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7,378 results for “case”
Evaluation of stormwater urban ecological infrastructure in Phoenix, Arizona (USA): a case study of a small-scale bioretention basin system
In 2017, Arizona State University finished construction on a pedestrian mall central to its Tempe campus, which included a small-scale bioretention basin system for stormwater management. This study analyzed the flood control and water quality improvement performance of the small-scale bioretention basin system in the Phoenix Metropolitan Area, AZ USA. Flood control efficacy was quantified by calculating discharge from the basin system using water level loggers and measuring soil moisture levels using soil moisture probes. Stormwater runoff samples were collected for twenty-one storm events and analyzed for nitrogen and phosphorus constituent concentrations. Nutrient concentrations at the system inflow and outflow were used to determine percent change in concentration. Water quality improvement performance was compared to results from previous studies on bioretention basin system performance. These data were used to create relevant graphical figures. Results were obtained by performing statistical analysis calculations on the data measurements. The results indicated that the bioretention basin system performed adequately for flood control and water quality improvement, supporting the use of stormwater urban infrastructure systems in arid and semi-arid climates. Further research can reveal how these systems may perform during more severe storm events and offer improvements for future designs.
OpenFOAM cases of the paper "Development and validation of an open-source CFD model for the efficiency assessment of data centers"
<p>This dataset contains the<em> underling data</em> for the paper "Development and validation of an open-source CFD model for the efficiency assessment of data centers”, submitted for the consideration and open review in Open Research Europe (ORE).</p> <p><strong>Validation1.tar.xz:</strong> OpenFOAM files and scripts for the simulation of flow and thermal structures in an enclosed environment (Wang and Chen, 2009).</p> <p><em>Wang, Miao; Chen, Qingyan (2009). Assessment of Various Turbulence Models for Transitional Flows in an Enclosed Environment (RP-1271). HVAC&R Research, 15(6), 1099–1119. doi:10.1080/10789669.2009.10390881</em></p> <p><strong>Validation2-kOmegaSSTModel.tar.xz:</strong> OpenFOAM files and scripts for the simulation of forced convection in a room (Zhang et al. 2007) using k-omega SST turbulence model. </p> <p><em>Zhao Zhang, Wei Zhang, Zhiqiang John Zhai & Qingyan Yan Chen (2007) Evaluation of Various Turbulence Models in Predicting Airflow and Turbulence in Enclosed Environments by CFD: Part 2—Comparison with Experimental Data from Literature, HVAC&R Research, 13:6, 871-886, DOI: 10.1080/10789669.2007.10391460</em></p> <p><strong>Validation2-RNGkEpsilonModel.tar.xz:</strong> OpenFOAM files and scripts for the simulation of forced convection in a room (Zhang et al. 2007) using RNG k-epsilon turbulence model. </p> <p><em>Zhao Zhang, Wei Zhang, Zhiqiang John Zhai & Qingyan Yan Chen (2007) Evaluation of Various Turbulence Models in Predicting Airflow and Turbulence in Enclosed Environments by CFD: Part 2—Comparison with Experimental Data from Literature, HVAC&R Research, 13:6, 871-886, DOI: 10.1080/10789669.2007.10391460</em></p> <p><strong>Validation3.tar.xz:</strong> OpenFOAM files and scripts for the simulation of strong natural convection in a model fire room (Murakami et al. 1995).</p> <p><em>Murakami, S., S. Kato, and R. Yoshie. 1995. Measurement of turbulence statistics in a model fire room by LDV. ASHRAE Transactions 101(2):287–301.</em></p> <p><strong>Validation4.tar.xz:</strong> OpenFOAM files and scripts for the simulation of thermal distribution in an open-aisle data center (Abdelmaksoud et al. 2013).</p> <p><em>W.A. Abdelmaksoud, T.Q. Dang, H. Ezzat Khalifa, R.R. Schmidt Improved computational fluid dynamics model for open-aisle air-cooled data center simulations J. Electron. Packag., 135 (2013), pp. 030901-30913</em></p> <p><strong>Results_Validation1.tar.xz:</strong> Simulation results of the Validation case 1.</p> <p><strong>Results_Validation2.tar.xz:</strong> Simulation results of the Validation case 2.</p> <p><strong>Results_Validation3.tar.xz:</strong> Simulation results of the Validation case 3.</p> <p><strong>Results_Validation4.tar.xz:</strong> Simulation results of the Validation case 4.</p> <p><strong>layout.csv:</strong> Input file for the Validation case 4.</p>
