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1,072 results for “Harvestable”
Anonymous Data on Swingers in Germany Harvested on the Web
<p>The data package consists of various files that contain different types of information, mainly focusing on anonymous swingers’ data in various regions:</p> <h2>1. Residents Data (tabular)</h2> <p><strong>Focus</strong>: Demographic and socio-economic data at the county level, focusing on the swinger community. It includes median ages, population<br>densities, and economic factors.<br><strong>Unique Aspects</strong>: Inclusion of demographic details like age groups, employment sectors, and divorce rates, allowing for a deeper socio-economic<br>analysis.<br><strong>Format</strong>: The data are provided in both *.xlsx and *.sav formats, allowing sharing and long-term access to the data.</p> <h2>2. Software</h2> <p>Python scripts used for data conversion and structuring are provided for transparency reasons.</p> <h2>3. Calculation Results Files</h2> <p>Files related to various calculations which had led to the specific design of the data are provided for transparency reasons. They are provided in<br>*.xlsx, *.pdf, and *md format, as is most convenient to adequately reflext the respective content.</p> <p><em><strong>Please refer to the file readme.md for more details.</strong></em></p>
Subsample of the maximum Water Area Extent of Telangana Rainwater Harvesting System from Pléiades DEM
<p>Small Reservoirs maximum water area extent polygones composing the Rainwater Harvesting System over the Telangana state, South-India. Maximum Water Area Extent are elevation isolines selected by hand from Very High Resolution Digital Elevation Model (VHR DEM at 2 meters resolution) derived from pairs of stereoscopic Pléiades images at 50cm resolution (10.5281/zenodo.10403040). The selection is made to find the area that contain both MWAE derived from Sentinel-2 (10.5281/zenodo.10402199) and Landsat archives (Global Surface Water, <span><a href="https://doi.org/10.1038/nature20584" target="_blank" rel="noopener">10.1038/nature20584</a></span>) curated from rivers and big dams (10.5281/zenodo.10402096).</p>
Maximum Water Area Extent of the Rainwater Harvesting System in Telangana from GSW
<p>Small Reservoirs maximum water area extent polygones composing the Rainwater Harvesting System over the Telangana state, South-India. Maximum water area extent (occurence >0) is extracted form the Global Surface Water dataset at 30 meters resolution (doi:10.1038/nature20584) and currated from rivers, canal and dammed reservoirs related surface water areas. The last access to GSW maximum water extent to build this dataset was in November 2022.</p>
Subsample of the maximum Water Area Extent of Telangana Rainwater Harvesting System from Sentinel-2
<p>Small Reservoirs Maximum Water Area Extent polygones (MWAE) composing the Rainwater Harvesting System (RHS) derieved from Sentinel-2 Multispectral data in the Telangana state, South-India. MWAE is extracted from Sentinel-2 cloud free images time serie collected from 2016 to 2021 (last access in 2021) over the area covered by stereoscopic images acquired from Pléiades satellites (DEM available 10.5281/zenodo.10403040). A random forest classification is used with a set of training and validation samples. These samples are Sentinel pixel locations (10 x 10 meters) corresponding to permanent water pixels extracted from Global Surface Water datasets (doi:10.1038/nature20584) and never flooded pixels derived from Height Above Nearest Drainage data-set (10.1016/j.jhydrol.2011.03.051).</p>
Energy Harvesting Using a Nonlinear Resonator with Asymmetric Potential Wells
<p><strong>This repository contains</strong> the results of numerical simulations of a nonlinear bistable system for harvesting energy from ambient vibrating mechanical sources. Detailed model tests were carried out on an inertial energy harvesting system consisting of a piezoelectric beam with additional springs attached. The mathematical model was derived using the bond graph approach. Depending on the spring selection, the shape of the bistable potential wells was modified including the removal of wells’ degeneration. Consequently, the broken mirror symmetry between the potential wells led to additional solutions with corresponding voltage responses. The probability of occurrence for different high voltage/large orbit solutions with changes in potential symmetry was investigated. In particular, the periodicity of different solutions with respect to the harmonic excitation period were studied and compared in terms of the voltage output. The results showed that a large orbit period-6 subharmonic solution could be stabilized while some higher subharmonic solutions disappeared with the increasing asymmetry of potential wells. Changes in frequency ranges were also observed for chaotic solutions.</p>
Relevance of criteria and indicators for sustainable timber harvesting
<p>For well-founded decisions in sustainable timber harvesting, it is important to know the preferences of the different stakeholders. The concept of sustainable timber harvesting is to incorporate economic, social, and environmental criteria in the selection, execution and assessment of harvesting operations. In a previous study, 33 criteria were identified by forest experts as relevant for evaluating sustainability. To assess the importance of these criteria, an online survey was conducted among Austrian stakeholders between April and May 2023, in which 610 people were invited to participate and which resulted in a response rate of 47%.</p> <p>The survey participants were primarily male (94%), with an average age of 47 and an average of 20 years of work experience. The key criteria for sustainable harvesting that were unanimously mentioned by the stakeholders on the basis of a Likert scale, included occupational hazards, residual stand damage, loss of wood quality due to poor work performance, biomass regeneration, water erosion, noise exposure, soil rutting, physical workload, working conditions, and vibration exposure. These identified preferences will inform the development of a decision support model for sustainable timber harvesting using these criteria as input parameters.</p>
