The NTRS is a valuable resource for researchers, students, educators, and the public to access NASA's current and historical technical literature and engineering results. Over 500,000 aerospace-related citations, over 200,000 full-text online documents, and over 500,000 images and videos are available. NTRS content continues to grow as new scientific and technical information (STI) is created or funded by NASA. The types of information found in the NTRS include: conference papers, journal articles, meeting papers, patents, research reports, images, movies, and technical videos. NTRS is Open Archives Initiative Protocol for Metadata Harvesting (OAI-PMH) enabled

The LEARN Project explores the creation of novel concepts and processes with the potential to create new capabilities in aeronautics research through awards to the external community including university and industry teams. The LEARN Project incorporates a competitive review process of the external teams’ proposals to develop integrated solutions for complex technical problems captured in the ARMD strategic thrusts, followed by short duration activities for feasibility assessment. Follow-on phases of the most promising ideas are also funded. LEARN also utilizes challenges and prizes to the external community.  With these processes, NASA funds also help catalyze investments from the aerospace and non-aerospace communities toward solving problems aligned with NASA interests.

The NASA Aeronautics Research Institute (NARI) has been established to achieve the LEARN Project’s goals.  NARI will complement other ARMD efforts in seeking early-stage innovative concepts applicable to a broad spectrum of aeronautical challenges in the nation’s air transportation system by sponsoring research solicitations and by hosting future competitive challenges. The Institute will coordinate these efforts and communicate the outcome of the research conducted to interested parties both internal and external to NASA. ARMD’s goal is to mature the new concepts in order to infuse them into current ARMD research programs, to enable new avenues of aeronautics research that are not currently supported by ARMD program and project funds, or to achieve practical application by the aeronautics community.

Multivariate Time-Series (MTS) are ubiquitous, and are generated in areas as disparate as sensor recordings in aerospace systems, music and video streams, medical monitoring, and financial systems. Domain experts are often interested in searching for interesting multivariate patterns from these MTS databases which can contain up to several gigabytes of data. Surprisingly, research on MTS search is very limited. Most existing work only supports queries with the same length of data, or queries on a fixed set of variables. In this paper, we propose an efficient and flexible subsequence search framework for massive MTS databases, that, for the first time, enables querying on any subset of variables with arbitrary time delays between them. We propose two provably correct algorithms to solve this problem — (1) an R-tree Based Search (RBS) which uses Minimum Bounding Rectangles (MBR) to organize the subsequences, and (2) a List Based Search (LBS) algorithm which uses sorted lists for indexing. We demonstrate the performance of these algorithms using two large MTS databases from the aviation domain, each containing several millions of observations. Both these tests show that our algorithms have very high prune rates (>95%) thus needing actual disk access for only less than 5% of the observations. To the best of our knowledge, this is the first flexible MTS search algorithm capable of subsequence search on any subset of variables. Moreover, MTS subsequence search has never been attempted on datasets of the size we have used in this paper.

The NASA Landsat Data Collection (NLDC) is a compilation of Landsat multispectral scanner (MSS) scenes and Landsat thematic mapper (TM) scenes. This compilation of scenes represents data collections from four distinct projects including: (1) the Global Change Landsat Data Collection (GCLDC);(2) the Humid Tropical Forest Project (HTFP) collection of source scenes and products; (3) a collection of data from the Committee on Environment and Natural Resources Research [formerly the Committee on Earth and Environmental Sciences (CEES)] that is historically referred to as the CEES collection; and (4) ongoing Landsat data purchases by NASA-funded investigators, starting with the 1996 fiscal year. The NLDC scenes have been screened for cloud cover and band quality resulting in a high grade,high quality data compilation. The GCLDC collection contains Landsat TM scenes that were purchased by NASA from Space Imaging, formerly the Earth Observation Satellite Company,under a special agreement to promote the use of shared data in global change research. The HTFP, the largest component of NASA's Landsat Pathfinder Program, contains Landsat MSS and TM scenes collected over the past 20 years. The goal of the HTFP is to globally map deforestation in the humid tropical forests. The CEES collection is the result of an effort to coordinate data needs among several Federal agencies (e.g.,Environmental Protection Agency, Department of the Interior agencies, National Oceanic and Atmospheric Administration, Department of Defense). These Landsat TM scenes were collected for a variety of research projects. Ongoing NASA purchases of Landsat TM data support NASA scientists and their affiliated researchers in programs and projects including the NASA Research and Analysis Program; the Global Land Cover Test Sites Project; the HTFP, the International Biosphere-Geosphere Programme, the NASA Applications Program; and the Landsat-7 Science Team.

