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NASA NTRS2017

Operational Overview for UAS Integration in the NAS Project Flight Test Series 3

The National Aeronautics and Space Administration Unmanned Aircraft Systems Integration in the National Airspace System Project has conducted a series of flight tests intended to support the reduction of barriers that prevent unmanned aircraft from flying without the required waivers from the Federal Aviation Administration. The 2015 Flight Test Series 3, supported two separate test configurations. The first configuration investigated the timing of Detect and Avoid alerting thresholds using a radar equipped unmanned vehicle and multiple live intruders flown at varying encounter geometries. The second configuration included a surrogate unmanned vehicle (flown from a ground control station, with a safety pilot on board) flying a mission in a virtual air traffic control airspace sector using research pilot displays and Detect and Avoid advisories to maintain separation from live and virtual aircraft. The test was conducted over an eight-week span within the R-2508 Special Use Airspace. Over 200 encounters were flown for the first configuration, and although the second configuration was cancelled after three data collection flights, Flight Test 3 proved to be invaluable for the purposes of planning, managing, and execution of this type of integrated flight test.

unmanned aircraft systems↗

NASA NTRS2024

Tracking Community Building in Open Science

Open Science is enabled by a vibrant community of researchers who regularly engage with the data, from its production to its organization, curation, archiving, dissemination, analysis, and publication. This presentation will examine community building in open science. The NASA Open Science Data Repository (OSDR) makes data available to the public following the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles. OSDR takes open science further with the OS Analysis Working Groups (AWGs) that facilitate community development and promotion. The primary activity of each AWG is to establish and validate analytical processes to generate higher-order data from data housed in OSDR. There are a number of these groups on various topics, including the Animal AWG, Plant AWG, Microbial AWG, Multi-Omics AWG, AI/ML AWG, and the Ames Life Sciences Data Archive (ALSDA) AWG. The international volunteers participating in these AWGs come from academia, citizen science initiatives, industry, and government. They include researchers, principal investigators, professors, trained hobbyists, and students from various domains and disciplines. Anyone may request to join the AWGs, and membership requests are vetted monthly by the group organizers before granting admission. Core to membership is demonstrated expertise through records of training, integrity, work in the professed domain(s), and good community standing. Regular virtual meetings are held for each AWG, with a varying cadence depending on the group's needs and goals. AWG communities share their expertise in research including cutting edge tools, software, frameworks, data formats, and libraries accelerating research collectively. This collaborative approach helps community members cross technology gaps and identify emerging challenges. These diverse communities encompass a wide range of individuals hailing from various sectors within the Science Mission Directorate and beyond. They serve as a means to promote and enhance transparency, accessibility, and inclusion. An annual AWG Symposium brings contributors together in person. Participation in AWGs can be synchronous or asynchronous, with some groups performing most of their work in off hours. Participants gain valuable skills and connections that allow them to add value to their communities and new organizations that they join, resulting in an expanded return on investment for the space life science community. Open science is increasingly a federal mandate and initiatives like NASA's Transform to Open Science and instruments like the Decadal Survey of Biological and Physical Sciences in Space demonstrate the need to carefully consider best practices in this domain. Here, we present greater detail about the makeup and participation metrics of the various AWGs affiliated with OSDR and details of successful peer-reviewed publication campaigns.

