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56 results for “power analysis”
Solar and interplanetary magnetic field data analyzed in "Optimal frequency-domain analysis for spacecraft time series: Introducing the missing-data multitaper power spectrum estimator"
<p>This dataset contains simultaneous measurements of the interplanetary magnetic field magnitude <B> and the sun's radio flux at 10.7 cm <F10.7>. <B> measurements come from a series of spacecraft located at the L1 point, while <F10.7> was measured by the ongoing monitoring program by Canada's Dominion Radio Astrophysical Observatory. Bartels rotation-averaged data were downloaded from NASA's OMNIWeb, https://omniweb.gsfc.nasa.gov/html/ow_data.html. The file contains other solar wind plasma parameters that were not used in the analysis.</p>
BOREALIS Power Analysis Code and Data
<p>This contains the code and data necessary to rerun the power analysis used in testing BOREALIS.</p> <p>Borealis is an R library performing outlier analysis for count-based bisulfite sequencing data. It detects outlier methylated CpG sites from bisulfite sequencing (BS-seq). The core of Borealis is modeling Beta-Binomial distributions. This can be useful for rare disease diagnoses.</p>
BIKE Key-Recovery: Combining Power Consumption Analysis and Information-Set Decoding
<p>Data used in the paper: "BIKE Key-Recovery: Combining Power Consumption Analysis and Information-Set Decoding". The paper has been accepted at <a href="https://sulab-sever.u-aizu.ac.jp/ACNS2023/">ACNS-2023</a>.</p> <p>The available dataset contains a file with a few power consumption curves taken from a Cortex-M4 (STM32F4) on a CW308 board.</p> <p>The file is a numpy array stored using the np.save API.<br> The file can be directly used for running the notebooks provided in the <a href="https://github.com/benoitgerard/sca-bike">publication github</a>.</p>
Database for Market uptake of concentrating solar power in Europe: model-based analysis of drivers and policy trade-offs. MUSTEC project.
<p>This dataset contains the data underlying the modelling activities of the MUSTEC (<em>Market Uptake of Solar Thermal Electricity through Cooperation</em>) project used in the models Green-X (TU Wien) and Enertile (Fraunhofer ISI).</p> <p>For description of the modelled scenarios, results and findings, see: Resch, G., Schöniger, F., Kleinschmitt, C., Franke, K., Sensfuß, F., Thonig, R., and Lilliestam, J.:<em> </em><em> Market uptake of concentrating solar power in Europe: model-based analysis of drivers and policy trade-offs. </em>Deliverable 8.2 MUSTEC project, TU Wien, Wien.</p> <p>For information on the project see: https://www.mustec.eu/</p> <p>For data descriptions, licence, and further information, see README.md.</p> <p> </p>
FIGURE 3 in A new solar powered species of the genus Phyllodesmium Ehrenberg, 1831 (Mollusca: Nudibranchia: Aeolidoidea) from Indonesia with analysis of its photosynthetic activity and notes on biology
FIGURE 3: Phyllodesmium jakobsenae, hard structures in digestive system: A: Right jaw seen from the outside. B: Left jaw from the inside, denticles at the masticatory border seen from the inside. C: Distal part of radula of specimen No. 5. D: Closeup of distal part of radula of specimen No. 5; note the worn denticles on the edge of the rhachidian teeth .. E: Distal part of radula of specimen No. 3. F: Different angle of view of rhachidian cusps from No. 3. G: Part of the radula of specimen No. 3.
FIGURE 4 in A new solar powered species of the genus Phyllodesmium Ehrenberg, 1831 (Mollusca: Nudibranchia: Aeolidoidea) from Indonesia with analysis of its photosynthetic activity and notes on biology
FIGURE 4: Phyllodesmium jakobsenae, histology: A: View of ceras from side orientated to light. Note the dense branches along the whole ceras. B: View of ceras from side orientated away from light. Note the main branch of digestive gland and the fewer ramifications. C: Cnidosac of large ceras with no nematocysts. D: Digestive glandular branches beneath epidermis in large ceras. Note the many zooxanthellae especially in the digestive glandular tissue. E: Longitudinal section of small ceras with cnidosac. The digestive glandular duct is not branching, and shows glandular cells. The cnidosac is filled with many tiny nematocysts. The epidermis shows many glandular cells, beneath the epidermis a layer of “ cellules spéciales ” can be seen. F: Section trough eye and statocyst with one statolith. G: Small part of the oral gland with outleading duct. Abbreviations: cn cnidosac, cs “ cellules spéciales ”, e eye, mc mucous cells, st statocyst.