Every one of the future exploration architectures being considered by NASA have, at their core, the need to rendezvous and dock with other vehicles or bodies.  Future manned vehicles need to be able to do so with both cooperative and uncooperative vehicles and objects.  To this end, the sensors being considered are all optical-based.  In fact, passive sensors, such as IR cameras and visual cameras, are at the heart of any exploration architecture.  There is a need for the onboard systems to be able to use the images provided by these sensors to rendezvous and dock/capture these objects.  Therefore, this project will develop this capability to operate around a variety of objects, without a priori knowledge of their geometry.  In particular, a technology called ‘optical flow’ or ‘visual odometry’ (VO), will be harnessed to develop a robust on-board capability using passive sensors; of course, if active sensors are available, they will be used as well. In fact, we will also apply this technique to navigating around a cratered object (such as an asteroid). This project will enhance the Agency’s ability to operate at distant locations, without the need for ground intervention.

To date, all of the on-board navigation development performed has focused on either Low Earth Orbit (LEO) or Low Lunar Orbit (LLO).  We seek to advance deep-space navigation technology by focusing this Internal Research and Development (IRAD) upon rendezvous and navigation in a weak gravity environment, either at Lagrangian point 2 (L2) or around an asteroid.  Of course, this will apply to any destinations that have a strong gravity field as well.  As well, the technology developed in this Internal Research and Development will apply to rendezvousing with vehicles such as ISS.  We choose to focus our IRAD effort on the navigation algorithms and software for the ARCM DRO Mission, thus broadening our scope, maintaining our cutting-edge capability, and advancing US manned space exploration.  The goal is to be flexible enough to meet the needs of the NASA vision, as it applies to any destination the Agency chooses to embark upon.

NASA's goal in Earth science is to observe, understand, and model the Earth system to discover how it is changing, to better predict change, and to understand the consequences for life on Earth. The Applied Sciences Program, within the Earth Science Division of the NASA Science Mission Directorate, serves individuals and organizations around the globe by expanding and accelerating societal and economic benefits derived from Earth science, information, and technology research and development.

The Prediction Of Worldwide Energy Resources (POWER) Project, funded through the Applied Sciences Program at NASA Langley Research Center, gathers NASA Earth observation data and parameters related to the fields of surface solar irradiance and meteorology to serve the public in several free, easy-to-access and easy-to-use methods. POWER helps communities become resilient amid observed climate variability by improving data accessibility, aiding research in energy development, building energy efficiency, and supporting agriculture projects.

The POWER project contains over 380 satellite-derived meteorology and solar energy Analysis Ready Data (ARD) at four temporal levels: hourly, daily, monthly, and climatology. The POWER data archive provides data at the native resolution of the source products. The data is updated nightly to maintain near real time availability (2-3 days for meteorological parameters and 5-7 days for solar). The POWER services catalog consists of a series of RESTful Application Programming Interfaces, geospatial enabled image services, and web mapping Data Access Viewer. These three service offerings support data discovery, access, and distribution to the project’s user base as ARD and as direct application inputs to decision support tools.

The latest data version update includes hourly-based source ARD, in addition to enhanced daily, monthly, annual, and climatology data. The daily time series for meteorology is available from 1981, while solar-based parameters start in 1984. The hourly source data are from Clouds and the Earth's Radiant Energy System (CERES) and Global Modeling and Assimilation Office (GMAO), spanning from 1984 for meteorology and from 2001 for solar-based parameters. The hourly data equips users with the ARD needed to model building system energy performance, providing information directly amenable to decision support tools introducing the industry standard EnergyPlus Weather file format.