Battery-less Environment Sensor Using Thermoelectric Energy Harvesting From Soil-Ambient Air Temperature Differences
<p>The data set contains the data collected from experiments sites in Belgium ( Campus Drie Eiken, University of Antwerp, 51.161° N, 4.408° W) and Iceland ( Forhot, 64.008° N, 21.178° W) for the research and evaluation of a battery-less environment sensor powered by energy harvesting. The device uses the temperature difference between soil and air to produce energy with the help of a Thermoelectric Generator (TEG) and powers a wireless sensor node. The data set includes data collected from 2 phases of the study. One during the initial evaluation phase where we collected soil temperatures at 15 cm and air temperature to evaluate the possibilities of producing energy from the temperature differences. Using these data, we estimated the energy production capacity for both sites. Further, a proof-of-concept device was developed, and its performance was evaluated with field experiments. During this process, we collected the voltage level of the storage unit, i.e, the capacitor, air and soil temperatures and the TEG output voltage. During both phases, the same methods were employed to collect data. The voltage values were measured with a 12-bit ADC and the temperature was measured with 1-Wire temperature sensor. Further, the collected data were transferred to cloud storage in real-time for further analysis and evaluation. </p> <ul> <li><strong>cde_mseasurements_oct2020-nov2020.csv</strong> <ul> <li> Soil temperature and air temperature data from the Campus Drie Eiken at the University of Antwerp, Belgium. The data were collected from 2 Oct 2020 to 17 Nov 2020.</li> </ul> </li> <li><strong>cde_teg_measurements.csv</strong> <ul> <li>Soil temperature, ambient temperature and the open-circuit voltage of TEG from Campus Drie Eiken at the University Antwerp, Belgium from 21 Apr 2021 to 25 Apr May 2021. Also includes the difference calculated between the two temperature values.</li> </ul> </li> <li><strong>cde_energy_simulated.csv</strong> <ul> <li>Energy production capacity estimated using the temperature data collected from Campus Drie Eiken at the University of Antwerp.</li> </ul> </li> <li><strong>aui_measurements_nov-2021.csv</strong> <ul> <li>Soil temperature and air temperature data from the Forhot research site in Iceland for the month of November 2021.</li> </ul> </li> <li><strong>aui_teg_measurements.csv</strong> <ul> <li>Soil temperature, ambient temperature and the open-circuit voltage of TEG collected from the Forhot research site in Iceland. Also includes the difference calculated between the two temperature values. The data were collected from 18 Nov 2021 to 30 Nov 2021</li> </ul> </li> <li><strong>aui_energy_simulated.csv</strong> <ul> <li>Energy production capacity estimated using the temperature data collected from the Forhot research site in Iceland.</li> </ul> </li> <li><strong>cde_capacitor_voltage.csv</strong> <ul> <li>The voltage level of the capacitor used by the battery-less device to buffer the harvested energy. The device was deployed at the Campus Drie Eiken and the data collection was carried out from 1 Mar 2022 to 12 Apr 2022. A 15 mF supercapacitor was used. </li> </ul> </li> </ul>
Sediment Budget for Timber Harvest in a California Coastal Watershed
<p>Dataset for Publication: Sediment Budget for Timber Harvest in a California Coastal Watershed</p> <p> </p>
Online Data for 'The role of wildfires in the interplay of forest carbon stocks and wood harvest in the contiguous United States during the 20th century'
<p>This data file (.xlsx) contains all data used to create table 1, figures 1a-d, figure 2, figure S1, S2, and S5 of the study "The role of wildfires in the interplay of forest carbon stocks and wood harvest in the contiguous United States during the 20th century". Main article is available under: https://doi.org/10.1029/2023GB007813</p>
Whole-tree weight and mensurational data for 13 Quercus montana, 12 Quercus rubra, 12 Acer saccharum, and 21 Betula lenta trees harvested between 2000 and 2022 from Black Rock Forest, Cornwall, NY.
Fifty-eight trees ranging from 1.5 to 54.2 centimeters diameter at breast height from four dominant forest tree species in Black Rock Forest were felled, sectioned, and weighed immediately. Subsections were then dried to determine a dry-to-wet-weight ratio for each tree, which was used to determine total dried aboveground biomass for each tree. Stumps and leaves were included. These data enabled construction of species-specific formulae for each species to predict total tree aboveground dry biomass from dbh measurements of live trees for these four species from around the Black Rock Forest region.
Biomass totals and root biomass (partitioned by percent of total leaf area) for species, tissue type, and functional group for the Arctic LTER experimental 1981 mesic acidic tussock tundra (MAT81) for the 2000 and 2015 harvests, Toolik Field Station, Alaska.
Whole plant biomass totals and root biomass (partitioned by percent of total leaf area) for species, tissue type, and functional group for the Arctic LTER experimental 1981 mesic acidic tussock tundra (MAT81) for the 2000 and 2015 harvests. Because most of the root biomass could not be identified to species in either 2000 or 2015, the calculation of root biomass and element content for roots not identified to species was estimated by the proportion of those species’ contributions to total leaf area. Specific Leaf Area (SLA = leaf area per gram leaf, centimeter squared per gram) values were available from several previous harvests of this experiment; in the present study, we used measurements from the 1995 harvest (Shaver et al. 2001).
