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NASA NTRS1996

WATCHMAN: A Data Warehouse Intelligent Cache Manager

Data warehouses store large volumes of data which are used frequently by decision support applications. Such applications involve complex queries. Query performance in such an environment is critical because decision support applications often require interactive query response time. Because data warehouses are updated infrequently, it becomes possible to improve query performance by caching sets retrieved by queries in addition to query execution plans. In this paper we report on the design of an intelligent cache manager for sets retrieved by queries called WATCHMAN, which is particularly well suited for data warehousing environment. Our cache manager employs two novel, complementary algorithms for cache replacement and for cache admission. WATCHMAN aims at minimizing query response time and its cache replacement policy swaps out entire retrieved sets of queries instead of individual pages. The cache replacement and admission algorithms make use of a profit metric, which considers for each retrieved set its average rate of reference, its size, and execution cost of the associated query. We report on a performance evaluation based on the TPC-D and Set Query benchmarks. These experiments show that WATCHMAN achieves a substantial performance improvement in a decision support environment when compared to a traditional LRU replacement algorithm.

Scheuermann, Peter↗

NASA NTRS2016

Probabilistic Prognosis of Non-Planar Fatigue Crack Growth

Quantifying the uncertainty in model parameters for the purpose of damage prognosis can be accomplished utilizing Bayesian inference and damage diagnosis data from sources such as non-destructive evaluation or structural health monitoring. The number of samples required to solve the Bayesian inverse problem through common sampling techniques (e.g., Markov chain Monte Carlo) renders high-fidelity finite element-based damage growth models unusable due to prohibitive computation times. However, these types of models are often the only option when attempting to model complex damage growth in real-world structures. Here, a recently developed high-fidelity crack growth model is used which, when compared to finite element-based modeling, has demonstrated reductions in computation times of three orders of magnitude through the use of surrogate models and machine learning. The model is flexible in that only the expensive computation of the crack driving forces is replaced by the surrogate models, leaving the remaining parameters accessible for uncertainty quantification. A probabilistic prognosis framework incorporating this model is developed and demonstrated for non-planar crack growth in a modified, edge-notched, aluminum tensile specimen. Predictions of remaining useful life are made over time for five updates of the damage diagnosis data, and prognostic metrics are utilized to evaluate the performance of the prognostic framework. Challenges specific to the probabilistic prognosis of non-planar fatigue crack growth are highlighted and discussed in the context of the experimental results.

Leser, Patrick E.↗

NASA NTRSDate not supplied

A Novel Machine Learning Method for Surface PM2.5 Estimations from Geostationary Satellites

Particulate matter (PM) with a diameter of less or equal to 2.5 μm, known as PM , affects human health as it penetrates the respiratory system. The Environmental Protection Agency (EPA) measures the atmospheric concentration of PM using air quality monitors stationed throughout the Continental United States (CONUS). Such measurements are points on a spatial domain and therefore, might not be representative of the air quality at nearby areas considering that the composition of the atmosphere is highly variable from place to place. Satellite based AOD permits a spatially uniform means of estimating PM and new geostationary satellites provide high temporal and spatial resolution estimation of AOD. However, the concentration of PM is non-linearly dependent on other atmospheric parameters that include relative humidity, temperature, and height of the planetary boundary layer. This information may be estimated at similar spatial and temporal resolutions as AOD from numerical modeling such as from the National Oceanic and Atmospheric Administration’s (NOAA) High Resolution Rapid Refresh (HRRR) model which resolves near real-time atmospheric conditions over the CONUS. The estimation of PM concentration is a multi-parametric problem that considers the effect of temporal dependencies among the different parameters. Deep learning approaches are appropriate for such complex estimation problems as they intrinsically capture relations among multiple non-linear parameters. This study compares deep-learning methods to traditional regression analysis to demonstrate the capabilities of these methods in predicting PM2.5 concentrations. Additionally, a novel ensemble learning approach is employed to identify scientific processes that could further improve the estimation of PM concentration. Utilizing Long Short-Term Memory (LSTM) neural networks, which are suitable for multivariate time series estimation problems as they are capable of learning long-term dependencies, individual models are created for each EPA station and trained on the aforementioned dataset collocated over each station. Individual station models are merged if the model's performance is improved by reducing the root mean squared error (RMSE) metric. This ensemble training method ultimately reduces the RMSE value. Evaluation of these results provide insights into physical processes and related observable parameters that may contribute to PM concentrations. Identified parameters evaluated to be statistically different between the merged and unmerged models are expected to improve overall performance. These new parameters are then utilized for reevaluation of the deep learning methods with an extreme gradient boosting model with an RMSE of 5.5 providing the best results.