NASA-QA Benchmark

NASA SMD and IBM research developed NASA-QA benchmark, an extractive question answering task focused on the Earth science domain. First, 39 paragraphs from Earth science papers which appeared in AGU and AMS journals were sourced. Subject matter experts from NASA formulated questions and marked the corresponding answers in these paragraphs, resulting in a total of 117 question-answer pairs. The dataset is split into a training set of 90 pairs and a validation set of… See the full description on the dataset page: https://huggingface.co/datasets/nasa-impact/nasa-smd-qa-benchmark.

OWLETS1_Pandora_Data_1 is the Ozone Water-Land Environmental Transition Study (OWLETS-1) ozone and nitrogen dioxide data collected by the NASA GSFC Pandora Spectrometer Project located at NASA Langley Research Center, the Chesapeake Bay Bridge Tunnel, SERC Research Vessel, Virginia Commonwealth University (VCU) and Wallops Flight Facility during the OWLETS field campaign. OWLETS was supported by the NASA Science Innovation Fund (SIF). Data collection is complete.Coastal regions have typically posed a challenge for air quality researchers due to a lack of measurements available over water and water-land boundary transitions. Supported by NASA’s Science Innovation Fund (SIF), the Ozone Water-Land Environmental Transition Study (OWLETS) field campaign examined ozone concentrations and gradients over the Chesapeake Bay from July 5, 2017 – August 3, 2017, with twelve intensive measurement days occurring during this time period. OWLETS utilized a unique combination of instrumentation, including aircraft, TOLNet ozone lidars (NASA Goddard Space Flight Center Tropospheric Ozone Differential Absorption Lidar and NASA Langley Research Center Mobile Ozone Lidar), UAV/drones, ozonesondes, AERONET sun photometers, and mobile and ship-based measurements, to characterize the land-water differences in ozone and other pollutants. Two main research sites were established as part of the campaign: an over-land site at NASA LaRC, and an over-water site at the Chesapeake Bay Bridge Tunnel. These two research sites were established to provide synchronous vertical measurements of meteorology and pollutants over water and over land. In combination with mobile observations between the two sites, pollutant gradients were able to be observed and used to better understand the fundamental processes occurring at the land-water interface. OWLETS-2 was completed from June 6, 2018 – July 6, 2018 in the upper Chesapeake Bay region. Research sites were established at the University of Maryland, Baltimore County (UMBC), Hart Miller Island (HMI), and Howard University Beltsville (HUBV), with HMI representing the over-water location and UMBC and HUBV representing the over-land sites. Similar measurements were carried out to further characterize water-land gradients in the upper Chesapeake Bay. The measurements completed during OWLETS are of importance in enhancing air quality models, and improving future satellite retrievals, particularly, NASA’s Tropospheric Emissions: Monitoring of Pollution, which is scheduled to launch in 2022.

One of the key problems that arises in many areas is to estimate a potentially nonlinear function [tex] G(x, \theta)[/tex] given input and output samples tex [/tex] so that [tex]y approx G(x, \theta)[/tex]. There are many approaches to addressing this regression problem. Neural networks, regression trees, and many other methods have been developed to estimate [tex]$G$[/tex] given the input output pair tex [/tex]. One method that I have worked with is called Gaussian process regression. There many good texts and papers on the subject. For more technical information on the method and its applications see: http://www.gaussianprocess.org/ A key problem that arises in developing these models on very large data sets is that it ends up requiring an [tex]O(N^3)[/tex] computation where N is the number of data points and the training sample. Obviously this becomes very problematic when N is large. I discussed this problem with Leslie Foster, a mathematics professor at San Jose State University. He, along with some of his students, developed a method to address this problem based on Cholesky decomposition and pivoting. He also shows that this leads to a numerically stable result. If ou're interested in some light reading, I’d suggest you take a look at his recent paper (which was accepted in the Journal of Machine Learning Research) posted on dashlink. We've also posted code for you to try it out. Let us know how it goes. If you are interested in applications of this method in the area of prognostics, check out our new paper on the subject which was published in IEEE Transactions on Systems, Man, and Cybernetics.

