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NASA NTRS1999

Adaptive Mesh Refinement for Microelectronic Device Design

Finite element and finite volume methods are used in a variety of design simulations when it is necessary to compute fields throughout regions that contain varying materials or geometry. Convergence of the simulation can be assessed by uniformly increasing the mesh density until an observable quantity stabilizes. Depending on the electrical size of the problem, uniform refinement of the mesh may be computationally infeasible due to memory limitations. Similarly, depending on the geometric complexity of the object being modeled, uniform refinement can be inefficient since regions that do not need refinement add to the computational expense. In either case, convergence to the correct (measured) solution is not guaranteed. Adaptive mesh refinement methods attempt to selectively refine the region of the mesh that is estimated to contain proportionally higher solution errors. The refinement may be obtained by decreasing the element size (h-refinement), by increasing the order of the element (p-refinement) or by a combination of the two (h-p refinement). A successful adaptive strategy refines the mesh to produce an accurate solution measured against the correct fields without undue computational expense. This is accomplished by the use of a) reliable a posteriori error estimates, b) hierarchal elements, and c) automatic adaptive mesh generation. Adaptive methods are also useful when problems with multi-scale field variations are encountered. These occur in active electronic devices that have thin doped layers and also when mixed physics is used in the calculation. The mesh needs to be fine at and near the thin layer to capture rapid field or charge variations, but can coarsen away from these layers where field variations smoothen and charge densities are uniform. This poster will present an adaptive mesh refinement package that runs on parallel computers and is applied to specific microelectronic device simulations. Passive sensors that operate in the infrared portion of the spectrum as well as active device simulations that model charge transport and Maxwell's equations will be presented.

Cwik, Tom↗

NASA NTRSDate not supplied

Open Source Application of Fusing Aerosol Products from GEO and LEO Satellites

Retrieving aerosol optical depths (AODs) from sun-synchronous polar orbiting (aka low earth orbit, LEO) satellites, such as MODISs, and VIIRSs, OMI, TROPOMI, etc, has become well-established as a tool for extracting information on particulate matter (PM) and related processes in the atmosphere. However, with recently launched geostationary satellites (GEO), such as GOES-16/17/18, and Himawari-8/9, and Meteosat Third Generation (MTG) they provide a much higher temporal resolution (order of 10 minutes), typically an image once or more per hour during daylight compared to LEO once per day. By combining these observations, we may be able to characterize the diurnal cycle of global AOD at the local, regional and global scale. While the science community is still exploring the new data from GEO observations, we have been thinking about how to properly combine/merge/fuse those data considering differences in their spatial and temporal resolutions. However, this poses a “Big Data” challenge. The big data challenge is not just about data storage, but also about data discoverability, and accessibility, and even more, about data migration/mirroring in the cloud-computing environment. This paper is merely showing some of the efforts and approaches we have attempted in fusing six satellites’ Level 2 aerosol data (three are from GEO (GOES-16/17 and Himawari-8), and the other three are from LEO (TERRA/MODIS, AQUA/MODIS, SNPP-VIIRS) from Dark Target (DT) aerosol retrieval algorithm. Having the on-demand capability of fusing remote sensing products onto the desired temporal and spatial domain enables researchers and application practitioners to better manipulate and work with satellite and sensor data. It is our hopeWe hope that by making such an open-source package, and the accompanying functionality, the scientific community will be granted easier access to aerosol data processing resources. The MEaSUREs Program (Making Earth System Data Records for Use in Research Environments) expands our understanding of the Earth's current system through atmospheric and surface measurements. In an effort to aid the scientific research component and improve open source methods, this project developed Python code for fusing six satellite Level 2 aerosol data (three are from geostationary satellites (GEO), and the other three are from low earth orbital satellites (LEO)) from Dark Target Aerosol Retrieval Algorithm.

