Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “science data processing”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 775 records · Page 43
All sourcesNASA NTRSDOE OSTI
NASA NTRS2017

Planetary Crater Detection and Registration Using Marked Point Processes, Multiple Birth and Death Algorithms, and Region-Based Analysis

Because of the large variety of sensors and spacecraft collecting data, planetary science needs to integrate various multi-sensor and multi-temporal images. These multiple data represent a precious asset, as they allow the study of targets spectral responses and of changes in the surface structure; because of their variety, they also require accurate and robust registration. A new crater detection algorithm, used to extract features that will be integrated in an image registration framework, is presented. A marked point process-based method has been developed to model the spatial distribution of elliptical objects (i.e. the craters) and a birth-death Markov chain Monte Carlo method, coupled with a region-based scheme aiming at computational efficiency, is used to find the optimal configuration fitting the image. The extracted features are exploited, together with a newly defined fitness function based on a modified Hausdorff distance, by an image registration algorithm whose architecture has been designed to minimize the computational time.

Image Processing; Pattern Recognition↗
NASA NTRS2018

Planetary Crater Detection and Registration Using Marked Point Processes, Multiple Birth and Death Algorithms, and Region-Based Analysis

Because of the large variety of sensors and spacecraft collecting data, planetary science needs to integrate various multi-sensor and multi-temporal images. These multiple data represent a precious asset, as they allow the study of targets spectral responses and of changes in the surface structure; because of their variety, they also require accurate and robust registration. A new crater detection algorithm, used to extract features that will be integrated in an image registration framework, is presented. A marked point process-based method has been developed to model the spatial distribution of elliptical objects (i.e. the craters) and a birth-death Markov chain Monte Carlo method, coupled with a region-based scheme aiming at computational efficiency, is used to find the optimal configuration fitting the image. The extracted features are exploited, together with a newly defined fitness function based on a modified Hausdorff distance, by an image registration algorithm whose architecture has been designed to minimize the computational time.

Image Processing:Pattern Recognition↗
NASA NTRS2006

Rapid, Repeat-sample Monitoring of Crustal Deformations and Environmental Phenomena with the Uninhabited Aerial Vehicle Synthetic Aperture Radar

The Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) is a precision repeat-pass Interferometric Synthetic Aperture Radar (InSAR) mission being developed by the Jet Propulsion Laboratory and the Dryden Flight Research Center in support of NASA s Science Mission Directorate. UAVSAR's unique ability to fly a repeatable flight path, along with an electronically steerable array, allows interferometric data to be obtained with accuracies measured in millimeters. Deploying the radar on an airborne platform will also allow for radar images to be collected and compared with images from the same area taken hours or even years later - providing for long-term trending and near real-time notification of changes and deformations. UAVSAR s data processing algorithms will provide for near-real time data reduction providing disaster planning and response teams with highly accurate data to aid in the prediction of, and response to, natural phenomena. UAVSAR data can be applied to increasing our understanding of the processes behind solid earth, cryosphere, carbon cycle and other areas of interest in earth science. Technologies developed for UAVSAR may also be applicable to a future earth-orbiting InSAR mission and possibly for missions to the Moon or Mars. The UAVSAR is expected to fly on a Gulfstream III aircraft this winter, followed by a flight test program lasting until the second half of 2007. Following radar calibration and data reduction activities, the platform will be ready for science users in the summer of 2008.

Smith, Robert C.↗
NASA NTRSDate not supplied

Language Model For Earth Science: Exploring Potential Downstream Applications As Well As Current Challenges

The use of deep learning techniques to build transformer language models such as SciBERT and GPT3 have transformed the natural language technology (NLT) landscape. These new NLTs are being used in speech to text and vice versa, auto-mated text classification, sentiment analysis, topic modeling, text summarization, and cognitive assistants. While Earth science has no shortage of unstructured data such as journal and conference papers, little efforts have focused on harnessing NLTs for knowledge extraction and supporting the scientific process. This paper surveys the use of language models in different science. BERT-E, a new Earth science-specific language model, is presented. BERT-E is generated using a transfer learning solution. A language model that has already been trained for general Science (SciBERT) is fine-tuned using abstracts and full text extracted from various Earth science-related articles. A downstream keywords classification application is used for evaluation, and the use of BERT-E shows improved performance. The need to develop a robust set of benchmarks in evaluating the language model such as BERT-E is discussed. Finally, example applications are presented to inspire additional ideas for applications using domain-specific language models.

