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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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At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRS1995

Development of a Robust star identification technique for use in attitude determination of the ACE spacecraft

The Advanced Composition Explorer (ACE) spacecraft is designed to fly in a spin-stabilized attitude. The spacecraft will carry two attitude sensors - a digital fine Sun sensor and a charge coupled device (CCD) star tracker - to allow ground-based determination of the spacecraft attitude and spin rate. Part of the processing that must be performed on the CCD star tracker data is the star identification. Star data received from the spacecraft must be matched with star information in the SKYMAP catalog to determine exactly which stars the sensor is tracking. This information, along with the Sun vector measured by the Sun sensor, is used to determine the spacecraft attitude. Several existing star identification (star ID) systems were examined to determine whether they could be modified for use on the ACE mission. Star ID systems which exist for three-axis stabilized spacecraft tend to be complex in nature and many require fairly good knowledge of the spacecraft attitude, making their use for ACE excessive. Star ID systems used for spinners carrying traditional slit star sensors would have to be modified to model the CCD star tracker. The ACE star ID algorithm must also be robust, in that it will be able to correctly identify stars even though the attitude is not known to a high degree of accuracy, and must be very efficient to allow real-time star identification. The paper presents the star ID algorithm that was developed for ACE. Results from prototype testing are also presented to demonstrate the efficiency, accuracy, and robustness of the algorithm.

Woodard, Mark↗
NASA NTRS2002

Analysis of Carbon Nanotube Metal-Semiconductor Diode Device

We study recently reported drain current Id-drain voltage Vd characteristics of a carbon nanotube metal semiconductor diode device with the gate voltage Vg applied to modulate the carrier density in the nanotube. The diode was kink-shaped at the metal-semiconductor interface. It was shown that (1) larger negative Vg blocked Id more effectively in the negative Vd region, resulting in the rectifying Id-Vd characteristics, and that (2) positive Vg allowed Id in the both Vd polarities, resulting in the non-rectifying characteristics. The negative Vd was the Schottky reverse direction, judging from the negligible Id behavior for a wide region of -4 V less than Vd less than 0 V, with Vg = -4 V. Such negative Vg would attract positive charges from the metallic electrodes (charge reservoir) to the nanotube and lower the nanotube Fermi energy (EF). With larger negative Vg, the experiment showed that the Schottky forward direction (Vd greater than 0) had a smaller turn-on voltage and the Schottky reverse direction (Vd less than 0) was more resistant to the tunneling breakdown. Therefore, the majority carriers in the transport would be electrons since they can see a lower tunneling barrier (shallower built-in potential) in the forward direction when EF is lowered, and a thicker tunneling barrier (Schottky barrier) in the reverse direction due to the reduction in the electron density when EF is lowered.

Yamada, Toshishige↗
NASA NTRS2010

Secure ADS-B authentication system and method

A secure system for authenticating the identity of ADS-B systems, including: an authenticator, including a unique id generator and a transmitter transmitting the unique id to one or more ADS-B transmitters; one or more ADS-B transmitters, including a receiver receiving the unique id, one or more secure processing stages merging the unique id with the ADS-B transmitter's identification, data and secret key and generating a secure code identification and a transmitter transmitting a response containing the secure code and ADSB transmitter's data to the authenticator; the authenticator including means for independently determining each ADS-B transmitter's secret key, a receiver receiving each ADS-B transmitter's response, one or more secure processing stages merging the unique id, ADS-B transmitter's identification and data and generating a secure code, and comparison processing comparing the authenticator-generated secure code and the ADS-B transmitter-generated secure code and providing an authentication signal based on the comparison result.