FIGURE 1 in A new solar powered species of the genus Phyllodesmium Ehrenberg, 1831 (Mollusca: Nudibranchia: Aeolidoidea) from Indonesia with analysis of its photosynthetic activity and notes on biology
FIGURE 1: Phyllodesmium jakobsenae, living animals from North Sulawesi: A: Specimen laying eggs in an aquarium. B: Two specimens sitting in their food coral Xenia: on the right side a specimen with more brownish cerata and to the left a bigger specimen with more whitish cerata. Polyps of Xenia surround both individuals. C: Bigger specimen from B; please note the smaller cerata in the anterior part of body and the oral tentacles stretched to the lateral sides. D: Animal sitting inactive and mimicking Xenia polyps. E: Specimen starting to crawl.
High-resolution analysis of power plant land requirements for GODEEEP
<p>This dataset contains data associated with Mongird et al. (under review). Files include output from the following three analyses found in the paper: (1) Projected power plant siting intersections with US Disadvantaged Communities (DACs), important farmland, and natural areas; (2) onshore wind and solar photovoltaic capacity factor availability under 27 different siting restriction cases, and (3) output from an analysis that determines how many DACs are projected to see both fossil fuel generation retirement and new renewable power plant development. Each of the files associated with these components are described below. </p> <p>For more detailed information please refer to Mongird et al. (under review), "High-resolution analysis of power plant land requirements for the evolving Western United States power grid indicates coordinated land use policies will be essential"</p> <p>Outputs included in this dataset are associated with two different scenarios. Summaries of each of the two scenarios included are provided below. For additional information, see <a href="https://doi.org/10.1016/j.egycc.2023.100117">Ou et al. 2023.</a></p> <h2>Scenario Descriptions</h2> <ul> <li><strong>business-as-usual</strong>: <ul> <li>This scenario does not include any long-term federal policies requiring decarbonization.</li> <li>It does include the US Inflation Reduction Act (IRA) incentives.</li> <li>It assumes that CCS technologies are available.</li> </ul> </li> <li><strong>high renewables</strong>: <ul> <li>This scenario includes a clean electricity grid in the U.S. by 2035 and a net-zero economy by 2050.</li> <li>It does include US IRA incentives.</li> <li>It assumes that CCS technologies are available.</li> </ul> </li> </ul> <h2>Data Descriptions</h2> <h3>1. Projected power plant siting intersections</h3> <p><strong>Description</strong></p> <p>These files identify the intersection of projected power plant locations with three types of land: federall identified disadvantaged communities (DACs), important farmland, and land in close proximity to natural areas.</p> <p><strong>Scenario Files:</strong></p> <table> <tbody> <tr> <td>File Name</td> <td>File Description</td> </tr> <tr> <td>bau_dac_analysis_2050.csv</td> <td>Results from analysis identifying how many projected power plant sitings through 2050 under the busines-as-usual scenario intersect with federally identified US DACs by technology type and Western US state</td> </tr> <tr> <td>bau_env_analysis_2050.csv</td> <td>Results from analysis identifying how many projected power plant sitings through 2050 under the busines-as-usual scenario intersect with areas within 1 km, 5 km, and 10km of environmental areas by technology type and Western US state</td> </tr> <tr> <td>bau_farm_analysis_2050.csv</td> <td>Results from analysis identifying how many projected power plant sitings through 2050 under the busines-as-usual scenario intersect with important farmland by technology type and Western US state</td> </tr> <tr> <td>hr_dac_analysis_2050.csv</td> <td>Results from analysis identifying how many projected power plant sitings through 2050 under the high renewables scenario intersect with