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DOE OSTI2026

NLR's FleetDNA Speed Distributions for EPA MOVES

The U.S. Environmental Protection Agency (EPA) MOtor Vehicle Emission Simulator (MOVES) models mobile sources of air toxics at small and large scales across the Unites States. Vehicle speed distributions based on the type of vehicle, type of road traveled, and hour of the day are inputs into MOVES. In conjunction with vehicle miles traveled (VMT) inputs, they are used to estimate total operation time and select the relevant driving cycles from which running operating modes and emissions are estimated. In an ongoing research effort, the National Laboratory of the Rockies (NLR) partnered with the EPA to provide default average speed data for heavy-duty vehicles based on NLR's FleetDNA database and the Bourns College of Engineering - Center for Environmental Research and Technology (CE-CERT) data set from the University of California, Riverside.

33 ADVANCED PROPULSION SYSTEMS↗

NASA NTRS1993

Autonomous vehicle motion control, approximate maps, and fuzzy logic

Progress on research on the control of actions of autonomous mobile agents using fuzzy logic is presented. The innovations described encompass theoretical and applied developments. At the theoretical level, results of research leading to the combined utilization of conventional artificial planning techniques with fuzzy logic approaches for the control of local motion and perception actions are presented. Also formulations of dynamic programming approaches to optimal control in the context of the analysis of approximate models of the real world are examined. Also a new approach to goal conflict resolution that does not require specification of numerical values representing relative goal importance is reviewed. Applied developments include the introduction of the notion of approximate map. A fuzzy relational database structure for the representation of vague and imprecise information about the robot's environment is proposed. Also the central notions of control point and control structure are discussed.

Ruspini, Enrique H.↗

NASA NTRS2023

Experimental Characterization of the Space Launch System Block 1B Liftoff and Transition Environment

A wind tunnel test was conducted at the NASA Langley Research Center 14- by 22-Foot Subsonic Tunnel to evaluate the liftoff and transition flow environment of the Space Launch System Block 1B launch vehicles and tower interference effects with the newly designed Mobile Launcher 2. This test leveraged several unique diagnostic capabilities, including eight distributed force and moment measurements on the launch tower tiers, new umbilical and crew access arm configurations, and unsteady pressure acquisition on the rocket nose in the cargo configuration. The tower loading revealed a strong impact of the SLS vehicle height on force coefficients for individual tiers. Although the umbilicals produced weak influence on the loading on both vehicle and launch tower, unsteady pressure measurements revealed a slight increase in fundamental pressure oscillation frequency at specific wind azimuth directions. Multimodal flow states were observed in the gap flow between the vehicle and solid rocket boosters, producing different load profiles along the vehicle that are now incorporated in the vehicle database. This phenomenon was investigated using time-series measurements of forces as well as tufts and smoke flow visualization.

Lee J Mears↗

NASA NTRS2023

Experimental Characterization of the Space Launch System Block 1B Liftoff and Transition Environment

SLS↗

DOE OSTI2024

Multi-Scale Integrated Monitoring System for Enhancing Methane Emission Detection, Quantification & Prediction

