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23 results for “cyber-physical system”
Datasets of synthetic workflows for cyber-physical edge-hub-cloud systems
<p>These datasets of synthetic workflows were generated to evaluate the performance and scalability of a multi-constrained scheduling approach for workflow applications of various structures, sizes, and sensing/actuating requirements in a cyber-physical system (CPS) following the edge-hub-cloud paradigm. The examined CPS comprised four edge devices (i.e., single-board computers, each attached to an unmanned aerial vehicle (UAV) equipped with sensors/actuators) interacting with a hub device (e.g., a laptop), which in turn communicated with a more computationally capable cloud server. All system devices featured heterogeneous multicore processors and varied sensing/actuating or other specialized capabilities. The problem objective was the minimization of the overall latency of the application under deadline, memory, storage, energy, capability, and task precedence constraints.</p> <p>We generated 25 random workflows (task graphs) with 10, 20, 30, 40, and 50 nodes (5 task graphs for each size), utilizing the Task Graphs For Free (TGFF) random task graph generator [1],[2]. Additional task parameters (e.g., execution time, power consumption, memory, storage, output data size, capability) were included post-generation, using appropriate values. More details are provided in README.txt and in [3].<br><br>References:<br>[1] R. P. Dick, D. L. Rhodes, and W. Wolf, "TGFF: Task graphs for free," in Proc. Sixth International Workshop on Hardware/Software Codesign (CODES/CASHE), 1998, pp. 97-101, doi: 10.1109/HSC.1998.666245.</p> <p>[2] R. P. Dick, D. L. Rhodes, and K. Vallerio, "TGFF," https://robertdick.org/projects/tgff/.</p> <p>[3] A. Kouloumpris, G. L. Stavrinides, M. K. Michael, and T. Theocharides, “Optimal multi-constrained workflow scheduling for cyber-physical systems in the edge-cloud continuum,” in Proc. 2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC), Jul. 2024, pp. 483-492, doi: 10.1109/COMPSAC61105.2024.00072.</p>
Design Methodologies and Engineering Applications for Ecosystem Biomimicry: An Interdisciplinary Review Spanning Cyber, Physical, and Cyber-Physical Systems
<p>This is the data for the interdisciplinary review on ecosystem biomimicry for engineering applications. </p>
Dataset of Optimization Methods for Model-Implemented Fault Injection in Cyber-Physical Systems: A Systematic Literature Review
<p>Data set for the paper entitled “<strong>Optimization Methods for Model-Implemented Fault Injection in Cyber-Physical Systems: a Systematic Literature Review</strong>”</p> <p>In this repo, we have some pictures and Excel files.</p> <ul> <li>Pictures are screenshots from the Parsifal tool (https://parsif.al/) which we use for performing the SLR.</li> <li>Excel files are as follows:</li> </ul> <table style="border-collapse: collapse; width: 100%;"><colgroup><col style="width: 21.8789%;"><col style="width: 78.1211%;"></colgroup> <tbody> <tr> <td><strong>Excel’s file name</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>Keyword_analysis </td> <td>In this file, you can see the evolution of our keyword selection.</td> </tr> <tr> <td>Articles_InclusionExclusion_QA </td> <td>In this file, you can find all found papers until Feb. 27, 2025. In the last column of this excel file, we can see the status of each paper, if it has been included, or excluded by authors. For the included paper (their status is “Accepted”) you can see their quality score in the last column.</td> </tr> <tr> <td>Extracted_data </td> <td>In this file, we logged the result of data extraction from qualified paper. In the first sheet “Articles”, you can see a list of the read papers with corresponding data. Other sheets in this Excel file are driven from the “Article” sheet for data visualization. So, they are not important.</td> </tr> </tbody> </table> <p> <br>If you have any questions, you can read the corresponding paper and contact the authors.</p>
Cyber-Physical System power Consumption
<h1>Files</h1> <p>This dataset is comprised of 5 CSV files contained in the data.zip archive. Each one represents a production machine from which various sensor data has been collected. The average cadence for collection was 5 measurements per second. The monitored devices where used for hydroforming.</p> <p>The collection period covered the period from 2023-06-01 until 2023-08-05.</p> <h2>Data</h2> <p>These files represent a complete data dump from the data available in the time-series database, InfluxDB, used for collection. Because of this some columns have no semantic value for detecting production cycles or any other analytics.</p> <p>Each file contains a total of 14 columns. Some of the columns are artefacts of the query used to extract the data from InfluxDB and can be discarded. These columns are: results, table _start, _stop</p> <ul> <li><em>results</em> - An artefact of the InfluxDB query, signifies postprocessing of results in this dataset. It is "mean".