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Design of Multifunctional Composites: New Strategy to Save Energy and Improve Mechanical Performance
<p>dataset on </p> <p>Dynamic Mechanical Analysis, Electro-Mechanical Measurement, Dynamic Light Scattering</p> <p>FTIR spectroscopy, Thermogravimetric analysis, Differential Scanning Calorimetry,</p> <p>Electro-Temperature Measurement, Thermal Image Camera, Water sorption measurement,</p> <p>Transmission Electron Microscopy and Stress Strain</p>
The European Energy Vision 2060 (EU EnVis-2060): Scenario Parametrization
<h3>Description</h3> <p>This repository contains the scenario parametrization for the European Energy Vision 2060 (EU EnVis-2060) scenarios, which have been created by the European research projects Man0EUvRE (funded by the CETPartnership) and iDesignRES (funded by the European Commission). The data is formatted in the IAMC data format (see <a href="https://pyam-iamc.readthedocs.io/en/stable/data.html">https://pyam-iamc.readthedocs.io/en/stable/data.html</a>). </p> <p>The underlying raw data, including all sources and assumptions used for each data point can be found at the Global Energy System Model (GENeSYS-MOD) data repository (see <a href="https://github.com/GENeSYS-MOD/GENeSYS_MOD.data">https://github.com/GENeSYS-MOD/GENeSYS_MOD.data</a>). </p> <p> </p> <p>Alongside the scenario parametrization, there is also included a short report about the qualitative storylines, the workflow, and some key assumptions as part of Deliverable 1.2 of the Man0EUvRE project, as well as the Q2Q (qualitative to quantitative) matrix used in the process of the parametrization.</p> <p> </p> <h3>Changelog</h3> <table> <tbody> <tr> <td>Version</td> <td>Date</td> <td>Changes</td> </tr> <tr> <td>3.1</td> <td>08.09.2025</td> <td> <p>Improvements in district heating, technology costs for wind, PV, and electrolyzers. Updated fossil fuel import prices.</p> </td> </tr> <tr> <td>3.0</td> <td>31.07.2025</td> <td> <p>Further refinement of data set, used for <a href="https://doi.org/10.5281/zenodo.16640689">quantification</a> of the scenarios with GENeSYS-MOD (v1.1.0)</p> <p>Data changes are based on partner feedback and further calibration for the European scenarios.</p> </td> </tr> <tr> <td>2.0.1</td> <td>11.03.2025</td> <td> <p>Added newest version of Q2Q matrix</p> </td> </tr> <tr> <td>2.0</td> <td>28.02.2025</td> <td> <p>Significantly overhauled data set, used for <a href="https://doi.org/10.5281/zenodo.14959447">quantification</a> of the scenarios with GENeSYS-MOD (v1.0.1)</p> </td> </tr> <tr> <td>1.0.2</td> <td>11.09.2024</td> <td>Fixed missing hydropower data in capacities due to an error in the conversion script</td> </tr> <tr> <td>1.0.1</td> <td>07.09.2024</td> <td>Fixed missing data in residual capacities</td> </tr> <tr> <td>1.0</td> <td>06.09.2024</td> <td>Initial Upload</td> </tr> </tbody> </table> <p> </p> <h3>Funding</h3> <p>This research was funded by CETPartnership, the European Partnership under Joint Call 2022 for research proposals, co-funded by the European Commission (GA N°101069750) and with the funding organisations listed on the CETPartnership website.</p>
A Data-driven Analysis of a Cloud Data Center: Statistical Characterization of Workload, Energy and Temperature