Viggiano, Marc J↗
NASA NTRS2020

Demonstration of Deployment Accuracy of the Starshade Inner Disk Subsystem

We present experimental results that demonstrate the required in-plane deployment accuracy of the starshade Inner Disk Subsystem (IDS). This effort was to address Milestone 7C of the Starshade Technology Activity S5, which requires that the in-plane deployment accuracy of the petal interfaces on the IDS be within +/-300um. A full-scale 10m-diameter IDS prototype was constructed. This prototype comprised a medium-fidelity perimeter truss, medium-fidelity spokes, and a low-fidelity optical shield (OS). The testbed also included gravity compensation systems and a laser tracker metrology system. The post-processed measurement uncertainties of the laser tracker were less than 30um at the 3 sigma level. Design, engineering and fabrication of these components was done by JPL, Tendeg, and Roccor. Testing was performed at the Tendeg facility in Louisville, Colorado. The IDS prototype was deployed 22 times and the locations of petal interfaces on the IDS were measured after each deployment. Based on this data, tolerance intervals were calculated that would contain 99.73% of future deployment accuracy errors with 90% confidence. These tolerance intervals are conservative estimates for 3 sigma bounds. The tolerance intervals for the three pertinent error components were found to be within the required allocations, with at least 19% margin.

Turse, Dana↗
NASA NTRS2020

InSight Robotic Arm Testing Activities for HP3 Mole Anomaly Recovery on Mars

The InSight lander’s Heat Flow and Physical Properties Package (HP3) was deployed on Mars in February 2019and began attempting to penetrate to its target depth range of3-5 meters shortly thereafter. However, the mole’s downwardprogress stopped after only 35 cm of penetration. In response,the project convened an Anomaly Response Team (ART) andsince then has been attempting to diagnose the problem andassist the mole using the tools available on Mars. The key assetused in this effort has been the Instrument Deployment System(IDS), which includes two cameras and a robotic InstrumentDeployment Arm (IDA). Since the IDS was originally intendedonly to deploy InSight’s primary instruments to the Martiansurface, new testbed setups, experiments, and operational protocols (e.g., command sequences) were required and had to bedeveloped on a short timeline. The HP3 Mole ART first focusedon gathering all observable data on Mars about the state ofthe mole and Support Structure Assembly (SSA), as well asthe physical properties of the Martian regolith. This includedusing the robotic arm to point the IDC at the SSA duringdiagnostic hammering attempts to observe motion of the SSAand science tether. Images taken during these attempts revealedsome motion of the SSA, but no apparent change in mole depth.At JPL, the IDS and Testbed teams re-created the hardwareconfiguration on Mars based on limited knowledge of the mole’sstate. They devised and tested techniques to use the roboticarm and cameras to accomplish previously untested activitieson Mars, including imaging the HP3, using the IDA to interactwith the terrain, and using the IDA to move the SSA awayfrom the partially-embedded mole. The team executed the morepromising techniques on Mars. After diagnostic hammering onMars, the team decided to move the SSA to gain visibility ofthe mole’s configuration and access to the soil around the mole.After developing the technique and practicing the maneuver inthe InSight testbed, the team lifted the SSA on Mars and placedit behind the mole. This revealed a pit surrounding the nowexposed mole, observations of which provided essential cluesfor determining the root cause of the mole’s lack of progress.The IDS and Testbed teams altered the testbed to match thesituation on Mars. They devised IDA techniques to determinethe Martian soil properties and assist the mole’s descent. Theytested these techniques in the testbed and executed the more978-1-7281-2734-7/20/$31.00 c 2021 IEEE. Copyright 2020 CaliforniaInstitute of Technology. U.S. Government sponsorship acknowledged.promising ones on Mars. These include using the robotic arm toalter the regolith near the mole and to push on the mole while ithammers. This paper discusses the anomaly resolution testing inthe testbed at JPL, describes how the IDS team prepared for theanomaly recovery activities on Mars, and provides preliminaryresults of the efforts to assist the HP3 mole on Mars.

