Results for “Data Collection” — Page 60

NASA NTRS2009

Evolution of Web Services in EOSDIS: Search and Order Metadata Registry (ECHO)

During 2005 through 2008, NASA defined and implemented a major evolutionary change in it Earth Observing system Data and Information System (EOSDIS) to modernize its capabilities. This implementation was based on a vision for 2015 developed during 2005. The EOSDIS 2015 Vision emphasizes increased end-to-end data system efficiency and operability; increased data usability; improved support for end users; and decreased operations costs. One key feature of the Evolution plan was achieving higher operational maturity (ingest, reconciliation, search and order, performance, error handling) for the NASA s Earth Observing System Clearinghouse (ECHO). The ECHO system is an operational metadata registry through which the scientific community can easily discover and exchange NASA's Earth science data and services. ECHO contains metadata for 2,726 data collections comprising over 87 million individual data granules and 34 million browse images, consisting of NASA s EOSDIS Data Centers and the United States Geological Survey's Landsat Project holdings. ECHO is a middleware component based on a Service Oriented Architecture (SOA). The system is comprised of a set of infrastructure services that enable the fundamental SOA functions: publish, discover, and access Earth science resources. It also provides additional services such as user management, data access control, and order management. The ECHO system has a data registry and a services registry. The data registry enables organizations to publish EOS and other Earth-science related data holdings to a common metadata model. These holdings are described through metadata in terms of datasets (types of data) and granules (specific data items of those types). ECHO also supports browse images, which provide a visual representation of the data. The published metadata can be mapped to and from existing standards (e.g., FGDC, ISO 19115). With ECHO, users can find the metadata stored in the data registry and then access the data either directly online or through a brokered order to the data archive organization. ECHO stores metadata from a variety of science disciplines and domains, including Climate Variability and Change, Carbon Cycle and Ecosystems, Earth Surface and Interior, Atmospheric Composition, Weather, and Water and Energy Cycle. ECHO also has a services registry for community-developed search services and data services. ECHO provides a platform for the publication, discovery, understanding and access to NASA s Earth Observation resources (data, service and clients). In their native state, these data, service and client resources are not necessarily targeted for use beyond their original mission. However, with the proper interoperability mechanisms, users of these resources can expand their value, by accessing, combining and applying them in unforeseen ways.

Mitchell, Andrew↗

NASA NTRS2009

Lunar Human Research Requirements (LHRR)

Biomedical research will be conducted during transit and on the surface of the Moon to prepare for extended stays on the Moon and to prepare for the exploration of Mars. The objective of the Human Research Program (HRP) is to preserve the health and enhance performance of astronaut explorers. Specific objectives of the HRP include developing the knowledge, capabilities, and necessary countermeasures and technologies in support of human space exploration; focusing on mitigating the highest risks to crew health and performance; and defining and improving human spaceflight medical, environmental, behavioral, and human factors standards. This document contains a detailed description of the resource accommodations, interfaces, and environments to be provided by the Constellation Program (CxP) to support the HRP research in transit and on the lunar surface. Covered, specifically, are the requirements for mass and volume transport; crew availability; ground operations, baseline data collection, and payload processing; power, and data. Volumes and mass are given for transport of conditioned samples only. They do not account for the engineering solution that the Constellation Program will implement (refrigerator/freezer volume/mass). This document does not account for requirements on the Orion vehicle for transportation to and from the International Space Station (ISS). The ISS Program has supplied requirements for this mission.

Denkins, Pamela↗

NASA NTRS2011

Drop Size Distribution Measurements Supporting the NASA Global Precipitation Measurement Mission: Infrastructure and Preliminary Results