<p>A characterization of cloud data center logs, analyzing its workload, energy and thermal characteristics. For more details of the dataset, please read the following paper: <a href="http://hpc.ec.tuwien.ac.at/files/UCC_23_data_center_analysis.pdf">http://hpc.ec.tuwien.ac.at/files/UCC_23_data_center_analysis.pdf.</a></p><p> </p><p>If you use the dataset, please cite the following work:</p><p>Shashikant Ilager, Adel N. Toosi, Mayank Raj Jha, Ivona Brandic, Rajkumar Buyya, "A Data-driven Analysis of a Cloud Data Center: Statistical Characterization of Workload, Energy and Temperature", In Proceedings of the 16th IEEE/ACM International Conference on Utility and Cloud Computing (UCC2023), Messina, Italy, December 4-7, 2023.</p>
Energy System Time Series Suite (ESTSS) - Data Archive
<h2>Energy System Time Series Suite - Data Archive</h2> <p> </p> <p>This archive contains variously sized sets of declustered time series within the context of energy systems. These series demonstrate low discrepancy and high heterogeneity in feature space, resulting in a roughly uniform distribution within this space.</p> <p>For detailed information, please refer to the corresponding GitHub project:<br><a href="https://github.com/s-guenther/estss/">https://github.com/s-guenther/estss/</a></p> <p>For associated research, see<br><a href="https://doi.org/10.1186/s42162-024-00304-8">https://doi.org/10.1186/s42162-024-00304-8</a></p> <p>Data is provided in .csv format. The GitHub project includes a Python function to load this data as a dictionary of pandas data frames.</p> <p>Should you utilize this data, kindly also cite the associated research paper. For any queries, please feel free to reach out to us through GitHub or the contact details provided at the end of this readme file.</p> <p> </p> <h3>Folder Content</h3> <ul> <li>`ts_*.csv`: Contains declustered load profile time series in tabular format. <ul> <li>Size: `(n+1) x (m+1)`, with `n` representing time steps (1000 per series) and `m` the number of series.</li> <li>Includes a header row and index column. Headers indicate series id, and the index column numbers each time step, starting from `0`.</li> <li>The first half of the series `(m/2)` consistently display a constant sign (negative). They are sequentially numbered from 0.</li> <li>The second half `(m/2)` display varying signs. Numbering starts from `1,000,000`.</li> </ul> </li> <li>`features_*.csv`: Tabulates features corresponding to the time series. <ul> <li>Size: `(m+1) x (f+1)`, where `m` is the number of time series and `f` is the number of features</li> <li>Includes a header row and index column. Indexes represent time series id (matching `ts_*.csv` headers), and headers name the features.</li> </ul> </li> <li>`norm_space_*.csv`: Shows feature vectors in normalized feature space where time series are declustered. Provided for completeness; typically not needed by users. <ul> <li>Size: `(m+1) x (g+1)`, where `m` is the number of timer series and `g` is the number of selected features space features. (a subset of `f` from `features_*.csv`).</li> <li>Format matches `features_*.csv`.</li> </ul> </li> <li>`info_*.csv`: Maps declustered datasets to the manifolded dataset. Provided for completeness; typically not needed by users. <ul> <li>Size: `(m+1) x 2`, with `m` as series count. Columns contain manifolded set time series ids.</li> <li>Includes an index column and a header. The index holds the remapped id of declustered series. Header `0` is non-significant.</li> </ul> </li> </ul> <p>Each `ts_*.csv`, `features_*.csv`, `norm_space_*.csv`, and `info_*.csv` file comes in four versions to accommodate various set sizes:</p> <ul> <li>`*_4096.csv`</li> <li>`*_1024.csv`</li> <li>`*_256.csv`</li> <li>`*_64.csv`</li> </ul> <p>These represent sets with 4096, 1024, 256, and 64 time series, respectively,offering different densities in feature space population. The objective is to balance computational load and resolution for individual research needs.