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At least 1,639 records · Page 91
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

TH-E-BRF-03: A Multivariate Interaction Model for Assessment of Hippocampal Vascular Dose-Response and Early Prediction of Radiation-Induced Neurocognitive Dysfunction

Purpose: Vascular injury could be a cause of hippocampal dysfunction leading to late neurocognitive decline in patients receiving brain radiotherapy (RT). Hence, our aim was to develop a multivariate interaction model for characterization of hippocampal vascular dose-response and early prediction of radiation-induced late neurocognitive impairments. Methods: 27 patients (17 males and 10 females, age 31–80 years) were enrolled in an IRB-approved prospective longitudinal study. All patients were diagnosed with a low-grade glioma or benign tumor and treated by 3-D conformal or intensity-modulated RT with a median dose of 54 Gy (50.4–59.4 Gy in 1.8− Gy fractions). Six DCE-MRI scans were performed from pre-RT to 18 months post-RT. DCE data were fitted to the modified Toft model to obtain the transfer constant of gadolinium influx from the intravascular space into the extravascular extracellular space, Ktrans, and the fraction of blood plasma volume, Vp. The hippocampus vascular property alterations after starting RT were characterized by changes in the hippocampal mean values of, μh(Ktrans)τ and μh(Vp)τ. The dose-response, Δμh(Ktrans/Vp)pre->τ, was modeled using a multivariate linear regression considering integrations of doses with age, sex, hippocampal laterality and presence of tumor/edema near a hippocampus. Finally, the early vascular dose-response in hippocampus was correlated with neurocognitive decline 6 and 18 months post-RT. Results: The μh(Ktrans) increased significantly from pre-RT to 1 month post-RT (p<0.0004). The multivariate model showed that the dose effect on Δμh(Ktrans)pre->1M post-RT was interacted with sex (p<0.0007) and age (p<0.00004), with the dose-response more pronounced in older females. Also, the vascular dose-response in the left hippocampus of females was significantly correlated with memory function decline at 6 (r = − 0.95, p<0.0006) and 18 (r = −0.88, p<0.02) months post-RT. Conclusion: The hippocampal vascular response to radiation could be sex and age dependent. The early hippocampal vascular dose-response could predict late neurocognitive dysfunction. (Support: NIH-RO1NS064973)

60 APPLIED LIFE SCIENCES↗
NASA NTRS2026

Machine Learning for Predicting Team Functioning in HERA Missions

Team functioning is integral to success in future long term space exploration missions. Proactively detecting declines in team functioning can mitigate conflict and ensure mission success. This project developed a speech-based artificial intelligence (AI) system that unobtrusively predicts degradation in team functioning, including performance and cohesion, in the Human Exploration Research Analog (HERA) Campaigns 4 and 5. The AI system conducted automated analysis of the prosodic (tone of voice) and linguistic (language content) components of speech, modeling interpersonal dynamics at both the turn-taking and day-wide levels. We investigated team functioning via observing structured interactions (i.e., multi-mission space exploration vehicle-extra vehicular activity [MMSEV-EVA], team interaction battery [TIB]) and unstructured interactions before the MMSEV-EVA task. We developed machine learning models to predict team functioning (objective task accuracy, self reported team efficacy and self reported team cohesion) by analyzing OpenSmile acoustic features, linguistic descriptors extracted via the linguistic inquiry and word count (LIWC) dictionary, and semantic embeddings. In the TIB, static models using logistic regression and random forests were not able to predict task accuracy, but predicted team efficacy and cohesion during both the decision making and relational tasks to a moderate level (60-70%). Majority voting on the individual turns to predict day long team efficacy further increased accuracies (70-80%). Finally, long short-term memory (LSTM) models showed the best performance across all variables (80-91%), including task performance. In the MMSEV-EVA, static models achieved an accuracy of 60% with majority voting, which increased to 80% through the incorporation of mission day as a variable, accounting for the learning effect. A key finding across both tasks was the "team-dependent" nature of these interactions; models achieved much higher accuracy when trained on prior days of the same team's data rather than attempting to generalize across entirely different teams, with even 1-2 days of prior data per team achieving 5-15% improvement over team-independent models. In addition, the incorporation of pre-task data from the same team also improves model performance, e.g., incorporating data from the decision-making task of the TIB, which preceded the relational task, improved the prediction of team efficacy and cohesion during the latter. We compared model performance when trained on machine-generated data compared to data that had been further corrected by human annotators. Overall, models trained on human-corrected data exhibited a modest improvement in performance, particularly when acoustic features were used. We found no significant correlation between word error rate (WER) and model accuracy (r(55) = -0.08, p = 0.51), but model’s accuracy was significantly higher for medium/high quality transcription (0.74 (SD = 0.48)) compared to the low-quality group (0.64 (SD = 0.36)) (t(63)=2.82, p = 0.006). Based on these, several design recommendation emerge, that could inform Standards at NASA. Models predicting team functioning should incorporate at least one to two days of historical interaction data, include brief pre-task discussions, and explicitly model temporal learning effects, especially for longer operational tasks. Minimum quality standards for automated speech-processing pipelines are needed, given the performance gains observed with manually corrected acoustic data. Finally, systems should leverage both acoustic features and language embeddings in complementary ways, with modality choices and fusion strategies tailored to mission context, task demands, and data quality requirements.

