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At least 451 records · Page 25

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DOE OSTI2020

Quantitative temporally and spatially resolved X-ray fluorescence microprobe characterization of the manganese dissolution-deposition mechanism in aqueous Zn/α-MnO 2 batteries

Rechargeable aqueous Zn/α-MnO 2 batteries are a possible alternative to lithium ion batteries for scalable stationary energy storage applications due to their low cost, safety and environmentally benign components. A critical need for advancement of this battery system is a full understanding of the electrochemical reaction mechanisms, which remain unclear. In this report, operando , spatiotemporal resolved synchrotron X-ray fluorescence mapping measurements on a custom aqueous Zn/α-MnO 2 cell provided direct evidence of a Mn dissolution-deposition faradaic mechanism that governs the electrochemistry. Simultaneous visualization and quantification of the Mn distribution in the electrolyte revealed the formation of aqueous Mn species during discharge and depletion on charge. The findings are supported by ex situ transmission electron microscopy (TEM), X-ray diffraction, Mn K-edge X-ray absorption near edge structure (XANES) and extended X-ray absorption fine structure (EXAFS) measurements. The elucidated mechanism is fundamentally different from the previously proposed Zn 2+ insertion or conversion reactions. These findings provide a foundation for developing dissolution- deposition chemistries suitable for scalable stationary energy storage with aqueous electrolyte.

25 ENERGY STORAGE↗

DOE OSTI2020

Macro-physical Properties of Shallow Cumulus from Integrated ARM Observations (Final Report)

Fair-weather shallow cumuli (ShCu) play an important role in many climate-related processes. Irregular geometry of ShCu and their strong temporal and spatial variability make it challenging to observe ShCu holistically and to represent them correctly in climate models. To improve ShCu parameterizations, information on both vertically and horizontally resolved cloud properties is required. Commonly, the vertically resolved cloud properties are provided by zenith pointing lidar-radar observations with a very narrow field of view (FOV). Thus, these “pencil-beam” properties may not be representative of a larger surrounding area. Limited number of areal-averaged cloud properties, such as fractional sky cover (FSC), are offered typically by wide-FOV observations. The main goal of our project was to integrate advantages of the narrow-FOV (vertical structure of clouds) and wide-FOV (spatial arrangement of clouds) observations for an improved characterization of single-layer ShCu observed at the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site for an 18-yr period (2000-2017). There are four major accomplishments of our project, First, an updated operational cloud classification for days with ShCu has been suggested and evaluated through a detailed comparison with the manually curated records. Our classification extends successfully the latest ARM cloud type Value Added Product (VAP) based on the Active Remote Sensing of Clouds (ARSCL) cloud product by incorporating both cloud fraction (CF) provided by narrow-FOV ceilometer data and FSC from wide-FOV images offered by a Total Sky Imager (TSI). Moreover, our classification allows one to identify impact of instrumentation changes at the SGP site, namely the transition to KAZRARSCL with the updated cloud radar, on the identification of periods with single-layer ShCu. Second, a new approach that resolves cloud area distributions for a given region (up to 4x4 km 2 ) has been suggested and cloud equivalent diameters (CEDs) have been estimated for the first time. These estimations have been performed over a wide range of cloud sizes (about 0.01–3.5 km) with high temporal resolution (30s) using wide-FOV TSI images and cloud base height (CBH) provided by complementary narrow-FOV lidar measurements. Our simple and computationally inexpensive approach offers a previously unavailable dataset for process studies in the convective boundary layer and evaluation of ShCu parameterizations in cloud-resolving models. Third, a long-term integrated record of ShCu macrophysical properties has been developed. The developed record represents the longest available compilation of events with ShCu and includes (i) a novel visualization of the spatial variability in cloud cover both along- and across-wind directions, (ii) updated estimates of narrow-FOV CF and wide-FOV FSC, (iii) updated narrow-FOV CBH, and (iv) complementary data, such as wind speed and direction from the 915-MHz Radar Wind Profiler (RWP) data. The developed record has been used successfully to assess conventional observational estimates of cloud cover and their sensitivity to the following two factors: (i) instrument-dependent cloud detection and data merging criteria and (ii) FOV configuration. Fourth, co-variability of the ShCu macrophysical properties and environmental parameters has been analyzed for a 3-yr period (2016-2018). Our initial analysis includes diurnal changes of FSCs obtained for clouds with small, moderate and large CEDs and several environmental parameters, such as lifted condensation level (LCL) and mixed layer height (zi). Preliminary results of our analysis suggest that the horizontal extent of ShCu is controlled substantially by the sign and magnitude of difference between these two parameters (zi-LCL): the CED tends to grow with increase of this difference (zi exceeds LCL). We have initiated relationships between the ShCu and key atmospheric parameters that control both the development and evolution of ShCu using our new data product, which combines effectively the advantages of narrow-FOV data offered by zenith pointing cloud radars and lidars and wide-FOV TSI images. While the latest instrumentation at the ARM sites may address these challenging relationships in the future, we believe that the historical ARM data at the SGP site has not yet been fully utilized. Overall, our data product can be used by researchers working on a wide range of climate-related projects. These projects may include (i) a comprehensive evaluation of outputs from the Large-Eddy Simulation (LES) and single-column models for their future improvement, (ii) the representativeness of “short-period” results obtained from the previous model and observational studies and (iii) the planning of future field campaigns with focus on improved understanding of the diurnal cycle of cumulus convection.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2003

