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Automated fire weather stations developed by NASA and now being used by the California Division of Forestry provide a means of predicting where a forest fire might occur. The stations I employ aerospace sensors and a combination of solar energy, wind energy and commercial power. To avoid interference by trees and mountains, weather data is relayed by satellite from outlying stations to a central fire protection office.
Solar cells, integrated circuits, and sensors are essential to manned and unmanned space flight and exploration, but such systems are highly susceptible to damage from radiation. Especially problematic, the Van Allen radiation belts encircle Earth in concentric radioactive tori at distances from about 6,300 to 38,000 km, though the inner radiation belt can dip as low as 700 km, posing a severe hazard to craft and humans leaving Earth s atmosphere. To avoid this radiation, the International Space Station and space shuttles orbit at altitudes between 275 and 460 km, below the belts range, and Apollo astronauts skirted the edge of the belts to minimize exposure, passing swiftly through thinner sections of the belts and thereby avoiding significant side effects. This radiation can, however, prove detrimental to improperly protected electronics on satellites that spend the majority of their service life in the harsh environment of the belts. Compact, high-performance electronics that can withstand extreme environmental and radiation stress are thus critical to future space missions. Increasing miniaturization of electronics addresses the need for lighter weight in launch payloads, as launch costs put weight at a premium. Likewise, improved memory technologies have reduced size, cost, mass, power demand, and system complexity, and improved high-bandwidth communication to meet the data volume needs of the next-generation high-resolution sensors. This very miniaturization, however, has exacerbated system susceptibility to radiation, as the charge of ions may meet or exceed that of circuitry, overwhelming the circuit and disrupting operation of a satellite. The Hubble Space Telescope, for example, must turn off its sensors when passing through intense radiation to maintain reliable operation. To address the need for improved data quality, additional capacity for raw and processed data, ever-increasing resolution, and radiation tolerance, NASA spurred the development of the Radiation Tolerant Intelligent Memory Stack (RTIMS).
A system, method, and computer-readable storage devices for a 6U CubeSat with a magnetometer boom. The example 6U CubeSat can include an on-board computing device connected to an electrical power system, wherein the electrical power system receives power from at least one of a battery and at least one solar panel, a first fluxgate sensor attached to an extendable boom, a release mechanism for extending the extendable boom, at least one second fluxgate sensor fixed within the satellite, an ion neutral mass spectrometer, and a relativistic electron/proton telescope. The on-board computing device can receive data from the first fluxgate sensor, the at least one second fluxgate sensor, the ion neutral mass spectrometer, and the relativistic electron/proton telescope via the bus, and can then process the data via an algorithm to deduce a geophysical signal.
Mesoscale periodic structures observed in solar wind plasma serve as an important diagnostic tool for constraining the processes that govern the formation of the solar wind. These structures have been observed in situ and in remote data as fluctuations in proton and electron density. However, only two events of this type have been reported regarding the elemental and ionic composition. Composition measurements are especially important in gaining an understanding of the origin of the solar wind as the composition is frozen into the plasma at the Sun and does not evolve as it advects through the heliosphere. Here, we present the analysis of four events containing mesoscale periodic solar wind structure during which the Iron and Magnesium number density data, measured by the Solar Wind Ion Composition Spectrometer (SWICS)on board the Advanced Composition Explorer spacecraft, are validated at statistically significant count levels. We use a spectral analysis method specifically designed to extract periodic signals from astrophysical time series and apply it to the SWICS 12-minute native resolution dataset. We find variations in the relative abundance of elements with low first ionization potential, mass dependencies, and charge state during time intervals in which mesoscale periodic structures are observed. These variations are linked to temporal or spatial variations in solar source regions and put constraints on the solar wind formation mechanisms that produce them. Techniques presented here are relevant for future, higher-resolution studies of data from new instruments such as Solar Orbiter’s Heavy Ion Sensor.
Unlike transmission systems, distribution systems historically lack enough measurements, making their real-time monitoring almost impossible. Recent deployment of diverse types of devices such as phasor measurement units (PMUs), smart meters, solar inverters and weather information sensors opens up new ways of monitoring these systems, with the assistance of customized machine learning (ML) applications. The paper describes a grid-model-informed machine learning (ML) tool which integrates heterogeneous data streams and creates synchronous measurement snapshots to be used by a hybrid robust state estimator (SE) which provides not only accurate state estimates but also real-time feedback for ML model refinement. Improved monitoring performance due to the use of developed computational framework is experimentally observed by simulated scenarios on an electric utility’s distribution system.