Jennifer Wei↗

DOE OSTI2025

Integration Methods for High-Density Integrated Electric Drives (Final Technical Report)

The project goal was to research, design and fabricate high-power-density, high-performance SiC power electronic modules for motor drive inverters in electric vehicles (EVs) to enable the subsequent motor drive to achieve a power density of 100 kW/l. EVs use electric motors instead of internal combustion engines as the prime mover, and their core components are semiconductor-based power electronic modules. Efficient power electronic modules play a vital role in power conversion systems. The performance of power modules directly affects the energy efficiency and overall performance of electric vehicles. Therefore, it is particularly important to develop power modules with high power density and high electrical performance that will perform reliably under EV environmental conditions. To achieve these goals, advanced electronic packaging technologies are required. This report describes a 5-year effort to integrate key aspects surrounding the basic power devices into the module itself to increase power density. These include sensing and sensing readout circuits, gate driver circuits, and two-sided cooling solutions.

33 ADVANCED PROPULSION SYSTEMS↗

NASA NTRS2009

GPS/MEMS IMU/Microprocessor Board for Navigation

A miniaturized instrumentation package comprising a (1) Global Positioning System (GPS) receiver, (2) an inertial measurement unit (IMU) consisting largely of surface-micromachined sensors of the microelectromechanical systems (MEMS) type, and (3) a microprocessor, all residing on a single circuit board, is part of the navigation system of a compact robotic spacecraft intended to be released from a larger spacecraft [e.g., the International Space Station (ISS)] for exterior visual inspection of the larger spacecraft. Variants of the package may also be useful in terrestrial collision-detection and -avoidance applications. The navigation solution obtained by integrating the IMU outputs is fed back to a correlator in the GPS receiver to aid in tracking GPS signals. The raw GPS and IMU data are blended in a Kalman filter to obtain an optimal navigation solution, which can be supplemented by range and velocity data obtained by use of (l) a stereoscopic pair of electronic cameras aboard the robotic spacecraft and/or (2) a laser dynamic range imager aboard the ISS. The novelty of the package lies mostly in those aspects of the design of the MEMS IMU that pertain to controlling mechanical resonances and stabilizing scale factors and biases.

Gender, Thomas K.↗

NASA NTRS2009

Alignment Jig for the Precise Measurement of THz Radiation

Javadi, Hamid H.↗

DOE OSTI2025

Machine learning model inputs, outputs, and scripts associated with “Artificial intelligence-guided iterations between observations and modeling significantly improve environmental predictions”