Shrivatsa Mishra↗
DOE OSTI2014

Field-induced domain wall motion of amorphous [CoSiB/Pt]{sub N} multilayers with perpendicular anisotropy

Amorphous CoSiB/Pt multilayer is a perpendicular magnetic anisotropy material to achieve high squareness, low coercivity, strong anisotropy, and smooth domain wall (DW) motion, because of the smoother interface compared with crystalline multilayers. For [CoSiB(6 Å)/Pt (14 Å)]{sub N} multilayers with N = 3, 6, and 9, we studied the field-induced DW dynamics. The effective anisotropy constant K{sub 1}{sup eff} is 1.5 × 10{sup 6} erg/cm{sup 3} for all the N values, and the linear increment of coercive field H{sub c} with N gives constant exchange coupling J. By analyzing the field dependence of DW images at room temperature, a clear creep motion with the exponent μ = 1/4 could be observed. Even though the pinning field H{sub dep} slightly increases with N, the pinning potential energy U{sub c} is constant (=35 k{sub B}T) for all the N values. These results imply that the amorphous [CoSiB/Pt]{sub N} multilayers are inherently homogeneous compared to crystalline multilayers. For N ≤ 6, the pinning site density ρ{sub pin} is less than 1000/μm{sup 2}, which is about 1 pinning site per the typical device junction size of 30 × 30 nm{sup 2}. Also, the exchange stiffness constant A{sub ex} is obtained to be 0.48 × 10{sup −6} erg/cm, and the domain wall width is expected to be smaller than 5.5 nm. These results may be applicable for spin-transfer-torque magnetic random access memory and DW logic device applications.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Superconducting Hyperdimensional Associative Memory Circuit for Scalable Machine Learning

Here we propose a generalized architecture for the first rapid-single-flux-quantum (RSFQ) associative memory circuit. The circuit employs hyperdimensional computing (HDC), a machine learning (ML) paradigm utilizing vectors with dimensionality in the thousands to represent information. HDC designs have small memory footprints, simple computations, and simple training algorithms compared to superconducting neural network accelerators (SNNAs), making them a better option for scalable SFQ machine learning (ML) solutions. The proposed superconducting HDC (SHDC) circuit uses entirely on-chip RSFQ memory which is tightly integrated with logic, operates at 33.3 GHz, is applicable to general ML tasks, and is manufacturable at practically useful scales given current SFQ fabrication limits. Tailored to a language recognition task, SHDC consists of ~ 2-20 M Josephson junctions (JJs) and consumes up to three times less power than an analogous CMOS HDC circuit while achieving 78-84% higher throughput. SHDC is capable of outperforming the state of the art RSFQ SNNA, SuperNPU, by 48-99% for all benchmark NN architectures tested while occupying up to 90% less area and consuming up to nine times less power. To the best of the authors' knowledge, SHDC is currently the only superconducting ML approach feasible at practically useful scales for real-world ML tasks and capable of online learning.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2015

Predicting amyloid status in corticobasal syndrome using modified clinical criteria, magnetic resonance imaging and fluorodeoxyglucose positron emission tomography

