Sensitivity Analysis of Multibody Dynamics Using Spatial Operators
This paper discusses an approach for sensitivity analysis of multibody dynamics using spatial operators.
Multibody dynamics sensitivities spatial operators↗
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Results for “Spatial analysis”
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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Fire in the Brazilian Amazon: A Spatially Explicit Model for Policy Impact Analysis
This article implements a spatially explicit model to estimate the probability of forest and agricultural fires in the Brazilian Amazon. We innovate by using variables that reflect farmgate prices of beef and soy, and also provide a conceptual model of managed and unmanaged fires in order to simulate the impact of road paving, cattle exports, and conservation area designation on the occurrence of fire. Our analysis shows that fire is positively correlated with the price of beef and soy, and that the creation of new conservation units may offset the negative environmental impacts caused by the increasing number of fire events associated with early stages of frontier development.
Arima, Eugenio Y.↗
Frames of reference for helicopter electronic maps - The relevance of spatial cognition and componential analysis
Computer-generated map displays for NOE and low-level helicopter flight were formed according to prior research on maps, navigational problem solving, and spatial cognition in large-scale environments. The north-up map emphasized consistency of object location, wheareas, the track-up map emphasized map-terrain congruency. A component analysis indicates that different cognitive components, e.g., orienting and absolute object location, are supported to varying degrees by properties of different frames of reference.
Harwood, Kelly↗
Focused ion beam for improved spatially-resolved mass spectrometry and analysis of radioactive materials for uranium isotopic analysis
Not Available
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Non-propagating structures and propagating waves in solar wind turbulence revealed by simulations and observations
Structures and waves are common features of solar wind turbulence at various scales. The interplay between structures and waves is important for processes such as the turbulent energy cascade, plasma heating, and particle scattering. Our understanding of turbulence has been advanced by not only new space missions and numerical simulations, but also techniques that have been developed to interpret the rapidly growing turbulence data. We review basic models of turbulence with a specific focus on the analysis methods for understanding magnetic structures and waves. MHD and kinetic waves in single-spacecraft time series measurements can be identified through mode decomposition or their characteristic polarization signatures. The structures in this paper are considered as zero-frequency, non-propagating or convected modes embedded in the solar wind. The synergy between observations and simulations is most evident in the application of spatial-temporal analysis to multi-spacecraft observation and turbulence simulations. The spatial-temporal analysis has greatly improved our understanding of structures and waves in turbulence. We conclude by discussing prospects for future research.
79 ASTRONOMY AND ASTROPHYSICS↗
Stream pattern analysis using optical processing of ERTS imagery of Kansas
A spatial frequency analysis was carried out to obtain large-scale geologic and other ground pattern spatial frequency and orientational data from an optical data processing system using Earth Resources Technology Satellite-1 (ERTS-1) imagery of Kansas as input. The Diffraction Pattern Sampling Unit (DPSU) of the optical data processing system consists of a photodiode array containing 32 wedge-shaped photodiodes and 32 annular ring diodes. The curve obtained from the intensity distribution of the frequency spectrum yielded by the ring diodes gives the spatial frequency contributions due to ground features on the image. The curve obtained from the wedge-shaped diodes indicates the orientation of features in the sample area. Ten sample sites were used for each of eight geologic-physiographic regions. Comparison of ERTS-1 images with their respective spatial frequency and orientational curves reveals that preferred orientations of drainage produce recognizable peaks.
Mccauley, J.↗
A study of spatial data management and analysis systems
The Earth Resources Laboratory of the NASA Stennis Space Center is a center of space related technology for Earth observations. It has assumed the task, in a joint effort with Jackson State University, to reach out to the science community and acquire information pertaining to characteristics of spatially oriented data processing.
Christopher, Clyde↗
Application of Image Analysis for Characterization of Spatial Arrangements of Features in Microstructure
A number of microstructural processes are sensitive to the spatial arrangements of features in microstructure. However, very little attention has been given in the past to the experimental measurements of the descriptors of microstructural distance distributions due to the lack of practically feasible methods. We present a digital image analysis procedure to estimate the micro-structural distance distributions. The application of the technique is demonstrated via estimation of K function, radial distribution function, and nearest-neighbor distribution function of hollow spherical carbon particulates in a polymer matrix composite, observed in a metallographic section.
Louis, Pascal↗
Robust two-colour pyrometry uncertainty analysis to acquire spatially-resolved measurements
Two-colour pyrometry (2CP) has been used over several decades to study engine-relevant combustion processes, but results are generally regarded as qualitative or semi-quantitative. In many current 2CP systems, large measurement errors are introduced by parallax because the two measured wavelengths are not from the same line of sight. Here this work presents a spatially-resolved 2CP system with the objective of quantifying and reducing measurement uncertainty. An optical setup that eliminates parallax in 2CP is used together with pixel-by-pixel calibration of the camera sensor to increase measurement accuracy. Primary uncertainty terms are identified, and an error propagation analysis is performed to compute uncertainties in the final results of soot temperature, soot concentration parameter, KL, and soot mass. These methodologies are applied to investigate an auto-igniting fuel spray in a constant pressure flow rig at diesel-like conditions of high ambient pressure and temperature. Results show bias uncertainty of around 200 K (≈10%) for temperature and about 40%–60% for KL. High uncertainty was found to occur on the diffusion flame front where both optical thickness and soot concentrations are small. However, these uncertain measurement zones with relatively low soot concentrations contribute minimally to the total soot mass present in the reacting jet during the temporal evolution of the flame.