</li> <li><em>table</em> - An artefact of the InfluxDB query, can be discarded.</li> <li><em>_start</em> and <em>_stop</em> - Refers to ingestion related data, used in monitoring ingestion. </li> <li><em>_field</em> - An artefact of the InfluxDB query, specifying what field to use for the query.</li> <li><em>_measurement</em> - An artefact of the InfluxDB query, specifying what measurement to use for the query. Contains the same information as device_id.</li> <li><em>host</em> - An artefact of the InfluxDB query, the unique name of the host used for the InfluxDB sink in Kubernetes.</li> <li><em>kafka_topic</em> - Name of the Kafka topic used for collection.</li> </ul> <p> </p> <p>Pertinent columns are:</p> <ul> <li><strong><em>_time</em></strong> - Denotes the time at which a particular event has been measured, it is used as index when creating a dataframe.</li> <li><em><strong>_time.1</strong></em> - Duplicate of _time for sanity check and ease of analysis when _time is set as index</li> <li><em><strong>_value</strong></em> - Represents the value measured by each sensor type.</li> <li><em><strong>device_id </strong></em>- Unique identifier of the manufacturing device, should be the same as the file name, i.e. B827EB8D8E0C.</li> <li><em><strong>ingestion_time</strong></em> - Timestamp when the data has been collected and ingested by influxDB.</li> <li><em><strong>sid</strong></em> - Unique sensor ID; the power measurements can be found at sid 1.</li> </ul> <p> </p> <h1>Annotations</h1> <p>There are two additional files which contain annotation data: </p> <ul> <li><em><strong>scamp_devices.csv</strong></em> - Contains mapping information between the dataset device ID (defined in column "<em>DeviceIDMonitoring</em>") and the ground truth file ID (defined in column "<em>DeviceID</em>")</li> <li><em><strong>scamp_report_3m.csv </strong></em>- Contains the ground truth, which can be used for validation of cycle detection and analysis methods. The columns are as follows: <ul> <li><strong><em>ReportID</em></strong> - Internal unique ID created during data collection. It can be discarded.</li> <li><em><strong>JobID</strong></em> - Internal Scheduling Job unique ID.</li> <li><em><strong>DeviceID</strong></em> - The unique ID of the devices used for manufacturing needs to be mapped using the <em>scamp_device.csv</em> data.</li> <li><em><strong>StartTime</strong></em> - Start time of operations</li> <li><em><strong>EndTime</strong></em> - End time of operations</li> <li><em><strong>ProductID</strong></em> - Unique identifier of the product being manufactured.</li> <li><em><strong>CycleTime</strong></em> - Average length of cycle in seconds, added manually by operators. It can be unreliable.</li> <li><em><strong>QuantityProduced</strong></em> - Number of products manufactured during the timeframe given by <em>StartTime</em> and <em>EndTime</em>.</li> <li><em><strong>QuantityScrap</strong></em> - Number of scraped/malformed products in the given timeframe. These are part of the <em>QuantityProduced</em><em>,</em><strong> </strong>not in addition to it.</li> <li><em><strong>IntreruptionMinuted</strong></em> - Minutes of production halt.</li> </ul> </li> <li><em><strong>scamp_patterns.csv</strong></em> - Contains the start and end timestamp for selected example production cycles. These where chosen based on expert users.</li> </ul> <h1>Jupyter Notebook</h1> <p>We have provided a sample Jupyter notebook (<em>verify_data.ipynb</em>), which gives examples of how the dataset can be loaded and visualised as well as examples of how the sample patterns and ground truth can be addressed and visualised.</p> <h2>Note</h2> <p>The Jupyter Notebook contains an example of how the data can be loaded and visualised. Please note that both data should be filtered based on sid; the power measurements are collected by sid 1. See Notebook for example.</p>
Dataset of the manuscript "Problems and Solutions in Applying Continuous Integration and Delivery to 20 Open-Source Cyber-Physical Systems"
<p>This archive contains the artifacts (datasets) for the manuscript "Problems and Solutions in Applying Continuous Integration and Delivery to 20 Open-Source Cyber-Physical Systems"</p>
Dataset of the paper "An Empirical Characterization of Software Bugs in Open-Source Cyber-Physical Systems"
<p><br> #Dataset Package for the paper "An Empirical Characterization of Software Bugs in Open-Source Cyber-Physical Systems"</p> <p><br> Description of the content:</p> <p><br> 1) "1_RQ-CPS-bugs-Taxonomy" folder contains all the main experimental data concerning the issues sampled and analyzed from all the Projects considered in the study,<br> including row-data on the taxonomy validtion steps.<br> <br> - Under "the sub-folder "1_Taxonomy-Raw-data" are reported the row-data concerning the taxonomy validtion steps </p> <p><br> 2) "2_Scripts" contains all scripts used to generate the issue data and sampled issue raw-data in the previous folders: </p> <p><br> - "setup.md" file in the folder describes how to set=up and run the script used for collecting and sampling the issues for the validation steps:<br> <br> - runJSONtoCSV.sh<br> - JSONtoCSV.py<br> - generateListOfAllSamples.py<br> - generateAllSamples.r<br> <br> Under "the sub-folder "1_Scripts/1_Data_Collection":<br> <br> <br> 3) "3_Final Taxonomy" folder contains the final Table representation (also reported in the previous folder) and main figures of the CPSs Bugs Taxonomy.</p>
