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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.

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NASA NTRSDate not supplied

Low SWaP Onboard Satellite Navigation, Guidance, and Control Technology

Onboard autonomy is a necessity for responsive space operations. Autonomous navigation, guidance, and control (NGC) enables space missions to reduce their dependence on high demand ground assets and costly ground personnel. It also allows for in-situ decision making and higher return on mission data. A flight software and hardware system providing this capability, called “autoNGC,” is currently being developed at NASA Goddard Space Flight Center for infusion into multiple future missions. The first build of autoNGC, providing autonomous navigation for lunar orbiting spacecraft, is targeted for completion by Fall 2024. It provides sensor fusion of multiple measurement types including pseudo-range from a weak signal Global Navigation Satellite Service (GNSS) receiver, 1-way and 2-way direct to Earth (DTE) range and Doppler, bearing and range from optical camera sensed images, and an accelerometer. AutoNGC is also being targeted for future missions that involve small body proximity operations, Sun Earth Libration point orbits, and distributed systems missions (DSMs) including those at outer planets. AutoNGC flight software is being built upon the plug-and-play architecture of the core Flight System (cFS) [Ref. 1]. Figure (Slide 7) shows the message-based software bus layout of various software applications (“apps”) consisting of the standard cFS apps and autoNGC interface apps and libraries. Accurate onboard navigation and timing is obtained through the Goddard Enhanced Onboard Navigation System (GEONS) software library [Ref. 2], which fuses different measurement types through an extended Kalman filter (EKF) framework. Optical measurements that are ingested in GEONS are provided by the cFS Goddard Image Analysis and Navigation Tool (cGIANT) app [Ref. 3]. This app processes optical images to extract the bearing angles of the centroid of the imaged body (near or far), the range to the imaged body, and/or of the features on the surface of a body to perform terrain relative navigation (TRN). Measurement of range to the body’s center of mass can also be derived from the detection of the limb. The first build of autoNGC for a lunar orbiting spacecraft is a minimal size, weight, and power (SWaP) hardware design allowing for inclusion into CubeSats and SmallSat-size class buses. Advancements in miniaturized space processors, such as the SpaceCube 3.0 Mini and the SpaceCube Mini-Z [Ref. 4] are utilized for low SWaP while maintaining a high level of performance. Figure (Slide 11) shows the composition of the first autoNGC build. The current enclosure design has dimensions 12 cm x 17 cm x 13.5 cm. The box mass is expected to be less than 2 kg, and the nominal power is 21 W. The hardware interfaces are designed for flexibility with a variety of sensor inputs. The achievable navigation performance depends on the sensors utilized, including the onboard clock for 1-way pseudo-range measurements. Analysis using a configuration that consists of weak signal GPS, TRN, and 1-way DTE has shown position and velocity accuracies of 10 meters and 2 cm/s (3-σ ) RSS, respectively, with onboard time knowledge estimated to better than 13 ns (3-σ ), for a spacecraft in a representative 12-hour eccentric lunar orbit. Other measurement types such as x-rays from known pulsars (called XNAV) and cross-links can also be processed in GEONS. With the plug-and-play architecture of autoNGC, cFS apps can easily be added and replaced, even after launch. Goddard is actively seeking partners to collaborate in the development of additional capabilities for autoNGC, including industry, academia, and others across the US Government. Plans are being formulated to make the autoNGC software platform available for use by any US government organization to leverage the non-recurring engineering associated with the development of onboard autonomous NGC 3 capabilities. As advancements in space qualified sensors, microprocessors, and algorithms are made, the autoNGC platform provides a ready starting point for inclusion of these technologies.

C. J. Gramling↗
NASA NTRS2013

NASA Tech Briefs, November 2013

Topics include: Cryogenic Liquid Sample Acquisition System for Remote Space Applications; 5 Spatial Statistical Data Fusion (SSDF); GPS Estimates of Integrated Precipitable Water Aid Weather Forecasters; Integrating a Microwave Radiometer into Radar Hardware for Simultaneous Data Collection Between the Instruments; Rapid Detection of Herpes Viruses for Clinical Applications; High-Speed Data Recorder for Space, Geodesy, and Other High-Speed Recording Applications; Datacasting V3.0; An All-Solid-State, Room-Temperature, Heterodyne Receiver for Atmospheric Spectroscopy at 1.2 THz; Stacked Transformer for Driver Gain and Receive Signal Splitting; Wireless Integrated Microelectronic Vacuum Sensor System; Fabrication Method for LOBSTER-Eye Optics in <110> Silicon; Compact Focal Plane Assembly for Planetary Science; Fabrication Methods for Adaptive Deformable Mirrors; Visiting Vehicle Ground Trajectory Tool; Workflow-Based Software Development Environment; Mobile Thread Task Manager; A Kinematic Calibration Process for Flight Robotic Arms; Magnetostrictive Alternator; Bulk Metallic Glasses and Composites for Optical and Compliant Mechanisms; Detection of Only Viable Bacterial Spores Using a Live/Dead Indicator in Mixed Populations; and Intravenous Fluid Generation System.

