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NASA NTRSDate not supplied

Planar Investigation of a CobraMRV Reentry Flowfield Using Pulse-Burst, Cross-Correlation DGV

The flowfield around a Co-Optimization Blunt-body Reentry Analysis Mid-lift/drag-ratio Rigid Vehicle (CobraMRV) vehicle is investigated with a combination of high-speed planar laser Mie scattering (PLMS) and pulse-burst cross-correlation Doppler global velocimetry (PB-CC-DGV). Tests were conducted in the NASA Langley 4-foot Supersonic Unitary Plan Wind Tunnel (UPWT) over a range of different tunnel operating conditions and model configurations. Results indicate a complex shock-boundary layer interaction. Scalar information extracted from the PLMS show the evolution of the bow shock structure, while streamwise velocity measurements indicate the spatial evolution of the shock-boundary layer interaction including the growth of the separation shock foot and eventual reacceleration of the flow at farther downstream locations. Assessment of multiple cases show strong Mach and Reynolds numbers driven effects on the character of the shock-boundary layer interactions. Measurement uncertainties ranged from 50 to 150 m/s throughout the region of interest, driven largely by angular uncertainties and instabilities in the laser pointing. The mean accuracy of the freestream measurements was found to be 5.2-percent of tunnel predicted values.

Laser↗

NASA NTRS2021

Acoustic Sensor Network for Planetary Exploration

This paper investigates the concept of an acoustic sensor network that can monitor a variety of geophysical processes occurring on other planetary bodies. In many cases sound is naturally omnidirectional and travels at known speeds which depend on the composition and density of the atmosphere. The differences in the time of flight of signals received by a distributed microphone network can be used to locate the source of the sound. We suggest this property is ideal for mobile planetary robots and can be used to expand the exploration envelope considerably by directing camera pointing or rover path planning thus extending beyond line-of-sight exploration. Acoustic signatures have been used in a variety of fields (e.g., sonar, heavy machinery) to identify and catalog sounds associated with a specific vessels and malfunctioning machinery. Our ears have cataloged hundreds of sounds and we continuously use these sounds both consciously and subconsciously to extract information about our surroundings. This paper investigates the use acoustic measurements on other planetary bodies that could be used to characterize specific environmental parameters such as rain droplet size, wind speed, thunder, or dust devil vortex diameter. The paper identified other important sound sources that are thought to occur on other bodies in our solar system include; booming or singing dunes, waves, rivers, streams, fluidfalls, geysers, hurricanes, tornados, ice flow, volcanoes, planetary quakes, avalanche, rock slides and ice cracking. In addition, this paper focused on issues associated with the development of appropriate sensors for the network including the specification of the sensitivity, frequency response, and directional response of each of the microphones in the network in order to aid in localization of sound sources. We also presented our initial development of the transducers for potential mission targets including Mars and Titan and investigated the use of signal processing techniques including windowing, time frequency plots and correlation techniques to resolve phase differences between sensors in the network to aid in localization. We also identified additional benefits of these sensor networks in that they could used as engineering sensors to diagnose mechanical malfunctions on a rover or lander actuators or mechanisms. We also noted that they could also enhance public outreach by adding sound to videos.