George Priftis↗

NASA NTRS2022

Idaho Wildfires II: Assessing the Relationship Between Drought Indicators and Wildfire Risk to Enhance Hazard Modeling and Inform Mitigation Planning

The western United States has experienced twenty years of increased and prolonged drought which have exacerbated wildfire hazards. These jeopardize population centers through increased risks to ecosystem services, local economies, and livelihoods. The Idaho Office of Emergency Management, Water Resources, and Department of Lands are seeking methods to dynamically monitor these conditions and update models that inform hazard mitigation planning and resource allocation. Towards this, these agencies partnered with NASA DEVELOP to produce drought-enhanced wildfire hazard models. Part of a two-term project, the two teams revised the state’s static wildfire hazard model with refined data layers and remotely-sensed data to reflect dynamic ecosystem responses to drought conditions and wildfire potential. Our team distinguished between rangeland and forestland ecosystems, and investigated relationships between drought metrics and vegetation condition using TerrSet Earth Trends Modeler. This analysis determined that total precipitation at a 5-month lag interval (r 2 = 0.72) along with the Evaporative Stress Index (r 2 = 0.69); and precipitation at a 5-month interval (r 2 = 0.42) were important drivers in rangeland and forestland, respectively. These driver variables were incorporated into a temporally dynamic wildfire hazard map. Our team used linear regression to correlate hazard ratings with wildfire frequency. For the year 2020, neither the enhanced hazard model (p < 0.10, r 2 = 0.01) nor the state’s static model (p < 0.05, r 2 = 0.03) were strongly correlated with actual wildfire frequency as they expressed an inverse relationship between wildfire hazard and frequency. This suggests wildfire occurrence is complex and not necessarily driven by the variables used.

Wildfire↗

NASA NTRS2022

The expanding network of mineral chemistry throughout earth history reveals global shifts in crustal chemistry during the Proterozoic.

Earth surface redox conditions are intimately linked to the co-evolution of the geosphere and biosphere. Minerals provide a record of Earth’s evolving surface and interior chemistry in geologic time due to many diferent processes (e.g. tectonic, volcanic, sedimentary, oxidative, etc.). Here, we show how the bipartite network of minerals and their shared constituent elements expanded and evolved over geologic time. To further investigate network expansion over time, we derive and apply a novel metric (weighted mineral element electronegativity coefcient of variation; wMEECV) to quantify intra-mineral electronegativity variation with respect to redox. We fnd that element electronegativity and hard soft acid base (HSAB) properties are central factors in mineral redox chemistry under a wide range of conditions. Global shifts in mineral element electronegativity and HSAB associations represented by wMEECV changes at 1.8 and 0.6 billion years ago align with decreased continental elevation followed by the transition from the intermediate ocean and glaciation eras to postglaciation, increased atmospheric oxygen in the Phanerozoic, and enhanced continental weathering. Consequently, network analysis of mineral element electronegativity and HSAB properties reveal that orogenic activity, evolving redox state of the mantle, planetary oxygenation, and climatic transitions directly impacted the evolving chemical complexity of Earth’s crust.

