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26 results for “power law”
On the power and control of a misaligned rotor - Beyond the cosine law
<p>This dataset is a supplement to the article entitled "On the power and control of a misaligned rotor - Beyond the cosine law", currently under review in the Wind Energy Science Journal. Each figure in the article can be reproduced using the python scripts provided in the form of Jupyter Notebooks (.ipynb).</p>
Randomly sampled coefficients for synchrotron radiative transfer in the Stokes basis, power law model, computed by rimphony, for consumption by neurosynchro
<p>This directory contains a training set of 22 million randomly-sampled radiative transfer coefficients generated by <a href="https://github.com/pkgw/rimphony/">rimphony</a>, suitable for use with the <a href="https://github.com/pkgw/neurosynchro/">neurosynchro</a> package. These coefficients can be used for numerical radiative transfer of synchrotron emission in the Stokes basis with a package such as <a href="https://github.com/jadexter/grtrans/">grtrans</a>.</p> <p>In this particular dataset, coefficients were computed using a model of a power law electron distribution isotropic in pitch angle. The input parameters, which were sampled randomly in a three-dimensional space, are:</p> <ul> <li><em>s</em>, the harmonic number, dimensionless, sampled logarithmically between 5 and 50,000,000.</li> <li><em>theta</em>, the angle between the ray path and the local magnetic field, measured in radians, sampled linearly between 0.001 and π/2 (namely, 1.5707963267948966).</li> <li><em>p</em>, the power-law index of the energetic electrons, dimensionless, sampled linearly between 1.5 and 7.</li> </ul> <p>The coefficients were computed on Harvard’s Odyssey cluster using Git commit <a href="https://github.com/pkgw/rimphony/commit/772161ebda0217b8c1ccb8ce3801ad9dc3701a4f">772161</a> of rimphony. A total of about 5,000 CPU hours were used, with 500 processes running for about 10 hours each. There are 2,748,835 data rows in total. The data are provided in their original format, split among 500 files, so that smaller subsamples of the data may be loaded easily. A README.md file provides more detailed information.</p>
Trained neural network data for synchrotron radiative transfer in the Stokes basis, power law model, computed by rimphony, for consumption by neurosynchro
<p>This archive contains data representing a trained-up neural network suitable for use with the <a href="https://github.com/pkgw/neurosynchro/">neurosynchro</a> package. The network generates coefficients that can be used for numerical radiative transfer of synchrotron emission in the Stokes basis with a package such as <a href="https://github.com/jadexter/grtrans/">grtrans</a>.</p> <p>In this particular dataset, networks were trained on a training set of coefficients generated by <a href="https://github.com/pkgw/rimphony/">rimphony</a> that is available as <a href="https://doi.org/10.5281/zenodo.1341154">DOI:10.5281/zenodo.1341154</a>. The data were generated using a model of a power law electron distribution isotropic in pitch angle. The input parameters, which were sampled randomly in a three-dimensional space, were:</p> <ul> <li><em>s</em>, the harmonic number, dimensionless, sampled logarithmically between 5 and 50,000,000.</li> <li><em>theta</em>, the angle between the ray path and the local magnetic field, measured in radians, sampled linearly between 0.001 and π/2 (namely, 1.5707963267948966).</li> <li><em>p</em>, the power-law index of the energetic electrons, dimensionless, sampled linearly between 1.5 and 7.</li> </ul> <p>The training set was computed on Harvard’s Odyssey cluster using Git commit <a href="https://github.com/pkgw/rimphony/commit/772161ebda0217b8c1ccb8ce3801ad9dc3701a4f">772161</a> of rimphony. A total of about 5,000 CPU hours were used, with 500 processes running for about 10 hours each, yielding about 22 million numbers. Training the networks took about 3 hours on an 8-core laptop.</p> <p>For the purposes of <em>neurosynchro</em>, the formats of the files in this package should be regarded as internal implementation details. The <a href="https://pypi.org/project/neurosynchro/">neurosynchro</a> Python package will load up the files in this archive and use them to predict synchrotron coefficients. For specifics, see <a href="https://neurosynchro.readthedocs.io/en/stable/">the neurosynchro documentation</a>.</p>
The speed-curvature power law in tongue movements of repetitive speech [dataset]
<p>Files in this record contain data used to produce results presented in the<br> paper:</p> <p>Title: The speed-curvature power law in tongue movements of repetitive speech<br> Authors: Stephan R. Kuberski and Adamantios I. Gafos<br> DOI: <a href="https://doi.org/10.1371/journal.pone.0213851">https://doi.org/10.1371/journal.pone.0213851</a></p> <p>For further details refer to the included file README.txt.</p>