Image data of co-localization of IgG and HEV ORF2 protein in a case of hepatitis E-associated kidney disease
<p><span>Image data for a co-localization study of IgG with HEV ORF2 protein in a </span><span>de novo immune complex-mediated glomerulonephritis (GN) case in</span><span> a kidney transplant recipient </span><span>with chronic hepatitis E (Leblond and Helmchen, et al. 2024).<span> </span>Immunofluorescence images are provided for 25 glomeruli at low magnification (20x, 0.227 micron/pixel) and for 16 glomeruli at high magnification (100x, 0.0454 micron/pixel). For each example glomeruli the green channel represents IgG antibody staining with FITC, and the magenta channel represent anti-HEV ORF2 staining using Alexa Fluor 546.</span></p> <p><span>Methods: </span></p> <p><span>Mouse monoclonal antibody clone 1E6 against the HEV ORF2 protein was incubated for 1h at a dilution of 1:125 followed by a mix of Alexa Fluor 546-conjugated goat anti-mouse antibody (Invitrogen BV, A11018) and FITC-conjugated Rabbit anti-Human IgG (Gamma chain, Diagnostic Biosystem, F008) for 1hat a dilution of 1:50. Following automated staining, the slides were hand -washed in distilled H<sub>2</sub>O. Tissue was covered with Vectashield® Antifade Mounting Medium with DAPI (VectorLaboratories, H-1200), covered with a coverslip and stored at 4°C until evaluation.</span></p> <p><span>Immunofluorescence images were acquired with an upright fluorescence microscope (AxioImager.Z2 controlled by ZEN Blue software; 89 North Photofluor LM-75 light source, and Axiocam 503 mono camera; Zeiss, Jena, Germany), equipped with the following objectives: 20x (NA 0.5, Plan-NEOFLUAR), 40x (NA 1.4 oil, Plan-APOCHROMAT), and 100x (NA 1.45 oil, Plan-APOCHROMAT) objectives. This setup provides an excellent spatial resolution (nominally about 200 nm lateral resolution in our study; pixel size was 45.4 nm for 100x objective). High resolution images were taken with the 100x objective using the ApoTome.2 module with deconvolution (grid 5 lp/mm; section thickness 0.7 µm). We used Vysis Abbott Chroma filter sets (Blue: excitation (ex) 335-383 nm, emission (em) 420-470; green: ex 481-507 nm; em 521-551 nm; red: ex 534-556 nm, em 574- 606 nm). Co-localization of IgG and HEV ORF2 staining was quantified using Fiji software (Schindelin et al., 2012) and the JACoP ImageJ plug-in. </span></p>
A blind test on wind turbine wake modelling based on wind tunnel experiments: Phase I – The benchmark case
<p>This data set ("Data files.zip") contains the wind tunnel measurement data from Phase I of the Blind test on wind turbine wake modelling based on wind tunnel experiments organised during the TWEET-IE project (www.tweet-ie.eu).</p> <p>This updated version <strong>replaces</strong> the older versions 1.0.0 (https://doi.org/10.5281/zenodo.10566401), 1.1.0 (https://doi.org/10.5281/zenodo.11370112), 2.0 (https://doi.org/10.5281/zenodo.12188194) and 2.1 (https://doi.org/ 10.5281/zenodo.13918935). In comparison to the previous version 2.1 the data documentation has been updated to follow the template of the TWEET-IE project documents, indicating the Grant Agreement Number with the European Union and the Call Topic of the project.</p> <p>All tests were conducted in the closed-loop, low-speed boundary layer wind tunnel of the Chair of Aerodynamics and Fluid Mechanics at Technische Universität München (TUM). The experiments concerned two wind turbines, aligned with the flow, one downstream of the other, at a distance of 5 diameters. For Phase I, no control was applied to the wind turbine models, which were operating at constant RPM. The turbine models, designed and manufactured by TUM, were instrumented with multiple sensors and actuators and had a diameter of 1.1M. Measurements include velocity, power and loads on the turbines. A detailed description of the experimental set up can be found in the accompanying document ("Data documentation.pdf"). </p> <p>File "Submission procedure.zip" includes the format description and the templates of the output data that should be submitted by the participants in the blind test comparison.</p>