federally identified US DACs by technology type and Western US state</td> </tr> <tr> <td>hr_env_analysis_2050.csv</td> <td>Results from analysis identifying how many projected power plant sitings through 2050 under the high renewables scenario intersect with areas within 1 km, 5 km, and 10km of environmental areas by technology type and Western US state</td> </tr> <tr> <td>hr_farm_analysis_2050.csv</td> <td>Results from analysis identifying how many projected power plant sitings through 2050 under the high renewables scenario intersect with important farmland by technology type and Western US state</td> </tr> </tbody> </table> <p> </p> <p><strong>Data Dictionary:</strong></p> <table> <tbody> <tr> <td><strong>Column</strong></td> <td><strong>Description</strong></td> <td><strong>Units</strong></td> </tr> <tr> <td>state</td> <td>Name of US state</td> <td>N/A</td> </tr> <tr> <td>technology</td> <td>Power plant technology type inclusive of turbine type, presence of CCS, and cooling type (as applicable)</td> <td>N/A</td> </tr> <tr> <td>technology_simple</td> <td>Power plant technology type excluding turbine type, presence of CCS, and cooling type (as applicable)</td> <td>N/A</td> </tr> <tr> <td>layer_name</td> <td>Descriptive name of geospatial raster layer used for intersection analysis</td> <td>N/A</td> </tr> <tr> <td>layer</td> <td>Name of geospatial raster layer used for intersection analysis</td> <td>N/A</td> </tr> <tr> <td>total_plants</td> <td>Number of projected power plants of specified technology in specified state under given scenario</td> <td>#</td> </tr> <tr> <td>intersection</td> <td>Number of projected power plant intersections of specified technology in specified state with given layer under given scenario </td> <td>#</td> </tr> <tr> <td>fraction</td> <td>ratio of intersection and total_plants</td> <td>fraction</td> </tr> </tbody> </table> <p> </p> <p><strong>Scenario Difference Analysis Files:</strong></p> <table> <tbody> <tr> <td>difference_dac_analysis_2050.csv</td> <td>Results from analysis identifying how many more projected power plant sitings through 2050 under the high renewables scenario intersect with federally identified US DACs by technology type and Western US state compared to projected power plant sitings through 2050 under the business-as-usual scenario. Negative results indicate that the business-as-usual scenario had a greater number of intersections.</td> </tr> <tr> <td>difference_env_analysis_2050.csv</td> <td>Results from analysis identifying how many more projected power plant sitings through 2050 under the high renewables scenario intersect with areas within 1 km, 5 km, and 10km of environmental areas by technology type and Western US state compared to projected power plant sitings through 2050 under the business-as-usual scenario. Negative results indicate that the business-as-usual scenario had a greater number of intersections.</td> </tr> <tr> <td>difference_farm_analysis_2050.csv</td> <td>Results from analysis identifying how many more projected power plant sitings through 2050 under the high renewables scenario intersect with important farmland by technology type and Western US state compared to projected power plant sitings through 2050 under the business-as-usual scenario. Negative results indicate that the business-as-usual scenario had a greater number of intersections.</td> </tr> </tbody> </table> <p> </p> <p><strong>Data Dictionary:</strong></p> <table style="width: 85.255198%; height: 152px;"> <tbody> <tr style="height: 19px;"> <td style="width: 8.458634%; height: 19px;"><strong>Column</strong></td> <td style="width: 82.845413%; height: 19px;"><strong>Description</strong></td> <td style="width: 4.029241%; height: 19px;"><strong>Units</strong></td> </tr> <tr style="height: 19px;"> <td style="width: 8.458634%; height: 19px;">state</td> <td style="width: 82.845413%; height: 19px;">Name of US state</td> <td style="width: 4.029241%; height: 19px;">N/A</td> </tr> <tr style="height: 19px;"> <td