NOTE: The manuscript associated with this data package is currently in review. The data may be revised based on reviewer feedback. Upon manuscript acceptance, this data package will be updated with the final dataset and additional metadata. This data package is associated with the manuscript “Artificial intelligence-guided iterations between observations and modeling significantly improve environmental predictions” (Malhotra et al., in prep). This effort was designed following ICON (integrated, coordinated, open, and networked) principles to facilitate a model-experiment (ModEx) iteration approach, leveraging crowdsourced sampling across the contiguous United States (CONUS). New machine learning models were created every month to guide sampling locations. Data from the resulting samples were used to test and rebuild the machine learning models for the next round of sampling guidance. Associated sediment and water geochemistry and in situ sensor data can be found at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1923689, https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1729719, and https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1603775. This data package is associated with two GitHub repositories found at https://github.com/parallelworks/dynamic-learning-rivers and https://github.com/WHONDRS-Hub/ICON-ModEx_Open_Manuscript. In addition to this readme, this data package also includes two file-level metadata (FLMD) files that describes each file and two data dictionaries (DD) that describe all column/row headers and variable definitions. This data package consists of two main folders (1) dynamic-learning-rivers and (2) ICON-ModEx_Open_Manuscript which contain snapshots of the associated GitHub repositories. The input data, output data, and machine learning models used to guide sampling locations are within dynamic-learning-rivers. The folder is organized into five top-level directories: (1) “input_data” holds the training data for the ML models; (2) “ml_models” holds machine learning (ML) models trained on the data in “input_data”; (3) “examples” contains files for direct experimentation with the machine learning model, including scripts for setting up “hindcast” run; (4) “scripts” contains data preprocessing and postprocessing scripts and intermediate results specific to this data set that bookend the ML workflow; and (5) “output_data” holds the overall results of the ML model on that branch. Each trained ML model resides on its own branch in the repository; this means that inputs and outputs can be different branch-to-branch. There is also one hidden directory “.github/workflows”. This hidden directory contains information for how to run the ML workflow as an end-to-end automated GitHub Action but it is not needed for reusing the ML models archived here. Please see the top-level README.md in the GitHub repository for more details on the automation. The scripts and data used to create figures in the manuscript are within ICON-ModEx_Open_Manuscript. The folder is organized into four folders which contain the scripts, data, and pdf for each figure. Within the “fig-model-score-evolution” folder, there is a folder called “intermediate_branch_data” which contains some intermediate files pulled from dynamic-learning-rivers and reorganized to easily integrate into the workflows. NOTE: THIS FOLDER INCLUDES THE FILES AT THE POINT OF PAPER SUBMISSION. IT WILL BE UPDATED ONCE THE PAPER IS ACCEPTED WITH ANY REVISIONS AND WILL INCLUDE A DD/FLMD AT THAT POINT. We thank the United States Forest Service, Washington Department of Fish and Wildlife, Washington Department of Natural Resources, Cowiche Canyon Conservatory, Washington State Parks and Recreation Commission (Scientific Research Permit #210901), and the Confederated Tribes and Bands of the Yakama Nation for access to field locations where the samples labeled “SSS” were collected. We also thank the Yakama Nation Tribal Council and Yakama Nation Fisheries for working with us to facilitate sample collection and optimization of data usage according to their values and worldview. WHONDRS consortium members were asked to provide any acknowledgments for the collection of samples labeled “CM” and the following is a list of acknowledgments that were submitted with their corresponding Site IDs: (MART) Research activities were conducted in part on the Wind River Experimental Forest within the Gifford Pinchot National Forest; (MP- 100379) Philadelphia is part of Lenapehoking, the ancestral homelands of the Lenape peoples; (MP-102398) Land surveyed is the ancestral homelands of the Nookhose'iinenno (Arapaho), Tsis tsis'tas (Cheyenne), and Nuuchu (Ute); (MP-100749 and MP- 100747) Georgia Coastal Ecosystem LTER, OCE-1832178; (SP-70 and SP-72) Eastern Shoshone, Shoshone-Bannock; (MP- 102944) Funded by Oregon Watershed Enhancement Board. On the traditional lands of the Confederated Tribes of the Siletz, Confederated Tribes of the Grand Rhonde, and the Clatsop-Nehalem Confederated Tribe; (MP- 100607) Holiday Creek is located on the traditional territory of the Monacan Indian Nation; (SP-45) Lafayette Blue Springs State Park; (MP-102420) NSF DEB-2016749; (MP-100019) New Hampshire Agriculture Experiment Station; (SP-35) Rayonier (land owner; https://www.rayonier.com/); (MP- 101276) US Department of Energy, Office of Science, Biological and Environmental Research, Subsurface Biogeochemical Research, Watershed Dynamics and Evolution SFA at ORNL; (MP- 103224) Watershed Dynamics and Evolution SFA at ORNL; (MP- 101584) Traditional lands of the Oceti Sakowin (Dakota, Lakota, Nakoda) and Anishinaabe Peoples.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1983

STS-3/OSS-1 Plasma Diagnostics Package (PDP) measurements of the temperature pressure and plasma

Designed to withstand the thermal extremes of the STS-3 mission through the use of heaters and thermal blankets, the plasma diagnostics package sat on the release/engagement mechanism on the OSS-1 payload pallet without a coldplate and was attached to the RMS for two extended periods. Plots show temperature versus mission elapsed time for two temperature sensors. Pressure in the range of 10 to the -3 power torr and 10 to the -7 power torr, measured 3 inches from the skin of the package is plotted against GMT during the mission. The most distinctive feature of the pressure profile is the modulation at the obit period. It was found that pressure peaks when the atmospheric gas is rammed into the cargo bay. Electric and magnetic noise spectra and time variability due to orbiter systems, UHF and S-band transmitter field strengths, and measurements of the ion spectra obtained both in the cargo bay and during experiments are plotted.