This paper describes the results of a significant research and development effort conducted at NASA Ames Research Center to develop new text mining algorithms to discover anomalies in free-text reports regarding system health and safety of two aerospace systems. We discuss two problems of significant import in the aviation industry. The first problem is that of automatic anomaly discovery concerning an aerospace system through the analysis of tens of thousands of free-text problem reports that are written about the system. The second problem that we address is that of automatic discovery of recurring anomalies, i.e., anomalies that may be described in different ways by different authors, at varying times and under varying conditions, but that are truly about the same part of the system. The intent of recurring anomaly identification is to determine project or system weakness or high-risk issues. The discovery of recurring anomalies is a key goal in building safe, reliable, and cost-effective aerospace systems.

NASA GLOBE Community science project Leveraging Online and User Data through GLOBE And Zooniverse Engagement, or CLOUD GAZE is a NASA funded pilot project aimed to help NASA better understand the effect clouds are having on Earth’s climate. The CLOUD GAZE project is a collaboration between two giants of citizen science: The GLOBE Program and the Zooniverse online platform and is funded through NASA’s Citizen Science for Earth Systems Program. The CLOUD GAZE citizen science project characterizes cloud properties from sky photographs sent in through GLOBE Clouds ground observations. The GLOBE Clouds/CLOUD GAZE team at NASA Langley Research Center extracts cloud properties from sky photographs submitted to the GLOBE Program using the Zooniverse online platform.

The team produces datasets from three sources: ground-cloud observations from The GLOBE Program collocated with NASA/NOAA satellite data and the CLOUD GAZE cloud cover and cloud type characterizations. The datasets are for cloud type worldwide investigations and serve as training sets for machine learning. This data is provided as CSV files.

NASA GLOBE CLOUD GAZE Data Description

The data obtained from the Zooniverse, NASA Langley Research Center (NASA LaRC), and The GLOBE Program are free of charge for use in research, publications, and commercial applications. When data from The Zooniverse, The GLOBE Program, and NASA LaRC are used in a publication, we request this acknowledgment be included, "These data were obtained from the Zooniverse online platform, the GLOBE Program and NASA Langley Research Center." Please include such statements, either where the use of the data or other resource is described, or within the Acknowledgements section of the publication.

A framework to compare and evaluate diagnosis algorithms (DAs) has been created jointly by NASA Ames Research Center and PARC. In this paper, we present the first concrete implementation of this framework as a competition called DXC’09. The goal of this competition was to evaluate and compare DAs in a common platform and to determine a winner based on diagnosis results. 12 DAs (model-based and otherwise) competed in this first year of the competition in 3 tracks that included industrial and synthetic systems. Specifically, the participants provided algorithms that communicated with the run-time architecture to receive scenario data and return diagnostic results. These algorithms were run on extended scenario data sets (different from sample set) to compute a set of pre-defined metrics. A ranking scheme based on weighted metrics was used to declare winners. This paper presents the systems used in DXC’09, description of faults and data sets, a listing of participating DAs, the metrics and results computed from running the DAs, and a superficial analysis of the results.

The field of Prognostic Health Management (PHM) has been undergoing rapid growth in recent years, with development of increasingly sophisticated techniques for diagnosing faults in system components and estimating fault progression tra- jectories. Research efforts on how to utilize prognostic health information (e.g. for extending the remaining useful life of the system, increasing safety, or maximizing operational ef- fectiveness) are mostly in their early stages, however. This process of using prognostic information to determine a sys- tem’s actions or its configuration is beginning to be referred to as Prognostic Decision Making (PDM). In this paper we, first, propose a formulation of the PDM problem with the at- tributes of the aerospace domain in mind, outline some of the key requirements on PDM methods, and explore techniques that can be used as a foundation of PDM development. The problem of Pareto set viability, i.e. satisfaction of perfor- mance goals set for objective functions, is discussed next, followed by ideas for possible solutions. The ideas, termed Dynamic Constraint Redesign (DCR), have roots in the fields of Multidisciplinary Design Optimization and Game Theory. Prototype PDM and DCR algorithms are also described and results of their testing are presented.

