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At least 649 records · Page 36

Software Implements a Space-Mission File-Transfer Protocol

CFDP is a computer program that implements the CCSDS (Consultative Committee for Space Data Systems) File Delivery Protocol, which is an international standard for automatic, reliable transfers of files of data between locations on Earth and in outer space. CFDP administers concurrent file transfers in both directions, delivery of data out of transmission order, reliable and unreliable transmission modes, and automatic retransmission of lost or corrupted data by use of one or more of several lost-segment-detection modes. The program also implements several data-integrity measures, including file checksums and optional cyclic redundancy checks for each protocol data unit. The metadata accompanying each file can include messages to users application programs and commands for operating on remote file systems.

Rundstrom, Kathleen↗

A Community Energy Operations and Planning System: Concept, Use cases, Metrics, and Benefits

Community and city leaders are interested in achieving sustainability goals, providing resilient energy infrastructure, and improving economic competitiveness. Community-level data acquisition and analysis can provide energy and associated benefits that are not possible at the single building level. However, there is a lack of organizational structure, common semantic data models, interoperable systems, and methods to support data-driven decision-making for community-scale energy supply and demand systems. We explored the need and opportunity for a Community Energy Operations and Planning System (Community EOPS), a potential data exchange platform. We conducted “customer discovery” interviews, and reviewed literature, public tools, and technology platforms to identify key energy data “users” and use cases in communities. The key users of the Community EOPS could be developers of mixed-use districts, corporate, defense and university campus energy managers, and city managers of cities that own their energy utility. The value could be for community planning and reporting (for energy data-integrated land use planning and community infrastructure investments in microgrids, storage, district heating and cooling), energy efficiency (leveraging optimizations for community scale energy supply and demand), flexible load management (grid-edge load management to offset, shift, and flatten loads for multiple buildings and EV fleets), cost savings and revenue generation (participating in grid services), and social benefits such as energy resilience, equity, and awareness. We developed a conceptual Community EOPS architecture with recommendations for streamlined and prioritized data acquisition, sharing, and integration driven by prioritized use cases, common metrics, and actionable visualizations that can provide value to a community’s users.

Singh, Reshma↗

Nuclear Data: What is the big deal? [Slides]

This presentation begins with a look at the practical application: differential and integral data. It also includes a nuclear data evaluation: cross section formalisms, data processing and cross section library generation, computer tools, and criticality safety assessment: nuclear data. The presentation ends with some concluding remarks.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Big data for ecologists: Highlighting the ORNL DAAC

Ecologists are increasingly confronted by questions that can be addressed only by integrating data from numerous sources, often across large geographic areas and broad time periods. The supply of ecological big data is increasing at a rapid pace as researchers are publishing their data sets and large, public science and data infrastructures (such as NEON, DataONE, LTER, & NCEAS) are producing and curating extensive volumes of complex data and metadata. While supply of, and demand for, ecological data is on the rise, many ecologists now face a new challenge in locating and synthesizing the data relevant for their particular question. Here we highlight selected popular big data products applicable to ecological research available from the NASA Distributed Active Archive Center (DAAC) located at Oak Ridge National Laboratory (ORNL).

Boyer, Alison G↗

Abstract - Cooperative Research and Development Agreement between Environmental Defense Fund and National Energy Technology Laboratory

Here, we summarize an effort to develop a global oil and gas infrastructure (GOGI) taxonomy and geodatabase, using a combination of big data computing, custom search and data integration algorithms, and expert driven spatio-temporal analytics to identify, access, and evaluate open oil and gas data resources and uncertainty trends worldwide. This approach leveraged custom National Energy Technology Laboratory (NETL) tools and capabilities in collaboration with Environmental Defense Fund (EDF) and Carbon Limits subject matter expertise, to identify over 380 datasets and integrate more than 4.8 million features into the GOGI database. In addition to acquisition of open oil and gas infrastructure data, information was collected and analyzed to assess the spatial, temporal, and source quality of these resources, and estimate their completeness relative to the top 40 hydrocarbon producing and consuming countries.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Assessment of NO2 Observations During DISCOVER-AQ and KORUS-AQ Field Campaigns