Eli K. Moore↗

NASA NTRSDate not supplied

SLS Integrated Modal Test Uncertainty Quantification using the Hybrid Parametric Variation Method

Uncertainty in structural loading during launch is a significant concern in the development of spacecraft and launch vehicles. Small variations in launch vehicle and payload mode shapes and their interaction can result in significant variation in system loads. In many cases involving large aerospace systems it is difficult, not economical, or impossible to perform a system modal test. However, it is still vital to obtain test results that can be compared with analytical predictions to validate models. Instead, the “Building Block Approach” is used in which system components are tested individually. Component models are correlated and updated to agree as best they can with test results. The Space Launch System consists of a number of components that are assembled into a launch vehicle. Finite element models of the components are developed, reduced to Hurty/Craig-Bampton models and assembled to represent different phases of flight. The only opportunity to obtain modal test data from an assembled Space Launch System will be during the Integrated Modal Test. There is always uncertainty in every model, which flows into uncertainty in predicted system results. Uncertainty Quantification is used to determine statistical bounds on prediction accuracy based on model uncertainty. For the Space Launch System, model uncertainty is at the Hurty/Craig-Bampton component level. Uncertainty in the Hurty/Craig-Bampton components is quantified using the hybrid parametric variation approach that combines parametric and nonparametric uncertainty. Uncertainty in model form is one of the biggest contributors to uncertainty in complex built-up structures. This type of uncertainty cannot be represented by variations infinite element model input parameters and thus cannot be included in a parametric approach. However, model-form uncertainty can be modeled using a nonparametric approach based on random matrix theory. The hybrid parametric variation method requires the selection of dispersion values for the Hurty/Craig-Bampton fixed-interface eigenvalues, and the Hurty/Craig-Bampton stiffness matrices. Component test/analysis frequency error is used to identify the fixed-interface eigenvalue dispersions, while test/analysis cross-orthogonality is used to identify stiffness dispersion values. The hybrid parametric variation uncertainty quantification approach is applied to the Space Launch System Integrated Modal Test configuration. Monte Carlo analysis is performed, and statistics are determined for modal correlation metrics, frequency response from Integrated Modal Test shakers to selected accelerometers, as well as other metrics for determining how well target modes are excited and identified. If the predicted uncertainty envelopes future Integrated Modal Test results, then there will be increased confidence in the utility of the component-based hybrid parametric variation uncertainty quantification approach.

Uncertainty Quantification↗

NASA NTRSDate not supplied

A Comparative Analysis of Occupant Response Between Component and Full Vehicle Tests of Fokker F28 Aircraft Hardware

In 2019, the National Aeronautics and Space Administration (NASA) Langley Research Center (LaRC) conducted a full-scale crash test of a Fokker F28 MK1000 aircraft. This test concluded a multi-year research effort in which two component fuselage sections of a matching Fokker F28 aircraft were previously tested under similar vertical impact conditions. Due to facility and cost constraints of full-scale testing, aircraft are typically evaluated through component level tests (i.e. vertical drops of fuselage subsections or isolated seat tests). Although more practical, these tests are limited in their ability fully replicate the complex multi-axis loading environment induced on the occupants during a full-aircraft crash event. Because of this, there is risk that component level testing does not provide a complete assessment of vehicle crashworthiness. Comparative analysis between full-scale and component level testing of the Fokker F28 aircraft provides an excellent opportunity to evaluate differences in crashworthiness prediction made between these levels of test fidelity. In this study, Anthropomorphic Test Device (ATD, a.k.a crash test dummies) responses measured during the Fokker full scale impact test were compared to those measured in the component fuselage section drops. A variety of ATD configurations (5th, 50th, 95th) and positions (upright, braced) were tested in both the full-scale and component tests. ATD injury metric response comparisons were made across these ATD variations in addition to comparisons made with respect to ATD location within the vehicle. Results found the addition of horizontal impact velocity, achieved in the full-scale testing, along with aircraft structural effects altered ATD based crashworthiness assessment of the vehicle.

