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At least 1,189 records · Page 66

NASA Historical Data Book: NASA Launch Systems, Space Transportation, Human Spaceflight and Space Science, 1979-1988 - Volume 5

In 1973, NASA published the first volume of the NASA Historical Data Book, a hefty tome containing mostly tabular data on the resources of the space agency between 1958 and 1968. There, broken into detailed tables, were the facts and figures associated with the budget, facilities, procurement, installations, and personnel of NASA during that formative decade. In 1988, NASA reissued that first volume of the data book and added two additional volumes on the agency's programs and projects, one each for 1958-1968 and 1969-1978. NASA published a fourth volume in 1994 that addressed NASA resources for the period between 1969 and 1978. This fifth volume of the NASA Historical Data Book is a continuation of those earlier efforts. This fundamental reference tool presents information, much of it statistical, documenting the development of four critical areas of NASA responsibility for the period between 1979 and 1988. This volume includes detailed information on the development and operation of launch systems, space transportation, human spaceflight, and space science during this era. As such, it contains in-depth statistical information about the early Space Shuttle program through the return to flight in 1988, the early efforts to build a space station, the development of new launch systems, and the launching of seventeen space science missions. A companion volume will appear late in 1999, documenting the space applications, support operations, aeronautics, and resources aspects of NASA during the period between 1979 and 1988. NASA began its operations as the nation's civilian space agency in 1958 following the passage of the National Aeronautics and Space Act. It succeeded the National Advisory Committee for Aeronautics (NACA). The new organization was charged with preserving the role of the United States "as a leader in aeronautical and space science and technology" and in its application, with expanding our knowledge of the Earth's atmosphere and space, and with exploring flight both within and outside the atmosphere. By the 1980s, NASA had established itself as an agency with considerable achievements on record. The decade was marked by the inauguration of the Space Shuttle flights and haunted by the 1986 Challenger accident that temporarily halted the program. The agency also enjoyed the strong support of President Ronald Reagan, who enthusiastically announced the start of both the Space Station program and the National Aerospace Plane program.

Rumerman, Judy A.↗

Overview of ATLAS PanDA Workload Management

The Production and Distributed Analysis System (PanDA) plays a key role in the ATLAS distributed computing infrastructure. All ATLAS Monte-Carlo simulation and data reprocessing jobs pass through the PanDA system. We will describe how PanDA manages job execution on the grid using dynamic resource estimation and data replication together with intelligent brokerage in order to meet the scaling and automation requirements of ATLAS distributed computing. PanDA is also the primary ATLAS system for processing user and group analysis jobs, bringing further requirements for quick, flexible adaptation to the rapidly evolving analysis use cases of the early datataking phase, in addition to the high reliability, robustness and usability needed to provide efficient and transparent utilization of the grid for analysis users. We will describe how PanDA meets ATLAS requirements, the evolution of the system in light of operational experience, how the system has performed during the first LHC data-taking phase and plans for the future.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Publication and Retrieval of Computational Chemical-Physical Data Via the Semantic Web. Final Technical Report

This research showed the feasibility of applying the concepts of the Semantic Web to Computation Chemistry. We have created the first web portal (www.chemsem.com) that allows data created in the calculations of quantum chemistry, and other such chemistry calculations to be placed on the web in a way that makes the data accessible to scientists in a semantic form never before possible. The semantic web nature of the portal allows data to be searched, found, and used as an advance over the usual approach of a relational database. The semantic data on our portal has the nature of a Giant Global Graph (GGG) that can be easily merged with related data and searched globally via a SPARQL Protocol and RDF Query Language (SPARQL) that makes global searches for data easier than with traditional methods. Our Semantic Web Portal requires that the data be understood by a computer and hence defined by an ontology (vocabulary). This ontology is used by the computer in understanding the data. We have created such an ontology for computational chemistry (purl.org/gc) that encapsulates a broad knowledge of the field of computational chemistry. We refer to this ontology as the Gainesville Core. While it is perhaps the first ontology for computational chemistry and is used by our portal, it is only a start of what must be a long multi-partner effort to define computational chemistry. In conjunction with the above efforts we have defined a new potential file standard (Common Standard for eXchange – CSX for computational chemistry data). This CSX file is the precursor of data in the Resource Description Framework (RDF) form that the semantic web requires. Our portal translates CSX files (as well as other computational chemistry data files) into RDF files that are part of the graph database that the semantic web employs. We propose a CSX file as a convenient way to encapsulate computational chemistry data.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Climate Lecture 20: The Educational Global Climate Model (EdGCM)

