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DOE OSTI2020

Herbaceous Feedstock 2018 State of Technology Report

The U.S. Department of Energy (DOE) promotes the production of advanced liquid transportation fuels from lignocellulosic biomass by funding fundamental and applied research that advances the state of technology (SOT). As part of its involvement with this mission, Idaho National Laboratory (INL) completes an annual SOT report for biomass feedstock logistics. This report summarizes supply system impacts of Bioenergy Technologies Office (BETO)-funded research and development efforts at INL and elsewhere (such as the High-Tonnage Feedstock Logistics projects (Webb et al. 2013a, Webb et al. 2013b, Webb et al. 2013c, Webb and Sokhansanj 2014, Sokhansanj et al. 2014) that lead to improvements in feedstock supply systems. These include improvements to and observed performance of innovative harvest and collection methods, storage technologies, transportation and handling approaches, and advanced preprocessing technologies. Biomass quality and variability, and the interface between feedstock quality and conversion performance are key drivers in addition to delivered feedstock cost. In this report, we estimate the benefits of R&D improvements to individual supply system unit operations, and present the status of feedstock logistics technology development for converting biomass into biofuels. These analyses are supported by experimental data where possible, and help to align the SOT relative to the cost goals defined in the Multi-Year Program Plan. The 2018 Herbaceous SOT aligned feedstock logistic design with current biorefinery’s design capacity utilized by biochemical conversion platform. Currently biochemical conversion platform utilizes a 725,000 dry ton/year biorefiney design for the techno economic analysis. Hence, feedstock delivered cost in the 2018 Herbaceous SOT is calculated based on biorefinery’s 725,000 dry ton design capacity instead of 800, 000 dry ton capacity utilized in the 2017 Herbaceous SOT. Biomass availabilities in this SOT were updated to year 2018 data from the 2016 Billion-Ton Report (BT16) (DOE 2016a), with the exception of switchgrass, for which the 2018 Herbaceous SOT utilized the 2019 switchgrass availability data from BT16. The BT16 report (DOE 2016a) does not project switchgrass availability in 2018; the soonest switchgrass is available in the BT16 report is 2019. Therefore, availability of switchgrass for this analysis was that projected for 2019. The 2018 Herbaceous SOT incorporates same technologies utilized in the 2017 Herbaceous SOT. However, a sensitivity analysis is performed to understand the impact of variation of process parameters on those technologies on feedstock logistic cost. New R&D data that shows the variations of process parameters affecting process performance is incorporated in the 2018 SOT to measure the variations in delivered feedstock cost. The 2018 Herbaceous SOT has also provided projected delivered feedstock of 2022 design case based on near term technical target under BETO funded R&D project. Finally, updated biorefinery size of 725,000 dry ton/year was incorporated within least-cost formulation model to select optimal siting and depot scales during optimization of the least cost blend. This modification to the optimization algorithm allows the trade-off between the cost of increased supply radius and the savings from selecting biomass from higher producing counties to be assessed. Such optimization has also showed the economic benefit of decentralized depots in comparison to centralized preprocessing co-located with the biorefinery by decoupling the biorefinery and feedstock locations. The 2018 Herbaceous SOT report documents the current modeled cost of a herbaceous feedstock supply system (from harvest to the pretreatment reactor throat, including grower payment) for hydrocarbon fuel production via biochemical conversion, based on equipment and processes now available or potentially available in the near term. The modeled cost also considers both the required quality and the availability of the biomass resources. The 2018 Herbaceous SOT predicts a modeled delivered feedstock cost of $83.67/dry ton (2016$); this is a $0.23/dry ton (2016$) decrease from the 2017 Herbaceous SOT. The modification of biorefinery’s designed capacity and increased projected biomass availability in the same supply shed contributed to this modeled cost reduction. The least-cost formulation model to optimally site and scale local distributed preprocessing depots also contributed to the cost reduction by considering county-level grower payment and distance from the biorefinery as variables in the optimization algorithm. Sensitivity analysis on various process parameters that affect delivered feedstock cost in the 2018 Herbaceous SOT shows that the delivered cost could varies from $80.45-$88.83/dry ton. The top factors that causes such variations are: effective baling rate, bale density, hammer mill throughput, interest rate and storage dry matter loss.