</p> <p> </p> <h3>Contact</h3> <p>ESTSS - Energy System Time Series Suite<br>Copyright (C) 2023<br>Sebastian Günther<br>sebastian.guenther@ifes.uni-hannover.de</p> <p>Leibniz Universität Hannover<br>Institut für Elektrische Energiesysteme<br>Fachgebiet für Elektrische Energiespeichersysteme</p> <p>Leibniz University Hannover<br>Institute of Electric Power Systems<br>Electric Energy Storage Systems Section</p> <p><a href="https://www.ifes.uni-hannover.de/ees.html">https://www.ifes.uni-hannover.de/ees.html</a></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>
Simulated heating energy demand for two residential neighbourhoods
<p>The large-scale and comprehensive artificial dataset introduced in this research reflects the energy demands of two neighbourhoods and with some reasonable limitations mimics monitoring campaigns otherwise collected on-site from buildings in use. The monitoring campaigns are created using white-box simulation models for single-family houses representing typical neighbourhoods in Flanders. The datasets are generated using Dymola and the IDEAS package embedded in TEASER. Each house varies in geometry, size, envelope properties, occupancy schedules, and installed gas heating systems. In this research, two datasets are created, one reflecting the properties of a low-performing building stock dating before the introduction of the EPBD (2006), and the other reflecting properties of a well-performing stock built after 2006. The envelope properties for older houses are allocated using EPC data grouped in four construction periods, while for newly built houses the properties are based on EPB reports, both were collected in Flanders. The datasets include heavy-weight houses in a detached, semi-detached, or terraced typology. Furthermore, the houses are simulated as one or two-zone buildings, depending on the number of floors which range from one to three floors. In the simulations, a natural infiltration model is implemented as well as a stochastic occupant behaviour model mimicking gains from occupants and appliances. Due to the complexity of the large-scale simulation, the heating system is post-processed in a data-driven approach and the heat source for both datasets are gas-fired heating systems. In total six system configurations are considered including condensing and non-condensing boilers with three types of domestic hot water (DHW) sub-systems (no integrated DHW, direct and with a storage tank). For all configurations, a variable production efficiency is considered dependent on the load ratio. The urban-scale simulation is carried out at a 10-minute frequency for the weather data assuming the location of Heverlee (Belgium) in the year 2016.<br>The original purpose of this dataset was the development of statistical tools for the assessment of the heat loss coefficient of the building fabric. However, the generated artificial datasets provide a large spectre of usually difficult-to-measure inputs suitable to assess the importance of different components in the overall energy balance. Even though the original work looked into individual building behaviour, the datasets can be also used from an urban perspective for energy planning purposes.</p>
Agrisolar Food, Energy, and Water and economic Lifecycle Scenario (FEWLS) Tool Data