Recovery of a tropical stream after a harvest-related chlorine poisoning event
1. Harvest-related poisoning events are common in tropical streams, yet research on stream recovery has largely been limited to temperate streams and generally does not include any measures of ecosystem function, such as leaf breakdown. 2. We assessed recovery of a second-order, high-gradient stream draining the Luquillo Experimental Forest, Puerto Rico, three months after a chlorine-bleach poisoning event. The illegal poisoning of freshwater shrimps for harvest caused massive mortality of shrimps and dramatic changes in those ecosystem properties influenced by shrimps. We determined recovery potential using an established recovery index and assessed actual recovery by examining whether the poisoned reach returned to conditions resembling an undisturbed upstream reference reach.3. Recovery potential was excellent (score=729 out of a possible 729) and can be attributed to nearby sources of organisms for colonization, the mobility of dominant organisms, unimpaired habitat, rapid flushing and processing of chlorine, and location within a national forest.4. Actual recovery was substantial. Comparison of the reference reach with the formerly poisoned reach indicated: (1) complete recovery of xiphocaridid and palaemonid shrimp population abundances, shrimp size distributions, leaf breakdown rates, and abundances of oligochaetes and mayflies on leaves, and (2) only small differences in atyid shrimp abundance and community and ecosystem properties influenced by atyid shrimps (standing stocks of epilithic fine inorganic and organic matter, chlorophyll a, and abundances of chironomids and copepods on leaves). 5. There was no detectable pattern between any measured variables and distance downstream from the poisoning. However, shrimp size-distributions indicated that the observed recovery may represent a source-sink dynamic, in which the poisoned reach acts as a sink which depletes adult shrimp populations from surrounding undisturbed habitats. Thus, the rapid recovery observe
Long-Term Tracing of Indoor Solar Harvesting
<p><strong>Dataset Information</strong></p> <p>This dataset presents long-term term indoor solar harvesting traces and jointly monitored with the ambient conditions. The data is recorded at 6 indoor positions with diverse characteristics at our institute at ETH Zurich in Zurich, Switzerland.</p> <p>The data is collected with a measurement platform [3] consisting of a solar panel (AM-5412) connected to a bq25505 energy harvesting chip that stores the harvested energy in a virtual battery circuit. Two TSL45315 light sensors placed on opposite sides of the solar panel monitor the illuminance level and a BME280 sensor logs ambient conditions like temperature, humidity and air pressure.</p> <p>The dataset contains the measurement of the energy flow at the input and the output of the bq25505 harvesting circuit, as well as the illuminance, temperature, humidity and air pressure measurements of the ambient sensors. The following timestamped data columns are available in the raw measurement format, as well as preprocessed and filtered HDF5 datasets:</p> <ul> <li><code>V_in</code> - Converter input/solar panel output voltage, in volt</li> <li><code>I_in</code> - Converter input/solar panel output current, in ampere</li> <li><code>V_bat</code> - Battery voltage (emulated through circuit), in volt</li> <li><code>I_bat</code> - Net Battery current, in/out flowing current, in ampere</li> <li><code>Ev_left</code> - Illuminance left of solar panel, in lux</li> <li><code>Ev_right</code> - Illuminance left of solar panel, in lux</li> <li><code>P_amb</code> - Ambient air pressure, in pascal</li> <li><code>RH_amb</code> - Ambient relative humidity, unit-less between 0 and 1</li> <li><code>T_amb</code> - Ambient temperature, in centigrade Celsius</li> </ul> <p>The following publication presents and overview of the dataset and more details on the deployment used for data collection. A copy of the abstract is included in this dataset, see the file <code>abstract.pdf</code>.</p> <blockquote> <p>L. Sigrist, A. Gomez, and L. Thiele. "Dataset: Tracing Indoor Solar Harvesting." In Proceedings of the 2nd Workshop on Data Acquisition To Analysis (DATA '19), 2019.</p> </blockquote> <p><strong>Folder Structure and Files</strong></p> <ul> <li><code>processed/</code> - This folder holds the imported, merged and filtered datasets of the power and sensor measurements. The datasets are stored in HDF5 format and split by measurement position <code>posXX</code> and and power and ambient sensor measurements. The files belonging to this folder are contained in archives named <code>yyyy_mm_processed.tar</code>, where <code>yyyy</code> and <code>mm</code> represent the year and month the data was published. A separate file lists the exact content of each archive (see below).</li> <li><code>raw/</code> - This folder holds the raw measurement files recorded with the RocketLogger [1, 2] and using the measurement platform available at [3]. The files belonging to this folder are contained in archives named <code>yyyy_mm_raw.tar</code>, where <code>yyyy</code> and <code>mm</code>represent the year and month the data was published. A separate file lists the exact content of each archive (see below).</li> <li><code>LICENSE</code> - License information for the dataset.