09 BIOMASS FUELS↗

DOE OSTI2021

Herbaceous Feedstock 2021 State of Technology Report

09 BIOMASS FUELS↗

DOE OSTI2022

The understudied winter: Evidence of how precipitation differences affect stream metabolism in a headwater

Climate change is causing pronounced shifts during winter in the US, including shortening the snow season, reducing snowpack, and altering the timing and volume of snowmelt-related runoff. These changes in winter precipitation patterns affect in-stream freeze-thaw cycles, including ice and snow cover, and can trigger direct and indirect effects on in-stream physical, chemical, and biological processes in ~60% of river basins in the Northern Hemisphere. We used high-resolution, multi-parameter data collected in a headwater stream and its local environment (climate and soil) to determine interannual variability in physical, chemical, and biological signals in a montane stream during the winter of an El Niño and a La Niña year. We observed ~77% greater snow accumulation during the El Niño year, which caused the formation of an ice dam that shifted the system from a primarily lotic to a lentic environment. Water chemistry and stream metabolism parameters varied widely between years. They featured anoxic conditions lasting over a month, with no observable gross primary production (GPP) occurring under the ice and snow cover in the El Niño year. In contrast, dissolved oxygen and GPP remained relatively high during the winter months of the La Niña year. These redox and metabolic changes driven by changes in winter precipitation have significant implications for water chemistry and biological functioning beyond the winter. Our study suggests that as snow accumulation and hydrologic conditions shift during the winter due to climate change, hot-spots and hot-moments for biogeochemical processing may be reduced, with implications for the downstream movement of nutrients and transported materials.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1982

Spectral estimates of solar radiation intercepted by corn canopies

Reflectance factor data were acquired with a Landsat band radiometer throughout two growing seasons for corn (Zea mays L.) canopies differing in planting dates, populations, and soil types. Agronomic data collected included leaf area index (LAI), biomass, development stage, and final grain yields. The spectral variable, greenness, was associated with 78 percent of the variation in LAI over all treatments. Single observations of LAI or greenness have limited value in predicting corn yields. The proportions of solar radiation intercepted (SRI) by these canopies were estimated using either measured LAI or greenness. Both SRI estimates, when accumulated over the growing season, accounted for approximately 65 percent of the variation in yields. Models which simulated the daily effects of weather and intercepted solar radiation on growth had the highest correlations to grain yields. This concept of estimating intercepted solar radiation using spectral data represents a viable approach for merging spectral and meteorological data for crop yield models.

Bauer, M. E.↗

NASA NTRS1990

Further study of terrain effects on the mesoscale spectrum of atmospheric motions

Wind and temperature data collected on commercial airliners are used to investigate the effects of underlying terrain on mesoscale variability. These results expand upon those of Nastrom et al., by including all available data from the Global Atmospheric Sampling Program (GASP) and by more closely focusing on the coupling of variance with the roughness of the underlying terrain over mountainous regions. The earlier results, showing that variances are larger over mountains than over oceans or plains, with greatest increases at wavelengths below about 80 km, are confirmed. Statistical tests are used to confirm that these differences are highly significant. Over mountainous regions the roughness of the underlying terrain was parameterized from topographic data and it was found that variances are highly correlated with roughness and, in the troposphere, with background windspeed. Average variances over the roughest terrain areas range up to about ten times larger than those over the oceans. These results are found to follow the scaling with stability predicted in the framework of linenar gravity wave theory. The implications of these results for vertical transports of momentum and energy, assuming they are due to gravity waves and considering the effects of intermittency and anisotroy, are also discussed.

Jasperson, W. H.↗

NASA NTRS1993

Scintillation fading on a low elevation angle satellite path: Assessing the Austin experiment at 11.2 GHz

The University of Texas at Austin is now in its fifth year of observing an 11.2 GHz INTELSAT satellite beacon with an elevation angle of only 5.8 degrees. Measurements of the rainfall rate, beacon level, the radiometric sky temperature, and of meteorological parameters are ongoing. The experiment is motivated by the fact that significant increases in coverage can be obtained from a given geostationary orbit location if acceptable performance can be achieved at elevation angles below the current standard at Ku-Band of 10 degrees. The topics covered include the following: data collection, data processing, scintillation fading vs. rain fading, diurnal and seasonal variations, power spectrum, and variability of the beacon standard deviation.

