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At least 1,657 records · Page 92

Calibration support for the Earth Observing System Project

The Earth Observing System Project (EOS) program guidelines establishes significantly more stringent requirements on calibrations of instruments. This requirement is driven by the need for long-term continuity of acquired data sets and the use of measurements in interdisciplinary investigations. Personnel from the Standards and Calibration Office have been supporting the Program and Project in interpreting these goals into specific requirements. Contributions to EOS have included participation in the Panel of Experts which produced a list of consensus items necessary for accomplishing an accurate calibration and suggested EOS Project Calibration Policy, and drafting the announcement of opportunity and bidders information package positions on instrument calibration and data product validation. Technical staffing was provided to the NASA delegates to the Committee on Earth Orbiting Satellites (club of space-faring nations) for the standing working group on Calibration and Data Validation.

Guenther, B. W.↗

Extreme Ultraviolet Imaging Telescope (EIT)

Efforts concentrated on development and implementation of the SolarSoft (SSW) data analysis system. From an EIT analysis perspective, this system was designed to facilitate efficient reuse and conversion of software developed for Yohkoh/SXT and to take advantage of a large existing body of software developed by the SDAC, Yohkoh, and SOHO instrument teams. Another strong motivation for this system was to provide an EIT analysis environment which permits coordinated analysis of EIT data in conjunction with data from important supporting instruments, including Yohkoh/SXT and the other SOHO coronal instruments; CDS, SUMER, and LASCO. In addition, the SSW system will support coordinated EIT/TRACE analysis (by design) when TRACE data is available; TRACE launch is currently planned for March 1998. Working with Jeff Newmark, the Chianti software package (K.P. Dere et al) and UV /EUV data base was fully integrated into the SSW system to facilitate EIT temperature and emission analysis.

Lemen, J. R.↗

SAS PDF: pair distribution function analysis of nanoparticle assemblies from small-angle scattering data

SASPDF, a method for characterizing the structure of nanoparticle assemblies (NPAs), is presented. The method is an extension of the atomic pair distribution function (PDF) analysis to the small-angle scattering (SAS) regime. The PDFgetS3 software package for computing the PDF from SAS data is also presented. An application of the SASPDF method to characterize structures of representative NPA samples with different levels of structural order is then demonstrated. The SASPDF method quantitatively yields information such as structure, disorder and crystallite sizes of ordered NPA samples. The method was also used to successfully model the data from a disordered NPA sample. The SASPDF method offers the possibility of more quantitative characterizations of NPA structures for a wide class of samples.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

SkICAT: A cataloging and analysis tool for wide field imaging surveys

We describe an integrated system, SkICAT (Sky Image Cataloging and Analysis Tool), for the automated reduction and analysis of the Palomar Observatory-ST ScI Digitized Sky Survey. The Survey will consist of the complete digitization of the photographic Second Palomar Observatory Sky Survey (POSS-II) in three bands, comprising nearly three Terabytes of pixel data. SkICAT applies a combination of existing packages, including FOCAS for basic image detection and measurement and SAS for database management, as well as custom software, to the task of managing this wealth of data. One of the most novel aspects of the system is its method of object classification. Using state-of-theart machine learning classification techniques (GID3* and O-BTree), we have developed a powerful method for automatically distinguishing point sources from non-point sources and artifacts, achieving comparably accurate discrimination a full magnitude fainter than in previous Schmidt plate surveys. The learning algorithms produce decision trees for classification by examining instances of objects classified by eye on both plate and higher quality CCD data. The same techniques will be applied to perform higher-level object classification (e.g., of galaxy morphology) in the near future. Another key feature of the system is the facility to integrate the catalogs from multiple plates (and portions thereof) to construct a single catalog of uniform calibration and quality down to the faintest limits of the survey. SkICAT also provides a variety of data analysis and exploration tools for the scientific utilization of the resulting catalogs. We include initial results of applying this system to measure the counts and distribution of galaxies in two bands down to Bj is approximately 21 mag over an approximate 70 square degree multi-plate field from POSS-II. SkICAT is constructed in a modular and general fashion and should be readily adaptable to other large-scale imaging surveys.

