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619 results for “configuration”
Benchmarking on Microservices Configurations and the Impact on the Performance in Cloud Native Environments
<p><strong>The peer reviewed publication for this dataset has been published in LCN 2022, 47th Annual IEEE Conference on Local Computer Networks. Please cite this paper when referring to the dataset: https://www.eurecom.fr/publication/6971.</strong></p> <p>Cloud-native and containerization have changed the way to develop and deploy applications. Cloud-native rethinks the application architecture by embracing a microservice approach, where each microservice is packaged into containers to run in a centralized or an edge cloud. When deploying the container running the micro-service, the tenant has to specify the needed computing resources to run their workload in terms of the amount of CPU and memory limit. However, it is not straightforward for a tenant to know in advance the computing amount that allows running the microservice optimally. This will have an impact not only on the service performances but also on the infrastructure provider, particularly if the resource overprovisioning approach is used. To overcome this issue, we conduct an experimental study aiming to detect if a tenant's configuration allows running its service optimally. We run several experiments on a cloud-native platform, using different types of applications under different resource configurations. The obtained results are presented in the accepted IEEE LCN paper (https://www.eurecom.fr/publication/6971) and are shared in this dataset.</p> <p>The datasets are collected for 3 types of applications: Web servers written in python and Golang, RabbitMQ data broker and the OpenAirInterface 5G Core network function AMF (Access and Mobility Management Function).</p> <p><br> </p> <p><strong>Web Servers:</strong></p> <p><strong>files: </strong>golang-web-server-performance.csv, python-web-server-performance.csv</p> <p>We used Golang and Python-based web servers for the test. Each request to the web server returns a video of a size 43 MB. For testing we used ApacheBench, a command-line program used for benchmarking HTTP web servers. ApacheBench allows parallel requests from multiple clients. For each web server instance we send a number of requests ranging from 100 to 1000 and a concurrency level between 1 and 100, representing the number of parallel clients performing the requests.</p> <p>The information available in the dataset are as follows:</p> <p><strong>time:</strong> timestamp of collection of metrics.</p> <p><strong>ram_limit:</strong> the memory allocated to the container in megabytes.</p> <p><strong>cpu_limit:</strong> the CPU allocated to the container.</p> <p><strong>ram_usage:</strong> the amount of memory used by the container at the time of the metrics collection in byte.</p> <p><strong>cpu_usage:</strong> the amount of CPU used by the container at the time of the metrics collection.</p> <p><strong>n:</strong> the number of requests sent to the container.</p> <p><strong>c:</strong> the concurrency level in the requests.</p> <p><strong>lat50:</strong> the least response time for the best 50% requests in microseconds.</p> <p><strong>lat66:</strong> the least response time for the best 66% requests in microseconds.</p> <p><strong>lat75:</strong> the least response time for the best 75% requests in microseconds.</p> <p><strong>lat80:</strong> the least response time for the best 80% requests in microseconds.</p> <p><strong>lat90:</strong> the least response time for the best 90% requests in microseconds.</p> <p><strong>lat95:</strong> the least response time for the best 95% requests in microseconds.</p> <p><strong>lat98:</strong> the least response time for the best 98% requests in microseconds.</p> <p><strong>lat99:</strong> the least response time for the best 99% requests in microseconds.</p> <p><strong>lat100:</strong> the least response time in microseconds.</p> <p> </p> <p><strong>5G Core network’s AMF:</strong></p> <p><strong>file: </strong>amf-performance.csv</p> <p>For testing we use my5G-RANTester, a tool for emulating control and data planes of the UE and gNB (5G base station). The number of simultaneous registration requests that are sent to each instance of the AMF varies between 10 and 400.</p> <p>The information available in the dataset are as follows:</p> <p><strong>time:</strong> timestamp of collection of metrics.</p> <p><strong>ram_limit:</strong> the memory allocated to the container in megabytes.</p> <p><strong>cpu_limit:</strong> the CPU allocated to the container.</p> <p><strong>ram_usage:</strong> the amount of memory used by the container at the time of the metrics collection in byte.</p> <p><strong>cpu_usage:</strong> the amount of CPU used by the container at the time of the metrics collection.</p> <p><strong>n:</strong> the number of parallel registration requests sent to the AMF.</p> <p><strong>mean:</strong> the mean registration time for all the registration requests in microseconds.</p> <p><strong>lat50:</strong> the median registration time for registration requests in microseconds.</p> <p><strong>lat75: </strong>the least registration time for the best 75% registration requests in microseconds.</p> <p><strong>lat80:</strong> the least registration time for the best 80% registration requests in microseconds.</p> <p><strong>lat90:</strong> the least registration time for the best 90% registration requests in microseconds.</p> <p><strong>lat95:</strong> the least registration time for the best 95% registration requests in microseconds.</p> <p><strong>lat98:</strong> the least registration time for the best 98% registration requests in microseconds.</p> <p><strong>lat99:</strong> the least registration time for the best 99% registration requests in microseconds.</p> <p><strong>lat100:</strong> the least registration time in microseconds.