A General Rogh-Surface Inversion Algorithm: Theory and Application to SAR Data
This paper discusses the small perturbation model (SPM) used to express backscattering coefficients of the rough surface.
rough-surface inversion algorithms SAR data↗
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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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A review of preserving privacy in data collected from buildings with differential privacy
Significant amounts of data are collected in buildings. While these data have great potential for maximizing the energy efficiency of buildings in general, only a small portion of the data are accessible to researchers, government, and industry for analyses. Concerns about privacy are one of the major barriers prohibiting access to these data. Privacy preservation techniques are generally applied to this problem not only to preserve underlying privacy but also to improve the usefulness of data. Among various privacy preserving techniques, differential privacy has become one of the more popular solutions since its introduction in 2006. Differential privacy is a mathematical measure for protecting privacy so that one's privacy cannot be incurred by participating in a database. Additionally, although significant research improvements have been made for more than a decade, applying differential privacy to data collected in buildings is still an immature field of study. Because implementing differential privacy on a certain use case is not straightforward and can be achieved with various configurations, it is important to understand variation of configurations with different use cases around data collected from buildings. This literature review aims to introduce what has been done to implement differential privacy in data collected in buildings, and to discuss associated challenges and potential future research opportunities.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Discard survival of coastal elasmobranchs in a small-scale fishery using acoustic telemetry and recapture data
Not Available
Alonso-Fernández, Alexandre↗
Leveraging Large Language Models for Understanding Fundamental Principles of Catalysis
Heterogeneous catalysis presents a distinct challenge for artificial intelligence (AI). Data sets are often small and inconsistently reported, catalyst representations are not standardized, and extracting fundamental knowledge requires integrating performance data, spectroscopic characterizations, and mechanistic models across multiple scales. Language offers a unifying representation across these modalities, making catalysis well suited for leveraging large language models (LLMs). By standardizing how catalytic data is represented, LLMs make dispersed experimental results more accessible to downstream statistical modeling. In this perspective, we focus our discussion around three opportunities where LLMs can significantly contribute to catalysis: (1) text to properties; (2) text to structure; and (3) text to mechanistic models. The discussion is followed by a perspective section on LLM-readiness of data, aligning LLM outputs with scientific correctness, and bridging lab-scale discovery to industrial deployment. Across each area, the most productive applications couple dispersed chemical knowledge with physics-grounded validation to produce verifiable hypotheses and actionable representations.
Catalysts↗
Workflows Community Summit 2022: A Roadmap Revolution
Scientific workflows have become integral tools in broad scientific computing use cases. Science discovery is increasingly dependent on workflows to orchestrate large and complex scientific experiments that range from the execution of a cloud-based data preprocessing pipeline to multi-facility instrument-to-edge-to-HPC computational workflows. Given the changing landscape of scientific computing (often referred to as a computing continuum) and the evolving needs of emerging scientific applications, it is paramount that the development of novel scientific workflows and system functionalities seek to increase the efficiency, resilience, and pervasiveness of existing systems and applications. Specifically, the proliferation of machine learning/artificial intelligence (ML/AI) workflows, need for processing large-scale datasets produced by instruments at the edge, intensification of near real-time data processing, support for long-term experiment campaigns, and emergence of quantum computing as an adjunct to HPC, have significantly changed the functional and operational requirements of workflow systems. Workflow systems now need to, for example, support data streams from the edge-to-cloud-to-HPC, enable the management of many small-sized files, allow data reduction while ensuring high accuracy, orchestrate distributed services (workflows, instruments, data movement, provenance, publication, etc.) across computing and user facilities, among others. Further, to accelerate science, it is also necessary that these systems implement specifications/standards and APIs for seamless (horizontal and vertical) integration between systems and applications, as well as enable the publication of workflows and their associated products according to the FAIR principles.
97 MATHEMATICS AND COMPUTING↗
Development of a high-speed H-alpha camera system for the observation of rapid fluctuations in solar flares
A solid-state digital camera was developed for obtaining H alpha images of solar flares with 0.1 s time resolution. Beginning in the summer of 1988, this system will be operated in conjunction with SMM's hard X-ray burst spectrometer (HXRBS). Important electron time-of-flight effects that are crucial for determining the flare energy release processes should be detectable with these combined H alpha and hard X-ray observations. Charge-injection device (CID) cameras provide 128 x 128 pixel images simultaneously in the H alpha blue wing, line center, and red wing, or other wavelength of interest. The data recording system employs a microprocessor-controlled, electronic interface between each camera and a digital processor board that encodes the data into a serial bitstream for continuous recording by a standard video cassette recorder. Only a small fraction of the data will be permanently archived through utilization of a direct memory access interface onto a VAX-750 computer. In addition to correlations with hard X-ray data, observations from the high speed H alpha camera will also be correlated and optical and microwave data and data from future MAX 1991 campaigns. Whether the recorded optical flashes are simultaneous with X-ray peaks to within 0.1 s, are delayed by tenths of seconds or are even undetectable, the results will have implications on the validity of both thermal and nonthermal models of hard X-ray production.