NASA’s Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ, conducted in 2011–2014) campaign in the United States and the joint NASA and National Institute of Environmental Research (NIER) Korea–United States Air Quality Study (KORUS-AQ, conducted in 2016) in South Korea were two field study programs that provided comprehensive, integrated data sets of airborne and surface observations of atmospheric constituents, including nitrogen dioxide (NO2), with the goal of improving the interpretation of spaceborne remote sensing data. Various types of NO2 measurements were made, including in situ concentrations and column amounts of NO2 using ground- and aircraft-based instruments, while NO2 column amounts were being derived from the Ozone Monitoring Instrument (OMI) on the Aura satellite. This study takes advantage of these unique datasets by first evaluating in situ data taken from two different instruments on the same aircraft platform, comparing coincidently sampled profile-integrated columns from aircraft spirals with remotely sensed column observations from ground-based Pandora spectrometers, intercomparing column observations from the ground (Pandora), aircraft (in situ vertical spirals), and space (OMI), and evaluating NO2 simulations from coarse Global Modeling Initiative (GMI) and high-resolution regional models. We then use these data to interpret observed discrepancies due to differences in sampling and deficiencies in the data reduction process. Finally, we assess satellite retrieval sensitivity to observed and modeled a priori NO2 profiles. Contemporaneous measurements from two aircraft instruments that likely sample similar air masses generally agree very well but are also found to differ in integrated columns by up to 31.9 %. These show even larger differences with Pandora, reaching up to 53.9 %, potentially due to a combination of strong gradients in NO2 fields that could be missed by aircraft spirals and errors in the Pandora retrievals. OMI NO2 values are about a actor of 2 lower in these highly polluted environments due in part to inaccurate retrieval assumptions (e.g., a priori pro-files) but mostly to OMI’s large footprint (>312 km2).

Nitrogen dioxide↗

Slycat v 2.0

Slycat™ is a web-based system for analysis of large, high-dimensional data such as that produced by High Performance Computing (HPC) platforms. The Slycat™ server integrates data ingestion, scalable analysis, data management, and visualization with commodity web clients using a multi-tiered hierarchy of data and model storage. Analysis inputs are pushed or pulled into Slycat™, where models are generated and analysis artifacts are stored in a project database. These artifacts are the basis for visualizations that are delivered to users’ desktops through ordinary web browsers, eliminating the need to build and deploy platform-specific client applications. Slycat™ currently provides four types of analysis: canonical correlation analysis (CCA) to model relationships between inputs and output metrics, time series analysis featuring clustering and comparative visualization of waveforms, parameter space visualization including remote retrieval of images and movies from HPC disks, and VideoSwarm for modeling video similarities over time.

Letter, Matthew↗

EMP Attachment 2: DOE-SC PNNL Site Data Management Plan

This Data Management Plan (DMP) describes the data management processes and activities under the Pacific Northwest National Laboratory (PNNL) Site Environmental Monitoring Plan. The activities currently in the Plan are limited to offsite environmental surveillance of PNNL Site radiological releases to the air. The DMP provides guidance on data capture, processing and transmittal, and database configuration management. The requirements for the PNNL Site Environmental Monitoring (SEM) Database and associated records are documented in order to assure that vital data are recorded accurately, stored in a manner that retains data integrity, and are suitable for analyst to use. Protocols and procedures must ensure the data will be adequate and robust.

99 GENERAL AND MISCELLANEOUS↗

Automation of Data Collection Methods for Online Monitoring of Nuclear Power Plants

Maintenance activities in the United States nuclear power industry are mainly centered on preventive and corrective maintenance. Predictive maintenance, which is a variable time basis maintenance strategy that is dependent on the condition of the equipment, is less utilized but potentially more effective. In the current maintenance practices, the nuclear power plants rely on manual processes to supply information to a human decision making process. The plants have several processes that generate information that is not typically used beyond the intended target for collecting that information. This information is a source of data that is manually collected by labor intensive tasks. The data is therefore expensive to collect, yet limited in use. The industry has realized the opportunity present in reducing the labor intensive tasks by automating them and increasing the uses of the data collected by either developing advanced predictive maintenance methods using data-driven decision making that could span from trending to machine learning and artificial intelligence. This shift in maintenance strategy may result in high cost saving impact. In addition to reducing labor requirements and frequency of maintenance activities, this shift in strategy will reduce the materials portion of the operations and maintenance cost as well. The aim of this specific effort is to focus on the automation of data collection process. It is part of a series of planned efforts to target multiple elements of migrating the current preventive maintenance approach in nuclear power plants to a data-driven approach. These elements include data collection, management, analysis, visualization, in addition to value analysis and change enabling. The other five elements of migration were not considered because they are planned to be explicitly studied in multiple parallel efforts. Challenges including regulations requirements or limitations are planned in a future effort that focuses on change enabling. The risk and benefit/cost of the approach shift is planned in another effort targeted towards value. The storage, communication, computational power, data architecture, and protection are part of the information infrastructure and are planned in dedicated data management effort. The best means to present the data for assisted human decision making is planned in a data visualization effort. The data interpretation and patterns recognition for machine decision making is planned through multiple efforts in data analytics over the following years. This work identified seventeen data sources collection methods in nuclear power plants that could be automated for an integrated data platform that enables comprehensive and informed decision making. For each of the identified seventeen data sources, the base, modern, and state of the art states were described. The current state of data collection used by most utilities was identified as the "base state". The current achievable state was identified as the "modern state", a state that has either been achieved by at least some US utilities or in other industries. The future state that requires research and development to reach was identified as the "state of the art" state. The described states used an out-of-the-box thinking approach. This effort found that there is a hug potential for task automation and data fidelity improvements by evolving the current industry data collection methods. The majority of the processes analyzed were found to be manual and labor intensive and fell in the base state. As a result, the modern state often targeted reducing the manual logging efforts by using electronic and semi-intelligent means or complementing the work with sensors and mobile or fixed technologies that can reduce the demand of labor activities. The state of the art state involved applications advanced methods of data correlation, data mining, and machine learning along with increasing the spatial distribution and fidelity of data, coupling multiple data sources, and enabling the use of sophisticated mobile devices and machine comprehension methods.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Advances in Nuclear Data Evaluation Theory for NCSP [Slides]

