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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 361 records · Page 20

Evaluating the Taylor hardening model in polycrystalline Ti using high energy X-ray diffraction microscopy

High energy X-ray diffraction microscopy (HEDM) was employed to index nearly 1000 grains in a polycrystalline Ti specimen and characterize their deformation during an incremental tensile test. For each grain, the positions of its associated diffraction peaks were used for analyzing its evolving crystal orientation and stress tensor. The azimuthal breadth of each peak at different load steps was measured, allowing measurement of initial yielding and providing an estimate of the dislocation density evolution in each grain. Furthermore, the Taylor hardening model was evaluated grain by grain using the above data, indicating that it can represent hardening in most grains. The yield stress and the strain hardening coefficient for all grains were statistically analyzed with respect to grain orientation, grain size, slip systems, and the surrounding neighborhood to examine how the yield stress and hardening are correlated.

36 MATERIALS SCIENCE↗

Organic matter distribution in the icy environments of Taylor Valley, Antarctica

Glaciers can accumulate and release organic matter affecting the structure and function of associated terrestrial and aquatic ecosystems. Here we analyzed 18 ice cores collected from six locations in Taylor Valley (McMurdo Dry Valleys), Antarctica to determine the spatial abundance and quality of organic matter, and the spatial distribution of bacterial density and community structure from the terminus of the Taylor Glacier to the coast (McMurdo Sound). Our results showed that dissolved and particulate organic carbon (DOC and POC) concentrations in the ice core samples increased from the Taylor Glacier to McMurdo Sound, a pattern also shown by bacterial cell density. Fluorescence Excitation Emission Matrices Spectroscopy (EEMs) and multivariate parallel factor (PARAFAC) modeling identified one humic-like (C1) and one protein-like (C2) component in ice cores whose fluorescent intensities all increased from the Polar Plateau to the coast. The fluorescence index showed that the bioavailability of dissolved organic matter (DOM) also decreased from the Polar Plateau to the coast. Partial least squares path modeling analysis revealed that bacterial abundance was the main positive biotic factor influencing both the quantity and quality of organic matter. Marine aerosol influenced the spatial distribution of DOC more than katabatic winds in the ice cores. Certain bacterial taxa showed significant correlations with DOC and POC concentrations. Collectively, our results show the tight connectivity among organic matter spatial distribution, bacterial abundance and meteorology in the McMurdo Dry Valley ecosystem.

54 ENVIRONMENTAL SCIENCES↗

CrossLink: Geometry API [Slides]

The mesh generation process is very challenging and time consuming when working with complex CAD models. The process of creating and sorting geometric entities into groups appropriate for meshing is labor intensive and prone to error. In addition, the common data exchange formats such as STEP and IGES do not propagate information such as entity names that may be defined in the original model. Finally, entity counts change frequently with parameter variation as a result of tolerance-based geometry operations. Thus, sorting by index does not provide a robust and repeatable means for grouping. xGeom is a geometry library that enables the creation of NURBS curves and surfaces via a python scripting interface. xGeom is ideal for studying relatively simple models and is fully integrated with CrossLink’s mesh generation capabilities. For more complex models, xCAD is a python-based Creo Parametric CAD model driver that enables the model to be generated, queried, parametrically modified, regenerated, and exported without data loss and in a fully repeatable manner.

97 MATHEMATICS AND COMPUTING↗

Sooting tendencies of 20 bio-derived fuels for advanced spark-ignition engines

The sooting tendencies of 20 bio-derived fuels indicated as potential blendstocks for spark-ignition engines by the Co-Optimization of Fuels & Engines (Co-Optima) Initiative are studied. The Yield Sooting Index (YSI) is used to quantify the sooting tendencies. The Co-Optima Chemical Model is used to predict the numerical YSIs for all test fuels, and these results are compared with measurements. Experimental YSIs are newly measured for 2 furans and are taken from our previous work for the other compounds. Overall, the predicted YSIs agree well with measurements, within the experimental uncertainties, except for 2,5-dimethylfuran (2,5-DMF). It is found that Polycyclic Aromatic Hydrocarbons (PAH) growth reactions have little influence on the relative soot production from each fuel and therefore on YSI predictions. A modified sensitivity coefficient formulation is proposed to evaluate reaction sensitivities specifically for YSI. This formulation is applied to the 2,5-DMF-doped flame, and six 2,5-DMF initial decomposition reactions, which lead to different major soot precursors, are identified as the most sensitive reactions. The impact of the chemical kinetic uncertainties embedded in these reactions is quantified by randomly perturbing their reaction rates within a factor of two. The resulting prediction uncertainty in the 2,5-DMF-doped flame is found to be ± 8 YSI units (±11%), which demonstrates that the test-fuel-specific decomposition reactions indeed have considerable influence on its YSI prediction. Our research suggests that more accurate kinetic parameters for fuel initial decomposition reactions can potentially improve YSI predictions effectively, without altering the YSI predictions of other fuels with significantly different molecular structures.