Continuous Integration and Delivery Practices for Cyber-Physical Systems: An Interview-Based Study - Online Dataset
<p>This package contains the online dataset of the manuscript:</p> <p>Continuous Integration and Delivery Practices for Cyber-Physical Systems: An Interview-Based Study</p>
Data set of paper Model-Driven System-Performance Engineering for Cyber-Physical Systems
<p>This data set contains the raw and processed data of the paper <em>Model-Driven System-Performance Engineering for Cyber-Physical Systems</em>, published in the proceedings of ESWEEK’21.</p>
philip928lin/Flood-Risks-of-Cyber-physical-Attacks-in-a-Smart-Storm-Water-System: Flood Risks of Cyber-physical Attacks in a Smart Storm Water System
<p>This is the code archive for the publication "Flood Risks of Cyber-physical Attacks in a Smart Storm Water System" in Water Resources Research.</p>
Inconsistency Situations in Engineering of Cyber-Physical Systems
<p>This dataset provided describes inconsistency situation descriptions in the Engineering of Cyber-Physical Systems found in industry environments. It includes 19 situations from 17 industry participants. The participants are mostly from automotive engineering, but also special machine construction and production automation are represented as industries. The 9 involved companies equaly represent Original Equipment Manufacturers (OEM) and Tier-1 suppliers. This dataset will be extended within our future work.</p>
[dataset] Runtime Equilibrium Verification for Resilient Cyber-physical Systems
<p>This package contains the <strong>raw data</strong> and <strong>scripts</strong> used to carry out the evaluation of the framework RUNE<sup>2</sup> (RUNtime Equilibrium verification and Enforcement).</p> <p>This package is paired with the following paper submitted for publication to the ACM Transactions on Autonomous and Adaptive Systems (TAAS). Invited contribution to the IEEE ACSOS 2021 Special Issue.</p> <p><strong>Title</strong>: Enforcing Resilience in Cyber-physical Systems via Equilibrium Verification at Runtime</p> <p><strong>Authors</strong>:<br> - Matteo Camilli, Free University of Bozen-Bolzano, Italy<br> - Raffaela Mirandola, Politecnico di Milano, Italy<br> - Patrizia Scandurra, University of Bergamo, Italy</p> <p>See README.md for further instructions.</p>
Online Package for the manuscript "Continuous Integration and Delivery for Cyber-Physical systems: A Grounded-Theory"
<p>This package contains the material of the (grounded theory) study related to the paper</p> <p>"Continuous Integration and Delivery for Cyber-Physical systems: A Grounded-Theory"</p> <p>The content of the various files and directories is detailed in the following.</p> <p>InterviewStructure.pdf: complete interview structure (the paper reports an overview in Table 1)</p> <p>open_coding/: this directory contains details from the open coding procedure. In particular:<br> - AllCodesUsedWhileCodingAndMapping.csv contains the list of all codes generated during the open coding, with a symbol near each one (first column) then used to compute the inter-rater agreement<br> - first_round.csv, second_round.csv, third_round.csv, fourth_round.csv: files used to compute the inter_rater agreement</p> <p><br> CodesContributingToMindMap.csv: final set of codes, that contributed to the taxonomy (see below)</p> <p>FinalCodingTraceability.csv: this file describes how the final set of codes is traced onto the ten interviews. Note that, at this stage, the transcripts have been redacted for confidentiality purposes.</p> <p>MindMap_Complete.pdf: complete taxonomy of codes, in the form of a mind map. The one reported in the paper (Figure 2) cuts leaves, unless (as for benefits, for example) they are necessary to properly describe and understand the category. Also, note that the complete mind map separates the benefits into "actual" and "expected" (based on what was collected from the interviews) whereas the summary mindmap shown in the paper (Figure 2) does not make this distinction.</p> <p>6C_Diagrams/ : This directory contains the detailed 6C diagrams (i.e., each box related to a "C" contains the list of codes pertaining to it) for the three dimensions investigated in the paper and addressed in RQ1, RQ2, and RQ3.</p>
data set for Design of architectural environment integration of cyber-physical systems based on image segmentation and retrieval technology