Shawhan, S. D.↗

NASA NTRS2006

The Space Technology-7 Disturbance Reduction System Precision Control Flight Validation Experiment Control System Design

As originally proposed, the Space Technology-7 Disturbance Reduction System (DRS) project, managed out of the Jet Propulsion Laboratory, was designed to validate technologies required for future missions such as the Laser Interferometer Space Antenna (LISA). The two technologies to be demonstrated by DRS were Gravitational Reference Sensors (GRSs) and Colloidal MicroNewton Thrusters (CMNTs). Control algorithms being designed by the Dynamic Control System (DCS) team at the Goddard Space Flight Center would control the spacecraft so that it flew about a freely-floating GRS test mass, keeping it centered within its housing. For programmatic reasons, the GRSs were descoped from DRS. The primary goals of the new mission are to validate the performance of the CMNTs and to demonstrate precise spacecraft position control. DRS will fly as a part of the European Space Agency (ESA) LISA Pathfinder (LPF) spacecraft along with a similar ESA experiment, the LISA Technology Package (LTP). With no GRS, the DCS attitude and drag-free control systems make use of the sensor being developed by ESA as a part of the LTP. The control system is designed to maintain the spacecraft s position with respect to the test mass, to within 10 nm/the square root of Hz over the DRS science frequency band of 1 to 30 mHz.

O'Donnell, James R.↗

DOE OSTI2026

Soil Water Retention and Hydraulic Conductivity Data and Model at Trail Creek in Taylor River Watershed, Colorado 2024-2025