Introduction Group comparisons demonstrate greater visuospatial and memory deficits and temporoparietal-predominant degeneration on neuroimaging in patients with corticobasal syndrome (CBS) found to have Alzheimer’s disease (AD) pathology versus those with underlying frontotemporal lobar degeneration (FTLD). The value of these features in predicting underlying AD pathology in individual patients is unknown. The goal of this study is to evaluate the utility of modified clinical criteria and visual interpretations of magnetic resonance imaging (MRI) and fluorodeoxyglucose positron emission tomography (FDG-PET) for predicting amyloid deposition (as a surrogate of Alzheimer’s disease neuropathology) in patients presenting with CBS. Methods In total, 25 patients meeting CBS core criteria underwent amyloid (Pittsburgh compound B; PIB) PET scans. Clinical records, MRI, and FDG scans were reviewed blinded to PIB results. Modified clinical criteria were used to classify CBS patients as temporoparietal variant CBS (tpvCBS) or frontal variant CBS (fvCBS). MRI and FDG-PET were classified based on the predominant atrophy/hypometabolism pattern (frontal or temporoparietal). Results A total of 9 out of 13 patients classified as tpvCBS were PIB+, compared to 2out of 12 patients classified as fvCBS ( P < 0.01, sensitivity 82%, specificity 71% for PIB+ status). Visual MRI reads had 73% sensitivity and 46% specificity for PIB+ status with moderate intra-rater reliability (Cohen’s kappa = 0.42). Visual FDG reads had higher sensitivity (91%) for PIB+ status with perfect intra-rater reliability (kappa = 1.00), though specificity was low (50%). PIB results were confirmed in all 8 patients with available histopathology (3 PIB+ with confirmed AD, 5 PIB- with FTLD). Conclusions Splitting CBS patients into frontal or temporoparietal clinical variants can help predict the likelihood of underlying AD, but criteria require further refinement. Temporoparietal-predominant neuroimaging patterns are sensitive but not specific for AD.

Neurosciences & Neurology↗
NASA NTRS2014

The Effects of Long Duration Bed Rest on Functional Mobility and Balance: Relationship to Resting State Motor Cortex Connectivity

NASA offers researchers from a variety of backgrounds the opportunity to study bed rest as an experimental analog for space flight. Extended exposure to a head-down tilt position during long duration bed rest can resemble many of the effects of a low-gravity environment such as reduced sensory inputs, body unloading and increased cephalic fluid distribution. The aim of our study is to a) identify changes in brain function that occur with prolonged bed rest and characterize their recovery time course; b) assess whether and how these changes impact behavioral and neurocognitive performance. Thus far, we completed data collection from six participants that include task based and resting state fMRI. The data have been acquired through the bed rest facility located at the University of Texas Medical Branch (Galveston, TX). Subjects remained in bed with their heads tilted down 6 degrees below their feet for 70 consecutive days. Behavioral measures and neuroimaging assessments were obtained at seven time points: a) 7 and 12 days before bed rest; b) 7, 30, and 65 days during bed rest; and c) 7 and 12 days after bed rest. Functional connectivity magnetic resonance imaging (FcMRI) analysis was performed to assess the connectivity of motor cortex in and out of bed rest. We found a decrease in motor cortex connectivity with vestibular cortex and the cerebellum from pre bed rest to in bed rest. We also used a battery of behavioral measures including the functional mobility test and computerized dynamic posturography collected before and after bed rest. We will report the preliminary results of analyses relating brain and behavior changes. Furthermore, we will also report the preliminary results of a spatial working memory task and vestibular stimulation during in and out of bed rest.

Erdeniz, B.↗
DOE OSTI2021

Role of element-specific damping in ultrafast, helicity-independent, all-optical switching dynamics in amorphous (Gd,Tb)Co thin films

Ultrafast control of the magnetization in ps timescales by fs laser pulses offers an attractive avenue for applications such as fast magnetic devices for logic and memory. However, ultrafast helicity-independent all-optical switching (HI-AOS) of the magnetization has thus far only been observed in Gd-based, ferrimagnetic amorphous (a-) rare earth-transition metal (a-RE-TM) systems, and a comprehensive understanding of the reversal mechanism remains elusive. Here, we report HI-AOS in ferrimagnetic a-Gd 22-x Tb x Co 78 thin films, from x=0 to 18, and elucidate the role of Gd in HI-AOS in a-RE-TM alloys and multilayers. Increasing Tb content results in increasing perpendicular magnetic anisotropy and coercivity, without modifying magnetization density, and slower remagnetization rates and higher critical fluences for switching but still shows picosecond HI-AOS. Simulations of the atomistic spin dynamics based on the two-temperature model reproduce these results qualitatively and predict that the lower damping on the RE sublattice arising from the small spin-orbit coupling of Gd (with L=0) is instrumental for the faster dynamics and lower critical fluences of the Gd-rich alloys. Annealing a-Gd 10 Tb 12 Co 78 leads to slower dynamics which we argue is due to an increase in damping. These simulations strongly indicate that accounting for element-specific damping is crucial in understanding HI-AOS phenomena. The results suggest that engineering the element-specific damping of materials can open up new classes of materials that exhibit low-energy, ultrafast HI-AOS.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS2003