<p>This data set is used to implement the project Design of architectural environment integration of cyber-physical systems based on image segmentation and retrieval technology</p>
Replication package of "How Do Deep Learning Faults Affect AI-Enabled Cyber-Physical Systems in Operation? A Preliminary Study Based on DeepCrime Mutation Operators"
<p>Cyber-Physical Systems (CPSs) combine digital cyber technologies with physical processes. As in any other software system, in the case of CPSs, the use of Artificial Intelligence (AI) techniques in general, and Deep Neural Networks (DNNs) in particular, is contantly increasing. While recent studies have considerably advanced the field of testing AI-enabled systems, it has not yet been investigated how different Deep Learning (DL) bugs affect AI-enabled CPSs in operation. This work-in-progress paper presents a preliminary evaluation on how such bugs can affect CPSs in operation by using a mobile robot as a case study system. For that, we generated DL mutants by using operators proposed by Humbatova et al., which are operators based on real-world DL faults. Our preliminary investigation suggests that such bugs are more difficult to detect when they are deployed in operation rather than when testing their DNN in an off-line setup, which contrast with related studies.</p> <p> </p> <p>This repository provides the replication data employed in our study.</p>
Artifact Demonstration Video for Negotiating Autonomy in Cyber-Physical Systems at Run Time
<p>This artifact demonstration video shows a novel approach to dynamically negotiate autonomy in Cyber-Physical Systems (CPS). As CPS become increasingly integrated into our daily lives, sharing autonomy between human operators and autonomous agents within these systems becomes a critical issue. Business case developers also face the design and integration of CPS. Our research presents a novel architecture and methodology that seamlessly enables this sharing of autonomy. We aim to encourage business case developers to consider autonomic CPS. The video provides an insightful overview of our approach and highlights its practicality. It illustrates how our system enables business case developers to back CPS with a mechanism to negotiate and share autonomy at runtime. Our artifact demonstration video is not only a good opportunity to visually grasp the concept of negotiating autonomy in CPS, but also to experience the practical implementation of this approach. It serves as a valuable resource for researchers, engineers, and anyone interested in understanding the future of sharing autonomy in the context of cyber-physical systems.</p>
Distributed Multi-objective Optimization in Cyber-Physical Energy Systems
<p>The data includes results for a distributed multi-objective optimization in Cyber-Physical Energy Systems. The respective implementation for the scenarios can be found here: https://github.com/Digitalized-Energy-Systems/MOO-CPES/releases/tag/Distributed_Multi-objective_Optimization_in_Cyber-Physical_Energy_Systems<br>In this case, a multi-agent system exists for the optimization in which agents represent chp units or wind plants. For the optimization using the agents, the agents have to fulfill a target schedule, with contains of the sum of all unit schedules. Regarding the target schedule, three objectives are considered: minimizing the difference between the<br>produced power in sum and the given target schedule, minimizing the emissions and minimizing the uncertainties.</p>
Adopting Microservices and DevOps in the Cyber-Physical Systems Domain: A Rapid Review and Case Study at Siemens AG
<p>This repository contains the artifacts for a rapid review and interview-based case study at Siemens AG. We analyzed challenges and practices related to microservices and DevOps in the context of the cyber-physical systems (CPS) domain.</p>
Dataset: Maturity Evaluation of Domain-Specific Language Ecosystems for Cyber-Physical Production Systems
<p>This online repository contains the accompanying data for the ETFA publication "Maturity Evaluation of Domain-Specific Language Ecosystems for Cyber-Physical Production Systems".<br> <br> This involves all tables which contain the values of the maturity evaluation criteria of the five examined subject DSLs.</p>
Replication Package for A Systematic Literature Review of Model-driven Security Engineering for Cyber-physical Systems
<p>This package contains supplemental material for the paper "A Systematic Literature Review of Model-driven Security Engineering for Cyber-physical Systems".</p> <p>In particular, we provide:</p> <ul> <li>The survey protocol</li> <li>The used search strings</li> <li>The search results for each library</li> <li>The data extraction template</li> <li>The data extraction sheet for each selected approach</li> <li>The list of all publications and their exclusion stage</li> </ul>
Safe Reinforcement Learning Through Reactive Synthesis and Behavioral Programming in Cyber-Physical Systems
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.