Vogel, W. J.↗

NASA NTRS2007

Reusable, Extensible High-Level Data-Distribution Concept

A framework for high-level specification of data distributions in data-parallel application programs has been conceived. [As used here, distributions signifies means to express locality (more specifically, locations of specified pieces of data) in a computing system composed of many processor and memory components connected by a network.] Inasmuch as distributions exert a great effect on the performances of application programs, it is important that a distribution strategy be flexible, so that distributions can be adapted to the requirements of those programs. At the same time, for the sake of productivity in programming and execution, it is desirable that users be shielded from such error-prone, tedious details as those of communication and synchronization. As desired, the present framework enables a user to refine a distribution type and adjust it to optimize the performance of an application program and conceals, from the user, the low-level details of communication and synchronization. The framework provides for a reusable, extensible, data-distribution design, denoted the design pattern, that is independent of a concrete implementation. The design pattern abstracts over coding patterns that have been found to be commonly encountered in both manually and automatically generated distributed parallel programs. The following description of the present framework is necessarily oversimplified to fit within the space available for this article. Distributions are among the elements of a conceptual data-distribution machinery, some of the other elements being denoted domains, index sets, and data collections (see figure). Associated with each domain is one index set and one distribution. A distribution class interface (where "class" is used in the object-oriented-programming sense) includes operations that enable specification of the mapping of an index to a unit of locality. Thus, "Map(Index)" specifies a unit, while "LocalLayout(Index)" specifies the local address within that unit. The distribution class can be extended to enable specification of commonly used distributions or novel user-defined distributions. A data collection can be defined over a domain. The term "data collection" in this context signifies, more specifically, an abstraction of mappings from index sets to variables. Since the index set is distributed, the addresses of the variables are also distributed.

James, Mark↗

NASA NTRS2010

Changes in the Mass Balance of the Greenland Ice Sheet in a Warming Climate During 2003-2009

Mass changes of the Greenland ice sheet (GIS) derived from ICESat and GRACE data both show that the net mass loss from GIS during 2003-2009 is about 175 Gt/year, which contributes 0.5mm/yr global sea-level rise. The rate of mass loss has increased significantly since the 1990's when the GIS was close to mass balance. Even though the GIS was close to mass balance during the 1990's, it was already showing characteristics of responding to8 warmer climate, specifically thinning at the margins and thickening inland at higher elevations. During 2003-2009, increased ice thinning due to increases in melting and acceleration of outlet glaciers began to strongly exceed the inland thickening from increases in accumulation. Over the entire GIS, the mass loss between the two periods, from increased melting and ice dynamics, increased by about 190 Gt/year while the mass gain, from increased precipitation and accumulation, increased by only about 15Gt/year. These ice changes occurred during a time when the temperature on GIS changed at rate of about 2K/decade. The distribution of elevation and mass changes derived from ICESat have high spatial resolution showing details over outlet glaciers, by drainage systems, and by elevation. However, information on the seasonal cycle of changes from ICESat data is limited, because the ICESat lasers were only operated during two to three campaigns per year of about 35 days duration each. In contrast, the temporal resolution of GRACE data, provided by the continuous data collection, is much better showing details of the seasonal cycle and the inter-annual variability. The differing sensitivity of the ICESat altimetry and the GRACE gravity methods to motion of the underlying bedrock from glacial isostatic adjustment (GIA) is used to evaluate the GIA corrections provided by models. The two data types are also combined to make estimates of the partitioning of the mass gains and losses among accumulation, melting, and ice discharge from outlet glaciers.