The generalized form of the Bayes’ Theorem improves UQ for NCSP of differential and/or integral data evaluations and of applications. It is being implemented in an API for use by SCALE, SAMMY, etc. Extended R-matrix formalism improves evaluations of RRR including direct reactions, direct capture, and the Reich-Moore approx., doorway state reactions with applications to RPI data on lead isotopes. Extended R-Matrix formalism will be implemented into SAMMY post modernization.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Framework programmable platform for the advanced software development workstation. Integration mechanism design document

The Framework Programmable Software Development Platform (FPP) is a project aimed at combining effective tool and data integration mechanisms with a model of the software development process in an intelligent integrated software development environment. Guided by this model, this system development framework will take advantage of an integrated operating environment to automate effectively the management of the software development process so that costly mistakes during the development phase can be eliminated.

Mayer, Richard J.↗

Assessment of Urbanization on the Integrated Land-Ocean-Atmosphere Environment in Coastal Metropolis in Preparation for HyspIRI

The need for comprehensive studies of the relationships between past and projected changes of regional climate and human activity in comple x urban environments has been well established. The HyspIRI preparato ry airborne activities in California, associated science and applicat ions research, and eventually HyspIRI itself provide an unprecedented opportunity for development and implementation of an integrated data and modeling analysis system focused on coastal urban environments. We will utilize HyspIRI preparatory data collections in developing ne w remote sensing-based tools for investigating the integrated urban e nvironment, emphasizing weather, climate, and energy demands in compl ex coastal cities.

Sequera, Pedro↗

Slycat v 1.0 Beta

Slycat is a web-based system for analysis of large, high-dimensional data such as that produced by High Performance Computing (HPC) platforms. The Slycat server integrates data ingestion, scalable analysis, data management, and visualization with commodity web clients using a multi-tiered hierarchy of data and model storage. Analysis inputs are pushed into Slycat, where models are generated and analysis artifacts are stored in a project database. These artifacts are the basis for visualizations that are delivered to users' desktops through ordinary web browsers, eliminating the need to build and deploy platform-specific client applications. Slycat currently provides two types of analysis: canonical correlation analysis (CCA) to model relationships between inputs and output metrics, and time series analysis featuring clustering and comparative visualization of waveforms.

Source record↗

Wearable Biosensor Monitor to Support Autonomous Crew Health and Readiness to Perform

For future human exploration missions, NASA needs a health monitoring system composed of hardware that is compact, fully interoperable with an integrated data management system, and requires minimal consumables. Such a system will be achieved through the integration of small, easy to use biomedical sensors that will have the ability to measure, store and transmit physiological parameters during operational and ambulatory activity. Since 2012, the Canadian Space Agency (CSA) has been active in funding the development of wearable biomonitoring sensors. The Astroskin is the first prototype and consists of a shirt-based garment and headband with embedded sensors, and associated software and technology that measure vital signs, sleep quality and activity level of the wearer. NASA and CSA have been collaborating since 2014 to test and validate this system in a lab environment at Ames Research Center and more recently in the Human Exploration Research Analog (HERA) located at Johnson Spaceflight Center. Specific objectives of the HERA study were: 1) to assess the performance of the Astroskin biosensor system for long-term health monitoring (24-hours) capabilities and during exercise as a measure of crew fitness; 2) to obtain crew feedback on comfort and usability of the Astroskin system; 3) to demonstrate performance of Bluetooth communication during real-time transmission and for verification of data in this environment; and 4) to obtain baseline data for further development of algorithms and tools that facilitate decision support for diagnosing and monitoring of a sick or injured crewmember. HERA Campaign 3 included four missions (each 30-days in duration) with four crewmembers assigned to each mission. A total of 9 men and 7 women participated in the Astroskin evaluation that included continuous physiological monitoring (24-hours) on mission days MD-11, MD1 (high workload), MD15 (low workload), MD19, MD29, and MD+7. Mission days 19 and 29 also included 30 minutes of sub-maximal exercise on a cycle ergometer. Following each 24-hour monitoring session crew physiological data were downloaded to laptops and each crewmember completed a 28 question survey on their experiences with the Astroskin hardware and software. This presentation will focus on lessons learned from the HERA missions. Specifically it will address Astroskin system performance in terms of data loss and data quality (no comparison to lab standard devices), wireless communication with the onboard mobile device, crew usability and comfort, and future development of a next generation biomonitoring system.

