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Data were collected to improve understanding of the Arctic troposphere, and to provide researchers with a focused case-study period for future observational and modeling studies pertaining to Arctic atmospheric processes.
The C-Band Scanning ARM Precipitation Radar is a dual-polarization Doppler weather radar that operates in a simultaneous transmit and receive (STAR) mode, splitting the transmit signal so that power is transmitted on both horizontal and vertical polarizations at the same time. The CSAPR can also transmit on a single polarization (horizontal or vertical) and measure linear depolarization ratio (LDR). In addition to the first three Doppler moments (reflectivity, radial velocity, and spectra width), the CSAPR also provides differential reflectivity, correlation coefficient, and specific differential phase. The dual-polarization variables enable the provision of rainfall rate estimates and identification of precipitation types. For the 2018–2019 Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign in Argentina, ARM deployed a second-generation CSAPR, known as CSAPR2. CACTI data from the CSAPR2 are now available as b1-level products. Building on the original CACTI operational data, the b1-level products feature improved data quality resulting from extensive analyses and corrections. The data are cross-calibrated to a common point, datastreams are corrected for operational issues that occurred during the campaign, and several data quality masks and basic derived products are incorporated. For more information, read the CACTI radar b1-level processing report.
The C-Band Scanning ARM Precipitation Radar is a dual-polarization Doppler weather radar that operates in a simultaneous transmit and receive (STAR) mode, splitting the transmit signal so that power is transmitted on both horizontal and vertical polarizations at the same time. The CSAPR can also transmit on a single polarization (horizontal or vertical) and measure linear depolarization ratio (LDR). In addition to the first three Doppler moments (reflectivity, radial velocity, and spectra width), the CSAPR also provides differential reflectivity, correlation coefficient, and specific differential phase. The dual-polarization variables enable the provision of rainfall rate estimates and identification of precipitation types. For the 2018–2019 Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign in Argentina, ARM deployed a second-generation CSAPR, known as CSAPR2. CACTI data from the CSAPR2 are now available as b1-level products. Building on the original CACTI operational data, the b1-level products feature improved data quality resulting from extensive analyses and corrections. The data are cross-calibrated to a common point, datastreams are corrected for operational issues that occurred during the campaign, and several data quality masks and basic derived products are incorporated. For more information, read the CACTI radar b1-level processing report.
The C-Band Scanning ARM Precipitation Radar is a dual-polarization Doppler weather radar that operates in a simultaneous transmit and receive (STAR) mode, splitting the transmit signal so that power is transmitted on both horizontal and vertical polarizations at the same time. The CSAPR can also transmit on a single polarization (horizontal or vertical) and measure linear depolarization ratio (LDR). In addition to the first three Doppler moments (reflectivity, radial velocity, and spectra width), the CSAPR also provides differential reflectivity, correlation coefficient, and specific differential phase. The dual-polarization variables enable the provision of rainfall rate estimates and identification of precipitation types. For the 2018–2019 Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign in Argentina, ARM deployed a second-generation CSAPR, known as CSAPR2. CACTI data from the CSAPR2 are now available as b1-level products. Building on the original CACTI operational data, the b1-level products feature improved data quality resulting from extensive analyses and corrections. The data are cross-calibrated to a common point, datastreams are corrected for operational issues that occurred during the campaign, and several data quality masks and basic derived products are incorporated. For more information, read the CACTI radar b1-level processing report.
The C-Band Scanning ARM Precipitation Radar is a dual-polarization Doppler weather radar that operates in a simultaneous transmit and receive (STAR) mode, splitting the transmit signal so that power is transmitted on both horizontal and vertical polarizations at the same time. The CSAPR can also transmit on a single polarization (horizontal or vertical) and measure linear depolarization ratio (LDR). In addition to the first three Doppler moments (reflectivity, radial velocity, and spectra width), the CSAPR also provides differential reflectivity, correlation coefficient, and specific differential phase. The dual-polarization variables enable the provision of rainfall rate estimates and identification of precipitation types. For the 2018–2019 Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign in Argentina, ARM deployed a second-generation CSAPR, known as CSAPR2. CACTI data from the CSAPR2 are now available as b1-level products. Building on the original CACTI operational data, the b1-level products feature improved data quality resulting from extensive analyses and corrections. The data are cross-calibrated to a common point, datastreams are corrected for operational issues that occurred during the campaign, and several data quality masks and basic derived products are incorporated. For more information, read the CACTI radar b1-level processing report.
