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All sources NASA NTRS DOE OSTI

DOE OSTI2022

A Fast VANET-Assisted Scheme for Event Data Recorders

An event data recorder (EDR) is a device installed in a vehicle to record information. Similar to a black box in an airplane, an EDR is used in the study of automobile accidents. Many schemes have been proposed that use vehicle network technology to help record EDR data, including schemes involving storing data on roadside units or nearby vehicles and schemes leveraging blockchain technology. However, these schemes do not take into account the vehicle company’s server; with the increased use of autonomous vehicles, the data related to these vehicles are always uploaded to the vehicle company’s server. In this scenario, we classify the situation into different cases, according to whether or not it is an emergency and whether the vehicle and the server are connected. For these cases, we propose a scheme whereby a vehicle uploads the EDR data to a cloud server and sends the evidence of storage to the nearby vehicle through a vehicular ad hoc network. Our scheme offers a fast response due to the use of symmetric cryptography algorithms while also considering security requirements.

Liu, Wei↗

DOE OSTI2025

Monthly Quality-filtered Aggregation of NOAA Climate Data Record (CDR) of AVHRR Leaf Area Index (LAI) and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Version 5

This dataset contains gridded monthly Leaf Area Index (LAI) derived from the daily NOAA Climate Data Record (CDR) of AVHRR Leaf Area Index (LAI) and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Version 5. This data record spans from 1981 to 2018 using data from eight NOAA polar orbiting satellites: NOAA-7, -9, -11, -14, -16, -17, -18 and -19. The data are projected on a 0.05 degree x 0.05 degree global grid, as in the original CDR. The original CDR is one of the Land Surface CDR Version 5 products produced by the NASA Goddard Space Flight Center (GSFC) and the University of Maryland (UMD), which is accompanied by algorithm documentation, data flow diagram and source code for the NOAA CDR Program. This dataset is in the netCDF-4 file format following ACDD and CF Conventions. This dataset has applied quality assurance information to only include "OK" data from the original CDR in the monthly aggregation.

Vermote, Eric [NASA Goddard Space Flight Center (G↗

DOE OSTI2026

Monthly Quality-filtered Aggregation of NOAA Climate Data Record (CDR) of AVHRR (Version 5) and VIIRS (Version 1) Leaf Area Index (LAI) and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR)

This dataset contains gridded monthly Leaf Area Index (LAI) derived from the daily NOAA Climate Data Record (CDR) of AVHRR (Version 5) and VIIRS (Version 1) Leaf Area Index (LAI) and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR). This data record spans from 1981 to 2024 using data from NOAA polar orbiting satellites: NOAA-7, -9, -11, -14, -16, -17, -18, -19 and S-NPP. The data are projected on a 0.05 degree x 0.05 degree global grid, as in the original CDR. The original CDR is one of the Land Surface CDR products produced by the NASA Goddard Space Flight Center (GSFC) and the University of Maryland (UMD), which is accompanied by algorithm documentation, data flow diagram and source code for the NOAA CDR Program. This dataset is in the netCDF-4 file format following ACDD and CF Conventions. This dataset has applied quality assurance information to only include "OK" data from the original CDR in the monthly aggregation.

Vermote, Eric [NASA Goddard Space Flight Center (G↗

DOE OSTI2021

PERSIANN-CCS-CDR, a 3-hourly 0.04° global precipitation climate data record for heavy precipitation studies