We detail all of the facets of adapting classical model checking to a real aerospace system, in- cluding deriving the formal model and a set of specifications from natural language descriptions. To ensure the model checking results are meaningful, we have to ensure that both the model and specifications correctly reflect the intentions of the designers, thus we employ model validation and property debugging techniques. We demonstrate the utility of enhancing LTL satisfiability checking by taking the fairness constraints of the system model into consideration. We argue that specification debugging in real applications deserves more attention in future research efforts, and the utility of a system formalization, model and specification debugging, and verification trilogy for model checking real systems under development. In this paper we assume there are no hardware failures or lost messages. As the AAC design develops and hardware details are decided by AAC designers, we plan to take the failure rates of the chosen components into consideration, i.e. by extending our work to probabilistic model checking using PRISM [19]. Previous work has reported on analyzing the safety of air traffic control systems using simulation [3] or fault trees [1]. By extending the model we designed in this paper, we can carry out safety analysis using PRISM to capture the dynamic interactions in the AAC.

This dataset provides the final report from the PAN tropical investigation of bioGeochemistry and Ecological Adaptation (PANGEA) scoping study. PANGEA is one of the two scoping studies funded by NASA in 2023 to identify the scientific questions and develop the initial study design and implementation concept for a new NASA Terrestrial Ecology field campaign. This report provides 1) the scientific rationale; 2) an initial study design concept; 3) a presentation of science questions, goals, and objectives; 4) the rationale in terms of state-of-the-art, relevance, and expected advances; 5) implementation concepts; and 6) other information to enable NASA to fully evaluate the project. This report outlines the PANGEA concept, including the PANGEA science themes, science questions, the scientific and technical advancement arising from PANGEA, the critical role of NASA remote sensing, PANGEA's research strategy and study design, PANGEA's capacity-building and training priorities, community engagement strategy, ability to enable Earth Action, and technical and logistical feasibility. The PANGEA concept reflects the voices of many and was developed in collaboration with over 800 individuals representing over 300 organizations from 42 countries across five continents. This report is provided in five languages including English, Spanish, French, Portuguese, and Indonesian.

Multivariate Time-Series (MTS) are ubiquitous, and are generated in areas as disparate as sensor recordings in aerospace systems, music and video streams, medical monitoring, and financial systems. Domain experts are often interested in searching for interesting multivariate patterns from these MTS databases which can contain up to several gigabytes of data. Surprisingly, research on MTS search is very limited. Most existing work only supports queries with the same length of data, or queries on a fixed set of variables. In this paper, we propose an efficient and flexible subsequence search framework for massive MTS databases, that, for the first time, enables querying on any subset of variables with arbitrary time delays between them. We propose two provably correct algorithms to solve this problem — (1) an R-tree Based Search (RBS) which uses Minimum Bounding Rectangles (MBR) to organize the subsequences, and (2) a List Based Search (LBS) algorithm which uses sorted lists for indexing. We demonstrate the performance of these algorithms using two large MTS databases from the aviation domain, each containing several millions of observations. Both these tests show that our algorithms have very high prune rates (>95%) thus needing actual disk access for only less than 5% of the observations. To the best of our knowledge, this is the first flexible MTS search algorithm capable of subsequence search on any subset of variables. Moreover, MTS subsequence search has never been attempted on datasets of the size we have used in this paper.

Distributed wireless architectures for prognostics is an important enabling step in prognostic research in order to achieve feasible real-time system health management. A significant problem encountered in implementation of such architectures is power management. In this paper, we present robust power management techniques for a generic health management architecture that involves diagnostics and prognostics for a system comprising multiple heterogeneous components. Our power management techniques are based on online dynamic monitoring of the sensor battery discharge profile which enables accurate predictions of when the device should be put into low power modes. In our architecture, low power mode is achieved by run-time sampling rate modification through sleep states. Our experiments with a cluster of smart sensors for a hybrid diagnostics and prognostics architecture show significant gains in power management without severe loss in performance.