Malaska, Mike↗

NASA NTRS2022

Event Report for The Ethical Artificial Intelligence Quantification Workshop

Artificial Intelligence (AI) is a powerful emerging technology area which requires special attention to using it ethically. AI ethics is still an emerging field, and the partners for this workshop and report seek to move AI ethics discussion ahead by experimenting with ways to measure AI ethics criteria. The following document describes the outcomes and learnings from The Ethical Artificial Intelligence Quantification Workshop held at the National Institute for Aerospace (NIA), Hampton, Virginia on May 12th, 2022. The purpose of the workshop was for participants to evaluate and experiment-with the methodology and process presented by AIEthics.World in cooperation with Intel Corporation. The meeting participants learned about the Ethical AI Certification and Maturity Model™ and applied the methodology to selected notional AI systems. The workshop facilitated the evaluation of the maturity of the AI system according to ethical considerations relevant to NASA, NIA and other participants. The workshop consisted of three main phases. The first phase focused on understanding and summarizing NASA’s ethical approaches, mission and values based on published documentation, discussions and individual insights & opinions of participants. This information was prioritized, weighted, ordered, and quantified in phase two, to formulate an alignment between human values (ethics) and their applicability to AI systems during all lifecycle phases. The first two phases were summarized as a form of ethical genealogy for artificial intelligence, specific to NASA’s ethical approaches. In the third and last phase of the workshop the participants evaluated notional examples of artificial intelligence to qualify and quantify its ability to adhere to the organizational ethics approaches, using the Ethical AI Certification and Maturity Model™. The workshop uses the concept of genealogy, in the traditional sense: the study and traceability of lines of ancestors in the process of evolutionary development from earlier forms. However, as it is applied to an Ethical AI definition, it is providing the insights to the necessary and mandatory traceability of content, data, metrics, telemetry, elements, and structures which are used in the AI’s lifecycle to foster and measure AI ethics in all steps of its lifecycle. The Ethical Artificial Intelligence Quantification Workshop provided NASA with the opportunity to apply the Ethical AI Certification and Maturity Model™, in combination with existing and well-known decision-making and quality control methods to identify the metrics and measurements for an Ethical AI and assess its ethical condition and quality aligned with NASA ethics approaches. The result of the workshop is the capacity for NASA to apply the maturity model assessment to its AI Systems as desired and if necessary, publish the ability of these AI Systems to adhere to the organizational ethical goals. AI ethics frameworks need to be customized for each application domain, for example, individual NASA Mission Directorates. General principles that work in one area such as AI/Machine Learning-based text analysis (the ethics of information-extraction) may need to be adapted for another such as sense-and-avoid decision-making in a flight environment. The workshop was conducted among approximately twenty NASA subject matter experts, so the elements noted above should be considered examples, not definitive NASA ethical AI principles, genealogy, etc. Generating a definitive AI ethics framework for an organization as diverse as NASA would require far more discussion, debate, review, etc. However, the workshop provided valuable insight into mechanisms and processes for quantifying AI ethical qualities.

Artificial Intelligence↗

NASA NTRS2018

Variability Timescale and Spectral Index of Sgr A* in the Near Infrared: Approximate Bayesian Computation Analysis of the Variability of the Closest Supermassive Black Hole

Sagittarius A* (Sgr A* ) is the variable radio, near-infrared (NIR), and X-ray source associated with accretion onto the Galactic center black hole. We present an analysis of the most comprehensive NIR variability data set of Sgr A* to date: eight 24 hr epochs of continuous monitoring of Sgr A* at 4.5 μm with the IRAC instrument on the Spitzer Space Telescope, 93 epochs of 2.18 μm data from Naos Conica at the Very Large Telescope, and 30 epochs of 2.12 μm data from the NIRC2 camera at the Keck Observatory, in total 94,929 measurements. A new approximate Bayesian computation method for fitting the first-order structure function extracts information beyond current fast Fourier transformation (FFT) methods of power spectral density (PSD) estimation. With a combined fit of the data of all three observatories, the characteristic coherence timescale of Sgr A* is b 243 57 82 t = - + minutes (90% credible interval). The PSD has no detectable features on timescales down to 8.5 minutes (95% credible level), which is the ISCO orbital frequency for a dimensionless spin parameter a = 0.92. One light curve measured simultaneously at 2.12 and 4.5 μm during a low flux-density phase gave a spectral index αs = 1.6 ± 0.1 (F µ n s n -a ). This value implies that the Sgr A* NIR color becomes bluer during higher flux-density phases. The probability densities of flux densities of the combined data sets are best fit by log-normal distributions. Based on these distributions, the Sgr A* spectral energy distribution is consistent with synchrotron radiation from a nonthermal electron population from below 20 GHz through the NIR.