Miniature Inchworm Actuators Fabricated by Use of LIGA

Miniature inchworm actuators that would have relatively simple designs have been proposed for applications in which there are requirements for displacements of the order of microns or tens of microns and for the ability to hold their positions when electric power is not applied. The proposed actuators would be members of the class of microelectromechanical systems (MEMS), but would be designed and fabricated following an approach that is somewhat unusual for MEMS. Like other MEMS actuators, the proposed inchworm actuators could utilize thermoplastic, bimetallic, shape-memory-alloy, or piezoelectric actuation principles. The figure depicts a piezoelectric inchworm actuator according to the proposal. As in other inchworm actuators, linear motion of an extensible member would be achieved by lengthening and shortening the extensible member in synchronism with alternately clamping and releasing one and then the other end of the member. In this case, the moving member would be the middle one; the member would be piezoelectric and would be shortened by applying a voltage to it. The two outer members would also be piezoelectric; the release of the clamps on the upper or lower end would be achieved by applying a voltage to the electrodes on the upper or lower ends, respectively, of these members. Usually, MEMS actuators cannot be fabricated directly on the side walls of silicon wafers, yet the geometry of this actuator necessitates such fabrication. The solution, according to the proposal, would be to use the microfabrication technique known by the German acronym LIGA - "lithographie, galvanoformung, abformung," which means lithography, electroforming, molding. LIGA involves x-ray lithography of a polymer film followed by selective removal of material to form a three-dimensional pattern from which a mold is made. Among the advantages of LIGA for this purpose are that it is applicable to a broad range of materials, can be used to implement a variety of designs, including those of structures >1 mm high, affords submicron precision, and is amenable to mass production at relatively low unit cost. Fabrication of the proposed actuators would involve some technological risks - in particular, in the integration of electrode connection lines and placement of actuator elements. It will also be necessary to perform an intensive study of the feasibility of growing piezoelectric crystals onto LIGA molds.

Yang, Eui-Hyeok↗
DOE OSTI2023

Rapid solidified ductile Cu-Al-Mn ribbon and its elastocaloric potential

Abstract Cu-Al-Mn alloys display martensitic transformation over a wide range of temperatures. In addition to low cost, this alloy is known for its low transformation stress with reasonable latent heat favoring elastocaloric applications. However, the ductility of Cu-Al-Mn can be limited owing to ordering and intergranular fracture. Through rapid solidification by melt spinning, we show that Cu-Al-Mn ribbon can be made highly ductile (greater than 8% tensile strain in the as-spun state and 10% tensile strain after heat treatment). The ductility of the melt-spun ribbon is related to the suppression of L2 1 ordering that is characterized through magnetic property measurement. Heat treatment of the ribbon promotes bamboo grain formation, and the latent heat is increased to 6.4 J g −1 . Under tensile conditions, we show that the ribbon exhibited about 4 °C temperature change (4.4 °C on heating and 4.2 °C on cooling from 6.3% strain).

36 MATERIALS SCIENCE↗
DOE OSTI2022

Fluid-Kinetic Coupling: Advanced Discretizations for Simulations on Emerging Heterogeneous Architectures (LDRD FY20-0643)

