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3,688 results for “Milling”
Three-dimensional super-Yang--Mills theory on the lattice and dual black branes --- data release
<p>This HDF5 file collects data and analysis results for non-perturbative lattice field theory calculations investigating three-dimensional maximally supersymmetric SU(N) Yang--Mills theory on a skewed euclidean torus, and its holographic connection to dual D2-brane solutions in supergravity. See the README for further information.</p>
Nonperturbative phase diagram of two-dimensional N=(2,2) super-Yang--Mills theory --- data release
<p>This HDF5 file collects data and analysis results for non-perturbative lattice field theory calculations investigating two-dimensional supersymmetric SU(N) Yang--Mills theory with four supercharges. See the README for further information.</p>
Dataset for "Impact of the flow-field distribution channel cross-section geometry on PEM fuel cell performance: stamped vs. milled channel"
<p>Experimental data comprises raw data from load curve characterisation of a PEM fuel cell used for the validation of the mathematical model. Model data comprise of space-dependent values of hydrogen and oxygen concentration, local current densities, gas pressures and gas velocities in the modelled cell. These data were used for the investigation of the effect of different geometric parameters of flow-field channels on the performance of a PEM fuel cell.</p>
Monitoring juvenile Chinook salmon outmigration using rotary screw traps on Deer and Mill creeks
The California Department of Fish and Wildlife (CDFW) conducts juvenile salmonid emigration monitoring on Mill and Deer Creek (Tehama County, CA) annually from October through June using rotary screw traps (RSTs). Data from this monitoring is used to estimate juvenile spring-run Chinook salmon (Oncorhynchus tshawytscha) (spring-run) abundance and passage, identify yearling outmigration timing and alert resource agencies of juvenile spring-run presence in the lower Sacramento-San Joaquin Delta. This data will be included in the development of a juvenile production estimate (JPE) for spring-run Chinook salmon in the Sacramento River as required by Condition of Approval 7.5.2 of Incidental Take Permit No. 2081-2019-006-00 (ITP) issued by CDFW to California Department of Water Resources (DWR) for the long-term operation of the State Water Project. Salmonid data collected from the Mill and Deer RSTs, among other datasets, is also used by the Salmon Monitoring Team (SaMT) to understand the movement of juvenile salmon in the Sacramento River Watershed to estimate the number of winter-run and spring-run Chinook salmon that have entered the Sacramento-San Joaquin Delta (Delta). SaMT is a real-time operations monitoring team required by Condition of Approval 8.1.2 of the ITP which meets weekly from October through June, to provide advice for real-time management of SWP operations to DWR, CDFW, and the Water Operation Management Team (WOMT) to minimize take of winter-run and spring-run Chinook salmon in the Delta.
Monitoring juvenile Chinook salmon outmigration using rotary screw traps on Deer and Mill creeks 2023 to present
The California Department of Fish and Wildlife (CDFW) conducts juvenile salmonid emigration monitoring on Mill and Deer Creek (Tehama County, CA) annually from October through June using rotary screw traps (RSTs). Data from this monitoring is used to estimate juvenile spring-run Chinook salmon (Oncorhynchus tshawytscha) (spring-run) abundance and passage, identify yearling outmigration timing and alert resource agencies of juvenile spring-run presence in the lower Sacramento-San Joaquin Delta. This data will be included in the development of a juvenile production estimate (JPE) for spring-run Chinook salmon in the Sacramento River as required by Condition of Approval 7.5.2 of Incidental Take Permit No. 2081-2019-006-00 (ITP) issued by CDFW to California Department of Water Resources (DWR) for the long-term operation of the State Water Project. Salmonid data collected from the Mill and Deer RSTs, among other datasets, is also used by the Salmon Monitoring Team (SaMT) to understand the movement of juvenile salmon in the Sacramento River Watershed to estimate the number of winter-run and spring-run Chinook salmon that have entered the Sacramento-San Joaquin Delta (Delta). SaMT is a real-time operations monitoring team required by Condition of Approval 8.1.2 of the ITP which meets weekly from October through June, to provide advice for real-time management of SWP operations to DWR, CDFW, and the Water Operation Management Team (WOMT) to minimize take of winter-run and spring-run Chinook salmon in the Delta. This data package is complimentary to edi.1504 and represents current RST monitoring on Deer and Mill creeks. Data within the current year’s monitoring season are considered provisional.