Source record↗
NASA NTRS2020

Fast Neutron Spectroscopy with Organic Scintillation Detectors in a High-Radiation Environment

Organic scintillators with pulse shape discrimination capability are widely used in both research and practical applications of neutron detection. The neutron and gamma-ray identification performance of the detector depends on the classification algorithms, noise filters and pileup rejection criteria in a high-flux bremsstrahlung radiation environment. In this paper, a technique has been developed and implemented for the neutron detection with multiple filter and discrimination steps, which to a high confidence level eliminates counting of gamma-ray pulses. Such a technique is merited when making measurements in a high flux bremsstrahlung and secondary fluorescence environment. The EJ-309 and stilbene detectors coupled to the digital data acquisition system were used for the calibration assessments with standard gamma-ray and neutron sources such as 137Cs, 60Co, 252Cf, and Am-Be. The MCNPX-PoliMi and GEANT4 toolkits were used to simulate the light output and the optical photon transport in the scintillators and create detector response functions for each type of detector. The neutron spectrum unfolding algorithm, GRAVELW, was used to recreate and calibrate with the Am-Be as final step before applying the neutron detection system to extract fusion neutron spectra generated in an intense bremsstrahlung radiation environment. This new technique described offers the user the ability to measure neutron spectra in a high-flux gamma-ray field and tune the parameters to meet required filtering needs.

Neutron Detector, Organic Scintillator, PulseShape↗
NASA NTRS2023

Toward Accurate Thermal Modeling of Phase Change Material Based Photonic Devices

Reconfigurable or programmable photonic devices are rapidly growing and have become an integral part of many optical systems. The ability to selectively modulate electromagnetic waves through electrical stimuli is crucial in the advancement of a variety of applications from data communication and computing devices to environmental science and space explorations. Chalcogenide-based phase change materials (PCMs) are one of the most promising material candidates for reconfigurable photonics due to their large optical contrast between their different solid-state structural phases. Although significant efforts have been devoted to accurate simulation of PCM-based devices, in this paper, we highlight three important aspects which have often evaded prior models yet having significant impacts on the thermal and phase transition behavior of these devices: the enthalpy of fusion, the heat capacity change upon glass transition, as well as the thermal conductivity of liquid-phase PCMs. We further investigated the important topic of switching energy scaling in PCM devices, which also helps explain why the three above-mentioned effects have long been overlooked in electronic PCM memories but only become important in photonics. Our findings offer insight to facilitate accurate modeling of PCM-based photonic devices and can inform the development of more efficient reconfigurable optics.