</li> <li><code>README.md</code> - The README file containing this information.</li> <li><code>abstract.pdf</code> - A copy of the above mentioned abstract submitted to the DATA '19 Workshop, introducing this dataset and the deployment used to collect it.</li> <li><code>raw_import.ipynb</code> [<a href="https://nbviewer.jupyter.org/urls/zenodo.org/record/3715472/files/raw_import.ipynb">open in nbviewer</a>] - Jupyter Python notebook to import, merge, and filter the raw dataset from the <code>raw/</code> folder. This is the exact code used to generate the processed dataset and store it in the HDF5 format in the <code>processed/</code>folder.</li> <li><code>raw_preview.ipynb</code> [<a href="https://nbviewer.jupyter.org/urls/zenodo.org/record/3715472/files/raw_preview.ipynb">open in nbviewer</a>] - This Jupyter Python notebook imports the raw dataset directly and plots a preview of the full power trace for all measurement positions.</li> <li><code>processing_python.ipynb</code> [<a href="https://nbviewer.jupyter.org/urls/zenodo.org/record/3715472/files/processing_python.ipynb">open in nbviewer</a>] - Jupyter Python notebook demonstrating the import and use of the processed dataset in Python. Calculates column-wise statistics, includes more detailed power plots and the simple energy predictor performance comparison included in the abstract.</li> <li><code>processing_r.ipynb</code> [<a href="https://nbviewer.jupyter.org/urls/zenodo.org/record/3715472/files/processing_r.ipynb">open in nbviewer</a>] - Jupyter R notebook demonstrating the import and use of the processed dataset in R. Calculates column-wise statistics and extracts and plots the energy harvesting conversion efficiency included in the abstract. Furthermore, the harvested power is analyzed as a function of the ambient light level.</li> </ul> <p><strong>Dataset File Lists</strong></p> <p><em>Processed Dataset Files</em></p> <p>The list of the processed datasets included in the <code>yyyy_mm_processed.tar</code> archive is provided in <code>yyyy_mm_processed.files.md</code>. The markdown formatted table lists the name of all files, their size in bytes, as well as the SHA-256 sums.</p> <p><em>Raw Dataset Files</em></p> <p>A list of the raw measurement files included in the <code>yyyy_mm_raw.tar</code> archive(s) is provided in <code>yyyy_mm_raw.files.md</code>. The markdown formatted table lists the name of all files, their size in bytes, as well as the SHA-256 sums.</p> <p><strong>Dataset Revisions</strong></p> <p><em>v1.0 (2019-08-03)</em></p> <p>Initial release.<br> Includes the data collected from 2017-07-27 to 2019-08-01. The dataset archive files related to this revision are <code>2019_08_raw.tar</code> and <code>2019_08_processed.tar</code>.<br> For position <em>pos06</em>, the measurements from 2018-01-06 00:00:00 to 2018-01-10 00:00:00 are filtered (data inconsistency in file <code>indoor1_p27.rld</code>).</p> <p><em>v1.1 (2019-09-09)</em></p> <p>Revision of the processed dataset v1.0 and addition of the final dataset abstract.<br> Updated processing scripts reduce the timestamp drift in the processed dataset, the archive <code>2019_08_processed.tar</code> has been replaced.<br> For position <em>pos06</em>, the measurements from 2018-01-06 16:00:00 to 2018-01-10 00:00:00 are filtered (<code>indoor1_p27.rld</code> data inconsistency).</p> <p><em>v2.0 (2020-03-20)</em></p> <p>Addition of new data.<br> Includes the raw data collected from 2019-08-01 to 2019-03-16. The processed data is updated with full coverage from 2017-07-27 to 2019-03-16. The dataset archive files related to this revision are <code>2020_03_raw.tar</code> and <code>2020_03_processed.tar</code>.</p> <p><strong>Dataset Authors, Copyright and License</strong></p> <ul> <li>Authors: Lukas Sigrist, Andres Gomez, and Lothar Thiele</li> <li>Contact: Lukas Sigrist (<a href="mailto:lukas.sigrist@tik.ee.ethz.ch">lukas.sigrist@tik.ee.ethz.ch</a>)</li> <li>Copyright: (c) 2017-2019, ETH Zurich, Computer Engineering Group</li> <li>License: Creative Commons Attribution 4.0 International License (<a href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</a>)</li> </ul> <p><strong>References</strong></p> <p>[1] L. Sigrist, A. Gomez, R. Lim, S. Lippuner, M. Leubin, and L. Thiele. <em>Measurement and validation of energy harvesting IoT devices.</em> In Design, Automation & Test in Europe Conference & Exhibition (DATE), 2017.</p> <p>[2] ETH Zurich, Computer Engineering Group. RocketLogger Project Website, <a href="https://rocketlogger.ethz.ch/">https://rocketlogger.ethz.ch/</a>.</p> <p>[3] L. Sigrist. <em>Solar Harvesting and Ambient Tracing Platform</em>, 2019. <a href="https://gitlab.ethz.ch/tec/public/employees/sigristl/harvesting_tracing">https://gitlab.ethz.ch/tec/public/employees/sigristl/harvesting_tracing</a></p>
Energy Harvesting Using a Nonlinear Resonator with Asymmetric Potential Wells