Climate change will profoundly impact Earth's environmental health as well as the world's economic and geopolitical landscape over the coming decades. The impacts of climate change are, in fact, already beginning to be experienced and have the potential to affect every living plant and animal on Earth within decades. Given this reality, every citizen of this planet should have the right to knowledge about the Earth's climate system and have the option to adapt to, or help mitigate the profound changes that are coming. In addition, a portion of the workforce needs to be capable of interpreting and analyzing climate information because, since the impacts of climate change will be widespread, pervasive, and continue to change over time, more professions will be interacting with climate data. We are already at, or past, the point where educators and their students require access to the scientific and technological resources - computer models, data, and visualization tools - that scientists use daily in the study of climate change. Although scientists use many methods to study Earth's climate system, global climate models (GCMs) have become the primary tools for exploring the complex interactions between components of the entire system: atmosphere, oceans, and land. GCMs are used to make projections of future climate change, to simulate climates of the past, and even to help scientists look for life on other planets. Like any model, a GCM can help people evaluate actions before they are taken. Like Business Intelligence software, they are Climate Intelligence tools. Unfortunately, GCMs are black boxes to most people. A previous chapter in this book by Gary Russell, entitled Building a Climate Model, is one example of the growing body of literature aimed at the general public describing the inner workings of global climate models. This literature goes a long way toward explaining climate model fundamentals. However, it will not be enough to alleviate their black-box nature unless people are afforded hands-on, authentic learning experiences as well.

Chandler, Mark A.↗

Data Fusion With Uncertainty Quantification for Sub-City-Scale Air Quality Assessment and Forecasting

Many information sources can support air quality assessment and forecasting, including atmospheric chemistry model outputs, satellite retrievals of column chemical and aerosol constituents, and surface-based air quality monitoring data from both regulatory and low-cost instruments. Systematic integration of these data sources provides a major opportunity to improve understanding and management of air quality, but also presents technical barriers, especially in resource- and data-constrained settings in the Global South. This presentation describes a data fusion system, currently under development using the Google Earth Engine platform, which aims to integrate the information sources listed above to support comprehensive sub-city-scale assessment and management of air quality. Furthermore, the data fusion framework includes provisions for the quantification of uncertainties in the resulting fused estimates based on the variability of and among the input data sources. These capabilities will allow air quality managers to better understand their local air quality situation, including relative confidence in the fused estimates for different constituents, locations, and times, leading to better informed air quality management decisions. This presentation covers the underlying methodology of the data fusion and uncertainty quantification approaches, provides an update on the status of its implementation, and presents early qualitative and quantitative results.

Carl Malings↗

Hydrologic applications of ERTS-1 data collection system in central Arizona

The Earth Resources Technology Satellite (ERTS-1) Data Collection System (DCS) was used to relay hydrologic data (streamflow rates, precipitation amounts, soil and air temperature, and snow-moisture content) from remote sites in central Arizona to those responsible for reservoir management. The ERTS-DCS was utilized to furnish near-real time information on snow-moisture content and streamflow rates to the Salt River Project for use in the management and operation of reservoirs on the Salt and Verde Rivers. The Salt River Project, aided by near-real time hydrologic data furnished by both microwave and ERTS-telemetry, was successful in predicting the volume of runoff into the reservoirs. Serious flooding in the downstream Phoenix metropolitan area was prevented by prudent water management.