Phase change materials↗
NASA NTRS2021

Petrology, Geochemistry, and Pairing of Lunar Meteorites from the Dominion Range

Introduction: During the 2018-2019 Antarctic Search for Meteorites (ANSMET) field season in the Dominion Range (DOM), 7 lunar meteorite stones were collected: DOM 18242 (15.1 g), DOM 18244 (25.1 g), DOM 18262 (6.8 g), DOM 18509 (16.5 g), DOM 18543 (13.6 g), DOM 18666 (45.9 g), and DOM 18678 (11.6 g). Here we present the initial results of electron microprobe and X-ray computed tomography (XCT) studies of these stones and look at the details of their petrography and mineral chemistry, as well as investigate possible pairing relationships, both with each other and with previously described lunar meteorites. Most of the work presented here is on the DOM 18509, 18543, and 18678 stones; subsamples of the other stones are in hand and similar measurements will be made on them by the time of the meeting. Methods: Textures in DOM 18509, DOM 18543, and DOM 18678 were characterized in 2D by optical microscopy, backscattered electron (BSE) imaging and elemental X-ray images on thin sections, as well as in 3D by X-ray computed tomography (XCT) on sample chips. Mineral compositions were assessed through a combination of wavelength dispersive spectroscopy EPMA (electron probe microanalysis) and x-ray mapping on the JEOL 8530 at NASA JSC. The bulk composition of all three meteorites was determined based on analyses of the fusion crust glass. XCT analyses were done on the Nikon XTH 320 at NASA JSC. ICP-MS data on bulk rock subsamples for each meteorite will be carried out in the near future. Results: The stones are all similar in macroscopic appearance with a dark aphanitic matrix hosting a variety of small- to medium-sized angular mineral and lithic fragments (often light colored in nature) [1,2]. Based on EPMA and XCT results, the three meteorites are polymict regolith breccias comprised of mineral, glass, and lithic clasts ranging up to several mm in length. Melt veins run through all three meteorite samples. Mineral clasts in all 3 stones are dominated by pyroxene and plagioclase (An82-96), with minor amounts of SiO2, olivine (Fo1-52), and FeTi-oxides. Pyroxene grains are mostly Fe-rich pigeonite and augite, and larger clasts are normally zoned and have fine exsolution lamellae. The lithic clasts in all stones consists of: (1) basalt clasts that contain zoned pyroxene, plagioclase laths, and ilmenite, with minor silica and Fe-rich olivine; (2) granulitic clasts; (3) anorthosite clasts; (4) Si-rich clasts that also contain ilmenite, troilite, high-Ca pyroxene, fayalite, and K-feldspar likely mesostasis from late stage basalts). All three meteorites contain glassy fusion crust that is highly vesicular, high in FeO and Al2O3 (15-17 wt% each), ferroan (Mg# of 23-24), and moderately rich in TiO2 (1.4-1.8 wt%). The composition of the fusion crust can serve as a proxy for the bulk meteorite composition and is identical within error for all three meteorites. Implications: The lithic and mineral clasts in all three stones are similar in clast population and assemblages as well as mineral chemistry. In addition, the fusion crust composition, a proxy for bulk composition, is within error of each other for all three stones. Thus DOM 18509, DOM 18543, and DOM 18678 are almost certainly paired. Based on similarities in macroscopic description as well as preliminary classification data [1,2], all 7 lunar stones from DOM are likely paired, though additional quantitative analyses are needed to confirm this. The presence of spherules and vesicular fusion crusts indicates that the DOM pairing group is a regolith breccia. The presence of basaltic and gabbroic clasts as well as more feldspathic materials, suggest that the regolith from which these meteorites formed contained a mixture of feldspathic highland material and mare material, suggesting a possible provenance near a marehighlands boundary. No evidence of KREEPy lithologies have been observed so far in these meteorites, however, future ICP-MS data on bulk rock chips for the stones will reveal any KREEP component if present. The DOM pairing group has many similarities to previously described lunar breccia meteorite MET 01210, however more detailed compositional data will be needed to make a definitive comparison.

R.A. Zeigler↗
NASA NTRS2014

In-Situ and Remote-Sensing Data Fusion Using Machine Learning Techniques to Infer Urban and Fire Related Pollution Plumes

Airmass type characterization is key in understanding the relative contribution of various emission sources to atmospheric composition and air quality and can be useful in bottom-up model validation and emission inventories. However, classification of pollution plumes from space is often not trivial. Sub-orbital campaigns, such as SEAC4RS (Studies of Emissions, Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys) give us a unique opportunity to study atmospheric composition in detail, by using a vast suite of in-situ instruments for the detection of trace gases and aerosols. These measurements allow identification of spatial and temporal atmospheric composition changes due to various pollution plumes resulting from urban, biogenic and smoke emissions. Nevertheless, to transfer the knowledge gathered from such campaigns into a global spatial and temporal context, there is a need to develop workflow that can be applicable to measurements from space. In this work we rely on sub-orbital in-situ and total column remote sensing measurements of various pollution plumes taken aboard the NASA DC-8 during 2013 SEAC4RS campaign, linking them through a neural-network (NN) algorithm to allow inference of pollution plume types by input of columnar aerosol and trace-gas measurements. In particular, we use the 4STAR (Spectrometer for Sky-Scanning, Sun-Tracking Atmospheric Research) airborne measurements of wavelength dependent aerosol optical depth (AOD), particle size proxies, O3, NO2 and water vapor to classify different pollution plumes. Our method relies on assigning a-priori ground-truth labeling to the various plumes, which include urban pollution, different fire types (i.e. forest and agriculture) and fire stage (i.e. fresh and aged) using cluster analysis of aerosol and trace-gases in-situ and auxiliary (e.g. trajectory) data and the training of a NN scheme to fit the best prediction parameters using 4STAR measurements as input. We explore our misclassification rates as related to our ground-truth labels, and with multi-layered pollution plume cases. The next step in our analysis is to optimize parameter selection for a scheme that can be applied to space-borne aerosol and trace-gas observation platforms such as OMI, and future geostationary satellites such as TEMPO and GEO-CAPE.

