SEARCH · Search NASA

Results for “data structure”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 199 records · Page 11

All sources NASA NTRS DOE OSTI

DOE OSTI2024

ISO_Fortran_binding_m v0.1.0

The Fortran programming language standard defines a broad feature set supporting the interoperability of Fortran programs with program written according to the C programming language standard. Among Fortran's C-interoperability features is a a C header file "ISO_Fortran_binding.h" This header file defines the interface to various C data structures and functions that C programs may use to access Fortran data entities. The ISO_Fortran_binding_m software defines a native Fortran module that presents an interface to these same data structures and functions. ISO_Fortran_bind_m thus enables Fortran programs to access and manipulate Fortran entities in ways that precisely mirror what C programs can do using ISO_Fortran_binding.h. ISO_Fortran_binding_m facilitates writing portable standard-conforming Fortran programs that emulate non-interoperable features, e.g., dynamic polymorphism, in a standard-conforming interoperable way similar but broader than what is demonstrated in Berkeley Lab's Caffeine software [1]. ISO_Fortran_binding_m also enables a Fortran programmer to extend Fortran's capabilities to emulate certain C functionality such as memory address arithmetic or computing C's "sizeof" function. [1] https://github.com/BerkeleyLab/caffeine/blob/213e3df1c319f0663306354414f352acda42a24f/src/caffeine/collective_subroutines/co_reduce_s.f90#L88 [2] https://github.com/BerkeleyLab/ISO_Fortran_binding_m/blob/0c585362bb4f2c72cf9049c800a7115b529ec533/src/iso_fortran_binding_m.F90#L196

Rouson, Damian↗

NASA NTRS1979

Impact of cell size on inventory and mapping errors in a cellular geographic information system

The author has identified the following significant results. The effect of grid position was found insignificant for maps but highly significant for isolated mapping units. A modelable relationship between mapping error and cell size was observed for the map segment analyzed. Map data structure was also analyzed with an interboundary distance distribution approach. Map data structure and the impact of cell size on that structure were observed. The existence of a model allowing prediction of mapping error based on map structure was hypothesized and two generations of models were tested under simplifying assumptions.

Wehde, M. E.↗

NASA NTRS1976

Analysis of structural dynamic data from Skylab. Volume 1: Technical discussion

The results of a study to analyze data and document dynamic program highlights of the Skylab Program are presented. Included are structural model sources, illustration of the analytical models, utilization of models and the resultant derived data, data supplied to organization and subsequent utilization, and specifications of model cycles.

Demchak, L.↗

DOE OSTI2025

Data and scripts from: “Denoising autoencoder for reconstructing sensor observation data and predicting evapotranspiration: noisy and missing values repair and uncertainty quantification”

This data package includes data and scripts from the manuscript “Denoising autoencoder for reconstructing sensor observation data and predicting evapotranspiration: noisy and missing values repair and uncertainty quantification”.The study addressed common challenges faced in environmental sensing and modeling, including uncertain input data, missing sensor observations, and high-dimensional datasets with interrelated but redundant variables. Point-scaled meteorological and soil sensor observations were perturbed with noises and missing values, and denoising autoencoder (DAE) neural networks were developed to reconstruct the perturbed data and further predict evapotranspiration. This study concluded that (1) the reconstruction quality of each variable depends on its cross-correlation and alignment to the underlying data structure, (2) uncertainties from the models were overall stronger than those from the data corruption, and (3) there was a tradeoff between reducing bias and reducing variance when evaluating the uncertainty of the machine learning models.This package includes:(1) Four ipython scripts (.ipynb): “DAE_train.ipynb” trains and evaluates DAE neural networks, “DAE_predict.ipynb” makes predictions from the trained DAE models, “ET_train.ipynb” trains and evaluates ET prediction neural networks, and “ET_predict.ipynb” makes predictions from trained ET models.(2) One python file (.py): “methods.py” includes all user-defined functions and python codes used in the ipython scripts.(3) A “sub_models” folder that includes five trained DAE neural networks (in pytorch format, .pt), which could be used to ingest input data before being fed to the downstream ET models in ‘ET_train.ipynb” or ‘ET_predict.ipynb’.(4) Two data files (.csv). Daily meteorological, vegetation, and soil data is in “df_data.csv”, where “df_meta.csv” contains the location and time information of “df_data.csv”. Each row (index) in “df_meta.csv” corresponds to each row in “df_data.csv”. These data files are formatted to follow the data structure requirements and be directly used in the ipython scripts, and they have been shuffled chronologically to train machine learning models. The meteorological and soil data was collected using point sensors between 2019-2023 at(4.a) Three shrub-dominated field sites in East River, Colorado (named “ph1”, “ph2” and “sg5” in “df_meta.csv”, where “ph1” and “ph2” were located at PumpHouse Hillslopes, and “sg5” was at Snodgrass Mountain meadow) and(4.b) One outdoor, mesoscale, and herbaceous-dominated experiment in Berkeley, California (named “tb” in “df_meta.csv”, short for Smartsoils Testbed at Lawrence Berkeley National Lab).- See "df_data_dd.csv" and "df_meta_dd.csv" for variable descriptions and the Methods section for additional data processing steps. See "flmd.csv" and "README.txt" for brief file descriptions.- All ipython scripts and python files are written in and require PYTHON language software.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1996