Supporting the Growing Needs of the GIS Industry

Visual Learning Systems, Inc. (VLS), of Missoula, Montana, has developed a commercial software application called Feature Analyst. Feature Analyst was conceived under a Small Business Innovation Research (SBIR) contract with NASA's Stennis Space Center, and through the Montana State University TechLink Center, an organization funded by NASA and the U.S. Department of Defense to link regional companies with Federal laboratories for joint research and technology transfer. The software provides a paradigm shift to automated feature extraction, as it utilizes spectral, spatial, temporal, and ancillary information to model the feature extraction process; presents the ability to remove clutter; incorporates advanced machine learning techniques to supply unparalleled levels of accuracy; and includes an exceedingly simple interface for feature extraction.

Source record↗

DOE OSTI2026

Predicting critical heat flux using localized sensing at invisible vapor-liquid interfaces

Predicting critical heat flux (CHF) in two-phase electronics cooling systems remains a significant challenge due to the sudden onset of boiling crisis and the difficulty in directly visualizing vapor-liquid interfaces. Existing sensing methods rely on lagging temperature measurements, optically accessible systems, or spatially averaged signals that cannot pinpoint CHF initiation at localized high-heat-flux regions. Here, we report a planar capacitive sensing approach that enables real-time, localized detection of vapor-liquid interface dynamics for CHF prediction in boiling heat transfer. The capacitive sensor exploits the dielectric constant difference between liquid and vapor phases to capture bubble nucleation, growth, and departure dynamics with a temporal resolution down to 2 ms. The capacitive sensing reveals distinct signals across boiling regimes: from high-frequency fluctuations during strong nucleate boiling to low-frequency fluctuations with increased amplitudes when approaching CHF. The multi-sensor array experiments demonstrate real-time localized sensing, where each sensor responds exclusively to boiling in its immediate vicinity without crosstalk from neighboring regions. This non-intrusive sensing approach provides predictive rather than lagging sensing signals of CHF occurrence, offering predictive diagnosis of two-phase liquid cooling for the thermal management of high-power-density electronics.

CHF↗

NASA NTRS1991

Perceiving the coherent movements of spatially-separated features

When a partially-occluded object is represented in an image, it is defined by a set of spatially-separated features that may be registered at different spatial scales. To understand the image, human vision must organize these fragmented optical features into common and distinct object surfaces. Although the common fate of moving features often is considered a primary source of reliable information for image segmentation, little is known of the visual system's capacity to discriminate the coherent relative movements of spatially-separated features. In a series of experiments, observers viewed elements whose movements were correlated (direction and magnitude) or were uncorrelated. Our results indicate that observers can discriminate the two types of movement about as well as they can detect any movement at all. Moreover, the ability to perceive coherent movements is maintained under a variety of conditions including differences in the elements' spatial frequency content, spatial position and contrast, and temporal phase shifts between the spatially-correlated displacements. These results suggest that coherent relative motion may be a fundamental source of information exploited by vision, despite considerable variability in the spatial and temporal characteristics of the individual features.