This data package includes soil water retention and hydraulic conductivity data and model fitting results from measurements of ex-situ soil samples and in-situ soil sensors near Trail Creek. Soil water retention curves (SWRC) characterize soil water content as a function of soil water potential. SWRC depends on soil texture and pore structure and can be used to describe the constraints on biogeochemical processes in terms of soil water availability. In this data package, the sample identification follows the format TR-X-Y, where TR refers to Trail Creek, X is the treatment block identifier, and Y is the location identifier. Specifically, TR-ASCC1 is the control treatment block under the Adaptive Silviculture for Climate Change (ASCC) project, and TR-ASCC2 is the clear-cut treatment block. TR-ASCC-EHSn is associated with ecohydrology sites under the East-Taylor Watershed Community Observatory Sites directory, and TR-ASCC-ERTn (upslope n=1) are ecohydrology sites along the electrical resistivity tomography transects. The sample and location information can be found in metadata.csv, and the data from the soil sensors will be included in a future data version when the observation period becomes sufficiently long for data analysis. Sampling and Measurements Each sample falls into one of the two sampling methods – (1) intact cores or (2) soil sensors – and one of the two measurement methods – (a) laboratory or (b) in-situ. The intact cores were measured using the laboratory methods, which include measurements of soil water potential (HYPROP & WP4C, METER), saturated (KSAT, METER) and unsaturated hydraulic conductivity (HYPROP). The in-situ method uses a pair of co-located soil sensors to measure volumetric water content (TEROS12, METER) and soil water potential (TEROS21, METER), and the hydraulic conductivity was not measured. In comparison, the laboratory methods progress from full saturation to dry conditions, and the in-situ method includes both dry-to-wet and wet-to-dry cycles. The sampling and measurement methods for each sample can be found in metadata.csv, and more information about the measurements is detailed in the Methods section below. Models Retention and hydraulic conductivity data were fitted with four van-Genuchten-type models (specified by “model_name” column in the files): (1) traditional constrained van Genuchten model (“vG_constrained”), (2) traditional unconstrained van Genuchten model (“vG_unconstrained”), (3) PDI-variant of the constrained van Genuchten model (“vG_constrained_PDI”), and (4) PDI-variant of the unconstrained van Genuchten model (“vG_unconstrained_PDI”). The difference between the constrained (1: n) and the unconstrained (2: n, m) van Genuchten models is the number of pore-size distribution parameters in the model equations, giving the unconstrained model more degrees of freedom when fitting the data. Between the traditional and the PDI-variant models, model fitting differs the most at the dry end of the measurements. The traditional models allow infinite suction at the residual water content (water content does not drop below residual water content), and the PDI-variant models enforce a soil water potential value of pF=6.8 (~ -630 MPa) at oven-dryness (water content reaches 0). The inclusion of the van-Genuchten-type models is due to their common application. If other retention models are required, users can access the data in data.csv for further data fitting. More information about the models can be found in the Methods section below. Fitting Tasks The model fitting can be categorized into three levels of tasks (specified by “fitting_task” column in the files). Level 1 (“fit_retention”) only includes retention data fitting (the only level available for the in-situ method). Level 2 (“fit_retention_conductivity”) includes both retention and hydraulic conductivity data fitting, and the saturated hydraulic conductivity (Ks, a parameter of the hydraulic conductivity functions) is fixed by the measurements from KSAT. Level 3 (“fit_retention_conductivity_Ks”) also includes both retention and hydraulic conductivity data fitting, but Ks is a fitted parameter without the constraints from KSAT measurements. Among the same retention models (e.g. vG_constrained models of the same sample), level 1 should produce the best retention data fitting. Level 2 should have the highest misfit of the retention and hydraulic conductivity data, because the retention and hydraulic conductivity functions share common model parameters, and the unsaturated hydraulic conductivity (HYPROP) data fitting is subject to Ks measured independently by KSAT. Level 3 should have mid-level misfits of the retention and hydraulic conductivity data. While level 3 fits the hydraulic conductivity data better than level 2, the fitted Ks value might be unreasonable due to the lack of constraints at the wet end of the measurements. General recommendation when using this data package: (1) Choice of sampling methods: Intact cores might suffer from sample gaps that would lead to overestimation of Ks (sample gaps can be inferred from the “soil_sample_volume” column in metadata.csv when the value is < 249). In-situ method has higher uncertainty in characterizing the wet end of the SWRC because of sensor limitations and the difficulty in reaching full saturation under natural conditions. (2) Choice of fitting tasks: When only retention data is needed, level 1 (“fit_retention”) should be prioritized. When both retention and hydraulic conductivity data are needed, level 2 (“fit_retention_conductivity”) could be prioritized. (3) Choice of models: This could depend on what the downstream models call for. If no specific model is required, model misfit could be used as a ranking criterion. Model misfit values in terms of RMSE can be found in model_parameters.csv. The following files are included in this data package: (1) metadata.csv – This file includes the general information of each sample, including location (description, geocoordinates, elevation), sampling and measurements details (method, depth, time or period, volume, instruments), and soil physical properties (bulk density, saturated hydraulic conductivity, only applicable to physical soil samples). (2) data.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity data of each sample. Column “instrument” specifies the instrument (HYPROP, WP4C, or TEROS) used to perform the measurements. (3) model_fit.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity fitted from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the variable does not apply to that fitting task. (4) model_parameters.csv – This file includes the fitted model parameters, model misfits, and conventional water content thresholds (field capacity and wilting point) from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the parameter does not apply to that model and/or that fitting task. (5) data_Ks.csv – This file includes the saturated hydraulic conductivity measurements from KSAT. (6) /figure/*.png – This folder includes three quick visualizations of the data, retention model fitting results and misfits, and hydraulic conductivity model fitting results, misfits, and parameters. The model fitting results are separated by samples and fitting tasks and colored by models. Zoom-in required. (7) /hyprop/*.bdhx – This folder includes proprietary hyprop files that require the free Labros SoilView-Analysis (METER) to open. Users can explore data fitting using other retention models (i.e. Brooks-Corey, Fredlund-Xing, Kosugi, bimodal models). Be aware that Ks value is pre-entered under “Fitting tab, Conductivity functions parameters” for level 2 fitting. If the value is lost, please refer to metadata.csv under “Ks” column. (8) Six file-level metadata that summarize file, header, column, and variable information of all files. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

EARTH SCIENCE > LAND SURFACE > SOILS↗

NASA NTRS2017

IMAPS Device Packaging Conference 2017 - Engineered Micro Systems & Devices Track