Snow reflectance in all 6 TM reflective bands, i.e., 1, 2, 3, 4, 5, and 7 was simulated using a delta-Eddington model. Snow reflectance in bands 4, 5, and 7 appear sensitive to grain size. It appears that the TM filters resemble a ""square-wave'' closely enough that a square-wave can be assumed in calculations. Integrated band reflectance over the actual response functions was calculated using sensor data supplied by Santa Barbara Research Center. Differences between integrating over the actual response functions and the equivalent square wave were negligible. Tables are given which show (1) sensor saturation radiance as a percentage of the solar constant, integrated through the band response function; (2) comparisons of integrations through the sensor response function with integrations over the equivalent square wave; and (3) calculations of integrated reflectance for snow over all reflective TM bands, and water and ice clouds with thickness of 1 mm water equivalent over TM bands 5 and 7. These calculations look encouraging for snow/cloud discrimination with TM bands 5 and 7.
The cavity pyrheliometer sensor of the Nimbus 7 Earth Radiation Experiment indicated low-level variability of the total solar irradiance. The variability appears to be inversely correlated with common solar activity indicators in an event sense. the limitations of the measuring system and available data sets are described.
Facets of sensor lighting conditions for Earth observatory satellite missions are considered. Assuming onboard sensors of a given width viewing perpendicular to the subsatellite ground track along sun-synchronous orbits with various nodes, the ground trace of the ends of the sensor coverage were found, as well as the variation in solar illumination on the ground across the line covered by the sensor during the day for any point along the orbit. The changes with season and variation during the year were also found.
PV power and irradiance sensor-based measurements are prone to error, resulting in issues such as abrupt time series data shifts. These shifts, which are usually unintentional, may be caused by software or hardware configuration changes on a PV system, and do not reflect an actual change in overall system performance. Locating these shifts and segmenting the associated time series aids in more accurate future PV analysis. In this research, an offline changepoint detection (CPD) algorithm that automatically detects these abrupt data shifts in sensor-based time series is introduced. Data shift periods in 101 daily PV power and irradiance time series were labeled manually by two solar experts. These data streams represent sensor-based measurements, and display a variety of data shift behaviors. A changepoint detection algorithm was tuned using the 101 labeled data streams, with each model configuration's ability to detect labeled changepoints benchmarked using metrics such as F1-score, recall, and Rand Index. Best performing models on seasonality-corrected data streams include the Pruned Exact Linear (PELT) method, the Binary Segmentation method, and the Bottom-Up method, all scoring an average F1-score of 0.76 or greater at detecting labeled changepoints within a 30-day window for the labeled data sets. To promote further research in this space, we are releasing the labeled data shift sets on U.S. Department of Energy's (DOE) DuraMAT Data Hub, and the associated algorithm in the Python PVAnalytics package.
PV power and irradiance sensor-based measurements are prone to error, resulting in issues such as abrupt time series data shifts. These shifts, which are usually unintentional, may be caused by software or hardware configuration changes on a PV system, and do not reflect an actual change in overall system performance. Locating these shifts and segmenting the associated time series aids in more accurate future PV analysis. In this research, an offline changepoint detection (CPD) algorithm that automatically detects these abrupt data shifts in sensor-based time series is introduced. Data shift periods in 101 daily PV power and irradiance time series were labeled manually by two solar experts. These data streams represent sensor-based measurements, and display a variety of data shift behaviors. A changepoint detection algorithm was tuned using the 101 labeled data streams, with each model configuration's ability to detect labeled changepoints benchmarked using metrics such as F1-score, recall, and Rand Index. Best performing models on seasonality-corrected data streams include the Pruned Exact Linear (PELT) method, the Binary Segmentation method, and the Bottom-Up method, all scoring an average F1-score of 0.76 or greater at detecting labeled changepoints within a 30-day window for the labeled data sets. To promote further research in this space, we are releasing the labeled data shift sets on U.S. Department of Energy's (DOE) DuraMAT Data Hub, and the associated algorithm in the Python PVAnalytics package.
