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At least 325 records · Page 18

Direct Detection Doppler Lidar for Spaceborne Wind Measurement

Aerosol and molecular based versions of the double-edge technique can be used for direct detection Doppler lidar spaceborne wind measurement. The edge technique utilizes the edge of a high spectral resolution filter for high accuracy wind measurement using direct detection lidar. The signal is split between an edge filter channel and a broadband energy monitor channel. The energy monitor channel is used for signal normalization. The edge measurement is made as a differential frequency measurement between the outgoing laser signal and the atmospheric backscattered return for each pulse. As a result the measurement is insensitive to laser and edge filter frequency jitter and drift at a level less than a few parts in 10(exp 10). We have developed double edge versions of the edge technique for aerosol and molecular-based lidar measurement of the wind. Aerosol-based wind measurements have been made at Goddard Space Flight Center and molecular-based wind measurements at the University of Geneva. We have demonstrated atmospheric measurements using these techniques for altitudes from 1 to more than 10 km. Measurement accuracies of better than 1.25 m/s have been obtained with integration times from 5 to 30 seconds. The measurements can be scaled to space and agree, within a factor of two, with satellite-based simulations of performance based on Poisson statistics. The theory of the double edge aerosol technique is described by a generalized formulation which substantially extends the capabilities of the edge technique. It uses two edges with opposite slopes located about the laser frequency at approximately the half-width of each edge filter. This doubles the signal change for a given Doppler shift and yields a factor of 1.6 improvement in the measurement accuracy compared to the single edge technique. The use of two high resolution edge filters substantially reduces the effects of Rayleigh scattering on the measurement, as much as order of magnitude, and allows the signal to noise ratio to be substantially improved in areas of low aerosol backscatter. We describe a method that allows the Rayleigh and aerosol components of the signal to be independently determined using the two edge channels and an energy monitor channel. The effects of Rayleigh scattering may then subtracted from the measurement and we show that the correction process does not significantly increase the measurement noise for Rayleigh to aerosol ratios up to 10. We show that for small Doppler shifts a measurement accuracy of 0.4 m/s can be obtained for 5000 detected photon, 1.2 m/s for 1000 detected photons, and 3.7 m/s for 50 detected photons for a Rayleigh to aerosol ratio of 5. Methods for increasing the dynamic range of the aerosol-based system to more than +/- 100 m/s are given.

Korb, C. Laurence↗

Instrument Performance of GISMO, a 2 Millimeter TES Bolometer Camera used at the IRAM 30 m Telescope

In November of 2007 we demonstrated a monolithic Backshort-Under-Grid (BUG) 8x16 array in the field using our 2 mm wavelength imager GISMO (Goddard IRAM Superconducting 2 Millimeter Observer) at the IRAM 30 m telescope in Spain for astronomical observations. The 2 mm spectral range provides a unique terrestrial window enabling ground-based observations of the earliest active dusty galaxies in the universe and thereby allowing a better constraint on the star formation rate in these objects. The optical design incorporates a 100 mm diameter silicon lens cooled to 4 K, which provides the required fast beam yielding 0.9 lambda/D pixels. With this spatial sampling, GISMO will be very efficient at detecting sources serendipitously in large sky surveys, while the capability for diffraction limited imaging is preserved. The camera provides significantly greater detection sensitivity and mapping speed at this wavelength than has previously been possible. The instrument will fill in the spectral energy distribution of high redshift galaxies at the Rayleigh-Jeans part of the dust emission spectrum, even at the highest redshifts. Here1 will we present early results from our observing run with the first fielded BUG bolometer array. We have developed key technologies to enable highly versatile, kilopixel, infrared through millimeter wavelength bolometer arrays. The Backshort-Under-Grid (BUG) array consists of three components: 1) a transition-edge-sensor (TES) based bolometer array with background-limited sensitivity and high filling factor, 2) a quarter-wave reflective backshort grid providing high optical efficiency, and 3) a superconducting bump-bonded large format Superconducting Quantum Interference Device (SQUID) multiplexer readout. The array is described in more detail elsewhere (Allen et al., this conference). In November of 2007 we demonstrated a monolithic 8x 16 array with 2 mm-pitch detectors in the field using our 2 mm wavelength imager GISMO (Goddard IRAM Superconducting 2 Millimeter Observer) at the IRAM 30 m telescope in Spain for astronomical observations. The 2 mm spectral range provides a unique terrestrial window enabling ground-based observations of the earliest active dusty galaxies in the universe and thereby allowing a better constraint on the star formation rate in these objects. The optical design incorporates a 100 mm diameter silicon lens cooled to 4 K, which provides the required fast beam yielding 0.9 lambda1D pixels. With this spatial sampling, GISMO will be very efficient at detecting sources serendipitously in large sky surveys, while the capability for diffraction limited imaging is preserved. The camera provides significantly greater detection sensitivity and mapping speed at this wavelength than has previously been possible. The instrument will fill in the spectral energy distribution of high redshift galaxies at the Rayleigh-Jeans part of the dust emission spectrum, even at the highest redshifts. Here I will we present early results from our observing run with the first fielded BUG bolometer array.