This report details the progress and findings of a comprehensive study on reviewing existing solutions, identifying technology gaps, and formulating an “all-in-one” integrated strategy for developing the next-generation multiscale methane monitoring and modeling platform, conducted under grant number DE-FE0032292. Co-led by Dr. David Ebert, Dr. Binbin Weng, and Dr. Chenghao Wang at the University of Oklahoma, the project’s goal was to develop an integrated approach for building this engineering platform to detect, quantify, and mitigate methane emissions across various temporal scale, spatial scales, and sectors. The planning grant study began with an extensive review of various methane sensing and monitoring technologies and systems, surveying over 100 technology providers globally. This review revealed the prevalence of optical methods over chemical methods in commercially available sensors, with Non-Dispersive Infrared (NDIR), Tunable Diode Laser Absorption Spectroscopy (TDLAS), and Optical Gas Imaging (OGI) cameras being the most prevalent options. A trend towards more advanced optical techniques was observed, driven by increased regulatory focus and technological advancements. The technical evaluation of these sensing technologies provided crucial insights into their capabilities and limitations. The study examined emerging technologies such as Differential Absorption LiDAR (DIAL), which show promise for high-precision and long-range detection. The team then investigated the features and application bandwidth of various sensing platforms, including handheld, fixed/stationary, mobile, aerials, and spaceborne monitors. Pilot field studies were conducted to assess the capabilities of solutions for different emission scenarios. Field work with sensor deployments was conducted at three distinct site types: an oil & gas industry site, a cattle ranching operation, and a waste processing facility. The team also conducted a thorough review of methane flux inverse modeling approaches, focused on physically based methods. These approaches were categorized into simple, intermediate, and advanced methods. A realtime WRF-GHG (Weather Research and Forecasting-Greenhouse Gas) modeling system was developed and applied, incorporating multiple data sources to guide field experiments and inform methane plume detection. The project identified and analyzed numerous categories of methane data sources, including satellite measurements, ground-based sensors, and inventory databases. Key platforms examined include EDGAR, EPA GHGI, NASA TROPOMI, Carbon Mapper, and Climate TRACE, among others. The team proposed an architecture for a comprehensive methane monitoring platform. This system incorporates multi-source data acquisition, advanced data processing and assimilation, interactive visualization tools, and analytical capabilities for emissions forecasting and scenario analysis. The proposed platform aims to provide a user-friendly interface catering to various stakeholders, from researchers to policymakers. The architecture includes sophisticated data ingestion methods, a centralized data warehouse, and advanced analytical tools for data fusion and interpretation. To ensure the relevance and effectiveness of the proposed system, a comprehensive survey was conducted to gather stakeholder input on system requirements. Key findings include a strong need for integrating various data types and formats, a preference for real-time data updates and advanced visualization tools, and a demand for user-friendly interfaces catering to different expertise levels.

03 NATURAL GAS↗

NASA NTRS2014

Tradespace Exploration of Distributed Propulsors for Advanced On-Demand Mobility Concepts

Combustion-based sources of shaft power tend to significantly penalize distributed propulsion concepts, but electric motors represent an opportunity to advance the use of integrated distributed propulsion on an aircraft. This enables use of propellers in nontraditional, non-thrust-centric applications, including wing lift augmentation, through propeller slipstream acceleration from distributed leading edge propellers, as well as wingtip cruise propulsors. Developing propellers for these applications challenges long-held constraints within propeller design, such as the notion of optimizing for maximum propulsive efficiency, or the use of constant-speed propellers for high-performance aircraft. This paper explores the design space of fixed-pitch propellers for use as (1) lift augmentation when distributed about a wing's leading edge, and (2) as fixed-pitch cruise propellers with significant thrust at reduced tip speeds for takeoff. A methodology is developed for evaluating the high-level trades for these types of propellers and is applied to the exploration of a NASA Distributed Electric Propulsion concept. The results show that the leading edge propellers have very high solidity and pitch well outside of the empirical database, and that the cruise propellers can be operated over a wide RPM range to ensure that thrust can still be produced at takeoff without the need for a pitch change mechanism. To minimize noise exposure to observers on the ground, both the leading edge and cruise propellers are designed for low tip-speed operation during takeoff, climb, and approach.

Borer, Nicholas K.↗

DOE OSTI2021

Large seasonal fluctuations of groundwater radioiodine speciation and concentrations in a riparian wetland in South Carolina