Christina M Johnson↗

NASA NTRSDate not supplied

Tracking Community Building in Open Science

Christina M Johnson↗

NASA NTRS2003

Combining Multiple Gyroscope Outputs for Increased Accuracy

A proposed method of processing the outputs of multiple gyroscopes to increase the accuracy of rate (that is, angular-velocity) readings has been developed theoretically and demonstrated by computer simulation. Although the method is applicable, in principle, to any gyroscopes, it is intended especially for application to gyroscopes that are parts of microelectromechanical systems (MEMS). The method is based on the concept that the collective performance of multiple, relatively inexpensive, nominally identical devices can be better than that of one of the devices considered by itself. The method would make it possible to synthesize the readings of a single, more accurate gyroscope (a virtual gyroscope) from the outputs of a large number of microscopic gyroscopes fabricated together on a single MEMS chip. The big advantage would be that the combination of the MEMS gyroscope array and the processing circuitry needed to implement the method would be smaller, lighter in weight, and less power-hungry, relative to a conventional gyroscope of equal accuracy. The method (see figure) is one of combining and filtering the digitized outputs of multiple gyroscopes to obtain minimum-variance estimates of rate. In the combining-and-filtering operations, measurement data from the gyroscopes would be weighted and smoothed with respect to each other according to the gain matrix of a minimum- variance filter. According to Kalman-filter theory, the gain matrix of the minimum-variance filter is uniquely specified by the filter covariance, which propagates according to a matrix Riccati equation. The present method incorporates an exact analytical solution of this equation.

Bayard, David S.↗

DOE OSTI2026

Three pairs of fungal Trametes strains isolated from distinct geographic origins show conserved genomic features and adaptive response to plant biomass

The genomes of white-rot fungi hold extended repertoires of enzymes active on virtually all the chemical bonds that intertwine lignocellulose polymers, and several Trametes species have been identified as powerful tools for biorefinery or bioremediation. However, only few studies have addressed the intra-species polymorphism one would expect from fungal strains collected in contrasted environments. We compared the genome sequence of pairs of strains collected in different geographic areas, for each of three fungal species. Using an updated list of the predicted functions for fungal ligno- and cellulolytic enzymes (CAZymes), we observed a high conservation of the gene repertoires among the six strains. We compared the adaptative response of the fungi grown on crystalline cellulose, wheat straw, aspen or pine sawdust by transcriptomics and secretomics. The gene regulation profiles were determined by the species and the substrates, rather than the strain. The secretomes did not show marked differences in the sets of secreted CAZymes after 3 day-growth on the substrates. We identified five transcription factor genes and two sesquiterpenoid synthesis genes induced during growth on lignocellulose. Wider studies using larger sets of strains will be necessary to evaluate the genericity of our findings, and to assess the phenotype diversity one could expect from geographic diversity as compared to taxonomic diversity in Trametes fungi.

Drula, E. [French National Research Institute for ↗

NASA NTRS1993

The possibilities for mobile and fixed services up to the 20/30 GHz frequency bands

Satellite Communications and broadcasting is presently in a period of considerable change. In the fixed service there is strong competition from terrestrial fiber optic systems which have virtually arrested the growth of the traditional satellite market for long distance high capacity communications. The satellite has however made considerable progress in areas where it has unique advantages; for example, in point to multipoint (broadcasting), multipoint to point (data collection) and generally in small terminal system applications where flexibility of deployment coupled with ease of installation are of importance. In the mobile service, in addition to the already established geostationary systems, there are numerous proposals for HEO, MEO and LEO systems. There are also several new frequency allocations as a result of the WARC 92 to be taken into account. At one extreme there are researchers working on Ka band 20/30 GHz mobile systems and there are other groups who foresee no future above the L-band frequency allocations. Amongst all these inputs it is difficult to see the direction in which development activities both for satellites and for earth segment should be focused. However, as an aid to understanding, this paper seeks to find some underlying relationships and to clarify some of the variables.

Hughes, Clifford D.↗

NASA NTRS2015

Dose Tracker Application for Monitoring Crew Medication Usage, Symptoms, and Adverse Effects During Missions

Medication usage records can be used as a relatively nonintrusive means of monitoring health. This has been attempted previously through crew medical records, but these records are incomplete from the perspective of a research pharmacologist. During the shuttle era, NASA operations did not include routine questioning of crewmembers about their medication use until after missions were complete. The (long!) questionnaire was on paper. Asking crewmembers to recall medication use from weeks before questioning made getting complete and accurate information virtually impossible. This study will document medication usage of crewmembers before and during their missions. It will capture previously unrecorded data regarding medication use during spaceflight, including side effect qualities, frequencies and severities. The research-oriented data will be collected for research purposes, separate from medical records. Dose Tracker employs an iOS application (app) for fast & easy collection of medication usage data from crewmember participants during their missions.