09 BIOMASS FUELS↗

Pan-Arctic daily streamflow at 0.5-degree resolution

Data was derived using daily runoff from models participating in the Coupled Model Intercomparison Project, version 6 (CMIP6) that have been routed through the Model for Scale Adaptive River Transport (MOSART) routing model to represent streamflow at individual streamflow gage locations. Data from 750 streamflow gage locations are represented, which are indexed by gage id according to the metadata.csv file. Streamflow gage location sites were based on gage records downloaded from the United States Geological Survey (USGS), National Water Data (HYDAT) Archive from Canada, and the State Hydrological Institute of Russia. Data are available from 11 Earth System Models (ESMs) in total at a daily timestep with a duration of 1920-2099 (1850-2014 for E3SM only). The spatial domain of the modeled data is 0.5 degree resolution. Earth System Models and Units of mean daily streamflow are presented in cubic meters per second (cms). Data files consist of 11 Earth System Model mean daily streamflow output per gage location in .csv format, 1 metadata file in .csv format and 1 Readme file in .docx format (13 files in total). These data were used to benchmark CMIP6 modeled representations of streamflow against gage records.

54 ENVIRONMENTAL SCIENCES↗

Data and scripts associated with a manuscript analyzing ELM-FATES parameter sensitivity under pre-fire and postfire scenarios using machine learning

NOTE: The manuscript associated with this data package is currently in review. The data may be revised based on reviewer feedback. Upon manuscript acceptance, this data package will be updated with the final dataset and additional metadata. This data package is associated with the manuscript “Fire Severity-Dependent Shifts in Vegetation Parameter Sensitivity: A Pre- and Post-Fire Analysis Using ELM-FATES and Explainable AI” submitted to Journal of Advances in Modeling Earth Systems (Zahura et al. 2026). The study examines vegetation physiological parameters controlling pre-fire and post-fire vegetation dynamics. To support this analysis, 73 vegetation parameters in Functionally Assembled Terrestrial Ecosystem Simulator (FATES) (Fisher et al., 2018) , which is coupled with E3SM (Energy Exascale Earth System Model) land model (ELM, ELM-FATES), were perturbed using a Sobol sequence to generate 1,024 ensemble members for two plant functional types: needleleaf evergreen extratropical trees (NEET) and C3 grass. Simulations were conducted for the pre-fire period (2016) and post-fire period (2018–2023). Burn severity was represented by modifying the Nesterov index in FATES to 75,000, 150,000, and 300,000 for low, moderate, and high severity, respectively. A no-fire scenario was also included. Simulations were performed for 16 grid cells in the American River Watershed across different burn severities and plant functional types. XGBoost (eXtreme Gradient Boosting) models were trained using the parameter ensembles and ELM-FATES-simulated outputs, including leaf area index (LAI), gross primary productivity (GPP), aboveground biomass, vegetation evaporation, transpiration, and soil evaporation. Models were trained separately for each year and burn severity, followed by SHAP (SHapley Additive exPlanations) analysis to identify changes in dominant parameters after fire disturbance. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. The data package contains the ELM-FATES simulation data. The scripts and data related to the analysis will be added later. The inputs and outputs from ELM-FATES are inside the “FATES” folder. “FATES_domain_surface” contains the domain and surface netcdfs that were used to run ELM-FATES in the study area. “FATES_parameters” contains the 1024 ensembles that were generated using Sobol sequence. “FATES_outputs” folder contains ELM-FATES simulated variables. All files are .csv and .nc (NetCDF).