Global Precipitation Measurement Mission (GPM) retrieval algorithm validation requires datasets that characterize the 4-D structure, variability, and correlation properties of hydrometeor particle size distributions (PSD) and accumulations over satellite fields of view (5 -- 50 km). Key to this process is the combined use of disdrometer and polarimetric radar platforms. Here the disdrometer measurements serve as a reference for up-scaling dual-polarimetric radar observations of the PSD to the much larger volumetric sampling domain of the radar. The PSD observations thus derived provide a much larger data set for assessing DSD variability, and satellite-based precipitation retrieval algorithm assumptions, in all three spatial dimensions for a range of storm types and seasons. As one component of this effort, the GPM Ground Validation program recently acquired five 3rd generation 2D Video disdrometers as part of its Disdrometer and Radar Observations of Precipitation Facility (DROP), currently hosted in northern Alabama by the NASA Marshall Space Flight Center and the University of Alabama in Huntsville. These next-generation 2DVDs were operated and evaluated in different phases of data collection under the scanning domain of the UAH ARMOR C-band dual-polarimetric radar. During this period approximately 7500 minutes of PSD data were collected and processed to create gamma size distribution parameters using a truncated method of moments approach. After creating the gamma parameter datasets the DSDs were then used as input to T-matrix code for computation of polarimetric radar moments at C-band. The combined dataset was then analyzed with two basic objectives in mind: 1) the investigation of seasonal variability in the rain PSD parameters as observed by the 2DVDs; 2) the use of combined polarimetric moments and observed gamma distribution parameters in a functional form to retrieve PSD parameters in 4-D using the ARMOR radar for precipitation occurring in different seasons and for different rain system types. Preliminary results suggest that seasonal variations in the DSD parameters do occur, but are most pronounced when comparing tropical PSDs to either winter or summer convective precipitation. For example the previously documented shift to relatively smaller drop diameters in higher number concentrations for equivalent rain rate bins was observed in tropical storm rainbands occurring over Huntsville. On a more inter seasonal basis empirical fits between parameters such as D0 and ZDR do not appear to exhibit robust seasonal biases- i.e., one fit seems to work for all seasons within acceptable standard error (O[10%]) for estimates of D0. In polarimetric retrievals of the vertical variability in PSD (rain layer) for a tropical rainband we find that the Do varies with height when partitioned by specified precipitation categories (e.g., convective or stratiform, heavy and light stratiform etc.) but this variation is of order 10-20% and is smaller than the difference in D0 observed between the basic delineation of convective and stratiform precipitation types. Currently we are expanding our analysis of the vertical structure of the PSD to include several seasonally and/or dynamically-different storm system types (e.g., winter convection and stratiform events; summer mid-latitude convective etc.) sampled by ARMOR. The study will present the results of our combined analyses.

Petersen, Walter A.↗

NASA NTRS2014

MERRA Analytic Services: Meeting the Big Data Challenges of Climate Science Through Cloud-enabled Climate Analytics-as-a-service

Climate science is a Big Data domain that is experiencing unprecedented growth. In our efforts to address the Big Data challenges of climate science, we are moving toward a notion of Climate Analytics-as-a-Service (CAaaS). We focus on analytics, because it is the knowledge gained from our interactions with Big Data that ultimately produce societal benefits. We focus on CAaaS because we believe it provides a useful way of thinking about the problem: a specialization of the concept of business process-as-a-service, which is an evolving extension of IaaS, PaaS, and SaaS enabled by Cloud Computing. Within this framework, Cloud Computing plays an important role; however, we it see it as only one element in a constellation of capabilities that are essential to delivering climate analytics as a service. These elements are essential because in the aggregate they lead to generativity, a capacity for self-assembly that we feel is the key to solving many of the Big Data challenges in this domain. MERRA Analytic Services (MERRAAS) is an example of cloud-enabled CAaaS built on this principle. MERRAAS enables MapReduce analytics over NASAs Modern-Era Retrospective Analysis for Research and Applications (MERRA) data collection. The MERRA reanalysis integrates observational data with numerical models to produce a global temporally and spatially consistent synthesis of 26 key climate variables. It represents a type of data product that is of growing importance to scientists doing climate change research and a wide range of decision support applications. MERRAAS brings together the following generative elements in a full, end-to-end demonstration of CAaaS capabilities: (1) high-performance, data proximal analytics, (2) scalable data management, (3) software appliance virtualization, (4) adaptive analytics, and (5) a domain-harmonized API. The effectiveness of MERRAAS has been demonstrated in several applications. In our experience, Cloud Computing lowers the barriers and risk to organizational change, fosters innovation and experimentation, facilitates technology transfer, and provides the agility required to meet our customers' increasing and changing needs. Cloud Computing is providing a new tier in the data services stack that helps connect earthbound, enterprise-level data and computational resources to new customers and new mobility-driven applications and modes of work. For climate science, Cloud Computing's capacity to engage communities in the construction of new capabilies is perhaps the most important link between Cloud Computing and Big Data.