</p> <p> </p> <p><strong>RabbitMQ data broker:</strong></p> <p><strong>file: </strong>rabbitmq-performance.csv</p> <p>For testing we used RabbitMQ PerfTest which is a throughput testing tool that simulates basic workloads and provides the throughput and the time that a message takes to be consumed by a consumer. For each deployed RabbitMQ server we used a number of producers and consumers that ranges from 50 to 500. Each producer sends messages to the broker with a rate of 100 messages per second for a period of time of 90 seconds.</p> <p>The information available in the dataset are as follows:</p> <p><strong>time:</strong> timestamp of collection of metrics.</p> <p><strong>ram_limit:</strong> the memory allocated to the container in megabytes.</p> <p><strong>cpu_limit:</strong> the CPU allocated to the container.</p> <p><strong>ram_usage:</strong> the amount of memory used by the container at the time of the metrics collection in byte.</p> <p><strong>cpu_usage:</strong> the amount of CPU used by the container at the time of the metrics collection.</p> <p><strong>n:</strong> the number of producers sending messages to the RabbitMQ server.</p> <p><strong>Min:</strong> the minimum consumption time for the producer messages.</p> <p><strong>lat50:</strong> the median consumption time for the producer messages.</p> <p><strong>lat75:</strong> the least consumption time for the best 75% messages in microseconds.</p> <p><strong>lat95:</strong> the least consumption time for the best 95% messages in microseconds.</p> <p><strong>lat99:</strong> the least consumption time for the best 99% messages in microseconds.</p>
X-PSI Parameter Recovery for Temperature Map Configurations Inspired by PSR J0030+0451
<p>Posterior sample files associated with the preprint "X-PSI Parameter Recovery for Temperature Map Configurations Inspired by PSR J0030+0451 " by Vinciguerra et al. (2023; <a href="https://doi.org/10.48550/arXiv.2209.12840">arXiv</a>; almost submitted to for publication in ApJ) and Jupyter notebook scripts to reproduce the corresponding figures.</p> <p>Also included are examples of model modules in the Python language using the X-PSI framework.</p> <p>Please refer to the READme for detailed information.</p>
Microscope-Cockpit find nuclei code and microscope simulation configuration
<p>This file contains instructions for setting up a simulated microscope<br> environment using Microscope-Cockpit and Python-Microscope. This<br> environment includes a large tiled image of which segments are<br> returned to simulate stage movement and different colour channels<br> returned to simulate changing an emission filter. This simulated<br> microscope is then used to test the findNuclei script showing the ease<br> of extending Cockpit functionality with Python libraries,<br> Python-openCV is used in this case.<br> </p>
Data to the journal article "The capping agent is the key: Structural alterations of Ag NPs during CO2 electrolysis probed in a zero-gap gas-flow configuration"
<p>This data set corresponds to the journal article "The capping agent is the key: Structural alterations of Ag NPs during CO2 electrolysis probed in a zero-gap gas-flow configuration"</p>
WS22 database: combining Wigner Sampling and geometry interpolation towards configurationally diverse molecular datasets
<p>The WS22 database provides a collection of molecular datasets that explores a broad configurational space of flexible organic molecules with varying sizes and complexity. It includes several chemical properties calculated with a quantum chemical (QM) method. Complementary to the structured datasets, this repository also provides the molecular geometries for the equilibrium structures together with the corresponding output of the QM frequency calculations. Details about the methodology, content, and structure of the WS22 datasets are provided in the README file included in this repository.</p>
H2020 ENODISE: UTWE Configuration A Experimental Databases with Mitigation
<p>This database contains the datasets for the aeroacoustic experiments conducted at the University of Twente on the H2020 ENODISE project, Task 6.1 (experimental databases with mitigation), Configuration A1 - Wall mounted.</p> <p><br>Configuration A1 consists of a propeller ingesting a zero-pressure-gradient boundary layer. Far-field acoustics are measured using two microphone arrays, to assess directivity and sound pressure levels. Different treatments are used at the flat plate underneath the propeller to attenuate the noise reflected by the plate. Two main strategies are analyzed: tuned quarter-wavelength resonator arrays - to reduce tonal noise - and a broadband noise absorber (metal foam). The quarter-wavelength resonator arrays are tested in different geometrical distributions. </p> <p><br>That forms part of deliverables D6.4, D6.5, and D6.6 - see reports for further information regarding measurement techniques. Included are descriptive READMEs. The data is in the HDF5 format and comprises mean, spectral, and other processed data types in engineering units. CAD files are also added, to properly describe the setup and its components.</p>
qc3C manuscript simuated sweep configuration and source code
<p>This is the repository of configuration details and source code necessary to reproduce the simulated sweep for the manuscript : qc3C - reference-free quality control for Hi-C sequencing data.</p> <p>The repository also contains the qc3C analysis results used in the paper.</p> <p>This now includes QC reports over the simulated sweep generated by HiCExplorer.</p>
Simulated TSCH dataset using different slotframe matrix configurations