<p>Input data and baseline outputs for the Agrisolar Food, Energy, Water, and economic Lifecycle Scenario (FEWLS) Tool. Note that corresponding code is linked in the attached Github doi (https://doi.org/10.5281/zenodo.10023281). </p> <p>The FEWLS tool was developed and used in the recently submitted research article, <em>Food-energy-water and economic outcomes of agrisolar co-location in irrigated regions</em>. In general, this code takes in a ground-mounted solar PV shape file (with some auxiliary information) and generates user set lifespan predictions for food (Calorie), energy (GWh), water (m3), and economic (USD) effects due to offsetting agricultural land with solar PV energy generation. </p>
Lalas_et_al_2021_Energies_DATASET
<p>This dataset contains the underlying data for the following publication: Lalas, D., Gakis, N., Mirasgedis, S., Georgopoulou, E., Sarafidis, Y., & Doukas, H. (2021). Energy and GHG Emissions Aspects of the COVID Impact in Greece. Energies, 14(7), 1955. <a href="https://doi.org/10.3390/en14071955">https://doi.org/10.3390/en14071955</a>.</p>
Sensor deployment to support the integrated energy management system in residential buildings in ReCO2ST LoRa Dataset
<p>LoRa Radio Testing Datasets for preliminary performance tests. These datasets were taken in order to ensure that the LoRa radios were capable of transmitting through concrete and testing various preamble settings of the radio. As per the paper,</p> <p>"Although these testing methodologies were indicative but not exact or perfect, to test in a manner that was qualitative would have been both costly and beyond the scope of the project." </p> <p>These tests were to help us verify feasibility of the chosen LoRa Radio</p>
Dataset supporting the paper "Superconducting Scanning Tunneling Microscope Tip to Reveal Sub-millielectronvolt Magnetic Energy Variations on Surfaces. J. Phys. Chem Lett. 12, 2983 (2021)"
<p>Dataset corresponding to theoretical calculations in the supporting information of the paper "Superconducting Scanning Tunneling Microscope Tip to Reveal Sub-millielectronvolt Magnetic Energy Variations on Surfaces" J. Phys. Chem Lett. 12, 2983 (2021), <a href="https://doi.org/10.1021/acs.jpclett.1c00328">https://doi.org/10.1021/acs.jpclett.1c00328</a></p> <p>List of files:</p> <p>Several folders corresponding to the figures of the supporting information. They contain:</p> <ul> <li>.siesta files: STM images in WsXM format (http://www.wsxm.eu/) simulated using STMpw (<a href="https://doi.org/10.5281/zenodo.3581159">https://doi.org/10.5281/zenodo.3581159</a>).</li> <li>CONTCAR files: relaxed structures in VASP format. They can be visualized with VESTA (<a href="https://jp-minerals.org/vesta/en/">https://jp-minerals.org/vesta/en/</a>).</li> <li>.agr files: grace files (<a href="https://plasma-gate.weizmann.ac.il/Grace/">https://plasma-gate.weizmann.ac.il/Grace/</a>).</li> </ul>
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>
Experimental HIL datasets of a heat pump controlled by MPC or rule-based controllers for energy flexibility
<p>Hardware-in-the-loop experiment performed in the SEILAB laboratory of IREC<br> Air-to-water heat pump including a DHW tank for production of SH and DHW, which external unit is placed in a climate chamber that reproduces the desired weather conditions dynamically<br> Control is MPC or rule-based, both triggered either by a signal of price or CO2 intensity from the grid (4 series of experiments)<br> Connected to virtual residential building (flat) in Spanish Mediterranean climate<br> More information:<br> https://doi.org/10.1109/ACCESS.2019.2903084</p>
MSP430FR5969 Basic Block Worst Case Energy Consumption (WCEC) and Worst Case Execution Time (WCET) dataset