The C-Band Scanning ARM Precipitation Radar is a dual-polarization Doppler weather radar that operates in a simultaneous transmit and receive (STAR) mode, splitting the transmit signal so that power is transmitted on both horizontal and vertical polarizations at the same time. The CSAPR can also transmit on a single polarization (horizontal or vertical) and measure linear depolarization ratio (LDR). In addition to the first three Doppler moments (reflectivity, radial velocity, and spectra width), the CSAPR also provides differential reflectivity, correlation coefficient, and specific differential phase. The dual-polarization variables enable the provision of rainfall rate estimates and identification of precipitation types. For the 2018–2019 Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign in Argentina, ARM deployed a second-generation CSAPR, known as CSAPR2. CACTI data from the CSAPR2 are now available as b1-level products. Building on the original CACTI operational data, the b1-level products feature improved data quality resulting from extensive analyses and corrections. The data are cross-calibrated to a common point, datastreams are corrected for operational issues that occurred during the campaign, and several data quality masks and basic derived products are incorporated. For more information, read the CACTI radar b1-level processing report.
The C-Band Scanning ARM Precipitation Radar is a dual-polarization Doppler weather radar that operates in a simultaneous transmit and receive (STAR) mode, splitting the transmit signal so that power is transmitted on both horizontal and vertical polarizations at the same time. The CSAPR can also transmit on a single polarization (horizontal or vertical) and measure linear depolarization ratio (LDR). In addition to the first three Doppler moments (reflectivity, radial velocity, and spectra width), the CSAPR also provides differential reflectivity, correlation coefficient, and specific differential phase. The dual-polarization variables enable the provision of rainfall rate estimates and identification of precipitation types. For the 2018–2019 Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign in Argentina, ARM deployed a second-generation CSAPR, known as CSAPR2. CACTI data from the CSAPR2 are now available as b1-level products. Building on the original CACTI operational data, the b1-level products feature improved data quality resulting from extensive analyses and corrections. The data are cross-calibrated to a common point, datastreams are corrected for operational issues that occurred during the campaign, and several data quality masks and basic derived products are incorporated. For more information, read the CACTI radar b1-level processing report.
The C-Band Scanning ARM Precipitation Radar is a dual-polarization Doppler weather radar that operates in a simultaneous transmit and receive (STAR) mode, splitting the transmit signal so that power is transmitted on both horizontal and vertical polarizations at the same time. The CSAPR can also transmit on a single polarization (horizontal or vertical) and measure linear depolarization ratio (LDR). In addition to the first three Doppler moments (reflectivity, radial velocity, and spectra width), the CSAPR also provides differential reflectivity, correlation coefficient, and specific differential phase. The dual-polarization variables enable the provision of rainfall rate estimates and identification of precipitation types. For the 2018–2019 Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign in Argentina, ARM deployed a second-generation CSAPR, known as CSAPR2. CACTI data from the CSAPR2 are now available as b1-level products. Building on the original CACTI operational data, the b1-level products feature improved data quality resulting from extensive analyses and corrections. The data are cross-calibrated to a common point, datastreams are corrected for operational issues that occurred during the campaign, and several data quality masks and basic derived products are incorporated. For more information, read the CACTI radar b1-level processing report.
The near-infrared is populated by numerous emission lines radiated from the Earth’s atmosphere, and these emission lines are often several orders of magnitude brighter in intensity than the typical astrophysical science target. The subtraction of these emission lines, sky subtraction, can create large systematic errors in ground-based astronomical spectra effectively limiting the number of usable resolution elements. A more effective sky subtraction, and the reduction of the systematic errors due to the sky subtraction process, is a major hurdle which ground-based astronomy must overcome to increase the amount of usable spectrum, and to be able to observe fainter scientific targets. Large high-quality data sets such as the Sloan Digital Sky Survey (SDSS) in themselves present opportunities for a reduction of the systematic sky subtraction errors through self-calibration. The sky residual correction method of Wild and Hewett is one such self-calibration technique which uses principal component analysis to reduce the systematic sky residual errors present in SDSS spectra. We apply sky residual corrections to the SDSS, Baryon Oscillation Spectroscopic Survey, and Apache Point Observatory Galactic Evolution Experiment data sets to optimize the number of subtraction components, and to quantify the reduction of the systematic errors in the science spectra. Finally as a proof of concept we use the sky-residual-corrected SDSS luminous red galaxy spectra to search for gravitationally lensed emission line Galaxies.
