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NASA NTRS2011

Using Satellite Aerosol Retrievals to Monitor Surface Particulate Air Quality

The MODIS and MISR aerosol products were designed nearly two decades ago for the purpose of climate applications. Since launch of Terra in 1999, these two sensors have provided global, quantitative information about column-integrated aerosol properties, including aerosol optical depth (AOD) and relative aerosol type parameters (such as Angstrom exponent). Although primarily designed for climate, the air quality (AQ) community quickly recognized that passive satellite products could be used for particulate air quality monitoring and forecasting. However, AOD and particulate matter (PM) concentrations have different units, and represent aerosol conditions in different layers of the atmosphere. Also, due to low visible contrast over brighter surface conditions, satellite-derived aerosol retrievals tend to have larger uncertainty in urban or populated regions. Nonetheless, the AQ community has made significant progress in relating column-integrated AOD at ambient relative humidity (RH) to surface PM concentrations at dried RH. Knowledge of aerosol optical and microphysical properties, ambient meteorological conditions, and especially vertical profile, are critical for physically relating AOD and PM. To make urban-scale maps of PM, we also must account for spatial variability. Since surface PM may vary on a finer spatial scale than the resolution of standard MODIS (10 km) and MISR (17km) products, we test higher-resolution versions of MODIS (3km) and MISR (1km research mode) retrievals. The recent (July 2011) DISCOVER-AQ campaign in the mid-Atlantic offers a comprehensive network of sun photometers (DRAGON) and other data that we use for validating the higher resolution satellite data. In the future, we expect that the wealth of aircraft and ground-based measurements, collected during DISCOVER-AQ, will help us quantitatively link remote sensed and ground-based measurements in the urban region.

Levy, Robert C.↗

NASA NTRS1985

Optical-data-processing properties of a liquid-crystal television spatial light modulator

The potential of an extremely inexpensive liquid-crystal television (LCTV) as a two-dimensional spatial light modulator has been investigated. The LCTV modulates the transmission of coherent or incoherent light and can either be electronically addressed through a microcomputer or optically addressed with a TV camera. The transmission characteristics of the device have been measured, its diffraction pattern is examined, and its use as an input device for an optical correlator is tested. It is discovered that, with proper modifications, it has potential for optical-data-processing applications.

Liu, H.-K.↗

NASA NTRS2000

Model Atmospheres and Spectra for Extrasolar Giant Planets

In the past few years much new observational data has become available for brown dwarfs and extra solar planets. Not only are new objects being discovered but the availability of higher resolution spectra is improving. This allows a better comparison between the models and the available data, and places new constraints on the models which now have to be made more physically realistic in order to better interpret the observations. Under this grant, an array of new opacities were calculated and successfully applied to a variety of physical situations that were used as input to model available observations of brown dwarfs and extra solar giant planets.

Freedman, Richard S.↗

NASA NTRS1998

Searching for Planets Around other Stars

In this colloquim presentation, Professor of Astronomy, Geoffrey Marcy discusses the discovery of planets orbiting other stars. Using the Doppler shift caused by stellar wobble that is caused by nearby planetary mass, astronomers have been able to infer the existence of Jupiter-sized planets around other stars. Using a special spectrometer at Lick Observatory, the wobble of several stars have been traced over the years required to generate an accurate pattern required to infer the stellar wobble. Professor Marcy, discusses the findings of planets around 47 Ursae Majoris, 16 Cygni B, 51 Pegasus, and 56 Rho 1 Cne. In the case of 56 Rho 1 Cne the planet appears to be close to the star, within 1.5 astronomical units. The observations from the smaller Lick Observatory will be augmented by new observations from the larger telescope at the Kek observatory. This move will allow observations of smaller planets, as opposed to the massive planets thus far discovered. The astronomers also hope to observe smaller stars with the Kek data. Future spaceborne observations will allow the discovery of even smaller planets. A spaceborne interferometer is in the planning stages, and an even larger observatory, called the Terrestrial Planet Finder, is hoped for. Professor Marcy shows artists' renderings of two of the planets thus far discovered. He also briefly discusses planetary formation and shows slides of both observations from the Orion Nebula and models of stellar system formation.