GoNEXUS SEF application: Andalusia practical case study
<p>Data for the figures in the journal article:</p> <p>Gonzalez-Rosell A, Arfa I and Blanco M (2023) Introducing GoNEXUS SEF: a solutions evaluation framework for the joint governance of water, energy, and food resources. Sustainability Science. https://doi.org/10.1007/s11625-023-01324-1</p> <p>Figure 3. Generation of new quantitative evidence: Percentage of variation of indicators between the water price scenario (WP) and baseline scenario (BS) for the year 2030. Source: Own elaboration based on the participatory SDM results (González-Rosell et al., 2020).</p> <p>Figure 4. Cross-impact matrix of the indicator system and solution from Table 2 for the water pricing in the Andalusia case study. Source: Own elaboration.</p> <p>Figure 5. Analysis of the degree of distribution of the network system based on the cross-impact matrix in Figure 4. Source: Own elaboration.</p> <p>Figure 6. (a) Full network graph: links between 16 indicators and WP solution based on the cross-impact matrix. (b) Tree network graph of the total influence of the WP solution at the first and second order based on the cross-impact matrix. Colour scale as in</p> <p>Figure 8. Synergies (green) and trade-off (red) of the WP solution on policy objectives and nexus objectives in Andalusia. Source: Own elaboration.</p> <p> </p>
Protecting the Aging Brain, Case-Study
Open the record for dataset details and reuse information.
Bird migration case study dataset v1.1
<p>Bird migration case study dataset, updated during the WG3 workshop ‘Visualisations: from show cases to production’ in 2015 by @peterdesmet.</p> <p>Changeset</p> <ul> <li>Add aggregation instructions for bird migration altitude profiles</li> <li>Add instructions to create basemap</li> <li>Add forward trajectory data</li> <li>Change license to CC0</li> <li>Update README & documentation where necessary</li> </ul>
Case study of self-compacting, fiber reinforced, lightweight concrete, intended for production of precast elements
<p>This a dataset set to paper entitled: "Case study of self-compacting, fiber reinforced, lightweight concrete, intended for production of precast elements".</p> <p>Dataset is one excel file divided in various sheets containing:</p> <ol> <li>Properties of used aggregates</li> <li>Initial properties of concrete</li> <li>Composition of concrete</li> <li>Concrete with fibres</li> <li>Final concrete</li> </ol>
Dataset for the paper "Anomaly Detection in Large-Scale Cloud Systems: An Industry Case and Dataset"
<p>We present a large-scale anomaly detection dataset collected from IBM Cloud's Console over approximately 4.5 months. This high-dimensional dataset captures telemetry data from multiple data centers, specifically designed to aid researchers in developing and benchmarking anomaly detection methods in large-scale cloud environments. It contains 39,365 entries, each representing a 5-minute interval, with 117,448 features/attributes, as interval_start is used as the index. The dataset includes detailed information on request counts, HTTP response codes, and various aggregated statistics. The dataset also includes labeled anomaly events identified through IBM's internal monitoring tools, providing a comprehensive resource for real-world anomaly detection research and evaluation.</p> <p><strong>File Descriptions</strong></p> <ul> <li><code>location_downtime.csv</code> - Details planned and unplanned downtimes for IBM Cloud data centers, including start and end times in ISO 8601 format.