Staguhn, Johannes↗

Characterization of energy response for photon-counting detectors using x-ray fluorescence

Purpose: To investigate the feasibility of characterizing a Si strip photon-counting detector using x-ray fluorescence. Methods: X-ray fluorescence was generated by using a pencil beam from a tungsten anode x-ray tube with 2 mm Al filtration. Spectra were acquired at 90° from the primary beam direction with an energy-resolved photon-counting detector based on an edge illuminated Si strip detector. The distances from the source to target and the target to detector were approximately 19 and 11 cm, respectively. Four different materials, containing silver (Ag), iodine (I), barium (Ba), and gadolinium (Gd), were placed in small plastic containers with a diameter of approximately 0.7 cm for x-ray fluorescence measurements. Linear regression analysis was performed to derive the gain and offset values for the correlation between the measured fluorescence peak center and the known fluorescence energies. The energy resolutions and charge-sharing fractions were also obtained from analytical fittings of the recorded fluorescence spectra. An analytical model, which employed four parameters that can be determined from the fluorescence calibration, was used to estimate the detector response function. Results: Strong fluorescence signals of all four target materials were recorded with the investigated geometry for the Si strip detector. The average gain and offset of all pixels for detector energy calibration were determined to be 6.95 mV/keV and −66.33 mV, respectively. The detector’s energy resolution remained at approximately 2.7 keV for low energies, and increased slightly at 45 keV. The average charge-sharing fraction was estimated to be 36% within the investigated energy range of 20–45 keV. The simulated detector output based on the proposed response function agreed well with the experimental measurement. Conclusions: The performance of a spectral imaging system using energy-resolved photon-counting detectors is very dependent on the energy calibration of the detector. The proposed x-ray fluorescence technique offers an accurate and efficient way to calibrate the energy response of a photon-counting detector.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Extensions of algebraic image operators: An approach to model-based vision

Researchers extend their previous research on a highly structured and compact algebraic representation of grey-level images which can be viewed as fuzzy sets. Addition and multiplication are defined for the set of all grey-level images, which can then be described as polynomials of two variables. Utilizing this new algebraic structure, researchers devised an innovative, efficient edge detection scheme. An accurate method for deriving gradient component information from this edge detector is presented. Based upon this new edge detection system researchers developed a robust method for linear feature extraction by combining the techniques of a Hough transform and a line follower. The major advantage of this feature extractor is its general, object-independent nature. Target attributes, such as line segment lengths, intersections, angles of intersection, and endpoints are derived by the feature extraction algorithm and employed during model matching. The algebraic operators are global operations which are easily reconfigured to operate on any size or shape region. This provides a natural platform from which to pursue dynamic scene analysis. A method for optimizing the linear feature extractor which capitalizes on the spatially reconfiguration nature of the edge detector/gradient component operator is discussed.