PV power and irradiance sensor-based measurements are prone to error, resulting in issues such as time series data shifts. In this research, a changepoint detection (CPD) algorithm that automatically detects data shifts in sensor-based time series is introduced. Data shift periods in 101 daily PV power and irradiance time series were labeled manually by two solar experts. These data streams represent sensor-based measurements, and display a variety of data shift behaviors. A changepoint detection algorithm was tuned using the 101 labeled data streams, with each model configuration's ability to detect labeled changepoints benchmarked using metrics such as F1-score, recall, and Rand Index. Best performing models on seasonality-corrected data streams include the Pruned Exact Linear (PELT) method, the Binary Segmentation method, and the Bottom-Up method, all scoring an average F1-score of 0.76 or greater at detecting labeled changepoints within a 30-day window across the labeled data sets. Pending approval, we plan to release the labeled data sets for this research on NREL's DuraMAT Data Hub, and the associated algorithm in the Python PVAnalytics package. By supplying the training sets and algorithm, we hope to encourage further development in this research space.
This white paper has a technical focus meant to provide background to those unfamiliar with the challenges of high temperature electronics, and an understanding of why new surface mission concepts are now viable for Venus and other applications. It describes that, for the first time, high temperature electronics have been developed to enable a paradigm change in extended duration Venus surface exploration. This white paper concentrates on the advances made in Silicon Carbide (SiC) integrated circuit (IC) electronics that have matured to a state where a simple long-life scientific probe is feasible for Venus surface operations. These electronics have been demonstrated for more than a year at 500˚C, 60 days in high-fidelity simulated Venus surface conditions chamber, and have been integrated and demonstrated with prototype long-lived scientific observation sensor technology. Continued maturation of these needed high temperature electronics and sensors is ongoing in the Long-Lived In-Situ Solar System Explorer (LLISSE) project to provide an operational system including sensor control and operation, signal processing, power management, and communications in the early part of the 2020’s. A key focus of this work includes increasing the complexity and decreasing the power consumption of the ICs which will pave the way to enhanced capabilities, such as long-duration Venus seismic measurements. Other IC development efforts include high temperature memory, terrestrial based characterization of volcanic magma, and development of a simple microprocessor. This electronics has also shown to have exceptionally high radiation tolerance, and thus have relevance for a broad range of solar system exploration. Although presently at the level of complexity of 1970-1980 silicon-based electronics, such electronics can enable breakthroughs in Venus planetary exploration akin to 1970s-launched planetary exploration successes (such as Viking and Voyager).
A 55 We free-piston Stirling Technology Demonstration Convertor (TDC) has been tested as part of an evaluation to determine its feasibility as a means for significantly reducing the amount of radioactive material required compared to Radioisotope Thermoelectric Generators (RTGs) to support long-term space science missions. Measurements were made to quantify the low frequency magnetic and electric fields radiated from the Stirling's 80 Hertz (Hz) linear alternator and control electronics in order to determine the magnitude of reduction that will be required to protect sensitive field sensors aboard some science missions. One identified "Solar Probe" mission requires a 100 dB reduction in the low frequency magnetic field over typical military standard design limits, to protect its plasma wave sensor. This paper discusses the electromagnetic interference (EMI) control options relative to the physical design impacts for this power system, composed of 3 basic electrical elements. They are (1) the Stirling Power Convertor with its linear alternator, (2) the power switching and control electronics to convert the 90 V, 80 Hz alternator output to DC for the use of the spacecraft, and (3) the interconnecting wiring including any instrumentation to monitor and control items 1 and 2.
The MODerate Resolution Imaging Spectroradiometer (MODIS) is one of the key instruments for the NASA s Earth Observing System (EOS). It is currently operating on both EOS Terra and Aqua satellites. The MODIS is a major advance over its heritage sensors in terms of its spectral, spatial, and temporal resolutions with frequent global observations and a broad range of science applications. There are 20 reflective solar bands (RSB) with center wavelengths ranging from 0.41 to 2.l microns and 16 thermal emissive bands (TEB) from 3.7 to 14.4 microns. The absolute radiometric accuracy requirements (lsigma) at the typical spectral radiance levels are plus or minus 2% for the RSB reflectance factors and plus or minus 5% for the RSB radiance products. With few exceptions, the TEB requirements are plus or minus 1%. To verify that the instruments met their specified design requirements both Terra and Aqua MODIS underwent extensive pre-launch calibration and characterization at various levels, including system-level thermal vacuum testing. On-orbit calibration and characterization are performed by the on-board calibrators: a solar diffuser (SD) and a solar diffuser stability monitor (SDSM), a V-groove flat panel blackbody (BB), and a spectro-radiometric calibration assembly (SRCA). In this paper, we present an overview of MODIS calibration and characterization activities, methodologies, and lessons learned from pre-launch testing and on-orbit operations. Key issues to be discussed include our on-orbit efforts of monitoring detectors noise characterization, tracking solar diffuser and optics degradation, and updating sensor s response versus scan-angle. The MODIS experience has provided invaluable lessons that are being used in designing and testing the Visible Infrared Imaging Radiometer Suite (VIIRS), a direct follow-on to the MODIS that will be flown on the National Polar-Orbit Operational Environmental Satellite System (NPOESS) missions.