This dataset is from a 48 hour sampling campaign at H.J. Andrews Experimental Forest, Oregon, USA. Filtered surface water and pore water samples were collected every three hours from the Watershed 1 (WS1) stream. Samples were delivered to the Environmental Molecular Sciences Laboratory (EMSL) for high resolution carbon characterization via Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS). In addition, basic geochemistry analyses (e.g., dissolved organic carbon concentration, cations, anions, SpC, pH) were conducted, standardized photos were taken, surface water hydrographs were collated from existing instrumentation, and extensive metadata were captured. All data types are provided in a standard format. In addition, the data package contains instructions to process the FTICR data based on the user’s specific needs. All data are free to be used for any purpose, such as for manuscripts, presentations, and grant proposals. Please use the data package’s DOI to cite the data package. Note that the included hydrograph has a separate DOI that should also be cited. We ask that you email us at WHONDRS@pnnl.gov to let us know that you're using the data and acknowledge WHONDRS and the U.S. Department of Energy's Subsurface Biogeochemical Research program—which generously provides funding to WHONDRS—in your documents, presentations, etc. There is no obligation to include WHONDRS members as co-authors.
Temperature and density asymmetry diagnosis is critical to advance inertial confinement fusion (ICF) science. A multi-monochromatic x-ray imager (MMI) is an attractive diagnostic for this purpose. The MMI records the spectral signature from an ICF implosion core with time resolution, 2-D space resolution, and spectral resolution. While narrow-band images and 2-D space-resolved spectra from the MMI data constrain temperature and density spatial structure of the core, the accuracy of the images and spectra depends not only on the quality of the MMI data but also on the reliability of the post-processing tools. In this paper, we synthetically quantify the accuracy of images and spectra reconstructed from MMI data. Errors in the reconstructed images are less than a few percent when the space-resolution effect is applied to the modeled images. The errors in the reconstructed 2-D space-resolved spectra are also less than a few percent except those for the peripheral regions. Spectra reconstructed for the peripheral regions have slightly but systematically lower intensities by ~6% due to the instrumental spatial-resolution effects. However, this does not alter the relative line ratios and widths and thus does not affect the temperature and density diagnostics. We also investigate the impact of the pinhole size variation on the extracted images and spectra. A 10% pinhole size variation could introduce spatial bias to the images and spectra of ~10%. A correction algorithm is developed, and it successfully reduces the errors to a few percent. Finally, it is desirable to perform similar synthetic investigations to fully understand the reliability and limitations of each MMI application.
The U.S. Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) Program is developing a scientific basis to extend the existing U.S. nuclear power plant (NPP) operating life beyond the current 60-year licensing period and to ensure NPP long-term reliability, productivity, safety, and security. Under the Advanced Instrumentation, Information, and Control (II and C) Systems Technologies pathway, the LWRS Program conducts targeted research and development to address long-term aging and obsolescence of existing instrumentation and control technologies, as well as to implement and test new digital II and C technology that enables broad innovation and business improvement in the NPP operating model. To this end, the role of human factors engineering (HFE) is important as part of ensuring that the control room modernization (CRM) activities account for the operator's cognitive and physiological characteristics. Part of the HFE process for CRM entails conducting tests and evaluations (T and Es) that will inform the design of new human-system interface (HSI) technologies. Qualitative data that captures operator feedback and important human-system interactions are an important part of T and E. However, the analyzing this qualitative data in order to provide actionable design recommendations can be time-consuming and error prone. This paper presents an early version of a tool, which uses a combination of natural language processing and supervised machine learning to support the analysis of qualitative data. The objectives of this tool is to improve the process of (1) sifting through freeform text for improved design issue identification, (2) maintaining consistency in theme coding conventions, and (3) readily providing actionable design guidance that can be traceable to state-of-the-art HFE guidelines such as NUREG-0700. (authors)
The Department of Energy's (DOE) Geothermal Data Repository (GDR) team has implemented data standards and automated data pipelines for the following data types: 1) drilling data, 2) geospatial datasets, and 3) DAS data. An additional data pipeline is proposed for stimulation data. These data standards and pipelines are intended to improve the real-world applicability of geothermal machine learning outputs through improving the quality of data. More specifically, through standardizing high-value datasets, the GDR is reducing project-specific data curation requirements, allowing more time to be spent on actual research. By automating this process, the burden of standardization is taken off of the user, overall increasing the availability of standardized data. This paper provides an update on the GDR's transition toward data standardization through automated data pipelines and calls for feedback from the community on how we can improve this process.