Crashworthiness↗

NASA NTRS2023

Risk Assessment for Asteroid Impact Threat Scenarios

Asteroid impacts can cause a wide range of damage through multiple potential hazards, from localized blast waves or thermal radiation, to tsunami inundation, to global climatic effects. The level of risk posed by these hazards depends not only upon their extent and severity, but also upon the likelihood of the various damage ranges. Some consequences may be more moderate but very likely, while others may be unlikely but catastrophic. Evaluating the risk from these hazards involves substantial uncertainties across all aspects of the problem, including the properties of the asteroid itself, the specifics of its entry, and the complex high-energy damage physics involved. NASA’s Asteroid Threat Assessment Project performs Probabilistic Asteroid Impact Risk (PAIR) assessments that use fast-running entry and hazard models to evaluate millions of impact cases representing the distributions of these many uncertain parameters. This paper presents current probabilistic asteroid impact risk assessment modeling tools and approaches used for evaluating specific asteroid impact threat cases. We give an overview of the current PAIR model used to support impact threat scenarios and discuss some of the key applications of these assessment in supporting response decisions and planetary defense preparedness. We then present the results and key findings from the recent 2023 PDC hypothetical impact exercise as an example of the primary types of risk results and metrics being developed to inform and support those mitigation and response decisions.

SMD↗

NASA NTRS2022

Artificial Neural Network Modeling for Airline Disruption Management

Since the 1970s, most airlines have incorporated computerized support for managing disruptions during flight schedule execution. However, existing platforms for airline disruption management (ADM) employ monolithic system design methods that rely on the creation of specific rules and requirements through explicit optimization routines, before a system that meets the specifications is designed. Thus, current platforms for ADM are unable to readily accommodate additional system complexities resulting from the introduction of new capabilities, such as the introduction of unmanned aerial systems (UAS), operations and infrastructure, to the system. To this end, we use historical data on airline scheduling and operations recovery to develop a system of artificial neural networks (ANNs), which describe a predictive transfer function model (PTFM) for promptly estimating the recovery impact of disruption resolutions at separate phases of flight schedule execution during ADM. Furthermore, we provide a modular approach for assessing and executing the PTFM by employing a parallel ensemble method to develop generative routines that amalgamate the system of ANNs. Our modular approach ensures that current industry standards for tardiness in flight schedule execution during ADM are satisfied, while accurately estimating appropriate time-based performance metrics for the separate phases of flight schedule execution.

Kolawole Ogunsina↗

NASA NTRS2024

Characterization of NUW-LHT-5m, A Lunar Highland Simulant

A new simulant of the lunar highlands regolith, NUW-LHT-5M, was designed by NASA and manufactured by Washington Mills. The simulant was based on Apollo 16 data and is a member of the NU-LHT-series. NASA’s Marshall Space Flight Center and Johnson Space Center have already purchased 3 metric tons of the simulant for advanced engineering work. In support of engineering uses of the simulant, we provided measurements of the simulant including: mineral abundance and composition, liberation, X-ray fluorescence (XRF), ferrous iron, carbon, sulfur, 60 element inductively coupled plasma (ICP), loss on ignition, particle size, both 2D and 3D particle shape, specific surface area, shear, cohesion, internal friction, helium pycnometry, minimum index density, tap density, magnetic susceptibility, cryogenic and high temperature permittivity, visible and near-infrared (VNIR) and middle infra-red spectroscopy (MIR), differential scanning calorimetry (DSC), viscosity, thermal diffusivity, thermal conductivity, thermal gravimetric analysis (TGA), evolved gas analysis (EGA), and spark sintering. For the crystalline components the design of the simulant called for two rocks from the Stillwater Complex, Montana: 17.6 wt% norite, 37.7% anorthosite, and 4.7 wt% olivine from an unspecified commercial source. The other 40% of the simulant was a high calcium (An100), vesicular glass that Washington Mills made specifically for the simulant. Fabrication and quality control processes for both the glass and the simulant are described. Importantly, most of the graphs and tables presented herein provide values for both the new simulant and data for the older NASA mare simulant, JSC-1A. Finally, we discussed the current limitations of NUW-LT-5M and most other lunar regolith simulants to replicate the lunar material.

