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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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We have extended a previously developed high-level data model, which combines numerical quantities and meta-data into a unified hybrid model, to distributed data. An elegant query language based on SQL is extended further to allow queries against such a distributed hybrid data base. The extension is realized by allowing statements in a non-SQL programming language to be embedded in SQL view definitions.
Most of the summer was spent developing software that would permit existing test report forms to be printed over the web on a printer that is supported by Adobe Acrobat Reader. The data is stored in a DBMS (Data Base Management System). The client asks for the information from the database using an HTML (Hyper Text Markup Language) form in a web browser. JavaScript is used with the forms to assist the user and verify the integrity of the entered data. Queries to a database are made in SQL (Sequential Query Language), a widely supported standard for making queries to databases. Java servlets, programs written in the Java programming language running under the control of network server software, interrogate the database and complete a PDF form template kept in a file. The completed report is sent to the browser requesting the report. Some errors are sent to the browser in an HTML web page, others are reported to the server. Access to the databases was restricted since the data are being transported to new DBMS software that will run on new hardware. However, the SQL queries were made to Microsoft Access, a DBMS that is available on most PCs (Personal Computers). Access does support the SQL commands that were used, and a database was created with Access that contained typical data for the report forms.
Networks of video cameras, meteorological sensors, and ancillary electronic equipment are under development in collaboration among NASA Ames Research Center, the Federal Aviation Administration (FAA), and the National Oceanic Atmospheric Administration (NOAA). These networks are to be established at and near airports to provide real-time information on local weather conditions that affect aircraft approaches and landings. The prototype network is an airport-approach-zone camera system (AAZCS), which has been deployed at San Francisco International Airport (SFO) and San Carlos Airport (SQL). The AAZCS includes remotely controlled color video cameras located on top of SFO and SQL air-traffic control towers. The cameras are controlled by the NOAA Center Weather Service Unit located at the Oakland Air Route Traffic Control Center and are accessible via a secure Web site. The AAZCS cameras can be zoomed and can be panned and tilted to cover a field of view 220 wide. The NOAA observer can see the sky condition as it is changing, thereby making possible a real-time evaluation of the conditions along the approach zones of SFO and SQL. The next-generation network, denoted a remote tower sensor system (RTSS), will soon be deployed at the Half Moon Bay Airport and a version of it will eventually be deployed at Los Angeles International Airport. In addition to remote control of video cameras via secure Web links, the RTSS offers realtime weather observations, remote sensing, portability, and a capability for deployment at remote and uninhabited sites. The RTSS can be used at airports that lack control towers, as well as at major airport hubs, to provide synthetic augmentation of vision for both local and remote operations under what would otherwise be conditions of low or even zero visibility.
The suite of exercise hardware aboard the International Space Station (ISS) generates an immense amount of data. The data collected from the treadmill, cycle ergometer, and resistance strength training hardware are basic exercise parameters (time, heart rate, speed, load, etc.). The raw data are post processed in the laboratory and more detailed parameters are calculated from each exercise data file. Updates have recently been made to how this valuable data are stored, adding an additional level of data security, increasing data accessibility, and resulting in overall increased efficiency of medical report delivery. Questions regarding exercise performance or how exercise may influence other variables of crew health frequently arise within the crew health care community. Inquiries over the health of the exercise hardware often need quick analysis and response to ensure the exercise system is operable on a continuous basis. Consolidating all of the exercise system data in a single repository enables a quick response to both the medical and engineering communities. A SQL server database is currently in use, and provides a secure location for all of the exercise data starting at ISS Expedition 1 - current day. The database has been structured to update derived metrics automatically, making analysis and reporting available within minutes of dropping the inflight data it into the database. Commercial tools were evaluated to help aggregate and visualize data from the SQL database. The Tableau software provides manageable interface, which has improved the laboratory's output time of crew reports by 67%. Expansion of the SQL database to be inclusive of additional medical requirement metrics, addition of 'app-like' tools for mobile visualization, and collaborative use (e.g. operational support teams, research groups, and International Partners) of the data system is currently being explored.
The primary role of the Technical Integration Office is to provide technical solutions and services to different branches at KSC (Kennedy Space Center) and NASA program customers. The Technical Integration Office helps support KSC's operational needs by providing services such as digital connectivity, data center services, modelling and simulation tools, and communication video services. To learn the necessary technology and processes for my internship, I am working on two projects: learning C# (C Sharp programming language) with SQL and developing requirements for a PX (Communication and Public Engagement) inventory management system. To learn how to efficiently program with C#, my mentor assigned me to complete a sports informatics application that would let users discover facts and rules about various sports. The sports informatics application comes with search capabilities, report generating features, rule lists that users can modify, and diagrams for various sport strategies. To further build upon this project, I also developed a sport simulation game with the application. Once I begin more SQL-based projects, I will have the opportunity to learn how to manage databases and link SQL servers with C# programs. To develop requirements for the inventory management system, I have met with PX representatives and toured their storage facilities to see how they organize and store their items and equipment. I will also be meeting with representatives from the budget office to find out what information must be in a system budget report. The main components the system must have are customer request management, a search feature for items and equipment, report generation capabilities, and automated system warnings when item quantities reach or go below administrator-specified threshold levels. I have drafted questions and shall statements that will ultimately become part of the inventory management system requirements document.