Mowafy, Lyn↗

DOE OSTI2024

Rapid detection of rare events from in situ X-ray diffraction data using machine learning

High-energy X-ray diffraction methods can non-destructively map the 3D microstructure and associated attributes of metallic polycrystalline engineering materials in their bulk form. These methods are often combined with external stimuli such as thermo-mechanical loading to take snapshots of the evolving microstructure and attributes over time. However, the extreme data volumes and the high costs of traditional data acquisition and reduction approaches pose a barrier to quickly extracting actionable insights and improving the temporal resolution of these snapshots. This article presents a fully automated technique capable of rapidly detecting the onset of plasticity in high-energy X-ray microscopy data. The technique is computationally faster by at least 50 times than the traditional approaches and works for data sets that are up to nine times sparser than a full data set. This new technique leverages self-supervised image representation learning and clustering to transform massive data sets into compact, semantic-rich representations of visually salient characteristics ( e.g. peak shapes). These characteristics can rapidly indicate anomalous events, such as changes in diffraction peak shapes. It is anticipated that this technique will provide just-in-time actionable information to drive smarter experiments that effectively deploy multi-modal X-ray diffraction methods spanning many decades of length scales.

Zheng, Weijian↗

NASA NTRS2012

Multi-Wavelength Observations of the Type IIb Supernova 2009mg

We present Swift UVOT and XRT observations, and visual wavelength spectroscopy of the Type IIb supernova (SN) 2009mg, discovered in the Sb galaxy ESO 121-G26. The observational properties of SN 2009mg are compared to the prototype Type IIb SNe 1993J and 2008ax, with which we find many similarities. However,minor differences are discernible including SN 2009mg not exhibiting an initial fast decline or micro-band upturn as observed in the comparison objects, and its rise to maximum is somewhat slower leading to slightly broader light curves. The late-time temporal index of SN 2009mg, determined from 40 days post-explosion, is consistent with the decay rate of SN 1993J, but inconsistent with the decay of Co-56. This suggests leakage of gamma-rays out of the ejecta and a stellar mass on the small side of the mass distribution. Our XRT nondetection provides an upper limit on the mass-loss rate of the progenitor of M less than 1.5 x 10(exp -5) solar mass yr(exp -1). Modelling of the SN light curve indicates a kinetic energy of 0.15 sup +0.02 sub -0.13 x 10(exp 51) erg, an ejecta mass of 0.56 sup+0.10 sub -.26 solar mass and a Ni-56 mass of 0.10 plus or minus 0.01 solar mass.

Oates, S. R.↗

NASA NTRS2017

Gaussian Mixture Models for Temporal Depth Fusion

Sensing the 3D environment of a moving robot is essential for collision avoidance. Most 3D sensors produce dense depth maps, which are subject to imperfections due to various environmental factors. Temporal fusion of depth maps is crucial to overcome those. Temporal fusion is traditionally done in 3D space with voxel data structures, but it can be approached by temporal fusion in image space, with potential benefits in reduced memory and computational cost for applications like reactive collision avoidance for micro air vehicles. In this paper, we present an efficient Gaussian Mixture Models based depth map fusion approach, introducing an online update scheme for dense representations. The environment is modeled from an ego-centric point of view, where each pixel is represented by a mixture of Gaussian inverse-depth models. Consecutive frames are related to each other by transformations obtained from visual odometry. This approach achieves better accuracy than alternative image space depth map fusion techniques at lower computational cost.

Matthies, Larry↗

DOE OSTI2019

Time-Resolved X-Ray Microscopy for Materials Science

X-ray microscopy has been an indispensable tool to image nanoscale properties for materials research. One of its recent advances is extending microscopic studies to the time domain to visualize the dynamics of nanoscale phenomena. Large-scale X-ray facilities have been the powerhouse of time-resolved X-ray microscopy. Their upgrades, including a significant reduction of the X-ray emittance at storage rings (SRs) and fully coherent ultrashort X-ray pulses at free-electron lasers (FELs), will lead to new developments in instrumentation and will open new scientific opportunities for X-ray imaging of nanoscale dynamics with the simultaneous attainment of unprecedentedly high spatial and temporal resolutions. As a result, this review presents recent progress in and the outlook for time-resolved X-ray microscopy in the context of ultrafast nanoscale imaging and its applications to condensed matter physics and materials science.