<p>This dataset contains around 30 000 basic blocks whose energy consumption and execution time have been measured in isolation on the MSP430FR5969 microcontroller, at 1MHz. Basic blocks were executed in a worst case scenario regarding the MSP430 FRAM cache and CPU pipeline. The dataset creation process is described thoroughly in [1].</p> <h2>Folder structure</h2> <p>This dataset is composed of the following files:</p> <ul> <li><code>basic_blocks.tar.xz</code> contains all basic blocks (BB) used in the dataset, in a custom JSON format,</li> <li><code>data.csv</code>/<code>data.xlsx</code> contains the measured energy consumption and execution time for each basic block</li> </ul> <p>We first details how the <code>basic_blocks.tar.gz</code> archive is organized, and then present the CSV/XSLX spreadsheet format.</p> <h2>Basic Blocks</h2> <p>We extracted the basic blocks from a subset of programs of the <em>AnghaBench</em> benchmark suite [2]. The <code>basic_blocks.tar.gz</code> archive consist of the extracted basic blocks organized as <code>json</code> files. Each <code>json</code> file correspond to a C source file from <em>AnghaBench</em>, and is given a unique identifier. An example json (<code>137.json</code>) is available here:</p> <div> <pre><code>{ "extr_pfctl_altq.c_pfctl_altq_init": [ # Basic block 1 [ # Instruction 1 of BB1 [ "MOV.W", "#queue_map", "R13" ], # Instruction 2 of BB1 [ "MOV.B", "#0", "R14" ], # Instruction 3 of BB1 [ "CALL", "#hcreate_r", null ] ], # Basic block 2 [ .... ] ] }</code></pre> </div> <p>The json contains a dict with only one key pointing to an array of basic blocks. This key is the name of the original C source file in <em>AnghaBench</em> from which the basic blocks were extracted (here <a href="https://github.com/brenocfg/AnghaBench/blob/master/freebsd/sbin/pfctl/extr_pfctl_altq.c_pfctl_altq_init.c">extr_pfctl_altq.c_pfctl_altq_init.c</a>). The array contains severals basic blocks, which are represented as an array of instructions, which are themselves represented as an array <code>[OPCODE, OPERAND1, OPERAND2]</code>.</p> <p>Then, each basic block can be identified uniquely using two ids : its <em>file id</em> and its <em>offset</em> in the file (<code>id=<file id>_<offset></code>). In our example, the basic block 1 can be identified by the json file id (<code>137</code>) and its offset in the file (<code>0</code>). Its ID is <code>137_0</code>. This ID is used to make the mapping between a basic block and its energy consumption/execution time, with the <code>data.csv</code>/<code>data.xlsx</code> spreadsheet.</p> <h2>Energy Consumption and Execution Time</h2> <p>Energy consumption and execution time data are stored in the <code>data.csv</code> file. Here is the extract of the csv file corresponding to the basic block <code>137_0</code>. The spreadsheet format is described below.</p> <pre><code>bb_id;nb_inst;max_energy;max_time;avg_time;avg_energy;energy_per_inst;nb_samples;unroll_factor 137_0;3;8.77;7.08;7.04;8.21;2.92;40;50</code></pre> <p>Spreadsheet format :</p> <ul> <li><code>bb_id</code>: the unique identifier of a basic block (cf. <a>Basic Blocks</a>)</li> <li><code>nb_inst</code>: the number of instructions in the basic block</li> <li><code>max_energy</code>: the maximum energy comsumption (in nJ) measured during the experiment</li> <li><code>max_time</code>: the maximum execution time (in us) measured during the experiment</li> <li><code>avg_time</code>: the average execution time (in us) measured during the experiment</li> <li><code>avg_energy</code>: the average energy comsumption (in nJ) measured during the experiment</li> <li><code>energy_per_inst</code>: the average energy consumption per instruction (correspond to <code>avg_energy</code>/<code>nb_inst</code>)</li> <li><code>nb_samples</code>: how much time the basic block energy consumption/execution time has been measured</li> <li><code>unroll_factor</code>: how much time the basic block was unrolled (cf Basic Block Unrolling)</li> </ul> <h3>Basic Block Unrolling</h3> <p>To measure the energy consumption and execution time of the msp430, we need to be able to handle the scale difference between the measurement tool and the basic block execution time. This is achieved by duplicating the basic block multiple times while making sure to keep the worst-case memory layout as explained in the paper. The number of time the basic block has been duplicated is called the <code>unroll_factor</code>.</p> <p>Values of energy and time are always given per basic block, so they have already been divided by the unroll factor.</p> <p> </p> <h2>Dataset description</h2> <h3>Features</h3> <p>The selected features after PCA analysis for both energy and time model are listed here: <strong> MOV.W_Rn_Rn, MOV.W_X(Rn)_X(Rn), CALL, MOV.B_#N_Rn, ADD.W_Rn_Rn, MOV.W_@Rn_Rn, MOV.W_X(Rn)_Rn, ADD.W_#N_Rn, PUSHM.W_#N_Rn, MOV.W_X(Rn)_ADDR, CMP.W_#N_Rn, MOV.W_&ADDR_X(Rn), MOV.W_Rn_X(Rn), BIS.W_Rn_Rn, RLAM.W_#N_Rn, SUB.W_#N_Rn, MOV.W_&ADDR_Rn, MOV.W_#N_X(Rn), CMP.W_Rn_Rn, BIT.W_ADDR_Rn, MOV.W_@Rn_X(Rn), ADD.W_#N_X(Rn), MOV.W_#N_Rn, AND.W_Rn_Rn, MOV.W_Rn_ADDR, SUB.W_Rn_Rn, MOV.W_ADDR_Rn, MOV.W_X(Rn)_&ADDR, MOV.W_ADDR_ADDR, JMP, ADD_#N_Rn, BIS.W_Rn_X(Rn), SUB_Rn_Rn, MOV.W_ADDR_X(Rn), ADDC_#N_X(Rn), MOV.B_Rn_Rn, CMP.W_X(Rn)_X(Rn), ADD_Rn_Rn, nb_inst, INV.W_Rn_, NOP__, ADD.W_X(Rn)_X(Rn), ADD.W_Rn_X(Rn), MOV.B_@Rn_Rn, BIS.W_X(Rn)_X(Rn), MOV.B_#N_X(Rn), MOV.W_#N_ADDR, AND.W_#N_ADDR, SUBC_X(Rn)_X(Rn), BIS.W_#N_X(Rn), SUB.W_X(Rn)_X(Rn), AND.B_#N_Rn, ADD_X(Rn)_X(Rn), MOV.W_@Rn_ADDR, MOV.W_&ADDR_ADDR, ADDC_Rn_Rn, AND.W_#N_X(Rn), SUB_#N_Rn, RRUM.W_#N_Rn, AND_ADDR_Rn, CMP.W_X(Rn)_ADDR, MOV.B_#N_ADDR, ADD.W_#N_ADDR, CMP.B_#N_Rn, SXT_Rn_, XOR.W_Rn_Rn, CMP.W_@Rn_Rn, ADD.W_@Rn_Rn, ADD.W_X(Rn)_Rn, AND.W_Rn_X(Rn), CMP.B_Rn_Rn, AND.W_X(Rn)_X(Rn), BIC.W_#N_Rn, BIS.W_#N_Rn, AND.B_#N_X(Rn), MOV.B_X(Rn)_X(Rn), AND.W_@Rn_Rn, MOV.W_#N_&ADDR, BIS.W_Rn_ADDR, SUB.W_X(Rn)_Rn, SUB.W_Rn_X(Rn), SUB_X(Rn)_X(Rn), MOV.B_@Rn_X(Rn), CMP.W_@Rn_X(Rn), ADD.W_X(Rn)_ADDR, CMP.W_Rn_X(Rn), BIS.W_@Rn_X(Rn), CMP.B_X(Rn)_X(Rn), RRC.W_Rn_, MOV.W_@Rn_&ADDR, CMP.W_#N_X(Rn), ADDC_X(Rn)_Rn, CMP.W_X(Rn)_Rn, BIS.W_X(Rn)_Rn, SUB_X(Rn)_Rn, MOV.B_X(Rn)_Rn, MOV.W_ADDR_&ADDR, AND.W_#N_Rn, RLA.W_Rn_, INV.W_X(Rn)_, XOR.W_#N_Rn, SUB.W_Rn_ADDR, BIC.W_#N_X(Rn), MOV.B_X(Rn)_ADDR, ADD_#N_X(Rn), SUB_Rn_X(Rn), MOV.B_&ADDR_Rn, MOV.W_Rn_&ADDR, ADD_X(Rn)_Rn, AND.W_X(Rn)_Rn, PUSHM.A_#N_Rn, RRAM.W_#N_Rn, AND.W_@Rn_X(Rn), BIS.B_Rn_X(Rn), SUB.W_@Rn_Rn, CLRC__, CMP.W_#N_ADDR, XOR.W_Rn_X(Rn), MOV.B_Rn_ADDR, CMP.B_X(Rn)_Rn, BIS.B_Rn_Rn, BIS.W_X(Rn)_ADDR, CMP.B_#N_X(Rn), CMP.W_Rn_ADDR, XOR.W_X(Rn)_Rn, MOV.B_Rn_X(Rn), ADD.B_#N_Rn</strong></p> <h3>Code</h3> <p>The trained machine learning model, tests, and local explanation code can be generated and found here: <a href="https://colab.research.google.com/drive/1zaCIkbKDRDtwzhY-r3Iff0OpOSkOUibW?usp=sharing" target="_blank" rel="noopener">WORTEX Machine learning code</a> </p> <h2>Acknowledgment</h2> <p>This work has received a French government support granted to the Labex CominLabs excellence laboratory and managed by the National Research Agency in the “Investing for the Future” program under reference ANR-10-LABX-07-01</p> <h2>Licensing</h2> <p>Copyright 2024 Hector Chabot Copyright 2024 Abderaouf Nassim Amalou Copyright 2024 Hugo Reymond Copyright 2024 Isabelle Puaut</p> <p>Licensed under the Creative Commons Attribution 4.0 International License</p> <h2>References</h2> <p>[1] Reymond, H., Amalou, A. N., Puaut, I. “WORTEX: Worst-Case Execution Time and Energy Estimation in Low-Power Microprocessors using Explainable ML” in 22nd International Workshop on Worst-Case Execution Time Analysis (WCET 2024) </p> <p>[2] Da Silva, Anderson Faustino, et al. “Anghabench: A suite with one million compilable C benchmarks for code-size reduction.” 