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This public dataset contains openly-documented, machine readable digital research data corresponding to figures published in J.D. Weberski et al., “Taylor Limit Studies for Local Helicity Injection Plasma Startup,” Physics of Plasmas 32 , 062510 (2025).
Public streets, easements, and rights-of-way, collectively referred to as Real Property Interests (RPI), pose challenging and complex problems for the FUSRAP Maywood Project Team. RPIs are components of a multi-faceted objective to evaluate all remaining sources of contamination for which the Government is responsible under the Maywood Record of Decision. USACE designed and implemented a MARSSIM-based radiological investigation within sixteen potentially impacted RPIs. These RPIs were not completely addressed on their own or in conjunction with nearby property investigations or remediations. However, these RPIs were investigated based on the potential for or known impact of FMSS contamination as determined through both a historic data review of the transport mechanism (downstream waste deposition) and process knowledge obtained during Final Status Survey of proximal properties. The primary objectives of this investigation were to confirm the presence or absence of FUSRAP contamination within a given RPI, provide sufficient data to release an RPI if contamination was not encountered, and provide sufficient data for either design of an appropriate remedial solution or implementation of Land Use Controls when contamination was encountered. Of additional value, knowledge gained during the RPI investigations will allow the team to share useful radiological data with local officials and utility service providers. This information will support better, more informed decisions by all parties involved in situations such as emergency subsurface utility repairs, roadway improvement planning, or even natural disaster response. Finally, certain unexpected outcomes learned during the final data analysis may lead to refinement of the conceptual site model. Logistical lessons learned and challenges encountered during the Maywood investigation may also be helpful to stakeholders, planners, and managers of similar projects wishing to identify their own potential liability within public streets, easements, rights-of-way, and other types of RPIs. (authors)
Computation of a single geoidal height from gravity acceleration data formally requires that the latter be known everywhere on the earth. A computational procedure based on linear inverse theory for estimating geoidal heights from incomplete sets of data is presented. The same scheme can be used to estimate gravity accelerations from altimetry-derived geoids. The systematic error owing to lack of data and the choice of a particular inverse operator is described by using resolution functions and their spherical harmonic expansions. An rms value of this error is also estimated by assuming a spectrum for the unknown geoid. The influence of the size of the data region, the spacing between data, the filtering applied to the data, and the model weighting function chosen are all quantified in a spherical geometry. The examples presented show that when low degree spherical harmonic coefficients are available - from satellite orbit analysis - a band-passed version of the geoid can be constructed from local gravity data, even with a relatively restricted data set.
Samples of ~1 µm films of CeO2 doped with 2 wt% Mo, 1.5 wt% Ru, 0.75 wt% Pd, 0.5 wt% Re and 0.25 wt% Rh grown with pulsed laser deposition were irradiated with I2+ ions (610°C and 730°C, 1016 and 5×1016 I2+/cm2). For selected samples post-irradiation heat treatment was conducted (900°C, 1100°C). The specimens were sectioned with focussed ion beam milling and characterized in a transmission electron microscope with energy dispesive x-ray spectroscopy, and with atom-probe tomography. Energy dispersive x-ray spectroscopy was used to obtain elemental maps showing the distribution of dopants in the specimen after exposure. Some of these maps are discussed in detail in our companion article “Formation of multicomponent alloy particles in doped ceria under I2+ ion irradiation and thermal annealing” in the Journal of Nuclear Materials [REF]. Advanced computational analysis could be used to more accurately quantify local compositions. Data is provided for additional regions of interest and one additional irradiation condition. The doped Ceria film that was heat treated at 1100 °C delaminated from the substrate in most places. Samples were extracted from the underside of a delaminated piece and analyzed with atom-probe tomography. The resulting data show Ceria and a Mo-rich particle and demonstrate that this approach is feasable in principle to study local compositions in a sample exposed to such extreme conditions.
Droplet collision–coalescence is a crucial process in cloud physics, but accurately representing this process under different dynamical conditions remains challenging. A proposed future convective‐cloud chamber aims to investigate this key process, but the method for observing it remains unclear, even though it is theoretically established that collision‐coalescence will occur. This study serves as a proof‐of‐concept demonstration of how knowledge‐informed deep learning, combined with measurement data from local sensors in the chamber, can be used to estimate the collision kernels, which determine how the droplet size distribution evolves during collision‐coalescence. In addition to estimating the collision kernel, we also address wall fluxes, another uncertain but important process that acts as a source of heat and moisture in the chamber. Ensemble runs of large‐eddy simulations are conducted by scaling the wall fluxes and the collision kernel, while the measured flow and cloud properties are used as inputs for a neural network. Results indicate that this approach successfully maps the scaling of wall fluxes and the collision kernel with biases of approximately 1% or less relative to the range of the target data. This proof‐of‐concept lays the groundwork for future applications; when the real measurements are available, real sensor data combined with the trained model presented in this work will enable estimation of the actual wall fluxes and collision kernel.