Aboveground biomass↗

Statistical framework to assess long-term spatio-temporal climate changes: East River mountainous watershed case study

Abstract Evaluation of long-term temporal and spatial climatic change in mountainous regions is a critical challenge because of the interactive effects of multiple land and climatic factors and processes. Here we present the application of the statistical framework to the assessment of changes of climatic conditions, using data from 17 meteorological stations across the East River watershed near Crested Butte, Colorado, USA, and spanning the period from 1966 to 2021. The framework is developed based on (1) a time-series analysis of daily, monthly, and yearly averaged meteorological parameters (temperature, relative humidity, precipitation, wind speed, etc.), (2) evaluation and time series analysis of potential evapotranspiration (ET o ), actual evapotranspiration (ET), aridity index (AI), standard precipitation index (SPI) and standard precipitation-evapotranspiration index (SPEI), and (3) a temporal-spatial climatic zonation of the studied area based on the hierarchical clustering and PCA analysis of the SPEI, because the SPEI can be considered an integrative characteristic of the changes of climatic conditions. The Budyko model, with the application of the Penman–Monteith equation for the estimation of ET o , was used to determine the ET. The time series analysis of the AI is used to identify the periods with energy limited and water limited conditions. Hierarchical clustering of site locations for the three temporal segments of the SPEI showed a significant temporal-spatial shifts, indicating that dynamic climatic processes drive zonation patterns. Therefore, the watershed climatic zonation requires periodic re-evaluation based on the structural time series analysis of meteorological and water balance data.

54 ENVIRONMENTAL SCIENCES↗

Resource selection functions based on hierarchical generalized additive models provide new insights into individual animal variation and species distributions

Habitat selection studies are designed to generate predictions of species distributions or inference regarding general habitat associations and individual variation in habitat use. Such studies frequently involve either individually indexed locations gathered across limited spatial extents and analyzed using resource selection functions (RSFs) or spatially extensive locational data without individual resolution typically analyzed using species distribution models. Both analytical methodologies have certain desirable features, but analyses that combine individual- and population-level inference with flexible non-linear functions may provide improved predictions while accounting for individual variation. Here, we describe how RSFs can be fit using hierarchical generalized additive models (HGAMs) using widely available software, providing a means to explore individual variation in habitat associations and to generate species distribution maps. We used GPS tracking data from golden eagles Aquila chrysaetos from across eastern North America with four environmental predictors to generate monthly distribution models. We considered three model structures that assumed different amounts of individual variation in the functional relationship between predictors and habitat use and used k-fold cross-validation to compare model performance. Models accounting for individual variability in shape and smoothness of functional responses performed best. Eagles exhibited the least amount of individual variation in response to land cover variables during winter months, with most individuals more closely adhering to the population-level trend. During the summer months, eagles exhibited more substantial individual variation in shape and smoothness of the functional relationships, suggesting some need to account for individual variation in eagle habitat use for both inferential and predictive purposes, during this time of year. Because they allow users to blend flexible functions with random effects structures and are well-supported by a variety of software platforms, we believe that HGAMs provide a useful addition to the suite of analyses used for modeling habitat associations or predicting species distributions.

54 ENVIRONMENTAL SCIENCES↗

Improved Characterization of the Astrophysical Muon–neutrino Flux with 9.5 Years of IceCube Data

We present a measurement of the high-energy astrophysical muon–neutrino flux with the IceCube Neutrino Observatory. The measurement uses a high-purity selection of 650k neutrino-induced muon tracks from the northern celestial hemisphere, corresponding to 9.5 yr of experimental data. With respect to previous publications, the measurement is improved by the increased size of the event sample and the extended model testing beyond simple power-law hypotheses. An updated treatment of systematic uncertainties and atmospheric background fluxes has been implemented based on recent models. The best-fit single power-law parameterization for the astrophysical energy spectrum results in a normalization of ${\phi }_{@100\mathrm{TeV}}^{{\nu }_{\mu }+{\bar{\nu }}_{\mu }}={1.44}_{-0.26}^{+0.25}\times {10}^{-18}\,{\mathrm{GeV}}^{-1}{\mathrm{cm}}^{-2}{{\rm{s}}}^{-1}{\mathrm{sr}}^{-1}$ and a spectral index ${\gamma }_{\mathrm{SPL}}={2.37}_{-0.09}^{+0.09}$, constrained in the energy range from 15 TeV to 5 PeV. The model tests include a single power law with a spectral cutoff at high energies, a log-parabola model, several source-class-specific flux predictions from the literature, and a model-independent spectral unfolding. The data are consistent with a single power-law hypothesis, however, spectra with softening above one PeV are statistically more favorable at a two-sigma level.