Weir, N.↗

Processed sap flow and fine-root trait data associated with summer drought responses in temperate trees in Lisle, Illinois, USA (2019–2021)

These data support the manuscript “Acquisitive root exploration strategies help maintain higher peak sap flux rates during summer drought, but more root biomass does not”. The dataset includes processed sap flow measurements and fine-root trait data collected between 2019 and 2021 from temperate monodominant tree plots established in the 1920s to 1930s ranging in size from 0.05 to 0.8 ha at The Morton Arboretum in Lisle, IL. Sap flow was measured with ICT sap flow sensors using the heat ratio method. Fine-root traits were measured from soil cores which includes specific root length (SRL), specific root area (SRA), diameter, biomass, and length for diameter classes ≤1 mm and ≤2 mm. The package contains comma separated value (CSV) data files and associated metadata that can be viewed and analyzed using common software such as spreadsheet programs, R, and Python. These data are used to investigate how variation in fine-root traits relate to tree water use and drought response during summer drought linking belowground root traits and aboveground physiological responses.

drought↗

Integrated Laser Characterization, Data Acquisition, and Command and Control Test System

Satellite-based laser technology has been developed for topographical measurements of the Earth and of other planets. Lasers for such missions must be highly efficient and stable over long periods in the temperature variations of orbit. In this innovation, LabVIEW is used on an Apple Macintosh to acquire and analyze images of the laser beam as it exits the laser cavity to evaluate the laser s performance over time, and to monitor and control the environmental conditions under which the laser is tested. One computer attached to multiple cameras and instruments running LabVIEW-based software replaces a conglomeration of computers and software packages, saving hours in maintenance and data analysis, and making very longterm tests possible. This all-in-one system was written primarily using LabVIEW for Mac OS X, which allows the combining of data from multiple RS-232, USB, and Ethernet instruments for comprehensive laser analysis and control. The system acquires data from CCDs (charge coupled devices), power meters, thermistors, and oscilloscopes over a controllable period of time. This data is saved to an html file that can be accessed later from a variety of data analysis programs. Also, through the LabVIEW interface, engineers can easily control laser input parameters such as current, pulse width, chiller temperature, and repetition rates. All of these parameters can be adapted and cycled over a period of time.

Stysley, Paul↗

An Overview of the Design and Development of the Geostationary Operational Environmental Satellite R-Series (GOES-R) Space Segment

The first of the National Oceanic and Atmospheric Administration (NOAA) Geostationary Operational Environmental Satellite R-series (GOES-R) satellites was launched in November 2016. GOES-R has been developed by NOAA in partnership with the National Aeronautics and Space Administration (NASA). The satellite represents a quantum leap in the state of the art for geostationary weather satellites by providing data from a suite of six new instruments. All instruments were developed expressly for this mission, and include two Earth-observing instruments (the Advanced Baseline Imager (ABI) and Geostationary Lightning Mapper (GLM)), two solar-viewing instruments (Solar Ultraviolet Imager (SUVI) and Extreme ultraviolet and X-ray Irradiance Sensors (EXIS)) and two in situ instruments (Space Environment In-Situ Suite (SEISS) and a magnetometer pair). In addition to hosting the instruments, GOES-R also accommodates several communication packages designed to collect and relay data for weather forecasting and emergency management. Accommodating the six instruments and four communication payloads imposed challenging and competing constraints on the satellite, including requirements for extremely stable earth and solar pointing, high-speed and nearly error-free instrument data transmission, and a very quiet electromagnetic background. To meet mission needs, GOES-R employed several technological innovations, including low-thrust rocket engines that allow instrument observations to continue during maneuvers, and the first civilian use of Global Positioning System-based orbit determination in geostationary orbit. This paper will provide a brief overview of the GOES-R satellite and its instruments as well as the developmental challenges involved in accommodating the instruments and communications payloads.

SUVI↗

An Overview of the Design and Development of the GOES R-Series Space Segment

The first of the National Oceanic and Atmospheric Administration (NOAA) Geostationary Operational Environmental Satellite R-series (GOES-R) satellites was launched in November 2016. GOES-R has been developed by NOAA in partnership with the National Aeronautics and Space Administration (NASA). The satellite represents a quantum leap in the state of the art for geostationary weather satellites by providing data from a suite of six new instruments. All instruments were developed expressly for this mission, and include two Earth-observing instruments (the Advanced Baseline Imager (ABI) and Geostationary Lightning Mapper (GLM)), two solar-viewing instruments (Solar Ultraviolet Imager (SUVI) and Extreme ultraviolet and X-ray Irradiance Sensors (EXIS)) and two in situ instruments (Space Environment In-Situ Suite (SEISS) and a magnetometer pair). In addition to hosting the instruments, GOES-R also accommodates several communication packages designed to collect and relay data for weather forecasting and emergency management. Accommodating the six instruments and four communication payloads imposed challenging and competing constraints on the satellite, including requirements for extremely stable earth and solar pointing, high-speed and nearly error-free instrument data transmission, and a very quiet electromagnetic background. To meet mission needs, GOES-R employed several technological innovations, including low-thrust rocket engines that allow instrument observations to continue during maneuvers, and the first civilian use of Global Positioning System-based orbit determination in geostationary orbit. This paper will provide a brief overview of the GOES-R satellite and its instruments as well as the developmental challenges involved in accommodating the instruments and communications payloads.