style="width: 8.458634%; height: 19px;">technology</td> <td style="width: 82.845413%; height: 19px;">Power plant technology type</td> <td style="width: 4.029241%; height: 19px;">N/A</td> </tr> <tr style="height: 19px;"> <td style="width: 8.458634%; height: 19px;">layer</td> <td style="width: 82.845413%; height: 19px;">Name of geospatial raster layer used for intersection analysis</td> <td style="width: 4.029241%; height: 19px;">N/A</td> </tr> <tr style="height: 19px;"> <td style="width: 8.458634%; height: 19px;">hr</td> <td style="width: 82.845413%; height: 19px;">Number of projected power plant intersections with given layer under the high renewables scenario</td> <td style="width: 4.029241%; height: 19px;">#</td> </tr> <tr style="height: 19px;"> <td style="width: 8.458634%; height: 19px;">bau</td> <td style="width: 82.845413%; height: 19px;">Number of projected power plant intersections with given layer under the business-as-usual scenario</td> <td style="width: 4.029241%; height: 19px;">#</td> </tr> <tr style="height: 38px;"> <td style="width: 8.458634%; height: 38px;">intersection</td> <td style="width: 82.845413%; height: 38px;">Difference in projected power plant intersections between the high renewables scenario and the business-as-usual scenario</td> <td style="width: 4.029241%; height: 38px;">#</td> </tr> </tbody> </table> <h3> </h3> <h3>2. Projected onshore wind and solar photovoltaic capacity factor availability under 27 siting restriction cases</h3> <p>Description:</p> <p>This file contains results from an analysis on the capability of reaching high renewables scenario solar and wind generation in 2050 under 27 different siting restriction cases.</p> <p><strong>Relevant File:</strong></p> <table> <tbody> <tr> <td>File Name</td> <td>File Description</td> </tr> <tr> <td>capacity_factor_analysis_2050.csv</td> <td>Amount of solar PV or onshore wind generation projected to be available in a given state under a specified siting restriction case </td> </tr> </tbody> </table> <p><strong>Data Dictionary:</strong></p> <table> <tbody> <tr> <td><strong>Column</strong></td> <td><strong>Description</strong></td> <td><strong>Units</strong></td> </tr> <tr> <td>region_name</td> <td>Name of US state</td> <td>N/A</td> </tr> <tr> <td>technology</td> <td>Power plant technology type (either solar PV or Wind)</td> <td>N/A</td> </tr> <tr> <td>capacity_density_mw</td> <td>Assumed MW per square-km</td> <td>MW</td> </tr> <tr> <td>case</td> <td>Name of siting exclusion case</td> <td>N/A</td> </tr> <tr> <td>total_generation_mwh</td> <td>Projected total generation available given remaining available land after exclusions</td> <td>MWh</td> </tr> <tr> <td>target_generation_mwh</td> <td>Projected target annual generation in 2050 for technology type under high renewables scenario</td> <td>MWh</td> </tr> <tr> <td>gcam_trading_region</td> <td>Name of zonal representation of electricity trading regions as defined in the capacity expansion model</td> <td>N/A</td> </tr> </tbody> </table> <h3> </h3> <h3>3. US DACs that see both fossil fuel generation retirement and new renewable power plant development by 2050</h3> <p><strong>Description:</strong></p> <p>This data contains US census tract GEOIDs that see both new renewable sitings and the retirement of fossil generating resources.</p> <p><strong>Relevant File:</strong></p> <table> <tbody> <tr> <td>File Name</td> <td>File Description</td> </tr> <tr> <td>dac_fossil_retire_analysis_2050.csv</td> <td>List of US census tracts that see both fossil fuel generation retirement and new renewable generation siting by 2050 </td> </tr> </tbody> </table> <p><strong>Data Dictionary:</strong></p> <table> <tbody> <tr> <td><strong>Column</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td> census_tract</td> <td> US census tract GEOID</td> </tr> <tr> <td>state_name</td> <td>Name of US state</td> </tr> <tr> <td>county_name</td> <td>Name of US county</td> </tr> <tr> <td>scenario</td> <td>scenario name</td> </tr> </tbody> </table> <p> </p> <h2>Funding statement</h2> <p>This research was supported by the Grid Operations, Decarbonization, Environmental and Energy Equity Platform (GODEEEP) Investment, under the Laboratory Directed Research and Development (LDRD) Program at Pacific Northwest National Laboratory (PNNL).