Recent studies evaluating multiple years of groundwater radioiodine ( 129 I) concentration in a riparian wetland located in South Carolina, USA identified strong seasonal concentration fluctuations, such that summer concentrations were much greater than winter concentrations. These fluctuations were observed only in the wetlands but not in the upland portion of the plume and only with 129 I, and not with other contaminants of anthropogenic origin: nitrate/nitrite, strontium-90, technecium-99, tritium, or uranium. This unexplained observation was hypothesized to be the result of strongly coupled processes involving hydrology, water temperature, microbiology, and chemistry. To test this hypothesis, an extensive historical groundwater database was evaluated, and additional measurements of total iodine and iodine speciation were made from recently collected samples. During the summer, the water table decreased by as much as 0.7 m, surface water temperature increased by as much as 15 °C, and total iodine concentrations were consistently greater (up to 680%) than the following winter months. Most of the additional iodine observed in the summer could be attributed to proportional gains in organo-iodine, and not iodide or iodate. Furthermore, 129 I concentrations were observed to be two-orders-of-magnitude greater at the bottom of the upland aquifer than at the top. A coupled hydrological and biogeochemical conceptual model is proposed to tie these observations together. First, as the surface water temperature increased during the summer, microbial activity was enhanced, which in turn stimulated the formation of mobile organo-I. Hydrological processes were also likely involved in the observed iodine seasonal changes: (1) as the water table decreased in summer, the remaining upland water entering the wetland was comprised of a greater proportion of water containing elevated iodine concentrations from the low depths, and (2) water flow paths in summer changed such that the wells intercepted more of the contaminant plume and less of the diluting rainwater (due to evapotranspiration) and streamwater (as the lower levels promote a predominantly recharging system). Furthermore, these results underscore the importance of coupled processes influencing contaminant concentrations, and the need to assess seasonal contaminant variations to optimize long-term monitoring programs of wetlands.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRSDate not supplied

Urban Air Mobility Noise: Current Practice, Gaps, and Recommendations

AN Air Mobility (UAM) is an opportunity for aviation to improve transportation systems across the world. Representative UAM vehicle attributes include electrical vertical takeoff and landing (eVTOL) vehicles that can accommodate up to 6 passengers (or equivalent cargo), are possibly autonomous, perform missions of up to 100 nautical miles at altitudes up to 3000 ft. above ground level, have flight speeds up to 200 knots, and weigh between 800 and 8000 pounds. Along with the many anticipated benefits, there will be noise issues that need to be addressed. In 2018, NASA formed an Urban Air Mobility Noise Working Group (UNWG) to assemble noise experts from industry, universities and government agencies to identify, discuss, and address UAM noise issues. This oral presentation summarizes technology gaps and goals associated with four areas of interest: Tools & Technologies, Ground & Flight Testing, Human Response & Metrics, and Regulation & Policy, and is drawn from a draft white paper [1] by the same title. Tools & Technologies include noise prediction tools and noise reduction technologies that have been developed for conventional rotorcraft and fixed-wing vehicles that may be applicable or need to be modified for UAM. Prediction tools need to be able to account for variable speed rotors and other temporal variation effects that impact community noise. A reprioritization of noise sources needs to be done since UAM vehicles include multiple rotors/propellers, often in proximity to one another and/or the airframe, with dynamic transition, and new noise sources such as electric motors or hybrid-electric propulsion. Scattering and propagation methods need to be developed that include the vehicle components and surfaces near a receiver such as buildings and vertiports. Validation databases are needed to quantify prediction uncertainties. Prediction tools used to evaluate community noise will need source models appropriate for a wide range of UAM vehicles. Existing noise reduction technologies need to be evaluated and new noise reduction technologies should be developed in anticipation of future noise requirements. Although the prediction and treatment of interior cabin noise is a secondary goal, it is recognized that new tools and methods may be needed due to the uniqueness of the vehicle design and the presence of both acoustic and structure-borne loads. Ground & Flight Testing has been a critical part of validating noise reduction technologies and verifying that an air vehicle is ready for certification. UAM vehicles introduce new challenges for test procedures such as different source noise directivity, unsteady sources due to maneuvers, and a variety of takeoff and approach trajectories. The operating environment will be more complex than current aircraft with the introduction of vertiports in populated areas with “urban canyons” making reflections an important part of noise prediction and annoyance. It is expected that new test procedures and measurement methods will be necessary. Consideration will need to be given for both piloted and autonomous operations. Human Response & Metrics may be very different for UAM noise compared to current experience with airport noise. Current metrics used to certify rotorcraft and fixed-wing aircraft may not be as useful for evaluating UAM noise. Operations at lower altitudes may influence annoyance. Psychoacoustic and community testing will be needed to quantify annoyance and assess appropriate metrics. In addition to conventional noise level metrics, considerations such as audibility and temporal variation of the sound may be required. Differences between indoor or outdoor exposure will have an impact with dependence on urban and residential flight paths. Aircraft noise is currently regulated at a national level and typically involves partnerships with the industry to establish regulations. Regulators realize that current policies and procedures may not be appropriate for some of the emerging air vehicles and new procedures may be needed to address UAM noise. Development of new policies and procedures are needed so that local communities do not hastily attempt to establish their own restrictions that will both limit growth of the market and create an inconsistent and confusing regulatory environment. To expedite this development, it is crucial early measurement data are shared through partnership arrangements to support both noise certification and noise modeling/noise assessment. At the same time, an effective engagement strategy should be developed to address local community noise issues associated with UAM vehicles and flight operations as they arise.