</li> <li><code>unpivoted_data.parquet</code> - Contains raw telemetry data with 413 million+ rows, covering details like location, HTTP status codes, request types, and aggregated statistics (min, max, median response times).</li> <li><code>anomaly_windows.csv</code> - Ground truth for anomalies, listing start and end times of recorded anomalies, categorized by source (Issue Tracker, Instant Messenger, Test Log).</li> <li><code>pivoted_data_all.parquet</code> - Pivoted version of the telemetry dataset with 39,365 rows and 117,449 columns, including aggregated statistics across multiple metrics and intervals.</li> <li><code>demo/demo.[ipynb|html]</code>: This demo file provides examples of how to access data in the Parquet files, available in Jupyter Notebook (<code>.ipynb</code>) and HTML (<code>.html</code>) formats, respectively.</li> </ul> <p>Further details of the dataset can be found in <strong>Appendix B: Dataset Characteristics</strong> of the <a href="https://arxiv.org/abs/2411.09047">paper</a> titled <strong><em>"Anomaly Detection in Large-Scale Cloud Systems: An Industry Case and Dataset."</em></strong> Sample code for training anomaly detectors using this data is provided in <a href="https://doi.org/10.5281/zenodo.14598119" target="_blank" rel="noopener">this package</a>.</p> <p> </p> <p>When using the dataset, please cite it as follows:</p> <pre><code>@misc{islam2024anomaly,</code><br><code> title={Anomaly Detection in Large-Scale Cloud Systems: An Industry Case and Dataset}, </code><br><code> author={Mohammad Saiful Islam and Mohamed Sami Rakha and William Pourmajidi and Janakan Sivaloganathan and John Steinbacher and Andriy Miranskyy},</code><br><code> year={2024},</code><br><code> eprint={2411.09047},</code><br><code> archivePrefix={arXiv},</code><br><code> url={https://arxiv.org/abs/2411.09047}</code><br><code>}</code></pre> <p> </p>
Inter-Chemical Correlation results for the study: HHEARx2016-1534 (A Nested Case-Control Study of Prenatal Exposure to Phthalates and Psychosocial Stress: Adverse Pregnancy Outcomes and the Mediating Role of Placental Function)
Title: A Nested Case-Control Study of Prenatal Exposure to Phthalates and Psychosocial Stress: Adverse Pregnancy Outcomes and the Mediating Role of Placental Function <br>Species: Homo sapiens <br>Number of samples: 5789 <br>Number of named analytes: 17 <br>Datasource url: https://hheardatacenter.mssm.edu/PublicFile/ViewPublicFile?projectid=14 <br>
H2020 Platone German Demonstrator Use Case 1 Asset and Topolgy Data
<p>This dataset asset and topology data of the field test setup. The dataset gives details about:</p> <p>- Low Voltage (LV) network</p> <p>- Tranformer located in the secondary substation</p> <p>- Community Battery Energy Storage System (CBES) connected to the LV-busbar in the 2nd. Substation</p> <p>- PV and number of households located in the energy community</p> <p>- PV installed generation power of the community</p> <p>- Domestic Storages and Inverter</p> <p> </p>
H2020 Platone German Demonstrator Use Case 1 Market Data
<p>This dataset contains settings of the Local Energy Management System, that have been set via a Graphical User Interface (GUI). The dataset contain follwowing data:</p> <p>H2020_Platone_GER_Market__ALF-C_GUI_Timestamp</p> <p>H2020_Platone_GER_Market__ALF-C_GUI_Use Case</p> <p>H2020_Platone_GER_Market__ALF-C_GUI_UC_ID</p> <p>H2020_Platone_GER_Market__ALF-C_GUI_Option</p> <p>H2020_Platone_GER_Market__ALF-C_GUI_Priority</p> <p>H2020_Platone_GER_Market__ALF-C_GUI_Submission_Time</p> <p>H2020_Platone_GER_Market__ALF-C_GUI_UC_Start_Date</p> <p>H2020_Platone_GER_Market__ALF-C_GUI_UC_End_Date</p> <p> </p> <p> </p>
H2020 Platone Italian Demonstrator Use Case 1-2 Measurements
