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The data consists of 2 sets of 2000 data points histograms and cumulative integral plots.

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sensor data

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Safety assessment of Cyber-Physical Systems (CPS) requires a tremendous amount of effort as the complexity of cyber-physical systems is increasing. A well-known approach for the safety assessment of CPS is Fault Injection (FI). The goal of fault injection is to find a catastrophic fault that can fail the system by injecting faults into it. These catastrophic faults are less likely to happen, and finding it requires tremendous labor and cost.

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This is a numerical example of parts deterioration monitoring constructed based on the literature "Preventive Maintenance and Joint Optimization of Spare Parts Inventory and Reservation Policy"

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The database contains information about three test systems, including the network connected to Bus 5 of the Roy Billinton Test System and two netwroks with 93 and 135 load nodes.

 

 

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README.txt for simulation files IEEE_Collaboration_N_entities_2021.mdl and IEEE_2_Platforms_federation_SF_2021.mdl

 

This is a README.txt for the model published on the paper titled:

 

Improving IoT Federation Resiliency with Distributed Ledger Technology, 2021, Elo T, et al.

 

This readme describes how to replicate the main simulation results from the paper using a Vensim

model file. The model file has been generated using the Vensim DSS Macintosh Version 

8.0.7 Double Precision x64.

Instructions: 

README.txt for simulation files IEEE_Collaboration_N_entities_2021.mdl and IEEE_2_Platforms_federation_SF_2021.mdl

 

This is a README.txt for the model published on the paper titled:

 

Improving IoT Federation Resiliency with Distributed Ledger Technology, 2021, Elo T, et al.

 

This readme describes how to replicate the main simulation results from the paper using a Vensim

model file. The model file has been generated using the Vensim DSS Macintosh Version 

8.0.7 Double Precision x64.

 

To replicate the results of the 2 member federation do the following:

Open the provided model file (“2_Platforms_federation_SF_2021.mdl”) with Vensim DSS Macintosh Version 8.0.7 Double Precision x64, or similar

Push "Simulate". 

You obtain the base case result graph of Figure 4 (leftmost sub picture).

 

Right click “fixes effect on harm multiplier” from the model.

Push “Equation”.

Edit the value in “Equations”. It reads: “1”.

Change it to “0.96”.

Push “OK”.

Push "Simulate". 

You obtain the middle case result graph of Figure 4 (middle sub picture).

 

Right click “fixes effect on harm multiplier” from the model.

Push “Equation”.

Edit the value in “Equations”. It reads: “0.96”.

Change it to “0.92”.

Push “OK”.

Push "Simulate". 

You obtain the middle case result graph of Figure 4 (rightmost sub picture).

 

 

To replicate the results of the associated paper do the following:

Open the provided model file (“IEEE_Collaboration_N_entities_2021.mdl”) with Vensim DSS Macintosh Version 8.0.7 Double Precision x64, or similar

Push “Simulate” to obtain the baseline simulation for the federation of 10 members

To obtain the spread graph around this push “Sensitivity”

Input “0.119935” to the “Minimum”. Input “0.119939” to the “Maximum”.

Choose “RANDOM_UNIFORM” as “Distribution”.

Input “500” to “number of rounds”.

Push “Parameter”

Choose “fixes effect on harm multiplier”. Push OK.

Push “Next”.

Answer “Yes” to the question: “Do you want to incorporate your current editing?”

Choose “Finnish” at “Savelist control” dialog.

Push Select All button from simulation setup control.

Push Finish button in the same dialog.

A Sensitivity Simulation begins.

After the run you return to model view. Then:

Choose “Federation health” from the model by left clicking it.

Choose Sensitivity Graph from the left button menu.

A Window appears replicating the result for Fig. 7 of the publication.

 

 

To replicate the results of the 3 member federation do the following:

Open the provided model file (“IEEE_Collaboration_N_entities_2021.mdl”) with Vensim DSS Macintosh Version 8.0.7 Double Precision x64, or similar

Right click “fixes effect on harm multiplier” from the model.

Push “Equation”.

Edit the value in “Equations”. It reads: “0.119935”.

Change it to “0.5”.

Push “OK”.

Push “Subscripts”.