Accurate long-term global precipitation estimates, especially for heavy precipitation rates, at fine spatial and temporal resolutions is vital for a wide variety of climatological studies. Most of the available operational precipitation estimation datasets provide either high spatial resolution with short-term duration estimates or lower spatial resolution with long-term duration estimates. Furthermore, previous research has stressed that most of the available satellite-based precipitation products show poor performance for capturing extreme events at high temporal resolution. Therefore, there is a need for a precipitation product that reliably detects heavy precipitation rates with fine spatiotemporal resolution and a longer period of record. Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System-Climate Data Record (PERSIANN-CCS-CDR) is designed to address these limitations. This dataset provides precipitation estimates at 0.04° spatial and 3-hourly temporal resolutions from 1983 to present over the global domain of 60°S to 60°N. Evaluations of PERSIANN-CCS-CDR and PERSIANN-CDR against gauge and radar observations show the better performance of PERSIANN-CCS-CDR in representing the spatiotemporal resolution, magnitude, and spatial distribution patterns of precipitation, especially for extreme events.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Model-independent search for the presence of new physics in events including H → γγ with $\sqrt{s}$ = 13 TeV pp data recorded by the ATLAS detector at the LHC

A model-independent search for new physics leading to final states containing a Higgs boson, with a mass of 125.09 GeV, decaying to a pair of photons is performed with 139 fb -1 of $\sqrt{s}$ = 13 TeV $pp$ collision data recorded by the ATLAS detector at the Large Hadron Collider at CERN. This search examines 22 final states categorized by the objects that are produced in association with the Higgs boson. These objects include isolated electrons or muons, hadronically decaying $τ$-leptons, additional photons, missing transverse momentum, and hadronic jets, as well as jets that are tagged as containing a $b$-hadron. No significant excesses above Standard Model expectations are observed and limits on the production cross section at 95% confidence level are set. Detector efficiencies are reported for all 22 signal regions, which can be used to convert detector-level cross-section limits reported in this paper to particle-level cross-section constraints.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2021

Clinical knowledge extraction via sparse embedding regression (KESER) with multi-center large scale electronic health record data

The increasing availability of electronic health record (EHR) systems has created enormous potential for translational research. However, it is difficult to know all the relevant codes related to a phenotype due to the large number of codes available. Traditional data mining approaches often require the use of patient-level data, which hinders the ability to share data across institutions. In this project, we demonstrate that multi-center large-scale code embeddings can be used to efficiently identify relevant features related to a disease of interest. We constructed large-scale code embeddings for a wide range of codified concepts from EHRs from two large medical centers. We developed knowledge extraction via sparse embedding regression (KESER) for feature selection and integrative network analysis. We evaluated the quality of the code embeddings and assessed the performance of KESER in feature selection for eight diseases. Besides, we developed an integrated clinical knowledge map combining embedding data from both institutions. The features selected by KESER were comprehensive compared to lists of codified data generated by domain experts. Features identified via KESER resulted in comparable performance to those built upon features selected manually or with patient-level data. The knowledge map created using an integrative analysis identified disease-disease and disease-drug pairs more accurately compared to those identified using single institution data. Analysis of code embeddings via KESER can effectively reveal clinical knowledge and infer relatedness among codified concepts. KESER bypasses the need for patient-level data in individual analyses providing a significant advance in enabling multi-center studies using EHR data.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2022

Case studies in bias reduction and inference for electronic health record data with selection bias and phenotype misclassification

Electronic health records (EHR) are not designed for population‐based research, but they provide easy and quick access to longitudinal health information for a large number of individuals. Many statistical methods have been proposed to account for selection bias, missing data, phenotyping errors, or other problems that arise in EHR data analysis. However, addressing multiple sources of bias simultaneously is challenging. We developed a methodological framework (R package, SAMBA ) for jointly handling both selection bias and phenotype misclassification in the EHR setting that leverages external data sources. These methods assume factors related to selection and misclassification are fully observed, but these factors may be poorly understood and partially observed in practice. As a follow‐up to the methodological work, we demonstrate how to apply these methods for two real‐world case studies, and we evaluate their performance. In both examples, we use individual patient‐level data collected through the University of Michigan Health System and various external population‐based data sources. In case study (a), we explore the impact of these methods on estimated associations between gender and cancer diagnosis. In case study (b), we compare corrected associations between previously identified genetic loci and age‐related macular degeneration with gold standard external summary estimates. These case studies illustrate how to utilize diverse auxiliary information to achieve less biased inference in EHR‐based research.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2022