Kim, Junggon↗
NASA NTRS2021

InSight Robotic Arm Testing Activities for HP3 Mole Anomaly Recovery on Mars

The InSight lander’s Heat Flow and Physical Properties Package (HP3 ) was deployed on Mars in February 2019 and began attempting to penetrate to its target depth range of 3-5 meters shortly thereafter. However, the mole’s downward progress stopped after only 35 cm of penetration. In response, the project convened an Anomaly Response Team (ART) and since then has been attempting to diagnose the problem and assist the mole using the tools available on Mars. The key asset used in this effort has been the Instrument Deployment System (IDS), which includes two cameras and a robotic Instrument Deployment Arm (IDA). Since the IDS was originally intended only to deploy InSight’s primary instruments to the Martian surface, new testbed setups, experiments, and operational protocols (e.g., command sequences) were required and had to be developed on a short timeline. The HP3 Mole ART first focused on gathering all observable data on Mars about the state of the mole and Support Structure Assembly (SSA), as well as the physical properties of the Martian regolith. This included using the robotic arm to point the IDC (Instrument Deployment Camera) at the SSA during diagnostic hammering attempts to observe motion of the SSA and science tether. Images taken during these attempts revealed some motion of the SSA, but no apparent change in mole depth. At JPL, the IDS and Testbed teams re-created the hardware configuration on Mars based on limited knowledge of the mole’s state. They devised and tested techniques to use the robotic arm and cameras to accomplish previously untested activities on Mars, including imaging the HP3 , using the IDA to interact with the terrain, and using the IDA to move the SSA away from the partially-embedded mole. The team executed the more promising techniques on Mars. After diagnostic hammering on Mars, the team decided to move the SSA to gain visibility of the mole’s configuration and access to the soil around the mole. After developing the technique and practicing the maneuver in the InSight testbed, the team lifted the SSA on Mars and placed it behind the mole. This revealed a pit surrounding the now exposed mole, observations of which provided essential clues for determining the root cause of the mole’s lack of progress. The IDS and Testbed teams altered the testbed to match the situation on Mars. They devised IDA techniques to determine the Martian soil properties and assist the mole’s descent. They tested these techniques in the testbed and executed the more promising ones on Mars. These include using the robotic arm to alter the regolith near the mole and to push on the mole while it hammers. This paper discusses the anomaly resolution testing in the testbed at JPL, describes how the IDS team prepared for the anomaly recovery activities on Mars, and provides preliminary results of the efforts to assist the HP3 mole on Mars.

Kim, Junggon↗
NASA NTRSDate not supplied

Impact of DORISx Post Seismic Deformation and Seasonal Corrections on the DPOD2020 Solution

As one of the tracking systems used for the altimeter missions (such as TOPEX/Poseidon, Envisat, Jason-1/-2/-3, CryoSat-2, Saral/AltiKa, Sentinel-3A/-3B, HY-2A/C/D, Jason-CS/Sentinel-6A, SWOT), the position of the DORIS tracking stations provides a fundamental reference for the estimation of the precise orbits and so, by extension is fundamental for the quality of the altimeter data analysis and derived products. Due to the time evolution of the DORIS ground network, stations included in ITRF2020 may have been decommissioned and stations that were added to the tracking network after 2021.0 are, by definition, not part of ITRF2020. Therefore, to satisfy operational requirements for precise orbit determination and routine delivery of geodetic products, the International DORIS Service maintains the DORIS terrestrial reference frame for Precise Orbit Determination (DPOD) solutions. Since 2016, the DPOD products are computed by the IDS Combination Center (CC) as a DORIS cumulative position and linear velocity solution aligned to the latest available ITRF and using the latest IDS weekly combined series. Mid-2023, the IDS CC started the computation of the second version of the DPOD2020 based on the IDS 20 weekly solutions from 1993.0 to 2023.0. With that new solution, the IDS CC will include Post Seismic Deformation (PSD) corrections for some DORIS sites such as Socorro Island. These DPOD PSD corrections are determined from the analysis of DORIS coordinate time series only. The second version will also include optional annual and semi-annual station corrections. The presentation will describe the analysis used for extracting the PSD and seasonal DORIS station corrections, compare such corrections to those derived for ITRF2020, and evaluate the impact these new corrections have on station positioning and satellite POD.