Wotring, Virginia↗

NASA NTRSDate not supplied

Linear Mixed-Effects Models for Human-in-the-Loop Tracking Experiment Data

Linear mixed-effects models provide several benefits over more traditional statistical inference tests that are particularly useful for most human-in-the-loop tracking experiment data. However, surprisingly, mixed models are virtually not used for the analysis of tracking experiment data. This paper uses of linear mixed-effects models to analyze combined tracking data from two previous human-in-the-loop roll tracking experiments that compared control behavior metrics collected in both a research aircraft and a motion-base simulator. In the experiments, pilots' behavior under 10 different motion configurations with varying motion filter gains and break frequencies was evaluated and compared to that in the real aircraft. The linear mixed-effects model analysis on the combined dataset confirmed the main statistical outcomes of the individual experiments. The main benefits of mixed models for this type of data were demonstrated by successfully combining data from two experiments that used different experimental conditions and of which one had an additional apparatus and the other a missing participant. Finally, the mixed-model analysis was able to explicitly test for scientifically relevant statistical differences in the dependent measures between the aircraft and simulator, as well as both experiments.

manual control↗

DOE OSTI2023

Time-Resolved Radioluminescence of the Polynuclear Copper(I) Phosphor Cu4I6²–. Different Temporal responses to Photo, X-Ray, β-Ray and α-Particle Excitation

Described for the first time are the time-resolved radioluminescence (TRRL) properties of the highly luminescent copper(I) cluster Cu4I62– using X-ray, b-ray and a-particle excitation. The copper(I) phosphors are two crystalline salts of this anion, [Na2(18-crown-6)2(H2O)3][Cu4I6] (I) and [Li(benzo-15-crown-5)2(H2O)]2[Cu4I6] (II), that had been shown to display strong photoluminescence (PL) when excited with ultraviolet light. The integrated radioluminescence (RL) spectra observed when pulsed X-rays were used as the excitation source are virtually the same as the PL spectra under comparable conditions. These temporal RL spectra are invariant at all observation times indicating that the observed scintillation occurs from a common excited state (or set of excited states). When the TRRL is collected from excitation with a pulsed X-ray source, b-ray decay from 90Sr or a-decay from 244Cm, the longer components (>2 μs) of the TRRL displayed (in each case) lifetimes comparable to those of the strictly exponential PL decays recorded under pulsed laser UV excitation. However, at shorter observation times (< 500 ns), the RL decay deviated from the exponential model, indicating a markedly different temporal luminescence behavior when these materials were excited with pulsed high energy radiation. Furthermore, this effect was the most pronounced for the a-particle excitation. These results suggest that the absorption of a very high energy X-ray photon, b-ray or a-particle at a site within the crystalline solid leads to a high local density of electronic excited states and that coupling between these states provides an alternative channel for excited state decay. Given that the patterns observed

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

DOE OSTI2025

Inclusive electron scattering in the resonance region off a hydrogen target with CLAS12

Inclusive electron scattering cross sections off a hydrogen target at a beam energy of 10.6 GeV have been measured with data collected from the CLAS12 spectrometer at Jefferson Laboratory. These first absolute cross sections from CLAS12 cover a wide kinematic area in invariant mass 𝑊 of the final state hadrons from the pion threshold up to 2.5 GeV for each bin in virtual photon four-momentum transfer squared 𝑄 2 from 2.55 to 10.4GeV 2 owing to the large scattering angle acceptance of the CLAS12 detector. Comparison of the cross sections with the resonant contributions computed from the CLAS results on the nucleon resonance electroexcitation amplitudes has demonstrated a promising opportunity to extend the information on their 𝑄 2 evolution up to 10 GeV 2 . Together these results from CLAS and CLAS12 offer good prospects for probing the nucleon parton distributions at large fractional parton momenta 𝑥 for 𝑊<2.5 GeV, while covering the range of distances where the transition from the strongly coupled to the perturbative regimes is expected.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2021