Data Analytics↗

NASA NTRS2017

Understanding the International Space Station Crew Perspective following Long-Duration Missions through Data Analytics & Visualization of Crew Feedback

The International Space Station (ISS) first became a home and research laboratory for NASA and International Partner crewmembers over 16 years ago. Each ISS mission lasts approximately 6 months and consists of three to six crewmembers. After returning to Earth, most crewmembers participate in an extensive series of 30+ debriefs intended to further understand life onboard ISS and allow crews to reflect on their experiences. Examples of debrief data collected include ISS crew feedback about sleep, dining, payload science, scheduling and time planning, health & safety, and maintenance. The Flight Crew Integration (FCI) Operational Habitability (OpsHab) team, based at Johnson Space Center (JSC), is a small group of Human Factors engineers and one stenographer that has worked collaboratively with the NASA Astronaut office and ISS Program to collect, maintain, disseminate and analyze this data. The database provides an exceptional and unique resource for understanding the "crew perspective" on long duration space missions. Data is formatted and categorized to allow for ease of search, reporting, and ultimately trending, in order to understand lessons learned, recurring issues and efficiencies gained over time. Recently, the FCI OpsHab team began collaborating with the NASA JSC Knowledge Management team to provide analytical analysis and visualization of these over 75,000 crew comments in order to better ascertain the crew's perspective on long duration spaceflight and gain insight on changes over time. In this initial phase of study, a text mining framework was used to cluster similar comments and develop measures of similarity useful for identifying relevant topics affecting crew health or performance, locating similar comments when a particular issue or item of operational interest is identified, and providing search capabilities to identify information pertinent to future spaceflight systems and processes for things like procedure development and training. In addition, the comments were scored for sentiment using a polarity scoring algorithm to identify both positive and negative comments for particular groups and clusters, allowing the team to make analytically informed decisions regarding future hardware and operating procedures. The use of polarity scoring with time series analysis was used to provide insight into how crew health and habitability is changing throughout various spaceflight increments or the station lifecycle as a whole. Finally, a visualization framework was developed to address the needs of the end users to search for and analyze comments by user, category or mission. This paper will discuss how the use of an analytical framework in conjunction with the current human interface, improved the understanding of crew perspective and shortened the time for analysis allowing for more informed decisions and rapid development of improvements. These methods are significantly optimizing the way that this valuable data can be assessed and applied to current and future spaceflight design and development. This collaboration allows the FCI OpsHab team to effectively analyze and share data in a more automated and timely fashion. Trends are no longer derived manually and can be illustrated effectively and accurately with these evolving techniques to an ever growing group of human spaceflight end users.

Bryant, Cody↗

NASA NTRS2018

A Laser-Induced Fluorescence Diagnostic for HERMeS and High-Power Electric Propulsion

NASA is continuing the development of a 12.5-kW Hall thruster system, which is baselined in a phased exploration concept to expand human presence to cis-lunar space and eventually to Mars. The development team is transitioning knowledge gained from the testing of the government-built Technology Development Unit (TDU) to the contractor-built Engineering Development Unit (EDU). A new laser-induced fluorescence diagnostic that is compatible with the testing of engineering hardware was developed to obtain data for thruster model validation in the lowest background pressure achievable. Prior to performing the test on the EDU, the team performed a functional checkout test of this new diagnostic using the TDU. In addition to providing a checkout of the diagnostic, this test provided data that can be correlated to electron mobility for comparison to the EDU at a later date. A number of technical challenges related to large test facilities and interfacing with engineering hardware were overcome while implementing the new laser diagnostic system. The initial data set was in good agreement with prior optical and non-optical diagnostics data collected on the TDU thrusters. This data set also revealed the spatial origin of high angle ions that have been of concern for spacecraft integration.