Guilhem Moreaux↗
DOE OSTI2026

Annual Summary Report for the Remote-Handled Low-Level Waste Disposal Facility—FY 2025

The U.S. Department of Energy (DOE) requires the performance assessment (PA) (Department of Energy Idaho Operations Office [DOE-ID] 2018a), composite analysis (CA) (DOE-ID 2012), and CA addendum (DOE-ID 2018b) for the Remote-Handled Low-Level Waste (RHLLW) Disposal Facility at the Idaho National Laboratory (INL) Site shall be maintained to evaluate changes that could affect the performance, design, and operating basis for the facility (DOE Manual 435.1-1 Change 3, “Radioactive Waste Management Manual,” Section IV.P. [4]). The RHLLW Disposal Facility became operational in September 2018 after the completion of operational readiness activities required by DOE Order 425.1D, “Verification of Readiness to Start Up or Restart Nuclear Facilities,” and the issuance of the startup authorization by the Startup Approval Authority (Boston 2018). The first waste disposals at the RHLLW Disposal Facility began in Fiscal Year (FY) 2019. In Fiscal Year (FY) 2025, no significant operational changes or other activities occurred that would cause deviation from the assumptions in the PA and CA pertaining to disposal geometry, verification of waste characteristics, tracking disposal inventories against total limits, facility closure design, or institutional controls. This FY 2025 annual summary report (ASR) documents the continued adequacy of the PA, CA, operating disposal authorization statement (ODAS) (ODAS 2018), ODAS technical-basis documents, and the radioactive waste management basis (RWMB) (INL 2024a) to meet DOE Order 435.1, “Radioactive Waste Management,” performance objectives for the RHLLW Disposal Facility. Annual review of the adequacy of the PA and CA at the RHLLW Disposal Facility ensures that conclusions of the analyses remain valid in accordance with requirements of DOE Order 435.1.

12 - MGMT OF RADIOACTIVE AND NON-RADIOACTIVE WASTE↗
DOE OSTI2024

Federated Access from DOE Labs to Distributed Storage in the EIC Era of Computing

The Electron Ion Collider (EIC) collaboration and future experiment is a unique scientific ecosystem within Nuclear Physics as the experiment starts right off as a crosscollaboration between Brookhaven National Lab (BNL) & Jefferson Lab (JLab). As a result, this muti-lab computing model tries at best to provide services accessible from anywhere by anyone who is part of the collaboration. While the computing model for the EIC is not finalized, it is anticipated that the computational and storage resources will be made accessible to a wide range of collaborators across the world. The use of federated ID seems to be a critical element to the strategy of providing such services, allowing seamless access to each lab site computing resources. However, providing Federated access to a Federated storage is not a trivial matter and has its share of technical challenges. In this contribution, we focus on the steps we took towards the deployment of a distributed object storage system that integrates with Amazon S3 and Federated ID. We will first cover for and explain the first stage storage solutions provided to the EIC during the detector design phase. Our initial test deployment consisted of Lustre storage using MinIO, hence providing an S3 interface. High Availability load balancers were added later to provide the initial scalability it lacked. Performance of that system will be shown. While this embryonic solution worked well, it had many limitations. Looking ahead, the Ceph object storage is considered a top-of-the-line solution in the storage community - since the Ceph Object Gateway is compatible with the Amazon S3 API out of the box, our next phase will use a native S3 storage. Our Ceph deployment will consist of erasure coded storage nodes to maximize storage potential along with multiple Ceph Object Gateways for redundant access. We will compare performance of our next stage implementations. Finally, we will present how to leverage OpenID Connect with the Ceph Object Gateway’s to enable Federated ID access. We hope this contribution will serve the community needs as we move forward with cross-lab collaborations and the need for Federated ID access to distributed compute facilities.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Microstructural stability and mechanical properties of the as-cast and heat-treated newly developed TiNbCrTa refractory complex concentrated alloy