<p><strong><span>This repository contains</span></strong><span> the results of numerical simulations of a nonlinear bistable system for harvesting energy from ambient vibrating mechanical sources. Detailed model tests were carried out on an inertial energy harvesting system consisting of a piezoelectric beam with additional springs attached. The mathematical model was derived using the bond graph approach. Depending on the spring selection, the shape of the bistable potential wells was modified including the removal of wells’ degeneration. Consequently, the broken mirror symmetry between the potential wells led to additional solutions with corresponding voltage responses. The probability of occurrence for different high voltage/large orbit solutions with changes in potential symmetry was investigated. In particular, the periodicity of different solutions with respect to the harmonic excitation period were studied and compared in terms of the voltage output. The results showed that a large orbit period-6 subharmonic solution could be stabilized while some higher subharmonic solutions disappeared with the increasing asymmetry of potential wells. Changes in frequency ranges were also observed for chaotic solutions.</span></p>
Piezomagnetic vibration energy harvester with an amplifier
<p>This repository contains results of simulation of the effect of an amplification mechanism in a nonlinear vibration energy harvesting system where a ferromagnetic beam resonator is attached to the vibration source through an additional linear spring with a damper. The beam moves in the nonlinear double-well potential caused by interaction with two magnets. The piezoelectric patches with electrodes attached to the electrical circuit support mechanical energy transduction into electrical power. The results show that the additional spring can improve energy harvesting. By changing its stiffness, we observed various solutions. At the point of the optimal stiffness of the additional spring, the power output is amplified a few times depending on the excitation amplitude.</p>
Double-Versus Triple-Potential Well Energy Harvesters: Dynamics and Power Output
<div>The present datasets and figures focus on the analysis of BEH and TEH systems where the corresponding depth of the potential well and the width of their characteristics are the same. The efficiency of energy harvesting for TEH and BEH systems assuming similar potential parameters is provided. The basic types of multistable energy harvesters are bistable energy harvesting systems (BEH) and tristable energy harvesting systems (TEH). Providing such parameters allows for reliable formulation of conclusions about the efficiency in both types of systems. These energy harvesting systems are based on permanent magnets and a cantilever beam designed to obtain energy from vibrations. Starting from the bond graphs, we derived the nonlinear equations of motion. Then we followed the bifurcations along the increasing frequency for both configurations. To identify the character of particular solutions, we estimated their corresponding phase portraits, Poincare sections, and Lyapunov exponents. The selected solutions are associated with their voltage output. The results in this numerical study show clearly that the bistable potential is more efficient for energy harvesting provided the corresponding excitation amplitude is large enough. However, the tristable one could work better in the limits of low-level and low-frequency excitations. </div> <div> <h2>Series information</h2> <p>Potential characteristics of energy harvesting systems caused by magnetic field of distributed permanent magnets:</p> <ul> <li>Fig4a_b_V_y1.txt</li> <li>Fig4a_r_V_y1.txt</li> </ul> <p>Potential characteristics of energy harvesting systems caused by magnetic field effect as in the previous case and an additional change in the stiffness of the flexible cantilever beam:</p> <ul> <li>Fig4b_b_V_y1.txt</li> <li>Fig4b_r_V_y1.txt</li> </ul> <p>Series of steady states of the system against frequency for two potential wells. </p> <ul> <li>Fig6a_p005.txt</li> <li>Fig6c_p025.txt</li> <li>Fig6e_p05.txt</li> <li>Fig6g_p085.txt</li> </ul> <p>Series of steady states of the system against frequency for three potential wells</p> <ul> <li>Fig6b_p005.txt</li> <li>Fig6d_p025.txt</li> <li>Fig6f_p05.txt</li> <li>Fig6h_p085.txt</li> </ul> <p>The excitation amplitude increases downwards from 0.05 to 0.85 and its values are listed in the corresponding description. The results were obtained for zero initial conditions. ω and x are dimensionless.</p> </div> <div><strong>Figures:</strong></div> <div> <p><a href="https://zenodo.org/api/records/14176152/draft/files/Fig%202.jpg/content" target="_blank" rel="noopener"><br>ig 2.jpg</a> - A graph of bonds representing the dynamics of the tested design solutions of energy harvesting systems.</p> <p><a href="https://zenodo.org/api/records/14176152/draft/files/Fig%203.jpg/content" target="_blank" rel="noopener">Fig 3.jpg</a> - A Lagrangian bond graph, with causality conflicts intentionally introduced.</p> <p><a href="https://zenodo.org/api/records/14176152/draft/files/Fig%204.jpg/content" target="_blank" rel="noopener">Fig 4.jpg</a> - Potential characteristics of energy harvesting systems caused by: (<strong>a</strong>) magnetic field of distributed permanent magnets (Fig. 1); (<strong>b</strong>) magnetic field effect as in previous case and an additional change in stiffness of the flexible cantilever beam (to satisfy equal potential barriers <em>V</em><sub>2</sub> = <em>V</em><sub>3</sub> ) used in further calculations.</p> <p><a href="https://zenodo.org/api/records/14176152/draft/files/Fig%206.jpg/content" target="_blank" rel="noopener">Fig 6.jpg</a> - Bifurcation diagrams (stroboscopic) of steady states of the system against frequency for: (<strong>a</strong>) Two potential wells; (<strong>b</strong>) three potential wells. The excitation amplitude increases downwards from 0.05 to 0.85 and its values are listed in the corresponding sub-figures. The results were obtained for zero initial conditions. <em>ω</em> and <em>x</em> are dimensionless.