crew fitness↗

Prime agricultural land monitoring and assessment component of the California Integrated Remote Sensing System

The use of digital LANDSAT techniques for monitoring agricultural land use conversions was studied. Two study areas were investigated: one in Ventura County and the other in Fresno County (California). Ventura test site investigations included the use of three dates of LANDSAT data to improve classification performance beyond that previously obtained using single data techniques. The 9% improvement is considered highly significant. Also developed and demonstrated using Ventura County data is an automated cluster labeling procedure, considered a useful example of vertical data integration. Fresno County results for a single data LANDSAT classification paralleled those found in Ventura, demonstrating that the urban/rural fringe zone of most interest is a difficult environment to classify using LANDSAT data. A general raster to vector conversion program was developed to allow LANDSAT classification products to be transferred to an operational county level geographic information system in Fresno.

Estes, J. E.↗

Feature Detection From LiDAR Point Cloud and Aerial Imagery Using Distance Dependent Chinese Restaurant Process [Slides]

It is possible to integrate data captured by UAS from multiple data modalities spatially. We can detect features by combining information across images and LiDAR point clouds. Performance issues exist with the current algorithms in place & could be improved. We hope to test it for at least 500 iterations. Post processing of data takes enormous efforts in case of data generated by proprietary software tools…We intend to develop a better framework for seamless integration.

97 MATHEMATICS AND COMPUTING↗

Titan Trek and IcyMoons Trek: Two New Online NASA Visualization and Analysis Portals for Saturn's Moons

Saturn’s moons are the focus of two new online visualization and analysis tools produced by NASA. The Solar System Treks family of online portals provides a suite of interactive visualization and analysis tools. These enable mission planners, lunar scientists, engineers, students, and the general public to access mapped data products from past and current missions for a growing number of planetary bodies. They integrate data from a variety of instruments aboard a number of missions. This presentation will introduce two new portals in the Solar System Trek suite. Titan Trek highlights Saturn’s largest moon, and IcyMoons Trek features a number of Saturn’s other moons as studied by the Cassini mission.

Law, E.↗

Datascope to Enable Earth Independent Medical Operations (EIMO)

BACKGROUND: NASA has amassed sixty years of knowledge and experience relevant to maintenance of crew health and performance in low earth orbit. The Apollo Program introduced the importance of ensuring progressively autonomous operational capability. Earth Independent Medical Operations (EIMO) will require a gradual shift in the balance of medical responsibility, management, and authority from terrestrial to space-based assets. Terrestrial assets will continue to be essential for pre-mission screening and planning in addition to maintenance of crew health and performance. However, new capabilities are needed to enable EIMO and the amount of data required to support these systems, and mitigate the impacts of data transmission delays and reduced bandwidth coupled with lack of cloud-like resources and on-board computing capacity that is currently unclear or operationally insufficient. OVERVIEW: The overall goal of EIMO is to develop artificial intelligence (AI)-based solutions to analyze crew health and performance data utilizing a clinical decision support system (CDSS) to provide crew medical officers (CMO) with the equivalent of real-time, on-board medical consults. The EIMO ecosystem is envisioned as a “system of systems” where embedded reference databases and real-time data streams from multiple input vectors continuously and seamlessly assess crew health and performance. EIMO will be designed to make recommendations to the CMO using multi-modal AI-based natural language processing and machine learning methods with interoperability to push/pull data within and between multiple vehicle and habitat architectures. DISCUSSION: Data flows and storage/retrieval capacity are severely constrained during space missions and the challenges will become even greater during exploration missions. Just as each past program from Mercury to the International Space Station (ISS) required rethinking the interaction between ground-based controllers and space-based crew, so too will future missions to the Moon and Mars. While the NASA High-Performance Spaceflight Computing Processor project aims to increase computational capacity by 100 times over current spaceflight computers, the projected deliverable still lags considerably behind what will be needed to enable an AI-driven CDSS. Restrictions in processing speed and data storage capacity, coupled with transmission bottlenecks and delays, necessitate definition and optimization of an integrated data architecture to enable a progressively autonomous medical capability.

Medical operations↗