Schumann, H. H.↗

TRUST: TDRSS Resource User Support Tool

TRUST-TDRSS (Tracking Data and Relay Satellite System) Resource User Support Tool is presented in the form of the viewgraphs. The following subject areas are covered: TRUST development cycle; the TRUST system; scheduling window; ODM/GCMR window; TRUST architecture; surpass; and summary.

Sparn, Thomas P.↗

The use of LANDSAT-1 imagery in mapping and managing soil and range resources in the Sand Hills region of Nebraska

The author has identified the following significant results. Evaluation of ERTS-1 imagery for the Sand Hills region of Nebraska has shown that the data can be used to effectively measure several parameters of inventory needs. (1) Vegetative biomass can be estimated with a high degree of confidence using computer compatable tape data. (2) Soils can be mapped to the subgroup level with high altitude aircraft color infrared photography and to the association level with multitemporal ERTS-1 imagery. (3) Water quality in Sand Hills lakes can be estimated utilizing computer compatable tape data. (4) Center pivot irrigation can be inventoried from satellite data and can be monitored regarding site selection and relative success of establishment from high altitude aircraft color infrared photography. (5) ERTS-1 data is of exceptional value in wide-area inventory of natural resource data in the Sand Hills region of Nebraska.

Seevers, P. M.↗

The carbon nanotube formation parameter space: data mining and mechanistic understanding for efficient resource use

Carbon nanotube (CNT) synthesis via catalytic chemical vapor deposition is relatively energy consumptive and among the least efficient reactions with respect to carbon conversion efficiency. Interestingly, these processes can be fed using a diverse set of hydrocarbon starting materials, including methane, ethylene, and acetylene, over a wide range of operating temperatures and carbon-to-hydrogen feedstock ratios. Mapping the parameter space for successful CNT growth through data extraction from published literature illuminated the most energy- and material- efficient synthetic pathways in practice to date and provided insights on thermodynamic limitation of CNT growth (i.e., the fundamental mechanisms of CNT formation). Further experimental investigations confirmed that emergent trends in the literature were the result of physicochemical constraints on the process rather than behavioral inertia in the community. The initiation temperatures for CNT growth from acetylene, ethylene, and methane feedstocks via direct experimentation were 550, 700, and 950 °C, respectively, consistent with the trend in literature-extracted mean optima (642 ± 128, 739 ± 82, and 858 ± 125 °C, respectively). These relative temperatures are consistent with a universal CNT growth mechanism, wherein all carbon feedstocks are converted to alkyne-containing species that serve as direct precursors for CNT growth. Mitigating this step with rational carbon precursor delivery, rather than relying on heat to generate the most reactive precursors in situ, could largely reduce the environmental burdens in CNT manufacturing. Indeed, manipulating the starting gas-phase composition and minimizing the thermal treatment through the use of C 2 H 2 increased carbon conversion yield by a factor of more than 10 compared to C 2 H 4 , and, consequently, should minimize hazardous volatile organic compound and polycyclic aromatic hydrocarbon emissions. Finally, the methodology utilized in this study is transferrable to guide the green synthesis of other materials and should be automated in the future for high-throughput screening of the vast process chemistry literature.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Regional inventories and mapping of land resources and environmental geology using remotely sensed data

Black-and-white stereoscopic aerial photographs at a scale of 1:20,000, 1:40,000, and 1:65,000 and controlled aerial photo mosaics at a scale of 1:24,000 were the basic data utilized for most mapping. Color and color infrared aerial photography (1:20,000) was employed to man the barrier islands, and color infrared photography (1:120,000) was used to map sand and mud units of the coastal plain north of Houston. LANDSAT-1 imagery in Bands 4, 5, and 7 (1:250,000) was utilized to examine land use and certain resource units statewide. Side scanning radar and LANDSAT imagery were studied experimentally toward mapping land suitability units in carbonate terranes with high relief and certain other terranes. Large-scale color and color infrared aerial stereoscopic photographs supply the most information for regional mapping. black-and-white photography is the most practical data for mapping because of scale, complete coverage, availability, and relatively low cost.