Zwally, H. Jay↗

NASA NTRS2010

Seagrass Health Modeling and Prediction with NASA Science Data

Previous research has demonstrated that MODIS data products can be used as inputs into the seagrass productivity model developed by Fong and Harwell (1994). To further explore this use to predict seagrass productivity, Moderate Resolution Imaging Spectroradiometer (MODIS) custom data products, including Sea Surface Temperature, Light Attenuation, and Chlorophyll-a have been created for use as model parameter inputs. Coastal researchers can use these MODIS data products and model results in conjunction with historical and daily assessment of seagrass conditions to assess variables that affect the productivity of the seagrass beds. Current monitoring practices involve manual data collection (typically on a quarterly basis) and the data is often insufficient for evaluating the dynamic events that influence seagrass beds. As part of a NASA-funded research grant, the University of Mississippi, is working with researchers at NASA and Radiance Technologies to develop methods to deliver MODIS derived model output for the northern Gulf of Mexico (GOM) to coastal and environmental managers. The result of the project will be a data portal that provides access to MODIS data products and model results from the past 5 years, that includes an automated process to incorporate new data as it becomes available. All model parameters and final output will be available through the use National Oceanic and Atmospheric Administration?s (NOAA) Environmental Research Divisions Data Access Program (ERDDAP) tools as well as viewable using Thematic Realtime Environmental Distributed Data Services (THREDDS) and the Integrated Data Viewer (IDV). These tools provide the ability to create raster-based time sequences of model output and parameters as well as create graphs of model parameters versus time. This tool will provide researchers and coastal managers the ability to analyze the model inputs so that the factors influencing a change in seagrass productivity can be determined over time.

Robinson, Harold D.↗

NASA NTRS2011

Adaptive Control Using Residual Mode Filters Applied to Wind Turbines

Many dynamic systems containing a large number of modes can benefit from adaptive control techniques, which are well suited to applications that have unknown parameters and poorly known operating conditions. In this paper, we focus on a model reference direct adaptive control approach that has been extended to handle adaptive rejection of persistent disturbances. We extend this adaptive control theory to accommodate problematic modal subsystems of a plant that inhibit the adaptive controller by causing the open-loop plant to be non-minimum phase. We will augment the adaptive controller using a Residual Mode Filter (RMF) to compensate for problematic modal subsystems, thereby allowing the system to satisfy the requirements for the adaptive controller to have guaranteed convergence and bounded gains. We apply these theoretical results to design an adaptive collective pitch controller for a high-fidelity simulation of a utility-scale, variable-speed wind turbine that has minimum phase zeros.

Frost, Susan A.↗

NASA NTRS2012

Mixed Layer Heights Derived from the NASA Langley Research Center Airborne High Spectral Resolution Lidar

The NASA airborne High Spectral Resolution Lidar (HSRL) has been deployed on board the NASA Langley Research Center's B200 aircraft to several locations in North America from 2006 to 2012 to aid in characterizing aerosol properties for over fourteen field missions. Measurements of aerosol extinction (532 nm), backscatter (532 and 1064 nm), and depolarization (532 and 1064 nm) during 349 science flights, many in coordination with other participating research aircraft, satellites, and ground sites, constitute a diverse data set for use in characterizing the spatial and temporal distribution of aerosols, as well as properties and variability of the Mixing Layer (ML) height. We describe the use of the HSRL data collected during these missions for computing ML heights and show how the HSRL data can be used to determine the fraction of aerosol optical thickness within and above the ML, which is important for air quality assessments. We describe the spatial and temporal variations in ML heights found in the diverse locations associated with these experiments. We also describe how the ML heights derived from HSRL have been used to help assess simulations of Planetary Boundary Layer (PBL) derived using various models, including the Weather Research and Forecasting Chemistry (WRF-Chem), NASA GEOS-5 model, and the ECMWF/MACC models.

Scarino, Amy J.↗

NASA NTRS2016

A Dark Energy Camera Search for Missing Supergiants in the LMC After the Advanced LIGO Gravitational-Wave Event GW150914