urban air mobility↗

NASA NTRS2016

Source Data Applicability Impacts on Epistemic Uncertainty for Launch Vehicle Fault Tree Models

Launch vehicle systems are designed and developed using both heritage and new hardware. Design modifications to the heritage hardware to fit new functional system requirements can impact the applicability of heritage reliability data. Risk estimates for newly designed systems must be developed from generic data sources such as commercially available reliability databases using reliability prediction methodologies, such as those addressed in MIL-HDBK-217F. Failure estimates must be converted from the generic environment to the specific operating environment of the system where it is used. In addition, some qualification of applicability for the data source to the current system should be made. Characterizing data applicability under these circumstances is crucial to developing model estimations that support confident decisions on design changes and trade studies. This paper will demonstrate a data-source applicability classification method for assigning uncertainty to a target vehicle based on the source and operating environment of the originating data. The source applicability is determined using heuristic guidelines while translation of operating environments is accomplished by applying statistical methods to MIL-HDK-217F tables. The paper will provide a case study example by translating Ground Benign (GB) and Ground Mobile (GM) to the Airborne Uninhabited Fighter (AUF) environment for three electronic components often found in space launch vehicle control systems. The classification method will be followed by uncertainty-importance routines to assess the need to for more applicable data to reduce uncertainty.

Al Hassan, Mohammad↗

DOE OSTI2023

Identification of mobile genetic elements with geNomad

Identifying and characterizing mobile genetic elements in sequencing data is essential for understanding their diversity, ecology, biotechnological applications and impact on public health. Here we introduce geNomad, a classification and annotation framework that combines information from gene content and a deep neural network to identify sequences of plasmids and viruses. geNomad uses a dataset of more than 200,000 marker protein profiles to provide functional gene annotation and taxonomic assignment of viral genomes. Using a conditional random field model, geNomad also detects proviruses integrated into host genomes with high precision. In benchmarks, geNomad achieved high classification performance for diverse plasmids and viruses (Matthews correlation coefficient of 77.8% and 95.3%, respectively), substantially outperforming other tools. Leveraging geNomad’s speed and scalability, we processed over 2.7 trillion base pairs of sequencing data, leading to the discovery of millions of viruses and plasmids that are available through the IMG/VR and IMG/PR databases. geNomad is available at https://portal.nersc.gov/genomad.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Real-Twin