accretion↗

NASA NTRSDate not supplied

Open Source Application of Fusing Aerosol Products from GEO and LEO Satellites

Retrieving aerosol optical depths (AODs) from sun-synchronous polar orbiting (aka low earth orbit, LEO) satellites, such as MODISs, and VIIRSs, OMI, TROPOMI, etc, has become well-established as a tool for extracting information on particulate matter (PM) and related processes in the atmosphere. However, with recently launched geostationary satellites (GEO), such as GOES-16/17/18, and Himawari-8/9, and Meteosat Third Generation (MTG) they provide a much higher temporal resolution (order of 10 minutes), typically an image once or more per hour during daylight compared to LEO once per day. By combining these observations, we may be able to characterize the diurnal cycle of global AOD at the local, regional and global scale. While the science community is still exploring the new data from GEO observations, we have been thinking about how to properly combine/merge/fuse those data considering differences in their spatial and temporal resolutions. However, this poses a “Big Data” challenge. The big data challenge is not just about data storage, but also about data discoverability, and accessibility, and even more, about data migration/mirroring in the cloud-computing environment. This paper is merely showing some of the efforts and approaches we have attempted in fusing six satellites’ Level 2 aerosol data (three are from GEO (GOES-16/17 and Himawari-8), and the other three are from LEO (TERRA/MODIS, AQUA/MODIS, SNPP-VIIRS) from Dark Target (DT) aerosol retrieval algorithm. Having the on-demand capability of fusing remote sensing products onto the desired temporal and spatial domain enables researchers and application practitioners to better manipulate and work with satellite and sensor data. It is our hopeWe hope that by making such an open-source package, and the accompanying functionality, the scientific community will be granted easier access to aerosol data processing resources. The MEaSUREs Program (Making Earth System Data Records for Use in Research Environments) expands our understanding of the Earth's current system through atmospheric and surface measurements. In an effort to aid the scientific research component and improve open source methods, this project developed Python code for fusing six satellite Level 2 aerosol data (three are from geostationary satellites (GEO), and the other three are from low earth orbital satellites (LEO)) from Dark Target Aerosol Retrieval Algorithm.

Jennifer Wei↗

NASA NTRSDate not supplied

An Overview of the Aerothermodynamic Database for the Mars Sample Return Earth Entry Vehicle

The Mars Sample Return Earth Entry System (MSR-EES) is a capsule that is part of the Mars Sample Return mission that will return Martian soil samples to Earth in 2033. Due to the steep flight path angle and relatively large vehicle size, the MSR-EES capsule will experience the highest peak heating rate of any previous Earth entry vehicle. The aerothermal database for MSR-EES is primarily characterized by numerical CFD, DSMC, and radiation simulations. Margins are applied to the convective and radiative heating rates. Surface roughness effects and margins are also included in the database formulation. The aerothermal database can be used to extract information at any body point location, perform trade studies in trajectory space, and provide inputs for material response simulations.