The suite of exercise hardware aboard the International Space Station (ISS) generates an immense amount of data. The data collected, treadmill, cycle ergometer, and resistance strength training hardware, are basic exercise parameters (time, heart rate, speed, load, etc.). The raw data are processed in the laboratory and more detailed parameters are calculated from each exercise data file. Updates recently have been made to how these valuable data are stored, adding an additional level of security, increasing accessibility, and resulting in overall increased efficiency of medical report delivery. Questions regarding exercise performance or how exercise may influence other variables of crew health frequently arise within the crew health care community. Inquiries regarding the health of the exercise hardware often need quick analysis and response to ensure the exercise system is operable on a continuous basis. Consolidating all of the exercise system data in a single repository enables a quick response to both the medical and engineering communities. A SQL server database is currently in use, and provides a secure location for all of the exercise data starting at ISS Expedition 1 to current date. The database has been structured to update derived metrics automatically, making analysis and reporting available within minutes of dropping the in-flight data into the database. Commercial tools were evaluated to help aggregate and visualize data from the SQL database. The Tableau software provides manageable interface, which has improved the laboratory’s output time of crew reports by 67%. Expansion of the SQL database, to be inclusive of additional medical requirement metrics, addition of ‘app-like’ tools for mobile visualization, and collaborative use (e.g., operational support teams, research groups, and International Partners) of the data system, is currently being explored.
The deliberate construction of isoreticular eea-metal–organic frameworks (MOFs) (Cu-eea-1, Cu-eea-2 and Cu-eea-3) and rtl-MOFs (Co-rtl-1 and Co-rtl-2) has been accomplished based on the ligand-to-axial pillaring of supermolecular building layers. The use of different metal ions resulted in two types of supermolecular building layers (SBLs): Kagome (kgm) and square lattices (sql) which further interconnect to form anticipated 3D-MOFs. The isoreticular expansion of (3,6)-connected Cu-MOFs has been achieved with desired eea-topology based on kgm building layers. In addition, two (3,6)-connected Co-rtl-MOFs were also successfully constructed based on sql building layers. The Cu-eea-MOFs were shown to act as hydrogen storage materials with appreciable amount of hydrogen uptake abilities. Moreover Cu-eea-MOFs have also exhibited remarkable CO2 capture ability at ambient condition compared to nitrogen and methane, due to the presence of amide functionalities.
Ordered interlayer stacking is intrinsic in two-dimensional covalent organic frameworks (2D COFs) and has strong implications on COF's optoelectronic properties. Reversible interlayer sliding, corresponding to shearing of 2D layers along their basal plane, is an appealing dynamic control of both structures and properties, yet it remains unexplored in the 2D COF field. In this paper, we demonstrate that the reversible interlayer sliding can be realized in an imine-linked tetrathiafulvalene (TTF)-based COF TTF-DMTA. The solvent treatment induces crystalline phase changes between the proposed staircase-like sql net structure and a slightly slipped eclipsed sql net structure. The solvation-induced crystallinity changes correlate well with reversible spectroscopic and electrical conductivity changes as demonstrated in oriented COF thin films. In contrast, no reversible switching is observed in a related TTF-TA COF, which differs from TTF-DMTA in terms of the absence of methoxy groups on the phenylene linkers. This work represents the first 2D COF example of which eclipsed and staircase-like aggregated states are interchangeably accessed via interlayer sliding, an uncharted structural feature that may enable applications such as chemiresistive sensors.
Abstract Spin systems are an attractive candidate for quantum-enhanced metrology. Here we develop a variational method to generate metrological states in small dipolar-interacting spin ensembles with limited qubit control. For both regular and disordered spatial spin configurations the generated states enable sensing beyond the standard quantum limit (SQL) and, for small spin numbers, approach the Heisenberg limit (HL). Depending on the circuit depth and the level of readout noise, the resulting states resemble Greenberger-Horne-Zeilinger (GHZ) states or Spin Squeezed States (SSS). Sensing beyond the SQL holds in the presence of finite spin polarization and a non-Markovian noise environment. The developed black-box optimization techniques for small spin numbers ( N ≤ 10) are directly applicable to diamond-based nanoscale field sensing, where the sensor size limits N and conventional squeezing approaches fail.