Neural-network↗
NASA NTRS2021

Mapping Boreal Forest Spruce Beetle Health Status at the Individual Crown Scale Using Fused Spectral and Structural Data

The frequency and severity of spruce bark beetle outbreaks are increasing in boreal forests leading to widespread tree mortality and fuel conditions promoting extreme wildfire. Detection of beetle infestation is a forest health monitoring (FHM) priority but is hampered by the challenges of detecting early stage (“green”) attack from the air. There is indication that green stage might be detected from vertical gradients of spectral data or from shortwave infrared information distributed within a single crown. To evaluate the efficacy of discriminating “non-infested”, “green”, and “dead” health statuses at the landscape scale in Alaska, USA, this study conducted spectral and structural fusion of data from: (1) Unoccupied aerial vehicle (UAV) multispectral (6 cm) + structure from motion point clouds (~700 pts per sq. m); and (2) Goddard Lidar Hyperspectral Thermal (G-LiHT) hyperspectral (400 to 1000 nm, 0.5 m) + SWIR-band lidar (~32 pts per sq.m). We achieved 78% accuracy for all three health statuses using spectral + structural fusion from either UAV or G-LiHT and 97% accuracy for non-infested/dead using G-LiHT. We confirm that UAV 3D spectral (e.g., greenness above versus below median height in crown) and lidar apparent reflectance metrics (e.g., mean reflectance at 99th percentile height in crown), are of high value, perhaps capturing the vertical gradient of needle degradation. In most classification exercises, UAV accuracy was lower than G-LiHT indicating that collecting ultra-high spatial resolution data might be less important than high spectral resolution information. While the value of passive optical spectral information was largely confined to the discrimination of non-infested versus dead crowns, G-LiHT hyperspectral band selection (~400, 675, 755, and 940 nm) could inform future FHM mission planning regarding optimal wavelengths for this task. Interestingly, the selected regions mostly did not align with the band designations for our UAV multispectral data but do correspond to, e.g., Sentinel-2 red edge bands, suggesting a path forward for moderate scale bark beetle detection when paired with suitable structural data.

Janice Cessna↗
NASA NTRS1989

Signal and data processing of small targets 1989; Proceedings of the Meeting, Orlando, FL, Mar. 27-29, 1989

The present conference on digital signal processing, association and filtering techniques, and multiple-sensor/multiple-tracking techniques, discusses single-frame velocity estimation, efficient target extraction for laser radar imagery, precision target tracking for small extended objects, IR clutter partitioning for matched filter design, the maximum-likelihood approach to gamma circumvention, position estimation for optical point targets using staring detector arrays, and a multiple-scan signal processing technique for area-moving target indication. Also discussed are a proportional integral estimator, the prediction of track purity in tracking performance evaluations, synchronization and fault-tolerance in a distributed tracker, the benefits of soft sensors and probabilistic fusion, and testing track initiation algorithms fusing two-dimensional tracks.

Drummond, Oliver E.↗
NASA NTRS1984

Fabrication of glass gas cells for the HALOE and MAPS satellite experiments

The Halogen Occultation Experiment (HALOE) and the Measurement of Air Pollution from Satellites (MAPS) experiment are satellite-borne experiments which measure trace constituents in the Earth's atmosphere. The instruments which obtain the data for these experiments are based on the gas filter correlation radiometer measurement technique. In this technique, small samples of the gases of interest are encapsulated in glass cylinders, called gas cells, which act as very selective optical filters. This report describes the techniques employed in the fabrication of the gas cells for the HALOE and MAPS instruments. Details of the method used to fuse the sapphire windows (required for IR transmission) to the glass cell bodies are presented along with detailed descriptions of the jigs and fixtures used during the assembly process. The techniques and equipment used for window inspection and for pairing the HALOE windows are discussed. Cell body materials and the steps involved in preparing the cell bodies for the glass-to-sapphire fusion process are given.