Plasma physics simulations are vital for a host of Sandia mission concerns, for fundamental science, and for clean energy in the form of fusion power. Sandia's most mature plasma physics simulation capabilities come in the form of particle-in-cell (PIC) models and magnetohydrodynamics (MHD) models. MHD models for a plasma work well in denser plasma regimes when there is enough material that the plasma approximates a fluid. PIC models, on the other hand, work well in lower-density regimes, in which there is not too much to simulate; error in PIC scales as the square root of the number of particles, making high-accuracy simulations expensive. Real-world applications, however, almost always involve a transition region between the high-density regimes where MHD is appropriate, and the low-density regimes for PIC. In such a transition region, a direct discretization of Vlasov is appropriate. Such discretizations come with their own computational costs, however; the phase-space mesh for Vlasov can involve up to six dimensions (seven if time is included), and to apply appropriate homogeneous boundary conditions in velocity space requires meshing a substantial padding region to ensure that the distribution remains sufficiently close to zero at the velocity boundaries. Moreover, for collisional plasmas, the right-hand side of the Vlasov equation is a collision operator, which is non-local in velocity space, and which may dominate the cost of the Vlasov solver. The present LDRD project endeavors to develop modern, foundational tools for the development of continuum-kinetic Vlasov solvers, using the discontinuous Petrov-Galerkin (DPG) methodology, for discretization of Vlasov, and machine-learning (ML) models to enable efficient evaluation of collision operators. DPG affords several key advantages. First, it has a built-in, robust error indicator, allowing us to adapt the mesh in a very natural way, enabling a coarse velocity-space mesh near the homogeneous boundaries, and a fine mesh where the solution has fine features. Second, it is an inherently high-order, high-intensity method, requiring extra local computations to determine so-called optimal test functions, which makes it particularly suited to modern hardware in which floating-point throughput is increasing at a faster rate than memory bandwidth. Finally, DPG is a residual-minimizing method, which enables high-accuracy computation: in typical cases, the method delivers something very close to the $L^2$ projection of the exact solution. Meanwhile, the ML-based collision model we adopt affords a cost structure that scales as the square root of a standard direct evaluation. Moreover, we design our model to conserve mass, momentum, and energy by construction, and our approach to training is highly flexible, in that it can incorporate not only synthetic data from direct-simulation Monte Carlo (DSMC) codes, but also experimental data. We have developed two DPG formulations for Vlasov-Poisson: a time-marching, backward-Euler discretization and a space-time discretization. We have conducted a number of numerical experiments to verify the approach in a 1D1V setting. In this report, we detail these formulations and experiments. We also summarize some new theoretical results developed as part of this project (published as papers previously): some new analysis of DPG for the convection-reaction problem (of which the Vlasov equation is an instance), a new exponential integrator for DPG, and some numerical exploration of various DPG-based time-marching approaches to the heat equation. As part of this work, we have contributed extensively to the Camellia open-source library; we also describe the new capabilities and their usage. We have also developed a well-documented methodology for single-species collision operators, which we applied to argon and demonstrated with numerical experiments. We summarize those results here, as well as describing at a high level a design extending the methodology to multi-species operators. We have released a new open-source library, MLC, under a BSD license; we include a summary of its capabilities as well.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Deep Learning Based Superconducting Radio-Frequency Cavity Fault Classification at Jefferson Laboratory

This work investigates the efficacy of deep learning (DL) for classifying C100 superconducting radio-frequency (SRF) cavity faults in the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab. CEBAF is a large, high-power continuous wave recirculating linac that utilizes 418 SRF cavities to accelerate electrons up to 12 GeV. Recent upgrades to CEBAF include installation of 11 new cryomodules (88 cavities) equipped with a low-level RF system that records RF time-series data from each cavity at the onset of an RF failure. Typically, subject matter experts (SME) analyze this data to determine the fault type and identify the cavity of origin. This information is subsequently utilized to identify failure trends and to implement corrective measures on the offending cavity. Manual inspection of large-scale, time-series data, generated by frequent system failures is tedious and time consuming, and thereby motivates the use of machine learning (ML) to automate the task. This study extends work on a previously developed system based on traditional ML methods (Tennant and Carpenter and Powers and Shabalina Solopova and Vidyaratne and Iftekharuddin, Phys. Rev. Accel. Beams, 2020, 23, 114601), and investigates the effectiveness of deep learning approaches. The transition to a DL model is driven by the goal of developing a system with sufficiently fast inference that it could be used to predict a fault event and take actionable information before the onset (on the order of a few hundred milliseconds). Because features are learned, rather than explicitly computed, DL offers a potential advantage over traditional ML. Specifically, two seminal DL architecture types are explored: deep recurrent neural networks (RNN) and deep convolutional neural networks (CNN). We provide a detailed analysis on the performance of individual models using an RF waveform dataset built from past operational runs of CEBAF. In particular, the performance of RNN models incorporating long short-term memory (LSTM) are analyzed along with the CNN performance. Furthermore, comparing these DL models with a state-of-the-art fault ML model shows that DL architectures obtain similar performance for cavity identification, do not perform quite as well for fault classification, but provide an advantage in inference speed.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2012

Promoting system-level learning from project-level lessons

A growing number of low and middle income nations (LMCs) have adopted some sort of system for environmental impact assessment (EIA). However, generally many of these EIA systems are characterised by a low performance in terms of timely information dissemination, monitoring and enforcement after licencing. Donor actors (such as the World Bank) have attempted to contribute to a higher performance of EIA systems in LMCs by intervening at two levels: the project level (e.g. by providing scoping advice or EIS quality review) and the system level (e.g. by advising on EIA legislation or by capacity building). The aims of these interventions are environmental protection in concrete cases and enforcing the institutionalisation of environmental protection, respectively. Learning by actors involved is an important condition for realising these aims. A relatively underexplored form of learning concerns learning at EIA system-level via project level donor interventions. This 'indirect' learning potentially results in system changes that better fit the specific context(s) and hence contribute to higher performances. Our exploratory research in Ghana and the Maldives shows that thus far, 'indirect' learning only occurs incidentally and that donors play a modest role in promoting it. Barriers to indirect learning are related to the institutional context rather than to individual characteristics. Moreover, 'indirect' learning seems to flourish best in large projects where donors achieved a position of influence that they can use to evoke reflection upon system malfunctions. In order to enhance learning at all levels donors should thereby present the outcomes of the intervention elaborately (i.e. discuss the outcomes with a large audience), include practical suggestions about post-EIS activities such as monitoring procedures and enforcement options and stimulate the use of their advisory reports to generate organisational memory and ensure a better information dissemination.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Magnetic properties of Ni 40+x Mn 39-x Sn 21 (x = 0, 2, 4, 6 and 8 at.%) Heusler alloys