lunar regolith simulant↗

NASA NTRS2017

Cart3D Simulations for the Second AIAA Sonic Boom Prediction Workshop

Simulation results are presented for all test cases prescribed in the Second AIAA Sonic Boom Prediction Workshop. For each of the four nearfield test cases, we compute pressure signatures at specified distances and off-track angles, using an inviscid, embedded-boundary Cartesian-mesh flow solver with output-based mesh adaptation. The cases range in complexity from an axisymmetric body to a full low-boom aircraft configuration with a powered nacelle. For efficiency, boom carpets are decomposed into sets of independent meshes and computed in parallel. This also facilitates the use of more effective meshing strategies - each off-track angle is computed on a mesh with good azimuthal alignment, higher aspect ratio cells, and more tailored adaptation. The nearfield signatures generally exhibit good convergence with mesh refinement. We introduce a local error estimation procedure to highlight regions of the signatures most sensitive to mesh refinement. Results are also presented for the two propagation test cases, which investigate the effects of atmospheric profiles on ground noise. Propagation is handled with an augmented Burgers' equation method (NASA's sBOOM), and ground noise metrics are computed with LCASB.

Prediction↗

NASA NTRSDate not supplied

Predicting Airport Runway Configurations for Decision-Support Using Supervised Learning

One of the most challenging tasks for air traffic controllers is runway configuration management (RCM). It deals with the optimal selection of runways to operate on (for arrivals and departures) based on traffic, surface wind speed, wind direction, other environmental variables, noise constraints, and several other airport-specific factors. It affects the efficiency of the National Airspace System (NAS) and both surface and airspace operations can benefit from better understanding future runway configurations. In this paper, we present a comprehensive implementation of predictive models for runway configuration estimation from large volumes of historical data. Specifically, operational data from two full years (2018 and 2019) is collected, analyzed, and fused together to build the data product used in this work. The data set differs from prior work in the field in terms of its scope, resolution, and variety of factors collected and considered. Meteorological data is collected from two different sources – current weather conditions from METAR (Meteorological Terminal Aviation Routine Weather Report) and forecast weather conditions from Localized Aviation MOS Program (LAMP). Operational data from the Federal Aviation Administration (FAA) Aviation System Performance Metrics (ASPM) related to scheduled and actual number of arrivals and departures, average taxi times, etc. are collected. NASA’s Sherlock Data Warehouse is used to identify critical information such as go-arounds, and other events that might impact RCM decision-making. All data is collected and aggregated over 15-minute intervals throughout the two years. This provides a resolution like the timescales that might be necessary for runway configuration management decision-making. A variety of supervised learning algorithms are tested including Support Vector Machine, Random Forest, Gradient Boosting, etc. including tuning of the model hyperparameters. The modeling process is applied and presented on two representative U.S. airports – Charlotte Douglas International Airport (KCLT) and Denver International Airport (KDEN). The two airports present different levels of complexity in terms of the total number of configurations used and provide a balanced perspective on the generalizability of the developed approach to other airports in the NAS. Initial results are promising (F1 score of 0.91 at KCLT and 0.83 at KDEN) for data in the test set. The final paper will contain a comprehensive comparison between different models and model building strategies as well as further refined results. Most important predictors for each airport will be identified along with a discussion and recommendations on adapting the framework to other scenarios.