Huang, Wensheng↗

NASA NTRS2018

Ion Velocity in the Discharge Channel and Near-Field of the HERMeS Hall Thruster

NASA is continuing the development of a 12.5-kW Hall thruster system, which is baselined in a phased exploration concept to expand human presence to cis-lunar space and eventually to Mars. The development team is transitioning knowledge gained from the testing of the government-built Technology Development Unit (TDU) to the contractor-built Engineering Development Unit (EDU). A new laser-induced fluorescence diagnostic that is compatible with the testing of engineering hardware was developed to obtain data for thruster model validation in the lowest background pressure achievable. Prior to performing the test on the EDU, the team performed a functional checkout test of this new diagnostic using the TDU. In addition to providing a checkout of the diagnostic, this test provided data that can be correlated to electron mobility for comparison to the EDU at a later date. A number of technical challenges related to large test facilities and interfacing with engineering hardware were overcome while implementing the new laser diagnostic system. The initial data set was in good agreement with prior optical and non-optical diagnostics data collected on the TDU thrusters. This data set also revealed the spatial origin of high angle ions that have been of concern for spacecraft integration.

Huang, Wensheng↗

NASA NTRS2018

A Laser-Induced Fluorescence Diagnostic for HERMeS and High-Power Electric Propulsion

NASA is continuing the development of a 12.5-kW Hall thruster system, which is baselined in a phased exploration concept to expand human presence to cis-lunar space and eventually to Mars. The development team is transitioning knowledge gained from the testing of the government-built Technology Development Unit (TDU) to the contractor-built Engineering Development Unit (EDU). A new laser-induced fluorescence diagnostic that is compatible with the testing of engineering hardware was developed to obtain data for thruster model validation in the lowest background pressure achievable. Prior to performing the test on the EDU, the team performed a functional checkout test of this new diagnostic using the TDU. In addition to providing a checkout of the diagnostic, this test provided data that can be correlated to electron mobility for comparison to the EDU at a later date. A number of technical challenges related to large test facilities and interfacing with engineering hardware were overcome while implementing the new laser diagnostic system. The initial data set was in good agreement with prior optical and non-optical diagnostics data collected on the TDU thrusters. This data set also revealed the spatial origin of high angle ions that have been of concern for spacecraft integration.

Huang, Wensheng↗

NASA NTRS2018

Ion Velocity in the Discharge Channel and Near-Field of the HERMeS Hall Thruster

NASA is continuing the development of a 12.5-kW Hall thruster system, which is baselined in a phased exploration concept to expand human presence to cis-lunar space and eventually to Mars. The development team is transitioning knowledge gained from the testing of the government-built Technology Development Unit (TDU) to the contractor-built Engineering Development Unit (EDU). A new laser-induced fluorescence diagnostic that is compatible with the testing of engineering hardware was developed to obtain data for thruster model validation in the lowest background pressure achievable. Prior to performing the test on the EDU, the team performed a functional checkout test of this new diagnostic using the TDU. In addition to providing a checkout of the diagnostic, this test provided data that can be correlated to electron mobility for comparison to the EDU at a later date. A number of technical challenges related to large test facilities and interfacing with engineering hardware were overcome while implementing the new laser diagnostic system. The initial data set was in good agreement with prior optical and non-optical diagnostics data collected on the TDU thrusters. This data set also revealed the spatial origin of high angle ions that have been of concern for spacecraft integration.

Huang, Wensheng↗

NASA NTRS2018

Ion Velocity in the Discharge Channel and Near-Field of the HERMeS Hall Thruster

NASA is continuing the development of a 12.5-kW Hall thruster system, which is baselined in a phased exploration concept to expand human presence to cis-lunar space and eventually to Mars. The development team is transitioning knowledge gained from the testing of the government-built Technology Development Unit (TDU) to the contractor-built Engineering Development Unit (EDU). A new laser-induced fluorescence diagnostic that is compatible with the testing of engineering hardware was developed to obtain data for thruster model validation in the lowest background pressure achievable. Prior to performing the test on the EDU, the team performed a functional checkout test of this new diagnostic using the TDU. In addition to providing a checkout of the diagnostic, this test provided data that can be correlated to electron mobility for comparison to the EDU at a later date. A number of technical challenges related to large test facilities and interfacing with engineering hardware were overcome while implementing the new laser diagnostic system. The initial data set was in good agreement with prior optical and non-optical diagnostics data collected on the TDU thrusters. This data set also revealed the spatial origin of high angle ions that have been of concern for spacecraft integration.