A columnar data representation is known to be an efficient way for data storage, specifically in cases when the analysis is often done based only on a small fragment of the available data structures. A data representation like Apache Parquet is a step forward from a columnar representation, which splits data horizontally to allow for easy parallelization of data analysis. Based on the general idea of columnar data storage, working on the [LDRD Project], we have developed a striped data representation, which, we believe, is better suited to the needs of High Energy Physics data analysis. A traditional columnar approach allows for efficient data analysis of complex structures. While keeping all the benefits of columnar data representations, the striped mechanism goes further by enabling easy parallelization of computations without requiring special hardware. We will present an implementation and some performance characteristics of such a data representation mechanism using a distributed no-SQL database or a local file system, unified under the same API and data representation model. The representation is efficient and at the same time simple so that it allows for a common data model and APIs for wide range of underlying storage mechanisms such as distributed no-SQL databases and local file systems. Striped storage adopts Numpy arrays as its basic data representation format, which makes it easy and efficient to use in Python applications. The Striped Data Server is a web service, which allows to hide the server implementation details from the end user, easily exposes data to WAN users, and allows to utilize well known and developed data caching solutions to further increase data access efficiency. We are considering the Striped Data Server as the core of an enterprise scale data analysis platform for High Energy Physics and similar areas of data processing. We have been testing this architecture with a 2TB dataset from a CMS dark matter search and plan to expand it to multiple 100 TB or even PB scale. We will present the striped format, Striped Data Server architecture and performance test results.
SAND2024-02520O Tensor SQL Database performs certain relational database management system/structured query language (RDBMS/SQL) queries faster than is possible using existing state-of-the-art databases. Tensor SQL is optimized for sparse linear algebra problems and other whole-table queries. Currently, the database is being used only for research and program development. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.
This software is an AI-powered data exploration with natural language. datasight connects an AI agent to your database and provides a web UI where you can ask questions in natural language. The agent writes SQL, runs queries, and generates interactive Plotly visualizations. Supports DuckDB, PostgreSQL, SQLite, and Flight SQL databases. Also queries local CSV and Parquet files directly — no database setup required. Supports Anthropic Claude (default), GitHub Models (open source), and Ollama (local) as LLM backends.
This paper discusses a physical meaning of the standard quantum limit (SQL) in quantum decision theory. It will be shown that a necessary condition for overcoming the SQL is quantum interference.
JSDTImport is a computer program for translating native Shuttle Data Tape (SDT) files from American Standard Code for Information Interchange (ASCII) format into databases in other formats. JSDTImport solves the problem of organizing the SDT content, affording flexibility to enable users to choose how to store the information in a database to better support client and server applications. JSDTImport can be dynamically configured by use of a simple Extensible Markup Language (XML) file. JSDTImport uses this XML file to define how each record and field will be parsed, its layout and definition, and how the resulting database will be structured. JSDTImport also includes a client application programming interface (API) layer that provides abstraction for the data-querying process. The API enables a user to specify the search criteria to apply in gathering all the data relevant to a query. The API can be used to organize the SDT content and translate into a native XML database. The XML format is structured into efficient sections, enabling excellent query performance by use of the XPath query language. Optionally, the content can be translated into a Structured Query Language (SQL) database for fast, reliable SQL queries on standard database server computers.
The Mars Relay Operational Service (MaROS) comprises a number of tools to coordinate, plan, and visualize various aspects of the Mars Relay network. These levels include a Web-based user interface, a back-end "ReSTlet" built in Java, and databases that store the data as it is received from the network. As part of MaROS, the innovators have developed and implemented a feature set that operates on several levels of the software architecture. This new feature is an advanced querying capability through either the Web-based user interface, or through a back-end REST interface to access all of the data gathered from the network. This software is not meant to replace the REST interface, but to augment and expand the range of available data. The current REST interface provides specific data that is used by the MaROS Web application to display and visualize the information; however, the returned information from the REST interface has typically been pre-processed to return only a subset of the entire information within the repository, particularly only the information that is of interest to the GUI (graphical user interface). The new, advanced query and data mining capabilities allow users to retrieve the raw data and/or to perform their own data processing. The query language used to access the repository is a restricted subset of the structured query language (SQL) that can be built safely from the Web user interface, or entered as freeform SQL by a user. The results are returned in a CSV (Comma Separated Values) format for easy exporting to third party tools and applications that can be used for data mining or user-defined visualization and interpretation. This is the first time that a service is capable of providing access to all cross-project relay data from a single Web resource. Because MaROS contains the data for a variety of missions from the Mars network, which span both NASA and ESA, the software also establishes an access control list (ACL) on each data record in the database repository to enforce user access permissions through a multilayered approach.