Tejas G Puranik↗

NASA NTRS2010

Coronal Mass Ejections: a Summary of Recent Results

Coronal mass ejections (CMEs) have been recognized as the most energetic phenomenon in the heliosphere, deriving their energy from the stressed magnetic fields on the Sun. This paper highlights some of the recent results on CMEs obtained from the Solar and Heliospheric Observatory (SOHO) and the Solar Terrestrial Relations Observatory (STEREO) missions. The summary of the talk follows. SOHO observations revealed that the CME rate is almost a factor of two larger than previously thought and varied with the solar activity cycle in a complex way (e.g., high-latitude CMEs occurred in great abundance during the solar maximum years). CMEs were found to interact with other CMEs as well as with other large-scale structures (coronal holes), resulting in deflections and additional particle acceleration. STEREO observations have confirmed the three-dimensional nature of CMEs and the shocks surrounding them. The EUV signatures (flare arcades, corona) dimming, filament eruption, and EUV waves) associated with CMEs have become vital in the identification of solar sources from which CMEs erupt. CMEs with speeds exceeding the characteristic speeds of the corona and the interplanetary medium drive shocks, which produce type II radio bursts. The wavelength range of type II bursts depends on the CME kinetic energy: type II bursts with emission components at all wavelengths (metric to kilometric) are due to CMEs of the highest kinetic energy. Some CMEs, as fast as 1600 km/s do not produce type II bursts, while slow CMEs (400 km/s) occasionally produce type II bursts. These observations can be explained as the variation in the ambient flow speed (solar wind) and the Alfven speed. Not all CME-driven shocks produce type II bursts because either they are subcritical or do not have the appropriate geometry. The same shocks that produce type II bursts also produce solar energetic particles (SEPs), whose release near the Sun seems to be delayed with respect to the onset of type II bursts. This may indicate a subtle difference in the acceleration of the ions and 10 keV electrons needed to produce type II bursts. Surprisingly, some shocks lacking type II bursts are associated with energetic storm particle events (ESPs) pointing to the importance of electron escape from the shock for producing the radio emission. CMEs slow down or accelerate in the interplanetary medium because of the drag force, which modifies the transit time of CMEs and shocks. Halo CMEs that appear to surround the occulting disk were known before the SOHO era as occasional events. During the SOHO era, they became very prominent because of their ability to impact Earth and producing geomagnetic storms. Halo CMEs are generally more energetic than ordinary CMEs, which means they can produce severe impact on Earth's magnetosphere. Their origin close to the disk center of the Sun ensures direct impact on the magnetosphere, although their internal magnetic structure is crucial in causing storms. The solar sources of CMEs that produce SEP events at Earth, on the other hand, are generally in the western hemisphere because of the magnetic connectivity. Thus, CMEs are very interesting from the point of view of plasma physics as well as practical implications because of their space weather impact.

Gopalswamy, Nat↗

NASA NTRS2014

NASA's Space Launch System: An Enabling Capability for Discovery

The National Aeronautics and Space Administration's (NASA's) Space Launch System (SLS) Program, managed at the Marshall Space Flight Center, is making progress toward delivering a new capability for human spaceflight and scientific missions beyond Earth orbit. Developed with the goals of safety, affordability, and sustainability in mind, the SLS rocket will launch the Orion Multi-Purpose Crew Vehicle (MPCV), equipment, supplies, and major science missions for exploration and discovery. Making its first uncrewed test flight in 2017 and its first crewed flight in 2021, the SLS will evolve into the most powerful launch vehicle ever flown, capable of supporting human missions into deep space and to Mars. This paper will summarize the planned capabilities of the vehicle, the progress the SLS Program has made in the years since the Agency formally announced its architecture in September 2011, and the path the program is following to reach the launch pad in 2017 and then to evolve the 70 metric ton (t) initial lift capability to 130 t lift capability. The paper will outline the milestones the program has already reached, from developmental milestones such as the manufacture of the first flight hardware and recordbreaking engine testing, to life-cycle milestones such as the vehicle's Preliminary Design Review in the summer of 2013. The paper will also discuss the remaining challenges in both delivering the 70 t vehicle and in evolving its capabilities to the 130 t vehicle, and how the program plans to accomplish these goals. In addition, this paper will demonstrate how the Space Launch System is being designed to enable or enhance not only human exploration missions, but robotic scientific missions as well. Because of its unique launch capabilities, SLS will support simplifying spacecraft complexity, provide improved mass margins and radiation mitigation, and reduce mission durations. These capabilities offer attractive advantages for ambitious science missions by reducing infrastructure requirements, cost, and schedule. A traditional baseline approach for a mission to investigate the Jovian system would require a complicated trajectory with several gravity-assist planetary fly-bys to achieve the necessary outbound velocity. The SLS rocket, offering significantly higher C3 energies, can more quickly and effectively take the mission directly to its destination, providing scientific results sooner and at lower operational cost. The SLS rocket will launch payloads of unprecedented mass and volume, such as "monolithic" telescopes and in-space infrastructure, and will revolutionize science mission planning and design for years to come. As this paper will explain, SLS is making measurable progress toward becoming a global infrastructure asset for robotic and human scouts of all nations by harnessing business and technological innovations to deliver sustainable solutions for space exploration.