2021 IEEE/ACM International Symposium on Code Generation and Optimization (CGO). IEEE, 2021.</p>
Half-kilowatt high energy third harmonic conversion to 50 J @ 10 Hz at 343 nm [dataset]
<p>Dataset relevant to the publication "Half-kilowatt high energy third harmonic conversion to 50 J @ 10 Hz at 343 nm" in HPLSE</p>
Dataset SUC1/S1-S4: Cyberattack scenarios on DER energy management and control
<p><span>This sandboxing use case focuses on advanced energy management and control applications for DERs, which are essential for optimizing their operation and achieving key objectives. These objectives include tracking the awarded power generation according to energy market clearing processes, avoiding intense power imbalances caused by intermittent weather-based RES, and increasing the profitability of DER owners. By leveraging real-time control strategies, the management system can dynamically adjust flexible DER operations to improve the overall response of aggregated DERs, based on both RES and Battery Storage Systems (BSS). Since this use case requires active control of an BSS and its operation can be severely affected in case of a cyber-attack, it is crucial to examine this scenario in a controlled and non-invasive environment enabled by a sandboxing testing environment (the KIOS CoE Cyber-physical sandbox) to avoid any disturbance to the actual power infrastructure.</span></p> <p><span>This dataset collection is related to the four comprehensive cyber attack scenarios which target the Modbus TCP communication ptotocol used for transmitting m<span>easurements from smart meters of RESs and DERs to DER controller, as well as s<span>et-points sent to flexible DER inverters from DER controller, aiming to </span></span>affect the proper operation of the DER energy managemnet and control function. The four scenarios are:</span></p> <p><span><span>SUC1/S1 - MITM with FDI cyber-attack on wind farm measurements<br></span></span><span>SUC1/S2 - MITM with FDI cyber-attack on BSS measurements</span></p> <p><span>SUC1/S3 - MITM with FDI cyber-attack on BSS set-points</span></p> <p><span>SUC1/S4 - MITM with DoS cyber-attack</span></p> <p><span><span>The dataset includes electrical measurements of the active power generation of the wind farm (attacked signal and actual state) and the BSS operation, as well as the set-point generated by the DER controller. The dataset is provided in the form of time-series measurements available as MATLAB (.mat) and CSV files. The measurements were recorded with a 5-second time resolution by the DER controller. <span> </span></span></span></p>
Equation-of-Motion Coupled-Cluster Theory based on the 4-component Dirac–Coulomb(–Gaunt) Hamiltonian. Energies for single electron detachment, attachment and electronically excited states: Figures