A balloon payload designed to study the processes of energy release, particle acceleration, and heating of the active corona in hard X-ray microflares and normal flares is described. An array of liquid nitrogen-cooled germanium detectors together with large area phoswich scintillation detectors provide the highest sensitivity (about 500 sq cm) and energy resolution (not greater than 0.7 keV) ever achieved for solar hard X-ray (about 15-600 keV) measurements. These detectors were flown in February 1987 from Australia on a long duration radiation controlled balloon flight (LDBF) which provided 12 days of observations before cutdown in Brazil. The payload includes solar cells for power, pointing, and navigation sensors, a microprocessor controlled data system with VCR tape storage, and transmitters for GOES and ARGOS spacecraft. This successful flight illustrates the potential of LDBFs for solar flare studies.
The total solar irradiance measurements made by the cavity sensor of the Earth Radiation Budget experiment on the Nimbus-7 spacecraft have detected the sunspot-blocking effect, established the downward trend of the declining solar cycle, and appear to be confirming an upturn in irradiance at the outset of the new cycle. Studies of the measurements' frequency spectra have advanced the interest in helioseismology or mode analysis, and studies of photospheric activity have advanced through the modeling of the sunspot blocking and photospheric brightening vs measured irradiance.
Missions to solar system bodies must meet increasingly ambitious objectives requiring highly reliable “precision landing”, and “hazard avoidance” capabilities. To meet these needs, we have developed two lidar sensor systems that can be used individually or in concert depending on the mission requirements. One is Navigation Doppler Lidar (NDL) capable of providing vehicle precision vector velocity and ground-relative altitude and the other is an Imaging Flash Lidar for Terrain Relative Navigation and Hazard Detection and Avoidance. Using both lidar sensors together will enable landing anywhere and under any lighting condition. The NDL performance has been extensively characterized through numerous ground and aircraft flight tests and will be soon demonstrated on two lunar landing missions (mid-2023). The Flash Lidar performance is currently being assessed onboard a drone and will soon be tested on helicopter and fixed-wing aircraft platforms. This paper describes both lidar sensors and their expected operation on landing vehicles.
A ceramic- and thick-film-materials-based prototype electronic package designed for silicon carbide (SiC) high-temperature sensors and electronics has been successfully tested at 500 C in an oxygen-containing air environment for 500 hours. This package was designed, fabricated, assembled, and electronically evaluated at the NASA Glenn Research Center at Lewis Field with an in-house-fabricated SiC semiconductor test chip. High-temperature electronics and sensors are necessary for harsh-environment space and aeronautical applications, such as space missions to the inner solar system or the emission control electronics and sensors in aeronautical engines. Single-crystal SiC has such excellent physical and chemical material properties that SiC-based semiconductor electronics can operate at temperatures over 600 C, which is significantly higher than the limit for Si-based semiconductor devices. SiC semiconductor chips were recently demonstrated to be operable at temperatures as high as 600 C, but only in the probe station environment because suitable packaging technology for sensors and electronics at temperatures of 500 C and beyond did not exist. Thus, packaging technology for SiC-based sensors and electronics is immediately needed for both application and commercialization of high-temperature SiC sensors and electronics. In response to this need, researchers at Glenn designed, fabricated, and assembled a prototype electronic package for high-temperature electronics, sensors, and microelectromechanical systems (MEMS) using aluminum nitride (AlN) substrate and gold (Au) thick-film materials. This prototype package successfully survived a soak test at 500 C in air for 500 hours. Packaging components tested included thick-film high-temperature metallization, internal wire bonds, external lead bonds, and a SiC diode chip die-attachment. Each test loop, which was composed of thick-film printed wire, wire bond, and lead bond was subjected to a 50-mA direct current for 250 hours at 500 C.