Real-Twin is a unified, model-agnostic scenario generation tool designed to streamline and standardize the evaluation of emerging mobility technologies. It provides an end-to-end framework that includes robust workflows, integrated tools, and comprehensive metrics to generate, calibrate, and benchmark microscopic traffic simulation scenarios across multiple platforms. Key Features of Real-Twin include: - Unified Scenario Generation: generate transferable, simulation-ready scenarios from heterogeneous data sources using a consistent workflow. - Automated Calibration Workflow: bridges simulation and real-world data, minimizing manual effort and making traffic simulation more accessible to researchers and engineers. - Model-Agnostic Compatibility: supports SUMO, VISSIM, and AIMSUN for cross-platform scenario generation and benchmarking. Enables reliable comparisons and reproducibility across different simulation tools. - Consistent Scenarios across Different Simulators: generate comparable simulation scenarios across different microscopic traffic simulators, providing users the ability to conduct benchmarking and cross-validation that are crucial for ensuring the reliability and reproducibility of simulation results. - Emerging Technology Support: includes a scenario database and pipeline for studying autonomous vehicles (AVs), with planned extensions to CAVs, EVs, and other advanced technologies.

Wang, Chieh (Ross) [Oak Ridge National Laboratory ↗

NASA NTRS1998

Engine Gaseous, Aerosol Precursor and Particulated Flight Altitude Conditions

The overall objective of the NASA Atmospheric Effects of Aviation Project (AEAP) is to develop scientific bases for assessing atmospheric impacts of the exhaust emissions by both current and future fleets of subsonic and supersonic aircraft. Among the six primary elements of the AEAP is Emissions Characterization. The objective of the Emission Characterization effort is to determine the exhaust emission constituents and concentrations at the engine exit plane. The specific objective of this engine test is to obtain a database of gaseous and particulate emissions as a function of fuel sulfur and engine operating conditions. The database of the particulate emission properties is to be used as a comparative baseline with subsequent flight measurement. The engine used in this test was a Pratt & Whitney F1OO-200E turbofan engine. Aviation fuel (Jet A) with a range of fuel sulfur was used. Low and high sulfur values are limited by commercially available fuels and by fuel specification limits of O.3% by weight. Test matrix was set by parametrically varying the combustor inlet temperature (T(sub 3) between idle and maximum power setting at simulated SLS and up to five other altitudes for each fuel. Four diagnostic systems, extractive and non-intrusive, were assembled for the gaseous and particulate emissions characterization measurements study. NASA extractive system includes smoke meter and analyzers for measurement of CO, CO2, NO, NOx, O2, total unburnt hydrocarbons (THC), and SO2. Particulate emissions were characterized by University of Missouri-Rolla Mobile Aerosol Sampling System. A chemical ionization mass spectrometer from the Air Force Research Laboratory at Hanscom AFB was used to measure SO2 and HNO3. Aerodyne Research. Inc. used infrared tunable diode laser absorption to measure SO2, SO3, NO, H2O and CO2.

Wey, C. C.↗

NASA NTRS2010

Wind Tunnel Investigation of Ground Wind Loads for Ares Launch Vehicle

A three year program was conducted at the NASA Langley Research Center (LaRC) Aeroelasticity Branch (AB) and Transonic Dynamics Tunnel (TDT) with the primary objective to acquire scaled steady and dynamic ground-wind loads (GWL) wind-tunnel data for rollout, on-pad stay, and on-pad launch configurations for the Ares I-X Flight Test Vehicle (FTV). The experimental effort was conducted to obtain an understanding of the coupling of aerodynamic and structural characteristics that can result in large sustained wind-induced oscillations (WIO) on such a tall and slender launch vehicle and to generate a unique database for development and evaluation of analytical methods for predicting steady and dynamic GWL, especially those caused by vortex shedding, and resulting in significant WIO. This paper summarizes the wind-tunnel test program that employed two dynamically-aeroelastically scaled GWL models based on the Ares I-X Flight Test Vehicle. The first model tested, the GWL Checkout Model (CM), was a relatively simple model with a secondary objective of restoration and development of processes and methods for design, fabrication, testing, and data analysis of a representative ground wind loads model. In addition, parametric variations in surface roughness, Reynolds number, and protuberances (on/off) were investigated to determine effects on GWL characteristics. The second windtunnel model, the Ares I-X GWL Model, was significantly more complex and representative of the Ares I-X FTV and included the addition of simplified rigid geometrically-scaled models of the Kennedy Space Center (KSC) Mobile Launch Platform (MLP) and Launch Complex 39B primary structures. Steady and dynamic base bending moment as well as model response and steady and unsteady pressure data was acquired during the testing of both models. During wind-tunnel testing of each model, flow conditions (speed and azimuth) where significant WIO occurred, were identified and thoroughly investigated. Scaled data from the Ares I-X GWL model test was used in the determination of worst-case loads for the analysis of Ares I-X FTV design wind conditions. Finally, this paper includes a brief discussion of the limited full-scale GWL data acquired during the rollout and on-pad stay of the Ares I-X FTV that was launched from KSC on October 28, 2009.

