored on tape. Likewise, in the disk-only configuration, the capacity of the system is only limited by the aggregate disk capacity of the data servers. In a multi-user environment, however, this has become problematic. This presentation will describe the design and implementation of a user- and group-based quota system, that allows to manage tape and disk space allocations, as part of dCache namespace.
The cyber risk landscape for BESS and IBR can be broken up by threats, vulnerabilities, and consequences for these systems. This presentation walks through the cyber risk landscape for BESS through the lens of consequence-informed awareness and mitigation for each risk factor. Threats with varying capabilities have been demonstrated in real-world events. Though threat actors can rarely be directly influenced by organizations, exposure of systems to adversaries can be limited (a known issue with IBR systems) to reduce likelihood of adversaries accessing systems with disruptive consequences. Common trends in disclosed IBR vulnerabilities include weak password generation or managements for various devices or services and web portal vulnerabilities that provide unauthorized access to data or capabilities or elevated user privileges. Understanding these common vulnerabilities and considering the consequences if these types of vulnerabilities were to occur can help mitigate risk. Consequences range from loss-of-view events that have no reliability impact to asset damage or grid stability impacts. Five case studies are briefly shared to highlight trends in real-world events affecting IBR.
A scenario of relational, hierarchial, and network data bases is presented and a distributed access view integrated data base system (DAVID) is described for uniformly accessing data bases which are heterogeneous and physically distributed. The DAVID system is based on data base logic so that the relational approach is generalized to the heterogeneous approach. The global data manager is explained as are global data manipulation languages which can operate on all the data bases and can query the data dictionary and the data directory.