Wermund, E. G.↗

Federal Metering Data Analysis Needs and Existing Tools

Agencies have been working to improve their metering data collection, management, and analysis efforts over the last decade (since EPAct 2005) and will continue to address these challenges as new requirements and data needs come into place. Unfortunately there is no “one-size-fits-all” solution. As agencies continue to expand their capabilities to use metered consumption data to reducing resource use and improve operations, the hope is that shared knowledge will empower others to follow suit. This paper discusses the Federal metering data analysis needs and some existing tools.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Access NASA Satellite Global Precipitation Data Visualization on YouTube

Since the satellite era began, NASA has collected a large volume of Earth science observations for research and applications around the world. The collected and archived satellite data at 12 NASA data centers can also be used for STEM education and activities such as disaster events, climate change, etc. However, accessing satellite data can be a daunting task for non-professional users such as teachers and students because of unfamiliarity of terminology, disciplines, data formats, data structures, computing resources, processing software, programming languages, etc. Over the years, many efforts including tools, training classes, and tutorials have been developed to improve satellite data access for users, but barriers still exist for non-professionals. In this presentation, we will present our latest activity that uses a very popular online video sharing Web site, YouTube (https://www.youtube.com/), for accessing visualizations of our global precipitation datasets at the NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC). With YouTube, users can access and visualize a large volume of satellite data without the necessity to learn new software or download data. The dataset in this activity is a one-month animation for the GPM (Global Precipitation Measurement) Integrated Multi-satellite Retrievals for GPM (IMERG). IMERG provides precipitation on a near-global (60 deg. N-S) coverage at half-hourly time interval, providing more details on precipitation processes and development compared to the 3-hourly TRMM (Tropical Rainfall Measuring Mission) Multisatellite Precipitation Analysis (TMPA, 3B42) product. When the retro-processing of IMERG during the TRMM era is finished in 2018, the entire video will contain more than 330,000 files and will last ~3.6 hours. Future plans include development of flyover videos for orbital data for an entire satellite mission or project. All videos, including the one-month animation, will be uploaded and available at the GES DISC site on YouTube (https://www.youtube.com/user/NASAGESDISC).

precipitation↗

Combining In-situ and In-transit Processing to Enable Extreme-Scale Scientific Analysis

With the onset of extreme-scale computing, I/O constraints make it increasingly difficult for scientists to save a sufficient amount of raw simulation data to persistent storage. One potential solution is to change the data analysis pipeline from a post-process centric to a concurrent approach based on either in-situ or in-transit processing. In this context computations are considered in-situ if they utilize the primary compute resources, while in-transit processing refers to offloading computations toa set of secondary resources using asynchronous data transfers. In this paper we explore the design and implementation of three common analysis techniques typically performed on large-scale scientific simulations: topological analysis, descriptive statistics, and visualization. We summarize algorithmic developments, describe a resource scheduling system to coordinate the execution ofvarious analysis workflows, and discuss our implementation using the DataSpaces and ADIOS frameworks that support efficient data movement between in-situ and in-transit computations. Wedemonstrate the efficiency of our lightweight, flexible framework by deploying it on the Jaguar XK6 to analyze data generated by S3D, a massively parallel turbulent combustion code. Our framework allows scientists dealing with the data deluge at extreme scale to perform analyses at increased temporal resolutions, mitigate I/O costs, and significantly improve the time to insight.