<p>The current dataset was obtained by using a specifically developed simulator, to analyze the behavior of a time slotted channel hopping (TSCH) wireless sensor network (WSN), under different operating conditions.</p> <p>The configuration of the simulator, the characteristics of the network, and the generated traffic patterns are reported in [1].<br>Different configurations of the slotframe matrix, which allows slots to be reserved for specific pairs of nodes, are analyzed.</p> <p>File "<code>network_topology.pdf</code>" reports the topology of the simulated wireless network.</p> <p>The four analyzed configurations, which are deeply described in [1], are:</p> <ul> <li><strong>Star</strong>, whose slotframe matrix configuration is reported in the "<code>Star.conf</code>" file.</li> <li><strong>Load</strong>, whose slotframe matrix configuration is reported in the "<code>Load.conf</code>" file.</li> <li><strong>Parallel</strong>, whose slotframe matrix configuration is reported in the "<code>Parallel.conf</code>" file.</li> <li><strong>LPR</strong>, whose slotframe matrix configuration is reported in the "<code>LPR.conf</code>" file.</li> </ul> <p>A typical "<code>.conf</code>" file has the following format:<br><code># Offset src dest</code><br><code>0 4 1</code><br><code>0 6 2</code><br><code>0 8 3</code><br><code>0 10 9</code><br><code>1 1 0</code><br>where the first column represents the slot offset, i.e., the time slot in the slotframe matrix (which repeats periodically over time), in which a transmission opportunity is scheduled. Since many concurrent transmissions between different couple of nodes and different channels are possible simultaneously, more than one transmission could be scheduled at the same time. In the example, four transmission opportunities are scheduled in slot offset number 0.<br>The second column of each row represents the source node, while the third column represents the destination node. For instance, the schedule "<code>0 10 9</code>" represents the scheduled transmission at slot offset 0 from the source node 10 to the destination node 9.</p> <p> </p> <p>For each configuration, a corresponding file with the extension "<code>.dat</code>" contains the log generated in the simulation. An example is the following: <br><code>00083 72204 FLOW: 6 11 -> 0 LOST: 0 TRIES: 3 LATENCY: 540</code><br><code>00082 72156 FLOW: 5 10 -> 0 LOST: 0 TRIES: 4 LATENCY: 3460</code><br><code>00084 78013 FLOW: 0 4 -> 0 LOST: 0 TRIES: 2 LATENCY: 1240</code><br>where the transmission in a path from the source node (e.g., 11) to a destination node (e.g., the root node 0) is summarized with a single line in the log.</p> <p>Each line is composed of the following fields:</p> <ul> <li><em><packet number></em>: an integer number (e.g., <code>00083</code>) that uniquely identifies a packet transmitted in a multi-hop fashion from the source node to the destination node.</li> <li><em><queuing_time></em>: the queuing time expressed in terms of number of slots. In the simulation, slots have a length of 20 ms.</li> <li><em><flow_index></em>: the word "<code>FLOW:</code>" followed by an integer number identifying the flow. The simulation contains seven periodic flows with periods 6001, 6003, 6005, 6007, 6011, 6013, and 6017 expressed in terms of number of slots, for flows with index 0, 1, 2, 3, 4, 5, 6, respectively. For instance, "FLOW: 6" has a period of 6017 slots, which corresponds to 120.34 s (i.e., about 2 minutes).</li> <li><em><path></em>: an integer value representing the source node of the path, followed by the characters "<code>-></code>", followed by another integer value representing the destination node. For instance, "<code>11 -> 0</code>" represents the transmission in the path between node 11 and node 0.</li> <li><em><lost></em>: is an indication if the packet was lost in the path ("<code>LOST: 1</code>") or the packet arrived correctly at the destination ("<code>LOST: 0</code>"). A packet is lost if on a given link reached the maximum number of retransmissions.</li> <li><em><tries></em>: is the sum of the transmissions performed in each link. For instance, the link "<code>10 -> 0</code>" is composed of 3 hops. The value "<code>TRIES: 4</code>" means that a retransmission was performed for one of the links in the path.</li> <li><em><latency></em>: the transmission latency of the packet from when it was queued to when it reached its destination. The latency is expressed in ms.</li> </ul> <p>For each condition, the number of logged packets (i.e., lines) is 36,742,162, corresponding to 20 years of simulation.</p> <p> </p> <p>In addition, the code of the simulator is provided in the file "<code>TSCHmodeler.zip</code>".</p> <p>To run the simulations reported in [1], you have to execute the command:</p> <ul> <li>For experiment in Section IV.A <ul> <li><code>python3 -m TSCHmodeler conf/simple.conf</code></li> <li><code>python3 -m TSCHmodeler conf/simple_1week.conf</code></li> </ul> </li> <li>For experiment in Section IV.B <ul> <li><code>python3 -m TSCHmodeler conf/star_minimal.conf</code> for the <strong>star</strong> minimal configuration</li> <li><code>python3 -m TSCHmodeler conf/star_load.conf</code> for the <strong>load</strong> minimal configuration</li> <li><code>python3 -m TSCHmodeler conf/star_parallel.conf</code> for the <strong>parallel</strong> minimal configuration</li> <li><code>python3 -m TSCHmodeler conf/star_LPR.conf</code>for the <strong>LPR</strong> minimal configuration</li> </ul> </li> <li>For experiment in Section IV.C <ul> <li><code>python3 -m TSCHmodeler conf/large_40_nodes.conf</code></li> <li><code>python3 -m TSCHmodeler conf/large_121_nodes.conf</code></li> </ul> </li> </ul> <p> </p> <p>References:<br>[1] S. Scanzio, P. Chiavassa, G. Formis, G. Paolini and G. Cena, “A Lightweight Simulation Environment for TSCH-Based Wireless Sensor Networks,” in IEEE Transactions on Industrial Cyber-Physical Systems, 2025. doi: <a title="https://doi.org/10.1109/TICPS.2025.3620370" href="https://doi.org/10.1109/TICPS.2025.3620370" target="_blank" rel="noopener">10.1109/TICPS.2025.3620370</a></p>
Data and scripts for the publication "Coil Optimization for Quasi-helically Symmetric Stellarator Configurations"