Sullivan, E. M.↗
NASA NTRSDate not supplied

MAB Phase Ceramics for Lunar Applications

During the ten-week internship my work focused on designing characterization protocol for 3D printed MAB phase ceramics on steel and Tiplates. I was mentored by Dr. Samuel Hocker of NASA and advised by Dr. Gupta from University of North Dakota. I also collaborated with Mackenzie Short from the University of North Dakota. The characterization protocol was divided into following steps; initially, we performed visual inspection of the plates. During this process, the coatings which spalled or delaminated were discarded for further evaluation. During the next step, we performed optical microscopy to discern and document surface features and porosities of the samples. We also noted some decomposition under the optical microscope as well. Thereafter, profilometry analysis was performed to understand the effect of power density on the surface roughness. Due to the deposited powder over the samples, the profilometry was inconclusive. We then cleaned the samples on the steel plate using ethanol and cotton swabs to wipe up the loose powder covering the samples. Detailed SEM analysis was then performed on the cleaned specimens. In parallel, the 3D printed samples on the Tiplate were cleaned by using an ultrasonic bath for 1h. Optical imaging showed that this cleaning process was more effective for cleaning except that small cracks propagated through the layered ceramics. The profilometry data gathered on the titanium plate after cleaning was more conclusive. The SEM data on the steel plate showed that there is decomposition at all wattages although at 100 W the extent of decomposition was lower. We are planning to characterize the steel samples after cleaning in ultrasonic bath for 5-10 min to minimize cracking during the cleaning process. In addition, we are also planning to study the fractured surface by SEM analysis.

Additive Manufacturing, Laser Powder Bed Fusion, S↗
NASA NTRS2023

Assessment of the Impact of Discontinuity in Satellite Instruments and Retrievals on Global PM2.5

Exposure to fine particulate matter (PM2.5) is the leading environmental risk factor for mortality globally. Satellite-derived estimates of surface PM2.5 developed from a combination of satellites, simulations, and ground monitor data are relied upon for health impact studies. The ability to develop satellite-derived PM2.5 estimates requires the continued availability of aerosol optical depth (AOD) sources. This work examines the impact of the addition or loss of satellite AOD data sources on global PM2.5 estimation and the impact of continuing the longterm record with AOD from the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi-National Polar orbiting Partnership (S-NPP) satellite after the loss of the MODIS (MODerate resolution Imaging Spectroradiometer) and MISR (Multi-angle Imaging Spectroradiometer) instruments on board the Terra and Aqua satellites. We find that the addition of VIIRS S-NPP AOD products to geophysical PM2.5 estimates from satellites and simulations causes regional differences that correspond to differences in the VIIRS and MODIS Deep Blue AOD algorithms and sampling. Changes in long-term trends and timeseries due to the addition or loss of AOD data sources are generally within their uncertainties. Statistical fusion with ground monitor data partially corrects for changes due to sampling differences when introducing the VIIRS AOD products, but uncertainty remains over desert regions where ground monitor coverage is sparse. This work provides promise for the sustained development of global satellite-derived PM2.5 estimates, despite discontinuities in instruments and retrieval methods.

Remote sensing↗
NASA NTRS2023

Manufacturing and Mechanical Testing of TC1225/LM-PAEK and TC1200/PEEK Thermoplastic Composite Panels

Development of thermoplastic composites (TPCs) for aerospace structures is experiencing renewed enthusiasm attributed to the availability of rapid manufacturing technology, ease of joining through fusion welding processes, and the successful utilization of the material in flight critical structures. Semicrystalline thermoplastics such as polyether ether ketone (PEEK), polyether ketone ketone (PEKK), and polyphenylene sulfide (PPS) have been successfully demonstrated as thermoplastic skins, keel beams, and angle brackets for commercial aircraft. Composite specimens were fabricated from the Toray TC1225 and TC1200 unidirectional prepreg systems and tested to gain confidence with NASA in-house TPC processing as part of the Thermoplastics Development for Exploration Application (TDEA) project. TC1225 is a relatively new material system that uses T700GC fibers and low-melt polyaryl ether ketone (LM-PAEK) resin. TC1200 is the latest version of a material system that has been available for several decades with AS4 fibers and PEEK resin. Results for ultrasonic inspection, optical microscopy, acid digestion, and differential scanning calorimetry establish the quality of the panels. Mechanical test data for unnotched and notched laminate tension and compression as well as compression after impact provide data for equivalency assessment and support material selection in TDEA. Satisfactory equivalence of the TC1225 results and the National Center for Advanced Materials Performance (NCAMP) database was found. The results indicate high-quality manufacturing was achieved, and the resulting mechanical test data are in agreement with that in the literature.