Bennett, Janine C.↗

Predicting Large‐Scale Systematic Missing Pipe Attributes in Water Distribution Networks

Water distribution network (WDN) models are an essential tool used by water utilities for hydraulic analysis. Unfortunately, missing data and insufficient resources often make creating and maintaining these models unfeasible. Existing methods to address missing pipe properties, like sequential imputation for missing values and reconstruction using graph metrics, are designed to accommodate random patterns of missing information and require a significant percentage of the system's attributes to be known. However, these data completeness assumptions do not always align with real‐world scenarios where large sections of the WDN model have missing data. To address this challenge, this study proposes a data‐driven approach for estimating pipe diameter when considering different spatial patterns and degrees of data completeness (i.e., 0%–90%). Using data from 16 WDNs in Kentucky, this study compares the use of machine learning (ML) using topological and geospatial features against an existing deterministic approach. Results demonstrate that WDN models with pipe diameters predicted by the proposed ML method had comparable hydraulic performance to the ground truth models. Moreover, results showed that ML method performance varies between WDNs of differing topological classification. Insights from this study help advance the ability to leverage partial data to create and maintain WDN models amid uncertainty and inadequate resources.

Poff, Jason W. [Oregon State Univ., Corvallis, OR ↗

Timely Result-Data Offloading for Improved HPC Center Scratch Provisioning and Serviceability

Modern High-Performance Computing (HPC) centers are facing a data deluge from emerging scientific applications. Supporting large data entails a significant commitment of the highthroughput center storage system, scratch space. However, the scratch space is typically managed using simple purge policies, without sophisticated end-user data services to balance resource consumption and user serviceability. End-user data services such as offloading are performed using point-to-point transfers that are unable to reconcile center s purge and users delivery deadlines, unable to adapt to changing dynamics in the end-toend data path and are not fault-tolerant. Such inefficiencies can be prohibitive to sustaining high performance. In this paper, we address the above issues by designing a framework for the timely, decentralized offload of application result data. Our framework uses an overlay of user-specified intermediate and landmark sites to orchestrate a decentralized fault-tolerant delivery. We have implemented our techniques within a production job scheduler (PBS) and data transfer tool (BitTorrent). Our evaluation using both a real implementation and supercomputer job log-driven simulations show that: the offloading times can be significantly reduced (90.4% for a 5 GB data transfer); the exposure window can be minimized while also meeting center-user Service Level Agreements.

99 GENERAL AND MISCELLANEOUS↗

Potential Applications of LANDSAT Data in Energy Management Associated with Kenya's Forests

LANDSAT can be effectively used to monitor the extent and magnitude of forest cover change in Kenya in order to evaluate the potential for energy supply. Digital processing of LANDSAT data provides a reliable monitoring technique for forest resource management in Kenya. Data analysis was used to illustrate that Kenya's forests are indeed diminishing. A model used to make projections for the availability of fuelwood as an energy source is presented. The resulting figures imply that Kenya's forest will all but disappear around the end of the 20th century. Analysis of LANDSAT data for Mau East substantiates these alarming findings.

Maghenda, M. M.↗

Object oriented development of engineering software using CLIPS

Engineering applications involve numeric complexity and manipulations of a large amount of data. Traditionally, numeric computation has been the concern in developing an engineering software. As engineering application software became larger and more complex, management of resources such as data, rather than the numeric complexity, has become the major software design problem. Object oriented design and implementation methodologies can improve the reliability, flexibility, and maintainability of the resulting software; however, some tasks are better solved with the traditional procedural paradigm. The C Language Integrated Production System (CLIPS), with deffunction and defgeneric constructs, supports the procedural paradigm. The natural blending of object oriented and procedural paradigms has been cited as the reason for the popularity of the C++ language. The CLIPS Object Oriented Language's (COOL) object oriented features are more versatile than C++'s. A software design methodology based on object oriented and procedural approaches appropriate for engineering software, and to be implemented in CLIPS was outlined. A method for sensor placement for Space Station Freedom is being implemented in COOL as a sample problem.

Yoon, C. John↗