Combined Mesonet and Tracker

Title: Combined Mesonet and Trackers (UNL Mobile Mesonets) Authors University of Nebraska PI: Adam Houston, UNL Professor (ahouston2@unl.edu) Mailing Address: 126 Bessey Hall P.O. Box 880340 Lincoln, NE 68588-0340 CoMeT Overview The University of Nebraska-Lincoln operates three Combined Mesonet and Tracker (CoMeTs). CoMeTs are Ford Explorers (model years 2015, 2017, and 2019) with forward-mounted suites of meteorological sensors and dual moonroofs, combining the capability of a mobile mesonet to collect near-surface observations with the capability of an unmanned aircraft systems (UAS) tracker vehicle, which enables an observer in the second row of seats to see the aircraft and maintain compliance with Federal Aviation Administration policies on UAS operation. The CoMeTs collect observations of slow temperature and humidity at ~2 m above ground level (AGL) using a Vaisala HMP155A, fast temperature at ~2 m AGL using a Campbell Scientific 109SS-L thermistor, pressure at ~2.5 m AGL using a Vaisala PTB210, wind speed and direction at ~3.25 m AGL using an R.M. Young 05103 propeller anemometer, and vehicle heading using a KVH Industries C-100 fluxgate compass (Barbieri et al. 2019). The HMP155A and 109SS-L thermistor are shielded and aspirated within a U-tube (Waugh and Frederickson 2010; Houston et al. 2016). This list of sensors is also included in the CoMeT data file metadata. Manufacturer specifications for these instruments are given in Table 1 of Hanft and Houston (2018). The reported measured quantities are summarized below.CoMeT-3 was funded through an equipment allocation included in the NSF TORUS award (AGS-1824649). Instrument Description The specific sensors included on each CoMeT are summarized in the table at the end of this section. In general each CoMeT collects observations of slow temperature and humidity at ~2 m above ground level (AGL) using a Vaisala HMP155, fast temperature at ~2 m AGL using a Campbell Scientific 109SS-L thermistor, pressure at ~2.5 m AGL using a Vaisala PTB210 barometer with a Gill pressure port, wind speed and direction at ~3.25 m AGL using an R.M. Young 05103 propeller anemometer, position using a Garmin 19x HVS receiver, and vehicle heading using a KVH Industries C-100 fluxgate compass. The HMP155 and 109SS are shielded and aspirated within a U-tube (Waugh and Frederickson 2010; Houston et al. 2016). Fast temperature and corrected RH measurements (using sensors housed within the U-tube) have a time constant of 10-12 s based on data collected across a temperature and RH shock during the CLOUD-MAP 2017 calibration/validation tests on June 26, 2017. Vehicle speed was < 10 kts for this test. CoMeT-1 CoMeT-2 CoMeT-3 Slow Temperature Slow RH Vaisala HMP155A-L20-PT Part #: 22280-7 Vaisala HMP155E Part #: E1AA11A0B1A1A0A Vaisala HMP155E Part #: E1AA11A0B1A1A0A Fast temperature Campbell Scientific 109SS-L20-PT Part #: 21448-3 Campbell Scientific 109SS-L12-PW Part #: 21448-109 Campbell Scientific 109SS-L12-PW Part #: 21448-150 Pressure Vaisala PTB-210 Part #: A1A1B Gill Pressure Port Part #: 61002 Vaisala PTB-210 Part #: A1A1B Gill Pressure Port Part #: 61002 Vaisala PTB-210 Part #: A1A1B Gill Pressure Port Part #: 61002 Wind RM Young 05103-L20-PT Part #: 18435-310 RM Young 05103-L20-PW Part #: 18435-244 RM Young 05103-L20-PW Part #: 18435-244 GPS Garmin GPS 19x HVS (NMEA 0183) Part #: 010-01010-00 Garmin GPS 19x HVS (NMEA 0183) Part #: 010-01010-00 Garmin GPS 19x HVS (NMEA 0183) Part #: 010-01010-00 Compass KVH C-100 Part #: 01-0177-15 KVH C-100 Part #: 01-0177-15 KVH C-100 Part #: 01-0177-15 Logger Campbell Scientific CR6-NA-XT-SW Part #: 28385-9 Campbell Scientific CR6-WIFI-XT-SW Part #: 28385-6 Campbell Scientific CR6-WIFI-XT-SW Part #: 28385-6 Data Collection and Real-Time Processing The reported measured quantities are summarized in the table below. Quantity Units Source Epoch time Seconds GPS Latitude and longitude Degrees GPS Altitude m GPS Pressure hPa PTB210 Temperature (fast) deg C 109SS-L Temperature (slow) deg C HMP155 RH (slow) % HMP155 Vehicle speed m/s GPS Vehicle heading deg C-100 and GPS In addition to the measured variables, several derived variables are calculated. Corrected/fast relative humidity (%) Relative humidity is adjusted to the fast temperature following Richardson et al. (1998) and Houston et al. (2016). Water vapor mixing ratio (g/kg) Dew point temperature (°C) Potential temperature (Kelvin) Virtual potential temperature (Kelvin) Equivalent potential temperature (Kelvin) Regular intercomparisons between all three CoMeTs were performed during TORUS 2019. Comparisons were also conducted between CoMeT-1 and CoMeT-2 during LAPSE-RATE (2018) on 14 July. In these intercomparisons, the vehicles were parked adjacent to each other aligned perpendicular to (and facing into) the wind. To minimize engine heating effects, intercomparisons were only conducted when the wind speed was >10 kts. Data Format Original data files for each deployment are saved as text files and then converted to NetCDF. NetCDF versions have units that are CF compliant and may not match the original units in the txt files. The naming convention for the NetCDF files is as follows: UNL.CoMeT3.