</p> <p><a href="https://zenodo.org/api/records/14176152/draft/files/Fig%207.jpg/content" target="_blank" rel="noopener">Fig 7.jpg</a> - Exemplary solutions showing the geometrical structures of chaotic phase flows and the corresponding Poincaré cross-sections of a BEH. <em>Dc</em> denotes the corresponding correlation dimension. <em>ω</em>, <em>p</em>, <em>x, </em>and x' are dimensionless.</p> <p><a href="https://zenodo.org/api/records/14176152/draft/files/Fig%208.jpg/content" target="_blank" rel="noopener">Fig 8.jpg</a> - Examples of periodic responses of a BEH system identified for dimensionless mechanical vibration amplitudes: (<strong>a</strong>) <em>p</em> = 0.05; (<strong>b</strong>) <em>p</em> = 0.25; (<strong>c</strong>) <em>p</em> = 0.5; (<strong>d</strong>) <em>p</em> = 0.85. <em>ω</em>, <em>p</em>, <em>x, </em>and<em> x'</em> are dimensionless.</p> <p><a href="https://zenodo.org/api/records/14176152/draft/files/Fig%209.jpg/content" target="_blank" rel="noopener">Fig 9.jpg</a> - Example solutions showing geometric structures of chaotic phase flows and corresponding Poincaré cross-sections, which were identified for a system with three potential wells (TEH). <em>Dc</em> denotes the corresponding correlation dimension. <em>ω</em>, <em>p</em>, <em>x, </em>and<em> x'</em> are dimensionless.</p> <p>Fig 10.jpg – Influence of external load characteristics on periodic induced solutions in a TEH. Trajectory shapes are plotted for selected frequencies ω. <em>ω</em>, <em>p</em>, <em>x, </em>and<em> x'</em> are dimensionless.</p> <p><a href="https://zenodo.org/api/records/14176152/draft/files/Fig%2011.jpg/content" target="_blank" rel="noopener">Fig 11.jpg</a> - Multicolored maps of the values of effective energy harvesting systems (RMS voltage outputs) with the potential: (<strong>a</strong>) two-well (BEH); (<strong>b</strong>) three-well (TEH) for zero initial conditions. <em>ω</em> and <em>p</em> are dimensionless, while <em>U<sub>RMS</sub></em> is expressed in Volts.</p> <p><a href="https://zenodo.org/api/records/14176152/draft/files/Fig%2012.jpg/content" target="_blank" rel="noopener">Fig 12.jpg</a> - Comparison of RMS<strong> </strong>voltage outputs for two and three wells potential systems versus amplitude and frequency. ω is dimensionless while <em>U<sub>RMS</sub></em> is expressed in Volts.</p> </div>
Dataset for publication: "Photosystem II supercomplexes lacking light-harvesting antenna protein LHCB5 and their organization in the thylakoid membrane"
<p>Data repository for "<strong>Photosystem II supercomplexes lacking light-harvesting antenna protein LHCB5 and their organization in the thylakoid membrane</strong>".</p> <p><strong>FIGURE </strong><strong>1</strong><strong><em> </em></strong><strong>Phenotype and photosynthetic characteristics of the <em>lhcb5</em> mutant. </strong>(A) Phenotype of <em>Arabidopsis thaliana</em> wild type (WT) and <em>lhcb5</em> mutant plants grown at controlled conditions for 6 weeks (8 h light/16 h dark cycle; 22/20°C; <br>110 µmol photons m<sup>-2</sup> s<sup>-1</sup>; 60% humidity). (B) Immunoblot analysis of thylakoid membranes of WT and <em>lhcb5</em> mutant plants with antibody directed against LHCB5. (C) Content of light-harvesting proteins LHCB1-6 evaluated relatively to the content of CP43 protein in the WT and the <em>lhcb5</em> mutant. The protein content was determined in isolated thylakoid membranes by liquid chromatography-tandem mass spectrometry (LC-MS/MS). The columns represent means ± SD, individual points show technical replicates. All data passed the normality and equal variance tests and according to Student t-test the datasets of WT and <em>lhcb5</em> were not significantly different (α ≤ 0,05), except for the relative content of LHCB5/CP43. (D) Protein ratios of photosynthesis-related thylakoid membrane proteins of the WT and the <em>lhcb5</em> mutant. The protein content was determined by LC-MS/MS in isolated thylakoid membranes. PSII represents the sum of relative PG intensities of D1, D2, CP43, and CP47 proteins, LHCII - LHCB1–3 proteins, PSI - PSAA and PSAB proteins, LHCI - LHCA1–4 proteins, ATPS - α and β subunits of ATP synthase, and cyt f represents cytochrome f component of cytochrome b<sub>6</sub>f complex. The columns represent means ± SD, individual points show technical replicates. All data passed the normality and equal variance tests and according to Student t-test the datasets of WT and <em>lhcb5</em> are not significantly different (α ≤ 0,05).