GOES-16↗

Space Acceleration Measurement System for Free Flyers

Experimenters from the fluids, combustion, materials, and life science disciplines all use the microgravity environment of space to enhance their understanding of fundamental physical phenomena caused by disturbances from events such as spacecraft maneuvers, equipment operations, atmospheric drag, and (for manned flights) crew movement. Space conditions reduce gravity but do not eliminate it. To quantify the level of these disturbances, NASA developed the Space Acceleration Measurement System (SAMS) series to collect data characterizing the acceleration environment on the space shuttles. This information is provided to investigators so that they can evaluate how the microgravity environment affects their experiments. Knowledge of the microgravity environment also helps investigators to plan future experiments. The original SAMS system flew 20 missions on the shuttle as well as on the Russian space station Mir. Presently, Lewis is developing SAMS-II for the International Space Station; it will be a distributed system using digital output sensor heads. The latest operational version of SAMS, SAMS-FF, was originally designed for free flyer spacecraft and unmanned areas. SAMS-FF is a flexible, modular system, housed in a lightweight package, and it uses advances in technology to improve performance. The hardware package consists of a control and data acquisition module, three different types of sensors, data storage devices, and ground support equipment interfaces. Three different types of sensors are incorporated to measure both high- and low-frequency accelerations and the roll rate velocity. Small, low-power triaxial sensor heads (TSH's) offer high resolution and selectable bandwidth, and a special low-frequency accelerometer is available for high-resolution, low-frequency applications. A state-of-the-art, triaxial fiberoptic gyroscope that measures extremely low roll rates is housed in a compact package. The versatility of the SAMS-FF system is shown in the three different types of missions SAMS-FF has supported.

Kacpura, Thomas J.↗

Overview of shuttle data on density

The HiRAP (High Resolution Accelerometer Package) used on the Shuttle is described and examples of flight-derived density-altitude profiles are compared to the 1976 Standard Atmosphere. By flying an accelerometer along with a mass spectrometer it is possible to obtain the drag coefficients for the Shuttle. However, problems may arise due to contamination in the near-shuttle environment.

Blanchard, Robert↗

Monitoring Disasters by Use of Instrumented Robotic Aircraft

Efforts are under way to develop data-acquisition, data-processing, and data-communication systems for monitoring disasters over large geographic areas by use of uninhabited aerial systems (UAS) robotic aircraft that are typically piloted by remote control. As integral parts of advanced, comprehensive disaster- management programs, these systems would provide (1) real-time data that would be used to coordinate responses to current disasters and (2) recorded data that would be used to model disasters for the purpose of mitigating the effects of future disasters and planning responses to them. The basic idea is to equip UAS with sensors (e.g., conventional video cameras and/or multispectral imaging instruments) and to fly them over disaster areas, where they could transmit data by radio to command centers. Transmission could occur along direct line-of-sight paths and/or along over-the-horizon paths by relay via spacecraft in orbit around the Earth. The initial focus is on demonstrating systems for monitoring wildfires; other disasters to which these developments are expected to be applicable include floods, hurricanes, tornadoes, earthquakes, volcanic eruptions, leaks of toxic chemicals, and military attacks. The figure depicts a typical system for monitoring a wildfire. In this case, instruments aboard a UAS would generate calibrated thermal-infrared digital image data of terrain affected by a wildfire. The data would be sent by radio via satellite to a data-archive server and image-processing computers. In the image-processing computers, the data would be rapidly geo-rectified for processing by one or more of a large variety of geographic-information- system (GIS) and/or image-analysis software packages. After processing by this software, the data would be both stored in the archive and distributed through standard Internet connections to a disaster-mitigation center, an investigator, and/or command center at the scene of the fire. Ground assets (in this case, firefighters and/or firefighting equipment) would also be monitored in real time by use of Global Positioning System (GPS) units and radio communication links between the assets and the UAS. In this scenario, the UAS would serve as a data-relay station in the sky, sending packets of information concerning the locations of assets to the image-processing computer, wherein this information would be incorporated into the geo-rectified images and maps. Hence, the images and maps would enable command-center personnel to monitor locations of assets in real time and in relation to locations affected by the disaster. Optionally, in case of a disaster that disrupted communications, the UAS could be used as an airborne communication relay station to partly restore communications to the affected area. A prototype of a system of this type was demonstrated in a project denoted the First Response Experiment (Project FiRE). In this project, a controlled outdoor fire was observed by use of a thermal multispectral scanning imager on a UAS that delivered image data to a ground station via a satellite uplink/ downlink telemetry system. At the ground station, the image data were geo-rectified in nearly real time for distribution via the Internet to firefighting managers. Project FiRE was deemed a success in demonstrating several advances essential to the eventual success of the continuing development effort.