R Ramachandran↗
NASA NTRS1991

Data compression for data archival, browse or quick-look

Soon after space and Earth science data is collected, it is stored in one or more archival facilities for later retrieval and analysis. Since the purpose of the archival process is to keep an accurate and complete record of data, any data compression used in an archival system must be lossless, and protect against propagation of error in the storage media. A browse capability for space and Earth science data is needed to enable scientists to check the appropriateness and quality of particular data sets before obtaining the full data set(s) for detailed analysis. Browse data produced for these purposes could be used to facilitate the retrieval of data from an archival facility. Quick-look data is data obtained directly from the sensor for either previewing the data or for an application that requires very timely analysis of the space or Earth science data. Two main differences between data compression techniques appropriate to browse and quick-look cases, are that quick-look can be more specifically tailored, and it must be limited in complexity by the relatively limited computational power available on space platforms.

Dozier, Jeff↗
NASA NTRS2024

Improved Soil Moisture Estimation and Detection of Irrigation Signal By Incorporating SMAP Soil Moisture Into the Indian Land Data Assimilation System (ILDAS)

Land surface models have facilitated the estimation of soil moisture over a range of spatiotemporal scales. However, limitations in model parameterization and under-representation of anthropogenic processes restrict their ability to estimate local-scale soil moisture variability, especially over irrigated areas. Assimilation of satellite-based soil moisture retrievals into land surface models can be a viable approach to overcome these constraints, specially over highly irrigated countries such as India, where such applications are rare. Additionally, large-scale validation of modeled soil moisture has been limited over India till now due to lack of a representative station network. By assimilating Soil Moisture Active Passive (SMAP)-based estimates into the state-of-the-art Indian Land Data Assimilation System (ILDAS) and combining with a new soil moisture station network of more than 200 stations, this study demonstrates improved soil moisture estimations and capture of irrigation signals over the region. The Noah-MP land surface model is forced by multiple local and global meteorological datasets and Ensemble Kalman Filter (EnKF) is used for assimilation of soil moisture. Comparison of open-loop and data assimilated soil moisture against station soil moisture data shows relative spatial mean improvement of 0.0178 in correlation and 0.0029 m3/m3 in RMSE. Further statistical comparison with in-situ data has also shown better results over most of the stations, as evident from improved correlations and reduced unbiased RMSE after assimilation. Finally, the climatology of soil moisture over the different irrigation fractions reveals that data assimilated outputs over irrigated grid cells tend to have higher soil moisture during dry winter season, demonstrating the ability to capture irrigation signals. These findings quantify the value of data assimilation in improving soil moisture estimates and the ability to capture unmodeled processes such as irrigation, which lays the science groundwork for upcoming space missions such as NASA ISRO Synthetic Aperture Radar (NISAR).

