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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Generating Uncertainty Distributions for Seismic Signal Onset Times

Signal arrival-time estimation plays a critical role in a variety of downstream seismic analyses, including location estimation and source characterization. Any arrival-time errors propagate through subsequent data-processing results. In this article, we detail a general framework for refining estimated seismic signal arrival times along with full estimation of their associated uncertainty. Using the standard short-term average/long-term average threshold algorithm to identify a search window, we demonstrate how to refine the pick estimate through two different approaches. In both cases, new waveform realizations are generated through bootstrap algorithms to produce full a posteriori estimates of uncertainty of onset arrival time of the seismic signal. The onset arrival uncertainty estimates provide additional data-derived information from the signal and have the potential to influence seismic analysis along several fronts.

58 GEOSCIENCES↗
DOE OSTI2021

Trans-ionospheric signal polarization

An electromagnetic (EM) wave originating at or near the earth’s surface can propagate upwards, traverse the ionosphere, and then be detected at a satellite. While the signal’s magnitude will affect its detectabilty, its polarization will play an important role as well. Furthermore the signal polarization can reveal properties about its source, and the ionospheric conditions through which it traveled. Thus, it is desirable to predict the polarization characteristics of trans-ionospheric EM signals. This situation is complicated by the birefringence imposed on an EM wave propagating in the magnetized plasma of the ionosphere. Each frequency component of the signal can excite two waves that propagate at different phase velocities due to their separate refractive characteristics.For this work, the amplitude and phase of each EM wave field spatial component is followed as it crosses the lower ionospheric boundary, traverses the ionosphere and then crosses the upper boundary. Results are for a single frequency, and can be easily extended to broadband waveforms using fourier transform techniques.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Auto-Curation of Seismic Event Data for Signal Denoising

Denoising contaminated seismic signals for later processing is a fundamental problem in seismic signals analysis. Neural network approaches have shown success denoising local signals when trained on short-time Fourier transform spectrograms. One challenge of this approach is the onerous process of hand-labeling event signals for training. By leveraging the SCALODEEP seismic event detector, we develop an automated set of techniques for labeling event data. Despite region specific challenges, training the neural network denoiser on machine curated events shows comparable performance to the neural network trained on hand curated events. We showcase our technique with two experiments, one using Utah regional data and one using regional data from the Korean peninsula.

58 GEOSCIENCES↗
DOE OSTI2021

LL18-Signal Analysis-NDD2Ad (Final Report)

Under favorable circumstances, seismic waveforms corresponding to an ensemble of events related by a common, spatially-distributed process collectively exhibit a regular, signal-space geometry. When events in the ensemble have a common, or nearly common, source mechanism, this geometry is a distorted image of the distribution of events in the source region. The signal space image can be visualized using a relatively simple waveform alignment and projection operation. Ensemble waveform correlation measurements can be inverted to estimate the distribution of the events in the source region, up to an arbitrary rotation, reflection, and scaling with residual distortion. We demonstrate these concepts with synthetic waveforms and with observations of long-wall mining induced seismicity for which substantial ground truth information is available. Our experience with these data has implications for location, correlation detection and machine learning, and possible application to studies of repeating events in induced, volcanic, and glacial seismicity. Our results place limits on the widely-held assumption that waveform correlation is a useful measure of event separation. We suggest that the constraints on event separation need to be evaluated in the context of a population of related events, whose waveforms sample the signal space image of the source region. A better indicator of event separation is the length of the shortest path in signal space along the image.

58 GEOSCIENCES↗
DOE OSTI2021

Illuminating gut microbiome signaling pathways that regulate host circadian rhythms

The gut microbiome affects host physiology by modulating nutrient acquisition and creating chemical signals that are received by host receptors. The molecular details of the orchestration of these microbial signaling processes remain largely unclear. We used a function-centric approach to interrogate the mechanistic links between gut microbiome metabolism and host circadian rhythms. We used rodent models, including germ-free mice, to determine how the gut microbiome influences host chronobiology by assaying metabolites in the gut and in circulation, and we used activity-based protein profiling to provide direct functional links to individual taxa. We specifically investigated how externally imposed behavioral schedules, such as feeding schedules, can desynchronize biological rhythms in metabolic organs (e.g., the gut, pancreas, and liver) from the central circadian rhythms regulated by the brain. We hypothesize that the gut microbiome creates chemical signals that can be entrained by feeding/fasting schedule to induce internal metabolic misalignment. Elaborating on the findings of this project will elucidate the chemical signals from the microbiome that modulate biological rhythms in the host’s metabolic organs.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Signal Transduction Pathways of Chloroplast Quality Control (Final Report)