The collapse of a stellar core is expected to produce gravitational waves (GWs), neutrinos, and in most cases a luminous supernova. Sometimes, however, the optical event could be significantly less luminous than a supernova and a direct collapse to a black hole, where the star just disappears, is possible. The GW event GW150914 was detected by the LIGO Virgo Collaboration via a burst analysis that gave localization contours enclosing the Large Magellanic Cloud (LMC). Shortly thereafter, we used DECam to observe 102 deg(exp.2) of the localization area,including 38 deg(exp. 2) on the LMC for a missing supergiant search. We construct a complete catalog of LMC luminous red supergiants, the best candidates to undergo invisible core collapse, and collected catalogs of other candidates:less luminous red supergiants, yellow supergiants, blue supergiants, luminous blue variable stars, and Wolf-Rayet stars. Of the objects in the imaging region, all are recovered in the images. The timescale for stellar disappearance is set by the free-fall time, which is a function of the stellar radius. Our observations at 4 and 13 days after the event result in a search sensitive to objects of up to about 200 solar radii. We conclude that it is unlikely that GW150914 was caused by the core collapse of a relatively compact supergiant in the LMC, consistent with the LIGO Collaboration analyses of the gravitational waveform as best interpreted as a high mass binary black hole merger. We discuss how to generalize this search for future very nearby core-collapse candidates.

galaxies: individual (LMC) â€“ gravitational waves↗

NASA NTRS2006

The Australian National Airborne Field Experiment 2005: Soil Moisture Remote Sensing at 60 Meter Resolution and Up

Spatially-distributed soil moisture observations have applications spanning a wide range of spatial resolutions from the very local needs of individual farmers to the progressively larger areas of interest to weather forecasters, water resource managers, and global climate modelers. To date, the most promising approach for space-based remote sensing of soil moisture makes use of passive microwave emission radiometers at L-band frequencies (1-2 GHz). Several soil moisture-sensing satellites have been proposed in recent years, with the European Space Agency's Soil Moisture Ocean Salinity (SMOS) mission scheduled to be launched first in a couple years. While such a microwave-based approach has the advantage of essentially allweather operation, satellite size limits spatial resolution to 10's of km. Whether used at this native resolution or in conjunction with some type of downscaling technique to generate soil moisture estimates on a finer-scale grid, the effects of subpixel spatial variability play a critical role. The soil moisture variability is typically affected by factors such as vegetation, topography, surface roughness, and soil texture. Understanding and these factors is the key to achieving accurate soil moisture retrievals at any scale. Indeed, the ability to compensate for these factors ultimately limits the achievable spatial resolution and/or accuracy of the retrieval. Over the last 20 years, a series of airborne campaigns in the USA have supported the development of algorithms for spaceborne soil moisture retrieval. The most important observations involved imagery from passive microwave radiometers. The early campaigns proved that the retrieval worked for larger and larger footprints, up to satellite-scale footprints. These provided the solid basis for proposing the satellite missions. More recent campaigns have explored other aspects such as retrieval performance through greater amounts of vegetation. All of these campaigns featured extensive ground truth collection over a range of grid spacings, to provide a basis for examining the effects of subpixel variability. However, the native footprint size of the airborne L-band radiometers was always a few hundred meters. During the recently completed (November, 2005) National Airborne Field Experiment (NAFE) campaign in Australia, a compact L-band radiometer was deployed on a small aircraft. This new combination permitted routine observations at native resolutions as high as 60 meters, substantially finer than in previous airborne soil moisture campaigns, as well as satellite footprint areal coverage. The radiometer, the Polarimetric L-band Microwave Radiometer (PLMR) performed extremely well and operations included extensive calibration-related observations. Thus, along with the extensive fine-scale ground truth, the NAFE dataset includes all the ingredients for the first scaling studies involving very-high-native resolution soil moisture observations and the effects of vegetation, roughness, etc. A brief overview of the NAFE will be presented, then examples of the airborne observations with resolutions from 60 m to 1 km will be shown, and early results from scaling studies will be discussed.

Kim, E. J.↗

DOE OSTI2023

Beryllium Isotopes in Rainfall for Stratospheric-Tropospheric Exchange Dynamics Experiment (BIRDIE) (Field Campaign Report)