In this study, a TiNbCrTa refractory complex concentrated alloy (RCCA) was prepared using vacuum arc remelting. The microstructural evolution and mechanical properties of both as-cast and heat-treated RCCA samples were analyzed. Heat treatment (HT) was performed at 800–1200 °C for 1 h in a vacuum-sealed environment. These samples exhibited a formation of Cr 2 Nb and Cr 2 Ti Laves phases. A variation in elemental distribution was observed, with interdendritic (ID) regions showing higher fractions of Ti and Cr, while the dendritic regions had a greater concentration of Ta and Nb. Micro-segregation at the IDs was confirmed through energy dispersive x-ray spectroscopy mapping, which inferred the formation of Cr- and Ti-rich phases during HT at 800–1200 °C. High-temperature HT at 1200 °C for 1 h led to the evolution of the hcp omega phase. Prolonged HT at 1200 °C for 96 h resulted in the evolution of a Cr-rich Laves phase (Cr 2 Ta), which was homogeneously distributed within the microstructure, indicating an unstable microstructure. Furthermore, despite prolonged HT, a variation in the elemental distribution persisted due to the presence of dendritic and ID regions. Electron backscattered diffraction analysis revealed the presence of bcc and hcp phases in the dendritic and ID regions, respectively, of the as-cast and HTed samples. The as-cast samples demonstrated a high compressive strength of approximately 2 GPa. Micro-hardness values increased with the HT temperature up to 1000 °C. Further increases under HT conditions did not significantly reduce the microhardness value, whereas prolonged HT at 1200 °C led to an increase in the microhardness value. Overall, the newly developed TiNbCrTa RCCA exhibited high-strength behavior even after the phase transformation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

static-subid: Deterministic subordinate UID/GID assignment for unprivileged containers

static-subid calculates predictable subordinate UID and GID ranges for Linux users based on their UID, ensuring consistent ID mappings across multiple systems. Unlike shadow-utils' sequential allocation (which depends on creation order), static-subid uses a deterministic formula that guarantees the same user UID always receives the same subordinate ID range. Subordinate IDs enable user namespaces for unprivileged container runtimes (Podman, Docker rootless mode, LXC) by mapping container UIDs/GIDs to host subordinate IDs without requiring root privileges.

Riehecky, Pat [Fermi National Accelerator Laborato↗
DOE OSTI2026

Long-term stabilization of intensity-difference squeezing from four-wave mixing in rubidium vapor

Generation of quantum states of light through off-resonance four-wave mixing in rubidium vapor is a straightforward and well-established technique. However, achieving a sufficiently high and consistent level of intensity difference squeezing (IDS) and intrinsic quantum correlations between photon pairs over an extended timescale, necessary for quantum-light-based nonlinear optical spectroscopy and microscopy, remains challenging and largely unexplored. Here, we report a simple stabilization method combining an active periodic laser frequency retuning with an automatic control algorithm based on quantitatively assessing the factors affecting the IDS level and squeezed light intensity. Validation of our method was performed by acquiring data over a 5-hour period, and the results demonstrate a remarkably stable squeezing level of -7.8 dB in combination with a > 4× reduction of the standard deviation of IDS level from 0.46 dB to 0.10 dB. The achieved stabilization further enables us to quantitatively assess the IDS reduction due to the scattering in a polystyrene bead suspension as a function of sample transmission. Our approach should enable a variety of applications requiring an extended squeezing stability over multiple hours, especially for those following biological processes and chemical reactions in real time.

Allen, Christian Harry [Oak Ridge National Laborat↗
DOE OSTI2026

DOE EV Data Collection - Vehicle Data

Vehicle data consist of electric vehicle performance data collected directly from the vehicle during standard operations. Data were collected using onboard data loggers that were either installed by the project team or preinstalled by the original equipment manufacturer. Data recorded by the data loggers were made accessible via an online web portal or an application programming interface. Different data loggers were used (HEM, ViriCiti, and Geotab), and the method for each vehicle is defined in the vehicle attributes file. Some systems collected data on a “trip-level” basis, in which each row of a table represents a single trip (the period between a key-on and key-off event), whereas other data were collected on a per-day basis, in which each row represents a single day of operation. Data were collected over a range of data collection periods, depending on the project. Data have been anonymized by removing information or decreasing information resolution as necessary so that fleets are not identifiable. Due to the wide range of vehicle types represented and variation in data collection, data parameters and frequencies differ between vehicles and fleets The **Performance Data Daily/Trip Data Dictionaries** contain definitions for each available parameter associated with a vehicle’s operations, aggregated at either a daily or trip level. The parameters available will vary from vehicle to vehicle, but every possible parameter will be defined. The **Vehicle Attributes Data Dictionary** contains definitions for each available parameter associated with a vehicle’s physical and functional attributes and fleet context. The **Vehicle Attributes** table contains specific vehicle characteristics, coded to an anonymous Vehicle ID. This Vehicle ID can be used as a key between vehicle data and vehicle attribute tables. The **Vehicle Data** tables contain the data from each vehicle’s operations, aggregated at either a daily or trip level, coded to an anonymous Vehicle ID. This Vehicle ID can be used as a key between vehicle data and vehicle attribute tables. Data is being uploaded quarterly through 2023 and subject to change until the conclusion of the project.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