Wegener, Steven S.↗

An Intelligent Adaptable Monitoring Package. Final Report

The “Intelligent Adaptable Monitoring Package” project was a four-year effort that demonstrated the feasibility of integrated sensing packages at tidal and wave energy sites. Such integration is generally required by the breadth of sensors required to understand environmental effects at marine energy sites and the operational difficulty of deploying, maintaining, and recovering such sensors. Over the course of the project, the Adaptable Monitoring Package (AMP) was deployed in multiple settings, each corresponding to a project budget period: - Budget Period 1: Demonstration of cabled deployment at Pacific Northwest National Laboratory’s Marine Science Laboratory. The deployment highlighted AMP hardware endurance over a 4-month deployment in a tidally-dominated environment and laid the groundwork for machine learning algorithms to detect and classify targets present in active sonar data. - Budget Period 2: Demonstration of an autonomous deployment at PacWave South off the coast of Newport, Oregon. The deployment highlighted the stability of AMP hardware and software, with the autonomous package collecting data on a duty cycle over a 1.5-month deployment. - Budget Period 3: Demonstration of an autonomous deployment powered by a wave energy converter at the U.S. Navy’s Wave Energy Test Site. The deployment highlighted the potential of wave energy to power ocean observatories and led to the development of machine learning algorithms to detect and classify targets in optical camera data. In aggregate, this project’s greatest success was demonstrating the AMP’s flexibility in a range of deployment scenarios. Each budget period represented a “first of a kind” demonstration of integrated instrumentation – cabled AMP, autonomous AMP, wave-energy powered AMP – and each deployment helped to identify and set goals for the next. Further, despite the exploratory nature of these deployments, each one achieved high system up-time and proved that flexible integration of multiple sensors in a single package represents a viable strategy for marine energy environmental monitoring. The key lessons learned from the project are: - Without continuous power, either from a shore cable or in situ source, many of the benefits of integration are lost (If continuous power is not available, the ability to detect rare events is lost, as is the ability to minimize the risk of behavioral changes through adaptive sensing. However, even on a duty cycle, there is still value in being able to acquire synchronous data from multiple sensors.); and - Observations from a moving platform present substantially greater data processing challenges than those from stationary platforms. Finally, these deployments also demonstrate an important truth: successful integration alone does not guarantee that relevant data are collected. To grow the knowledge base about environmental interactions with marine energy converters, integrated systems, like the AMP, need to include the right sensor mix and connect the data pipelines to effective processing algorithms. These deployments establish a strong foundation for future collaborations with the environmental research community: not only to understand the environmental effects of marine energy, but also to improve our general ability to study life in the sea.

16 TIDAL AND WAVE POWER↗

TomocuPy – efficient GPU-based tomographic reconstruction with asynchronous data processing

Fast 3D data analysis and steering of a tomographic experiment by changing environmental conditions or acquisition parameters require fast, close to real-time, 3D reconstruction of large data volumes. Here a performance-optimized TomocuPy package is presented as a GPU alternative to the commonly used central processing unit (CPU) based TomoPy package for tomographic reconstruction. TomocuPy utilizes modern hardware capabilities to organize a 3D asynchronous reconstruction involving parallel read/write operations with storage drives, CPU–GPU data transfers, and GPU computations. In the asynchronous reconstruction, all the operations are timely overlapped to almost fully hide all data management time. Since most cameras work with less than 16-bit digital output, the memory usage and processing speed are furthermore optimized by using 16-bit floating-point arithmetic. As a result, 3D reconstruction with TomocuPy became 20–30 times faster than its multi-threaded CPU equivalent. Full reconstruction (including read/write operations and methods initialization) of a 2048 3 tomographic volume takes less than 7 s on a single Nvidia Tesla A100 and PCIe 4.0 NVMe SSD, and scales almost linearly increasing the data size. To simplify operation at synchrotron beamlines, TomocuPy provides an easy-to-use command-line interface. Efficacy of the package was demonstrated during a tomographic experiment on gas-hydrate formation in porous samples, where a steering option was implemented as a lens-changing mechanism for zooming to regions of interest.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

The Future is Hera! Analyzing Astronomical Over the Internet

Hera is the data processing facility provided by the High Energy Astrophysics Science Archive Research Center (HEASARC) at the NASA Goddard Space Flight Center for analyzing astronomical data. Hera provides all the pre-installed software packages, local disk space, and computing resources need to do general processing of FITS format data files residing on the users local computer, and to do research using the publicly available data from the High ENergy Astrophysics Division. Qualified students, educators and researchers may freely use the Hera services over the internet of research and educational purposes.