Lerner, Bao-Ting↗

The Edge of Exploration: An Edge Storage and Computing Framework for Ambient Noise Seismic Interferometry Using Internet of Things Based Sensor Networks

Recent technological advances have reduced the complexity and cost of developing sensor networks for remote environmental monitoring. However, the challenges of acquiring, transmitting, storing, and processing remote environmental data remain significant. The transmission of large volumes of sensor data to a centralized location (i.e., the cloud) burdens network resources, introduces latency and jitter, and can ultimately impact user experience. Edge computing has emerged as a paradigm in which substantial storage and computing resources are located at the “edge” of the network. In this paper, we present an edge storage and computing framework leveraging commercially available components organized in a tiered architecture and arranged in a hub-and-spoke topology. The framework includes a popular distributed database to support the acquisition, transmission, storage, and processing of Internet-of-Things-based sensor network data in a field setting. We present details regarding the architecture, distributed database, embedded systems, and topology used to implement an edge-based solution. Lastly, a real-world case study (i.e., seismic) is presented that leverages the edge storage and computing framework to acquire, transmit, store, and process millions of samples of data per hour.

58 GEOSCIENCES↗

Outer Wall Segmentation of Abdominal Aortic Aneurysm by Variable Neighborhood Search Through Intensity and Gradient Spaces

Aortic aneurysm segmentation remains a challenge. Manual segmentation is a time-consuming process which is not practical for routine use. To address this limitation, several automated segmentation techniques for aortic aneurysm have been developed, such as edge detection-based methods, partial differential equation methods, and graph partitioning methods. However, automatic segmentation of aortic aneurysm is difficult due to high pixel similarity to adjacent tissue and a lack of color information in the medical image, preventing previous work from being applicable to difficult cases. This paper uses uses a variable neighborhood search that alternates between intensity-based and gradient-based segmentation techniques. By alternating between intensity and gradient spaces, the search can escape from local optima of each space. The experimental results demonstrate that the proposed method outperforms the other existing segmentation methods in the literature, based on measurements of dice similarity coefficient and jaccard similarity coefficient at the pixel level. In addition, it is shown to perform well for cases that are difficult to segment.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

Robust system size reduction of discrete fracture networks: a multi-fidelity method that preserves transport characteristics

In this paper, we propose a multi-fidelity system reduction technique that uses weighted graphs paired with three-dimensional discrete fracture network (DFN) modelling for efficient simulation of subsurface flow and transport in fractured media. DFN models are used to simulate flow and transport in subsurface fractured rock with low-permeability. One method to alleviate the heavy computational overhead associated with these simulations is to reduce the size of the DFN using a graph representation of it to identify the primary flow sub-network and only simulate flow and transport thereon. The first of these methods used unweighted graphs constructed solely on DFN topology and could be used for accurate predictions of first-passage times. However, these techniques perform poorly when predicting later stages of the mass breakthrough. We utilize a weighted-graph representation of the DFN where edge weights are based on hydrological parameters in the DFN that allows us to exploit the kinematic quantities derivable a posteriori from the flow solution obtained on the graph representation of the DFN to perform system reduction and predict the later stages of the breakthrough curve with high fidelity. We also propose and demonstrate the use of an adaptive pruning algorithm with error control that produces a pruned DFN sub-network whose predicted mass breakthrough agrees with the original DFN within a user-specified tolerance. Lastly, the method allows for the level of accuracy to be a user-controlled parameter.

58 GEOSCIENCES↗

Updated design of the CMB polarization experiment satellite LiteBIRD

Recent developments of transition-edge sensors (TESs), based on extensive experience in ground-based experiments, have been making the sensor techniques mature enough for their application on future satellite CMB polarization experiments. LiteBIRD is in the most advanced phase among such future satellites, targeting its launch in Japanese Fiscal Year 2027 (2027FY) with JAXA's H3 rocket. It will accommodate more than 4000 TESs in focal planes of reflective low-frequency and refractive medium-and-high-frequency telescopes in order to detect a signature imprinted on the cosmic microwave background (CMB) by the primordial gravitational waves predicted in cosmic inflation. The total wide frequency coverage between 34GHz and 448GHz enables us to extract such weak spiral polarization patterns through the precise subtraction of our Galaxy's foreground emission by using spectral differences among CMB and foreground signals. Telescopes are cooled down to 5Kelvin for suppressing thermal noise and contain polarization modulators with transmissive half-wave plates at individual apertures for separating sky polarization signals from artificial polarization and for mitigating from instrumental 1/f noise. Passive cooling by using V-grooves supports active cooling with mechanical coolers as well as adiabatic demagnetization refrigerators. Sky observations from the second Sun-Earth Lagrangian point, L2, are planned for three years. An international collaboration between Japan, USA, Canada, and Europe is sharing various roles. In May 2019, the Institute of Space and Astronautical Science (ISAS), JAXA selected LiteBIRD as the strategic large mission No. 2.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Mechanistic study of direct coupling of CO 2 and C 2 H 4 over atomically dispersed metal at graphene edges