HydroFish: freshwater fish co-occurrence with hydropower plants and non-powered dams in conterminous United States sub-basins

The HydroFish dataset lists all existing hydropower plants (EHAs) and non-powered dams (NPDs; ≥ 0.001 MW potential nominal capacity), delineates the hydrologic sub-basins in which they are situated, and then lists all freshwater fish species reported to occur in those sub-basins. This dataset was compiled using the HydroBio dataset (https://hydrosource.ornl.gov/data/datasets/hydrobio/) and contains 24 total variables that describe hydrologic sub-basins, each unique EHA (plant ID and name, geographic coordinates, permit type, capacity, etc.) and NPD (ID value, known names, geographic coordinates, and estimated potential nominal capacity), and freshwater fish species in the sub-basin (common and scientific name, origin, and migratory and threat status). The HydroBio dataset was built using Oak Ridge National Laboratory’s Existing Hydropower Assets Dataset (2024 version) and Non-powered Dam Technical Potential Dataset (2024 version), and NatureServe’s fish species distribution dataset (2023 version). The dataset also contains summary variables that report the unique number of EHAs, NPDs, and freshwater fish species per sub-basin. The HydroFish dataset contains two unique data files: 1) a .csv metadata file describing the dataset variables, and 2) a .csv data file containing the actual dataset. Note that there may be many rows per unique existing hydropower plant or non-powered dam given that distinct species are listed per existing plant or NPD per sub-basin. The dataset is downloadable as a zip file containing the metadata and dataset files.

Bozeman, Bryan [Oak Ridge National Laboratory (ORN↗
DOE OSTI2025

Vehicular Re-Identification from Uncontrolled Multiple Views

Vehicle re-identification (re-ID) across disparate sensing modalities remains a fundamental challenge for transportation research. In this work, we introduce a deep multi-view vehicle re-ID framework that leverages Siamese networks to compare pairs of vehicle images and produce matching scores, enabling robust association across drastically different viewpoints such as those from UAVs, surveillance cameras, and ground sensors. The model exploits convolutional neural networks to learn features that remain discriminative under changes in angle, distance, and illumination, supporting more generalizable re-ID performance. As part of this effort, we also developed an automated pipeline to synchronize roadside and UAV video streams, producing a multi-perspective dataset that complements preexisting real collections and a synthetic dataset generated in this study. Together, these contributions advance the capability to re-identify vehicles across wide viewing baselines; establish a foundation for scalable, reproducible research in vehicle re-ID; and open pathways for future applications, such as inferring routine behaviors, movement patterns, and daily habits of the individual associated with the vehicle.

convolutional neural networks↗
DOE OSTI2026

Sensitivity Analysis of Drivers Water Shortage in the Los Angeles Region During Drought