Late in fiscal year 2011, the Crew Earth Observations (CEO) team was tasked to upgrade and replace its mission planning and mission operations software systems, which were developed in the Space Shuttle era of the 1980s and 1990s. The impetuses for this change were the planned transition of all workstations to the Windows 7 64-bit operating system and the desire for more efficient and effective use of Satellite Tool Kit (STK) software required for reliable International Space Station (ISS) Earth location tracking. An additional requirement of this new system was the use of the same SQL database of CEO science sites from the SMMS, which was also being developed. STK Integrated Message Production List Editor (SIMPLE) is the essential, all-in-one tool now used by CEO staff to perform daily ISS mission planning to meet its requirement to acquire astronaut photography of specific sites on Earth. The sites are part of a managed, long-term database that has been defined and developed for scientific, educational, and public interest. SIMPLE's end product is a set of basic time and location data computed for an operator-selected set of targets that the ISS crew will be asked to photograph (photography is typically planned 12 to 36 hours out). The CEO operator uses SIMPLE to (a) specify a payload operations planning period; (b) acquire and validate the best available ephemeris data (vectors) for the ISS during the planning period; (c) ingest and display mission-specific site information from the CEO database; (d) identify and display potential current dynamic event targets as map features; (e) compute and display time and location information for each target; (f) screen and select targets based on known crew availability constraints, obliquity constraints, and real-time evaluated constraints to target visibility due to illumination (sun elevation) and atmospheric conditions (weather); and finally (g) incorporate basic, computed time and location information for each selected target into the daily CEO Target List product (message) for submission to ISS payload planning and integration teams for their review and approval prior to uplink. SIMPLE requires and uses the following resources: an ISS mission planning period Greenwich Mean Time start date/time and end date/time), the best available ISS mission ephemeris data (vectors) for that planning period, the STK software package configured for the ISS, and an ISS mission-specific subset of the CEO sites database. The primary advantages realized by the development and implementation of SIMPLE into the CEO payload operations support activity are a smooth transition to the Windows 7 operating system upon scheduled workstation refresh; streamlining of the input and verification of the current ISS ephemeris (vector data); seamless incorporation of selected contents of the SQL database of science sites; the ability to tag and display potential dynamic event opportunities on orbit track maps; simplification of the display and selection of encountered sites based on crew availability, illumination, obliquity, and weather constraints; the incorporation of high-quality mapping of the Earth with various satellite-based datasets for use in describing targets; and the ability to encapsulate and export the essential selected target elements in XML format for use by onboard Earth-location systems, such as Worldmap. SIMPLE is a carefully designed and crafted in-house software package that includes detailed help files for the user and meticulous internal documentation for future modifications. It was delivered in February 2012 for test and evaluation. Following acceptance, it was implemented for CEO mission operations support in May 2012.
The unprecedented growth of climate data creates new opportunities for climate studies, and yet big climate data pose a grand challenge to climatologists to efficiently manage and analyze big data. The complexity of climate data content and analytical algorithms increases the difficulty of implementing algorithms on high performance computing systems. This paper proposes an in-memory, distributed computing framework, ClimateSpark, to facilitate complex big data analytics and time-consuming computational tasks. Chunking data structure improves parallel I/O efficiency, while a spatiotemporal index is built for the chunks to avoid unnecessary data reading and preprocessing. An integrated, multi-dimensional, array-based data model (ClimateRDD) and ETL operations are developed to address big climate data variety by integrating the processing components of the climate data lifecycle. ClimateSpark utilizes Spark SQL and Apache Zeppelin to develop a web portal to facilitate the interaction among climatologists, climate data, analytic operations and computing resources (e.g., using SQL query and Scala/Python notebook). Experimental results show that ClimateSpark conducts different spatiotemporal data queries/analytics with high efficiency and data locality. ClimateSpark is easily adaptable to other big multiple- dimensional, array-based datasets in various geoscience domains.
The Data Mining & Knowledge Management team at Kennedy Space Center is providing data management services to the Frangible Joint Empirical Test (FJET) project at Langley Research Center (LARC). FJET is a project under the NASA Engineering and Safety Center (NESC). The purpose of FJET is to conduct an assessment of mild detonating fuse (MDF) frangible joints (FJs) for human spacecraft separation tasks in support of the NASA Commercial Crew Program. The Data Mining & Knowledge Management team has been tasked with creating and managing a database for the efficient storage and retrieval of FJET test data. This paper details the Extract, Transform, and Load (ETL) process as it is related to gathering FJET test data into a Microsoft SQL relational database, and making that data available to the data users. Lessons learned, procedures implemented, and programming code samples are discussed to help detail the learning experienced as the Data Mining & Knowledge Management team adapted to changing requirements and new technology while maintaining flexibility of design in various aspects of the data management project.