Search for invisible Higgs-boson decays in events with vector-boson fusion signatures using 139 fb -1 of proton-proton data recorded by the ATLAS experiment

A direct search for Higgs bosons produced via vector-boson fusion and subsequently decaying into invisible particles is reported. The analysis uses 139 fb -1 of pp collision data at a centre-of-mass energy of $\sqrt{s}$ = 13 TeV recorded by the ATLAS detector at the LHC. The observed numbers of events are found to be in agreement with the background expectation from Standard Model processes. For a scalar Higgs boson with a mass of 125 GeV and a Standard Model production cross section, an observed upper limit of 0.145 is placed on the branching fraction of its decay into invisible particles at 95% confidence level, with an expected limit of 0.103. These results are interpreted in the context of models where the Higgs boson acts as a portal to dark matter, and limits are set on the scattering cross section of weakly interacting massive particles and nucleons. Invisible decays of additional scalar bosons with masses from 50 GeV to 2 TeV are also studied, and the derived upper limits on the cross section times branching fraction decrease with increasing mass from 1.0 pb for a scalar boson mass of 50 GeV to 0.1 pb at a mass of 2 TeV.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2021

From CAN to ROS: A Monitoring and Data Recording Bridge

The Controller Area Network (CAN) bus protocol is used in modern vehicles for sharing messages between several control units within a vehicle. CAN bus messages are encoded with unknown scheme and decoding these messages provide unlimited access to valuable information that is used in many autonomous vehicles applications. This paper proposes a ROS based package (CAN-to-ROS) for monitoring, recording, and real-time and offline decoding of CAN bus messages. The package is developed in the ROS framework to add modularity and ease of integration with other software, and it is written in C++ to guarantee speed of the execution during run-time. For realtime decoding of CAN bus data, CAN-to-ROS package used in conjunction with other library called Libpanda that provide access to CAN bus message from a vehicle. The package was evaluated and tested on a Raspberry Pi with real CAN bus data from a Toyota RAV4. The results confirm the capabilities of CAN-to-ROS package and resulted in using the package in other research projects.

Elmadani, Safwan↗

DOE OSTI2021

A strategy for validation of variables derived from large-scale electronic health record data

Not Available

Liu, Lin↗

DOE OSTI2021

Seismic Imaging of the Mw 7.1 Ridgecrest Earthquake Rupture Zone From Data Recorded by Dense Linear Arrays

Abstract We analyze seismograms recorded by four arrays (B1–B4) with 100 m station spacing and apertures of 4–8 km that cross the surface rupture of the 2019 Mw 7.1 Ridgecrest earthquake. The arrays extend from B1 in the northwest to B4 in the southeast of the surface rupture. Delay times between P wave arrivals associated with ∼1,200 local earthquakes and four teleseismic events are used to estimate local velocity variations beneath the arrays. Both teleseismic and local P waves travel faster on the northeast than the southwest side of the fault beneath arrays B1 and B4, but the velocity contrast is less reliably resolved at arrays B2 and B3. We identify several 1–2 km wide low‐velocity zones with much slower inner cores that amplify S waveforms, inferred as damage zones, beneath each array. The damage zones at arrays B2 and B4 also generate fault‐zone head and trapped waves. An automated detector, based on peak ground velocities and durations of high‐amplitude waves, identifies candidate fault‐zone trapped waves (FZTWs) in a localized zone for ∼600 earthquakes at array B4. Synthetic waveform modeling of averaged FZTWs, generated by ∼30 events with high‐quality signals, indicates that the trapping structure at array B4 has a width of ∼300 m, depth of 3–5 km, S wave velocity reduction of ∼20% with respect to the surrounding rock, Q ‐value of ∼30, and S wave velocity contrast of ∼4% across the fault (faster on the northeast side). The results show complex fault‐zone internal structures (velocity contrasts and low‐velocity zones) that vary along fault strike.