Direct coupling of CO 2 and ethylene (hereinafter DCCE) to acrylic acid is valuable for valorizing CO 2 to manufacture acrylate-derived products. However, previous studies in DCCE have been limited on molecular catalysts with challenges in improving catalytic performance. In this work, we employed density functional theory calculations and ab initio molecular dynamics simulations to investigate the heterogeneous catalysis of DCCE over atomically dispersed metal centers at nitrogen-doped zigzag edge of graphene. Based on competitive adsorption and structural stability, Mo, Cr, V, Ru, and Ni active sites are chosen to explore the reaction kinetics. Here, we find that the activation barriers are determined by the charge redistribution at transition states, which explains the trend of activity for the C-C coupling and the hydrogen transfer, two key steps in DCCE. Furthermore, we show that the intramolecular hydrogen transfer (rate-limiting step) is hindered due to the lack of local coordinate at the active sites. We thus propose to use co-adsorbed water as a “proton-exchanger” following a water-assisted route, and show that the activation barriers are reduced over all metal centers. Particularly, water promotes the hydrogen transfer over metals with strong CO 2 -ethylene co-activation and facile C-C coupling kinetics, which could be considered promising for DCCE. In both mechanisms, the stability of metallactone intermediate can be used to predict the catalytic activity. It is anticipated that the insights from this work can provide guidelines for mimicking well-defined multifunctional active sites in molecular catalysts to design heterogeneous catalysts for such C-C coupling, which advances catalytic utilization of CO 2 .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Common Column Identification for Table Similarity Detection in Electrified Transportation Data Lakes

Electrified transportation often requires researchers and operators to interact with datasets from a wide range of sources and disciplines, such as transportation, power systems, public health, policies, and regulations. These datasets vary in quality and format, making it difficult to understand, preprocess, and identify key columns representing real-world entities or values for indexing and joining, which can negatively impact downstream analysis and operation. Existing solutions are limited, requiring extensive manual customization or data expertise to utilize. In this article, we propose a multi-layered approach to automatically identify key columns to expedite preprocessing and aid in analysis of electrified transportation data. Our method leverages a dynamic ontology to identify common fields and an information theory-based strategy for edge cases that are difficult to generalize. Evaluations on a number of datasets from data.gov and kaggle.com show improved performance of our methods over several baseline techniques, and our ablation analyses illustrate the efficacy of individual components of our method. Our case studies also demonstrate that our methods have the potential to improve analysis of electrified transportation data and aid in automatic integration of such datasets.

33 ADVANCED PROPULSION SYSTEMS↗

Transmission-distribution long-term volt-var planning considering reactive power support capability of distributed PV

High penetration of grid-edge, inverter-based photovoltaic (PV) can cause significant voltage fluctuations not only at the distribution but also at the sub-transmission levels due to PV output intermittency. Traditional reactive power planning approaches do not consider intermittency, nor the possibility of coordinating the control of existing and future volt-ampere reactive resources. This paper proposes a reactive power planning tool for sub-transmission systems to mitigate voltage violations and fluctuations caused by high PV penetration and intermittency with a minimum investment cost. The planning tool coordinates with an optimization-based volt-var operational tool for: a) modeling the coordination of all existing var assets in both sub-transmission and distribution systems to reduce the need of new equipment, and b)selecting a set of scenarios with voltage violations, derived from PV intermittency c) testing the final investment decision. The tool obtains an investment need for each intermittency scenario with a proposed optimal power-flow framework with efficient techniques to handle a high number of discrete variables. Two options are provided for final planning decision: i) a conservative direct combination of investment need solutions and ii) a machine learning-based selection of representative investment needs at most time steps. The final investment decision options are verified using a realistic large-scale sub-transmission system and 5-minute PV and load data. The results show a significant voltage performance improvement with a lower investment cost for additional var equipment compared to conventional approaches.