<p>Coils and VMEC configurations for the three stellarator configurations presented in "Coil Optimization for Quasi-helically Symmetric Stellarator Configurations", including the optimization scripts used to find the coils and plot scripts used to produce the figures in the text.</p>
Data and configuration files for "Expansion of accreting main-sequence stars during rapid mass transfer"
<p>Data and configuration files that can be used to reproduce results from the paper <a href="https://ui.adsabs.harvard.edu/abs/2024ApJ...966L...7L/abstract">Expansion of Accreting Main-sequence Stars during Rapid Mass Transfer</a>. This directory contains MESA inlists and starting models used for calculations performed with MESA r15140, and YAML configuration files for calculations performed with COMPAS v02.41.04.</p> <p>See README.txt for a description of all files.</p> <p> </p> <p>Any work making use of these files should cite</p> <p>Lau, M., Hirai, R., Mandel, I., Tout, C., 2024, Expansion of Accreting Main-sequence Stars during Rapid Mass Transfer, ApJL, 966, 1</p> <div></div>
Dataset for configurational entropy of a finite number of dumbbells close to a wall
<p><strong>Introduction:</strong> Dataset from numerical simulations to quantify the reduction in configurational entropy of dumbbells due to the presence of a wall, as developed and described in detail in the paper</p> <ul> <li>Markus Hütter: Configurational entropy of a finite number of dumbbells close to a wall. Eur. Phys. J. E, 45(1): 6 (19 pages), 2022. DOI: 10.1140/epje/s10189-022-00160-y WWW: https://doi.org/10.1140/epje/s10189-022-00160-y</li> </ul> <p>which should be cited whenever this dataset is used. The data compiled here is the basis for figures 5, 6, and 9 in that paper.</p> <p><strong>Format</strong>: The files are provided in plain-text format (ascii).</p> <p><strong>Filenames</strong>: The nomenclature for the filenames follows the following scheme:</p> <ul> <li>data-normal-Lone{L1}-Ltwo{L2}-N{N}-{method}-{timestamp}.txt</li> </ul> <p>where (see the original paper for details) {L1} and {L2} specify the confining slab, {N} denotes the number of dumbbells, and {method} is either "SPLIT" (for the data presented in figures 5 and 6) or "WangLandau-MERGED" (for the data presented in figure 9).</p> <p><strong>File content</strong>: Each datafile contains 8 headerlines, in which the values of L1, L2, and N are repeated, and furthermore the following quantities are specified: nsteps is the total number of steps for the random sampling; clow = (4*L1^2)/N; cupp = 4*L2^2.</p> <p>After these headerlines, the data is presented in tab-delimited columns, as<br> follows for the "SPLIT"-files:</p> <ul> <li>column 1: conformation value (mid-bin position)</li> <li>column 2: number of successful placings in that bin (i.e., compatible with wall confinement)</li> <li>column 3: number of attempted placings in that bin</li> <li>column 4: (not used)</li> </ul> <p>(where the ratio of column 2 to column 3 gives the partition coefficient), whereas for the "WangLandau-MERGED"-files the columns represent the following:</p> <ul> <li>column 1: conformation value (mid-bin position)</li> <li>column 2: natural logarithm of the partition coefficient</li> </ul> <p>Details are explained in the paper mentioned above.</p>
Data set for: Mapping magnetic signals of individual magnetite grains to their internal magnetic configurations using micromagnetic models
<p>This data set contains the simulations and data analysis files used in the publication: "<em>Mapping magnetic signals of individual magnetite grains to their internal magnetic configurations using micromagnetic models</em>", by D. Cortés-Ortuño, K. Fabian and L. V. de Groot.</p> <p>The data set includes:</p> <ul> <li>Scripts and output files from MERRILL simulations</li> <li>Jupyter notebooks with data analysis</li> <li>Figures</li> </ul> <p>A preprint of this work can be found in:</p> <p>David Cortés-Ortuño, Karl Fabian and Lennart V. de Groot. <em>Mapping magnetic signals of individual magnetite grains to their internal magnetic configurations using micromagnetic models.</em> DOI: 10.1002/essoar.10510574.1. Earth and Space Science Open Archive. <a href="https://doi.org/10.1002/essoar.10510574.1">https://doi.org/10.1002/essoar.10510574.1</a></p> <p>The README file in this dataset (in markdown format) contains full details about the simulations. The dataset also contains pre-computed data files to calculate the inversions and produce the figures and analyze the inversion data without processing the vbox files.</p> <p>To cite this dataset you can use the following bibtex entry:</p> <pre><code>@Misc{Cortes2022, author = {Cortés-Ortuño, David and Fabian, Karl and de Groot, Lennart V.}, title = {{Data set for: Mapping magnetic signals of individual magnetite grains to their internal magnetic configurations using micromagnetic models}}, publisher = {Zenodo}, year = {2022}, doi = {10.5281/zenodo.6501818}, url = {https://doi.org/10.5281/zenodo.6501818}, } </code></pre> <p> </p>
Gaia Data Release 3: Basis function configuration for internally calibrated BP/RP spectra
<p>This XML file contains the basis function configuration adopted for the internally calibrated BP and RP spectra published in Gaia Data Release 3. The same file is included in the GaiaXPy (https://gaia-dpci.github.io/GaiaXPy-website/index.html) python package offering some useful functions to use the spectra.</p> <p>The content of the file and its basic usage are described in detail in Appendix C in the paper "Gaia Data Release 3: Processing and validation of BP/RP low-resolution spectral data", De Angeli, F. et al. A&A (2022).</p>
study of second harmonic generation in periodically poled fiber in double pass configuration
<p>This dataset includes the experimental measurements and the numerical simulations of the power of second harmonic generated inside a periodically poled fiber traversed in single and double pass by a fundamental signal whose wavelength is included in a certain range of values. This measurements are the preliminary study for situation where the PPSF can be exploited in multiple pass configuration, such as in a cavity. </p>