Scanning Electron Microscopes (SEMs) are widely used in experimental science laboratories, often requiring cumbersome and repetitive user analysis. Automating SEM image analysis processes is highly desirable to address this challenge. In particle sample analysis, Machine Learning (ML) has emerged as the most effective approach for particle segmentation. However, the time-intensive process of manually annotating thousands of SEM images limits the applicability of supervised learning approaches. Self-Supervised Learning (SSL) offers a promising alternative by enabling knowledge extraction from raw, unlabeled data. This study presents a framework for evaluating SSL techniques in SEM image analysis, focusing on novel methods leveraging the ConvNeXtV2 architecture for particle detection. A dataset comprising 25,000 SEM images is curated to benchmark these proposed SSL methods. The results demonstrate that ConvNeXtV2 models, with varying parameter counts, consistently outperform other techniques in particle detection across different length scales, achieving up to a 34% reduction in relative error compared to established SSL methods. Furthermore, an ablation study explores the relationship between dataset size and SSL performance, providing actionable insights for practitioners regarding model selection and resource efficiency. This research advances the integration of SSL into autonomous analysis pipelines and supports its application in accelerating materials science discovery.
Wire arc additive manufacturing (WAAM) is an energy-efficient manufacturing technique used for near-net-shape production of functional industrial components. However, heat accumulation during deposition and the associated mechanical and metallurgical changes result in complex residual stress profiles across the cross section of the fabricated components. These residual stresses are detrimental to the service life of the components. In this study, sequentially coupled thermomechanical analysis of WAAM B91 steel is conducted to quantify the residual stress variation across the component. The thermomechanical analysis includes a transient heat transfer model and a static stress model that incorporates the transformation-induced plasticity due to martensitic phase transformation. Here, the experimentally calibrated heat transfer model mirrors the temperature variation of the system during the deposition. The results from the stress model are validated via x-ray diffraction measurements, and the numerical results are in good agreement with the experimental data.
Unwanted motion of the probe with respect to the sample is a ubiquitous problem in scanning probe and scanning transmission electron microscopies, causing both linear and nonlinear artifacts in experimental images. We have designed a procedure to correct these artifacts by using orthogonal scan pairs to align each measurement line-by-line along the slow scan direction, by fitting contrast variation along the lines. We demonstrate the accuracy of our algorithm on both synthetic and experimental data and provide an implementation of our method.
We extend a previously introduced model of stochastic gene regulation of cancer to a nonlinear case having both gene and pseudogene messenger RNAs (mRNAs) self-regulated. The model consists of stochastic Boolean genetic elements and possesses noise-induced multistability (multimodality). We obtain analytical expressions for probabilities for the case of constant but finite number of microRNA molecules which act as a noise source for the competing gene and pseudogene mRNAs. The probability distribution functions display both the global bistability regime as well as even-odd number oscillations for a certain range of model parameters. Statistical characteristics of the mRNA’s level fluctuations are evaluated. The obtained results of the extended model advance our understanding of the process of stochastic gene and pseudogene expressions that is crucial in regulation of cancer.
Purpose: This paper introduces a nonlinear weighting scheme into the backprojection operation within the simultaneous algebraic reconstruction technique (SART). It is designed for tomosynthesis imaging of objects with high-attenuation features in order to reduce limited angle artifacts. Methods: The algorithm estimates which projections potentially produce artifacts in a voxel. The contribution of those projections into the updating term is reduced. In order to identify those projections automatically, a four-dimensional backprojected space representation is used. Weighting coefficients are calculated based on a dissimilarity measure, evaluated in this space. For each combination of an angular view direction and a voxel position an individual weighting coefficient for the updating term is calculated. Results: The feasibility of the proposed approach is shown based on reconstructions of the following real three-dimensional tomosynthesis datasets: a mammography quality phantom, an apple with metal needles, a dried finger bone in water, and a human hand. Datasets have been acquired with a Siemens Mammomat Inspiration tomosynthesis device and reconstructed using SART with and without suggested weighting. Out-of-focus artifacts are described using line profiles and measured using standard deviation (STD) in the plane and below the plane which contains artifact-causing features. Artifacts distribution in axial direction is measured using an artifact spread function (ASF). The volumes reconstructed with the weighting scheme demonstrate the reduction of out-of-focus artifacts, lower STD (meaning reduction of artifacts), and narrower ASF compared to nonweighted SART reconstruction. It is achieved successfully for different kinds of structures: point-like structures such as phantom features, long structures such as metal needles, and fine structures such as trabecular bone structures. Conclusions: Results indicate the feasibility of the proposed algorithm to reduce typical tomosynthesis artifacts produced by high-attenuation features. The proposed algorithm assigns weighting coefficients automatically and no segmentation or tissue-classification steps are required. The algorithm can be included into various iterative reconstruction algorithms with an additive updating strategy. It can also be extended to computed tomography case with the complete set of angular data.