Source record↗

NASA NTRS1974

The ERTS-1 investigation (ER-600). Volume 5: ERTS-1 urban land use analysis

The Urban Land Use Team conducted a year's investigation of ERTS-1 MSS data to determine the number of Land Use categories in the Houston, Texas, area. They discovered unusually low classification accuracies occurred when a spectrally complex urban scene was classified with extensive rural areas containing spectrally homogeneous features. Separate computer processing of only data in the urbanized area increased classification accuracies of certain urban land use categories. Even so, accuracies of urban landscape were in the 40-70 percent range compared to 70-90 percent for the land use categories containing more homogeneous features (agriculture, forest, water, etc.) in the nonurban areas.

Erb, R. B.↗

DOE OSTI2025

From clutter to clarity: Emergent neural operators via questionnaire metrics

Real-world datasets in chemical engineering and bioengineering processes—such as those from catalytic reactors, multiphase flows, polymerization reactors, bioreactors, and clinical trials—can often be unlabeled or disorganized, rendering the training of existing supervised learning models ineffective at learning the underlying dynamics. To salvage these datasets for decision-making, we first seek to obtain clarity from the cluttered data. Here, we present a framework for developing “structural” generative models, discovering emergent equations, and constructing efficient emulators from scrambled datasets by integrating unsupervised organizational learning techniques (Questionnaires) with advanced deep learning architectures (Deep Hidden Physics Models and Deep Operator Networks). Our approach is demonstrated on two illustrative model systems: (a) a 1D advection–diffusion partial differential equation representing a winding underground pipe and (b) an ensemble of Stuart–Landau oscillators, an agent-based system of coupled ordinary differential equations. In both cases, we successfully reconstruct meaningful spatial, temporal, and parameter embeddings from scrambled data, enabling good predictions of system dynamics. As a result, we highlight the framework’s potential for broader applications, enabling data-driven system identification in fields with inherently disorganized or hidden parameter spaces.

42 ENGINEERING↗

NASA NTRS2005

First Imaging Results from the Iapetus B/C Flyby of the Cassini Spacecraft

The first of two relatively close Iapetus flybys in Cassini's primary mission occured on Dec 31, 2004 18:49 UTC near apoapsis from orbit "B" to "C" at an altitude of approximately 123,400 km over the northern leading hemisphere, resulting in a minimum pixel scale of 740 m for the ISS narrow angle camera (NAC). Data revealed details of a greater than 1300-km-long ridge that had been discovered just one week earlier in optical navigation images. Individual mountains within the western part of the ridge reach heights of approximately 20 km over surrounding terrain. The data set provides constraints on the origin of the albedo dichotomy. It appears very likely that the dark material is overlying an ice crust, but no evidence for emplacement of dark material via surface flows is apparent. Instead, signs for dark-material emplacement through processes that included ballistic transportation are visible. No bright-floor ("punch-through") craters have been found on the dark hemisphere. The ridge discovery may revive the idea of an endogenic origin of the dark side.

Denk, T.↗

NASA NTRS1999

Constraints on the Martian Plate Tectonic Hypothesis from Gravity and Topography Data

The Mars Global Surveyor Magnetic Fields Experiment/ Electron Reflectometer (MGS MAG/ER) experiment serendipitously discovered unanticipated and unprecedented regions of high amplitude crustal magnetic anomalies, indicating strong sources of remanent crustal magnetism. In one area of the southern hemisphere, the anomalies appear lineated and alternate in direction, resembling the stripes formed at terrestrial oceanic spread-ing regions. However, many significant differences exist. The inferred magnetization are easily an order of magnitude greater in strength than terrestrial counterparts. The width of the anomalies appears to be approximately 200 km, in comparison to a variable width of order 10-1000 km at terrestrial spreading centers. However, the spacecraft altitude of 100-200 km may be such that narrower anomalies are simply unresolved. Although the majority of strong anomalies are found in the southern highlands, there is no clear correlation with landforms at the surface. The lack of a correlation between magnetism and topography hinders the confident interpretation of magnetic sources. Additional information is contained in the original extended abstract.