</p> <p><strong>FIGURE </strong><strong>2</strong><strong><em> </em></strong><strong>Separation and structural characterization of PSII supercomplexes from <em>lhcb5</em> mutant plants. </strong>(A) Separation of pigment–protein complexes from thylakoid membranes from <em>Arabidopsis thaliana</em> WT and <em>lhcb5</em> mutant plants by clear native polyacrylamide gel electrophoresis. Thylakoid membranes were solubilized by n-dodecyl α-D-maltopyranoside (detergent/chlorophyll mass ratio of 10). (B) Electron density maps of characteristic PSII supercomplexes from the separated green gel bands of the <em>lhcb5 </em>mutant designated as C<sub>2</sub>S<sub>2</sub>M<sub>2</sub>, C<sub>2</sub>S<sub>2</sub>M and C<sub>2</sub>SM. Projection maps are fitted by corresponding structural high-resolution models of PSII supercomplexes (Van Bezouwen et al., 2017) without LHCB5. Individual PSII subunits are color-coded according to (E). (C), (D) Comparison of structural models of the PSII C<sub>2</sub>S<sub>2</sub>M<sub>2</sub> supercomplexes from <em>Arabidopsis thaliana</em> WT and the <em>lhcb5</em> mutant. (C) Projection map of the PSII C<sub>2</sub>S<sub>2</sub>M<sub>2</sub> supercomplex from <em>Arabidopsis thaliana</em> wild type (Ilíková et al., 2021) fitted by the high-resolution structure from Van Bezouwen et al. (2017). (D) Overlay of structural models of the PSII C<sub>2</sub>S<sub>2</sub>M<sub>2</sub> supercomplex from <em>Arabidopsis thaliana</em> wild type (surface representation, partially transparent) and the <em>lhcb5</em> mutant shows a specific shift of the S and M LHCII trimers as well as the monomeric antenna LHCB6 (see arrows in the corresponding colors) due to the absence of LHCB5. Individual PSII subunits are color-coded according to (E). (E) Legend of individual PSII subunits, which are color-coded as follows: PSII core complex in green, S and M LHCII trimers in red and blue, respectively, and the monomeric antenna proteins, LHCB4, LHCB5, LHCB6, in yellow, cyan, and dark orange, respectively.</p> <p><strong>FIGURE </strong><strong>3</strong><strong> </strong><strong>Organization of photosystem II in thylakoid membranes of the <em>lhcb5</em> mutant. </strong>(A, B) Examples of electron micrographs of negatively stained thylakoid membrane isolated from the <em>lhcb5</em> mutant with densities corresponding to the PSII core complex. Representative picture of PSII supercomplexes “randomly” organized (A) and organized into 2D semi-crystalline array (B). (C, D, E) Projection maps of PSII megacomplexes obtained using image analysis of PSII particles in thylakoid membranes. Three specific associations of PSII supercomplexes are shown and fitted by the model of PSII supercomplex C<sub>2</sub>S<sub>2</sub>M<sub>2</sub> without LHCB5 (see Figure 2B). Megacomplexes are averaged projections of 1 925 (C), 2 241 (D), and 2 305 (E) particles. (F) Isolated PSII particle from thylakoid membranes with “randomly” organized PSII as an average projection of 3 741 particles fitted by the model of PSII supercomplex C<sub>2</sub>S<sub>2</sub>M<sub>2</sub> without LHCB5 (see Figure 2B). (G) PSII supercomplexes organized into 2D semi-crystalline array as an average projection of 418 sub-areas together with the fitted model of PSII C<sub>2</sub>S<sub>2</sub>M<sub>2</sub> supercomplexes (see Figure 2B). Projection maps of PSII supercomplexes show core complexes in green, S trimers in red, M trimers in blue, LHCB4 in yellow, and LHCB6 in dark orange color.</p> <p><strong>FIGURE </strong><strong>4</strong><strong><em> </em></strong><strong>Distribution of mutual distances between neighboring photosystem II particles in thylakoid membranes of <em>Arabidopsis thaliana</em> WT and the <em>lhcb5 </em>mutant. </strong>The distances between two closest neighboring PSII supercomplexes were analyzed using EM. Histograms are normalized to the maximum.</p> <p><strong>SUPPORTING FIGURE 1 Analysis of chosen photosystem I and II photosynthesis related parameters. </strong>(A) Quantum yield of photochemistry of PSI - Y(I). (B) Quantum yield of photochemistry of PSII - Y(II). (C) Non-photochemical quenching – NPQ. Parameters were measured during actinic light exposure (800 µmol photons m<sup>-2</sup> s<sup>-1</sup>) and dark relaxation using saturating light pulses (300 ms, 10 000 µmol photons m<sup>-2</sup> s<sup>-1</sup>) in WT and <em>lhcb5</em> mutant plants. Results represent mean values ± SD from 4 measurements. Plants were dark-adapted for 30 min before the measurement.</p> <p><strong>SUPPORTING FIGURE 2 Single-particle image analysis and classification of protein complexes from CN−PAGE C<sub>2</sub>S<sub>2</sub>M<sub>2 </sub>band from the Arabidopsis <em>lhcb5</em> mutant (Figure 2A). </strong>Number of averaged projections in given classes are indicated.</p> <p><strong>SUPPORTING FIGURE 3 Single-particle image analysis and classification of protein complexes from CN−PAGE C<sub>2</sub>S<sub>2</sub>M<sub> </sub>band from the Arabidopsis <em>lhcb5</em> mutant (Figure 2A). </strong>Number of averaged projections in given classes are indicated.</p> <p><strong>SUPPORTING FIGURE 4 Single-particle image analysis and classification of protein complexes from CN−PAGE C<sub>2</sub>SM band from the Arabidopsis <em>lhcb5</em> mutant (Figure 2A). </strong>Number of averaged projections in given classes are indicated.</p> <p><strong>SUPPORTING FIGURE 5<em> </em>A histogram of the relative abundance of PSII semi-crystalline arrays </strong><strong>in thylakoid membranes of Arabidopsis </strong><strong><em>lhcb5</em></strong><strong> mutant. </strong>The bars represent the number of electron micrographs where the 2D arrays cover the indicated percentage of the membrane. The histogram was obtained by evaluation of 50 randomly selected images.</p> <p><strong>SUPPORTING TABLE 1</strong> Physiological parameters of Arabidopsis WT and lhcb5 mutant plants.</p> <p><strong>SUPPORTING TABLE 2 </strong>Density of bands corresponding to LHCB5-less PSII supercomplexes evaluated relatively to WT.