Study of the velocity gradient tensor in turbulent flow

The behavior of the velocity gradient tensor, A(ij)=delta u(i)/delta x(j), was studied using three turbulent flows obtained from direct numerical simulation The flows studies were: an inviscid calculation of the interaction between two vortex tubes, a homogeneous isotropic flow, and a temporally evolving planar wake. Self-similar behavior for each flow was obtained when A(ij) was normalized with the mean strain rate. The case of the interaction between two vortex tubes revealed a finite sized coherent structure with topological characteristics predictable by a restricted Euler model. This structure was found to evolve with the peak vorticity as the flow approached singularity. Invariants of A(ij) within this structure followed a straight line relationship of the form: gamma(sup 3)+gammaQ+R=0, where Q and R are the second and third invariants of A(ij), and the eigenvalue gamma is nearly constant over the volume of this structure. Data within this structure have local strain topology of unstable-node/saddle/saddle. The characteristics of the velocity gradient tensor and the anisotropic part of a related acceleration gradient tensor H(ij) were also studied for a homogeneous isotropic flow and a temporally evolving planar wake. It was found that the intermediate principal eigenvalue of the rate-of-strain tensor of H(ij) tended to be negative, with local strain topology of the type stable-node/saddle/saddle. There was also a preferential eigenvalue direction. The magnitude of H(ij) in the wake flow was found to be very small when data were conditioned at high local dissipation regions. This result was not observed in the relatively low Reynolds number simulation of homogeneous isotropic flow. A restricted Euler model of the evolution of A(ij) was found to reproduce many of the topological features identified in the simulations.

Cheng, Wei-Ping↗

DOE OSTI2021

Timely Reporting of Heavy Hitters Using External Memory

Given an input stream S of size N, a Φ-heavy hitter is an item that occurs at least ΦN times in S. The problem of finding heavy-hitters is extensively studied in the database literature. In this work, we study a real-time heavy-hitters variant in which an element must be reported shortly after we see its T = Φ N-th occurrence (and hence it becomes a heavy hitter). We call this the Timely Event Detection (TED) Problem. The TED problem models the needs of many real-world monitoring systems, which demand accurate (i.e., no false negatives) and timely reporting of all events from large, high-speed streams with a low reporting threshold (high sensitivity). Like the classic heavy-hitters problem, solving the TED problem without false-positives requires large space (Ω (N) words). Thus in-RAM heavy-hitters algorithms typically sacrifice accuracy (i.e., allow false positives), sensitivity, or timeliness (i.e., use multiple passes). We show how to adapt heavy-hitters algorithms to external memory to solve the TED problem on large high-speed streams while guaranteeing accuracy, sensitivity, and timeliness. Our data structures are limited only by I/O-bandwidth (not latency) and support a tunable tradeoff between reporting delay and I/O overhead. With a small bounded reporting delay, our algorithms incur only a logarithmic I/O overhead. We implement and validate our data structures empirically using the Firehose streaming benchmark. Multi-threaded versions of our structures can scale to process 11M observations per second before becoming CPU bound. In comparison, a naive adaptation of the standard heavy-hitters algorithm to external memory would be limited by the storage device’s random I/O throughput, i.e., ≈100K observations per second.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

On the vulnerability of data-driven structural health monitoring models to adversarial attack

Many approaches at the forefront of structural health monitoring rely on cutting-edge techniques from the field of machine learning. Recently, much interest has been directed towards the study of so-called adversarial examples; deliberate input perturbations that deceive machine learning models while remaining semantically identical. This article demonstrates that data-driven approaches to structural health monitoring are vulnerable to attacks of this kind. In the perfect information or ‘white-box’ scenario, a transformation is found that maps every example in the Los Alamos National Laboratory three-storey structure dataset to an adversarial example. Also presented is an adversarial threat model specific to structural health monitoring. The threat model is proposed with a view to motivate discussion into ways in which structural health monitoring approaches might be made more robust to the threat of adversarial attack.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

Electric Vehicle Charging Analytics and Reporting Tool (EV-ChART): Data Format and Preparation Guidance (V.5.0)