Valencic, L. A.↗

Analysis of space radiation data of semiconductor memories

This article presents an analysis of radiation effects for several select device types and technologies aboard the Combined Release and Radiation Effects Satellite (CRRES) satellite. These space-flight measurements covered a period of about 14 months of mission lifetime. Single Event Upset (SEU) data of the investigated devices from the Microelectronics Package (MEP) were processed and analyzed. Valid upset measurements were determined by correcting for invalid readings, hard failures, missing data tapes (thus voids in data), and periods over which devices were disabled from interrogation. The basic resolution time of the measurement system was confirmed to be 2 s. Lessons learned, important findings, and recommendations are presented.

Flight Experiment↗

Evaluating Model Performance of an Ensemble-based Chemical Data Assimilation System During INTEX-B Field Mission

We present a global chemical data assimilation system using a global atmosphere model, the Community Atmosphere Model (CAM3) with simplified chemistry and the Data Assimilation Research Testbed (DART) assimilation package. DART is a community software facility for assimilation studies using the ensemble Kalman filter approach. Here, we apply the assimilation system to constrain global tropospheric carbon monoxide (CO) by assimilating meteorological observations of temperature and horizontal wind velocity and satellite CO retrievals from the Measurement of Pollution in the Troposphere (MOPITT) satellite instrument. We verify the system performance using independent CO observations taken on board the NSFINCAR C-130 and NASA DC-8 aircrafts during the April 2006 part of the Intercontinental Chemical Transport Experiment (INTEX-B). Our evaluations show that MOPITT data assimilation provides significant improvements in terms of capturing the observed CO variability relative to no MOPITT assimilation (i.e. the correlation improves from 0.62 to 0.71, significant at 99% confidence). The assimilation provides evidence of median CO loading of about 150 ppbv at 700 hPa over the NE Pacific during April 2006. This is marginally higher than the modeled CO with no MOPITT assimilation (-140 ppbv). Our ensemble-based estimates of model uncertainty also show model overprediction over the source region (i.e. China) and underprediction over the NE Pacific, suggesting model errors that cannot be readily explained by emissions alone. These results have important implications for improving regional chemical forecasts and for inverse modeling of CO sources and further demonstrate the utility of the assimilation system in comparing non-coincident measurements, e.g. comparing satellite retrievals of CO with in-situ aircraft measurements. The work described above also brought to light several short-comings of the data assimilation approach for CO profiles. Because of the limited vertical resolution of the measurement, the retrievals at different altitudes are correlated which can lead to problems with numerical error and overall efficiency. This has resulted in a manuscript that is about to be submitted to JGR:

Arellano, A. F., Jr.↗

High redshift QSOs and the x ray background

ROSAT pointed observations were made of 9 QSO's from the Large Bright Quasar Survey (LBQS). The LBQS is based on machine measurement of objective prism plates taken with the UK Schmidt Telescope. Software has been used to select QSO's by both color and by the presence of spectral features and continuum breaks. The probability of detection can be calculated as a function of magnitude, redshift and spectral features, and the completeness of the survey can be accurately estimated. Nine out of 1040 QSO's in the LBQS have z greater than 3. The observations will provide an important data point in the X-ray luminosity function of QSO's at high redshift. The QSO's with z greater than 3 span less than a magnitude in M(sub B), so can be combined as a homogeneous sample. This analysis is only possible with a sample drawn from a large and complete catalog such as the LBQS. Four of the 9 QSO's that were observed with the ROSAT PSPC for this proposal were detected, including one of the most luminous X-ray sources ever observed. The April 1992 version of the PROS DETECT package was used to reduce the data. The results have been used to search for evolution of the X-ray properties of QSO's in redshift. The 9 QSO's lie in the range -28.7 less than M(sub B) less than -27.8. When combined with data for 16 QSO's in a similar luminosity range at lower redshift correlations with luminosity and redshift can be separated out. The LBQS sample also yields a new constraint on the contribution of high redshift QSO's to the X-ray background. An initial requirement is knowledge of the X-ray properties (alpha(sub OX)) as a function of redshift. Integration over the evolving luminosity function of the LBQS then gives the QSO contribution to the source counts.

Impey, Chris↗