Soil Moisture↗
NASA NTRS2013

Shuttle Topography Data Inform Solar Power Analysis

The next time you flip on a light switch, there s a chance that you could be benefitting from data originally acquired during the Space Shuttle Program. An effort spearheaded by Jet Propulsion Laboratory (JPL) and the National Geospatial-Intelligence Agency (NGA) in 2000 put together the first near-global elevation map of the Earth ever assembled, which has found use in everything from 3D terrain maps to models that inform solar power production. For the project, called the Shuttle Radar Topography Mission (SRTM), engineers at JPL designed a 60-meter mast that was fitted onto Shuttle Endeavour. Once deployed in space, an antenna attached to the end of the mast worked in combination with another antenna on the shuttle to simultaneously collect data from two perspectives. Just as having two eyes makes depth perception possible, the SRTM data sets could be combined to form an accurate picture of the Earth s surface elevations, the first hight-detail, near-global elevation map ever assembled. What made SRTM unique was not just its surface mapping capabilities but the completeness of the data it acquired. Over the course of 11 days, the shuttle orbited the Earth nearly 180 times, covering everything between the 60deg north and 54deg south latitudes, or roughly 80 percent of the world s total landmass. Of that targeted land area, 95 percent was mapped at least twice, and 24 percent was mapped at least four times. Following several years of processing, NASA released the data to the public in partnership with NGA. Robert Crippen, a member of the SRTM science team, says that the data have proven useful in a variety of fields. "Satellites have produced vast amounts of remote sensing data, which over the years have been mostly two-dimensional. But the Earth s surface is three-dimensional. Detailed topographic data give us the means to visualize and analyze remote sensing data in their natural three-dimensional structure, facilitating a greater understanding of the features and processes taking place on Earth."

Source record↗
NASA NTRS2001

ICESat (GLAS) Science Processing Software Document Series: GLAS Science Software Requirements Document - Volume 3

This document addresses the software requirements of the Geoscience Laser Altimeter System (GLAS) Standard Data Software (SDS) supporting the GLAS instrument on the EOS ICESat Spacecraft. This Software Requirements Document represents the initial collection of the technical engineering information for the GLAS SDS. This information is detailed within the second of four main volumes of the Standard documentation, the Product Specification volume. This document is a "roll-out" from the governing volume outline containing the Concept and Requirements sections.

Jester, Peggy L.↗
NASA NTRS2019

Planetary Boundary Layer Height from AIRS and MERRA-2 Products at NASA GES DISC, and Insights from Data Intercomparison

The Atmospheric Infrared Sounder (AIRS) is the hyperspectral infrared sounder onboard NASA's Aqua satellite, launched in 2002. The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), in collaboration with NASA Sounder Team at JPL, provides processing, archiving, and distribution services for NASA sounders: the Aqua AIRS mission and the subsequent Suomi-National Polar-orbiting Partnership Cross-track Infrared Sounder (CrIS) mission. The Planetary Boundary Layer (PBL) Height is a new variable added in the AIRS Version 6 support product. It is derived based on gradients of the retrieved atmospheric thermodynamic profile, and gives the pressure at the top of PBL over the ocean. The GES DISC also provides services for the second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) product generated by the Goddard Earth Observing System Model, Version 5 (GEOS-5) data assimilation system. The monthly PBL Height variable has been available in the Giovanni system, which is a Web-based application developed by the GES DISC providing a simple and intuitive way to visualize, analyze, and access vast amounts of Earth science remote sensing data. In this work, we will present the monthly PBL Height data from AIRS and MERRA-2 and the services to support data intercomparison, such as access, plotting, subsetting, re-gridding, and generation of a multi-year monthly mean. We will also show intercomparison results, and evaluate whether (over the ocean) AIRS can observe PBL features similar to the reanalysis product at monthly and longer-term scales.

AIRS↗
NASA NTRS2008

The NPOESS Preparatory Project (NPP) Science Data Segment (SDS) Data Depository and Distribution Element (SD3E) System Architecture