The Joint Office of Energy and Transportation maintains the Electric Vehicle Charging Analytics and Reporting Tool (EV-ChART), which provides a centralized hub for submitting electric vehicle (EV) charging infrastructure data directed by the Federal Highway Administration (23 CFR 680.112(a)-(c)). EV-ChART provides a streamlined data submission process and an integrated set of analytic tools, connects to other data sources, and empowers data sharing and access across stakeholders, including the public. Any data shared publicly will be aggregated and anonymized to stay in accordance with 23 CFR 680. This EV-ChART Data Format and Preparation Guidance provides a comprehensive overview of the data reporting requirements as authorized under 23 CFR 680.112(a)-(c)). The guidance is intended to be used alongside the EV-ChART Data Input Template, which defines the tabular data structure that these data submissions must follow. Per 23 CFR 680.112(a)-(c), the annual and quarterly data submissions are required of all National Electric Vehicle Infrastructure (NEVI) Formula Program projects, as well as projects for the construction of publicly accessible EV chargers that are funded with funds made available under Title 23, United States Code, including any EV charging infrastructure project funded with federal funds that is treated as a project on a federal-aid highway. One-time data submissions are required of both the NEVI Formula Program projects and grants awarded under 23 U.S.C. 151(f) for projects that are for EV charging stations located along and designed to serve the users of designated Alternative Fuel Corridors (AFCs). Other information and data required in 23 CFR 680, such as 23 CFR 680.112(d), 23 CFR 680.116(c), and 23 CFR 680.106(a), are not discussed in this guidance.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2024

On a Simplified Approach to Achieve Parallel Performance and Portability Across CPU and GPU Architectures

This paper presents software advances to easily exploit computer architectures consisting of a multi-core CPU and CPU+GPU to accelerate diverse types of high-performance computing (HPC) applications using a single code implementation. The paper describes and demonstrates the performance of the open-source C++ matrix and array (MATAR) library that uniquely offers: (1) a straightforward syntax for programming productivity, (2) usable data structures for data-oriented programming (DOP) for performance, and (3) a simple interface to the open-source C++ Kokkos library for portability and memory management across CPUs and GPUs. The portability across architectures with a single code implementation is achieved by automatically switching between diverse fine-grained parallelism backends (e.g., CUDA, HIP, OpenMP, pthreads, etc.) at compile time. The MATAR library solves many longstanding challenges associated with easily writing software that can run in parallel on any computer architecture. This work benefits projects seeking to write new C++ codes while also addressing the challenges of quickly making existing Fortran codes performant and portable over modern computer architectures with minimal syntactical changes from Fortran to C++. We demonstrate the feasibility of readily writing new C++ codes and modernizing existing codes with MATAR to be performant, parallel, and portable across diverse computer architectures.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1975

Case studies on the geological application of LANDSAT imagery in Brazil

The author has identified the following significant results. Sao Domingos Range, Pocos de Caldas, and Araguaia and Tocantins Rivers in Brazil were selected as test sites for LANDSAT imagery. The satellite images were analyzed using conventional photointerpretation techniques, and the results indicate the application of small scale image data in regional structural data analysis, geological mapping, and mineral exploration.

Demendonca, F.↗

NASA NTRS1986

The Tasseled Cap de-mystified

The fundamental concepts on which the Tasseled Cap transformations of MSS and TM data are based - particularly the identification of inherent data structures - are explained and discussed. Emphasis on the structures present in data from any given sensor, which are themselves the expression of physical characteristics of scene classes, provides a number of advantages, including (a) reduction in data volume with minimal information loss; (b) spectral features which can be applied, without re-definition or adjustment, to any data set for a given sensor; (c) spectral features which can be directly associated with important physical parameters; and (d) easier integration of data from multiple sensors.

Crist, E. P.↗

NASA NTRS2001

A strategy for compression and analysis of massive geophysical data sets

This paper describes a method for summaraizing data in a way that approximately preserves high-resolution data structure while reducing data volume and maintaining global integrity of very large, remote sensing data sets. The method is under development for one of Terra's instruments, the Multi-angle Imaging SpectroRadiometer (MISR).

geophysical data sets compression↗

DOE OSTI2023

Electric Vehicle Charging Analytics and Reporting Tool (EV-ChART): Data Format and Preparation Guidance, Version 2.0