H2020 OPERA Project: Power Take-Off and Control Law testing in MARMOK-A-5 Wave Energy Converter at BiMEP
<p>Funded under European Union's Horizon 2020 Programme, <a href="http://opera-h2020.eu/">OPERA</a> project’s main objective is to reduce the time to market of wave energy, by further advancing in 4 key innovations aiming to reduce up to 50% the Levelized Cost of Energy (LCOE) projections of a floating Oscillating Water Column (OWC) technology.</p> <p>As part of project activities, a series of Power Take-Off (PTO) and Control Law (CL) tests were carried out using IDOM's MARMOK-A-5 wave energy converter, while this was deployed in the Biscay Marine Energy Platform (BiMEP) from October 2018 to June 2019.</p> <p>The datasets herein contain a collection of PTO and CL testing results obtained during this extensive testing campaign, providing quantitative evidence of the performance of both innovations. </p>
Data from: Ecological tradeoffs drive a power-law relationship between group size and population density in social foragers
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Data from: Exploiting nozzle geometry to predict resolution in extrusion-based bioprinting: mathematical modelling of a power-law fluid
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Supplementary material for "A Partitioned Finite Element Method for power-preserving discretization of open systems of conservation laws"
<p>This archive contains supplementary material for the paper "A Partitioned Finite Element Method for power-preserving discretization of open systems of conservation laws", containing the source codes for the numerial results presented in the paper. An arXiv pre-print version of the paper is available <a href="https://arxiv.org/abs/1906.05965">here</a>.</p> <p>The following codes are provided:</p> <ul> <li> <p><code>codes/simulation1D_small.jl</code>: small amplitudes (linear) 1D simulation</p> </li> <li> <p><code>codes/simulation1D_large.jl</code>: large amplitudes (nonlinear) 1D simulation</p> </li> <li> <p><code>codes/simulation1D_analytical_gradient</code>: large amplitudes 1D simulation, but using an analytical nonlinear Hamiltonian gradient expression</p> </li> <li> <p><code>codes/simulation2D.jl</code>: large amplitudes (nonlinear) 2D simulation</p> </li> <li> <p><code>codes/convergence1D.jl</code>: convergence analysis of the 1D linear case</p> </li> <li> <p><code>codes/convergence2D.m</code>: convergence analysis of the 2D linear case</p> </li> </ul> <p>A GitHub with the codes and a few instructions on usage is available <a href="http://github.com/flavioluiz/PFEM-article-supplementary-material">here</a>.</p> <p><strong>Acknowledgements</strong></p> <p>This work has been performed in the frame of the Collaborative Research DFG and ANR project INFIDHEM, entitled "Interconnected of Infinite-Dimensional systems for Heterogeneous Media", nº ANR-16-CE92-0028. Further information is available <a href="http://websites.isae-supaero.fr/infidhem/the-project">here</a>.</p>
Data for coherence measurements of polaritons in thermal equilibrium reveal a power law for two-dimensional condensates
<p>All the raw data sets collected for this project are included in this submission. The code for the numerics is also included. 'Readme.text' files are included with the data sets explaining what the data sets are and how to read them. </p>
Non-Newtonian Power-law fluid simulations in rectangular channel
<h1>Velocity distribution in rectangular channel for Power law fluid.</h1> <p>Using viscosity: eta(x)=K*(x/gamma0)^(n-1)/gamma0</p> <p>Solved the PDE equation: https://doc.comsol.com/5.5/doc/com.comsol.help.comsol/comsol_ref_equationbased.23.008.html<br>in rectangular channel in COMSOL with<br>c=eta(sqrt(d(u,x)^2+d(u,y)^2), f=0.013333333 [Pa*s]<br>e_a, d_a, alpha, beta, gamma=0<br>gamma0=1[1/s]<br>Boundary conditions on x=0, y=0, x=witdh, y=height is u=0</p> <p>Output file contains: Coordinates X, Y [mm] on triangular grid and Velocity u [m/s]</p> <p>File specific parameters:<br>"H2O_100x096.txt" - n=1, K=0.013333333 [Pa*s], height=1 [mm], width=0.96 [mm]<br>"10pFBS_37C_100x115.txt" - n=0.5, K=0.00541540444218501 [Pa*s], height=1 [mm], width=1.15 [mm]<br>"10pFBS_097x092.txt" - n=0.32, K=0.00541540444218501 [Pa*s], height=0.97 [mm], width=0.92 [mm]</p>
Data from: Biological and statistical processes jointly drive population aggregation: using host–parasite interactions to understand Taylor's power law
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Quantitative evaluation of mantle flow traction on overlying tectonic plate: Linear versus power-law mantle rheology
<p>Dataset for <strong>Quantitative evaluation of mantle flow traction on overlying tectonic plate: Linear versus power-law mantle rheology</strong></p>
Data from: A generalized distribution interpolated between the exponential and power law distributions and applied to pill bug (Armadillidium vulgare) walking data
