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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 109 records · Page 6

Computational Workflow for Accelerated Molecular Design Using Quantum Chemical Simulations and Deep Learning Models

Efficient methods for searching the chemical space of molecular compounds are needed to automate and accelerate the design of new functional molecules such as pharmaceuticals. Given the high cost in both resources and time for experimental efforts, computational approaches play a key role in guiding the selection of promising molecules for further investigation. Here, we construct a workflow to accelerate design by combining approximate quantum chemical methods [i.e. density-functional tight-binding (DFTB)], a graph convolutional neural network (GCNN) surrogate model for chemical property prediction, and a masked language model (MLM) for molecule generation. Property data from the DFTB calculations are used to train the surrogate model; the surrogate model is used to score candidates generated by the MLM. The surrogate reduces computation time by orders of magnitude compared to the DFTB calculations, enabling an increased search of chemical space. Furthermore, the MLM generates a diverse set of chemical modifications based on pre-training from a large compound library. We utilize the workflow to search for near-infrared photoactive molecules by minimizing the predicted HOMO-LUMO gap as the target property. Our results show that the workflow can generate optimized molecules outside of the original training set, which suggests that iterations of the workflow could be useful for searching vast chemical spaces in a wide range of design problems.

Blanchard, Andrew↗

Eastern North Atlantic Aerosol Mask (ENA-AM) with the Condensation Particle Counter (CPC) at Central Facility (C1) during Winter 2017

Eastern North Atlantic (ENA) Aerosol Mask (ENA-AM) is a mask developed to isolate high concentration aerosol events and identify the regional baseline in one minute time resolution CPC measurements collected at ENA central site during Winter 2017(December 1-30). ENA-AM is based on the statistically different behavior of adjacent data points during background and pollution peaks measurements. Measurements that differ by more than α times the standard deviation (σm) of the data below the median plus the two points after are masked by ENA-AM.

54 ENVIRONMENTAL SCIENCES↗

Eastern North Atlantic Aerosol Mask (ENA-AM) with the Condensation Particle Counter (CPC) at Central Facility (C1) during Summer 2017

Eastern North Atlantic (ENA) Aerosol Mask (ENA-AM) is a mask developed to isolate high concentration aerosol events and identify the regional baseline in one minute time resolution CPC measurements collected at ENA central site during summer 2017(July 22 to August 20). ENA-AM is based on the statistically different behavior of adjacent data points during background and pollution peaks measurements. Measurements that differ by more than α times the standard deviation (σm) of the data below the median plus the two points after are masked by ENA-AM.

54 ENVIRONMENTAL SCIENCES↗

Galaxy clustering in harmonic space from the dark energy survey year 1 data: compatibility with real-space results

ABSTRACT We perform an analysis in harmonic space of the Dark Energy Survey Year 1 Data (DES-Y1) galaxy clustering photometric data using products obtained for the real-space analysis. We test our pipeline with a suite of lognormal simulations, which are used to validate scale cuts in harmonic space as well as to provide a covariance matrix that takes into account the DES-Y1 mask. We then apply this pipeline to DES-Y1 data taking into account survey property maps derived for the real-space analysis. We compare with real-space DES-Y1 results obtained from a similar pipeline. We show that the harmonic space analysis we develop yields results that are compatible with the real-space analysis for the bias parameters. This verification paves the way to performing a harmonic space analysis for the upcoming DES-Y3 data.

79 ASTRONOMY AND ASTROPHYSICS↗

Eastern North Atlantic Aerosol Mask (ENA-AM) with the Condensation Particle Counter (CPC) at Supplementary Facility (S01)

Eastern North Atlantic (ENA) Aerosol Mask (ENA-AM) is a mask developed to isolate high concentration aerosol events and identify the regional baseline in one minute time resolution CPC measurements collected at the ENA supplementary site (S1) during summer (July 22 to August 20) 2017. ENA-AM is based on the statistically different behavior of adjacent data points during background and pollution peaks measurements. Measurements that differ by more than α times the standard deviation (σm) of the data below the median plus the two points after are masked by ENA-AM.

54 ENVIRONMENTAL SCIENCES↗

Bowtie Manufacturing Images, Round 1&2

This dataset contains over 3,600 image files of images of a semiconductor manufactured part called 'Bowtie'. The data is grouped into 'accept' and 'reject' but does not contain masks for why the inspector rejected a part. There are further groupings such as different zoom magnifications, types of rejection (e.g. gouge, debris, etc.). Some of the parts have been laminated and are organized as such. For the 2nd round of data also included are Excel spreadsheets which can be used to identify position on the wafer where the images came from. The included PDF has example images and explains this in more detail.