42 ENGINEERING↗
Vision Foundation Models in Remote Sensing: A survey
Artificial intelligence (AI) technologies have profoundly transformed the field of remote sensing (RS), revolutionizing data collection, processing, and analysis. Traditionally reliant on manual interpretation and task-specific models, RS research has been significantly enhanced by the advent of foundation models (FMs)—large-scale pretrained AI models capable of performing a wide array of tasks with unprecedented accuracy and efficiency. This article provides a comprehensive survey of FMs in the RS domain. We categorize these models based on their architectures, pretraining datasets, and methodologies. Through detailed performance comparisons, we highlight emerging trends and the significant advancements achieved by those FMs. Additionally, we discuss technical challenges, practical implications, and future research directions, addressing the need for high-quality data, computational resources, and improved model generalization. Our research also finds that pretraining methods, particularly self-supervised learning (SSL) techniques like contrastive learning (CL) and masked autoencoders (MAEs), remarkably enhance the performance and robustness of FMs. This survey aims to serve as a resource for researchers and practitioners by providing a panorama of advances and promising pathways for the continued development and application of FMs in RS.
data models↗
Intrinsic spatial chirp of subcycle terahertz pulsed beams
Here, we present an analysis of spatial chirp in subcycle pulsed beams corroborated by spatiotemporal electro-optic sampling measurements of terahertz radiation. Modeling the subcycle pulsed beam as a superposition of monochromatic Gaussian beams, free-space propagation is shown to directly lead to lateral spatial chirp with the spectrum on-axis substantially bluer than the overall energy spectrum. The two-transverse + one-temporal dimensional profile of terahertz subcycle pulsed beams from organic crystals DSTMS and OH1 are measured and compared with observed spatiospectral correlations consistent with the model.
43 PARTICLE ACCELERATORS↗
Alkali-silica reaction products and cracks: X-ray micro-tomography-based analysis of their spatial-temporal evolution at a mesoscale
In this study, we propose a laboratory-scale methodology, based on X-ray micro-tomography and caesium (Cs) as a contrast agent, to advance the understanding of cracking due to alkali-silica reaction (ASR) in concrete. The methodology allows achieving a completely non-destructive and time-lapse characterization of the spatial-temporal patterns of both the cracks and the ASR products. While Cs addition slightly accelerated the ASR kinetics, the crack patterns, with and without Cs addition, were statistically equivalent. Cracks with ASR products appeared first in the aggregates, close to the interface with the cement paste. They propagated afterwards towards the aggregates interior. Some products were then extruded for several mm into air voids and cracks in the cement paste. This process suggests that, in the early stage, the ASR products may be a low-viscosity gel that can flow away from the source aggregate and may settle later elsewhere as a rigid phase, upon calcium uptake.
36 MATERIALS SCIENCE↗
A spectroscopic analysis code for spatially-resolved X-ray absorption data from the COAX platform
Explore the source record for details and available documents.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Multi-criteria spatial screening and uncertainty analysis applied to direct-use geothermal projects
Not provided.
Computer Science↗
Spatial and Temporal Interpolation Analysis Process of Shock Loading
The following document serves to describe the current process that is utilized by W-13 analysts to approximate the pressure-time boundary condition seen by a test object from shock tube loading. The intent of this report is to capture the current capability and to encourage continued growth and development. Information on the required input variables, mapping process, and generated output information is covered in the following sections. Throughout the document, gaps and weaknesses of the mapping process are noted to encourage future development efforts.
42 ENGINEERING↗
Quantitative analysis of properties and spatial relations of fuzzy image regions
Properties of objects and spatial relations between objects play an important role in rule-based approaches for high-level vision. The partial presence or absence of such properties and relationships can supply both positive and negative evidence for region labeling hypotheses. Similarly, fuzzy labeling of a region can generate new hypotheses pertaining to the properties of the region, its relation to the neighboring regions, and finally, the labels of the neighboring regions. In this paper, we present a unified methodology to characterize properties and spatial relationships of object regions in a digital image. The proposed methods can be used to arrive at more meaningful decisions about the contents of the scene.
Krishnapuram, Raghu↗
A review on recent machine learning applications for imaging mass spectrometry studies
Imaging mass spectrometry (IMS) is a powerful analytical technique widely used in biology, chemistry, and materials science fields that continue to expand. IMS provides a qualitative compositional analysis and spatial mapping with high chemical specificity. The spatial mapping information can be 2D or 3D depending on the analysis technique employed. Due to the combination of complex mass spectra coupled with spatial information, large high-dimensional datasets (hyperspectral) are often produced. Therefore, the use of automated computational methods for an exploratory analysis is highly beneficial. The fast-paced development of artificial intelligence (AI) and machine learning (ML) tools has received significant attention in recent years. These tools, in principle, can enable the unification of data collection and analysis into a single pipeline to make sampling and analysis decisions on the go. There are various ML approaches that have been applied to IMS data over the last decade. Here, in this review, we discuss recent examples of the common unsupervised (principal component analysis, non-negative matrix factorization, k-means clustering, uniform manifold approximation and projection), supervised (random forest, logistic regression, XGboost, support vector machine), and other methods applied to various IMS datasets in the past five years. The information from this review will be useful for specialists from both IMS and ML fields since it summarizes current and representative studies of computational ML-based exploratory methods for IMS.
47 OTHER INSTRUMENTATION↗
Ground pattern analysis in the Great Plains
The author has identified the following significant results. Spatial frequency analysis of ERTS-1 images appears to be useful in discriminating between large scale ground patterns in Kansas. Using parameters derived from the optical data processing of ERTS-1 images, sample areas from large physiographic categories have been accurately identified.
Davis, J. C.↗