Kiplinger, Alan L.↗
Small-scale microwave background anisotropies implied by large-scale data
In the absence of reheating microwave background radiation (MBR) anisotropies on arcminute scales depend uniquely on the amplitude and the coherence length of the primordial density fluctuations (PDFs). These can be determined from the recent data on galaxy correlations, xi(r), on linear scales (APM survey). We develop here expressions for the MBR angular correlation function, C(theta), on arcminute scales in terms of the power spectrum of PDFs and demonstrate their accuracy by comparing with detailed calculations of MBR anisotropies. We then show how to evaluate C(theta) directly in terms of the observed xi(r) and show that the APM data give information on the amplitude, C(O), and the coherence angle of MBR anisotropies on small scales.
Kashlinsky, A.↗
Canadian aeronautical mobile data trials
This paper describes a series of aeronautical mobile data trials conducted on small aircraft (helicopters and fixed wing) utilizing a low-speed store-and-forward mobile data service. The paper outlines the user requirements for aeronautical mobile satellite communications. 'Flight following' and improved wide-area dispatch communications were identified as high priority requirements. A 'proof-of-concept' trial in a Cessna Skymaster aircraft is described. This trial identified certain development work as essential to the introduction of commercial service including antenna development, power supply modifications and doppler software modifications. Other improvements were also proposed. The initial aeronautical mobile data service available for pre-operational (Beta) trials is outlined. Pre-operational field trials commenced in October 1992 and consisted of installations on a Gralen Communications Inc. Cessna 177 and an Aerospatiale Astar 350 series light single engine helicopter. The paper concludes with a discussion of desirable near term mobile data service developments, commercial benefits, current safety benefits and potential future applications for improved safety.
Pedersen, Allister↗
AveBoost2: Boosting for Noisy Data
AdaBoost is a well-known ensemble learning algorithm that constructs its constituent or base models in sequence. A key step in AdaBoost is constructing a distribution over the training examples to create each base model. This distribution, represented as a vector, is constructed to be orthogonal to the vector of mistakes made by the pre- vious base model in the sequence. The idea is to make the next base model's errors uncorrelated with those of the previous model. In previous work, we developed an algorithm, AveBoost, that constructed distributions orthogonal to the mistake vectors of all the previous models, and then averaged them to create the next base model s distribution. Our experiments demonstrated the superior accuracy of our approach. In this paper, we slightly revise our algorithm to allow us to obtain non-trivial theoretical results: bounds on the training error and generalization error (difference between training and test error). Our averaging process has a regularizing effect which, as expected, leads us to a worse training error bound for our algorithm than for AdaBoost but a superior generalization error bound. For this paper, we experimented with the data that we used in both as originally supplied and with added label noise-a small fraction of the data has its original label changed. Noisy data are notoriously difficult for AdaBoost to learn. Our algorithm's performance improvement over AdaBoost is even greater on the noisy data than the original data.
Oza, Nikunj C.↗
Multiple Satellite Observations of Cloud Cover in Extratropical Cyclones
Using cloud observations from NASA Moderate Resolution Imaging Spectroradiometer, Multiangle Imaging Spectroradiometer, and CloudSat-CALIPSO, composites of cloud fraction in southern and northern hemisphere extratropical cyclones are obtained for cold and warm seasons between 2006 and 2010, to assess differences between these three data sets, and between summer and winter cyclones. In both hemispheres and seasons, over the open ocean, the cyclone-centered cloud fraction composites agree within 5% across the three data sets, but behind the cold fronts, or over sea ice and land, the differences are much larger. To supplement the data set comparison and learn more about the cyclones, we also examine the differences in cloud fraction between cold and warm season for each data set. The difference in cloud fraction between cold and warm season southern hemisphere cyclones is small for all three data sets, but of the same order of magnitude as the differences between the data sets. The cold-warm season contrast in northern hemisphere cyclone cloud fractions is similar for all three data sets: in the warm sector, the cold season cloud fractions are lower close to the low, but larger on the equator edge than their warm season counterparts. This seasonal contrast in cloud fraction within the cyclones warm sector seems to be related to the seasonal differences in moisture flux within the cyclones. Our analysis suggests that the three different data sets can all be used confidently when studying the warm sector and warm frontal zone of extratropical cyclones but caution should be exerted when studying clouds in the cold sector.
imaging spectrometers↗
Midwest Agriculture & Food Security: Leveraging NASA Earth Observations to Analyze and Display Crop Phenology Data and Weather Conditions to Support Expansion of Small Grain Crops in the Midwest
Explore the source record for details and available documents.
Sophie Barrowman↗
Validation of Small Satellite Dynamics Simulation Modules using ASTERIA Flight Data
No abstract provided
Mohan, Swati↗
Leveraging NASA Earth Observations to Analyze and Display Crop Phenology Data and Weather Conditions to Support Expansion of Small Grain Crops in the Midwest
Explore the source record for details and available documents.
Olivia Landry↗
Effect of small‐scale ionospheric variability on GNSS radio occultation data quality
Explore the source record for details and available documents.