The low electron concentration region (e/a < 7.75) of the magnetic phase diagram of the off-stoichiometric Ni–Mn–Sn Heusler alloys was investigated in detail by DSC and magnetization measurements of the Ni 40+x Mn 39-x Sn 21 (x = 0, 2, 4, 6 and 8 at.%) alloys. The alloys show a stable austenitic phase without any martensitic transformation down to 5 K even after heat treatment. The Curie temperature exhibits a broad maximum over a large composition range. The evolution of the magnetic moment with the electron concentration fits the data of previous studies and confirms the peak-like dependence in the extended range of e/a values predicted by ab initio calculations. Finally, the explored part of the moment versus e/a curve can be explained in terms of a localized magnetic moment model and full atomic order in the alloys.

36 MATERIALS SCIENCE↗
NASA NTRS2010

Initial Results of DC Electric Fields, Associated Plasma Drifts, Magnetic Fields, and Plasma Waves Observed on the C/NOFS Satellite

Initial results are presented from the Vector Electric Field Investigation (VEFI) on the Air Force Communication/Navigation Outage Forecasting System (C/NOFS) satellite, a mission designed to understand, model, and forecast the presence of equatorial ionospheric irregularities. The VEFI instrument includes a vector DC electric field detector, a fixed-bias Langmuir probe operating in the ion saturation regime, a flux gate magnetometer, an optical lightning detector, and associated electronics including a burst memory. Compared to data obtained during more active solar conditions, the ambient DC electric fields and their associated E x B drifts are variable and somewhat weak, typically < 1 mV/m. Although average drift directions show similarities to those previously reported, eastward/outward during day and westward/downward at night, this pattern varies significantly with longitude and is not always present. Daytime vertical drifts near the magnetic equator are largest after sunrise, with smaller average velocities after noon. Little or no pre-reversal enhancement in the vertical drift near sunset is observed, attributable to the solar minimum conditions creating a much reduced neutral dynamo at the satellite altitude. The nighttime ionosphere is characterized by larger amplitude, structured electric fields, even where the plasma density appears nearly quiescent. Data from successive orbits reveal that the vertical drifts and plasma density are both clearly organized with longitude. The spread-F density depletions and corresponding electric fields that have been detected thus far have displayed a preponderance to appear between midnight and dawn. Associated with the narrow plasma depletions that are detected are broad spectra of electric field and plasma density irregularities for which a full vector set of measurements is available for detailed study. Finally, the data set includes a wide range of ELF/VLF/HF oscillations corresponding to a variety of plasma waves, in particular banded ELF hiss, whistlers, and lower hybrid wave turbulence triggered by lightning-induced sferics. The VEFI data represents a new set of measurements that are germane to numerous fundamental aspects of the electrodynamics and irregularities inherent to the Earth's low latitude ionosphere.

Pfaff, R.↗
NASA NTRS2017

On the Development and Application of High Data Rate Architecture (HiDRA) in Future Space Networks