36 MATERIALS SCIENCE↗

NASA NTRS2015

Evaluation of the Surface PM2.5 in Version 1 of the NASA MERRA Aerosol Reanalysis over the United States

We use surface fine particulate matter (PM2.5) measurements collected by the United States Environmental Protection Agency (US EPA) and the Interagency Monitoring of Protected Visual Environments (IMPROVE) networks as independent validation for Version 1 of the Modern Era Retrospective analysis for Research and Applications Aerosol Reanalysis (MERRAero) developed by the Global Modeling Assimilation Office (GMAO). MERRAero is based on a version of the GEOS-5 model that is radiatively coupled to the Goddard Chemistry, Aerosol, Radiation, and Transport (GOCART) aerosol module and includes assimilation of bias corrected Aerosol Optical Depth (AOD) from Moderate Resolution Imaging Spectroradiometer (MODIS) sensors on both Terra and Aqua satellites. By combining the spatial and temporal coverage of GEOS-5 with observational constraints on AOD, MERRAero has the potential to provide improved estimates of PM2.5 compared to the model alone and with greater coverage than available observations.Importantly, assimilation of AOD data constrains the total column aerosol mass in MERRAero subject to assumptions about optical properties for each of the species represented in GOGART. However, single visible wavelength AOD data does not contain sufficient information content to correct errors in either aerosol vertical placement or composition, critical elements for a proper characterization of surface PM2.5. Despite this, we find that the data-assimilation equipped version of GEOS-5 better represents observed PM2.5 between 2003 and 2012 compared to the same version of the model without AOD assimilation. Compared to measurements from the EPA-AQS network, MERRAero shows better PM2.5 agreement with the IMPROVE network measurements, which are composed essentially of rural stations. Regardless the data network, MERRAero PM2.5 are closer to observation values during the summer while larger discrepancies are observed during the winter. Comparing MERRAero to PM2.5 data collected by the Chemical Speciation Network (CSN) offers greater insight on the species MERRAero predicts well and those for which there are biases relative to the EPA observations. Analysis of this speciated data indicates that the lack of nitrate emissions in MERRAero and an underestimation of carbonaceous emissions in the Western US explains much of the reanalysis bias during the winter. To further understand discrepancies between the reanalysis and observations, we use complimentary data to assess two important aspects of MERRAero that are of relevance to the diagnosis of PM2.5, in particular AOD and vertical structure

Air Pollution↗

DOE OSTI2024

Quantifying Dissolution Dynamics in Porous Media Using a Spatial Flow Focusing Profile

Abstract The diverse range of patterns in porous media formed by dissolution processes depends on the relative magnitude of flow, transport, and chemical reactions at pore surfaces. However, distinguishing between regimes often relies solely on qualitative, visual comparisons of emergent structures. Here, we propose a quantitative measure capable of identifying different regimes using the concept of the spatial flow focusing profile, which segments the medium into cross sections along the flow direction to calculate the flow focusing index for each section. We employ this measure in numerical simulations of a dissolving porous medium using a pore network model. We obtain a morphological phase diagram of dissolution patterns, which we characterize using the flow focusing profile. In particular, we demonstrate that analyzing the temporal changes in the profile allows one to quantitatively distinguish between wormholing and channeling. The transition between them is shown to be affected by the heterogeneity of the system.

58 GEOSCIENCES↗

NASA NTRS2001

Spatiotemporal relationships between growth and microtubule orientation as revealed in living root cells of Arabidopsis thaliana transformed with green-fluorescent-protein gene construct GFP-MBD

Arabidopsis thaliana plants were transformed with GFP-MBD (J. Marc et al., Plant Cell 10: 1927-1939, 1998) under the control of a constitutive (35S) or copper-inducible promoter. GFP-specific fluorescence distributions, levels, and persistence were determined and found to vary with age, tissue type, transgenic line, and individual plant. With the exception of an increased frequency of abnormal roots of 35S GFP-MBD plants grown on kanamycin-containing media, expression of GFP-MBD does not appear to affect plant phenotype. The number of leaves, branches, bolts, and siliques as well as overall height, leaf size, and seed set are similar between wild-type and transgenic plants as is the rate of root growth. Thus, we conclude that the transgenic plants can serve as a living model system in which the dynamic behavior of microtubules can be visualized. Confocal microscopy was used to simultaneously monitor growth and microtubule behavior within individual cells as they passed through the elongation zone of the Arabidopsis root. Generally, microtubules reoriented from transverse to oblique or longitudinal orientations as growth declined. Microtubule reorientation initiated at the ends of the cell did not necessarily occur simultaneously in adjacent neighboring cells and did not involve complete disintegration and repolymerization of microtubule arrays. Although growth rates correlated with microtubule reorientation, the two processes were not tightly coupled in terms of their temporal relationships, suggesting that other factor(s) may be involved in regulating both events. Additionally, microtubule orientation was more defined in cells whose growth was accelerating and less stringent in cells whose growth was decelerating, indicating that microtubule-orienting factor(s) may be sensitive to growth acceleration, rather than growth per se.