Smrekar, S.↗

NASA NTRS1992

Near-Earth asteroids: Observer alert network and physical observations

This project strives to obtain physical observations on newly discovered Near-Earth Objects (NEO's) in order to provide fundamental data needed to assess the resources available in the population. The goal is acquiring data on all objects brighter than magnitude V= 17.0. To accomplish this, an electronic mail alert and observer information service that informs observers around the world as to the status of physical observations on currently observable NEO's was established. Such data is also acquired ourselves through a cooperative program with European colleagues that uses telescopes on La Palma to obtain spectra of NEO's and through observations made from a local telescope on Tumamoc Hill. This latter telescope has the advantage that large amounts of observing time are available, so that whenever a new NEO's discovered, we can be assured of getting time to observe it.

Davis, Donald R.↗

NASA NTRSDate not supplied

Airborne for All Engaging the ESIP Community to Explore Findings from a Recent Airborne and Field Data Workshop and Identify Synergies Across Organizations

The highly diverse, heterogeneous, and cross-disciplinary nature of airborne Earth observation provides unique challenges for those who want to use the data for scientific research and applications. NASA recently held its first Airborne and Field data workshop as a collaborative, open forum for users and producers of airborne Earth observations to discuss their experiences and concerns. During the two day event, NASA collected feedback from attendees about what NASA is doing well and what needs are still unmet in order to determine how NASA can better help the community make use of airborne and field data. In this session, we will report on the workshop findings. We will discuss the needs discovered from the workshop and we look for community feedback about how NASA can best address community concerns and work with other organizations that collect airborne data.

Sara Lubkin↗

NASA NTRS2007

Does the Blazar Gamma-ray Spectrum Harden with Increasing Flux? - Analysis of Nine Years of EGRET Data

The Energetic Gamma Ray Experiment Telescope (EGRET) on the Compton Gamma Ray Observatory (CGRO) discovered gamma-ray emission from more than 67 blazars during its nine-year lifetime. We conducted an exhaustive search of the EGRET archives and selected all the blazars that were observed multiple times and were bright enough to enable a spectral analysis using standard powerlaw models. The sample consists of 18 flat-spectrum radio quasars (FSRQs), 6 low-frequency-peaked BL Lacs (LBLs) and 2 high-frequency-peaked BL Lacs (HBLs). We do not detect any clear pattern in'the variation of spectral index with flux. Some of the blazars do not show any statistical evidence for spectral variability. The spectrum hardens with increasing flux in a few cases. There is also evidence for a flux-hardness anticorrelation at lo\v fluxes in five blazars. The well observed blazars (3C 279,3C 273, PKS 0528-i-134, PKS 1622-297, PKS 0208- 512) do not show any overall trend in the long-term spectral dependence on flux, but the sample shows a mixture of hard and soft states. We observed spectral hysteresis at weekly timescales in all the three FSRQs for which data from flares lasting for 3 approx. 4 weeks were available. All three sources show a counterclockwise rotation despite the widely different flux profiles. Hysteresis in the spectral index vs. flux space has never been observed in FSRQs in gamma-rays at weekly timescales. itre analyze the observed spectral behavior in the context of various inverse-Compton mechanisms believed to be responsible for emission in the EGRET energy range. Our analysis uses the EGRET skymaps that were regenerated to include the changes in performance during the mission.