Historically, space missions have been severely constrained by their ability to downlink the data they have collected. These constraints are a result of relatively low link rates on the spacecraft as well as limitations on the time during which data can be sent. As part of a coherent strategy to address existing limitations and get more data to the ground more quickly, the Space Communications and Navigation (SCaN) program has been developing an architecture for a future solar system Internet. The High Data Rate Architecture (HiDRA) project is designed to fit into such a future SCaN network. HiDRA's goal is to describe a general packet-based networking capability which can be used to provide assets with efficient networking capabilities while simultaneously reducing the capital costs and operational costs of developing and flying future space systems.Along these lines, this paper begins by reviewing various characteristics of modern satellite design as well as relevant characteristics of emerging technologies (such as free-space optical links capable of working at 100+ Gbps). Next, the paper describes HiDRA's design, and how the system is able to both integrate and support the operation of not only today's high-rate systems, but also the high-rate systems likely to be found in the future. This section also explores both existing and future networking technologies, such as Delay Tolerant Networking (DTN) protocol (RFC4838 citeRFC:1, RFC5050citeRFC:2), and explains how HiDRA supports them. Additionally, this section explores how HiDRA is used for scheduling data movement through both proactive and reactive link management. After this, the paper moves on to explore a reference implementation of HiDRA. This implementation is currently being realized based on a Field Programmable Gate Array (FPGA) memory and interface controller that is itself controlled by a local computer running DTN software. Next, this paper explores HiDRA's natural evolution, which includes an integration path for software-defined networking (SDN) switches. This section also describes considerations for both near-Earth and deep-space instantiations of HiDRA, describing how differences in latencies between the environments will necessarily influence how the system is configured and the networks operate. Finally, this paper describes future work. This section includes a description of a potential ISS implementation which will allow rapid advancement through the technology readiness levels (TRL). This section also explores work being done to support HiDRA's successful implementation and operation in a heterogeneous network: such a network could include communications equipment spanning many vintages and capabilities, and one significant aspect of HiDRA's future development involves balancing compatibility with capability.

DTN↗
DOE OSTI2022

Cabana: A Performance Portable Library for Particle-Based Simulations

Particle-based simulations are ubiquitous throughout many fields of computational science and engineering, spanning the atomistic level with molecular dynamics (MD), to mesoscale particle-in-cell (PIC) simulations for solid mechanics, device-scale modeling with PIC methods for plasma physics, and massive N-body cosmology simulations of galaxy structures, with many other methods in between (Hockney & Eastwood, 1989). While these methods use particles to represent significantly different entities with completely different physical models, many low-level details are shared including performant algorithms for short- and/or long-range particle interactions, multi-node particle communication patterns, and other data management tasks such as particle sorting and neighbor list construction. Cabana is a performance portable library for particle-based simulations, developed as part of the Co-Design Center for Particle Applications (CoPA) within the Exascale Computing Project (ECP) (Alexander et al., 2020). The CoPA project and its full development scope, including ECP partner applications, algorithm development, and similar software libraries for quantum MD, is described in (Mniszewski et al., 2021). Cabana uses the Kokkos library for on-node parallelism (Edwards et al., 2014; Trott et al., 2022), enabling simulation on multi-core CPU and GPU architectures, and MPI for GPU-aware, multi-node communication. Cabana provides particle simulation capabilities on almost all current Kokkos backends, including serial execution, OpenMP (including OpenMP-Target for GPUs), CUDA (NVIDIA GPUs), HIP (AMD GPUs), and SYCL (Intel GPUs), providing a clear path for the coming generation of accelerator-based exascale hardware. Cabana builds on Kokkos by providing new particle data structures and particle algorithms resulting in a similar execution policy-based, node-level programming model that is intended to be used in addition to the core Kokkos library within an application. Cabana is designed as an application and physics agnostic, but particle-specific toolkit which can either be used to generate a new application, or to be used as needed in existing applications at various levels of invasiveness including through interfaces that wrap user memory in existing data structures.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2013