Huang, Wensheng↗

NASA NTRS2018

Cumulus: NASA Archives in the Cloud

NASA's Earth Observing System Data and Information System (EOSDIS) houses nearly 30PBs of critical Earth Science data and with upcoming missions is expected to balloon to between 200PBs-300PBs over the next seven years. The magnitude of data collected makes it infeasible to download data and process it locally, forcing us to re-think how we store and work with earth science data. NASA has looked to the cloud to address this, building its open source Cumulus software to manage the ingest of diverse data in a wide variety of formats into the cloud and provide services to manage and access the data. In this talk, we will describe how Cumulus provides common features needed to manage a cloud archive in the realm of ingest, data stewardship, and cost-controlled distribution of science data to users and services.

EOSDIS↗

NASA NTRS2019

NASA Archives and Data Stewardship in the Cloud with Cumulus

NASA's Earth Observing System Data and Information System (EOSDIS) houses nearly 30PB (petabytes) of critical Earth Science data and with upcoming missions is expected to balloon to between 200PBs-300PBs over the next seven years. The magnitude of data collected makes it infeasible to download data and process it locally, forcing us to re-think how we store and work with earth science data. NASA has looked to the cloud to address this, building its open source Cumulus software to manage the ingest of diverse data in a wide variety of formats into the cloud and provide services to manage and access the data. In this talk, we will describe how Cumulus provides common features needed to manage a cloud archive in the realm of ingest, data stewardship, and cost-controlled distribution of science data to users and services.

NASA↗

NASA NTRS2019

Using Big Data Technologies with Earth Science Data in HDF5: HDF5 Scalable Solutions

HDF5 (Hierarchical Data Format 5) is open-source, high-performance software that consists of an abstract data model, library, and fileformat used for storing and managing extremely large and/or complex data collections. NASA Earth Observing System (EOS) Data and Information Systems use HDF5 as an archival format to store remote sensing data from EOS satellites. HDF5 is also used to store other types of Geoscience and Strophysical data, e.g., seismic data and data from Low-Frequency Array (LOFAR) radio telescopes. Data stored in HDF5 has reached tens of petabytes and is growing at an accelerated rate.With the growing amout of HDF5 Earth Science data to analyze and process, scientists need to adopt big data technologies including new storage paradigms such as cloud and object storage. To run models and perform data analysis they also need to utilizied efficient and diverse ways to access data, from high-performance computing's (HPC) Message Passing Interface (MPI) I/O and deep memory hierarchies (DMH) to non-HPC frameworks such as Apache Hadoop, Spark, and Drill. The HDF Group continually works to enable usage of big data technologies in HDF software.

Knox, Larry↗

NASA NTRS2013

Thermal Modeling and Analysis of the Hurricane Imaging Radiometer (HIRad)

The Hurricane Imaging Radiometer (HIRad) is a payload carried by an unmanned aerial vehicle (UAV) at altitudes up to 60,000 ft with the purpose of measuring ocean surface wind speeds and near ocean surface rain rates in hurricanes. The payload includes several components that must maintain steady temperatures throughout the flight. Minimizing the temperature drift of these components allows for accurate data collection and conclusions to be drawn concerning the behavior of hurricanes. HIRad has flown on several different UAVs over the past two years during the fall hurricane season. Based on the data from the 2011 flight, a Thermal Desktop model was created to simulate the payload and reproduce the temperatures. Using this model, recommendations were made to reduce the temperature drift through the use of heaters controlled by resistance temperature detector (RTD) sensors. The suggestions made were implemented for the 2012 hurricane season and further data was collected. The implementation of the heaters reduced the temperature drift for a portion of the flight, but after a period of time, the temperatures rose. With this new flight data, the thermal model was updated and correlated. Detailed analysis was conducted to determine a more effective way to reduce the temperature drift. The final recommendations made were to adjust the set temperatures of the heaters for 2013 flights and implement hardware changes for flights beyond 2013.