Nandikotkur, Giridhar↗

DOE OSTI2025

Evaluation of normalization strategies for mass spectrometry-based multi-omics datasets

Introduction Data normalization is crucial for multi-omics integration, reducing systematic errors and maximizing the likelihood of discovering true biological variation. Most studies assess normalization for a single omics type or use datasets from separate experiments. Few address time-course data, where normalization might bias temporal differentiation. In this study, we compared common normalization methods and a machine learning approach, Systematical Error Removal using Random Forest (SERRF), using multi-omics datasets generated from the same experiment—even from the same cell lysate. Objectives To develop a straightforward process to assess normalization effects and identify the most robust methods across multi-omics datasets. Methods We analyzed metabolomics, lipidomics, and proteomics datasets from primary human cardiomyocytes and motor neurons exposed to acetylcholine-active compounds over time. Normalization effectiveness was evaluated based on improvement in QC features consistency and observing the change in treatment and time-related variance. Results Probabilistic Quotient Normalization (PQN) and Locally Estimated Scatterplot Smoothing (LOESS) QC were identified as optimal for metabolomics and lipidomics, while PQN, Median, and LOESS normalization excelled for proteomics. These methods consistently enhanced QC feature consistency in metabolomics and lipidomics, and preserved time-related variance or treatment-related variance in proteomics, demonstrating their effectiveness and robustness. SERRF normalization, applied only to metabolomics in this study, outperformed other methods in some datasets but inadvertently masked treatment-related variance in others. Conclusion Our evaluation identified PQN and LoessQC as the top methods for metabolomics and lipidomics, and PQN, Median, and Loess normalization for proteomics, in multi-omics integration in a temporal study.

60 APPLIED LIFE SCIENCES↗

NASA NTRS1989

Geometric bias and time coincidence in 3-dimensional laser Doppler velocimeter systems

The measurement by a three-dimensional laser Doppler velocimeter of a turbulent flow has been numerically simulated. Errors associated with the probe volume geometry and the coincidence time window concept are revealed. One type of error occurs for high system data rates when multiple particles lead to system realizations. Another error occurs associated with a geometric bias discovered in the present study. This three-dimensional ldv geometric bias exists even for single-particle realizations and regardless of the system data rate. A technique for the elimination of the geometric bias is presented.

Brown, J. L.↗

NASA NTRS2010

Experimental Space Shuttle Orbiter Studies to Acquire Data for Code and Flight Heating Model Validation