NASA Tech Briefs, January 2013

Topics include: Single-Photon-Sensitive HgCdTe Avalanche Photodiode Detector; Surface-Enhanced Raman Scattering Using Silica Whispering-Gallery Mode Resonators; 3D Hail Size Distribution Interpolation/Extrapolation Algorithm; Color-Changing Sensors for Detecting the Presence of Hypergolic Fuels; Artificial Intelligence Software for Assessing Postural Stability; Transformers: Shape-Changing Space Systems Built with Robotic Textiles; Fibrillar Adhesive for Climbing Robots; Using Pre-Melted Phase Change Material to Keep Payloads in Space Warm for Hours without Power; Development of a Centrifugal Technique for the Microbial Bioburden Analysis of Freon (CFC-11); Microwave Sinterator Freeform Additive Construction System (MS-FACS); DSP/FPGA Design for a High-Speed Programmable S-Band Space Transceiver; On-Chip Power-Combining for High-Power Schottky Diode-Based Frequency Multipliers; FPGA Vision Data Architecture; Memory Circuit Fault Simulator; Ultra-Compact Transputer-Based Controller for High-Level, Multi-Axis Coordination; Regolith Advanced Surface Systems Operations Robot Excavator; Magnetically Actuated Seal; Hybrid Electrostatic/Flextensional Mirror for Lightweight, Large-Aperture, and Cryogenic Space Telescopes; System for Contributing and Discovering Derived Mission and Science Data; Remote Viewer for Maritime Robotics Software; Stackfile Database; Reachability Maps for In Situ Operations; JPL Space Telecommunications Radio System Operating Environment; RFI-SIM: RFI Simulation Package; ION Configuration Editor; Dtest Testing Software; IMPaCT - Integration of Missions, Programs, and Core Technologies; Integrated Systems Health Management (ISHM) Toolkit; Wind-Driven Wireless Networked System of Mobile Sensors for Mars Exploration; In Situ Solid Particle Generator; Analysis of the Effects of Streamwise Lift Distribution on Sonic Boom Signature; Rad-Tolerant, Thermally Stable, High-Speed Fiber-Optic Network for Harsh Environments; Towed Subsurface Optical Communications Buoy; High-Collection-Efficiency Fluorescence Detection Cell; Ultra-Compact, Superconducting Spectrometer-on-a-Chip at Submillimeter Wavelengths; UV Resonant Raman Spectrometer with Multi-Line Laser Excitation; Medicine Delivery Device with Integrated Sterilization and Detection; Ionospheric Simulation System for Satellite Observations and Global Assimilative Model Experiments - ISOGAME; Airborne Tomographic Swath Ice Sounding Processing System; flexplan: Mission Planning System for the Lunar Reconnaissance Orbiter; Estimating Torque Imparted on Spacecraft Using Telemetry; PowderSim: Lagrangian Discrete and Mesh-Free Continuum Simulation Code for Cohesive Soils; Multiple-Frame Detection of Subpixel Targets in Thermal Image Sequences; Metric Learning to Enhance Hyperspectral Image Segmentation; Basic Operational Robotics Instructional System; Sheet Membrane Spacesuit Water Membrane Evaporator; Advanced Materials and Manufacturing for Low-Cost, High-Performance Liquid Rocket Combustion Chambers; Motor Qualification for Long-Duration Mars Missions.

Source record↗
DOE OSTI2016

Pd overlayer on Oxygen pre-covered Graphene/Ru(0001): Thermal Stability

The behaviors of metal atoms on graphene and graphene oxide are quite important since the composite materials consisting of graphene and transition metal(s) can be employed in numerous technological applications. Despite that the great importance of metal particles on graphene or graphene oxide substrates, fundamental studies of the physical and chemical properties of metal clusters on graphene or graphene oxide are still needed. In this study, in order to elucidate the properties of metal on graphene (GR) and oxygen-precovered graphene (O-GR), the systems consisting of Pd deposited either on to pristine graphene or oxygen-precovered graphene layers have been investigated. The oxygen-precovered graphene was prepared by hot filament method under oxygen atmosphere. Exposure of the graphene layer to atomic oxygen resulted in both intercalation of oxygen between the graphene layer and the Ru(0001) substrate and functionalization of the graphene layer (mainly epoxide formation). Pd deposited onto the oxidized graphene layer interacted strongly with the intercalated oxygen, resulting in intercalation of some of the deposited Pd even at lower temperature than we have seen for Pd on pristine graphene. At the low coverage of 0.25 MLE, Pd mainly formed large planar domains (spreading of Pd on the oxidized graphene layer) and intercalated between the graphene layer and the Ru substrate at lower temperature due to the interaction between Pd and intercalated oxygen. At higher temperatures, both systems experience the same phenomenon, i.e., the intercalation of all of palladium between the graphene layer and Ru substrate. The intercalated Pd desorbed from the substrate at temperatures >1100 K. At higher Pd coverages, some of the Pd intercalated between the GR and the Ru substrate, while small Pd particles spread on the surface. The structure of the Pd overlayer after the 800 K annealing cycle seems to be very different from that observed at 300 K. The Pd that was present in small clusters spread out on the graphene surface due to the interaction with the intercalated Pd layer through the graphene layer. This interaction stabilized the Pd and even after 1000 K annealing the metal film was stable. After even higher temperature annealing all the Pd intercalated, and then desorbed above 1100 K. The graphene layer preserved its perfect structure (i.e., continuity) without any damage after the intercalation and desorption of Pd. We gratefully acknowledge the US Department of Energy (DOE), Office of Science, Office of Basic Energy Sciences, Chemical Sciences, Geosciences, and Biosciences Division for the support of this work. The research described in this article was performed at the Environmental Molecular Sciences Laboratory (EMSL), a national scientific user facility sponsored by the DOE Office of Biological and Environmental Research and located at Pacific Northwest National Laboratory (PNNL). PNNL is operated for the US DOE by Battelle Memorial Institute under contract number DE-AC05-76RL01830. CWY gratefully acknowledges the support of this work by the Sungshin Women’s University Research Grant of 2015.

Yi, Cheol-Woo W.↗