The Beryllium Isotopes in Rainfall for Stratospheric-Tropospheric Exchange Dynamics Experiment or BIRDIE was a precipitation collection campaign designed to measure cosmogenic isotopes of beryllium in rainfall and other forms of precipitation across the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility’s Southern Great Plains (SGP) atmospheric observatory. Precipitation samples were collected over three weeks (May 21-June 10, 2022) at 11 SGP sites across a 120-km 2 domain in northern and central Oklahoma. The field campaign targeted summertime convection rain events. We aimed to capture any spatial and/or temporal variability in the concentrations of beryllium-10 (10Be) and beryllium-7 (7Be) across the ARM SGP site during these rainfall events. The ratio of 10Be to 7Be (10/7Be) has been shown to be a powerful tracer for transport processes in the stratosphere-troposphere exchange (STE) system (e.g., Koch and Rind 1998). Any observed variability is analyzed to see if it could be linked to STE processes, including overshooting of deep convection clouds that penetrate the lower stratosphere. Six wet weather system events were collected within our three-week span. Five out of six of these rainfall events are classified as moderately to strongly convective. Those events are studied in more detail. The five systems also follow each other over a short period of 11 days. In our rainfall samples we observed spatial and event variability in the concentrations of beryllium-10 and -7 that resulted in variability of 10/7Be ratios. Each weather event appeared to exhibit a unique 10/7Be pattern across the domain that was not directly linked to storm total precipitation amount. To better understand the causes for this spatial variability, cloud characteristics in the domain were looked at to assess cloud type, cloud height, and depth information. We also ran Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model back trajectories to identify possible air mass and moisture origins across our events. Detailed analysis is ongoing. The purpose of this technical report is to provide background on using cosmogenic isotopes as atmospheric tracers, describe the experimental design, and present the beryllium concentration and ratio data. Lastly, we note that BIRDIE coincided with two other deep convection field campaigns in the area: The National Aeronautics and Space Administration (NASA)’s Dynamics and Chemistry of the Summer Stratosphere (DCOTSS) experiment (https://dcotss.org/) centered in Salina, Kansas, and the Targeted Observation by Radars and (uncrewed aerial systems) UAS of Supercells (TORUS) experiment (https://catalog.eol.ucar.edu/torus_2022) across the U.S. Central Plains. Additionally, a Lawrence Livermore National Laboratory ( LLNL) team at DCOTSS attempted to measure 10Be/7Be ratios on aerosols collected in situ in the lower and mid-stratosphere over Kansas using high-altitude balloons. Precipitation samples were also collected on the ground in Salina, Kansas.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2022

Multiwavelength Variability of Sagittarius A* in 2019 July

We report a timing analysis of near-infrared (NIR), X-ray, and submillimeter data during a 3 day coordinated campaign observing Sagittarius A* . Data were collected at 4.5 μm with the Spitzer Space Telescope, 2–8 keV with the Chandra X-ray Observatory, 3–70 keV with NuSTAR, 340 GHz with ALMA, and 2.2 μm with the GRAVITY instrument on the Very Large Telescope Interferometer. Two dates show moderate variability with no significant lags between the submillimeter and the infrared at 99% confidence. A moderately bright NIR flare (FK ∼ 15 mJy) was captured on July 18 simultaneous with an X-ray flare (F2−10 keV ∼ 0.1 counts s−1 ) that most likely preceded bright submillimeter flux (F340 GHz ∼ 5.5 Jy) by about + - + 34 33 14 minutes at 99% confidence. The uncertainty in this lag is dominated by the fact that we did not observe the peak of the submillimeter emission. A synchrotron source cooled through adiabatic expansion can describe a rise in the submillimeter once the synchrotron self-Compton NIR and X-ray peaks have faded. This model predicts high GHz and THz fluxes at the time of the NIR/X-ray peak and electron densities well above those implied from average accretion rates for Sgr A* . However, the higher electron density postulated in this scenario would be in agreement with the idea that 2019 was an extraordinary epoch with a heightened accretion rate. Since the NIR and X-ray peaks can also be fit by a nonthermal synchrotron source with lower electron densities, we cannot rule out an unrelated chance coincidence of this bright submillimeter flare with the NIR/X-ray emission.

