Collection of data from in situ sensors via satellite
Earth resources and meteorological data collection from in situ sensors via satellite, discussing system considerations
Dorfman, S. D.↗
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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Use of MAGSAT anomaly data for crustal structure and mineral resources in the US Midcontinent
Personnel matters related to the processing and interpretation of MAGSAT data are reported. Efforts are being initiated to determine the crustal geology, structure, and potential economic consequences to be deduced from the satellite magnetic anomalies in conjuction with correlative data.
Carmichael, R. S.↗
Use of MAGSAT anomaly data for crustal structure and mineral resources in the US Midcontinent
The analysis and preliminary interpretation of investigator-B MAGSAT data are addressed. The data processing included: (1) removal of spurious data points; (2) statistical smoothing along individual data tracks, to reduce the effect of geomagnetic transient disturbances; (3) comparison of data profiles spatially coincident in track location but acquired at different times; (4) reduction of data by weighted averaging to a grid with 1 deg xl deg latitude/longitude spacing, and with elevations interpolated and weighted to a common datum of 400 km; (5) wavelength filtering; and (6) reduction of the anomaly map to the magnetic pole. Agreement was found between a magnitude data anomaly map and a reduce-to-the-pole map supporting the general assumption that, on a large scale (long wavelength), it is induced crustal magnetization which is responsible for major anamalies. Anomalous features are identified and explanations are suggested with regard to crustal structure, petrologic characteristics, and Curie temperature isotherms.
Carmichael, R. S.↗
NASA Ames DEVELOP Interns: Helping the Western United States Manage Natural Resources One Project at a Time
The western half of the United States is made up of a number of diverse ecosystems ranging from arid desert to coastal wetlands and rugged forests. Every summer for the past 7 years students ranging from high school to graduate level gather at NASA Ames Research Center (ARC) as part of the DEVELOP Internship Program. Under the guidance of Jay Skiles [Ames Research Center (ARC) - Ames DEVELOP Manager] and Cindy Schmidt [ARC/San Jose State University Ames DEVELOP Coordinator] they work as a team on projects exploring topics including: invasive species, carbon flux, wetland restoration, air quality monitoring, storm visualizations, and forest fires. The study areas for these projects have been in Washington, Utah, Oregon, Nevada, Hawaii, Alaska and California. Interns combine data from NASA and partner satellites with models and in situ measurements to complete prototype projects demonstrating how NASA data and resources can help communities tackle their Earth Science related problems.
Justice, Erin↗
Toward unrestricted use of public genomic data
Despite some notable progress in data sharing policies and practices, restrictions are still often placed on the open and unconditional use of various genomic data after they have received official approval for release to the public domain or to public databases. These restrictions, which often conflict with the terms and conditions of the funding bodies who supported the release of those data for the benefit of the scientific community and society, are perpetuated by the lack of clear guiding rules for data usage. Existing guidelines for data released to the public domain recognize but fail to resolve tensions between the importance of free and unconditional use of these data and the “right” of the data producers to the first publication. This self-contradiction has resulted in a loophole that allows different interpretations and a continuous debate between data producers and data users on the use of public data. We argue that the publicly available data should be treated as open data, a shared resource with unrestricted use for analysis, interpretation, and publication.
59 BASIC BIOLOGICAL SCIENCES↗
A mass-conserving lattice Boltzmann method with dynamic grid refinement for immiscible two-phase flows
A mass-conserving lattice Boltzmann method (LBM) for multiphase flows is presented in this paper. The proposed LBM improves a previous model (Lee and Liu, 2010 [21]) in terms of mass conservation, speed-up, and efficiency, and also extends its capabilities for implementation on non-uniform grids. The presented model consists of a phase-field lattice Boltzmann equation (LBE) for tracking the interface between different fluids and a pressure-evolution LBM for recovering the hydrodynamic properties. In addition to the mass conservation property and the simplicity of the algorithm, the advantages of the current phase-field LBE are that it is an order of magnitude faster than the previous interface tracking LBE proposed by Lee and Liu (2010) [21] and it requires less memory resources for data storage. Meanwhile, the pressure-evolution LBM is equipped with a multi-relaxation-time (MRT) collision operator to facilitate attainability of small relaxation rates thereby allowing simulation of multiphase flows at higher Reynolds numbers. Additionally, we reformulate the presented MRT-LBM on nonuniform grids within an adaptive mesh refinement (AMR) framework. Various benchmark studies such as a rising bubble and a falling drop under buoyancy, droplet splashing on a wet surface, and droplet coalescence onto a fluid interface are conducted to examine the accuracy and versatility of the proposed AMR-LBM. The proposed model is further validated by comparing the results with other LB models on uniform grids. A factor of about 20 in savings of computational resources is achieved by using the proposed AMR-LBM. As a more demanding application, the Kelvin–Helmholtz instability (KHI) of a shear-layer flow is investigated for both density-matched and density-stratified binary fluids. The KHI results of the density-matched fluids are shown to be in good agreement with the benchmark AMR results based on the sharp-interface approach. When a density contrast between the two fluids exists, a typical chaotic structure in the flow field is observed at a Reynolds number of 10000, which indicates that the proposed model is a promising tool for direct numerical simulation of two-phase flows.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