First-principles prediction of the Co-Al phase diagram including configurational, vibrational and magnetic contributions
<p>Documentation for the Dataset used in the publication entitled "First-principles prediction of the Co–Al phase diagram including configurational, vibrational and magnetic contributions" <br>** These datasets comprise all configurations used in Co-Al system and their formation enthalpies at different temperatures, where configurational, vibrational and magnetic contributions were considered. Hcp Co and fcc Al were used as reference states. **<br>** More details about the methodology can be found in the paper "First-principles prediction of the Co-Al phase diagram including configurational, vibrational and magnetic contributions, Journal of Materials Research and Technology, 2024" **</p> <p>1. bcc-Co-Al.zip<br>- Description: bcc-Co-Al.zip is a compressed folder. It contains Al1-xCox configurations with bcc lattice used to fit the cluster expansion (CE). Each folder contains a POSCAR file that correspons to a configuration. The POSCAR can be opened with Notepad and visualized with VESTA software.</p> <p>2. fcc-Co-Al.zip<br>- Description: fcc-Co-Al.zip is a compressed folder. It contains Al1-xCox configurations with fcc lattice used to fit the CE. Each folder contains a POSCAR file that correspons to a configuration. The POSCAR can be opened with Notepad and visualized with VESTA software.</p> <p>3. hcp-Co-Al.zip<br>- Description: hcp-Co-Al.zip is a compressed folder. It contains Al1-xCox configurations with hcp lattice used to fit the CE. Each folder contains a POSCAR file that correspons to a configuration. The POSCAR can be opened with Notepad and visualized with VESTA software.</p> <p><br>4. Formation enthalpies of bcc-Co-Al.xlsx<br>- Description: Formation enthalpies of bcc lattice in Co-Al system at different temperatures, which includes the effect of lattice vibration and magnetic excitation. Fcc Al and hcp Co were used as reference states.</p> <p>- Variable description by columns:<br> 1-(Folder name) - type: numerical (integer)<br> Description: Each folder name in the bcc-Co-Al.zip corresponds to a configuration.<br> 2- (at. fraction of Co (%)) - type: numerical (float)<br> Description: The atomic fraction of Co in each configuration.<br> 3- (H_f^(conf)(DFT) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 0 K calculated by density functional theory (DFT) following eq.(18) in the paper.<br> 4- (H_f^(conf)(CE) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 0 K fitted by CE. <br> 6- (at. fraction of Co (%)) - type: numerical (float)<br> Description: The atomic fraction of Co in each configuration.<br> 7- (H_f^(conf+vib+mag)(Cal.) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 400 K calculated by DFT, the bond length vs. bond stiffness relationship and Monte Carlo simulation of the Heisenberg Hamiltonian following eq.(20) in the paper.<br> 8- (H_f^(conf+vib+mag)(CE) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 400 K fitted by CE. <br> 10- (at. fraction of Co (%)) - type: numerical (float)<br> Description: The atomic fraction of Co in each configuration.<br> 11- (H_f^(conf+vib+mag)(Cal.) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 800 K calculated by DFT, the bond length vs. bond stiffness relationship and Monte Carlo simulation of the Heisenberg Hamiltonian following eq.(20) in the paper.<br> 12- (H_f^(conf+vib+mag)(CE) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 800 K fitted by CE. <br> 14- (at. fraction of Co (%)) - type: numerical (float)<br> Description: The atomic fraction of Co in each configuration.<br> 15- (H_f^(conf+vib+mag)(Cal.) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 1200 K calculated by DFT, the bond length vs.bond stiffness relationship and Monte Carlo simulation of the Heisenberg Hamiltonian following eq.(20) in the paper.<br> 16- (H_f^(conf+vib+mag)(CE) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 1200 K fitted by CE.<br> 18- (at. fraction of Co (%)) - type: numerical (float)<br> Description: The atomic fraction of Co in each configuration.<br> 19- (H_f^(conf+vib+mag)(Cal.) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 1600 K calculated by DFT, the bond length vs.bond stiffness relationship and Monte Carlo simulation of the Heisenberg Hamiltonian following eq.(20) in the paper.<br> 20- (H_f^(conf+vib+mag)(CE) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 1600 K fitted by CE.</p> <p><br>5. Formation enthalpies of fcc Co-Al.xlsx<br>- Description: Formation enthalpies of fcc lattice in Co-Al system at different temperatures, which includes the effect of lattice vibration and magnetic excitation. Fcc Al and hcp Co were used as reference states.</p> <p>- Variable descriptions by columns are the same as those of Formation enthalpies of bcc-Co-Al.xlsx.</p> <p><br>6. Formation enthalpies of hcp-Co-Al.xlsx<br>- Description: Formation enthalpies of hcp lattice in Co-Al system at different temperatures, which includes the effect of lattice vibration and magnetic excitation. Fcc Al and hcp Co were used as reference states.</p> <p>- Variable descriptions by columns are the same as those of Formation energies of bcc-Co-Al.xlsx.</p> <p><br>7. ECIs of bcc-Co-Al at different temperatures.txt<br>- Description: ECIs of bcc lattice in Co-Al system from 0 to 2000 K with increment step of 10 K. The ECIs at different temperatures are separated by blank lines. ECIs at 0 K means that only configurational contribution was considered. ECIs at finite temperature means that configurational, vibrational and magnetic contributions were considered.