Creech, Stephen D.↗

NASA NTRS2014

NASA's Space Launch System: An Enabling Capability for Discovery

Creech, Stephen D.↗

NASA NTRS1996

LCTV Holographic Imaging

Astronauts are required to interface with complex systems that require sophisticated displays to communicate effectively. Lightweight, head-mounted real-time displays that present holographic images for comfortable viewing may be the ideal solution. We describe an implementation of a liquid crystal television (LCTV) as a spatial light modulator (SLM) for the display of holograms. The implementation required the solution of a complex set of problems. These include field calculations, determination of the LCTV-SLM complex transmittance characteristics and a precise knowledge of the signal mapping between the LCTV and frame grabbing board that controls it. Realizing the hologram is further complicated by the coupling that occurs between the phase and amplitude in the LCTV transmittance. A single drive signal (a gray level signal from a framegrabber) determines both amplitude and phase. Since they are not independently controllable (as is true in the ideal SLM) one must deal with the problem of optimizing (in some sense) the hologram based on this constraint. Solutions for the above problems have been found. An algorithm has been for field calculations that uses an efficient outer product formulation. Juday's MEDOF 7 (Minimum Euclidean Distance Optimal Filter) algorithm used for originally for filter calculations has been successfully adapted to handle metrics appropriate for holography. This has solved the problem of optimizing the hologram to the constraints imposed by coupling. Two laboratory methods have been developed for determining an accurate mapping of framegrabber pixels to LCTV pixels. A friendly software system has been developed that integrates the hologram calculation and realization process using a simple set of instructions. The computer code and all the laboratory measurement techniques determining SLM parameters have been proven with the production of a high quality test image.

Knopp, Jerome↗

NASA NTRS1998

Feature Detection in Linked Derived Spaces

This paper describes by example a strategy for plotting and interacting with data in multiple metric spaces. The example system was designed for use with time-varying computational fluid dynamics (CFD) datasets, but the methodology is directly applicable to other types of field data. The central objects embodied by the tool are {\em portraits}, which show the data in various coordinate systems, while preserving their spatial connectivity and temporal variability. The coordinates are derived in various ways from the field data, and an important feature is that new and derived portraits can be created interactively. The primary operations supported by the tool are brushing and linking: the user can select a subset of a given portrait, and this subset is highlighted in all portraits. The user can combine highlighted subsets from an arbitrary number of portraits with the usual logical operators, thereby indicating where an arbitrarily complex set of conditions holds. The system is useful for exploratory visualization and feature detection in multivariate data.

Henze, Chris↗

NASA NTRS2023

Automated Global-Scale Detection and Characterization of Anthropogenic Activity using Multi-Source Satellite-Based Remote Sensing Imagery

Satellite-based remote sensing imagery is an effective means for detecting objects and structures in support of many applications. However, detecting the spatial and temporal bounds of a specific activity in satellite imagery is inherently more complex and research in this area is nascent. One reason for this is that describing an activity implies defining both spatial and temporal bounds and while activity is inherently continuous in nature, the geospatial (imagery) time series for any particular swath of ground provided by satellite imagery is relatively sparse and discrete in comparison. The IARPA Space-Based Machine Automated Recognition Technique (SMART)1 program is the first large-scale research program to target advancing the state of the art for automatically detecting, characterizing, and monitoring large-scale anthropogenic activity in global, multispectral satellite imagery. The program has two primary research objectives: 1) the “harmonization” of multiple imagery sources and 2) automated reasoning at scale to detect, characterize, and monitor activities of interest. This paper provides details on the goals, dataset, metrics, and lessons learned of the IARPA SMART program. By releasing the annotated dataset, the program aims to foster additional research in this area by the community at large.

Hirsh R Goldberg↗