NASA Discipline Plant Biology↗

DOE OSTI2025

Toward Drilling the Perfect Geothermal Well: An International Research Coordination Network for Geothermal Drilling Optimization Supported by Deep Machine Learning and Cloud Based Data Aggregation

The EDGE project, supported by the U.S. Department of Energy Geothermal Technologies Office under award DE-EE0008793, established a data-driven framework for improving the efficiency, cost-effectiveness, and reliability of geothermal well drilling. The project focused on developing scalable data infrastructure, advanced machine learning and probabilistic models, and integrated analytics tools to support continuous drilling optimization. A central objective was to reduce geothermal drilling costs by up to seventy percent while minimizing the risk of well failure through predictive diagnostics and adaptive planning. Over the project period, a comprehensive data repository was designed and deployed, incorporating records from over one hundred geothermal wells across varied geological settings. This repository supported both structured and unstructured data and adhered to FAIR data principles, enabling provenance tracking, quality control, and standardized metadata. The project introduced automated ingestion pipelines and a cloud-hosted platform that facilitated access to raw, processed, and derived datasets. This infrastructure served as the foundation for model development and analysis. Machine learning workflows were developed to predict key drilling metrics including rate of penetration, non-productive time, and total drilling costs. Self-organizing maps and dimensionality reduction methods were used to uncover operational patterns and outliers, while supervised learning algorithms such as random forests and deep neural networks were applied to forecast performance outcomes. The models were validated on heterogeneous datasets from both U.S. and Icelandic fields, demonstrating variable but significant predictive accuracy. The results indicated that finer temporal resolution, inclusion of lithological data, and consistency in operational annotations could substantially improve model performance. The project also implemented process mining techniques to reconstruct state-transition models from drilling event logs. These models enabled the identification of deviations from optimal workflows and provided insights into recurring failure modes. Analysis of non-productive time highlighted the impact of equipment failures, geological challenges, and human factors, offering opportunities for targeted mitigation strategies. The EDGE Dashboard was developed as a web-based expert system integrating data visualization, model outputs, and user-driven queries. It provided an accessible interface for operators to explore historical data, evaluate predicted outcomes, and compare drilling scenarios. Initial feedback from project partners suggested that the dashboard could serve as a foundation for more advanced advisory and optimization tools. Overall, the EDGE project demonstrated the feasibility and value of applying modern data science techniques to geothermal drilling. It delivered a set of interoperable tools and models that can support more efficient, lower-risk well development. The findings point toward a viable path for transitioning from advisory analytics to semi-autonomous drilling systems, contingent on continued collaboration, expanded datasets, and field validation. The project results have immediate relevance for drilling operations, data management practices, and future geothermal R&D efforts aimed at achieving reliable, cost-competitive geothermal energy at scale.

15 GEOTHERMAL ENERGY↗

DOE OSTI2018

CLARITY reveals a more protracted temporal course of axon swelling and disconnection than previously described following traumatic brain injury

Abstract Diffuse axonal injury (DAI) is an important consequence of traumatic brain injury (TBI). At the moment of trauma, axons rarely disconnect, but undergo cytoskeletal disruption and transport interruption leading to protein accumulation within swellings. The amyloid precursor protein (APP) accumulates rapidly and the standard histological evaluation of axonal pathology relies upon its detection. APP+ swellings first appear as varicosities along intact axons, which can ultimately undergo secondary disconnection to leave a terminal “axon bulb” at the disconnected, proximal end. However, sites of disconnection are difficult to determine with certainty using standard, thin tissue sections, thus limiting the comprehensive evaluation of axon degeneration. The tissue‐clearing technique, CLARITY, permits three‐dimensional visualization of axons that would otherwise be out of plane in standard tissue sections. Here, we examined the morphology and connection status of APP+ swellings using CLARITY at 6 h, 24 h, 1 week and 1 month following the controlled cortical impact (CCI) model of TBI in mice. Remarkably, many APP+ swellings that appeared as terminal bulbs when viewed in standard 8‐µm‐thick regions of tissue were instead revealed to be varicose swellings along intact axons when three dimensions were fully visible. Moreover, the percentage of these potentially viable axon swellings differed with survival from injury and may represent the delayed onset of distinct mechanisms of degeneration. Even at 1‐month post‐CCI, ~10% of apparently terminal bulbs were revealed as connected by CLARITY and are thus potentially salvageable. Intriguingly, the diameter of swellings decreased with survival, including varicosities along intact axons, and may reflect reversal of, or reduced, axonal transport interruption in the chronic setting. These data indicate that APP immunohistochemistry on standard thickness tissue sections overestimates axon disconnection, particularly acutely post‐injury. Evaluating cleared tissue demonstrates a surprisingly delayed process of axon disconnection and thus longer window of therapeutic opportunity than previously appreciated. Intriguingly, a subset of axon swellings may also be capable of recovery.