Thermoplastic Composites↗
NASA NTRS2017

Fusion-Enabled Pluto Orbiter and Lander

The Pluto orbiter mission proposed here is credible and exciting. The benefits to this and all outer-planet and interstellar-probe missions are difficult to overstate. The enabling technology, Direct Fusion Drive, is a unique fusion engine concept based on the Princeton Field-Reversed Configuration (PFRC) fusion reactor under development at the Princeton Plasma Physics Laboratory. The truly game-changing levels of thrust and power in a modestly sized package could integrate with our current launch infrastructure while radically expanding the science capability of these missions. During this Phase I effort, we made great strides in modeling the engine efficiency, thrust, and specific impulse and analyzing feasible trajectories. Based on 2D fluid modeling of the fusion reactors outer stratum, its scrape-off-layer (SOL), we estimate achieving 2.5 to 5 N of thrust for each megawatt of fusion power, reaching a specific impulse, Isp, of about 10,000 s. Supporting this model are particle-in-cell calculations of energy transfer from the fusion products to the SOL electrons. Subsequently, this energy is transferred to the ions as they expand through the magnetic nozzle and beyond. Our point solution for the Pluto mission now delivers 1000 kg of payload to Pluto orbit in 3.75 years using 7.5 N constant thrust. This could potentially be achieved with a single 1 MW engine. The departure spiral from Earth orbit and insertion spiral to Pluto orbit require only a small portion of the total delta-V. Departing from low Earth orbit reduces mission cost while increasing available mission mass. The payload includes a lander, which utilizes a standard green propellant engine for the landing sequence. The lander has about 4 square meters of solar panels mounted on a gimbal that allows it to track the orbiter, which beams 30 to 50 kW of power using a 1080 nm laser. Optical communication provides dramatically high data rates back to Earth. Our mass modeling investigations revealed that if current high-temperature superconductors are utilized at liquid nitrogen temperatures, they drive the mass of the engine, partly because of the shielding required to maintain their critical temperature. Second generation materials are thinner but the superconductor is a very thin layer deposited on a substrate with additional layers of metallic classing. Tremendous research is being performed on a variety of these superconducting materials, and new irradiation data is now available. This raises the possibility of operating nearfuture high-temperature superconductors at a moderately low temperature to dramatically reduce the amount of shielding required. At the same time, a first generation space engine may require low-temperature superconductors, which are higher TRL and have been designed for space coils before (AMS-02 experiment for the ISS). We performed detailed analysis of the startup system and thermal conversion system components. The ideal working fluid was determined to be a blend of Helium and Xenon. No significant problems were identified with these subsystems. For the RF system, we conceived of a new, more efficient design using state-of-the-art switch amplifiers, which have the potential for 100% efficiency. This report presents details of our engine and trajectory analyses, mass modeling efforts, and updated vehicle designs.

temperature↗
NASA NTRSDate not supplied

Comparing Low-Cost Sensors with Ground-Based and Airborne In-Situ and Column Observations of NO2 and HCHO during the FRAPPE Field Campaign in Colorado, USA

Even in the presence of more reliable air quality tools, low-cost sensors have the benefit of recording data on highly localized spatial and temporal scales, allowing for multiple measurements within a single satellite pixel and on pixel boundaries. However, they are less accurate than their regulatory-grade counterparts, requiring regular co-locations with accepted instruments to ensure their validity. Thus, the addition of low-cost sensors to a field campaign – where reference-grade air quality instruments are abundant – not only provides ample opportunities for low-cost sensor co-location and calibration, but also allows the low-cost instruments to be used for sub-pixel validation, covering more surface area than the regulatory instruments alone with a network of sensors. During the summer of 2014, our low-cost sensor network was deployed during the Front Range Air Pollution and Photochemistry Experiment (FRAPPÉ) campaign conducted to sample the composition of air at and above ground level in northeastern Colorado, USA. The low-cost sensor platform included a suite of gas-phase sensors, notably NO2 and two generalized volatile organic compound (VOC) sensors, which were leveraged together to quantify speciated hydrocarbons such as formaldehyde. These key pollutants were chosen for their impacts on human health and climate change, as well as their inclusion on the TEMPO satellite launching this year. Airborne campaign measurements included slant column optical observations of formaldehyde (HCHO), nitrogen dioxide (NO2), and ozone (O3). Myriad additional in-situ instruments described chemical composition up to approximately 5 km above surface level. Ground-based instrumentation included both stationary and mobile regulatory-grade instruments, which were used for sensor calibration. Machine learning techniques such as artificial neural networks (ANNs) were used to match the low-cost signals to that of the reference-grade instruments. Here, we compare calibrated low-cost sensor data collected at ground level in a variety of locations along Colorado’s Front Range to various data sources from the FRAPPÉ campaign to better understand how well airborne and regulatory ground-based measurements can be extrapolated to other locations. Further, as the slant column measurements act as satellite simulators, we explore how low-cost instruments can be used for satellite validation purposes. Comparisons among these different data types also have important implications in data fusion.