Production Data Set for Five-Axis CNC Milling with Multiple Changeovers
<p>This dataset is an extensive production data set for a five-axis CNC milling process. Three geometrically different products were manufactured and production data from the machine was recorded. The recorded manufacturing process contains the preparation of the machine for the next product (changeover) as well as the machining process (production). An experimental manufacturing was organized with the aid of a changeover matrix to ensure that all possible changeover combinations for the three products were considered. The production was repeated five times, resulting in 30 manufacturing sessions and five complete changeover matrices. The data set was recorded from a Siemens 840D-SL machine control on a five-axis milling machine tool of type "Spinner U5-620" in a laboratory environment. A rich feature set is provided including rich supplementary material i.e. the NC-codes of the products, tool information, and a Jupyter notebook to illustrate the usage of the dataset.</p> <p>The supplementary material can be found at GitHub: <a title="Supplementary material" href="https://github.com/ElMoe/Production-Data-Set-for-Five-Axis-CNC-Milling-with-Multiple-Changeovers" target="_blank" rel="noopener">Link</a></p> <p>The corresponding data descriptor is available here: <a href="https://doi.org/10.1038/s41597-025-05294-0">Link</a></p> <p><strong>Changes from version 1.0.0 to version 1.0.1:</strong></p> <p>Feature smoothed_DC_voltage_Drive4 should be Tool_number_Magazine_Place_49</p>
Dataset for the publication entitled "An exact system of generation for face-milled hypoid gears with uniform depth taper: application to hypoid gear drives with high gear ratio"
Open the record for dataset details and reuse information.
Artificial Intelligence for Quality Control of manufacturing operations: Macro-mechanical milling in the Pilot Line GAMHE 5.0.
<p>Quality is defined as the extent to which a product conforms to the design specifications and how it complies with the requirements of component functionality. For some industries, such as automotive and aeronautical, the quality of their parts is very important given the high requirements to which they are subject. However, difficulties arise from the fact that a measure of quality can only be evaluated ‘‘out-of-process”, resulting in losses because there is no alternative to removing defective parts from the production line. Therefore, it is necessary to apply Artificial Intelligence-based kits/solutions that provide in-process estimation to predict quality from some measured variables. </p> <p>The main goal of these datasets is to monitor the final quality of the manufactured components or parts by estimating surface roughness from vibration signals and cutting parameters information using Artificial Intelligence-based solutions. Surface roughness is an essential feature in quality control defined by the deviation in the direction of the normal vector of a real surface from its ideal form. Because the roughness measurement is an offline and post process procedure, being able to estimate this value online brings a series of benefits in terms of time and cost reduction in manufacturing lines, energy efficiency, unnecessary wear of tools and machines, etc. Once a part has been detected with a surface quality below what is desired, a series of corrective measures can be applied for the following operations, such as: reducing the feed rate percentage, increasing the percentage of spindle speed or reducing the axial depth per pass, etc.</p>
Artificial Intelligence for quality control in manufacturing operations: Micro-mechanical milling in the Pilot Line GAMHE 5.0
<p>Quality is defined as the extent to which a product conforms to the design specifications and how it complies with the requirements of component functionality. For some industries, such as automotive and aeronautical, the quality of of manufactured parts is very important due to the high requirements. However, difficulties arise from the fact that a measure of quality can only be evaluated ‘‘out-of-process”, resulting in losses because there is no alternative to removing defective parts from the production line. Therefore, it is necessary to incorporate AI-based kits/solutions that provide in-process estimation to predict quality from some measured variables.</p> <p>The main goal of these datasets is to enable monitoring of final quality of the manufactured components or parts by estimating surface roughness from vibration signals and cutting parameters information. Surface roughness is an essential feature in quality control defined by the deviation in the direction of the normal vector of a real surface from its ideal form. Because the roughness measurement is an offline and post process procedure, being able to estimate this value online brings a series of benefits in terms of time and cost reduction in manufacturing lines, energy efficiency, unnecessary wear of tools and machines, etc. Once a part has been detected with a surface quality below what is desired, a series of corrective measures can be applied for the following operations, such as: reducing the feed rate percentage, increasing the percentage of spindle speed or reducing the axial depth per pass, etc.</p> <p>Workstation 4 (WS4) of the GAMHE 5.0 pilot line is a Kern Evo high-precision machining centre, with a maximum spindle speed of 50 000 rpm and Blum laser system and is used to run micro-milling and micro-drilling operations. In this experimental dataset, five cutting parameters were considered in the processes: spindle speed, <em>n</em>; feed rate, <em>f</em>; and axial depth of cut, <em>a<sub>P</sub></em>. The radial depth of cut, <em>a<sub>e</sub></em>; was equal to the mill tool radius, <em>r</em>, in all of the slots.</p> <p>These experiments were micro-milling operations with 0.3 mm, 0.5 mm, 0.8 mm and 1 mm-diameter mills on a sintered tungsten-copper alloy (W78Cu22). The data collected for each micro milling operation was the rms and peak value of the vibrations in the three-machine axis. In addition, five cutting parameters were also collected: position in <em>X</em> of the last point of the sample, feed rate, spindle speed, tool radius and axial depth.</p>
Dataset for the publication "Implementation of an exact completing method of generation for face-milled spiral bevel gears with uniform depth taper"
<p>This dataset contains geometric and graphics data associated with the referenced paper, enabling the reproduction of the conducted research. </p>
Monitoring adult spring-run Chinook salmon throughout the adult lifespan on Deer and Mill Creek
The California Department of Fish and Wildlife (CDFW) collects data on adult salmonids on Deer Creek and Mill Creek. Data is collected annually via redd surveys on Mill Creek and holding surveys on Deer Creek; video camera systems on both creeks collect data on upstream passage 24 hours a day, 7 days a week from February to August. Data from this monitoring is used to estimate adult escapement (upstream passage) abundance and timing, spawner abundance, and other important metrics for adult salmonids in the watershed. These data will also be used to inform the development of a juvenile production estimate (JPE) for spring-run Chinook salmon in the Sacramento River Watershed as required by Condition of Approval 7.5.2 of Incidental Take Permit No. 2081-2019-006-00 (ITP) issued by CDFW to California Department of Water Resources (DWR) for the long-term operation of the State Water Project.