Chloroplasts (photosynthetic plastids) of higher plants contain about 3000 proteins of which more than 95% are encoded by nuclear genes. To avoid the accumulation of reactive oxygen species that are the inherent by-product of photosynthesis, gene expression of these spatially separated genomes is regulated by two-way signaling. Thus, while plastid differentiation and development are largely under nuclear control, developmentally arrested or damaged plastids can regulate expression of nuclear genes via retrograde signaling pathways. In previous years of DOE-funded study, we performed a number of genetic screens that implicated the chloroplast-localized tetrapyrrole biosynthesis pathway as the source of both positive and stress-related retrograde signals. Using that work as a foundation, we have recently uncovered a new type of retrograde signal that leads to the selective removal of damaged chloroplasts from the cell. However, the mechanisms of such a chloroplast quality control pathway are not well characterized or understood.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Passband Signal Detection at the Edge

Algorithms for radio frequency (RF) spectrum awareness need to be compatible with edge hardware to be practical for many applications. We developed a signal detection and classification model for the ZCU111 RF System-on-a-Chip (RFSoC) that operates on the fast Fourier transform of passband RF data. The system can detect and classify multiple signals of interest and display the predictions in real-time. The model consists of a modified ConvNeXt backbone and YOLOv3 head to operate on the Deep Learning Processing Unit on the RFSoC. We gathered datasets for training and testing by using a software defined radio to transmit example signals of Wi-Fi 802.11 b/g, Wi-Fi 802.11 n, FM Radio, LTE and LTE-M. By leveraging multiple inputs on the RFSoC frontend, the datasets span up to 4 GHz of bandwidth. The models showed high performance in classification accuracy, center frequency error, bandwidth error, and detection accuracy for both single and multi-signal datasets.

42 ENGINEERING↗
DOE OSTI2026

Developing a Lightning All-Clear Signal for the Savannah River Site

Lightning safety is a major concern at the Savannah River Site (SRS). Notifications are provided when 3-strikes occur onsite within a 15-minute period. It is generally left up to individual contractors at SRS to determine when it is safe for workers to return to outside work, generally following a 3-hour period of no lightning, after the last lightning strike occurs. This work tries to improve safe return to work decisions by developing a reliable all-clear signal using an electric field mill. The best signal used thresholds from multiple electric field values to provide an average delay for return to work of 2 hours and 13 minutes. This average was heavily influenced by lingering storms in the area or missing data from power or network outages. One event out of 98 signaled the all-clear prior to another event occurring less than 60 minutes later. Regardless, 62% of the all clear signals had delay times less than the mean and 72% had delay times less than the current 3-hour delay after the last strike. This provides better worker safety for returning to work than 30 minutes after the last lightning strike (e.g. OSHA Lightning Safety) and improves the delay time from the current 3-hour delay.

Noble, Stephen [Savannah River National Laboratory↗
DOE OSTI2021

The Role of Sugar Signaling in Regulating Plant Fatty Acid Synthesis

Photosynthates such as glucose, sucrose, and some of their derivatives play dual roles as metabolic intermediates and signaling molecules that influence plant cell metabolism. Such sugars provide substrates for de novo fatty acid (FA) biosynthesis. However, compared with the well-defined examples of sugar signaling in starch and anthocyanin synthesis, until recently relatively little was known about the role of signaling in regulating FA and lipid biosynthesis. Recent research progress shows that trehalose 6-phosphate and 2-oxoglutarate (2-OG) play direct signaling roles in the regulation of FA biosynthesis by modulating transcription factor stability and enzymatic activities involved in FA biosynthesis. Specifically, mechanistic links between sucrose non-fermenting−1–related protein kinase 1 (SnRK1)–mediated trehalose 6-phosphate (T6P) sensing and its regulation by phosphorylation of WRI1 stability, diacylglycerol acyltransferase 1 (DGAT1) enzyme activity, and of 2-OG–mediated relief of inhibition of acetyl-CoA carboxylase (ACCase) activity by protein PII are exemplified in detail in this review.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Wounding rapidly alters transcription factor expression, hormonal signaling, and phenolic compound metabolism in harvested sugarbeet roots