Keller, Donald F.↗

DOE OSTI2021

A high-fidelity residential building occupancy detection dataset

Abstract This paper describes development of a data acquisition system used to capture a range of occupancy related modalities from single-family residences, along with the dataset that was generated. The publicly available dataset includes: grayscale images at 32-by-32 pixels, captured every second; audio files, which have undergone processing to remove personally identifiable information; indoor environmental readings, captured every ten seconds; and ground truth binary occupancy status. The data acquisition system, coined the mobile human presence detection (HPDmobile) system, was deployed in six homes for a minimum duration of one month each, and captured all modalities from at least four different locations concurrently inside each home. The environmental modalities are available as captured, but to preserve the privacy and identity of the occupants, images were downsized and audio files went through a series of processing steps, as described in this paper. This dataset adds to a very small body of existing data, with applications to energy efficiency and indoor environmental quality.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS2017

Fireballs in the Sky: An Augmented Reality Citizen Science Program

Fireballs in the Sky is an innovative Australian citizen science program that connects the public with the research of the Desert Fireball Network (DFN). This research aims to understand the early workings of the solar system, and Fireballs in the Sky invites people around the world to learn about this science, contributing fireball sightings via a user-friendly augmented reality mobile app. Tens of thousands of people have downloaded the app world-wide and participated in the science of meteoritics. The Fireballs in the Sky app allows users to get involved with the Desert Fireball Network research, supplementing DFN observations and providing enhanced coverage by reporting their own meteor sightings to DFN scientists. Fireballs in the Sky reports are used to track the trajectories of meteors - from their orbit in space to where they might have landed on Earth. Led by Phil Bland at Curtin University in Australia, the Desert Fireball Network (DFN) uses automated observatories across Australia to triangulate trajectories of meteorites entering the atmosphere, determine pre-entry orbits, and pinpoint their fall positions. Each observatory is an autonomous intelligent imaging system, taking 1000 by 36 megapixel all-sky images throughout the night, using neural network algorithms to recognize events. They are capable of operating for 12 months in a harsh environment, and store all imagery collected. We developed a completely automated software pipeline for data reduction, and built a supercomputer database for storage, allowing us to process our entire archive. The DFN currently stands at 50 stations distributed across the Australian continent, covering an area of 2.5 million square kilometers. Working with DFN's partners at NASA's Solar System Exploration Research Virtual Institute, the team is expanding the network beyond Australia to locations around the world. Fireballs in the Sky allows a growing public base to learn about and participate in this exciting research.

Day, Brian↗

NASA NTRS2017

A 3D Active Learning Application for NeMO-Net, the NASA Neural Multi-Modal Observation and Training Network for Global Coral Reef Assessment