<p>Description of the database "areti_profile_flexibility_customer_2021":</p> <p>Data about the flexibility measurement of the users involved in the trial.</p> <p>In the database you will find: </p> <p>Date: Measurement date (Mmm dd, yyyy);<br> Timestamp: Measurement time (hh:mm:ss.sss @UTC);<br> pod: Point of Delivery of the users' place (PoD) identification code;<br> measures.energy.absorbedActiveEnergy.value: Quarter-hour sample of active energy absorbed (kWh) [as per ID 6 in Tab. A.5 - CEI 13-82];<br> measures.energy.injectedActiveEnergy.value: Quarter-hour sample of active energy injected (kWh) [as per ID 7 in Tab. A.5 - CEI 13-82];<br> measures.energy.absorbedInductiveReactiveEnergy.value: Quarter-hour sample of inductive reactive energy when active energy absorbed (kVARh) [as per ID 9 in Tab. A.5 - CEI 13-82]; <br> measures.energy.absorbedCapacitiveReactiveEnergy.value: Quarter-hour sample of capacitive reactive energy when active energy absorbed (kVARh) [as per ID 10 in Tab. A.5 - CEI 13-82]; <br> measures.energy.injectedInductiveReactiveEnergy.value: Quarter-hour sample of inductive reactive energy when active energy injected (kVARh) [as per ID 11 in Tab. A.5 - CEI 13-82]; <br> measures.energy.injectedCapacitiveReactiveEnergy.value: Quarter-hour sample of capacitive reactive energy when active energy injected (kVARh) [as per ID 12 in Tab. A.5 - CEI 13-82];<br> measures.power.activePower.value: Quarter-hour average of active power exchange (kW) [as per ID 16 in Tab. A.5 - CEI 13-82];</p> <p> </p> <p>(Useful link to consult Italian UC:</p> <p><a href="https://eur01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fsmart-grid-use-cases.github.io%2Fdocs%2Fusecases%2Fplatone%2Fuc-it-1-voltage-management%2F&data=04%7C01%7Cfabio.bastianelli%40mail-bip.com%7C0bb5a42aac0c49cb876708d9f2c89510%7Cbb1a63ebeb09471aa00537b07792a5b5%7C0%7C0%7C637807765572787908%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=ptAsS52VBberHmZqIzYEXZs1PQrXQ6TDz6mNK%2FNWnk0%3D&reserved=0">https://smart-grid-use-cases.github.io/docs/usecases/platone/uc-it-1-voltage-management/</a></p> <p><a href="https://eur01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fsmart-grid-use-cases.github.io%2Fdocs%2Fusecases%2Fplatone%2Fuc-it-2-congestion-management%2F&data=04%7C01%7Cfabio.bastianelli%40mail-bip.com%7C0bb5a42aac0c49cb876708d9f2c89510%7Cbb1a63ebeb09471aa00537b07792a5b5%7C0%7C0%7C637807765572787908%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=xWNOqSeS5JxDoBEWZ4aB63gLmsnTA8YGGfCOoLjo1eo%3D&reserved=0">https://smart-grid-use-cases.github.io/docs/usecases/platone/uc-it-2-congestion-management/</a></p> <p><a href="https://platone-h2020.eu/data/deliverables/864300_M12_D1.1.pdf">https://platone-h2020.eu/data/deliverables/864300_M12_D1.1.pdf</a>)</p>
Modelling NBSs for coastal erosion and marine flooding: the Emilia-Romagna case studies
<p>The study was conducted in the context of the OPEn-air laboRAtories for Nature baseD solUtions to Manage environmental risks (OPERANDUM) project which is an H2020 project which aims at providing tools and methodologies for the assessment of NBS efficiency around the world. Two NBs were tested via modelling simulations on the Bellocchio Beach at Lido di Spina (Italy) located in the northern part of the Emilia-Romagna coast (northern Adriatic Sea): an artificial dune built with natural materials and a marine seagrass meadow.</p> <p>The artificial dune is an engineered structure that will mimic the functioning of natural dunes. Its aims are reducing both natural dune erosion and flooding in adjacent coastal lowlands. It consists of a barrier between the sea and land, in a similar way to a seawall. Unlike the latter, the NBS are ‘dynamic’, i.e. the dune/beach system interacts a great deal and is constantly undergoing small adjustments in response to changes in wind and wave climate or sea level. Its construction involves the placement of sediment from dredged sources on the beach and it will be reinforced with a structure composed of biodegradable material. Different typologies of experimental solutions are foreseen.</p> <p>The second NBS consists of an alongshore seagrass belt located in front of the coastal area. It was investigated as a potential mechanism for wave amplitude reduction. Among the few species that can live in the northern Adriatic Sea, Zostera Marina was chosen due to its ability to live in a marine environment influenced by freshwaters. A more detailed description can be found in (Pillai et al., 2021).