<p>This entry contains the figures included in the paper titled "Equation-of-Motion Coupled-Cluster Theory based on the 4-component Dirac--Coulomb(--Gaunt) Hamiltonian. Energies for single electron detachment, attachment and electronically excited states", by Avijit Shee, Trond Saue, Lucas Visscher and Andre Severo Pereira Gomes.</p> <p>It accompanies the dataset found at the DOI: 10.5281/zenodo.1320320</p> <p>There are three figures that use the (original) png files included in <a href="https://zenodo.org/api/files/7bda2e2b-ac69-41aa-a21e-821e88bfb973/original-figures.tar.bz2">original-figures.tar.bz2 </a>:</p> <p>figure 1: Potential energy curves of the spin-orbit split X<sup>2</sup>Π and A<sup>2</sup>Π states of the XO molecules, obtained with EOM-IP and the <sup>2</sup>DCG<sup>M</sup> Hamiltonian.</p> <p>figure 2: Internuclear distances (in Angstrom), harmonic vibrational frequencies (in cm<sup>−1</sup>) and the vertical Ω = 3/2 − 1/2 energy difference (in eV) for the X<sup>2</sup>Π and A<sup>2</sup>Π states of the XO molecules, obtained with EOM-IP and the <sup>2</sup>DCG<sup>M</sup> Hamiltonian.</p> <p>figure 3: SO-ZORA/QZ4P/Hartree-Fock (ADF) spinor magnetization plots (isosurfaces at 0.03 a.u.) and energies (in Eh) for the valence spinors of the XO<sup>−</sup> species (from left to right: X = Cl, Br, I, At, Ts).</p>
Predictive simulations of ionization energies of solvated halide ions with relativistic embedded Equation of Motion Coupled-Cluster Theory: Figures
<p>This entry contains the sources for the figures included in the body of the paper titled "Predictive simulations of ionization energies of solvated halide ions with relativistic embedded Equation of Motion Coupled-Cluster Theory", by Yassine Bouchafra, Avijit Shee, Florent Réal, Valérie Vallet and André Severo Pereira Gomes, as well as those found in the supplementary information.</p> <p>It accompanies the dataset found at the DOI: 10.5281/zenodo.1477004</p> <p> </p> <p> </p>
Data to "Object visibility, not energy expenditure, accounts for spatial biases in human grasp selection"
<p>This record contains experimental and analysis scripts (written in Matlab) as well as raw and processed data to reproduce the results shown in:</p> <p><strong>Maiello, G</strong>.<sup> †</sup>, Paulun, V. C.<sup> †</sup>, Klein, L. K. , & Fleming, R. W. (2018) Object visibility, not energy expenditure, accounts for spatial biases in human grasp selection. <em>i-Perception,10</em>(1), 1–5. doi:10.1177/2041669519827608.</p> <p><sup>†</sup>co-first authors</p>
Projected fresh water use from the European energy sector on NUTS2 level by 2050 following EU Energy Reference Scenario 2016
<p>The dataset contains projections of fresh water withdrawal and consumption from the European energy sector on NUTS2 level by 2050 following EU Energy Reference Scenario 2016.</p> <p>The energy sector in this scope includes energy production (production of coal, oil and gas) and energy transformation in oil refineries and power plants (nuclear, solid fuels, oil, gas, biomass and geothermal).</p> <p>The information in provided on NUTS 2 level following the NUTS2 2013 definition.</p> <p>The dataset is explained in more detail in the report <a href="https://ec.europa.eu/jrc/en/publication/projected-fresh-water-use-european-energy-sector">Projected fresh water use from the European energy sector</a>.</p>
Energy Levels of ThII in the range of 7 to 10 eV
<p>Data is presented on 166 previously unknown energy levels of Th+ ions, observed using resonant two-step laser excitation of trapped ions. The levels are of even parity and within the energy range from 7.8 to 9.8 eV. Observed lines are listed.<br> The observed levels can be relevant for the excitation or decay of the Th-229m isomeric nuclear state which lies in this energy range.</p>
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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