Injuries sustained by sugarbeet (Beta vulgaris L.) roots during harvest and postharvest operations seriously reduce the yield of white sugar produced from stored roots. Although wound healing is critically important to reduce losses, knowledge of these processes is limited for this crop as well as for roots in other species. To better understand the metabolic signals and changes that occur in wounded roots, dynamic changes in gene expression were determined by RNA sequencing and the activity of products from key genes identified in this analysis were determined in the 0.25 to 24 h following injury. Nearly five thousand differentially expressed genes that contribute to a wide range of cellular and molecular functions were identified in wounded roots. Highly upregulated genes included transcription factor genes, as well as genes involved in ethylene and jasmonic acid (JA) biosynthesis and signaling and phenolic compound biosynthesis and polymerization. Enzyme activities for key genes in ethylene and phenolic compound biosynthesis and polymerization also increased due to wounding. Results indicate that wounding causes a major reallocation of metabolism in sugarbeet taproots. Although both ethylene and JA are likely involved in triggering wound responses, the greater and more sustained upregulation of ethylene biosynthesis and signaling genes relative to those of JA, suggest a preeminence of ethylene signaling in wounded sugarbeet roots. Changes in gene expression and enzymes involved in phenolic compound metabolism additionally indicate that barriers synthesized to seal off wounds, such as suberin or lignin, are initiated within the first 24 h after injury.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Lectin Receptor-like Kinase Signaling during Engineered Ectomycorrhiza Colonization

Mutualistic association can improve a plant’s health and productivity. G-type lectin receptor-like kinase (PtLecRLK1) is a susceptibility factor in Populus trichocarpa that permits root colonization by a beneficial fungus, Laccaria bicolor. Engineering PtLecRLK1 also permits L. bicolor root colonization in non-host plants similar to Populus trichocarpa. The intracellular signaling reprogramed by PtLecRLK1 upon recognition of L. bicolor to allow for the development and maintenance of symbiosis is yet to be determined. In this study, phosphoproteomics was utilized to identify phosphorylation-based relevant signaling pathways associated with PtLecRLK1 recognition of L. bicolor in transgenic switchgrass roots. Our finding shows that PtLecRLK1 in transgenic plants modifies the chitin-triggered plant defense and MAPK signaling along with a significant adjustment in phytohormone signaling, ROS balance, endocytosis, cytoskeleton movement, and proteasomal degradation in order to facilitate the establishment and maintenance of L. bicolor colonization. Moreover, protein–protein interaction data implicate a cGMP-dependent protein kinase as a potential substrate of PtLecRLK1.

ectomycorrhiza colonization↗
DOE OSTI2023

AI ensemble for signal detection of higher order gravitational wave modes of quasi-circular, spinning, non-precessing binary black hole mergers

We introduce spatiotemporal-graph models that concurrently process data from the twin advanced LIGO detectors and the advanced Virgo detector. We trained these AI classifiers with 2.4 million IMRPhenomXPHM waveforms that describe quasi-circular, spinning, non-precessing binary black hole mergers with component masses m{1,2}∈[3M⊙,50M⊙], and individual spins sz{1,2}∈[−0.9,0.9]; and which include the (ℓ,|m|)={(2,2),(2,1),(3,3),(3,2),(4,4)} modes, and mode mixing effects in the ℓ=3,|m|=2 harmonics. We trained these AI classifiers within 22 hours using distributed training over 96 NVIDIA V100 GPUs in the Summit supercomputer. We then used transfer learning to create AI predictors that estimate the total mass of potential binary black holes identified by all AI classifiers in the ensemble. We used this ensemble, 3 classifiers for signal detection and 2 total mass predictors, to process a year-long test set in which we injected 300,000 signals. This year-long test set was processed within 5.19 minutes using 1024 NVIDIA A100 GPUs in the Polaris supercomputer (for AI inference) and 128 CPU nodes in the ThetaKNL supercomputer (for post-processing of noise triggers), housed at the Argonne Leadership Computing Facility. These studies indicate that our AI ensemble provides state-of-the-art signal detection accuracy, and reports 2 misclassifications for every year of searched data. This is the first AI ensemble designed to search for and find higher order gravitational wave mode signals.

Tian, Minyang↗
DOE OSTI2020

Structure of human Frizzled5 by fiducial-assisted cryo-EM supports a heterodimeric mechanism of canonical Wnt signaling

Frizzleds (Fzd) are the primary receptors for Wnt morphogens, which are essential regulators of stem cell biology, yet the structural basis of Wnt signaling through Fzd remains poorly understood. Here we report the structure of an unliganded human Fzd5 determined by single-particle cryo-EM at 3.7 Å resolution, with the aid of an antibody chaperone acting as a fiducial marker. We also analyzed the topology of low-resolution XWnt8/Fzd5 complex particles, which revealed extreme flexibility between the Wnt/Fzd-CRD and the Fzd-TM regions. Analysis of Wnt/β-catenin signaling in response to Wnt3a versus a ‘surrogate agonist’ that cross-links Fzd to LRP6, revealed identical structure-activity relationships. Thus, canonical Wnt/β-catenin signaling appears to be principally reliant on ligand-induced Fzd/LRP6 heterodimerization, versus the allosteric mechanisms seen in structurally analogous class A G protein-coupled receptors, and Smoothened. These findings deepen our mechanistic understanding of Wnt signal transduction, and have implications for harnessing Wnt agonism in regenerative medicine.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Integrated circuits with programmable gate timing signal generation for power converters and apparatus comprising the same