Mars↗

NASA NTRSDate not supplied

Real World Applications of AI/ML in Optimizing Airspace Operations

As National Airspace System (NAS) is going through the Digital Transformation journey, data science and analytics methods can significantly contribute to improving the traditional physics-based decision-making tools. The adoption of AI/ML methods will not only help accelerate Federal Aviation Administration (FAA)’s vision of Info-centric NAS but also contribute to the overall objective of sustainable aviation. AI/ML can improve the ground and airspace operations by enhancing the accuracy of current decision-making tools used by the airlines and the FAA to manage traffic on the ground and in the air. Huge amount of data that gets collected during a flight. AI/ML methods can extract information from this data and provide valuable insights to make better operational decisions. NASA has partnered with the FAA and commercial airlines such as American and Southwest Airlines on this effort and has successfully demonstrated the benefits of using ML in real world environment by reducing delays and optimizing ground operations at the US airports. In 2022 itself, NASA demonstrated real-world benefits (over 24K lbs. of fuel savings, over 76.6K lbs. CO2 emission savings, and several hours of delay savings) by deploying ML based prediction models to optimize ground operations at Dallas/Fort Worth International and Dallas Love Field Airports in Texas. These tools are being deployed on the cloud for broader deployment, adaptability, and scalability. NASA is developed a reference implementation of the cloud-based platform to significantly lower the bar to development and distribution of these digital services for aviation. In this talk, I will share information about the Digital Information Platform project, the novel AI/ML based approaches used for optimizing ground operations and the opportunities to partner with NASA on these demonstrations.

air traffic management↗

NASA NTRSDate not supplied

Global Sensitivity Analyses for Test Planning with Black-Box Models for Mars Sample Return

This work describes sensitivity analyses performed on complex black-box models used to support experimental test planning under limited resources in the context of the Mars Sample Return program, which aims at bringing to Earth rock and atmospheric samples from Mars. We develop a systematic workflow that allows the analysts to simultaneously obtain quantitative insights on key drivers of uncertainty, on the direction of impact, and the presence of interactions. We apply novel optimal transport-based global sensitivity measures to tackle the multivariate nature of the output. On the modeling side, we apply multi-fidelity techniques that leverage low-fidelity models to speed up the calculations and make up for the limited amount of high-fidelity samples, while keeping these in the loop for accuracy guarantees. The sensitivity analysis reveals insights useful for the analysts to understand the model's behavior and identify the factors to focus on during testing in order to maximize the value of information extracted from them to ensure mission success when limited resources are available.

Giuseppe Cataldo↗

NASA NTRSDate not supplied

GeoAI Advances in Specific Landform Mapping

Landform mapping (also referred to as geomorphology or geomorphometry) can be divided into two domains: general and specific (Evans 2012). Whereas general landform mapping categorizes all elements of the study area into landform classes, such as ridges, valleys, peaks, and depressions, the mapping of specific landforms requires the delineation (even if fuzzy) of individual landforms. The former is mainly driven by physical properties such as elevation, slope, and curvature. The latter, however, must consider the cognitive (human) reasoning that discriminates individual landforms in addition to these physical properties (Arundel and Sinha 2018). Both mapping forms are important. General geomorphometry is needed to understand geological and ecological processes and as boundary layer input to climate and environmental models. Specific geomorphometry supports such activities as disaster management and recovery, emergency response, transportation, and navigation. In the United States, individual landforms of interest are named in the U.S. Geological Survey (USGS) Geographic Names Information System, a point dataset captured specifically to digitize geographic names from the USGS Historical Topographic Map Collection (HTMC). Named landform extent is represented only by the name placement in the HTMC. Recent work has investigated CNN-based deep learning methods to capture these extents in machine-readable form. These studies first relied on physical properties (Arundel et al. 2020) and then included the HTMC as a band in RGB images in limited testing (Arundel et al. 2023). Results from the HTMC dataset surpassed those using just physical properties and using the HTMC alone performed best due to the hillshading and elevation (contour) data incorporated into the topographic maps. However, results fell short of an operational capacity to map all named landforms in the United States. Thus, our current work expands upon past research by focusing on the HTMC and physical information as inputs and the named landform label extents. Specifically, we propose to leverage pre-trained foundation models for segmentation and optical character recognition (OCR) models to jointly map landforms in the United States. Our approach aims to bridge the disparities among the independent information sources to facilitate informed decision-making. The modeling pipeline performs (1) segmentation using the physical information and (2) information extraction using OCR, in parallel. Then a computer vision approach merges the two branches into a labeled segmentation.

machine learning↗

NASA NTRS1975

A statistical analysis of data extraction for land cover information from high-altitude and satellite images

Land cover information from a 10 by 30 km test site in eastern Wisconsin extracted using 70 mm positive LANDSAT transparencies in an additive color viewer at a scale of 1:500,000 was compared with land cover information extracted using LANDSAT 9 x 9 inch positive transparencies at a scale of 1:1,000,000. Color infrared photography (NASA RB-57) at a scale of 1:60,000 provided ground truth for each land cover type. Interpretations were made for four land cover classes: 1) forest-brushland; (2) structures-barren land; (3) surface water; and (4) agricultural-open land. Statistical comparisons showed that neither method for the extraction of this data was clearly superior.