</p> <p><br>8. ECIs of fcc-Co-Al at different temperatures.txt<br>- Description: ECIs of fcc lattice in Co-Al system from 0 to 2000 K with increment step of 10 K. The ECIs at different temperatures are separated by blank lines. ECIs at 0 K means that only configurational contribution was considered. ECIs at finite temperature means that configurational, vibrational and magnetic contributions were considered.</p> <p><br>9. ECIs of hcp-Co-Al at different temperatures.txt<br>- Description: ECIs of hcp lattice in Co-Al system from 0 to 2000 K with increment step of 10 K. The ECIs at different temperatures are separated by blank lines. ECIs at 0 K means that only configurational contribution was considered. ECIs at finite temperature means that configurational, vibrational and magnetic contributions were considered.</p> <p><br>10. Clusters of bcc-Co-Al.txt<br>- Description: Cluster information of bcc lattice in Co-Al system. Each cluster is separated by a blank line. Each cluster contains: multiplicity; Length of the longest pair within the cluster; number of points in cluster; coordinates of point. They are arranged in a row.</p> <p><br>11. Clusters of fcc-Co-Al.txt<br>- Description: Cluster information of fcc lattice in Co-Al system. Each cluster is separated by a blank line. Each cluster contains: multiplicity; Length of the longest pair within the cluster; number of points in cluster; coordinates of point. They are arranged in a row.</p> <p><br>12. Clusters of hcp-Co-Al.txt<br>- Description: Cluster information of hcp lattice in Co-Al system. Each cluster is separated by a blank line. Each cluster contains: multiplicity; Length of the longest pair within the cluster; number of points in cluster; coordinates of point. They are arranged in a row.</p>
First principles prediction of the Al-Li phase diagram including configurational and vibrational entropic contributions
<p>Documentation for the Dataset used in the publication entitled "First principles prediction of the Al-Li phase diagram including configurational and vibrational entropic contributions" <br>** These datasets comprise all configurations used in Al-Li system and their formation enthalpies at different temperatures, where both configurational and vibrational contribution were considered. Bcc Li and fcc Al were used as reference state. **<br>** More details about the methodology can be found in the paper "Wei Shao, Sha Liu, Javier LLorca, First principles prediction of the Al-Li phase diagram including configurational and vibrational entropic contributions, Computational Materials Science, 2023"**</p> <p>1. bcc-Al-Li.zip<br>- Description: bcc-Al-Li.zip is a compressed folder. It contains Al1-xLix configurations with bcc lattice used to fit the cluster expansion (CE). Each folder contains a POSCAR file that corresponds to a configuration. The POSCAR can be opened with Notepad and visualized with VESTA software.</p> <p><br>2. fcc-Al-Li.zip<br>- Description: fcc-Al-Li.zip is a compressed folder. It contains Al1-xLix configurations with fcc lattice used to fit the CE. Each folder contains a POSCAR file that corresponds to a configuration. The POSCAR can be opened with Notepad and visualized with VESTA software.</p> <p>3. Formation enthalpies of bcc-Al-Li.xlsx<br>- Description: Formation enthalpy of each configuration in bcc Al-Li system at different temperatures, which includes the effect of lattice vibration. Bcc Li and fcc Al were used as reference state.</p> <p>- Variable descriptions by columns:<br> 1-(Folder nam) - type: numerical (integer)<br> Description: Each folder name in the bcc-Al-Li.zip corresponds to a configuration.<br> 2- (at. fraction of Li (%)) - type: numerical (float)<br> Description: The atomic fraction of Li in each configuration.<br> 3- (H_f^(conf)(DFT) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 0 K calculated by density functional theory (DFT).<br> 4- (H_f^(conf)(CE) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration fitted by CE at 0 K. <br> 6- (at. fraction of Li (%)) - type: numerical (float)<br> Description: The atomic fraction of Li in each configuration.<br> 7- (H_f^(conf+vib)(DFT+L-S) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 100 K calculated by DFT and bond length vs. bond stiffness relationship (L-S).<br> 8- (H_f^(conf+vib)(CE) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 100 K fitted by CE. <br> 10- (at. fraction of Li (%)) - type: numerical (float)<br> Description: The atomic fraction of Li in each configuration.<br> 11- (H_f^(conf+vib)(DFT+L-S) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 200 K calculated by DFT and L-S.<br> 12- (H_f^(conf+vib)(CE) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 200 K fitted by CE. <br> 14- (at. fraction of Li (%)) - type: numerical (float)<br> Description: The atomic fraction of Li in each configuration.<br> 15- (H_f^(conf+vib)(DFT+L-S) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 300 K calculated by DFT and L-S.<br> 16- (H_f^(conf+vib)(CE) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 300 K fitted by CE.<br> 18- (at. fraction of Li (%)) - type: numerical (float)<br> Description: The atomic fraction of Li in each configuration.<br> 19- (H_f^(conf+vib)(DFT+L-S) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 400 K calculated by DFT and L-S.<br> 20- (H_f^(conf+vib)(CE) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 400 K fitted by CE.<br> 22- (at. fraction of Li (%)) - type: numerical (float)<br> Description: The atomic fraction of Li in each configuration.<br> 23- (H_f^(conf+vib)(DFT+L-S) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 500 K calculated by DFT and L-S.<br> 24- (H_f^(conf+vib)(CE) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 500 K fitted by CE. <br> 26- (at. fraction of Li (%)) - type: numerical (float)<br> Description: The atomic fraction of Li in each configuration.<br> 27- (H_f^(conf+vib)(DFT+L-S) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 600 K calculated by DFT and L-S.<br> 28- (H_f^(conf+vib)(CE) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 600 K fitted by CE.