{deployment date YYYYMMDD}.{start time of observation collection in UTC HHMM}.L2.{post-processing codes}.cdf example: UNL.CoMeT3.20190627.1931.L2.g1.f1.cdf Post-processing codes are included to track modifications to the raw data. These codes are closely connected to error flags associated with each record. Each letter corresponds to a particular instrument: g: GPS p: Barometer tf: Fast temperature ts: Slow temperature rh: Relative humidity f: Compass w: Wind monitor a: All instruments Each number corresponds to a particular post-processing action described more below. Measured and derived variables are included in the following table. Variable Heading Standard Name Units time Time seconds since 00:00:00, 01-01-1970 Alt Altitude meters lat Latitude degrees north lon Longitude degrees east fast_temp Air Temperature kelvin slow_temp Air Temperature kelvin pressure Air Pressure pascals logger_RH Relative Humidity percent calc_corr_RH Relative Humidity percent wind_speed Wind Speed meters per second wind_dir Wind From Direction degrees vehicle_dir Vehicle Direction degrees dewpoint Dew Point Temperature kelvin mixing_ratio Humidity Mixing Ratio g/g theta Air Potential Temperature kelvin theta_v Virtual Potential Temperature kelvin theta_e Equivalent Potential Temperature Kelvin error_flag The error_flag variable is a string that matches the post-processing codes listed above. All instruments will have an associated code, but will have a “0” if the datum is unchanged from the initial processed value. Error Codes The following table summarizes the error codes for data collected before 2020: Error Code Relevant CoMeT Description g1 1,2,3 Exact correction. GPS position and time reprocessed from raw data g2 1 As far as we can tell this is an exact correction to an error in the GPS time. During the correct time periods the time suddenly went backwards ~250s and stayed at this offset for 750s when it corrected itself. The offset was applied to the “time warp” period. p1 2 Approximate correction. Hole in the pressure tube connecting the pressure port to the barometer. Resulted in erroneously low air pressure measurements when the vehicle was in motion. Derived variables recalculated (dew point temperature [e depends on qv and p], water vapor mixing ratio, potential temperature, virtual potential temperature, equivalent potential temperature) a1 3 Exact correction. Missing data reprocessed from raw data a2 1 Bug fix to bias correction for ts1, ts2, and rh1: water vapor mixing ratio was off by a factor of 10 and virtual potential temperature was wrong because of this. f1 3 No correction, missing data. Fluxgate compass inoperable. Wind speed and direction calculated using GPS-derived vehicle heading instead. rh1 1 Approximate correction. Constant bias of +1.7% removed from relative humidity. Derived variables recalculated (corrected/fast relative humidity, dew point temperature, water vapor mixing ratio, virtual potential temperature, equivalent potential temperature) ts1 1 Approximate correction. Constant bias of +0.6 K removed from slow temperature. Derived variables recalculated (corrected/fast relative humidity, dew point temperature, water vapor mixing ratio, virtual potential temperature, equivalent potential temperature) ts2 1 Approximate correction. Constant bias of +1.0 K removed. Derived variables recalculated (corrected/fast relative humidity, dew point temperature, water vapor mixing ratio, virtual potential temperature, equivalent potential temperature) References Bolton, D., 1980: The Computation of Equivalent Potential Temperature. Mon. Wea. Rev., 108, 1046–1053, https://doi.org/10.1175/1520-0493(1980)108<1046:TCOEPT>2.0.CO;2. Hanft, W., and A. L. Houston, 2018: An Observational and Modeling Study of Mesoscale Air Masses with High Theta-E. Mon. Wea. Rev., 146, 2503–2524, https://doi.org/10.1175/MWR-D-17-0389.1.Wexler Houston, A. L., R. J. Laurence III, T. W. Nichols, S. Waugh, B. Argrow, and C. L. Ziegler, 2016: Intercomparison of unmanned aircraft-borne and mobile mesonet atmospheric sensors. Journal of Atmospheric and Oceanic Technology. 33, 1569-1582, doi: 10.1175/JTECH-D-15-0178.1. Lowe, P. R., 1977: An Approximating Polynomial for the Computation of Saturation Vapor Pressure. J. Applied Meteorology, 16, 100–103. Richardson, S. J., S. E. Frederickson, F. V. Brock, and J. A. Brotzge, 1998: Combination temperature and relative humidity probes: Avoiding large air temperature errors and associated relative humidity errors. Preprints, 10th Symp. On Meteorological Observations and Instrumentation, Phoenix, AZ, Amer. Meteor. Soc., 278–283. Waugh, S., and S. E. Frederickson, 2010: An improved aspirated temperature system for mobile meteorological observations, especially in severe weather. 25th Conf. on Severe Local Storms, Denver, CO, Amer. Meteor. Soc., P5.2. [Available online at https://ams.confex.com/ams/25SLS/techprogram/paper_176205.htm.]