NASA field center Marshall Space Flight Center (Huntsville, AL), has invested in advanced wireless sensor technology development. Developments for a wireless microcontroller back-end were primarily focused on the commercial Synapse Wireless family of devices. These devices have many useful features for NASA applications, good characteristics and the ability to be programmed Over-The-Air (OTA). The effort has focused on two widely used sensor types, mechanical strain gauges and thermal sensors. Mechanical strain gauges are used extensively in NASA structural testing and even on vehicle instrumentation systems. Additionally, thermal monitoring with many types of sensors is extensively used. These thermal sensors include thermocouples of all types, resistive temperature devices (RTDs), diodes and other thermal sensor types. The wireless thermal board will accommodate all of these types of sensor inputs to an analog front end. The analog front end on each of the sensors interfaces to the Synapse wireless microcontroller, based on the Atmel Atmega128 device. Once the analog sensor output data is digitized by the onboard analog to digital converter (A/D), the data is available for analysis, computation or transmission. Various hardware features allow custom embedded software to manage battery power to enhance battery life. This technology development fits nicely into using numerous additional sensor front ends, including some of the low-cost printed circuit board capacitive moisture content sensors currently being developed at Auburn University.

Varnavas, Kosta↗

DOE OSTI2018

280 GHz Focal Plane Unit Design and Characterization for the Spider-2 Suborbital Polarimeter

We describe the construction and characterization of the 280 GHz bolometric focal plane units (FPUs) to be deployed on the second flight of the balloon-borne Spider instrument. These FPUs are vital to Spider’s primary science goal of detecting or placing an upper limit on the amplitude of the primordial gravitational wave signature in the cosmic microwave background (CMB) by constraining the B-mode contamination in the CMB from Galactic dust emission. Each 280 GHz focal plane contains a $16 \times 16$ grid of corrugated silicon feedhorns coupled to an array of aluminum–manganese transition-edge sensor (TES) bolometers fabricated on 150 mm diameter substrates. In total, the three 280 GHz FPUs contain 1530 polarization-sensitive bolometers (765 spatial pixels) optimized for the low loading environment in flight and read out by time-division SQUID multiplexing. In this paper, we describe the mechanical, thermal, and magnetic shielding architecture of the focal planes and present cryogenic measurements which characterize yield and the uniformity of several bolometer parameters. The assembled FPUs have high yields, with one array as high as 95% including defects from wiring and readout. We demonstrate high uniformity in device parameters, finding the median saturation power for each TES array to be $\sim $ 3 pW at 300 mK with a less than 6% variation across each array at $1\sigma $ . These focal planes will be deployed alongside the 95 and 150 GHz telescopes in the Spider-2 instrument, slated to fly from McMurdo Station in Antarctica in December 2018.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

NASA NTRS2018

Sensor Uncertainty Mitigation and Dynamic Well Clear Volumes in DAIDALUS

This paper presents recent updates to DAIDALUS (Detect & Avoid Alerting Logic for Unmanned Systems), a detect and avoid (DAA) software package for the integration of civil UAS into the airspace. DAIDALUS is the reference implementation of detect and avoid for unmanned aircraft systems chosen by RTCA Special Committee 228 (SC-228), and it is included in its corresponding Minimum Operational Performance Standards (MOPS) document, DO-365. This paper reports on the integration into DAIDALUS of two new capabilities, namely dynamic well clear volumes and sensor uncertainty mitigation.'