BAAD: a Biomass And Allometry Database for woody plants
Quantifying the amount of mass or energy invested in plant tissues is of fundamental interest across a range of disciplines, including ecology, forestry, ecosystem science, and climate change science (Niklas, 1994; Chave et al. 2005; Falster et al. 2011). The allocation of net primary production into different plant components is an important process affecting the lifetime of carbon in ecosystems, and resource use and productivity by plants (Cannell & Dewar, 1994; Litton et al. 2007; Poorter et al. 2012). While many studies in have destructively harvested woody plants in the name of science, most of these data have only been made available in the form of summary tables or figures included in publications. Until now, the raw data has resided piecemeal on the hard drives of individual scientists spread around the world. Several studies have gathered together the fitted (allometric) equations for separate datasets (Ter-Mikaelian & Korzukhin, 1997; Jenkins et al. 2003; Zianis et al. 2005; Henry et al. 2013), but none have previously attempted to organize and share the raw individual plant data underpinning these equations on a large scale. Gathered together, such data would represent an important resource for the community, meeting a widely recognised need for rich, open data resources to solve ecological problems (Costello et al. 2013; Fady et al. 2014; Harfoot & Roberts, 2014; Costello et al. 2013). We (D.S. Falster and R.A. Duursma, with the help of D.R. Barneche, R.G. FitzJohn and A. Vårhammar) set out to create such a resource, by asking authors directly whether they would be willing to make their raw data files freely available. The response was overwhelming: nearly everyone we contacted was interested to contribute their raw data. Moreover, we were invited to incorporate another compilation led by M. Ishihara and focussing on Japanese literature. As a result, we present BAAD: a Biomass And Allometry Database for woody plants, comprising data collected in 174 different published and unpublished studies.
Falster, Daniel↗
Maximum Likelihood Synchronization for Pulse Position Modulation with Inter-Symbol Guard Times
Deep space optical communications promises orders-of-magnitude growth in communication capacity, supporting high data rate applications such as video streaming and high-bandwidth science instruments. Pulse position modulation is the modulation format of choice for deep space applications, and by inserting inter-symbol guard times between the symbols, the signal carries the timing information needed by the demodulator. Accurately extracting this timing information is crucial to demodulating and decoding this signal. In this paper we propose a low complexity maximum likelihood timing estimator for pulse position modulation with inter-symbol guard times which significantly outperforms the prior art in this domain. We show that this estimator can achieve the same performance as prior estimators with an order-of-magnitude less signal flux, or multiple orders-of-magnitude less flux-accumulation time. Further we show that this estimator achieves the Cramer-Rao bound, making it asymptotically efficient. This method does not require an explicit synchronization sequence, freeing up channel resources for data transmission.
Rogalin, Ryan↗
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↗
An XML Representation for Crew Procedures
NASA ensures safe operation of complex systems through the use of formally-documented procedures, which encode the operational knowledge of the system as derived from system experts. Crew members use procedure documentation on the ground for training purposes and on-board space shuttle and space station to guide their activities. Investigators at JSC are developing a new representation for procedures that is content-based (as opposed to display-based). Instead of specifying how a procedure should look on the printed page, the content-based representation will identify the components of a procedure and (more importantly) how the components are related (e.g., how the activities within a procedure are sequenced; what resources need to be available for each activity). This approach will allow different sets of rules to be created for displaying procedures on a computer screen, on a hand-held personal digital assistant (PDA), verbally, or on a printed page, and will also allow intelligent reasoning processes to automatically interpret and use procedure definitions. During his NASA fellowship, Dr. Simpson examined how various industries represent procedures (also called business processes or workflows), in areas such as manufacturing, accounting, shipping, or customer service. A useful method for designing and evaluating workflow representation languages is by determining their ability to encode various workflow patterns, which depict abstract relationships between the components of a procedure removed from the context of a specific procedure or industry. Investigators have used this type of analysis to evaluate how well-suited existing workflow representation languages are for various industries based on the workflow patterns that commonly arise across industry-specific procedures. Based on this type of analysis, it is already clear that existing workflow representations capture discrete flow of control (i.e., when one activity should start and stop based on when other activities start and stop), but do not capture the flow of data, materials, resources or priorities. Existing workflow representation languages are also limited to representing sequences of discrete activities, and cannot encode procedures involving continuous flow of information or materials between activities.