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Development and validation of a mathematical model of heart rate response to fluid perturbation

Physiological closed-loop controlled (PCLC) medical devices monitor and automatically adjust the patient’s condition by using physiological variables as feedback, ideally with minimal human intervention to achieve the target levels set by a clinician. PCLC devices present a challenge when it comes to evaluating their performance, where conducting large clinical trials can be expensive. Virtual physiological patients simulated by validated mathematical models can be utilized to obtain preclinical evidence of safety and assess the performance of the PCLC medical device during normal and worst-case conditions that are unlikely to happen in a limited clinical trial. A physiological variable that plays a major role during fluid resuscitation is heart rate (HR). For in silico assessment of PCLC medical devices regarding fluid perturbation, there is currently no mathematical model of HR validated in terms of its predictive capability performance. This paper develops and validates a mathematical model of HR response using data collected from sheep subjects undergoing hemorrhage and fluid infusion. The model proved to be accurate in estimating the HR response to fluid perturbation, where averaged between 21 calibration datasets, the fitting performance showed a normalized root mean square error (NRMSE) of 7.41 ± 2.8%. The model was also evaluated in terms of model predictive capability performance via a leave-one-out procedure (21 subjects) and an independent validation dataset (6 subjects). Two different virtual cohort generation tools were used in each validation analysis. The generated envelope of virtual subjects robustly met the defined acceptance criteria, in which 95% of the testing datasets presented simulated HR patterns that were within a deviation of 50% from the observed data. In addition, out of 16000 and 18522 simulated subjects for the leave one-out and independent datasets, the model was able to generate at least one virtual subject that was close to the real subject within an error margin of 9.56 ± 3.15% and 11.1 ± 1.22% NRMSE, respectively. In conclusion, the model can generate valid virtual HR physiological responses to fluid perturbation and be incorporated into future non-clinical simulated testing setups for assessing PCLC devices intended for fluid resuscitation.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Strong constraints on the b → sγ photon polarisation from B0 → K*0e+e-decays