<br> 30- (at. fraction of Li (%)) - type: numerical (float)<br> Description: The atomic fraction of Li in each configuration.<br> 31- (H_f^(conf+vib)(DFT+L-S) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 700 K calculated by DFT and L-S.<br> 32- (H_f^(conf+vib)(CE) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 700 K fitted by CE.<br> 34- (at. fraction of Li (%)) - type: numerical (float)<br> Description: The atomic fraction of Li in each configuration.<br> 35- (H_f^(conf+vib)(DFT+L-S) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 800 K calculated by DFT and L-S.<br> 36- (H_f^(conf+vib)(CE) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 800 K fitted by CE. <br> 38- (at. fraction of Li (%)) - type: numerical (float)<br> Description: The atomic fraction of Li in each configuration.<br> 39- (H_f^(conf+vib)(DFT+L-S) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 900 K calculated by DFT and L-S.<br> 40- (H_f^(conf+vib)(CE) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 900 K fitted by CE.<br> 42- (at. fraction of Li (%)) - type: numerical (float)<br> Description: The atomic fraction of Li in each configuration.<br> 43- (H_f^(conf+vib)(DFT+L-S) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 1000 K calculated by DFT and L-S.<br> 44- (H_f^(conf+vib)(CE) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 1000 K fitted by CE.</p> <p>4. Formation enthalpies fcc-Al-Li.xlsx<br>- Description: Formation enthalpy of each configuration in fcc Al-Li system at different temperatures, which includes the effect of lattice vibration. Bcc Li and fcc Al were used as reference state.</p> <p>- Variable descriptions by columns are the same as those of Formation enthalpies of bcc-Al-Li.xlsx.</p> <p><br>5. ECIs of bcc-Al-Li at different temperatures.txt<br>- Description: ECIs of bcc lattice in Al-Li system from 0 to 2000 K with increment step of 10 K. The ECIs at different temperatures are separated by blank lines. ECIs at 0 K means that only configurational contribution was considered. ECIs at finite temperature means that both configurational and vibrational contributions were considered.</p> <p><br>6. ECIs of fcc-Al-Li at different temperatures.txt<br>- Description: ECIs of fcc lattice in Al-Li system from 0 to 2000 K with increment step of 10 K. The ECIs at different temperatures are separated by blank lines. ECIs at 0 K means that only configurational contribution was considered. ECIs at finite temperature means that both configurational and vibrational contributions were considered.</p> <p><br>7. Clusters of bcc-Al-Li.txt<br>- Description: Cluster information of bcc lattice in Al-Li system. Each cluster is separated by a blank line. Each cluster contains: multiplicity; Length of the longest pair within the cluster; number of points in cluster; coordinates of point. They are arranged in a row.</p> <p><br>8. Clusters of fcc-Al-Li.txt<br>- Description: Cluster information of fcc lattice in Al-Li system. Each cluster is separated by a blank line. Each cluster contains: multiplicity; Length of the longest pair within the cluster; number of points in cluster; coordinates of point. They are arranged in a row.</p>
Simulated dMRI images and ground truth of random fiber phantoms in various configurations
<p>This archive contains simulated dMRI images of random fiber phantoms in various configurations created with Fiberfox and other tools available in MITK Diffusion (<a href="http://mitk.org/wiki/DiffusionImaging">http://mitk.org/wiki/DiffusionImaging</a>). RandomFibers_Example.png illustrates one of the random fiber configurations used for these phantoms.</p> <p>If you are using any of these datasets or the tools used to generate them, please don't forget to cite the dataset itself as well as other relevant publications.</p> <p>Each subfolder contains the following elements:<br> The simulated dMRI image with b-values and gradient directions: dwi.nii.gz, dwi.bvals, dwi.bvecs<br> The fibers used for simulation: AllBundles.fib (binary vtk format)<br> parameters.ffp: Fiberfox simulation parameters<br> parameters.ffp.bvals: b-value file for Fiberfox simulation<br> parameters.ffp.bvecs: gradient vector file for Fiberfox simulation<br> parameters.ffp_VOLUME1.nii.gz: fiber compartment volume fraction map for Fiberfox simulation<br> The logfile detailing all steps of the generation process of the respective phantom: LOGFILE.json</p> <p>bundles: folder containing the individual fiber bundles (binary vtk format .fib)<br> centroids: folder containing the centerlines of each bundle<br> masks: folder containing the binary envelope of each bundle<br> peaks: folder containing the principal fiber direction image (peaks) of each bundle</p> <p>Each subfolder contains the fibers and dMRI simulations with the following fiber specifications:<br> Phantom 1:<br> - Number of bundles: 25<br> - Fiber density: 250 streamlines per cm²<br> - Bundle curvature: 0-30 in degree<br> - Bundle start radius: 5-15 in mm</p> <p>Phantom 2:<br> - Number of bundles: 25<br> - Fiber density: 250 streamlines per cm²<br> - Bundle curvature: 0-30 in degree<br> - Bundle start radius: 15-30 in mm</p> <p>Phantom 3:<br> - Number of bundles: 25<br> - Fiber density: 250 streamlines per cm²<br> - Bundle curvature: 30-60 in degree<br> - Bundle start radius: 5-15 in mm</p> <p>Phantom 4:<br> - Number of bundles: 25<br> - Fiber density: 250 streamlines per cm²<br> - Bundle curvature: 30-60 in degree<br> - Bundle start radius: 15-30 in mm</p> <p>Phantom 5:<br> - Number of bundles: 25<br> - Fiber density: 50-500 streamlines per cm²<br> - Bundle curvature: 0-30 in degree<br> - Bundle start radius: 5-15 in mm</p> <p>Phantom 6:<br> - Number of bundles: 25<br> - Fiber density: 50-500 streamlines per cm²<br> - Bundle curvature: 0-30 in degree<br> - Bundle start radius: 15-30 in mm</p> <p>Phantom 7:<br> - Number of bundles: 25<br> - Fiber density: 50-500 streamlines per cm²<br> - Bundle curvature: 30-60 in degree<br> - Bundle start radius: 5-15 in