Narkawicz, Anthony↗

NASA NTRS2022

GPM Ground Validation at NASA Wallops Precipitation Research Facility

The Wallops Precipitation Research Facility (WPRF) at NASA Goddard Space Flight Center, Wallops Island, VA has been established as a semi-permanent super-site for the Global Precipitation Measurement (GPM) Ground Validation (GV) program. WPRF is home to research quality precipitation instruments, including NASA’s S-band dual-polarimetric radar (NPOL), and a network of profiling radars, disdrometers, and rain gauges. This study investigates the statistical agreement of the GPM Core Observatory Dual Frequency Precipitation Radar (DPR), combined DPR-GPM Microwave Imager (GMI) and GMI Level II precipitation retrievals compared to WPRF ground observations from a six-year collection of satellite overpasses. Multi-sensor observations are integrated using the System for Integrating Multiplatform Data to Build the Atmospheric Column (SIMBA) software package. SIMBA ensures measurements recorded in a variety of formats are synthesized into a common reference frame for ease in comparison and analysis. Given that instantaneous satellite measurements are observed above ground level, this study investigates the possibility of a time lag between satellite and surface mass-weighted mean diameter (D(sub m)), reflectivity (Z), and precipitation rate (R) observations. Results indicate that time lags vary up to 30 minutes after overpass time but are not consistent between cases. In addition, GPM Core D(sub m) retrievals are within Level I mission science requirements as compared to WPRF ground observations. Results also indicate GPM algorithms overestimate light rain (< 1.0 mm hr(exp -1)). Two very different stratiform rain vertical profiles show differing results when compared to ground reference data. A key finding of this study indicates multi-sensor DPR/GMI combined algorithms outperform single sensor DPR algorithm.

Charanjit S Pabla↗

NASA NTRS2001

An Evaluation of Fractal Surface Measurement Methods for Characterizing Landscape Complexity from Remote-Sensing Imagery

The rapid increase in digital data volumes from new and existing sensors necessitates the need for efficient analytical tools for extracting information. We developed an integrated software package called ICAMS (Image Characterization and Modeling System) to provide specialized spatial analytical functions for interpreting remote sensing data. This paper evaluates the three fractal dimension measurement methods: isarithm, variogram, and triangular prism, along with the spatial autocorrelation measurement methods Moran's I and Geary's C, that have been implemented in ICAMS. A modified triangular prism method was proposed and implemented. Results from analyzing 25 simulated surfaces having known fractal dimensions show that both the isarithm and triangular prism methods can accurately measure a range of fractal surfaces. The triangular prism method is most accurate at estimating the fractal dimension of higher spatial complexity, but it is sensitive to contrast stretching. The variogram method is a comparatively poor estimator for all of the surfaces, particularly those with higher fractal dimensions. Similar to the fractal techniques, the spatial autocorrelation techniques are found to be useful to measure complex images but not images with low dimensionality. These fractal measurement methods can be applied directly to unclassified images and could serve as a tool for change detection and data mining.

Lam, Nina Siu-Ngan↗

NASA NTRS1999

Assessment of Selected CERES Electronic Component Survivability under Simulated Overvoltage Conditions

In August, 1998 a Clouds and the Earth's Radiant Energy System (CERES) instrument telemetry housekeeping parameter generated a yellow warning message that indicated an on-board + 15V Data Acquisition Assembly (DAA) power converter deregulation anomaly. An exhaustive investigation was undertaken to understand this anomaly and the long-term consequences which have severely reduced CERES operations on the Tropical Rainfall Measuring Mission (TRMM) spacecraft. Among investigations performed were ground tests that approximated the on-board electronic circuitry using a small quantity of flight identical components exposed to maximum spacecraft bus over-voltage conditions. These components include monolithic integrated microcircuits that perform analog signal conditioning on instrument sensor signals and an analog- to-digital converter (ADC) for the entire DAA. All microcircuit packages have either a bipolar silicon design with internal current limiting protections or have a complementary metal oxide semiconductor (CMOS) design with bias protections. Ground tests that have been running for approximately 8 months have indicated that these components are capable of withstanding as much as twice their input supply voltage ratings without noticeable performance degradation. These data provide CERES operators with confidence of being able to continue science operations over the remaining life of the TRMM mission. This paper will discuss this anomaly and some possible causes, a simulator of affected electronics, test results, prognosis for future CERES operations, and conclusions.