Kristen Okorn↗
NASA NTRS2017

NeMO-Net The Neural Multi-Modal Observation Training Network for Global Coral Reef Assessment

In the past decade, coral reefs worldwide have experienced unprecedented stresses due to climate change, ocean acidification, and anthropomorphic pressures, instigating massive bleaching and die-off of these fragile and diverse ecosystems. Furthermore, remote sensing of these shallow marine habitats is hindered by ocean wave distortion, refraction and optical attenuation, leading invariably to data products that are often of low resolution and signal-to-noise (SNR) ratio. However, recent advances in UAV and Fluid Lensing technology have allowed us to capture multispectral 3D imagery of these systems at sub-cm scales from above the water surface, giving us an unprecedented view of their growth and decay. Exploiting the fine-scaled features of these datasets, machine learning methods such as MAP, PCA, and SVM can not only accurately classify the living cover and morphology of these reef systems (below 8 error), but are also able to map the spectral space between airborne and satellite imagery, augmenting and improving the classification accuracy of previously low-resolution datasets.We are currently implementing NeMO-Net, the first open-source deep convolutional neural network (CNN) and interactive active learning and training software to accurately assess the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology. NeMO-Net will be built upon the QGIS platform to ingest UAV, airborne and satellite datasets from various sources and sensor capabilities, and through data-fusion determine the coral reef ecosystem makeup globally at unprecedented spatial and temporal scales. To achieve this, we will exploit virtual data augmentation, the use of semi-supervised learning, and active learning through a tablet platform allowing for users to manually train uncertain or difficult to classify datasets. The project will make use of Pythons extensive libraries for machine learning, as well as extending integration to GPU and High-End Computing Capability (HECC) on the Pleiades supercomputing cluster, located at NASA Ames. The project is being supported by NASAs Earth Science Technology Office (ESTO) Advanced Information Systems Technology (AIST-16) Program.

Remote Sensin↗
NASA NTRS2017

NeMO-Net: The Neural Multi-Modal Observation and Training Network for Global Coral Reef Assessment

In the past decade, coral reefs worldwide have experienced unprecedented stresses due to climate change, ocean acidification, and anthropomorphic pressures, instigating massive bleaching and die-off of these fragile and diverse ecosystems. Furthermore, remote sensing of these shallow marine habitats is hindered by ocean wave distortion, refraction and optical attenuation, leading invariably to data products that are often of low resolution and signal-to-noise (SNR) ratio. However, recent advances in UAV and Fluid Lensing technology have allowed us to capture multispectral 3D imagery of these systems at sub-cm scales from above the water surface, giving us an unprecedented view of their growth and decay. Exploiting the fine-scaled features of these datasets, machine learning methods such as MAP, PCA, and SVM can not only accurately classify the living cover and morphology of these reef systems (below 8 percent error), but are also able to map the spectral space between airborne and satellite imagery, augmenting and improving the classification accuracy of previously low-resolution datasets. We are currently implementing NeMO-Net, the first open-source deep convolutional neural network (CNN) and interactive active learning and training software to accurately assess the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology. NeMO-Net will be built upon the QGIS platform to ingest UAV, airborne and satellite datasets from various sources and sensor capabilities, and through data-fusion determine the coral reef ecosystem makeup globally at unprecedented spatial and temporal scales. To achieve this, we will exploit virtual data augmentation, the use of semi-supervised learning, and active learning through a tablet platform allowing for users to manually train uncertain or difficult to classify datasets. The project will make use of Pythons extensive libraries for machine learning, as well as extending integration to GPU and High-End Computing Capability (HECC) on the Pleiades supercomputing cluster, located at NASA Ames. The project is being supported by NASAs Earth Science Technology Office (ESTO) Advanced Information Systems Technology (AIST-16) Program.