In an experimental study to obtain detailed heating data over the Space Shuttle Orbiter, CUBRC has completed an extensive matrix of experiments using three distinct models and two unique hypervelocity wind tunnel facilities. This detailed data will be employed to assess heating augmentation due to boundary layer transition on the Orbiter wing leading edge and wind side acreage with comparisons to computational methods and flight data obtained during the Orbiter Entry Boundary Layer Flight Experiment and HYTHIRM during STS-119 reentry. These comparisons will facilitate critical updates to be made to the engineering tools employed to make assessments about natural and tripped boundary layer transition during Orbiter reentry. To achieve the goals of this study data was obtained over a range of Mach numbers from 10 to 18, with flight scaled Reynolds numbers and model attitudes representing key points on the Orbiter reentry trajectory. The first of these studies were performed as an integral part of Return to Flight activities following the accident that occurred during the reentry of the Space Shuttle Columbia (STS-107) in February of 2003. This accident was caused by debris, which originated from the foam covering the external tank bipod fitting ramps, striking and damaging critical wing leading edge heating tiles that reside in the Orbiter bow shock/wing interaction region. During investigation of the accident aeroheating team members discovered that only a limited amount of experimental wing leading edge data existed in this critical peak heating area and a need arose to acquire a detailed dataset of heating in this region. This new dataset was acquired in three phases consisting of a risk mitigation phase employing a 1.8% scale Orbiter model with special temperature sensitive paint covering the wing leading edge, a 0.9% scale Orbiter model with high resolution thin-film instrumentation in the span direction, and the primary 1.8% scale Orbiter model with detailed thin-film resolution in both the span and chord direction in the area of peak heating. Additional objectives of this first study included: obtaining natural or tripped turbulent wing leading edge heating levels, assessing the effectiveness of protuberances and cavities placed at specified locations on the orbiter over a range of Mach numbers and Reynolds numbers to evaluate and compare to existing engineering and computational tools, obtaining cavity floor heating to aid in the verification of cavity heating correlations, acquiring control surface deflection heating data on both the main body flap and elevons, and obtain high speed schlieren videos of the interaction of the orbiter nose bow shock with the wing leading edge. To support these objectives, the stainless steel 1.8% scale orbiter model in addition to the sensors on the wing leading edge was instrumented down the windward centerline, over the wing acreage on the port side, and painted with temperature sensitive paint on the starboard side wing acreage. In all, the stainless steel 1.8% scale Orbiter model was instrumented with over three-hundred highly sensitive thin-film heating sensors, two-hundred of which were located in the wing leading edge shock interaction region. Further experimental studies will also be performed following the successful acquisition of flight data during the Orbiter Entry Boundary Layer Flight Experiment and HYTHIRM on STS-119 at specific data points simulating flight conditions and geometries. Additional instrumentation and a protuberance matching the layout present during the STS-119 boundary layer transition flight experiment were added with testing performed at Mach number and Reynolds number conditions simulating conditions experienced in flight. In addition to the experimental studies, CUBRC also performed a large amount of CFD analysis to confirm and validate not only the tunnel freestream conditions, but also 3D flows over the orbiter acreage, wing leading edge, and controlurfaces to assess data quality, shock interaction locations, and control surface separation regions. This analysis is a standard part of any experimental program at CUBRC, and this information was of key importance for post-test data quality analysis and understanding particular phenomena seen in the data. All work during this effort was sponsored and paid for by the NASA Space Shuttle Program Office at the Johnson Space Center in Houston, Texas.

Wadhams, T. P.↗

NASA NTRS1979

Observations of an X-ray QSO

This paper reports X-ray, optical, radio, and IR measurements of the z = 0.068 QSO MR2251-178, which was initially discovered on the basis of its X-ray emission. Data are provided on the position, optical colors, and 2-10-keV X-ray luminosity of this low-redshift QSO. An absolute V magnitude of -24.2 is implied, and the X-ray/optical luminosity ratio is estimated to be about 3:1 at peak X-ray brightness. It is shown that the optical spectrum of MR2251-178 is quite normal for a QSO, except for an extremely broad H-alpha emission line with a FWZI of 30,000 to 40,000 km/s. The overall spectrum of MR2251-178 from the radio to the X-ray region is plotted, and the photometric history of this QSO since 1889 is briefly discussed.