The National Polar-orbiting Operational Environmental Satellite System (NPOESS), the U.S. Government's future low-Earth orbiting satellite system, will monitor global weather and environmental conditions. Serving as a risk reduction for NPOESS, the NPOESS Preparatory Project (NPP) will provide remotely sensed atmospheric, land, ocean, ozone, and sounder data that will serve the meteorological and global climate change scientific communities. The National Aeronautics and Space Administration (NASA) NPP Science Data Segment's (SDS) primary role is to independently assess the quality of the NPP science and environmental data records for their ability to support climate research. The SDS is composed of nine elements; an input element that receives data from the operational agencies and acts as a buffer, a calibration analysis element, five elements devoted to measurement based quality assessment, an element used to test algorithmic improvements, and an element that provides overall science direction. Each element requires a set of sensor specific science data products for their evaluation. There are four NPP sensors that will be flown on the NPP observatory. They are the Visible Infrared Imagining Radiometer Suite (VIIRS), the Advanced Technology Microwave Sounder (ATMS), the Cross-Track Infrared Sounder (CrIS), and the Ozone Mapper/Profiler Suite (OMPS). It is estimated that these four sensors combined will make daily data requests for approximately six terabytes of NPP science products from the operational data providers. As a result, issues associated with duplicate data requests, data transfers of large volumes of diverse products, and data transfer failures raised concerns with respect to the network traffic and bandwidth consumption. Therefore, a central data broker system for receiving and buffering data requests and data products for the SDS was developed. The data element for this system is called the SDS Data Depository and Distribution Element (SD3E). It supports science mission data assessment by assuring the timely and validated acquisition and subsequent transfer of the NPP Science Mission data to the SDS Elements and NPP Science Team. The six science elements that interface with the SD3E span across the NASA Goddard Space Flight Center (GSFC), the NASA Jet Propulsion Laboratory (JPL), and the University of Wisconsin. As the primary communication vehicle for the science elements and science team, the SD3E has an interface to the operational data providers: National Environment Satellite, Data, and Information Service (NESDIS) Interface Data Processing System (IDPS) and the National Oceanic Atmospheric Administration's (NOAA) Comprehensive Large Array-data Stewardship system (CLASS) Archive Data System (ADS), that are responsible for product generation and archive and distribution respectively. The SD3E is designed to be a semi-customizable and semi-automated system. This system is designed to provide flexibility and ease of use for the science users in accessing the latest data products by creating a rolling data cache that temporarily stores the products locally before transferring the data to the SDS Measurement based elements for the land, ocean, atmosphere, sounder, and ozone. This paper describes the design and architecture of one of the nine SDS elements, the SD3E, and how this system has provided a mechanism for efficient data exchange, how it has helped in alleviating some of the network traffic and usage, and how it has contributed to reducing operational costs.

Ho, Evelyn L.↗
NASA NTRS2015

SRT Status and Plans for Version-7

The AIRS Science Team Version-6 retrieval algorithm is currently producing level-3 Climate Data Records (CDRs) from AIRS that have been proven useful to scientists in understanding climate processes. CDRs are gridded level-3 products which include all cases passing AIRS Climate QC. SRT has made significant further improvements to AIRS Version-6. Research is continuing at SRT toward the development of AIRS Version-7. At the last Science Team Meeting, we described results using SRT AIRS Version-6.19. SRT Version-6.19 is now an official build at JPL called 6.2. SRTs latest version is AIRS Version-6.22. We have also adapted AIRS Version-6.22 to run with CrISATMS. AIRS Version-6.22 and CrIS Version- 6.22 both run now on JPL computers, but are not yet official builds. The main reason for finalization of Version-7, and using it in the relatively near future for the future processing and reprocessing of old AIRS data, is to produce even better CDRs for use by climate scientists. For this reason all results shown in this talk use only AIRS Climate QC.

CDRs↗
NASA NTRSDate not supplied

Fostering Open Science Inclusiveness for Interdisciplinary Users of Earth Observations

The term Open Science is subject to a variety of interpretations because of a key (and useful) ambiguity in the meaning of “Open”. Open in the sense of Transparency enables more trust in science research by making the details of the scientific process visible and accessible to anyone. “Open” in the sense of Inclusiveness enables more scientists from other disciplines to participate in research in a given discipline, thus producing more interdisciplinary research. Data Systems can play a major role in enabling Open (Inclusive) Science by making it easier for users from other disciplines to work with data within a given discipline. This is challenging for Earth Observation datasets, most of which are the product of advanced instrumentation and sophisticated, specialized variable retrieval algorithms and code. Serving the “extra-disciplinary”communities begins with simple things, like accessible, readable data documentation with adequate scaffolding. But just as important is provisioning Analysis-Ready data that does not require expert pre-processing. Disciplines also often have dominant toolsets, such as R in the biomass community or GIS in many applications communities. Ensuring that EO data are easy to use in the tools favored in other communities will enable more interdisciplinary research. Ideally, interdisciplinary research also benefits from scientists with different domain expertise. Platforms and frameworks that facilitate frictionless collaboration with discipline experts, together with capacity building efforts in those external disciplines also improve the inclusiveness aspect of Open Science. In short, Open Science is at root a way of thinking about how users from diverse discipline can best access and use data and services from a particular discipline.