14 SOLAR ENERGY↗

Comparison of dislocation content measured with transmission electron microscopy and micro-Laue diffraction based streak analysis

The subsurface dislocation content in a Ti-5Al-2.5Sn (wt%) uniaxial tension sample deformed at ambient temperature was characterized by peak streak analysis of micro-Laue diffraction patterns collected non-destructively by differential aperture X-raymicroscopy, and with focused ion beam transmission electron microscopy of material in the same volume. This comparison reveals that micro-Laue diffraction streak analysis based on an edge dislocation assumption can accurately identify the dominant dislocation slip system history (Burgers vector and plane observed by TEM), despite the fact that dislocations have predominantly screw character. As a result, other dislocations identified by TEM were not convincingly discernible from the peak streak analysis.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Topologically Guided, Automated Construction of Metal–Organic Frameworks and Their Evaluation for Energy-Related Applications

Metal-organic frameworks (MOFs) are promising materials for a range of energy and environmental applications. Here we describe in detail a computational algorithm and code to generate MOFs based on edge-transitive topological nets for subsequent evaluation via molecular simulation. This algorithm has been previously used by us to construct and evaluate 13 512 MOFs of 41 different topologies for cryo-adsorbed hydrogen storage. Grand canonical Monte Carlo simulations are used here to evaluate the 13 512 structures for the storage of gaseous fuels such as hydrogen and methane and nondistillative separation of xenon/krypton mixtures at various operating conditions. MOF performance for both gaseous fuel storage and xenon/krypton separation is influenced by topology. In conclusion, simulation data suggest that gaseous fuel storage performance is topology-dependent due to MOF properties such as void fraction and surface area combining differently in different topologies, whereas xenon/krypton separation performance is topology-dependent due to how topology constrains the pore size distribution.

36 MATERIALS SCIENCE↗

Spatiotemporal Graph Convolutional Networks for Earthquake Source Characterization

Abstract Accurate earthquake location and magnitude estimation play critical roles in seismology. Recent deep learning frameworks have produced encouraging results on various seismological tasks (e.g., earthquake detection, phase picking, seismic classification, and earthquake early warning). Many existing machine learning earthquake location methods utilize waveform information from a single station. However, multiple stations contain more complete information for earthquake source characterization. Inspired by recent successes in applying graph neural networks (GNNs) in graph‐structured data, we develop a Spatiotemporal Graph Neural Network (STGNN) for estimating earthquake locations and magnitudes. Our graph neural network leverages geographical and waveform information from multiple stations to construct graphs automatically and dynamically by adaptive message passing based on graphs' edges. Using a recent graph neural network and a fully convolutional neural network as baselines, we apply STGNN to earthquakes recorded by the Southern California Seismic Network from 2000 to 2019 and earthquakes collected in Oklahoma from 2014 to 2015. STGNN yields more accurate earthquake locations than those obtained by the baseline models and performs comparably in terms of depth and magnitude prediction, though the ability to predict depth and magnitude remains weak for all tested models. Our work demonstrates the potential of using GNNs and multiple stations for better automatic estimation of earthquake epicenters.