An apparatus is disclosed that includes a semiconductor device to control a power converter having two or more power FETs. The semiconductor device includes a gate signal generator configured to produce two or more gate signals able to drive the two or more FETs. The gate signal generator has programmable timing configuration parameters to control operation of the two or more gate signals, wherein the timing configuration parameters are digitally programmed via a memory in the semiconductor device. The memory may be implemented with fuses, other non-volatile memory or volatile memory. The parameters may be fixed or updated during a lifetime of the apparatus. A serial-to-parallel conversion may be used to input the parameters. Optimization methods may be performed to determine parameters considered to be optimal. The apparatus may also include the power converter.

42 ENGINEERING↗
DOE OSTI2021

Extracting low energy signals from raw LArTPC waveforms using deep learning techniques -- A proof of concept

We investigate the feasibility of using deep learning techniques, in the form of a one-dimensional convolutional neural network (1D-CNN), for the extraction of signals from the raw waveforms produced by the individual channels of liquid argon time projection chamber (LArTPC) detectors. A minimal generic LArTPC detector model is developed to generate realistic noise and signal waveforms used to train and test the 1D-CNN, and evaluate its performance on low-level signals. We demonstrate that our approach overcomes the inherent shortcomings of traditional cut-based methods by extending sensitivity to signals with ADC values below their imposed thresholds. This approach exhibits great promise in enhancing the capabilities of future generation neutrino experiments like DUNE to carry out their low-energy neutrino physics programs.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Generating clock signals for a cycle accurate, cycle reproducible FPGA based hardware accelerator

A method, system and computer program product are disclosed for generating clock signals for a cycle accurate FPGA based hardware accelerator used to simulate operations of a device-under-test (DUT). In one embodiment, the DUT includes multiple device clocks generating multiple device clock signals at multiple frequencies and at a defined frequency ratio; and the FPG hardware accelerator includes multiple accelerator clocks generating multiple accelerator clock signals to operate the FPGA hardware accelerator to simulate the operations of the DUT. In one embodiment, operations of the DUT are mapped to the FPGA hardware accelerator, and the accelerator clock signals are generated at multiple frequencies and at the defined frequency ratio of the frequencies of the multiple device clocks, to maintain cycle accuracy between the DUT and the FPGA hardware accelerator. In an embodiment, the FPGA hardware accelerator may be used to control the frequencies of the multiple device clocks.

Asaad, Sameh W.↗
DOE OSTI2021

Radio frequency passband signal generation using photonics

Devices, methods and systems for generating wideband, high-fidelity arbitrary radio frequency (RF) passband signals are described. A voltage tunable optical filter for arbitrary RF passband signal generation includes a first input configured to receive a broadband optical pulse train, a second input configured to receive a first control voltage representative of an amplitude signal, an electrooptic modulator to receive the broadband optical pulse train and the first control voltage, to modulate the broadband optical pulse train in accordance with the amplitude signal, and to produce two complementary optical outputs that form two arms of an interferometer, an optical delay component to impart an optical path difference into one of the complementary outputs of the electrooptic modulator, and a combiner or a splitter to receive two complementary optical outputs of the electrooptic modulator after impartation of the optical path difference and to produce an output interference pattern of fringes.

Gowda, Apurva Shantharaj↗
DOE OSTI2022

CANShield: Signal-based Intrusion Detection for Controller Area Networks

Modern vehicles rely on complex cyber-physical systems made up of hundreds of electronic control units (ECUs) connected through controller area network (CAN) buses. However, the CAN bus attack surface is increasing due to advanced features in automobiles, making it prone to injection attacks. The ordinary injection attacks disrupt the typical timing properties of the CAN data stream, and the rule-based intrusion detection systems (IDS) can easily detect them. However, advanced attackers can inject false data to the signal level, maintaining the regular pattern/frequency of the CAN messages. Such attacks can bypass the rule-based IDS or any anomaly-based IDS built on binary payload data. To make the vehicles robust against such intelligent attacks, we propose CANShield, a signal-based intrusion detection framework for the CAN bus that consists of three modules. A data preprocessing module handles the high-dimensional CAN data stream at the signal level and make them suitable for any machine learning model. A data analyzer module consists of multiple deep autoencoder networks, each analyzing the time series data from a different perspective. Finally, an attack detection module uses an ensemble method to make the final decision. Evaluation results on a standard signal-based dataset show the effectiveness of the CANShield in detecting five advanced attacks.

Shahriar, Md Hasan↗