79 ASTRONOMY AND ASTROPHYSICS↗

Dust and the intrinsic spectral index of quasar variations: hints of finite stress at the innermost stable circular orbit

ABSTRACT We present a study of 9 242 spectroscopically confirmed quasars with multiepoch ugriz photometry from the SDSS Southern Survey. By fitting a separable linear model to each quasar’s spectral variations, we decompose their five-band spectral energy distributions into variable (disc) and non-variable (host galaxy) components. In modelling the disc spectra, we include attenuation by dust on the line of sight through the host galaxy to its nucleus. We consider five commonly used attenuation laws, and find that the best description is by dust similar to that of the Small Magellanic Cloud, inferring a lack of carbonaceous grains from the relatively weak 2175-Å absorption feature. We go on to construct a composite spectrum for the quasar variations spanning 700–8000 Å. By varying the assumed power-law Lν ∝ να spectral slope, we find a best-fitting value α = 0.71 ± 0.02, excluding at high confidence the canonical Lν ∝ ν1/3 prediction for a steady-state accretion disc with a T ∝ r−3/4 temperature profile. The bluer spectral index of the observed quasar variations instead supports the model of Agol & Krolik, and Mummery & Balbus, in which a steeper temperature profile, T ∝ r−7/8, develops as a result of finite magnetically induced stress at the innermost stable circular orbit extracting energy and angular momentum from the black hole spin.

79 ASTRONOMY AND ASTROPHYSICS↗

Assessment of Potential Pennycress Availability and Suitable Sites for Sustainable Aviation Fuel Refineries in Ohio

Pennycress grain has a relatively high oil content (25–36%) and it is considered a desirable feedstock to produce sustainable aviation fuel (SAF). Pennycress crop can be integrated into the corn–soybean rotation as a winter cover crop in the midwestern U.S. to provide both ecosystem services and economic benefits for the farmers, while serving as a promising feedstock for SAF production. For pennycress-based SAF biorefineries to be established at the commercial scale, a sustainable design of the supply system is required to provide reliable information on feedstock availability and optimal facility locations. The objectives of this research were to assess the pennycress production potential in Ohio, and to identify the best locations to establish the SAF biorefineries. To estimate the pennycress production potential in Ohio, a geographic information system (GIS)-based model was developed using the spatially explicit six-year historical data on areas that were planted in the corn–soybean rotation for the period of 2013 through 2018, pennycress yield estimates from field-based experiments reported in the literature, and the soil productivity index for the region of study. Optimal SAF biorefinery locations were identified using a GIS-based location-allocation model. Annual land potentially available for pennycress production in Ohio was estimated to be ~0.6 million ha, which could produce ~1.1 million metric tons of pennycress grain as feedstock to produce ~210 million liters of SAF, depending on the pennycress yield level, oil content, and conversion efficiencies. In addition, the optimum locations for 12 biorefineries, each at an annual capacity of 18.9 million liters of SAF, were identified, and the average transportation distance was estimated to be 35 and 58 km for maximizing attendance and coverage conditions, respectively. The outcomes of this research would help minimize the risks associated with feedstock supply and cost variabilities for pennycress-based SAF production in the region.