58 GEOSCIENCES↗

Tuning the structure and habit of iron oxide mesocrystals

A precise control over the meso- and microstructure of ordered and aligned nanoparticle assemblies, i.e., mesocrystals, is essential in the quest for exploiting the collective material properties for potential applications. In this work, we produced evaporation-induced self-assembled mesocrystals with different mesostructures and crystal habits based on iron oxide nanocubes by varying the nanocube size and shape and by applying magnetic fields. A full 3D characterization of the mesocrystals was performed using image analysis, high-resolution scanning electron microscopy and Grazing Incidence Small Angle X-ray Scattering (GISAXS). This enabled the structural determination of e.g. multi-domain mesocrystals with complex crystal habits and the quantification of interparticle distances with sub-nm precision. Mesocrystals of small nanocubes (l = 8.6 12.6 nm) are isostructural with a body centred tetragonal (bct ) lattice whereas assemblies of the largest nanocubes in this study (l = 13.6 nm) additionally form a simple cubic (sc) lattice. The mesocrystal habit can be tuned from a square, hexagonal to star-like and pillar shapes depending on the particle size and shape and the strength of the applied magnetic field. Finally, we outline a qualitative phase diagram of the evaporation-induced self-assembled superparamagnetic iron oxide nanocube mesocrystals based on nanocube edge length and magnetic field strength.

36 MATERIALS SCIENCE↗

Domain alignment within ferroelectric/dielectric PbTiO 3 /SrTiO 3 superlattice nanostructures

The ferroelectric domain pattern within lithographically defined PbTiO 3 /SrTiO 3 ferroelectric/dielectric heteroepitaxial superlattice nanostructures is strongly influenced by the edges of the structures. Synchrotron X-ray nanobeam diffraction reveals that the spontaneously formed 180° ferroelectric stripe domains exhibited by such superlattices adopt a configuration in rectangular nanostructures in which domain walls are aligned with long patterned edges. The angular distribution of X-ray diffuse scattering intensity from nanodomains indicates that domains are aligned within an angular range of approximately 20° with respect to the edges. Computational studies based on a time-dependent Landau–Ginzburg–Devonshire model show that the preferred direction of the alignment results from lowering of the bulk and electrostrictive contributions to the free energy of the system due to the release of the lateral mechanical constraint. This unexpected alignment appears to be intrinsic and not a result of distortions or defects caused by the patterning process. Thus, our work demonstrates how nanostructuring and patterning of heteroepitaxial superlattices allow for pathways to create and control ferroelectric structures that may appear counterintuitive.

36 MATERIALS SCIENCE↗

Strain-balanced Si/SiGe type-II superlattices for near-infrared photodetection

Strain balanced silicon-silicon germanium type-II superlattice p-i-n photodetectors grown on a silicon germanium relaxed buffer layer are shown to exhibit an absorption band that extends beyond 0.7 eV (λ = 1.77 μm) with dark current densities of 27 μA cm{sup −2}. Simulations of the absorption edge, which are based on x-ray diffraction characterization, low observed dark current densities, and low dislocation densities, are consistent with fully strained heterostructures. Potential applications for devices made from this heterostructure design could include integrated silicon detectors, or low-noise absorption regions for infrared-extended silicon based avalanche photodiodes.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

CORSICA modelling of ITER hybrid operation scenarios

The hybrid operating mode observed in several tokamaks is characterized by further enhancement over the high plasma confinement (H-mode) associated with reduced magneto-hydro-dynamic (MHD) instabilities linked to a stationary flat safety factor ($q$ ) profile in the core region. The proposed ITER hybrid operation is currently aiming at operating for a long burn duration (>1000 s) with a moderate fusion power multiplication factor, $Q$ , of at least 5. This paper presents here candidate ITER hybrid operation scenarios developed using a free-boundary transport modelling code, CORSICA, taking all relevant physics and engineering constraints into account. The ITER hybrid operation scenarios have been developed by tailoring the 15 MA baseline ITER inductive H-mode scenario. Accessible operation conditions for ITER hybrid operation and achievable range of plasma parameters have been investigated considering uncertainties on the plasma confinement and transport. ITER operation capability for avoiding the poloidal field coil current, field and force limits has been examined by applying different current ramp rates, flat-top plasma currents and densities, and pre-magnetization of the poloidal field coils. Various combinations of heating and current drive (H&CD) schemes have been applied to study several physics issues, such as the plasma current density profile tailoring, enhancement of the plasma energy confinement and fusion power generation. A parameterized edge pedestal model based on EPED1 added to the CORSICA code has been applied to hybrid operation scenarios. Finally, fully self-consistent free-boundary transport simulations have been performed to provide information on the poloidal field coil voltage demands and to study the controllability with the ITER controllers.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