</p> <p><strong>Figure 1 C-D</strong> - source data for Figure 1. (panels C-D) Documentation of similar physiology of Arabidopsis thaliana wild type (WT), and its mutant with loss of LHCB5 protein subunit (lhcb5): (C) relative content of photosysthesis related proteins in thylakoid membranes of Arabidopsis thaliana lhcb5 genotype normalised to WT determined by LC-MS/MS; (D) relative protein ratios normalised to WT of photosynthesis related thylakoid membrane proteins of Arabidopsis thaliana lhcb5 genotype determined in isolated thylakoid membranes by LC-MS/MS.</p> <p><strong>Figure 4</strong> - source data for Figure 4. Relative distribution of photosystem II (PSII) distances in thylakoid grana membranes of Arabidopsis thaliana wild type (WT) and mutant with missing LHCB5 protein (lhcb5).</p> <p><strong>Supporting figure 1</strong> Source data for supporting figure 1 Photosynthesis related parametres describing PSI and PSII function. (A) quantum yield of photochemistry of PSI (Y(I)) in Arabidopsis thaliana WT and lhcb5 genotype leaves during red acitinic light exposure and dark relaxation; (B) quantum yield of photochemistry of PSII (Y(II)) in Arabidopsis thaliana WT and lhcb5 genotype leaves during red acitinic light exposure and dark relaxation; (C) non-photochemical quenching of Arabidopsis thaliana genotypes: The level of NPQ estimated during red acitinic light exposure and dark relaxation of WT and lhcb5 leaves.</p> <p><strong>Supporting figure 5</strong> Source data for Supplement figure 4. Relative abundance of 2D PSII arrays in the thylakoid membranes of Arabidopsis thaliana lhcb5 mutant from 50 randomly selected images.</p> <p><strong>Supporting table 1 - source data</strong> Source data for supporting table 1. Physiological parameters of witl type (WT) Arabidopsis thaliana and its mutant lacking LHCB5 protein (lhcb5): Repetitions of data measured for each genotypes.</p> <p><strong>Supporting table 2 - source data</strong> Source data for supporting table 2. Density of bands corresponding to LHCB5-less PSII supercomplexes evaluated relatively to WT: Repetitions of data measured for each genotypes.</p> <p><strong>Figure 1B source WB </strong>Source WB picture for FIGURE 1B.</p> <p><strong>Figure 1B source WB, marker </strong>Source WB picture with molecular marker for FIGURE 1B.</p>
HARVEST_200130_MOD_ANIS_4.2_APDL_UNIPD_CONSORTIUM_COUPLED+THERMAL+ELECTRIC.txt
<p>Ansys APDL files for the electric, thermal and thermo-electric properties of two-ply laminates with periodic boundary conditions.</p>
Dataset for "Tuning the thermoelectric properties of boron-doped silicon nanowires integrated in a micro-harvester"
<p>Dataset including all data used for the elaboration of the work "Tuning the thermoelectric properties of boron-doped silicon nanowires integrated in a micro-harvester" published in Advanced Materials Technologies, 2022</p> <p><a href="https://doi.org/10.1002/admt.202101715">https://doi.org/10.1002/admt.202101715</a></p> <p>The files includes:</p> <p>· INDIVIDUAL NW data:</p> <p> - I-V data of each NW at different temperatures</p> <p> - 3w data of each NW at different temperatures<br> - 4 SEM images of the NW, each of them used for assessing one NW parameter<br> - Tip: NW diameter 2<br> - Base: NW diameter 1<br> - Overall: NW length<br> - Tilted view at 45º: Relative NW heigh over substrate</p> <p>· SEEBECK MEASUREMENT data:</p> <p> - Voc versus applied dT data for each substrate temperature<br> - File containig calibration data for all resistors</p> <p>· TEM data:</p> <p>-TEM images of the studied NWs in .dm3 format.</p> <p>· X-RAY FLUORESCENCE data:</p> <p>- Maps containing one energy spectrum per pixel in .hdf files.</p> <p>· TIP-ENHANCED RAMAN SPECTROSCOPY data:</p> <p>- Maps containing one energy spectrum per pixel in a tabulated .txt file.</p> <p>· POWER HARVESTED data:</p> <p>- IV curves of each microthermocouple connection X-Y upon different substrate temperatures in tabulated separated .txt files</p>
Dataset for "A 21 m Operation Range RFID Tag for "Pick to Light" Applications with a Photovoltaic Harvester"
<p>In the paper, a novel Radio-Frequency Identification (RFID) tag for “pick to light” applications is presented. The proposed tag architecture shows the implementation of a novel voltage limiter and a supply voltage (VDD) monitoring circuit to guarantee a correct operation between the tag and the reader for the “pick to light” application. The feasibility to power the tag with different photovoltaic cells is also analyzed, showing the influence of the illuminance level (lx), type of source light (fluorescent, LED or halogen) and type of photovoltaic cell (photodiode or solar cell) on the amount of harvested energy. Measurements show that the photodiodes present a power per unit package area for low illuminance levels (500 lx) of around 0.08 μW/mm<sup>2</sup>, which is slightly higher than the measured one for a solar cell of 0.06 μW/mm<sup>2</sup>. However, solar cells present a more compact design for the same absolute harvested power due to the large number of required photodiodes in parallel. Finally, an RFID tag prototype for “pick to light” applications is implemented, showing an operation range of 3.7 m in fully passive mode. This operation range can be significantly increased to 21 m when the tag is powered by a solar cell with an illuminance level as low as 100 lx and a halogen bulb as source light.</p> <p>This dataset contains some of the data gathered during the experimental work developed and used in the paper.</p>
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