O. P. Verkhoglyadova↗
Systematic Evaluation of Backdoor Data Poisoning Attacks on Image Classifiers
Backdoor data poisoning attacks have recently been demonstrated in computer vision research as a potential safety risk for machine learning (ML) systems. Traditional data poisoning attacks manipulate training data to induce unreliability of an ML model, whereas backdoor data poisoning attacks maintain system performance unless the MLmodel is presented with an input containing an embedded“trigger” that provides a predetermined response advantageous to the adversary. Our work builds upon prior back-door data-poisoning research for ML image classifiers and systematically assesses different experimental conditions including types of trigger patterns, persistence of trigger patterns during retraining, poisoning strategies, architectures (ResNet-50, NasNet, NasNet-Mobile), datasets (Flowers, CIFAR-10), and potential defensive regularization techniques (Contrastive Loss, Logit Squeezing, Manifold Mixup,Soft-Nearest-Neighbors Loss). Experiments yield four key findings. First, the success rate of backdoor poisoning at-tacks varies widely, depending on several factors, including model architecture, trigger pattern and regularization technique. Second, we find that poisoned models are hard to detect through performance inspection alone. Third, regularization typically reduces backdoor success rate, although it can have no effect or even slightly increase it, depending on the form of regularization. Finally, backdoors inserted through data poisoning can be rendered ineffective after just a few epochs of additional training on a small set of clean data without affecting the model’s performance.
Truong, Loc T.↗
Shield fields: Concentrations of small volcanic edifices on Venus
Pre-Magellan analysis of the Venera 15/16 data indicated the existence of abundant small volcanic edifices, each less than or equal to 20 km diameter, interpreted to be predominantly shield volcanoes and occurring throughout the plains terrain, most common in equidimensional clusters. With the analysis of Magellan data, these clusters of greater than average concentration of small volcanic edifices have been called 'shield fields'. Although individual small shields can and do occur almost everywhere on the plains terrain of Venus, they most commonly occur in fields that are well-defined, predominantly equant, clusters of edifices. Major questions include why the edifices are concentrated in this way, how they relate to the source of the eruptive material, and what the possible relationship of shield fields to plains terrain is. There are three possible models for the origin of fields and small shields: (1) a field represents an 'island' of higher topography subsequently surrounded by later plains material; and (2) a field represents the area of magma reservoir.
Aubele, J. C.↗
Performance evaluation of a Terry GS-2 steam impulse turbine with air-water mixtures
Terry steam turbines are widely used in various industries because of their robust design. Within the nuclear power generation industry, they are used in the Reactor Core Isolation Cooling System to remove decay heat during reactor isolation events. During the Fukushima Daiichi nuclear power station disaster in Japan in 2011, the Reactor Core Isolation Cooling System and associated Terry turbine operated for over 70 hours in Unit 2; this runtime is well beyond the expected operating duration. Theories suggest the turbine was subjected to a two-phase inlet flow, which could degrade the turbine performance. In this work, an experimental test rig was constructed to test a full-scale Terry model GS-2 steam turbine under two-phase air/water flows. Steady-state efficiency and torque performance maps of the turbine were developed over a range of turbine inlet pressures (1.38–4.83 bar or 20–70 psia), air mass fractions (0.05–1.0) and rotational speeds up to 4000 RPM. Furthermore, turbine performance followed expected trends with torque varying linearly and efficiency varying quadratically with rotational speed. In addition, high-speed images of the two-phase flow entering the turbine were also analyzed to understand how changes in inlet pressure and air mass fraction affect the flow regime and homogenization. The present tests with air–water two-phase mixtures are an important step towards providing an understanding of the full-scale Terry turbine’s behavior and performance curves under two-phase conditions. The results of this work will be combined with air/water and steam/water data gathered using a small-scale Terry ZS-1 steam turbine in order to understand the scaling relationship between large and small size Terry turbines and fluid pairs. The combined data set will enable further development of analytical models over a wide range of conditions and may be used to provide technical justification for expanded use of the Terry turbines in nuclear power plant safety systems and other systems.
42 ENGINEERING↗
DIAS: A Data-Informed Active Subspace Regularization Framework for Inverse Problems
This paper presents a regularization framework that aims to improve the fidelity of Tikhonov inverse solutions. At the heart of the framework is the data-informed regularization idea that only data-uninformed parameters need to be regularized, while the data-informed parameters, on which data and forward model are integrated, should remain untouched. We propose to employ the active subspace method to determine the data-informativeness of a parameter. The resulting framework is thus called a data-informed (DI) active subspace (DIAS) regularization. Four proposed DIAS variants are rigorously analyzed, shown to be robust with the regularization parameter and capable of avoiding polluting solution features informed by the data. They are thus well suited for problems with small or reasonably small noise corruptions in the data. Furthermore, the DIAS approaches can effectively reuse any Tikhonov regularization codes/libraries. Though they are readily applicable for nonlinear inverse problems, we focus on linear problems in this paper in order to gain insights into the framework. Various numerical results for linear inverse problems are presented to verify theoretical findings and to demonstrate advantages of the DIAS framework over the Tikhonov, truncated SVD, and the TSVD-based DI approaches.
Nguyen, Hai↗