</p> <p>The numerical model chain, specifically developed for the study, consists of an Ocean Circulation model, so-called SHYFEM (Umgiesser et al., 2004), a wave model, so-called WWIII (Alves and Ardhuin, 2016), and a morphological model, so-called XBeach (Roelvink et al., 2009). Ten years of XBeach simulations have been executed to simulate the morphological impacts on the coastal strip for the present (2010-19) and future climate (2040-49). For each 10 years period, four scenarios were simulated: the baseline scenario without NBS (baseline_run), the scenario with the dune (dune_run), the scenario with the seagrass effect (seagrass_run) and the scenario with the two NBS integration (dune_seagrass_run).XBeach was forced with sea level and wave time series predicted by the SHYFEM and WWIII models respectively.</p> <p>The model domain consists in a curvilinear structured grid of about 3.2 km (longshore) x 2.8 km (cross-shore) covering the coastal stretch of Bellocchio beach at Lido di Spina (Italy) and extends seaward up to about 10 m depth.</p> <p>The performance of the NBSs and their impact on coastal erosion and marine flooding were investigated. For both present and future scenarios (201-2019 and 2040-2049), the reduction in wave intensity obtained with the seagrass provided greater benefits in terms of erosion mitigation and flood reduction. The analysis highlighted the limited scale of the dune intervention, in particular under present conditions, highlighting that the longer the artificial dune implemented, the larger the beach and dune area protected. For the future scenarios, the results are still significant and even small projects are expected to help in mitigating coastal erosion and marine flooding.</p> <p>For long-period simulations, no relevant improvements in reducing beach erosion was observed when the artificial dune was combined with the seagrass meadows with respect to the seagrass effects only. Instead, a dominant increase in sea levels will probably highlight the dune functions in hindering the marine ingression into the lagoon area behind and the consequent sediment redistribution.</p> <p>This dataset consists of XBeach model results, mainly:</p> <ul> <li>Morphological evolution of the coastal bottom at Bellocchio beach (Lido di Spina, Italy) in terms of initial and final bed levels, for the current (201-2019) and the future (2040-2049) scenarios. Results are available for the four NBS scenarios described above (and detailed in the Presentation.pdf)</li> <li>Maximum flood depth, defined as the non-simultaneous maximum water depth on the beach domain of Bellocchio (Lido di Spina, Italy) for the current (201-2019) and the future (2040-2049) scenarios. Results are available for the four NBS scenarios described above (and detailed in the Presentation.pdf) </li> <li>Erosion-deposition maps for the current (201-2019) and the future (2040-2049) scenarios. Results are available for the four NBS scenarios described above (and detailed in the Presentation.pdf).</li> </ul>
Data-Driven Computational Intelligence Applied to Dengue Outbreak Forecasting: a case study at the scale of the city of Natal, RN-Brazil
<p><strong>The dataset comprises survey data from the following sources:dengue_incidence_data.csv: public data provided by Municipal Health Department of Natal, State of Rio Grande do Norte, Brazil; and data of Brazilian Notifiable Diseases Information System (Sinan). The objective of this paper was to analyze incidence data of dengue cases registered in each neighborhood of Natal city, weekly sampled (52 epidemiological weeks a year) between 2016 – 2019). </strong></p>
The potential of low-cost UAVs and open-source photogrammetry software for high-resolution monitoring of alpine glaciers: A case study from the Kanderfirn (Swiss Alps)