NVAP_OCEAN_Total-Precipitable-Water data set includes only data from the Special Sensor Microwave/Imager (SSM/I) and intends to mirror other available SSM/I-only water vapor data sets. The data set is used for studies of climate change, interannual variability, and independent comparison to other ocean-only data sets. The new NASA Water Vapor Project (NVAP) data sets are produced under the NASA Making Earth Science Data Records for Use in Research Environments (MEaSUREs) program and is named NVAP-M. It supersedes the previous NVAP data set. NVAP-M continues the legacy of providing high-quality, model-independent global estimates of total column and layered water vapor. The use of improved, intercalibrated data sets and algorithms that were not available for the heritage NVAP data set results in an improved and extended water vapor data set that is stable enough for climate research and of a resolution appropriate for studies on smaller spatial and temporal scales. The true value of NVAP-M will be seen in outcomes from applied and research users of the data set in various fields. Some initial NVAP-M findings are presented in Vonder Haar et al. (2012). In addition to the time-dependent artifacts present in the previous NVAP data set, a wealth of new data has become available since the last NVAP processing in 2003. These include an additional SSM/I instrument, additional NOAA satellites, the NASA Earth Observing System (EOS)-Aqua Satellite, which carries the Atmospheric Infrared Sounder (AIRS), as well as water vapor information from Global Positioning System (GPS) satellites. This extension and reprocessing effort increases the temporal coverage from 14 to 22 (1988-2009) years, making the data set more useful and consistent for investigation of the long-term trends which are hypothesized to occur as Earth warms. In addition to the long-standing daily, 1-degree gridded Total Precipitable Water (TPW) and layered Precipitable Water (PW) products, NVAP-M includes additional products geared towards different scientific needs. Three separate processing streams produced products directed towards specific research goals. These are NVAP-M Climate, designed to provide the most stable water vapor data set over time for use in climate applications, and NVAP-M Weather, designed to provide higher spatial and temporal resolution products for use in studies on shorter time scales as well as weather case studies. Additionally, an ocean-only (NVAP-M Ocean) version includes only data from the SSM/I and is intended to mirror other available SSM/I-only water vapor data sets.

NVAP_CLIMATE_Total-Precipitable-Water data set is designed to provide the most stable water vapor dataset over time for use in climate applications. NASA Water Vapor Project MEaSUREs (NVAP-M) Climate only includes data from stable instruments that have undergone intercalibration efforts to ensure consistency between data from the same instrument flying on multiple satellite platforms. The new NVAP data sets are produced under the NASA Making Earth Science Data Records for Use in Research Environments (MEaSUREs) program and is named NVAP-M. It supersedes the previous NVAP data set. NVAP-M continues the legacy of providing high-quality, model-independent global estimates of total column and layered water vapor. The use of improved, intercalibrated data sets and algorithms that were not available for the heritage NVAP data set results in an improved and extended water vapor data set that is stable enough for climate research and of a resolution appropriate for studies on smaller spatial and temporal scales. The true value of NVAP-M will be seen in outcomes from applied and research users of the data set in various fields. Some initial NVAP-M findings are presented in Vonder Haar et al. (2012). In addition to the time-dependent artifacts present in the previous NVAP data set, a wealth of new data has become available since the last NVAP processing in 2003. These include an additional SSM/I instrument, additional NOAA satellites, the NASA Earth Observing System (EOS)-Aqua Satellite, which carries the Atmospheric Infrared Sounder (AIRS), as well as water vapor information from Global Positioning System (GPS) satellites. This extension and reprocessing effort increases the temporal coverage from 14 to 22 (1988-2009) years, making the data set more useful and consistent for investigation of the long-term trends which are hypothesized to occur as Earth warms. In addition to the long-standing daily, 1-degree gridded Total Precipitable Water (TPW) and layered Precipitable Water (PW) products, NVAP-M includes additional products geared towards different scientific needs. Three separate processing streams produced products directed towards specific research goals. These are NVAP-M Climate, designed to provide the most stable water vapor data set over time for use in climate applications, and NVAP-M Weather, designed to provide higher spatial and temporal resolution products for use in studies on shorter time scales as well as weather case studies. Additionally, an ocean-only (NVAP-M Ocean) version includes only data from the SSM/I and is intended to mirror other available SSM/I-only water vapor data sets.

Measurements taken off the Antarctic coast in the Ross Sea between 1996 and 1998 under the Research on Ocean-Atmosphere Variability and Ecosystem Response in the Ross Sea (ROAVERRS).