Simpson, Richard C.↗
A Model Tree Generator (MTG) Framework for Simulating Hydrologic Systems: Application to Reservoir Routing
Data-driven algorithms have been widely used as effective tools to mimic hydrologic systems. Unlike black-box models, decision tree algorithms offer transparent representations of systems and reveal useful information about the underlying process. A popular class of decision tree models is model tree (MT), which is designed for predicting continuous variables. Most MT algorithms employ an exhaustive search mechanism and a pre-defined splitting criterion to generate a piecewise linear model. However, this approach is computationally intensive, and the selection of the splitting criterion can significantly affect the performance of the generated model. These drawbacks can limit the application of MTs to large datasets. To overcome these shortcomings, a new flexible Model Tree Generator (MTG) framework is introduced here. MTG is equipped with several modules to provide a flexible, efficient, and effective tool for generating MTs. The application of the algorithm is demonstrated through simulation of controlled discharge from several reservoirs across the Contiguous United States (CONUS).
54 ENVIRONMENTAL SCIENCES↗
Sustaining the National Geothermal Data System: Considerations for a System Wide Approach and Node Maintenance, Geothermal Resources Council 37th Annual Meeting, Las Vegas, Nevada, September 29-October 2, 2013
Since the 2009 American Recovery and Reinvestment Act the U.S. Department of Energy’s Geothermal Technologies Office has funded $33.7 million for multiple data digitization and aggregation projects focused on making vast amounts of geothermal relevant data available to industry for advancing geothermal exploration. These projects are collectively part of the National Geothermal Data System (NGDS), a distributed, networked system for maintaining, sharing, and accessing data in an effort to lower the levelized cost of electricity (LCOE). Determining “who owns” and “who maintains” the NGDS and its data nodes (repositories in the distributed system) is yet to be determined. However, the invest- ment in building and populating the NGDS has been substantial, both in terms of dollars and time; it is critical that this investment be protected by ensuring sustainability of the data, the software and systems, and the accessibility of the data. Only then, will the benefits be fully realized. To keep this operational system sustainable will require four core elements: continued serving of data and applications; maintenance of system operations; a governance structure; and an effective business model. Each of these presents a number of challenges. Data being added to the NGDS are not strictly geothermal but data considered relevant to geothermal exploration and develop- ment, including vast amounts of oil and gas and groundwater wells, among other data. These are relevant to a broader base of users. By diversifying the client base to other users and other fields, the cost of maintaining core infrastructure can be spread across an array of stakeholders and clients. It is presumed that NGDS will continue to provide free and open access to its data resources. The next-phase NGDS operation should be structured to eventually pursue revenue streams to help off-set sustainability expenses as necessary and appropriate, potentially including income from: grants and contracts (agencies, foundations, pri- vate sector), membership, fees for services (consulting, training, customization, ‘app’ development), repository services (data, services, apps, models, documents, multimedia), advertisements, fees for premier services or applications, subscriptions to value added services, licenses, contributions and donations, endow- ments, and sponsorships.
15 GEOTHERMAL ENERGY↗
Accuracy of Genomic Prediction for Foliar Terpene Traits in Eucalyptus polybractea
Unlike agricultural crops, most forest species have not had millennia of improvement through phenotypic selection, but can contribute energy and material resources and possibly help alleviate climate change. Yield gains similar to those achieved in agricultural crops over millennia could be made in forestry species with the use of genomic methods in a much shorter time frame. Here we compare various methods of genomic prediction for eight traits related to foliar terpene yield in Eucalyptus polybractea , a tree grown predominantly for the production of Eucalyptus oil. The genomic markers used in this study are derived from shallow whole genome sequencing of a population of 480 trees. We compare the traditional pedigree-based additive best linear unbiased predictors (ABLUP), genomic BLUP (GBLUP), BayesB genomic prediction model, and a form of GBLUP based on weighting markers according to their influence on traits (BLUP|GA). Predictive ability is assessed under varying marker densities of 10,000, 100,000 and 500,000 SNPs. Our results show that BayesB and BLUP|GA perform best across the eight traits. Predictive ability was higher for individual terpene traits, such as foliar α-pinene and 1,8-cineole concentration (0.59 and 0.73, respectively), than aggregate traits such as total foliar oil concentration (0.38). This is likely a function of the trait architecture and markers used. BLUP|GA was the best model for the two biomass related traits, height and 1 year change in height (0.25 and 0.19, respectively). Predictive ability increased with marker density for most traits, but with diminishing returns. The results of this study are a solid foundation for yield improvement of essential oil producing eucalypts. New markets such as biopolymers and terpene-derived biofuels could benefit from rapid yield increases in undomesticated oil-producing species.