<p>This dataset contains high-resolution orthophotos (5 x 5 cm) and digital surface models (25 x 25 cm) of the Kandernfirn Glacier located in the Swiss Alps. Aerial images were aquired with a self-developed fixed-wing Unmanned Aerial Vehicle during ten surveys on five different days in 2017 and 2018. The open-source photogrammetry software OpenDroneMap (version 0.4.1) was used for image processing.</p> <p>The orthophotos and digital surface models were validated through dGNSS point measurements of ground control points. Please refer to the corresponding paper for information on the horizontal and vertical accuracy of the files.</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>
Film Programming in the USSR: A Case Study of Moscow Cinemas (1946–1955)
<p>This paper presents a database on film programming in Moscow cinemas between 1946 and 1955. It outlines the place of this research at the intersection of new cinema history and academic debates on film distribution in the field of Soviet history. The paper describes the data collection, the coding to present the data, and the structure of the database, which consists of the three datasets on Moscow film programming (1946–1955), Moscow cinemas (1946–1955), and the 1952 film calendar. Concluding remarks summarize the knowledge obtained from the database and introduce the Soviet case into the international context of digital data collections for historical cinema studies.</p> <p>The database includes Cyrillic; it might require additional text encoding. </p> <p>The updated version includes the unique IDs for cinemas to increase the usability of the data. </p>
Can household storage conditions reduce food waste and environmental impact ? A broccoli case study
<p>The consumption stage has been identified as the largest producer of food waste (FW) across the food supply chain (FSC), with fruit and vegetables being the most affected product category. The present study aims to determine the op- timal storage scenario at household level to avoid food waste and which has the lowest environmental footprint. Broc- coli was stored under different storage conditions: unbagged or bagged (periodically opened) in bioplastic bags inside a domestic refrigerator at 5 or 7 °C for 34 days and then analysed for relative humidity (RH), sensory properties and bioactive compounds. A life cycle assessment (LCA) was conducted to evaluate the environmental profile of 1 kg of broccoli purchased by the consumer (cradle-to-grave). At day 0 (base scenario) the carbon footprint was 0.81 kg CO2 eq/kg, with the vegetable farming being the main contributor to this environmental impact, mostly driven by fertiliser (production and its emissions to air and water) and irrigation (due to electricity consumption for water pumping). Quality and food waste depended on time and storage conditions: For short storage times, within three days, the best quality combined with the lowest environmental footprint was for unbagged broccoli at 7 °C and no household food waste. However, this scenario had the highest food waste level from day 3 onwards, with increased resource loss and overall environmental footprint. For long-term storage, using a bag and storing at 5 °C helped to re- duce food waste with the lowest environmental footprint. For example, at 16 days, this scenario (bagged at 5 °C) could save 4.63 kg/FU ofbroccoli and 3.16 kg CO2 eq/FU compared to the worst scenario (unbagged at 7 °C). Consumers are the key to reducing household food waste and this research provides the knowledge for improvement.</p>
Software Architecture Assessment for Sustainability: A Case Study
<h3>Replication Package: Software Architecture Assessment for Sustainability: A Case Study</h3> <p><br>This repository contains the supplementary material to support the paper published at the International Conference on Software Architecture (ECSA) 2024 titled, "Software Architecture Assessment for Sustainability: A Case Study". This repository can be used to replicate the study and carry out a Software Architecture Evaluation of other software systems.<br><br>The online version can be browsed on the linked <a href="https://github.com/S2-group/rep-pkg-ecsa-2024-SA-assessment-for-sustainability-a-case-study/tree/v1.0.0" target="_blank" rel="noopener">Github Repository</a></p> <p> </p> <p> </p>
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