Mousavi-Avval, Seyed Hashem↗

Global impacts of vegetation clumping on regulating land surface heat fluxes

The clumping index (CI) quantifies the non-random distribution of vegetation across space, which regulates the canopy radiative transfer processes and land surface carbon, water, and energy cycles. However, its impact on global surface energy budget, particularly sensible heat fluxes and surface temperature, is not well understood. Additionally, while there have been studies showing significant seasonal variations in CI, the impacts of these variations on surface energy fluxes remain unclear. In this study, we incorporated satellite-derived spatially and temporally explicit CI data into the Community Land Model version 5 (CLM5) to evaluate the effects of CI on global land energy fluxes. Our results showed that including CI increased the global mean sensible heat flux dissipated from ground by 3.9 W m -2 (~18%), while decreasing the global mean vegetation sensible heat flux by 4.9 W m -2 (~65%), resulting in a total sensible heat decrease of 1.0 W m -2 (~3%). In contrast, CI increased the global mean latent heat flux by 0.8 W m -2 (~2%), primarily due to increased evapotranspiration (up to 11 W m -2 ) in tropical regions. We also found considerable impacts of seasonal variations in CI, particularly on sensible heat fluxes from ground and vegetation in evergreen needleleaf forests and deciduous needleleaf forests. Using constant CI rather than considering seasonal variations resulted in significant overestimation and underestimation of the sensible heat fluxes from vegetation and ground, respectively, in boreal summer. In conclusion, these changes in surface energy fluxes caused by CI and its seasonal variations led to up to 1.7 and 0.5 K differences in simulated mean ground temperature. These findings highlight the importance of including CI and considering its seasonal variations in modeling land surface energy fluxes.

54 ENVIRONMENTAL SCIENCES↗

Boron coordination change in barium borate melts and glasses and its contribution to configurational heat capacity, entropy, and fragility

High-energy x-ray diffraction from molten and glassy BaB 2 O 4 and BaB 4 O 7 has been performed using aerodynamic levitation and laser heating over a wide range of temperatures. Remarkably, even in the presence of a heavy metal modifier dominating x-ray scattering, it was possible to extract accurate values for the tetrahedral, sp 3 , boron fraction, N 4 , which declines with increasing temperature, using bond valence-based mapping from the measured mean B–O bond lengths while accounting for vibrational thermal expansion. These are used within a boron-coordination-change model to extract enthalpies, ΔH, and entropies, ΔS, of isomerization between sp 2 and sp 3 boron. The results for BaB 4 O 7 , ΔH = 22(3) kJ mol -1 boron, ΔS = 19(2) J mol -1 boron K -1 , agree quantitatively with those found previously for Na 2 B 4 O 7 . Analytical expressions for N 4 (J, T) and associated configurational heat capacity, $C_{P}^{conf}$(J, T), and entropy, S conf (J, T), contributions are extended to cover a wide composition range 0 ≤ J = BaO/B 2 O 3 ≤ 3 using a model for ΔH(J) and ΔS(J) derived empirically for lithium borates. Maxima in the $C_{P}^{conf}$(J, T g ) and fragility index contributions are thereby predicted for J ≲ 1, higher than the maximum observed and predicted in N 4 (J, T g ) at J ≃ 0.6. We discuss the utility of the boron-coordination-change isomerization model in the context of borate liquids containing other modifiers and the prospect of neutron diffraction to aid in empirical determinations of modifier-dependent effects, illustrated by new neutron diffraction data on Ba 11 B 4 O 7 glass, its well-known α-polymorph, and lesser-known δ-phase.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Multivariate Drought Monitoring, Propagation, and Projection Using Bias-Corrected General Circulation Models

Understanding how droughts are characterized, propagated, and projected, particularly multivariate droughts, is necessary to explain the variability and changes in drought characteristics. This study aims to understand multimodel global drought monitoring, propagation, and projection by utilizing a multivariate standardized drought index (MSDI) during the historical (1959–2014) and future (2045–2100) periods under two socioeconomic pathways SSPs (370 and 585), derived from the bias-corrected Coupled Model Intercomparison Project Phase 6 (CMIP6). Based on the energy metrics, the multivariate bias correction method outperformed other techniques in correcting the biases in the CMIP6 drought representation. The drought indicators demonstrate distinct categories for meteorological, hydrological, and multivariate droughts. There were significant high cross correlations between Heatwave Total Length (HWTL) and MSDI in Africa and South America for all lagged times. Europe and North America generally saw the maximum MSDI drought duration (228 months) during the historical period. For future projections, Africa recorded the maximum drought duration (197 months), while Europe witnessed the minimum drought duration for SSP 370 (171 months), and North America (149 months) for SSP 585. Furthermore, during the historical period in tropical Africa, the propagation of meteorological to hydrological drought was slower during the wet months than during the dry months. Under the SSP 370 future projection, there was a shift in the long period of meteorological-hydrological propagation from the middle and late wet months to the beginning of the wet months in tropical Africa. Therefore, tracking and projecting drought characteristics is vital for understanding the risk of drought-related consequences.