54 ENVIRONMENTAL SCIENCES↗
Information Power Grid: Distributed High-Performance Computing and Large-Scale Data Management for Science and Engineering
We use the term "Grid" to refer to distributed, high performance computing and data handling infrastructure that incorporates geographically and organizationally dispersed, heterogeneous resources that are persistent and supported. This infrastructure includes: (1) Tools for constructing collaborative, application oriented Problem Solving Environments / Frameworks (the primary user interfaces for Grids); (2) Programming environments, tools, and services providing various approaches for building applications that use aggregated computing and storage resources, and federated data sources; (3) Comprehensive and consistent set of location independent tools and services for accessing and managing dynamic collections of widely distributed resources: heterogeneous computing systems, storage systems, real-time data sources and instruments, human collaborators, and communications systems; (4) Operational infrastructure including management tools for distributed systems and distributed resources, user services, accounting and auditing, strong and location independent user authentication and authorization, and overall system security services The vision for NASA's Information Power Grid - a computing and data Grid - is that it will provide significant new capabilities to scientists and engineers by facilitating routine construction of information based problem solving environments / frameworks. Such Grids will knit together widely distributed computing, data, instrument, and human resources into just-in-time systems that can address complex and large-scale computing and data analysis problems. Examples of these problems include: (1) Coupled, multidisciplinary simulations too large for single systems (e.g., multi-component NPSS turbomachine simulation); (2) Use of widely distributed, federated data archives (e.g., simultaneous access to metrological, topological, aircraft performance, and flight path scheduling databases supporting a National Air Space Simulation systems}; (3) Coupling large-scale computing and data systems to scientific and engineering instruments (e.g., realtime interaction with experiments through real-time data analysis and interpretation presented to the experimentalist in ways that allow direct interaction with the experiment (instead of just with instrument control); (5) Highly interactive, augmented reality and virtual reality remote collaborations (e.g., Ames / Boeing Remote Help Desk providing field maintenance use of coupled video and NDI to a remote, on-line airframe structures expert who uses this data to index into detailed design databases, and returns 3D internal aircraft geometry to the field); (5) Single computational problems too large for any single system (e.g. the rotocraft reference calculation). Grids also have the potential to provide pools of resources that could be called on in extraordinary / rapid response situations (such as disaster response) because they can provide common interfaces and access mechanisms, standardized management, and uniform user authentication and authorization, for large collections of distributed resources (whether or not they normally function in concert). IPG development and deployment is addressing requirements obtained by analyzing a number of different application areas, in particular from the NASA Aero-Space Technology Enterprise. This analysis has focussed primarily on two types of users: the scientist / design engineer whose primary interest is problem solving (e.g. determining wing aerodynamic characteristics in many different operating environments), and whose primary interface to IPG will be through various sorts of problem solving frameworks. The second type of user is the tool designer: the computational scientists who convert physics and mathematics into code that can simulate the physical world. These are the two primary users of IPG, and they have rather different requirements. The results of the analysis of the needs of these two types of users provides a broad set of requirements that gives rise to a general set of required capabilities. The IPG project is intended to address all of these requirements. In some cases the required computing technology exists, and in some cases it must be researched and developed. The project is using available technology to provide a prototype set of capabilities in a persistent distributed computing testbed. Beyond this, there are required capabilities that are not immediately available, and whose development spans the range from near-term engineering development (one to two years) to much longer term R&D (three to six years). Additional information is contained in the original.
Johnston, William E.↗
A Big Data Analytics Framework for HPC Log Data: Three Case Studies Using the Titan Supercomputer Log
Reliability, availability and serviceability (RAS) logs of high performance computing (HPC) resources, when closely investigated in spatial and temporal dimensions, can provide invaluable information regarding system status, performance, and resource utilization. These data are often generated from multiple logging systems and sensors that cover many components of the system. The analysis of these data for finding persistent temporal and spatial insights faces two main difficulties: the volume of RAS logs makes manual inspection difficult and the unstructured nature and unique properties of log data produced by each subsystem adds another dimension of difficulty in identifying implicit correlation among recorded events. To address these issues, we recently developed a multi-user Big Data analytics framework for HPC log data at Oak Ridge National Laboratory (ORNL). This paper introduces three in-progress data analytics projects that leverage this framework to assess system status, mine event patterns, and study correlations between user applications and system events. We describe the motivation of each project and detail their workflows using three years of log data collected from ORNL's Titan supercomputer.
Park, Byung↗
Use of MAGSAT anomaly data for crustal structure and mineral resources in the US midcontinent
Progress in the correlation of MAGSAT anomaly maps with geological and geophysical data sets is reported. An excerpt from Bouguer gravity map of the U.S. was filtered to retain wavelengths of 250 km, thus being physically somewhat analogous to MAGSAT data at 400 km height. Residual anomalies were extracted to compare with the satellite magnetics.
Carmichael, R. S.↗
A database and framework for carbon ore resources and associated supply chain data
Not Available
Justman, Devin↗