Kiefer, R. W.↗

NASA NTRSDate not supplied

Knowledge Discovery for Early Failure Assessment of Complex Engineered Systems Using Natural Language Processing

Emerging complex engineered systems may have unexpected safety issues due to novel operational environments, increasing autonomy, human-machine interaction, and other factors. To prevent failures in operation or testing that necessitate costly redesign, it is desirable to predict likely failure modes early in the design process. Information about past engineering failures in natural language format presents one possible solution by enabling the retrieval of information that can inform new designs. However, identifying documents containing usable information and extracting the required information can be prohibitively time-consuming when implemented at scale. In this research, an automated natural language processing (NLP) framework is proposed to discover relevant knowledge from documents containing failure-related design information. The framework is applied to NASA’s Lessons Learned Information System (LLIS),which is publicly available. Documents containing usable information are filtered using two different NLP-based models. Next, from the identified usable documents, a failure taxonomy is extracted using a partitioned hierarchical topic modeling approach. Partitions of the document describe different sections of the failure taxonomy – i.e., failure, cause of failure, and recommendations – as indicated by the structure of the original document. The extracted failure taxonomy can be leveraged in early design failure assessment methods. Moreover, the framework can be used to identify documents containing usable failure-related design information from other databases and extract relevant information from these documents.

Documentation and Information Science↗

NASA NTRSDate not supplied

Knowledge Discovery for Early Failure Assessment of Complex Engineered Systems Using Natural Language Processing

Documentation and Information Science↗

NASA NTRS2016

Earth Science Data Analytics: Preparing for Extracting Knowledge from Information

Data analytics is the process of examining large amounts of data of a variety of types to uncover hidden patterns, unknown correlations and other useful information. Data analytics is a broad term that includes data analysis, as well as an understanding of the cognitive processes an analyst uses to understand problems and explore data in meaningful ways. Analytics also include data extraction, transformation, and reduction, utilizing specific tools, techniques, and methods. Turning to data science, definitions of data science sound very similar to those of data analytics (which leads to a lot of the confusion between the two). But the skills needed for both, co-analyzing large amounts of heterogeneous data, understanding and utilizing relevant tools and techniques, and subject matter expertise, although similar, serve different purposes. Data Analytics takes on a practitioners approach to applying expertise and skills to solve issues and gain subject knowledge. Data Science, is more theoretical (research in itself) in nature, providing strategic actionable insights and new innovative methodologies. Earth Science Data Analytics (ESDA) is the process of examining, preparing, reducing, and analyzing large amounts of spatial (multi-dimensional), temporal, or spectral data using a variety of data types to uncover patterns, correlations and other information, to better understand our Earth. The large variety of datasets (temporal spatial differences, data types, formats, etc.) invite the need for data analytics skills that understand the science domain, and data preparation, reduction, and analysis techniques, from a practitioners point of view. The application of these skills to ESDA is the focus of this presentation. The Earth Science Information Partners (ESIP) Federation Earth Science Data Analytics (ESDA) Cluster was created in recognition of the practical need to facilitate the co-analysis of large amounts of data and information for Earth science. Thus, from a to advance science point of view: On the continuum of ever evolving data management systems, we need to understand and develop ways that allow for the variety of data relationships to be examined, and information to be manipulated, such that knowledge can be enhanced, to facilitate science. Recognizing the importance and potential impacts of the unlimited ways to co-analyze heterogeneous datasets, now and especially in the future, one of the objectives of the ESDA cluster is to facilitate the preparation of individuals to understand and apply needed skills to Earth science data analytics. Pinpointing and communicating the needed skills and expertise is new, and not easy. Information technology is just beginning to provide the tools for advancing the analysis of heterogeneous datasets in a big way, thus, providing opportunity to discover unobvious scientific relationships, previously invisible to the science eye. And it is not easy It takes individuals, or teams of individuals, with just the right combination of skills to understand the data and develop the methods to glean knowledge out of data and information. In addition, whereas definitions of data science and big data are (more or less) available (summarized in Reference 5), Earth science data analytics is virtually ignored in the literature, (barring a few excellent sources).