Chapman, John J.↗

NASA NTRS2005

Coherent Laser Instrument Would Measure Range and Velocity

A proposed instrument would project a narrow laser beam that would be frequency-modulated with a pseudorandom noise (PN) code for simultaneous measurement of range and velocity along the beam. The instrument performs these functions in a low mass, power, and volume package using a novel combination of established techniques. Originally intended as a low resource- footprint guidance sensor for descent and landing of small spacecraft onto Mars or small bodies (e.g., asteroids), the basic instrument concept also lends itself well to a similar application guiding aircraft (especially, small unmanned aircraft), and to such other applications as ranging of topographical features and measuring velocities of airborne light-scattering particles as wind indicators. Several key features of the instrument s design contribute to its favorable performance and resource-consumption characteristics. A laser beam is intrinsically much narrower (for the same exit aperture telescope or antenna) than a radar beam, eliminating the need to correct for the effect of sloping terrain over the beam width, as is the case with radar. Furthermore, the use of continuous-wave (CW), erbium-doped fiber lasers with excellent spectral purity (narrow line width) permits greater velocity resolution, while reducing the laser s power requirement compared to a more typical pulsed solid-state laser. The use of CW also takes proper advantage of the increased sensitivity of coherent detection, necessary in the first place for direct measurement of velocity using the Doppler effect. However, measuring range with a CW beam requires modulation to "tag" portions of it for time-of-flight determination; typically, the modulation consists of a PN code. A novel element of the instrument s design is the use of frequency modulation (FM) to accomplish both the PN-modulation and the Doppler-bias frequency shift necessary for signed velocity measurements. This permits the use of a single low-power waveguide electrooptic phase modulator, while simultaneously mitigating the effects of speckle as a noise source in the coherent detection.

Chang, Daniel↗

DOE OSTI2022

Temporal Study 2021-2022: Sample-Based Surface Water Chemistry and Organic Matter Characterization across Watersheds in the Yakima River Basin, Washington, USA (v3)

This dataset supports a broader study examining the drivers of temporal variability in sediment respiration rates in the Yakima River Basin. The dataset provides geochemistry and organic matter characterization data generated from samples collected at weekly or bi-weekly intervals at six sites across the Yakima River Basin in Washington, USA. Related sensor data will be published separately and can be used to link sediment respiration rates to biogeochemical processing rates. The data package was originally published in November 2022. It was updated in November 2023 (v2; modified files) and April 2025 (new and modified files). See the change history section in readme for more details. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. This dataset is comprised of one main data folder containing (1) file-level metadata; (2) data dictionary; (3) field metadata; (4) dissolved inorganic carbon (DIC), dissolved organic carbon (DOC; reported as non-purgeable organic carbon; NPOC), total nitrogen (TN), total suspended solids (TSS), and ions; (5) averaged values from water chemistry data; (6) surface water sampling protocol; (7) sensor protocol; (8) readme; (9) methods codes; (10) international generic sample number (IGSN) mapping file; and (11) folder of high resolution characterization of organic via 12 Tesla Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) through the Environmental Molecular Sciences Laboratory (EMSL; https://www.pnnl.gov/environmental-molecular-sciences-laboratory). This folder contains two subfolders, one containing the .xml data files and the other containing instructions for using Formularity (https://omics.pnl.gov/software/formularity) and an R script to process the data based on the user's specific needs. All files are .csv, .pdf, .R, .ref, or .xml. We acknowledge the Yakama Nation as owners and caretakers of the lands where we collected our data. We thank the Confederated Tribes and Bands of the Yakama Nation Tribal Council and Yakama Nation Fisheries for working with us to facilitate sample collection and optimization of data usage according to their values and worldview.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1976

An Experiment to Evaluate Skylab Earth Resources Sensors for Detection of the Gulf Stream

The author has identified the following significant results. An experiment to evaluate the Skylab earth resources package for observing ocean currents was performed in the Straits of Florida in January 1974. Data from the S190 photographic facility, S191 spectroradiometer and S192 multispectral scanner, were compared with surface observations. The anticyclonic edge of the Gulf Stream could be identified in the Skylab S190A and B photographs, but the cyclonic edge was obscured by clouds. The aircraft photographs were judged not useful for spectral analysis because vignetting caused the blue/green ratios to be dependent on the position in the photograph. The spectral measurement technique could not identify the anticyclonic front, but mass of Florida Bay water which was in the process of flowing into the Straits could be identified and classified. Monte Carlo simulations of the visible spectrum showed that the aerosol concentration could be estimated and a correction technique was devised.

Maul, G. A.↗