The code and detailed step-by-step instructions for generating the model output data, processing results, and analysis and plotting are provided at https://github.com/IMMM-SFA/Ferencz_et_al_2026_ER_Water. The PyArtes model is a python adaptation of the Artes model. PyArtes uses many of the same input data and optimization model architecture as Artes. Documentation for the PyArtes model is provided in the Supplement to the paper. The primary data product are simulated monthly water shortages for indoor and outdoor demand under a large ensemble of drought scenarios (>13,000). The droughts are hypothetical and are not based on historical time series data of supply sources - though historical data did help inform ranges explored for supply parameters. Demands are informed by recent 2017-2021 water supply data. Demands used for the model can be accessed at https://github.com/IMMM-SFA/Ferencz_et_al_2026_ER_Water. Simulations resolve demand for over 90 water providers in the study region. The results report 36 months of water shortage data for each indoor and outdoor demand node. The study also developed a multilayer perceptron (MLP) neural network trained on a subset of the simulated shortage ensemble to emulate worst annual water shortage for a given set of parameter multipliers -- provided the parameter values fall within the ranges sampled in the ensemble. Emulated water shortages for synthetic ensembles are in the MLP-generated shortages folder. The MLP model was used to generate larger ensembles to support Sobol analysis that would have been extremely computationally expensive to simulate. Datasets provided in this repository*: Simulated shortages. These results are used for the analysis for Figures 5, 8, and 9 in the paper, and also to train the MLP emulator. .zip file containing outputs for the 13,312 scenario ensemble. Separate .csv files for indoor and outdoor shortage for each scenario. Rows = demand ids (~100), Columns = months (36) Units = acre-feet/month of shortage (shortage = monthly demand - supply). 1 acft = 1233.48 m^3 .csv files of aggregated shortages derived from the 13,312 ensemble Rows = scenarios (13,312), Columns = demand ids (~100) Units = acre-feet/year (either worst annual shortage or total shortage over the 3-year drought) .csv file of the parameter multipliers scenarios for the ensemble .csv file of the parameter ranges and baseline values the multipliers were applied to MLP-generated shortages. These results are used for Figures 4, 6, and 7 in the paper. mwd higher folder: scenario ensembles, emulated worst year total shortages (acft), and Sobol results Emulated shortages. Rows = scenarios, columns = demand ids, units acft Sobol results. Rows = demand ids, columns Sobol (S1, ST, or 95% confidence interval) value for each parameter mwd lower folder: scenario ensembles, emulated worst year total shortages (acft), and Sobol results same organization as mwd higher MLP performance: performance metrics (R^2, RMSE, BIAS, MAPE) for the testing subset (20% or 2,662 scenarios) and simulated vs emulated worst year shortage (acre-feet/year) for every demand node, MWD wholesale regions, and the entire study region (LAC). Supporting data for figures. Figure plotting scripts in the associated GitHub repo. These files support analysis and visualization. Geospatial Data used for plotting simulated water shortages and Sobol results. Dictionary of full names for demand nodes in the model and estimates of water supply by source type informed by Artes input files and California Urban Water Management Planning data: https://water.ca.gov/Programs/Water-Use-And-Efficiency/Urban-Water-Use-Efficiency/Urban-Water-Management-Plans *Readme files provided for each folder.

drought↗
NASA NTRS1990

The intensity dependent spread model and color constancy

Odetics is investigating the use of the intensity dependent spread (IDS) model for determining color constancy. Object segmentation is performed effortlessly by the human visual systems, but creating computer vision that takes an image as input and performs object identification on the basis of color has some difficulties. The unknown aspects of the light illuminating a scene in space or anywhere can seriously interfere with the use of color for object identification. The color of an image depends not only on the physical characteristics of the object, but also on the wavelength composition of the incident illumination. IDS processing provides the extraction of edges and of reflectance changes across edges, independent of variations in scene illumination. IDS depends solely on the ratio of the reflectances on the two sides of the edge. Researchers are in the process of using IDS to recover the reflectance image.

Kurrasch, Ellie↗
NASA NTRS1990

Intensity dependent spread processor and workstation

The Intensity Dependent Spread (IDS) is an adaptive algorithm which is modified according to the local intensity in the scene. (This results in a nonlinear process which cannot take advantage of rather nice linear transform methods.) The computation is similar to a neural net whereby intensity information is moving from each input pixel to a set of surrounding output pixels in a manner described by Cornsweet and Yellott. A prototype of a very large scale integration IDS processor is being developed and implemented in a workstation environment. The workstation consists of a SUN 3/260 and a DATACUBE pipeline processor. The IDS prototype is a board set which operates in the DATA CUBE processor. The SUN 3/260 performs control, background processing, IDS simulation and image display functions.

Westrom, George↗