data analytics↗

NASA NTRS1974

Geologic information from satellite images

Extracting geologic information from ERTS and Skylab/EREP images is best done by a geologist trained in photo-interpretation. The information is at a regional scale, and three basic types are available: rock and soil, geologic structures, and landforms. Discrimination between alluvium and sedimentary or crystalline bedrock, and between units in thick sedimentary sequences is best, primarily because of topographic expression and vegetation differences. Discrimination between crystalline rock types is poor. Folds and fractures are the best displayed geologic features. They are recognizable by topographic expression, drainage patterns, and rock or vegetation tonal patterns. Landforms are easily discriminated by their familiar shapes and patterns. Several examples demonstrate the applicability of satellite images to tectonic analysis and petroleum and mineral exploration.

Lee, K.↗

NASA NTRS1974

Extracting land use information from the earth resources technology satellite data by conventional interpretation methods

A procedure for obtaining land use data from satellite imagery by the use of conventional interpretation methods is presented. The satellite is described briefly, and the advantages of various scales and multispectral scanner bands are discussed. Methods for obtaining satellite imagery and the sources of this imagery are given. Equipment used in the study is described, and samples of land use maps derived from satellite imagery are included together with the land use classification system used. Accuracy percentages are cited and are compared to those of a previous experiment using small scale aerial photography.

Vegas, P. L.↗

NASA NTRS1989

Extracting ocean surface information from altimeter returns - The deconvolution method

An evaluation of the deconvolution method for estimating ocean surface parameters from ocean altimeter waveforms is presented. It is shown that this method presents a fast, accurate way of determining the ocean surface parameters from noisy altimeter data. Three parameters may be estimated by using this method, including the altimeter-height error, the ocean-surface standard deviation, and the ocean-surface skewness. By means of a Monte Carlo experiment, an 'optimum' deconvolution algorithm and the accuracies with which the above parameters may be estimated using this algorithm are determined. Then the influence of instrument effects, such as errors in calibration and pointing-angle estimation, on the estimated parameters is examined. Finally, the deconvolution algorithm is used to estimate height and ocean-surface parameters from Seasat data.

Rodriguez, Ernesto↗

NASA NTRS1999

Parallel Visualization Co-Processing of Overnight CFD Propulsion Applications

An interactive visualization system pV3 is being developed for the investigation of advanced computational methodologies employing visualization and parallel processing for the extraction of information contained in large-scale transient engineering simulations. Visual techniques for extracting information from the data in terms of cutting planes, iso-surfaces, particle tracing and vector fields are included in this system. This paper discusses improvements to the pV3 system developed under NASA's Affordable High Performance Computing project.

Edwards, David E.↗

NASA NTRS2000

Classification of the Gabon SAR Mosaic Using a Wavelet Based Rule Classifier

A method is developed for semi-automated classification of SAR images of the tropical forest. Information is extracted using the wavelet transform (WT). The transform allows for extraction of structural information in the image as a function of scale. In order to classify the SAR image, a Desicion Tree Classifier is used. The method of pruning is used to optimize classification rate versus tree size. The results give explicit insight on the type of information useful for a given class.

Simard, Marc↗