Push “Edit…”.

Edit the value in “Equations”. It currently reads: “(f1-f10)”.

Change it to “(f1-f3)” to simulate a three member federation.

Push “OK”.

Push “Close” in “Subscript Control” dialog.

Push simulate to get a new baseline for a three member federation.

Push “Sensitivity”.

Check that “Number of” reads “500”.

Select the simulation setup line from "Currently active parameters".

Push "Modify Selected".

Input “0.500” to the “Minimum”.

Input “0.502” to the “Maximum”.

Check that “Distribution” is “RANDOM_UNIFORM”.

Push “Parameter”.

Choose “fixes effect on harm multiplier”. Push OK.

Push “Next”.

Answer “Yes” to the question: “Do you want to incorporate your current editing?”

Choose “Finnish” at “Savelist control” dialog.

A Sensitivity Simulation begins.

This simulation is visible faster due to 3 member federation being much more simple to simulate that the 10 member federation.

Choose “Federation health” from the model by left clicking it.

Choose Sensitivity Graph from the left button menu.

A Window appears replicating the result for Fig. 6 of the publication.

 

 

To replicate the results of the 3 member federation do the following:

Open the provided model file (“IEEE_Collaboration_N_entities_2021.mdl”) with Vensim DSS Macintosh Version 8.0.7 Double Precision x64, or similar

Push “Subscripts”.

Push “Edit…”.

Edit the value in “Equations”. It currently reads: “(f1-f10)”.

Change it to “(f1-f5)” to simulate a three member federation.

Push “OK”.

Push “Close” in “Subscript Control” dialog.

Right click “initial success” from the model.

Push “Equation”.

Edit the value in “Equations”. It reads: “50”.

Input a vector from Table 3: "100,50,50,5,5".

Push “Close”.

Right click “fixes effect on harm multiplier” from the model.

Push “Equation”.

Edit the value in “Equations”. It reads: “0.119935”.

Change it to “0.2”.

Push “OK”.

To obtain the spread graph around this push “Sensitivity”

Input “0.20” to the “Minimum”. Input “0.26” to the “Maximum”.

Choose “RANDOM_UNIFORM” as “Distribution”.

Input “500” to “number of rounds”.

Push “Parameter”

Choose “fixes effect on harm multiplier”. Push OK.

Push “Next”.

Answer “Yes” to the question: “Do you want to incorporate your current editing?”

Choose “Finnish” at “Savelist control” dialog.

Push Select All button from simulation setup control.

Push Finish button in the same dialog.

A Sensitivity Simulation begins.

After the run you return to model view. Then:

Choose “Federation health” from the model by left clicking it.

Choose Sensitivity Graph from the left button menu.

A Window appears replicating the result for Fig. 8 of the publication.

 

 

IEEE_Collaboration_N_entities_qualitative_with_DLT_2021.png:

This model is qualitative so only a graphical presentation as a bitmap is given.

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This dataset is the experimental data for the manuscript entitled "Standardisation Workflow Technology of Software Testing Processes and its Application to SRGM on RSA Timing Attack Tasks". The software fault data is generated from the testing of related software with RSA timing attack tasks, including the test data of three versions of RSA timing attack program and workflow verification system respectively. The attributes of test data contain no. of faults, fault type, interval time between faults and cumulative time, as well as estimate values for different SRGMs' parameters.

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Resource Efficient Real-Time Reliability Model for Multi-Agent IoT Systems

Resource Efficient Real-Time Reliability Model for Multi-Agent IoT Systems is called ERT-CORE. It defines specific input parameters, i.e., system's workload, average request processing time and availability. Defined parameters reflect system's state and react on its changes. Based on these parameters system reliability evaluation is performed.

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these files uploaded mainly used for supporting the research results displayed in paper.

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This dataset contains multimodal sensor data collected from side-channels while printing several types of objects on an Ultimaker 3 3D printer. Our related research paper titled "Sabotage Attack Detection for Additive Manufacturing Systems" can be found here: https://doi.org/10.1109/ACCESS.2020.2971947. In our work, we demonstrate that this sensor data can be used with machine learning algorithms to detect sabotage attacks on the 3D printer.

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