Christopher Lynnes↗
NASA NTRS1985

USSR Space Life Sciences Digest, issue 1

The first issue of the bimonthly digest of USSR Space Life Sciences is presented. Abstracts are included for 49 Soviet periodical articles in 19 areas of aerospace medicine and space biology, published in Russian during the first quarter of 1985. Translated introductions and table of contents for nine Russian books on topics related to NASA's life science concerns are presented. Areas covered include: botany, cardiovascular and respiratory systems, cybernetics and biomedical data processing, endocrinology, gastrointestinal system, genetics, group dynamics, habitability and environmental effects, health and medicine, hematology, immunology, life support systems, man machine systems, metabolism, musculoskeletal system, neurophysiology, perception, personnel selection, psychology, radiobiology, reproductive system, and space biology. This issue concentrates on aerospace medicine and space biology.

Hooke, L. R.↗
NASA NTRS1985

The AMPTE IRM Science Data Center

The creation of artificial ion clouds is a major aspect of the AMPTE program. The IRM Science Data center supports real-time decision for a release with real-time scientific data processing and display including model calculations of ion trajectories. Additionally, survey plots and summary data records are generated in near real time thus allowing to start the data analysis as early as possible. For detailed analyses, interactive programs were developed so that physical parameters of all IRM experiments can be combined to produce common spectra or line plots.

Bauer, O. H.↗
NASA NTRS1989

Research and technology 1989

The Marshall Space Flight Center annual report summarizes their advanced studies, research programs, and technological developments. Areas covered include: transportation systems; space systems such as Gravity Probe-B and Gamma Ray Imaging Telescope; data systems; microgravity science; astronomy and astrophysics; solar, magnetospheric, and atomic physics; aeronomy; propulsion; materials and processes; structures and dynamics; automated systems; space systems; and avionics.

Source record↗
NASA NTRS1994

Planning applications in image analysis

We describe two interim results from an ongoing effort to automate the acquisition, analysis, archiving, and distribution of satellite earth science data. Both results are applications of Artificial Intelligence planning research to the automatic generation of processing steps for image analysis tasks. First, we have constructed a linear conditional planner (CPed), used to generate conditional processing plans. Second, we have extended an existing hierarchical planning system to make use of durations, resources, and deadlines, thus supporting the automatic generation of processing steps in time and resource-constrained environments.

Boddy, Mark↗
NASA NTRS1996

Research and Technology Report. Goddard Space Flight Center

This issue of Goddard Space Flight Center's annual report highlights the importance of mission operations and data systems covering mission planning and operations; TDRSS, positioning systems, and orbit determination; ground system and networks, hardware and software; data processing and analysis; and World Wide Web use. The report also includes flight projects, space sciences, Earth system science, and engineering and materials.

Soffen, Gerald↗
NASA NTRS1993

Attitude Determination by Using Horizon and Sun Sensors

The Pointing and Alignment Workstation (PAWS) developed by Teledyne Brown Engineering (TBE) has successfully supported the first and second Atmospheric Laboratory for Applications and Science (ATLAS 1, 2) spacelab missions for NASA. The primary PAWS objective was to provide realtime pointing information to instruments whose line of-sight is dependent on Shuttle attitude and to study/quantify the causes and effects of Shuttle and payload pointing errors. In addition to Shuttle IMU attitude information, PAWS used atmospheric science sensors data to determine the spacecraft attitude. PAWS successfully achieved these goals by acquiring and processing data during the ATLAS 1, 2 mission. This paper presents the attitude determination algorithm real time processing, and results of post mission analysis. The findings of this study include the quality of the horizon sensor and IMU measurements as well as accuracy of attitude processor algorithm.

Huang, Allen K. H.↗