<p>The walking pattern of an organism is typically designated as either a Lévy walk or a Brownian walk based on whether the frequency distribution of its linear step lengths follows a power law distribution or an exponential distribution. However, there are many cases where actual data cannot be classified into either of these categories. In this paper, we propose a general distribution that includes the power law and exponential distributions as special cases. This distribution has two parameters: one parameter represents the exponent, similar to the power law and exponential distributions, and the other is a shape parameter representing the shape of the distribution. By introducing this distribution, an intermediate distribution model can be interpolated between the power law and exponential distributions. In this study, the proposed distribution was fitted to the frequency distribution of the step length calculated from the walking data of pill bugs. The autocorrelation coefficients were also calculated from the time-series data of the step length, and the relationship between the shape parameter and time dependency was investigated. The results indicate that individuals whose step length frequency distributions are closer to the power law distribution have stronger time dependence.</p> <p>C++ program for parameter estimation of generalized distributions and source code for statistical analysis using R.</p>
data and codes for paper "Understanding power-law photoluminescence decays and bimolecular recombination in lead-halide perovskites"
<p>These are the data and Matlab codes used in the paper "Understanding power-law photoluminescence decays and bimolecular recombination in lead-halide perovskites".</p>
Raw data for the article 'Revisiting power-law distributions in empirical outage data of power systems'
<p>Raw data for the article 'Revisiting power-law distributions in empirical outage data of power systems' (see the manuscript preprint https://arxiv.org/abs/2303.12714 for more details).</p>
Data from: A generalized distribution interpolated between the exponential and power law distributions and applied to pill bug (Armadillidium vulgare) walking data
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Common power laws for cities and spatial fractal structures
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Data from: Taylor's Power Law and its decomposition in urban facilities
As one of the few generalities in ecology, Taylor's power law admits a power function relationship $V=aM^{b}$ between the variance $V$ and mean number $M$ of organisms in a quadrat. We examine the spatial distribution data of seven urban service facilities in 37 major cities in China, and find that Taylor's law is validated among all types of facilities. Moreover, Taylor's law is robust if we shift the observation window or vary the size of the quadrats. The exponent $b$ increases linearly with the logarithm of the quadrat size, i.e., $b(s) = b_0 + A\log(s)$. Furthermore, the ANOVA test indicates that $b$ takes distinct values for different facilities in different cities. We decompose $b$ into two different factors, a city-specific factor (CSF) and a facility-specific factor (FSF). variations in $b$ can be explained to a large extent by the differences between cities and types of facilities. Facilities are more evenly distributed in larger and more developed cities. Competitive interchangeable facilities (e.g. pharmacy), with larger FSFs and smaller $b$s, are less aggregated than complimentary services (e.g., restaurants).
Data from: The speed–curvature power law in Drosophila larval locomotion
We report the discovery that the locomotor trajectories of Drosophila larvae follow the power-law relationship between speed and curvature previously found in the movements of human and non-human primates. Using high-resolution behavioural tracking in controlled but naturalistic sensory environments, we tested the law in maggots tracing different trajectory types, from reaching-like movements to scribbles. For most but not all flies, we found that the law holds robustly, with an exponent close to three-quarters rather than to the usual two-thirds found in almost all human situations, suggesting dynamic effects adding on purely kinematic constraints. There are different hypotheses for the origin of the law in primates, one invoking cortical computations, another viscoelastic muscle properties coupled with central pattern generators. Our findings are consistent with the latter view and demonstrate that the law is possible in animals with nervous systems orders of magnitude simpler than in primates. Scaling laws might exist because natural selection favours processes that remain behaviourally efficient across a wide range of neural and body architectures in distantly related species.
On the Identification of Power-Law Creep Parameters from Conical Indentation
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.