Galactic center↗

NASA NTRS2010

Motion Perception and Manual Control Performance During Passive Tilt and Translation Following Space Flight

This joint ESA-NASA study is examining changes in motion perception following Space Shuttle flights and the operational implications of post-flight tilt-translation ambiguity for manual control performance. Vibrotactile feedback of tilt orientation is also being evaluated as a countermeasure to improve performance during a closed-loop nulling task. METHODS. Data has been collected on 5 astronaut subjects during 3 preflight sessions and during the first 8 days after Shuttle landings. Variable radius centrifugation (216 deg/s) combined with body translation (12-22 cm, peak-to-peak) is utilized to elicit roll-tilt perception (equivalent to 20 deg, peak-to-peak). A forward-backward moving sled (24-390 cm, peak-to-peak) with or without chair tilting in pitch is utilized to elicit pitch tilt perception (equivalent to 20 deg, peak-to-peak). These combinations are elicited at 0.15, 0.3, and 0.6 Hz for evaluating the effect of motion frequency on tilt-translation ambiguity. In both devices, a closed-loop nulling task is also performed during pseudorandom motion with and without vibrotactile feedback of tilt. All tests are performed in complete darkness. PRELIMINARY RESULTS. Data collection is currently ongoing. Results to date suggest there is a trend for translation motion perception to be increased at the low and medium frequencies on landing day compared to pre-flight. Manual control performance is improved with vibrotactile feedback. DISCUSSION. The results of this study indicate that post-flight recovery of motion perception and manual control performance is complete within 8 days following short-duration space missions. Vibrotactile feedback of tilt improves manual control performance both before and after flight.

Clement, Gilles↗

DOE OSTI2022

Utilizing machine learning to improve the precision of fluorescence imaging of cavity-generated spin squeezed states

We present a supervised learning model to calibrate the photon collection rate during the fluorescence imaging of cold atoms. The linear regression model finds the collection rate at each location on the sensor such that the atomic population difference equals that of a highly precise optical cavity measurement. This 192 variable regression results in a measurement variance 27% smaller than our previous single variable regression calibration. The measurement variance is now in agreement with the theoretical limit due to other known noise sources. This model efficiently trains in less than a minute on a standard personal computer's CPU and requires less than 10 min of data collection. Furthermore, the model is applicable across a large change in population difference and across data collected on different days.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2026

Integrating very-high-resolution imagery, Sentinel-2 time-series data, and machine learning to map shrub fractional abundance across arid and semi-arid ecosystems in China

Shrub fractional abundance (SFA), the proportion of shrub cover per unit area, serves as a critical indicator of environmental aridity and ecosystem health in arid and semi-arid regions, particularly across the Mongolian steppe. However, large-scale SFA mapping in Mongolian steppe ecosystems remains challenging due to the small crown size of shrubs, their sparse distribution, and spectral overlap with coexisting low vegetation (e.g., grasses and herbs), which hinders accurate detection using coarser-resolution satellite data or traditional field surveys. To address these challenges, we developed a two-step approach that integrates very-high-resolution (VHR) imagery, time-series Sentinel-2 data, and deep learning techniques. First, we generated high-accuracy benchmark maps of individual shrub crowns from 0.5 m VHR imagery by combining manual segmentation with a hybrid deep learning framework (Dino V2 and convolutional neural networks). Second, we used these shrub crown maps as training data to build an XGBoost model for predicting SFA from 20 m Sentinel-2 time-series data, leveraging phenological information to improve estimation. We validated our approach across 70 sites (1km 2 each) in the Inner Mongolia Autonomous Region, which is representative of Mongolian steppe ecosystems. From VHR imagery, we mapped 1.31 million shrub crowns with an accuracy of R 2 = 0.92. Scaling up with Sentinel-2 data yielded regional SFA maps with an R 2 = 0.60. Further SHAP (SHapley Additive exPlanations) analysis on the developed XGBoost model revealed that phenological metrics (particularly observations in early-May, mid-July, and late-September), which distinguish shrub phenology from that of other land cover types (e.g., grasses and bare soil), were the most influential predictors of SFA. Finally, our regional SFA maps uncovered unimodal relationships between shrub distribution and climate variables, peaking at mean annual minimum temperatures near 0 °C and annual precipitation around 200 mm. Collectively, these findings demonstrate how the integration of multi-source remote sensing and machine learning can overcome historical limitations in SFA mapping, enabling accurate, spatially continuous assessments across vast Inner-Mongolian steppe ecosystems. Our framework has the potential to be applied to other steppe ecosystems and dryland ecosystems across the Mongolian steppe and beyond, offering a foundation for improved monitoring and ecological impact assessments in the face of global climate changes.

Arid and semi-arid landscapes↗