PIE LTER year 2016, 15 minute measurements of specific conductance, water temperature in a small headwater stream draining a highly suburban catchment (72% residential), Saw Mill Brook, Burlington, MA.
Year 2016, continuous measurements, every 15 minutes were made of conductivity, water temperature in a small headwater stream, Saw Mill Brook, Burlington, MA, draining a highly suburban catchment (72% residential) in the Ipswich River watershed. Due to sedimentation within the datalogger housing, there are periods when the datalogger lost hydrological connection with the stream (i.e., during low flow). As such, data has been flagged to remove these erroneous readings.
Dataset for the project "Evaluation of the effects of trace elements from street dust under urban – industrial conditions on the ecophysiology of Acer platanoides L. and Tilia cordata Mill.
<p>Description of the project: The rapid growth of cities, industry and transport has significantly deteriorated environmental quality, especially in areas with the highest population densities. It applies to water, soil, and air, especially in urban areas. Air pollutants include particulate matter (PM), which harms human health. According to WHO reports (2021), PM pollution is the cause of cardiovascular and respiratory diseases, leading to 4.2 million premature deaths worldwide in 2016. Although improving every year, the situation in Poland is still worse than in many European countries. The particulate matter also includes heavy metals, which have a toxic effect on plants. Plants in urban areas are particularly vulnerable, especially trees, which perform several vital functions, including mitigating climate change, filtering pollutants, and improving air quality. The aim of the project was to determine and compare the morphological and physiological responses of selected tree species to particulate pollution stress under urban conditions. Tree leaves are an essential barrier to atmospheric dust by trapping it on their surface. However, this may come at the cost of reduced light absorption, increased leaf temperature, damage to leaf blades and consequently impaired photosynthesis and plant productivity. However, the ability to absorb dust varies between tree species. It depends on the leaf surface structure, and the response may be due to the species' sensitivity to pollutants. Investigations were conducted in the Upper Silesian Industrial Area around various emission sources, such as heavy metal smelters, combined heat and power plants, and busy streets. The research focused on two tree species common in urban areas, the Norway maple (<i>Acer platanoides</i>) and the small-leaved lime (<i>Tilia cordata</i>). It included measurement of heavy metal concentrations in leaf blades and dust collected on their surface, analysis of concentrations of selected pigments and ascorbic acid as markers of environmental stress. The study provided a preliminary assessment of the impact of particulate pollution on tree function under harsh urban conditions and determined the potential of the studied species to reduce atmospheric dust.</p>
Series production data set for 5-axis CNC milling
<p>The data set described encompasses features extracted from the machine control of a five-axis milling machine across thirteen series of productions. Each production series entails a setup changeover to prepare the machine for another product type. Alongside timestamps and twenty features derived from Numerical Control (NC) variables, the data set includes labels denoting various production phases. These labels, up to 23 in total, are structured around a generalized milling process. Comprising thirteen .csv files, each corresponding to a series production, the dataset was gathered within a production company operating in the contract manufacturing sector. These components are tied to actual series orders within ongoing industrial production.</p> <p>The complete description of the data set is published here: <a href="https://www.mdpi.com/2306-5729/9/5/66" target="_blank" rel="noopener">https://doi.org/10.3390/data9050066</a></p>
Sp(2N) Yang-Mills theories on the lattice: scale setting and topology—data release