Qiu, Hongrui↗

DOE OSTI2021

Deep Learning Denoising of Seismic Data Recorded at the IMS Station MKAR.

Abstract not provided.

Tibi, Rigobert↗

DOE OSTI2020

Neutral gas analysis for JET DT operation

Neutral gas analysis, the measurement and evaluation of total and partial pressures, is a key technique to study the impact of neutral gas dynamics on retention, recycling and release processes of fuel or impurity species in fusion devices. At JET, the experiment closest to ITER in terms of operating parameters and size, various detectors and techniques for partial pressure and total pressure measurements are deployed together to characterise neutral gas dynamics during and after plasma operation on various toroidal and poloidal locations. An extensive modification of JET's sub-divertor neutral gas diagnostic system aims at retaining and extending established measurement capabilities in the forthcoming Deuterium-Tritium (DT) experiments (DTE2). To achieve DT compatibility, a separation of radiation-sensitive electronics from the sensor and adequate radiation shielding is required, as well as utilisation of a DT compatible differential pumping system with adjustable throughput to account for the strong pressure variation in the sub-divertor region. Finally, the sub-divertor neutral gas diagnostic will be equipped with multiple Residual Gas Analysers (RGAs), utilising quadrupole mass spectrometry and electrostatic ion-trap-principles, all operating with remote electronics located behind the biological radiation shield. These RGAs will record data in a fast selected discrete mass mode during plasma pulses (cycle ~2 s) and will automatically switch back to continuous data recording (cycle ~100 s) afterwards. They will be complemented by a newly improved Penning gauge spectroscopy configuration in particular supporting the He and D 2 separation relevant for DT operation. The distance between these devices and their associated control unit is typically 15m . A newly developed RGA with a cable length of 80 m, compatible with the ITER environment, will also be employed for the first time. Finally, this set-up and its operation in DTE2 will provide vital input to the development of the ITER divertor RGA in the most relevant environment currently available.

47 OTHER INSTRUMENTATION↗

DOE OSTI2025

Mathematical Morphological Filtering with a Self-Adaptive Reconstruction Technique and Application to Local Seismic Data

Recorded seismic data are generally contaminated by noise from different sources, which masks the signals of interest. In the seismology community, frequency filtering (FF) is the standard method for noise suppression. However, when the signal of interest and noise share the same frequency band, the latter cannot be filtered out without infringing on the former. We implemented a noise suppression approach based on the mathematical morphology theorem. The method involves compound operations of dilation and erosion using structuring elements of varying lengths and decomposes an input noisy waveform into several time functions with differing characteristics. Further, the filtered waveform is constructed from the time functions using a self-adaptive reconstruction technique. Application to a data set of >4700 local waveforms suggests that the implemented mathematical morphological filtering (MMF) approach is efficient for data with low signal-to-noise ratio (SNR) and significantly outperforms FF in that SNR range. For most of the dataset, FF, machine learning (ML) denoising, and continuous wavelet transform (CWT) thresholding result in higher SNR values compared with the MMF method. However, for ~42% of the waveforms, MMF outperforms FF, and the SNR gain achieved with MMF is as large as ~23 dB. Compared to ML denoising and CWT thresholding, this proportion drops to only ~10%–14%. Our results suggests that in an operational setting, MMF cannot replace the other noise suppression methods; however, signal detection can be improved if MMF is used to supplement them in some scenarios. MMF could help detect signals in problematic low-SNR data, which are currently being missed particularly when using FF alone.

58 GEOSCIENCES↗

DOE OSTI2026

220EV Box Truck Telemetry Dataset

This dataset contains telemetry data from the 220EV box truck as provided by the manufacturer, Dana. Data are recorded in a time series interval of 1 second. Recorded data contain odometer reading (kilometers), axle speed, state of charge of the battery, and cabin heater setting.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2026