In an attempt to determine whether clear air turbulence (CAT) which caused a brief, intense turbulence encounter to a jetliner might have been related to gravity waves generated by a line of thunderstorms, local sounding data taken within two hours of the incident have been used as input to a numerical model designed to simulate stratified flow over obstacles. In the model a rigid obstacle at the lower boundary acted as the source of the gravity waves. The model results show a large amplitude disturbance localized over the obstacle at and above flight level, in regions where CAT would be expected. Stratification studies have been conducted, varying the height and shape of the obstacle and the input wind profile. The success of the experiment leads to the conclusion that atmospheric gravity wave simulation can be useful in understanding and possibly avoiding CAT.
Radiation source localization and characterization are challenging tasks that currently require complex analyses for interpretation. Mixed reality (MR) technologies are at the verge of wide scale adoption and can assist in the visualization of complex data. Herein, we demonstrate real-time visualization of gamma ray and neutron radiation detector data in MR using the Microsoft HoloLens 2 smart glasses, significantly reducing user interpretation burden. Radiation imaging systems typically use double-scatter events of gamma rays or fast neutrons to reconstruct the incidence directional information, thus enabling source localization. The calculated images and estimated ’hot spots’ are then often displayed in 2D angular space projections on screens. By combining a state-of-the-art dual particle imaging system with HoloLens 2, we propose to display the data directly to the user via the head-mounted MR smart glasses, presenting the directional information as an overlay to the user’s 3D visual experience. We describe an open source implementation using efficient data transfer, image calculation, and 3D engine. We thereby demonstrate for the first time a real-time user experience to display fast neutron or gamma ray images from various radioactive sources set around the detector. We also introduce an alternative source search mode for situations of low event rates using a neural network and simulation based training data to provide a fast estimation of the source’s angular direction. Using MR for radiation detection provides a more intuitive perception of radioactivity and can be applied in routine radiation monitoring, education & training, emergency scenarios, or inspections.
For exascale HPC applications, silent data corruption (SDC) is one of the most dangerous problems because there is no indication that there are errors during the execution. We propose an adaptive impact-driven method that can detect SDCs dynamically. The key contributions are threefold. (1) We carefully characterize 18 real-world HPC applications and discuss the runtime data features, as well as the impact of the SDCs on their execution results. (2) We propose an impact-driven detection model that does not blindly improve the prediction accuracy, but instead detects only influential SDCs to guarantee user-acceptable execution results. (3) Our solution can adapt to dynamic prediction errors based on local runtime data and can automatically tune detection ranges for guaranteeing low false alarms. Experiments show that our detector can detect 80-99.99% of SDCs with a false alarm rate less that 1% of iterations for most cases. The memory cost and detection overhead are reduced to 15% and 6.3%, respectively, for a large majority of applications.
The Scientifically Calibrated In-Flight Imagery (SCIFLI) team captured high-resolution, calibrated, near-infrared imagery of the Orion capsule during atmospheric reentry of the EFT-1 mission. A US Navy NP-3D aircraft equipped with a multi-band optical sensor package, referred to as Cast Glance, acquired imagery of the Orion capsule's heatshield during a period when Orion was slowing from approximately Mach 10 to Mach 7. The line-of-sight distance ranged from approximately 65 to 40 nmi. Global surface temperatures of the capsule's thermal heatshield derived from the near-infrared intensity measurements complemented the in-depth (embedded) thermocouple measurements. Moreover, these derived surface temperatures are essential to the assessment of the thermocouples' reliance on inverse heat transfer methods and material response codes to infer the surface temperature from the in-depth measurements. The paper describes the image processing challenges associated with a manually-tracked, high-angular rate air-to-air observation. Issues included management of significant frame-to-frame motions due to both tracking jerk and jitter as well as distortions due to atmospheric effects. Corrections for changing sky backgrounds (including some cirrus clouds), atmospheric attenuation, and target orientations and ranges also had to be made. The image processing goal is to reduce the detrimental effects due to motion (both sensor and capsule), vibration (jitter), and atmospherics for image quality improvement, without compromising the quantitative integrity of the data, especially local intensity (temperature) variations. The paper will detail the approach of selecting and utilizing only the highest quality images, registering several co-temporal image frames to a single image frame to the extent frame-to-frame distortions would allow, and then co-adding the registered frames to improve image quality and reduce noise. Using preflight calibration data, the registered and averaged infrared intensity images were converted to surface temperatures on the Orion capsule's heatshield. Temperature uncertainties will be discussed relative to uncertainties of surface emissivity and atmospheric transmission loss. Comparison of limited onboard surface thermocouple data to the image derived surface temperature will be presented.