ADVANCED PROPULSION SYSTEMS,MATHEMATICS AND COMPUT↗

DOE OSTI2024

Geospatial Data Platform for All

Spatiotemporal data has evolved in scale due to augmented use in cross-domain applications. Simultaneously, there is substantial growth in the availability of Geographic Information Systems (GIS) data provided by the United States Geological Survey (USGS) along with other federal, state, county, or local agencies through open-data portals and public access APIs. However, data availability does not equate with accessibility. Large-scale analyses and applications require robust, performant data management with co-location of data storage and computing. The insufficiency of data management infrastructure compels researchers to adopt ad hoc project- specific GIS data storage solutions (e.g., copying data to High-Performance computer file systems). As an ad hoc storage strategy does not scale, it hampers cross-domain analyses causing difficulty in data reuse and utilizing existing code bases. Furthermore, GIS data is complex and requires expertise to analyze and manipulate due to its intricate data structures and data-specific projection transformations. Despite the challenges, we recognize that derived GIS data products, e.g., satellite or LIDAR-based images, can be used in downstream applications such as AI by domain, but non-GIS experts. To address the data needs and overcome the challenges, we are working towards a GIS Data Platform focused on efficient data storage, data discovery and access, and an API to enable common workflows. We propose a knowledge-graph (KG) approach for data discovery, whereby datasets are semantically linked to higher- level constructs such as projects and research areas. The semantic data links enable researchers to explore datasets in a top-down approach by specifying relevant and meaningful terms (assists in finding hidden data). An advantage is that the nodes and edges in a knowledge graph create built-in semantic documentation. Deeper spatiotemporal connections between data sources can be encoded via Graph Neural Networks (GNN) (Zhang et al., 2021). The KG approach can be extended to integrate the data itself in a Virtual KG (VKG). Our work will derive inspiration from large-scale VKG efforts that have been undertaken or are currently underway as part of the OpenStreetMap project (Ding et al., 2021). For DOE Data Days, we share the proposed geospatial data platform hybrid (cloud/on-prem) architecture, our work-to-date on storing, retrieving, and transforming LiDAR and raster data relevant to two important NREL use-cases, including the Renewable Energy Potential (reV) Model, and present our proposal for a KG based data discovery engine.

data platform↗

DOE OSTI2020

Tracktable

Tracktable is a toolkit for analysis and visualization of the trajectories of moving objects. Its main focus is on air and sea traffic. It can also work with more abstract trajectories such as eye tracking data. We supply a set of core data structures, input/output routines, math and machine learning algorithms that operate on those data structures, and visualization algorithms to produce movies and still images of the results.

Wilson, Andrew↗

NASA NTRS1989

Model reduction for flexible structures - Test data approach

A reduced model of a system is obtained by truncating part of its state variables. Hankel singular values and component costs determine which component is deleted or retained in the reduced model. In this paper Hankel singular values and component costs of a flexible structure are obtained from the resonance test data, rather than from the system matrices. Test data, besides system dynamics, include also actuators and sensors dynamics. For this reason, the reduced model obtained from test data can be far from the optimal one. In this paper the reconstruction of the flexible structure indices from the joint actuator-sensor-flexible structure indices is discussed.

Gawronski, Wodek↗

NASA NTRS1985

STAR (Simple Tool for Automated Reasoning): Tutorial guide and reference manual

STAR is an interactive, interpreted programming language for the development and operation of Artificial Intelligence application systems. The language is intended for use primarily in the development of software application systems which rely on a combination of symbolic processing, central to the vast majority of AI algorithms, with routines and data structures defined in compiled languages such as C, FORTRAN and PASCAL. References to routines and data structures defined in compiled languages are intermixed with symbolic structures in STAR, resulting in a hybrid operating environment in which symbolic and non-symbolic processing and organization of data may interact to a high degree within the execution of particular application systems. The STAR language was developed in the course of a project involving AI techniques in the interpretation of imaging spectrometer data and is derived in part from a previous language called CLIP. The interpreter for STAR is implemented as a program defined in the language C and has been made available for distribution in source code form through NASA's Computer Software Management and Information Center (COSMIC). Contained within this report are the STAR Tutorial Guide, which introduces the language in a step-by-step manner, and the STAR Reference Manual, which provides a detailed summary of the features of STAR.

Borchardt, G. C.↗

DOE OSTI2024

RNTuple: A CMS Perspective

ROOT is planning to move from TTree to RNTuple as the data storage format for HL-LHC in order to, for example, speed up the IO, make the files smaller, and have a modern C++ API. Initially, RNTuple was not planned to support the same set of C++ data structures as TTree supports. CMS has explored the necessary transformations in its standard persistent data types to switch to RNTuple. Many challenges were encountered as alternative data structures were explored. This contribution will discuss the challenges uncovered and how collaboration with the ROOT team allowed them to be overcome. The solution to the challenges allows progressive changes to the CMS data types rather than requiring a sudden change to all data types to be stored in RNTuple. Once the solution was achieved, storage performance comparisons using the CMS data types were possible between RNTuple and TTree. This contribution will also present the results of those comparisons.

Smith, Nick↗