</p> <p>PNNL is a multi-program national laboratory operated for the U.S. Department of Energy (DOE) by Battelle Memorial Institute under Contract No. DE-AC05-76RL01830.</p> <p> </p> <h2>Changelog</h2> <p>v1.1</p> <p> The following updates were made following manuscript revision:</p> <ul> <li>"power_density_mw" variable name in `capacity_factor_analysis_2050.csv` file changed to "capacity_density_mw"</li> <li>More estimates are provided in `capacity_factor_analysis_2050.csv` reflecting additional capacity density and turbine hub height assumptions.</li> <li>Scenario naming adjusted to align with manuscript naming</li> </ul>
UK Power Station Transformer Dissolved Gas Analysis Data (2010-2015)
<p>This dataset includes dissolved gas analysis records from coolant oil in 13 UK power station transformers for various timespans between 2010-2015. They form the basis of the paper "Assessing the impact of weak and moderate geomagnetic storms on UK power station transformers" submitted to the AGU "Space Weather" Journal by Z.M. Lewis, J.A. Wild and M. Allcock in December 2021.<br> <br> Please cite Lewis et al. if using these data. The authors thank D. Barker, EDF Energy Nuclear Generation, for providing these data.</p> <p> </p> <p>The data are presented in comma separated variable format files, as described in the readme.txt file.</p>
Transient analysis of power loss density with time-harmonic electromagnetic waves in Debye media
<p>Due to the complex permittivity, it is difficult to directly clarify the transient mechanism between electromagnetic waves and Debye media. To overcome above problem, the temporal relationship between the electromagnetic waves and permittivity is explicitly derived by applying the Fourier inversion and introducing the remnant displacement. With the help of the Poynting theorem and energy conservation equation, the transient power loss density is derived to describe the transient dissipation of electromagnetic field and the mechanism on phase displacement has been explicitly revealed. Besides, the unique solution can be obtained by applying the time-domain analysis method rather than involving the frequency-domain characteristics. The effectiveness of transient analysis is demonstrated by giving a comparison simulation on one-dimensional example.</p>
"Demand-side policies for power generation in response to the energy crisis: A model analysis for Italy", scripts and data
<p>This repository contains the data, scripts and results for the paper "Demand-side policies for power generation in response to the energy crisis: A model analysis for Italy", https://doi.org/10.1016/j.esr.2024.101329.</p> <p>Results in the paper are divided into three sections, corresponding to the numbers of the folders inside this dataset. They are described as follows:</p> <p>1 - EU policy impact: What is the impact on the Italian electricity of the european proposal of cutting power demand and shifting it during peak hours on gas consumption, system costs and emissions?</p> <p>2 - Gas cost sensitivity: Which would be Italy’s most convenient power system considering different gas prices?</p> <p>3 - DSM in mitigation: What could be the role of demand side measures in power systems with a high penetration of RES?</p>
Can green hydrogen drive economic transformation in Saudi Arabia? - An input-output analysis of different Power-to-X configurations. Supplementary Data
<p>Supplementary material for peer review</p> <ul> <li>Modelling Data (input & results)</li> <li>Literature Review</li> </ul>
Transient analysis of power loss density with time-harmonic electromagnetic waves in Debye media
Open the record for dataset details and reuse information.