Ricker, G. R.↗

NASA NTRS2019

Deep Learning Method for Detecting Precursors to Adverse Events

With the recent advancements in Deep Learning methods, the ability to model large complex heterogeneous data sets are fundamentally changing industry and research. Coupled with hardware improvements, and ease of implementation, a wide variety of deep neural network architectures can quickly be developed to solve a sweeping range of problems such as: object detection in images, automatic healthcare diagnosis using heterogenous data sources, real time language translating and sentence prediction, upscaling low resolution images, and forecasting of multivariate timeseries. Generally, many of these architectures outperform classical machine learning approaches in their respective tasks, however, this typically comes at a cost of interpretability. These black box algorithms generally suffer from lack of transparency in both model complexity as well as the rationale behind the prediction. This lack of comprehension, is driving an emerging area of interest in “Explainable AI”. An algorithm called: “Deep Temporal Multiple Instance Learning”1 was a recently developed to identify precursors to adverse events and has been applied in the aviation domain. The deep learning architecture is designed to capture the evolution of the probability of the outcome over the time preceding the adverse event using a multiple instance learning approach as illustrated in Figure 1. Precursors are defined when the probability of the event has exceeded a threshold at some point in the timeseries, at which point, a sensitivity analysis is performed to determine contributing factors. The contributing factors are used to explain and define the precursor during the periods where the probability score is high. The identified contributing factors are then presented to subject matter experts to provide objective insights into the leading factors associated with the particular adverse event. The algorithm has been tested on flight data from a commercial airline and has the ability to discover precursors to known adverse events that take the form of safety critical operations, such as unstable approach events on final approach. Apart from detecting precursors to adverse events, the converse can also be leveraged to discover corrective actions. These positive actions manifest themselves as periods in the timeseries when the precursor score has been lowered from an elevated state; meaning that if the system had been left uncorrected, it would have eventually reached the adverse event state. Characterizing these state changes can help identify successful interventions that may not have been known before. Policy makers and procedure designers can use this additional knowledge to craft more safety and efficient resilient procedures for future operations and therefore improve the overall performance of the National Airspace.

Matthews, Bryan L.↗

DOE OSTI2026

Performance and Reliability Assessment of the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) Data Advisor (ADA)

The Atmospheric Radiation Measurement (ARM) User Facility provides one of the world's largest openly accessible repositories of atmospheric observations through the ARM Data Discovery platform. Although the repository contains more than three decades of measurements collected from permanent observatories, mobile facilities, aircraft campaigns, and field experiments, identifying appropriate datasets can be challenging, particularly for new users unfamiliar with ARM instrumentation and datastream organization. To improve data accessibility, the ARM Data Center developed the ARM Data Advisor (ADA), an artificial intelligence-powered assistant designed to facilitate scientific data discovery, dataset interpretation, and user guidance. This report evaluates ADA's performance as a domain-specific scientific assistant using realistic atmospheric science workflows. The evaluation examines five key capabilities: data retrieval and curation efficiency, hallucination resistance, scientific reasoning, response to ambiguous queries, and content retention and session continuity. Representative prompts were developed to simulate typical interactions between researchers and the ARM Data Discovery platform, and ADA's responses were assessed for retrieval completeness, scientific accuracy, consistency, and practical usefulness. In these representative tests, ADA reduced the complexity of discovering and accessing ARM datasets by recommending appropriate datastreams, explaining instrumentation, interpreting metadata, and assisting with data processing workflows. ADA also exhibits strong domain knowledge of atmospheric science terminology and generally resists hallucination by acknowledging unavailable datasets and requesting clarification when appropriate. Overall, the results indicate that ADA represents a promising advancement in scientific data discovery within the ARM User Facility and has considerable potential to improve researcher productivity, particularly for new users and interdisciplinary scientists seeking efficient access to ARM observations.

Salvador, Christian [ORNL] (ORCID:0000000283287777↗

DOE OSTI2025

Factorization Machine‐Based Active Learning for Functional Materials Design with Optimal Initial Data

The optimization of functional materials is important to enhance their properties, but their complex geometries pose great challenges to optimization. Data-driven algorithms efficiently navigate such complex design spaces by learning relationships between material structures and performance metrics to discover high-performance functional materials. Surrogate-based active learning, continually improving its surrogate model by iteratively including high-quality data points, has emerged as a cost-effective data-driven approach. Furthermore, it can be coupled with quantum computing to enhance optimization processes, especially when paired with a special form of surrogate model (i.e., quadratic unconstrained binary optimization), formulated by factorization machine (FM). However, current practices often overlook the variability in design space sizes when determining the initial data size for optimization. In this work, we investigate the optimal initial data sizes required for efficient convergence across various design space sizes. By employing averaged piecewise linear regression, we identify initiation points where convergence begins, highlighting the crucial role of employing adequate initial data in achieving efficient optimization. These results contribute to the efficient optimization of functional materials by ensuring faster convergence and reducing computational costs in FM-based active learning.

active learning↗