An angular analysis of the B 0 → K* 0 e + e - decay is performed using a data sample corresponding to an integrated luminosity of 9 fb - 1 of pp collisions collected with the LHCb experiment. The analysis is conducted in the very low dielectron mass squared ( q 2 ) interval between 0 . 0008 and 0 . 257 GeV 2 , where the rate is dominated by the B 0 → K * 0 γ transition with a virtual photon. The fraction of longitudinal polarisation of the K * 0 meson, F L , is measured to be F L = (4 . 4 ± 2 . 6 ± 1 . 4)%, where the first uncertainty is statistical and the second systematic. The \( {A}_{\mathrm{T}}^{\mathrm{Re}} \) observable, which is related to the lepton forward-backward asymmetry, is measured to be \( {A}_{\mathrm{T}}^{\mathrm{Re}} \) = - 0 . 06 ± 0 . 08 ± 0 . 02. The \( {A}_{\mathrm{T}}^{(2)} \) and \( {A}_{\mathrm{T}}^{\mathrm{Im}} \) transverse asymmetries, which are sensitive to the virtual photon polarisation, are found to be \( {A}_{\mathrm{T}}^{(2)} \) = 0 . 11 ± 0 . 10 ± 0 . 02 and \( {A}_{\mathrm{T}}^{\mathrm{Im}} \) = 0 . 02 ± 0 . 10 ± 0 . 01. The results are consistent with Standard Model predictions and provide the world’s best constraint on the b → sγ photon polarisation.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2021

Development and validation of a second-order thermal network model for residential buildings

Heating, Ventilation, and Air Conditioning (HVAC) systems can maintain the space air temperature of residential buildings, either directly by heating/cooling the air, or indirectly via heat transfer to and from the building structure that acts as a thermal mass. Hence, HVAC systems can help achieve load shifting, peak load reduction, and/or energy cost saving, thus enabling grid-interactive HVAC operation. A home thermal model that can accurately reflect the dynamics of the space air and interior wall surface temperatures, is therefore valuable. This paper develops such a model using the standard RC (resistance-capacitance) approach. The model contains a virtual envelope node and an internal space node and is thus second-order. A hybrid parameter identification scheme, made up of the least-squares and optimal search methods, is also developed. The proposed model and scheme were validated using data collected from a test home. It was found that a modest amount of training data was sufficient to yield reliable parameter estimates and accurate prediction. It was also found that when making 24-hour-ahead prediction of the space air temperature, both methods had comparable performances when the training data began in a transition season. However, when they began in an HVAC season, the optimal search method performed better. Furthermore, the least-squares method is recommended during a transition season due to its lower computational burden, while the optimal search method is recommended during an HVAC season due to its better estimation performance.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

ATom (Acoustic Tomography Processing Suite) [SWR-24-120]

Acoustic tomography seeks the best-fit fluctuating velocity and temperature fields that explain a collection of signal travel times in a region of interest. This codebase defines an end-to-end framework for executing turbulent field retrievals from acoustic signals, acoustic signal design and processing tools for the physical array, and analysis tool that leverage virtual acoustic tomography arrays based on large-eddy simulations of the atmospheric boundary layer.