<p>This release contains all data and metadata used to prepare the publications <a href="https://arxiv.org/abs/2205.09254">Topological susceptibility in Yang-Mills theories</a> and <a href="https://arxiv.org/abs/2205.09364">Sp(2N) Yang-Mills theories on the lattice: scale setting and topology</a>.</p> <p>Included are:</p> <ul> <li>The raw log output from the Wilson flow computation, as well as metadata describing the ensembles used, in `raw_data.zip`. These include all numbers used in the publication (aside from fit parameters) in plaintext form. The archive contains a separate `README.md` describing the layout of the data.</li> <li>All numbers included in the above logs, restructured into HDF5 format for convenience, in `datapackage.h5`.</li> <li>The data presented in all tables in both papers, in CSV format, as described in more detail below.</li> </ul> <p>Further details are given in the file README.md.</p>
Symbol Representation of the Three Gluon Form Factor in N=4 Planar Super Yang-Mills Theory
<p>Datasets describing the symbol of the three-gluon form factor in N=4 planar super Yang-Mills theory, generated using the amplitude bootstrap approach. The file "EZ_symb_new_norm" contains the symbol form of this quantity at 1 through 5 loops of precision, while the file "EZ6_symb_new_norm" contains the symbol at 6 loops. The file "EZ_symb_quad_new_norm" contains the symbol at 1 through 6 loops in compressed "quad" form, where the final-entry conditions described in (https://arxiv.org/pdf/2204.11901) are used to dramatically reduce the total number of terms in the symbol. The file "EZ7_symb_quad_new_norm" contains the symbol at 7 loops in the "quad" form. </p> <p>The tag “new_norm” refers to the fact that in the symbols given here, the letters a,b,c are defined by a = sqrt(u/(v*w)), b = sqrt(v/(w*u)), c = sqrt(w/(u*v)), as in arXiv:2405.06107, in order to make all coefficients integers. In contrast, in arXiv:2204.11901, the letters a,b,c were defined by a = u/(v*w), b = v/(w*u), c = w/(u*v).</p> <p>In addition to the funding sources listed, MW was supported by research grant 00025445 from Villum Fonden.</p>
Arthur John Mills (m2839)
<b>-- <a href="https://doi.org/10.5281/zenodo.11582199">Documentation</a> --</b><br><br><u>Name</u>: Arthur John Mills<br><u>musiXplora-ID</u>: m2839<br><u>musiXplora-URI</u>: <a href="https://musixplora.de/mxp/m2839">https://musixplora.de/mxp/m2839</a><br><u>Gender</u>: m<br><u>Date of Birth</u>: 1872<br><u>Place of Birth</u>: London<br><u>Date of Death</u>: October 1919<br><u>Place of Death</u>: London<br><u>First Mentioned</u>: 1901<br><u>Professions (Musical)</u>: Komponist<br><u>Other Places of Activity</u>: London<br><br><br><br><u>Changelog</u>:<br> - v0.0.1: Initial Upload.<br>
Herbarium specimen image of Libinhania fontinalis A. G. Mill., R. Sommerer & N. Kilian, part of the collection of Botanic Garden and Botanical Museum Berlin
Part of a training dataset of scanned herbarium specimens. The data paper and a summary landing page will be published on Zenodo as it gets published.<br><br>Content of this deposition:<br><br>- A JSON-LD datafile listing the label data associated with this herbarium specimen. The Darwin and Dublin Core data standards are used for most values.<br>- A JPEG image file of the scanned herbarium sheet.<br>- A lossless TIFF image from which the JPEG image has been derived.
Herbarium specimen image of Arctium tomentosum Mill., part of the collection of Botanic Garden and Botanical Museum Berlin
Part of a training dataset of scanned herbarium specimens. The data paper and a summary landing page will be published on Zenodo as it gets published.<br><br>Content of this deposition:<br><br>- A JSON-LD datafile listing the label data associated with this herbarium specimen. The Darwin and Dublin Core data standards are used for most values.<br>- A JPEG image file of the scanned herbarium sheet.<br>- A lossless TIFF image from which the JPEG image has been derived.
Herbarium specimen image of Erinus verticillatus Mill., part of the collection of Natural History Museum London
Part of a training dataset of scanned herbarium specimens. The data paper and a summary landing page will be published on Zenodo as it gets published.<br><br>Content of this deposition:<br><br>- A JSON-LD datafile listing the label data associated with this herbarium specimen. The Darwin and Dublin Core data standards are used for most values.<br>- A JPEG image file of the scanned herbarium sheet.<br>- A lossless TIFF image from which the JPEG image has been derived.
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International Brain Laboratory public data
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OpenNeuro
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