The new generation of power systems involve a large number of autonomous operating units with the flow of data being restricted by privacy concerns or infrastructure hurdles. The systems are also time-varying so the control should adjust in a timely manner to avoid failures that usually have very negative economical implications. Adaptive neurocontrol which takes elements from adaptive control (great for time-varying problems) and model identification (could be in the form of neural network) by using local available data is a compelling tool. In this work, we summarize analytical results for adaptive neurocontrol, followed with numerical verification of how the method could work for future grids.
Interactions among electrons create novel many-body quantum phases of matter with wavefunctions that often reflect electronic correlation effects, broken symmetries, and novel collective excitations. A wide range of quantum phases has been discovered in MATBG, including correlated insulating, unconventional superconducting, and magnetic topological phases. The lack of microscopic information, including precise knowledge of possible broken symmetries, has thus far hampered our understanding of MATBG's correlated phases. Here we use high-resolution scanning tunneling microscopy to directly probe the wavefunctions of the correlated phases in MATBG. The squares of the wavefunctions of gapped phases, including those of the correlated insulators, pseudogap, and superconducting phases, show distinct patterns of broken symmetry with a √3 x √3 super-periodicity on the graphene atomic lattice that has a complex spatial dependence on the moiré superlattice scale. We introduce a symmetry-based analysis to describe our measurements of the wavefunctions of MATBG's correlated phases with a set of complex-valued local order parameters. For the correlated insulators in MATBG, at fillings of ±2 electrons per moiré unit cell relative to charge neutrality, we compare the observed quantum textures to those expected for proposed theoretical ground states. In typical MATBG devices, the textures of correlated insulators' wavefunctions closely match those of the theoretically proposed IKS order, while in ultra-low-strain samples our data has local symmetries like those of a T-IVC phase. Here, we also study the wavefunction of MATBG's superconducting state, revealing strong signatures of intervalley coherence that can only be distinguished from those of the insulator with our phase-sensitive measurements.
Deep, narrowband H alpha Charge Coupled Device (CCD) surveys of HII regions were carried out in several dwarf irregular galaxies in and near the local group. Data are now complete for these galaxies: NGC 6822, GR 8, IC 10, IC 1613, Sextans A, Sextans B, and Sag Irr. Observations are complete for DDO 47, 53, 167, 168 and 187. Details of some of the results for the surveys completed so far are discussed. For NGC 6822, CCD survey at H alpha resulted in the detection of 145 HII regions in the local group irregular galaxy NGC 6822. Most of them are newly detected, faint surface-brightness objects. Positions, maps and dimensions are being published elsewhere. For GR 8, a deep narrowband H alpha imaging of the nearby dwarf irregular galaxy GR 8 revealed a total of 32 HII regions. Positions, H alpha luminosities, and sizes of these objects were determined. The H alpha luminosity function has the same shape as that for more luminous galaxies, except for size of sample effects. Most HII regions detected are at the very low luminosity end of the general luminosity function. For IC 10, a deep CCD narrowband H alpha imaging of the local group dwarf irregular galaxy IC 10 revealed a total of 144 HII regions. Positions, H alpha luminosities, and sizes of these objects were determined. The H alpha luminosity function has the same shape as that for more luminous galaxies.
Radiometric calibration of satellite-acquired data is essential for quantitative scientific studies, as well as for a variety of image-processing applications. This paper describes a multiyear, on-orbit radiometric calibration of the Landsat Thematic Mapper (TM) Band 6 conducted at DOE's Pacific Northwest Laboratory. Numerous Landsat TM scenes acquired and analyzed included day and night coverages at several geographical locations over several seasons. Concurrent with Landsat overpasses, thermal field and local meteorological (surface and radiosonde) measurements were collected. At-satellite (uncorrected) radiances and temperatures for water and nonwater land cover were compared to ground truth (GT) measurements after making adjustments for atmospheric (using LOWTRAN), mixed-pixel, and emissivity effects. Results indicate that, for both water and nonwater features, TM Band 6 average corrected temperature determinations using local radiosonde data to adjust for atmospheric effects, and using appropriate emissivities, are within 1.0 C of GT temperature values. Temperatures of water pixels derived from uncorrected TM Band 6 data varied roughly between 1 and 3 C of ground truth values for water temperatures ranging between 4 and 24 C. Moreover, corrections using nonlocal and noncoincident radiosonde data resulted in errors as large as 12 C. Corrections using the U.S. Standard Atmosphere gave temperature values within 1 to 2 C of GT. The average uncertainty for field instruments was + or - 0.2 C; average uncertainty for Landsat TM corrected temperature determinations was + or - 0.4 C.