mm</p> <p>Phantom 8:<br> - Number of bundles: 25<br> - Fiber density: 50-500 streamlines per cm²<br> - Bundle curvature: 30-60 in degree<br> - Bundle start radius: 15-30 in mm</p> <p>Phantom 9:<br> - Number of bundles: 50<br> - Fiber density: 250 streamlines per cm²<br> - Bundle curvature: 0-30 in degree<br> - Bundle start radius: 5-15 in mm</p> <p>Phantom 10:<br> - Number of bundles: 50<br> - Fiber density: 250 streamlines per cm²<br> - Bundle curvature: 0-30 in degree<br> - Bundle start radius: 15-30 in mm</p> <p>Phantom 11:<br> - Number of bundles: 50<br> - Fiber density: 250 streamlines per cm²<br> - Bundle curvature: 30-60 in degree<br> - Bundle start radius: 5-15 in mm</p> <p>Phantom 12:<br> - Number of bundles: 50<br> - Fiber density: 250 streamlines per cm²<br> - Bundle curvature: 30-60 in degree<br> - Bundle start radius: 15-30 in mm</p> <p>Phantom 13:<br> - Number of bundles: 50<br> - Fiber density: 50-500 streamlines per cm²<br> - Bundle curvature: 0-30 in degree<br> - Bundle start radius: 5-15 in mm</p> <p>Phantom 14:<br> - Number of bundles: 50<br> - Fiber density: 50-500 streamlines per cm²<br> - Bundle curvature: 0-30 in degree<br> - Bundle start radius: 15-30 in mm</p> <p>Phantom 15:<br> - Number of bundles: 50<br> - Fiber density: 50-500 streamlines per cm²<br> - Bundle curvature: 30-60 in degree<br> - Bundle start radius: 5-15 in mm</p> <p>Phantom 16:<br> - Number of bundles: 50<br> - Fiber density: 50-500 streamlines per cm²<br> - Bundle curvature: 30-60 in degree<br> - Bundle start radius: 15-30 in mm</p>
Photovoltaic time series for European countries and different system configurations
<p>This repository comprises 38 years-long hourly time series representing the photovoltaic (PV) capacity factors in every European country (EU-28 plus Serbia, Bosnia-Herzegovina, Norway, and Switzerland). The term capacity factor is defined as the ratio between the delivered power and the cumulative installed capacity (DC). 3 letter codes (ISO-3166-3) are used to identify the countries. Time series include years from 1979 to 2017.</p> <p>To obtain PV time series irradiance from Climate Forecast System Reanalysis (CFSR) dataset has been converted into electricity generation and aggregated at country level. The PV model used for the conversion is described in the article linked below. Prior to conversion, reanalysis irradiance is bias corrected using satellite-based SARAH dataset and a globally-applicable methodology, which is also described in the article.</p> <p>For every country, four different time series assuming alternative PV configurations, <em>i.e</em>., rooftop, optimum tilt, 2-axis tracking, and delta are provided. To obtain the PV hourly capacity factors for a country, different assumptions on the shares of the alternative configurations can be made and the weighted time series can be aggregated accordingly.</p> <p>The license for the AU REatlas photovoltaic time series dataset is: <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International (CC BY 4.0)</a></p> <p>When using this data please make sure you include the following citation:</p> <p><em>M. Victoria and Gorm B. Andresen, Using validated reanalysis data to investigate the impact of the PV system configurations at high penetration levels in European countries, Progress in Photovoltaics: Research and Applications (2019) </em><a href="https://doi.org/10.1002/pip.3126">https://doi.org/10.1002/pip.3126</a></p> <p>More information can be requested from M. Victoria (<a href="mailto:mvp@eng.au.dk">mvp@eng.au.dk</a>) and Gorm B. Andresen (<a href="mailto:gba@eng.au.dk">gba@eng.au.dk</a>).</p> <p>Version 2 assumes tilt angle of 60º for PV panels in delta configuration (in version 1, tilt angle in delta configuration is equal to latitude). The remaining files do not change.</p> <p>Version 3 includes one additional file corresponding to country-wise time series obtained assuming 1 axis-tracker (horizontal axis oriented North-South). In addition, small corrections of the previous time series have been implemented affecting only early hours in the day.<br> </p>
The third Met Office Unified Model-JULES Regional Atmosphere and Land configuration, RAL3
<p>Supporting data for figures in GMD draft paper: The third Met Office Unified Model-JULES Regional Atmosphere and Land configuration, RAL3</p>
H2020 ENODISE: ONERA Numerical Aeroacoustic Database Configuration A2-Ducted
<p>This database contains the numerical results obtained by ONERA on the configuration A2-ducted of the H2020 ENODISE project. In this configuration, a ducted propeller is placed above an S-plate to ingest an adverse pressure gradient boundary layer in a partially buried configuration. The present results can be compared to the measurements carried out by the University of Bristol.</p> <p>All following parameters are kept constant in the present simulations:</p> <ul> <li>Free-stream velocity <em>u_inf</em> = 32 m/s,</li> <li>Propeller tip clearance <em>d/D</em> = 0.002,</li> <li>Propeller position inside the duct <em>x/D</em> = -0.0627.</li> </ul> <p>Five simulations have been realized by varying the installation (with or without the S-plate) and the propeller rotation speed:</p> <ul> <li>Isolated propeller (without the S-plate), <em>N</em> = 6000 rpm, 8000 rpm, 11000 rpm,</li> <li>Installed propeller (with the S-plate), <em>N</em> = 6000 rpm, 8000 rpm.</li> </ul> <p>The predictions were obtained using the ProLB solver which is based on the Lattice Boltzmann method (<a href="http://www.prolb-cfd.com/">http://www.prolb-cfd.com/</a>). Aerodynamic and acoustic results are provided for each simulation. One-dimensional results are saved into ASCII column files (<em>.dat</em> extension) while geometry and raw flow extractions are saved into HDF5 CGNS files (<em>.cgns</em> extension). <em>README.txt</em> files are included for detailed description of the data.</p> <p> </p>
ScienceDex guides
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