Mauro, Stephanie↗

NASA NTRS2013

Modeling and Analysis of the Hurricane Imaging Radiometer (HIRAD)

The Hurricane Imaging Radiometer (HIRad) is a payload carried by an unmanned aerial vehicle (UAV) at altitudes up to 60,000 ft with the purpose of measuring ocean surface wind speeds and near ocean surface rain rates in hurricanes. The payload includes several components that must maintain steady temperatures throughout the flight. Minimizing the temperature drift of these components allows for accurate data collection and conclusions to be drawn concerning the behavior of hurricanes. HIRad has flown on several different UAVs over the past two years during the fall hurricane season. Based on the data from the 2011 flight, a Thermal Desktop model was created to simulate the payload and reproduce the temperatures. Using this model, recommendations were made to reduce the temperature drift through the use of heaters controlled by resistance temperature detector (RTD) sensors. The suggestions made were implemented for the 2012 hurricane season and further data was collected. The implementation of the heaters reduced the temperature drift for a portion of the flight, but after a period of time, the temperatures rose. With this new flight data, the thermal model was updated and correlated. Detailed analysis was conducted to determine a more effective way to reduce the temperature drift. The final recommendations made were to adjust the set temperatures of the heaters for 2013 flights and implement hardware changes for flights beyond 2013.

Mauro, Stephanie↗

NASA NTRSDate not supplied

Shock-Buffet Prediction Report in Support of the High Angle Working Group at the Third Aeroelastic Prediction Workshop

This paper presents a summary of the computational shock-buffet results associated with the third Aeroelastic Prediction Workshop (AePW-3) High-Angle Working Group. The computational results are compared against experimental data collected during the Oscillating Turn Table (OTT) Benchmark Supercritical Wing (BSCW) test campaign conducted in NASA Langley Research Center Transonic Dynamics Tunnel (TDT) in early 2000. In addition to experimental forced oscillation data, unsteady pressure data was collected at a single spanwise station at transonic Mach numbers, several values of dynamic pressure, and fixed angles of attack. It is acknowledged that the current experimental data are limited, but future experiments are planned to obtain pressure data at two wingspan stations in addition to unsteady Pressure Sensitive Paint (uPSP). With that, the computational results presented here are considered to be semi-blind. The results are presented in the form of time-varying pressure coefficients, running-mean values of the pressure coefficients, Power Spectral Density (PSD) and Strouhal number computations. The results are split into two parts. In the first part, the computational results obtained by each team using their flow solvers and grids are compared. In the second part, a single computational tool was used to compute unsteady pressure on participant-generated grids. In addition, a description of each participating team’s software and methods is included.

Pawel Chwalowski↗

NASA NTRS1976

Aviation safety research and transportation/hazard avoidance and elimination

Data collected by the Scanning Laser Doppler Velocimeter System (SLDVS) was analyzed to determine the feasibility of the SLDVS for monitoring aircraft wake vortices in an airport environment. Data were collected on atmospheric vortices and analyzed. Over 1600 landings were monitored at Kennedy International Airport and by the end of the test period 95 percent of the runs with large aircraft were producing usable results in real time. The transport was determined in real time and post analysis using algorithms which performed centroids on the highest amplitude in the thresholded spectrum. Making use of other parameters of the spectrum, vortex flow fields were studied along with the time histories of peak velocities and amplitudes. The post analysis of the data was accomplished with a CDC-6700 computer using several programs developed for LDV data analysis.

Sonnenschein, C. M.↗

NASA NTRS2012

Two Years Onboard the MER Opportunity Rover

The Autonomous Exploration for Gathering Increased Science (AEGIS) system provides automated data collection for planetary rovers. AEGIS is currently being used onboard the Mars Exploration Rover (MER) mission's Opportunity to provide autonomous targeting of the MER Panoramic camera. Prior to AEGIS, targeted data was collected in a manual fashion where targets were manually identified in images transmitted to Earth and the rover had to remain in the same location for one to several communication cycles. AEGIS enables targeted data to be rapidly acquired with no delays for ground communication. Targets are selected by AEGIS through the use of onboard data analysis techniques that are guided by scientist-specified objectives. This paper provides an overview of the how AEGIS has been used on the Opportunity rover, focusing on usage that occurred during a 21 kilometer historic trek to the Mars Endeavour crater.

Mars Exploration Rover (MER)↗