NeMO-Net, the NASA neural multi-modal observation and training network for global coral reef assessment, is an open-source deep convolutional neural network and interactive active learning training software aiming to accurately assess the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology as well as mapping of spatial distribution. We present an interactive video game prototype for tablet and mobile devices where users interactively label morphology classifications over mm-scale 3D coral reef imagery captured using fluid lensing to create a dataset that will be used to train NeMO-Nets convolutional neural network. The application currently allows for users to classify preselected regions of coral in the Pacific and will be expanded to include additional regions captured using our NASA FluidCam instrument, presently the highest-resolution remote sensing benthic imaging technology capable of removing ocean wave distortion, as well as lower-resolution airborne remote sensing data from the ongoing NASA CORAL campaign. Active learning applications present a novel methodology for efficiently training large-scale Neural Networks wherein variances in identification can be rapidly mitigated against control data. NeMO-Net periodically checks users input against pre-classified coral imagery to gauge their accuracy and utilize in-game mechanics to provide classification training. Users actively communicate with a server and are requested to classify areas of coral for which other users had conflicting classifications and contribute their input to a larger database for ranking. In partnering with Mission Blue and IUCN, NeMO-Net leverages an international consortium of subject matter experts to classify areas of confusion identified by NeMO-Net and generate additional labels crucial for identifying decision boundary locations in coral reef assessment.

NeMO=NET↗

DOE OSTI2025

Differentially Private Map Matching (DPMM) v1.0

Human mobility trajectories provide valuable information for developing mobility applications, as they contain diverse and rich information about the users. User mobility data is valuable for various applications such as intelligent transportation systems (ITS), commercial business models, and disease-spread models. However, such spatio-temporal traces may pose a threat to user privacy. GPS trajectories in their raw form are not suitable for transportation studies, as they require matching locations with nearest road links — a process called map-matching. This software implements a differential privacy (DP)-based map-matching algorithm, called DPMM, that generates link-level location trajectories in a privacy-preserving manner to protect users' origin destinations (OD) and travel paths. OD privacy is achieved by injecting Planar Laplace noise to the user OD GPS points. Travel-path privacy is provided with randomized travel path construction using exponential DP mechanism. The injected noise level is selected adaptively, by considering the link density of the location and the functional category of the localized links. For path privacy, our mechanism samples waypoints and selects candidate paths between waypoints. DPMM provides privacy effectively with respect to link density instead of other trajectory samples in the database compared to other privacy mechanisms. Compared to the different baseline models our DP-based privacy model offers closer query responses to the raw data in terms of individual and aggregate trajectory-level statistics with an average at absolute deviation from the baseline for individual statistics on ϵ = 1.0. Beyond individual trajectory statistics, the DPMM outperforms the other benchmark DP-based mechanisms on different aggregate statistics with up to 8x improvement in utility.

Peisert, Sean [Lawrence Berkeley National Laborato↗

DOE OSTI2025

Detection of Isotopes in Urban Source Search Low-Count Gamma Spectra Using Hopfield Neural Networks

Source search campaigns involve measurements of background gamma-ray spectra with a mobile detector-spectrometer traveling along arbitrarily chosen trajectories over a wide screening area. Radiation counts are typically measured with a tellurium-doped sodium iodide [NaI(Tl)] scintillator detector-spectrometer in short acquisition intervals, usually 1 s. The objective is to detect orphan isotopes with half-lives shorter than those of the isotopes in the natural background. In principle, radioisotopes can be identified by their unique gamma emission spectrum. However, detecting orphan isotopes in search data is challenging because low counts measured in short acquisition intervals result in incomplete spectral lines. In this study, we investigate the performance of a Hopfield neural network (HNN) that implements an auto-associative memory for the detection of isotopes of interest in an urban search campaign. The HNN is trained on one example of gamma spectra with well-resolved spectral lines of each isotope of interest. During testing, the auto-associative memory implementation of the HNN processes low-count gamma spectra with partially complete isotopic lines by matching incoming measurements to the closest one of its memory-stored patterns. The testing database consisted of almost 10 000 1-s gamma spectra, including measurements of orphan isotopes 137 Cs, 241 Am, and 131 I, obtained during two urban search surveys with a NaI(Tl) detector. The performance of the HNN detection algorithm was evaluated using precision, recall, and F1 scores, and benchmarked with a multiple linear regression (MLR) identification algorithm. In conclusion, the test results demonstrate that HNN outperforms MLR in the detection of all the isotopes of interest.

Auto associative memory↗