Weber, Maura T.↗

DOE OSTI2024

Sub-millisecond keyhole pore detection in laser powder bed fusion using sound and light sensors and machine learning

Laser powder bed fusion is a mainstream additive manufacturing technology widely used to manufacture complex parts in prominent sectors, including aerospace, biomedical, and automotive industries. However, during the printing process, the presence of an unstable vapor depression can lead to a type of defect called keyhole porosity, which is detrimental to the part quality. In this study, we developed an effective approach to locally detect the generation of keyhole pores during the printing process by leveraging machine learning and a suite of optical and acoustic sensors. Simultaneous synchrotron x-ray imaging allows the direct visualization of pore generation events inside the sample, offering high-fidelity ground truth. A neural network model adopting SqueezeNet architecture using single-sensor data was developed to evaluate the fidelity of each sensor for capturing keyhole pore generation events. Our comparative study shows that the near infrared images gave the highest prediction accuracy, followed by 100 kHz and 20 kHz microphones, and the photodiode sensitive to processing laser wavelength had the lowest accuracy. Using a single sensor, over 90% prediction accuracy can be achieved with a temporal resolution as short as 0.1 ms. A data fusion scheme was also developed with features extracted using SqueezeNet neural network architecture and classification using different machine learning algorithms. Our work demonstrates the correlation between the characteristic optical and acoustic emissions and the keyhole oscillation behavior, and thereby provides strong physics support for the machine learning approach.

36 MATERIALS SCIENCE↗

NASA NTRS2009

Coastal Online Analysis and Synthesis Tool 2.0 (COAST)

The Coastal Online Assessment and Synthesis Tool (COAST) 3D geobrowser has been developed to integrate disparate coastal datasets from NASA and other sources into a desktop tool that provides new data visualization and analysis capabilities for coastal researchers, managers, and residents. It is built upon the widely used NASA-developed open source World Wind geobrowser from NASA Ames (Patrick Hogan et al.) .Net and C# version is used for development. It is leveraged off of World Wind community shared code samples and COAST 2.0 enhancement direction is based on Coastal science community feedback and needs assessment (GOMA). The main objective is to empower the user to bring more user-meaningful data into multi-layered, multi-temporal spatial context.

Brown, Richard B.↗

DOE OSTI2026

A call to standardize metrics for monitoring baleen whales near marine construction activities

Effective monitoring is necessary to protect marine mammal species during the construction of offshore infrastructure. The tools for detecting or monitoring marine mammals span traditional (e.g., visual observers, optical cameras), to newer (e.g., passive acoustic monitoring, infrared cameras, tags), and emerging (e.g., satellite imagery, environmental DNA, dimethyl sulfide concentration) technologies. Some are better suited for use during offshore development; however, peer-reviewed literature does not typically evaluate and report on the performance of these various technologies. We define a minimum set of metrics related to efficacy (i.e., confusion matrix, precision and recall, probability of missed mitigation), detection range (i.e., maximum and reliable detection range, spatial resolution), and data delivery (i.e., detection latency, system reliability, temporal resolution) that we recommend are needed to assess the utility of monitoring technologies for this purpose. Following a literature review of relevant studies, we highlight which publications reported these metrics and used multiple technologies to compare relative performance. We also emphasize the benefits of multi-modal approaches and recommend performance assessments through modeling or large-scale collaborative field testing. These metrics will standardize data collection, reporting, and analysis; promote consistent and comparable results; and foster collaboration among developers, regulatory agencies, and scientists. This may lead to the co-development of technology that achieves multiple goals, has greater application, and can answer research questions while collecting data to fulfill permitting requirements. These metrics may also inform decisions on what systems regulatory agencies might consider using and reduce monitoring costs, which is critical to support the marine sector's rapid growth alongside marine mammal conservation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

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↗