97 MATHEMATICS AND COMPUTING↗

Components of aircraft life support systems interact with each other and the user

The life support system in a tactical aircraft provides necessary supplemental oxygen to the aircrew. However, interactions among its various components may generate unexpected breathing loads. We focus here on the interactions between a regulator and breathing mask commonly used together in the U.S. Navy, the CRU-103 regulator and MBU 23/P mask, and some effects of the interactions on the user. The data reported were collected during a larger research effort examining potential physiological and cognitive effects of low regulator inlet pressures. Seventeen participants completed a series of tasks under mild exercise while breathing 40% O 2 (balance N 2 ) from an MBU-23/P mask supplied by a CRU-103 regulator with supply pressures 10, 6, 4, and 2 psig (CRU-103 specifications are for inlet pressures from 5 to 120 psig). Variables measured included flow to the mask and pressures at the regulator supply, in the hose to the mask, and in the mask. In addition to restricting inspiratory flow, low inlet pressure to the CRU-103 caused a counterintuitive overshoot in gas delivery pressure at end-inspiration, a mean increase of 1.5 cm H 2 O between the 10- and 2 psig conditions. The added pressure to the exhalation valve increased the expiratory threshold, the pressure to start expiratory flow, by approximately 2 cm H 2 O, increasing the effort needed to exhale.

60 APPLIED LIFE SCIENCES↗

SITCOMTN-165: Surface brightness profiles around massive galaxies in LSSTComCam data

Low surface brightness photometry of massive galaxies can probe the extended stellar halos and intracluster light but is often limited by background subtraction and masking systematics. Here we present radial surface brightness profiles for the brightest cluster galaxy of Abell 360 and 2dFGRS TGS323Z113 using Rubin Observatory Commissioning Camera (LSSTComCam) Data Preview 1 in the g, r, and z bands. Our results show that the LSSTComCam data can go down to ~28 mag arcsec^-2 in the r band. Comparison to DECaLS measurements processed with Legacyhalos shows good agreement of surface brightness profiles at small radii while the LSSTComCam data extend deeper at larger radii.

79 ASTRONOMY AND ASTROPHYSICS↗

KAZRCORGE

For the Ka ARM Zenith Radar (KAZR) data stream, kazrcorge.c1 (ge=general sensitivity), produces significant detection mask, corrects reflectivity for gaseous attenuation, and dealiases mean Doppler velocity.

54 ENVIRONMENTAL SCIENCES↗

KAZRCORMD

For the Ka ARM Zenith Radar (KAZR) data stream, kazrcormd.c1 (md=moderate sensitivity), produces significant detection mask, corrects reflectivity for gaseous attenuation, and dealiases mean Doppler velocity.

54 ENVIRONMENTAL SCIENCES↗

KAZRCORHI

For the Ka ARM Zenith Radar (KAZR) data stream, kazrcorhi.c1 (hi=high sensitivity), produces significant detection mask, corrects reflectivity for gaseous attenuation, and dealiases mean Doppler velocity.

54 ENVIRONMENTAL SCIENCES↗

KAZRCORGE-c0

For the Ka ARM Zenith Radar (KAZR) data stream, kazrcorge.c1 (ge=general sensitivity), produces significant detection mask, corrects reflectivity for gaseous attenuation, and dealiases mean Doppler velocity. Note that reflectivity in this product is not calibrated.

54 ENVIRONMENTAL SCIENCES↗

KAZRCORHI-c0

For the Ka ARM Zenith Radar (KAZR) data stream, kazrcorhi.c1 (hi=high sensitivity), produces significant detection mask, corrects reflectivity for gaseous attenuation, and dealiases mean Doppler velocity. Note that reflectivity in this product is not calibrated.

54 ENVIRONMENTAL SCIENCES↗

KAZRCORMD-c0

For the Ka ARM Zenith Radar (KAZR) data stream, kazrcorge.c1 (ge=general sensitivity), produces significant detection mask, corrects reflectivity for gaseous attenuation, and dealiases mean Doppler velocity. Note that reflectivity in this product is not calibrated.

54 ENVIRONMENTAL SCIENCES↗

CSAPR2 cell-tracking data collected during TRACER

One of the challenges of analyzing convective cell properties is quick evolution of the individual convective cells. While the operational radar data provide great a data set to analyze the evolution of radar observables of convective precipitation clouds statistically, previous studies also suggested that, because of the quick evolution of cell life cycle, conventional radar volume scan strategies taking ~5-7 minutes might not capture the detailed evolution. The TRACER campaign deployed CSAPR2, which performed frequent update of RHI and sector PPI scans to track convective cells every < 2 minutes guided by a new cell-tracking framework, Multisensor Agile Adaptive Sampling (MAAS; Kollias et al. 2020). This allows for capturing fast-evolving radar observables. The submitted data files are CSAPR2 data in CfRadial format collected during the TRACER field campaign from June to September 2020. The data files include processed radar variables including: noise-masked reflectivity and differential reflectivity corrected for rain attenuation and systematic biases, noise-masked dealiased radial velocity, specific differential phase, locations of target cells (latitude, longitude, radar range), and radar-echo classification.