NeMO-Net↗
NASA NTRS2012

NASA Tech Briefs, January 2012

Contents of this issue are: (1) Energy-Based Tetrahedron Sensor for High-Temperature, High-Pressure Environments (2) Handheld Universal Diagnostic Sensor (3) Large-Area Vacuum Ultraviolet Sensors (4) Fiber Bragg Grating Sensor System for Monitoring Smart Composite Aerospace Structures (5) Health-Enabled Smart Sensor Fusion Technology (6) Extended-Range Passive RFID and Sensor Tags (7) Hybrid Collaborative Learning for Classification and Clustering in Sensor Networks (8) Self-Healing, Inflatable, Rigidizable Shelter (9) Improvements in Cold-Plate Fabrication (10) Technique for Radiometer and Antenna Array Calibration - TRAAC (11) Real-Time Cognitive Computing Architecture for Data Fusion in a Dynamic Environment (12) Programmable Digital Controller (13) Use of CCSDS Packets Over SpaceWire to Control Hardware (14) Key Decision Record Creation and Approval Module (15) Enhanced Graphics for Extended Scale Range (16) Debris Examination Using Ballistic and Radar Integrated Software (17) Data Distribution System (DDS) and Solar Dynamic Observatory Ground Station (SDOGS) (18) Integration Manager (19) Eclipse-Free-Time Assessment Tool for IRIS (20) Automated and Manual Rocket Crater Measurement Software (21) MATLAB Stability and Control Toolbox Trim and Static Stability Module (22) Patched Conic Trajectory Code (23) Ring Image Analyzer (24) SureTrak Probability of Impact Display (25) Implementation of a Non-Metallic Barrier in an Electric Motor (26) Multi-Mission Radioisotope Thermoelectric Generator Heat Exchangers for the Mars Science Laboratory Rover (27) Uniform Dust Distributor for Testing Radiative Emittance of Dust-Coated Surfaces (28) MicroProbe Small Unmanned Aerial System (29) Highly Stable and Active Catalyst for Sabatier Reactions (30) Better Proton-Conducting Polymers for Fuel-Cell Membranes (31) CCD Camera Lens Interface for Real-Time Theodolite Alignment (32) Peregrine 100-km Sounding Rocket Project (33) SOFIA Closed- and Open-Door Aerodynamic Analyses (34) Sonic Thermometer for High-Altitude Balloons (35) Near-Infrared Photon-Counting Camera for High-Sensitivity Observations (36) Integrated Optics Achromatic Nuller for Stellar Interferometry (37) High-Speed Digital Interferometry (38) Ultra-Miniature Lidar Scanner for Launch Range Data Collection (39) Shape and Color Features for Object Recognition Search (40) Explanation Capabilities for Behavior-Based Robot Control (41) A DNA-Inspired Encryption Methodology for Secure, Mobile Ad Hoc Networks (42) Quality Control Method for a Micro-Nano-Channel Microfabricated Device (43) Corner-Cube Retroreflector Instrument for Advanced Lunar Laser Ranging (44) Electrospray Collection of Lunar Dust (45) Fabrication of a Kilopixel Array of Superconducting Microcalorimeters with Microstripline Wiring Spacecraft Attitude Tracking and Maneuver Using Combined Magnetic Actuators (46) Coherent Detector for Near-Angle Scattering and Polarization Characterization of Telescope Mirror Coatings

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NASA NTRS2010

Future Flight Opportunities and Calibration Protocols for CERES: Continuation of Observations in Support of the Long-Term Earth Radiation Budget Climate Data Record

The goal of the Clouds and the Earth s Radiant Energy System (CERES) project is to provide a long-term record of radiation budget at the top-of-atmosphere (TOA), within the atmosphere, and at the surface with consistent cloud and aerosol properties at climate accuracy. CERES consists of an integrated instrument-algorithm validation science team that provides development of higher-level products (Levels 1-3) and investigations. It involves a high level of data fusion, merging inputs from 25 unique input data sources to produce 18 CERES data products. Over 90% of the CERES data product volume involves two or more instruments. Continuation of the Earth Radiation Budget (ERB) Climate Data Record (CDR) has been identified as critical in the 2007 NRC Decadal Survey, the Global Climate Observing System WCRP report, and in an assessment titled Impacts of NPOESS Nunn-McCurdy Certification on Joint NASA-NOAA Climate Goals . Five CERES instruments have flown on three different spacecraft: TRMM, EOS-Terra and EOS-Aqua. In response, NASA, NOAA and NPOESS have agreed to fly the existing CERES Flight Model (FM-5) on the NPP spacecraft in 2011 and to procure an additional CERES Sensor with modest upgrades for flight on the JPSS C1 spacecraft in 2014, followed by a CERES follow-on sensor for flight in 2018. CERES is a scanning broadband radiometer that measures filtered radiance in the SW (0.3-5 m), total (TOT) (0.3-200 m) and WN (8-12 m) regions. Pre-launch calibration is performed on each Flight Model to meet accuracy requirements of 1% for SW and 0.5% for outgoing LW observations. Ground to flight or in-flight changes are monitored using protocols employing onboard and vicarious calibration sources. Studies of flight data show that SW response can change dramatically due to optical contamination. with greatest impact in blue-to UV radiance, where tungsten lamps are largely devoid of output. While science goals remain unchanged for ERB Climate Data Record, it is now understood that achieving these goals is more difficult for two reasons. The first is an increased understanding of the dynamics of the Earth/atmosphere system which demonstrates that separation of natural variability from anthropogenic change on decadal time scales requires observations with higher accuracy and stabilit

Priestley, Kory J.↗