Hamilton, Nicholas↗

NASA NTRS1997

Validity of a heart rate monitor during work in the laboratory and on the Space Shuttle

Accurate heart rate measurement during work is required for many industrial hygiene and ergonomics situations. The purpose of this investigation was to determine the validity of heart rate measurements obtained by a simple, lightweight, commercially available wrist-worn heart rate monitor (HRM) during work (cycle exercise) sessions conducted in the laboratory and also during the particularly challenging work environment of space flight. Three different comparisons were made. The first compared HRM data to simultaneous electrocardiogram (ECG) recordings of varying heart rates that were generated by an ECG simulator. The second compared HRM data to ECG recordings collected during work sessions of 14 subjects in the laboratory. Finally, ECG downlink and HRM data were compared in four astronauts who performed cycle exercise during space flight. The data were analyzed using regression techniques. The results were that the HRM recorded virtually identical heart rates compared with ECG recordings for the data set generated by an ECG simulator. The regression equation for the relationship between ECG versus HRM heart rate data during work in the laboratory was: ECG HR = 0.99 x (HRM) + 0.82 (r2 = 0.99). Finally, the agreement between ECG downlink data and HRM data during space flight was also very high, with the regression equation being: Downlink ECG HR = 1.05 x (HRM) -5.71 (r2 = 0.99). The results of this study indicate that the HRM provides accurate data and may be used to reliably obtain valid data regarding heart rate responses during work.

STS Shuttle Project↗

NASA NTRS2012

Visual System for Browsing, Analysis, and Retrieval of Data (ViSBARD)

ViSBARD software provides a way of visualizing multiple vector and scalar quantities as measured by many spacecraft at once. The data are displayed three-dimensionally along the orbits that may be shown either as connected lines or as points. The data display allows the rapid determination of vector configurations, correlations among many measurements at multiple points, and global relationships. Things such as vector field rotations and dozens of simultaneous variables are very difficult to see in (complementary) panel plot representations. The current and next generations of space physics missions require a means to display from tens to hundreds of time series of data in such a way that the mind can comprehend them for the purposes of browsing data, retrieving them in directly useful form, and analyzing them in a global context. Sets of many spacecraft, each carrying many instruments yielding nearly continuous data at high time resolution, have become one of the most effective ways to make progress in understanding the extended, ionized (plasma) atmosphere of the Earth and the Sun. For large collections of data to be effective, they must be extremely readily accessible, with simple, comprehensible overviews of what is available. ViSBARD provides a means to answer these concerns. The ViSBARD package also acts as a remote repository browser; an interface to a Virtual Observatory. Therefore, data can be pulled directly into the application, as opposed to searching for it and downloading separately.

Roberts, Aaron↗

NASA NTRS2002

The Virtual Glovebox (VGX): An Immersive Simulation System for Training Astronauts to Perform Glovebox Experiments in Space

The era of the International Space Station (ISS) has finally arrived, providing researchers on Earth a unique opportunity to study long-term effects of weightlessness and the space environment on structures, materials and living systems. Many of the physical, biological and material science experiments planned for ISS will require significant input and expertise from astronauts who must conduct the research, follow complicated assay procedures and collect data and samples in space. Containment is essential for Much of this work, both to protect astronauts from potentially harmful biological, chemical or material elements in the experiments as well as to protect the experiments from contamination by air-born particles In the Space Station environment. When astronauts must open the hardware containing such experiments, glovebox facilities provide the necessary barrier between astronaut and experiment. On Earth, astronauts are laced with the demanding task of preparing for the many glovebox experiments they will perform in space. Only a short time can be devoted to training for each experimental task and gl ovebox research only accounts for a small portion of overall training and mission objectives on any particular ISS mission. The quality of the research also must remain very high, requiring very detailed experience and knowledge of instrumentation, anatomy and specific scientific objectives for those who will conduct the research. This unique set of needs faced by NASA has stemmed the development of a new computer simulation tool, the Virtual Glovebox (VGB), which is designed to provide astronaut crews and support personnel with a means to quickly and accurately prepare and train for glovebox experiments in space.

Smith, Jeffrey D.↗