54 ENVIRONMENTAL SCIENCES↗

Automated Assembly Progress Monitoring in Modular Construction Factories Using Computer Vision-Based Instance Segmentation

Modular construction has recently gained interest as a transformative construction method. In this method, a large portion of the construction is performed inside factories, where processes are fast-paced and interdependent; therefore, any deviation from the schedule can delay the production. Such deviations are frequent in modular factories due to the labor-intensive nature of the tasks. This propagation of delays can be mitigated by continuously monitoring each process; however, current manual monitoring methods are laborious, and recently proposed contact sensor-based methods are intrusive to the work. In addition, recent computer vision-based monitoring methods inside factories are limited to detection algorithms that fail to provide the pixel-level accuracy required for assembly progress monitoring in highly occluded factory scenes, and they require a large number of manual annotations. Therefore, this paper proposes a method to monitor the installation of subassemblies in modular construction factories using mask R-CNN instance segmentation and improves the data efficiency of the model using a copy-paste augmentation method. This method was validated on the CCTV videos captured from a modular construction factory in the US, resulting in a 9% mAP improvement in segmentation.

computer vision↗

CHESS 2025: Spectrometer orthorectified at-sensor radiance from NEON AOP imaging spectroscopy surveys

This dataset provides Level 1 (L1) orthorectified at-sensor radiance derived from measurements collected by the Imaging Spectrometer-1 (NIS-1) onboard the NEON (National Ecological Observatory Network) Airborne Observation Platform (AOP) for the 2025 Colorado Headwaters Ecological Spectroscopy Study (CHESS). NIS-1 captures light reflected from the Earth’s surface in 426 discrete wavelength bands as raw digital numbers (DNs; Level 0). These data are then calibrated to physical units (uW/cm²·sr·nm) following the processing steps described in the NEON Imaging Spectrometer Level 1B Calibrated Radiance Algorithm Theoretical Basis Document (ATBD; Gallery 2022). The data delivered here are the primary inputs for the surface reflectance product in “Custom surface reflectance, shade masks, and equivalent water thickness maps for the Colorado Headwaters Ecological Spectroscopy Study” (Carroll et al. 2026). For intertemporal comparison, the radiance data here are most directly relatable to the v2 radiance data in “NEON AOP Imaging Spectroscopy Survey of Upper East River Colorado Watersheds: Raw-Space Radiance and Observational Variable Dataset” (Goulden et al. 2018), to which the same processing methodology was applied. Together, the radiance and reflectance data enable users to exploit the unique reflection signatures of different surface objects for land cover classification, foliar trait mapping, plant vigor assessment, water content estimation, trace-element identification, and other scientific applications. The data were acquired over three study domains in the Upper Gunnison river basin: the upper East River watershed (CRBU); Almont Triangle and Taylor Canyon (ALMO); and Upper Taylor River watershed (UPTA) between 2025-06-13 and 2025-07-15. Within each domain, data are delivered by flightline as orthorectified and calibrated hyperspectral rasters in Hierarchical Data Format version 5 (HDF5) format, with radiance values provided in uW/cm²·sr·nm on a fixed, uniform Universal Transverse Mercator (UTM) grid at 1 meter spatial resolution. The radiance rasters include all 426 NIS-1 spectral bands, along with associated quality-assurance (QA) and diagnostic and ancillary layers needed for atmospheric correction workflows. Orthorectified radiance is produced from pushbroom spectrometer observations by applying NEON’s radiometric calibration (including bad pixel masking, dark subtract, dark pedestal shift correction, electronic panel ghost correction, grating ghost correction, deblur correction and flat-fielding) and spectral calibration (using spectral response function band centers and full-width at half-maximum intensity), followed by geolocation and regridding to the fixed grid. CHESS Project Description: The Colorado Headwaters Ecological Spectroscopy Study (CHESS) comprised a multi-week airborne remote sensing and field observation campaign in the Upper Gunnison Basin, Colorado, conducted in June and July of 2025. Airborne remote sensing was conducted by the National Ecological Observatory Network Airborne Observation Platform (NEON AOP), concurrent with a field campaign run by the Rocky Mountain Biological Laboratory (RMBL), the Lawrence Berkeley National Laboratory (LBNL) and SLAC National Accelerator Laboratory Watershed Function Science Focus Area (SFA), and NASA-JPL (Jet Propulsion Laboratory) Earth Surface Mineral Dust Source Investigation (EMIT) program. Between June 10 and July 18, 2025, the NEON AOP flight team collected high-resolution aerial imaging spectroscopy and Light Detection and Ranging (LiDAR) data over three domains: the Upper East River (CRBU), Almont Triangle (ALMO), and the Upper Taylor Basin (UPTA). In coordination with the flights, a field campaign acquired ground-truth observations, including observations of vegetation composition, foliar traits, forest demography, and subsurface properties in 18 core sampling areas within the domains. Additional surface water observations were taken at over 380 point locations. All CHESS campaign datasets can be found within the CHESS ESS-DIVE data portal: https://data.ess-dive.lbl.gov/portals/chess. Funding Acknowledgement: Field and remote-sensing data acquisition was performed under a grant from the National Aeronautics and Space Administration (80NSSC24K1005). This work was also supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

2018 NEON and 2025 CHESS Campaigns↗