The MapReduce programming model, due to its simplicity and scalability, has become an essential tool for processing large data volumes in distributed environments. Recent Stream Processing Systems (SPS) extend this model to provide low-latency analysis of high-velocity continuous data streams. However, integrating MapReduce with streaming poses challenges: first, the runtime variations in data characteristics such as data-rates and key-distribution cause resource overload, that inturn leads to fluctuations in the Quality of the Service (QoS); and second, the stateful reducers, whose state depends on the complete tuple history, necessitates efficient fault-recovery mechanisms to maintain the desired QoS in the presence of resource failures. We propose an integrated streaming MapReduce architecture leveraging the concept of consistent hashing to support runtime elasticity along with locality-aware data and state replication to provide efficient load-balancing with low-overhead fault-tolerance and parallel fault-recovery from multiple simultaneous failures. Our evaluation on a private cloud shows up to 2:8 improvement in peak throughput compared to Apache Storm SPS, and a low recovery latency of 700 -1500 ms from multiple failures.
Speckle noise is inherent to synthetic aperture radar (SAR) imagery. Since the degradation of the image due to this noise results in uncertainties in the interpretation of the scene and in a loss of apparent resolution, it is desirable to filter the image to reduce this noise. In this paper, an adaptive algorithm based on the calculation of the local statistics around a pixel is applied to 1-look SAR imagery. The filter adapts to the nonstationarity of the image statistics since the size of the blocks is very small compared to that of the image. The performance of the filter is measured in terms of the equivalent number of looks (ENL) of the filtered image and the resulting resolution degradation. The results are compared to those obtained from different techniques applied to similar data. The local adaptive filter (LAF) significantly increases the ENL of the final image. The associated loss of resolution is also lower than that for other commonly used speckle reduction techniques.
This study analyzes time series of stratospheric water vapor (SWV) data at 30 hPa, based on frost-point hygrometer (FPH) measurements at Boulder, CO (40°N, 255°E), and on sets of HALOE scans above that region for 1993 through 2005; their differing trends agree with previously published findings. Yet, there is an apparent switch over in 2002 from positive to negative trends in the HALOE time series, and that change follows the episodic decrease of tropical, lower stratospheric SWV in early 2001. The HALOE data points only resolve medium to large scale spatial variations, as they are based on an average of four or more limb scans within a latitude/longitude sector near Boulder. The FPH time series do not indicate a similar change of trend in 2002, although its time series points have variations about the mean that are twice as large as those from HALOE. The FPH soundings are sensitive to variations from all spatial scales, however, including the effects of a local vertical mixing of the air due to gravity waves. The vertical gradient SWV mixing ratio with altitude is positive at 30 hPa. There is also a positive trend in methane and its conversion to water vapor during this time that may explain a slightly larger SWV trend from the local FPH data than from the HALOE data. The Nimbus 7 LIMS experiment provides daily SWV plots at medium to large scales during the dynamically active, winter and springtime periods, for assessing the effects of meridional transport of SWV from its source regions. There is transport of high SWV from polar regions and of lower SWV from the subtropics at those times. SWV variations and trends from both the FPH and HALOE time series are reasonable and in accord with the spatial scales that they observed.
We present new, fundamentally combinatorial and topological characterizations of the amplituhedron. Upon projecting external data through the amplituhedron, the resulting configuration of points has a specified (and maximal) generalized “winding number”. Equivalently, the amplituhedron can be fully described in binary: canonical projections of the geometry down to one dimension have a specified (and maximal) number of “sign flips” of the projected data. The locality and unitarity of scattering amplitudes are easily derived as elementary consequences of this binary code. Minimal winding defines a natural “dual” of the amplituhedron. This picture gives us an avatar of the amplituhedron purely in the configuration space of points in vector space (momentum-twistor space in the physics), a new interpretation of the canonical amplituhedron form, and a direct bosonic understanding of the scattering super-amplitude in planar N = 4 SYM as a differential form on the space of physical kinematical data.
Here, we present a new finite difference shock-capturing scheme for hyperbolic equations on static uniform grids. The method provides selectable high-order accuracy by employing a kernel-based Gaussian Process (GP) data prediction method which is an extension of the GP high-order method originally introduced in a finite volume framework by the same authors. The method interpolates Riemann states to high order, replacing the conventional polynomial interpolations with polynomial-free GP-based interpolations. For shocks and discontinuities, this GP interpolation scheme uses a nonlinear shock handling strategy similar to Weighted Essentially Non-Oscillatory (WENO), with a novelty consisting in the fact that nonlinear smoothness indicators are formulated in terms of the Gaussian likelihood of the local stencil data, replacing the conventional L 2 -type smoothness indicators of the original WENO method. We demonstrate that these GP-based smoothness indicators play a key role in the new algorithm, providing significant improvements in delivering high – and selectable – order accuracy in smooth flows, while successfully delivering non-oscillatory solution behavior in discontinuous flows.