54 ENVIRONMENTAL SCIENCES↗

Influence of defects on the femtosecond laser damage resistance of multilayer dielectric gratings

Multilayer dielectric (MLD) gratings with high diffraction efficiency and a high laser-induced damage (LID) threshold for pulse compressors are key to scaling the peak and average power of chirped pulse amplification lasers. However, surface defects introduced by manufacturing, storage, and handling processes can reduce the LID resistance of MLD gratings and impact the laser output. The underlying mechanisms of such defect-initiated LID remain unclear, especially in the femtosecond regime. In this Letter, we model dynamic processes in interactions of a 20-fs near-infrared (NIR) laser pulse and a MLD grating design in the presence of cylindrically symmetrical nodules and particle contaminants and cracks at the surface. Utilizing a dynamic model based on a 2D finite difference in time domain (FDTD) field solver coupled with photoionization, electron collision, and refractive index modification, we study the simulation results for the damage site distribution initiated by defects of various types and sizes and its impact on the LID threshold of the grating design.

Zhang, Simin (ORCID:0000000174948230)↗

Layer-Dependent Pressure Effect on the Electronic Structure of 2D Black Phosphorus

Through infrared spectroscopy, we systematically study the pressure effect on electronic structures of few-layer black phosphorus (BP) with layer number ranging from 2 to 13. We reveal that the pressure-induced shift of optical transitions exhibits strong layer dependence. In sharp contrast to the bulk counterpart which undergoes a semiconductor to semimetal transition under ~1.8 GPa, the band gap of 2 L increases with increasing pressure until beyond 2 GPa. Meanwhile, for a sample with a given layer number, the pressure-induced shift also differs for transitions with different indices. Through the tight-binding model in conjunction with a Morse potential for the interlayer coupling, this layer- and transition-index-dependent pressure effect can be fully accounted. Our study paves a way for versatile van der Waals engineering of two-dimensional BP.

2-dimensional systems↗

Solidification cracking of refractory alloys: a computational and machine learning study to investigate composition-dependence for improved weldability and additive manufacturability

Large-batch numerical, CALculation of PHAse Diagrams (CALPHAD)-based solidification cracking calculations are performed and then analyzed with machine learning methods to generate models that relate chemistry of refractory alloys to cracking susceptibility. Kou’s solidification cracking index is used to study the refractory alloys including O, N, C binary mixtures with Mo, Ta, Nb, and W, the molybdenum-based TZM, Niobium-based C103, and Tantalum-based T111 and Ta-10 W, as well as hypothetical refractory ternary alloys. Findings strongly validate Kou’s Crack Susceptibility Index (CSI) against Varestraint test data for Nb- and Ta-based alloys, establishing CSI thresholds where refractory alloys with CSI < 15,000 K are likely weldable, CSI > 15,000 K are prone to cracking, and CSI > 25,000 K are likely unweldable (or unprintable). Furthermore, interstitial elements C, N, and O significantly increase crack susceptibility, with some existing material specifications coinciding with peak cracking susceptibility concentrations. Finally, machine learning-derived elemental potency factors enable rapid prediction of CSI from alloy chemistry for C103, TZM, Ta-10 W, and T-111 alloys. These results provide practical guidance for feedstock selection, powder reuse limits, and alloy specification amendments for welding and additive manufacturing applications.

36 MATERIALS SCIENCE↗

Harnessing Satellite Data Alone for Mapping Global Thermal Anisotropy

Mapping thermal anisotropy across global lands is critical for advancing a wide range of Earth science studies. However, a comprehensive understanding of global thermal anisotropy intensity (TAI) and its governing factors remains missing. We introduce a novel data-driven methodology to quantify global TAI exclusively using multi-angle MODIS land surface temperature time series observations. Our analysis reveals distinct seasonal and diurnal TAI patterns, with global mean summertime TAI exceeding 2.9°C. Furthermore, we identify strong associations between TAI and key surface and atmospheric parameters, such as leaf area index and downward shortwave radiation. Our findings advocate for a paradigm shift from model-based to data-driven approaches in correcting thermal anisotropy, thereby addressing a critical bottleneck in Earth observation.

54 ENVIRONMENTAL SCIENCES↗