Nanoparticle Tracking Analysis: A powerful tool for characterizing magnetosome preparations - Supplementary
<p>Nanoparticle Tracking Analysis: A powerful tool for characterizing magnetosome preparations - Supplementary materials</p>
Analysis of the water-power nexus in the North, Eastern and Central African Power Pools
<p>This dataset underpins the report provided to the project "Analysis of the water-power nexus in the North, Eastern and Central African Power Pools"</p> <p>The report provides insights into the balance between energy supply and demand, power generation, total system costs, water consumption and withdrawal as well as carbon dioxide emissions for the North, Eastern and Central African power pools.</p>
Data from: Specimen-based analysis of morphology and the environment in ecologically dominant grasses: the power of the herbarium
Herbaria contain a cumulative sample of the world's flora, assembled by thousands of people over several hundred years. Recent advances in computation, DNA sequencing, and image manipulation have allowed us to capitalize on this resource. Using herbarium material, we conducted a species-level analysis of a major clade in the grass tribe Andropogoneae, which includes the dominant species of the world's grasslands, from the genera Andropogon, Schizachyrium, Hyparrhenia, and other groups. We imaged 188 of the 250 available species of the clade, georeferenced the specimens, and extracted climatic variables for each. Using semiand fully automated image analysis techniques, we extracted spikelet morphological characters and correlated these with environmental variables. We are currently generating chloroplast genome sequences to correct for phylogenetic covariance and here present an analysis of a subset of 81 species, representing the power of this approach. In addition to taxonomic/phylogenetic observations, we find all morphological and ecological characters are homoplasious but variable among clades. For example, sessile spikelet length is positively correlated with awn length when all accessions are considered, but when separated by clade, the relationship is positive for five sub-clades and negative for three others. Macrohair density and pedicel length were negatively correlated with precipitation.
River dams and the stability of bird communities: A hierarchical Bayesian analysis in a tropical hydroelectric power plant
<ol> <li>The effects of anthropogenic disturbance upon the stability of wildlife communities depend on the heterogeneity and connectivity of habitat remnants on multiple scales. The number of hydroelectric dams in biodiversity hotspots (Africa, South America and Asia) is growing rapidly. To establish their environmental impact, it is essential to understand the dynamics of wildlife communities before and following the establishment of dams.</li> <li>We evaluated the impacts of the filling of the Serra do Facão hydroelectric reservoir in the São Marcos river, central Brazil, upon the bird community. Using data from 1,145 surveys across 20 sampling sites over eight years, two years before and six years after the filling of the reservoir, we assessed the resistance, i.e., maintenance close to an equilibrium state during the disturbance, and resilience, i.e., ability to return to the original state following the disturbance, of the bird community. We used spatiotemporal hierarchical Bayesian models to assess the effects of reservoir filling on five community parameters: abundance, richness, phylogenetic diversity, functional diversity and species composition.</li> <li>In the period subsequent to reservoir filling, there was (i) a marked reduction in bird abundance, richness, phylogenetic diversity and functional diversity, and (ii) a reduction in the proportion of forest species, coupled with an increase in the proportion of savanna species. Except for bird abundance, none of the other community attributes returned to their original levels, even after six years. Our findings indicate that Cerrado bird communities have both low resistance and low resilience to habitat loss associated with the establishment of hydroelectric reservoirs.</li> <li> <i>Synthesis and applications.</i> The environmental costs of hydroelectric dams are still underestimated or neglected in Brazil. A new paradigm in the assessment of their environmental impacts is warranted, incorporating (i) models of spatiotemporal variations based on long-term monitoring with surveys initiated before disturbances and (ii) measures of functional and phylogenetic diversity, such that society can understand the costs and benefits of the establishment of new hydroelectric dams and make informed decisions. Biodiversity loss could be minimized by ensuring the preservation and connectivity of alluvial habitats, capable of maintaining the supply of resources and the functional and phylogenetic attributes of bird communities associated with such habitats.</li> </ol>
Analysis data for location- and scale-invariant power transformations
<p>This repository contains various files and folders related to the machine learning experiments in a forthcoming manuscript on location- and scale-invariant power transformations.</p>
Datasets used in the Paper of "Analysis of leading edge protection application on wind turbine performance through energy and power decomposition approaches"
<p>These are the datasets used in the <em>Wind Energy</em> paper "Analysis of leading edge protection application on wind turbine performance through energy and power decomposition approaches." The paper can be accessed <a href="https://onlinelibrary.wiley.com/doi/10.1002/we.2722">here</a>. The computer code used to produce the results in the paper can be found <a href="../records/6321157">here</a>.</p>
Multi-physic analysis of power electronic control parameters in a simulation framework
<p>Dataset for conference paper "Multi-physic analysis of power electronic control parameters in a simulation framework"</p>
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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.