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.

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 "Standardization Technology of Software Testing in 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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Many real-world systems can be modeled by multistate flow networks (MFNs) and their reliability evaluation features in designing and control of these systems. Considering the cost constraint makes the problem of reliability evaluation of an MFN more realistic. For a given demand value d and a given cost limit c, the reliability of an MFN at level (d, c) is the probability of transmitting at least d units from the source node to the sink node through the network within the cost of c.

The data set contains three folders: 'MCs lists', 'd_c_MCs lists', 'd_c_MCs lists2', and an additional file arr_tib.csv.

The folder 'MCs lists' contains 7 files in the csv format. Each of these files contains the list of all minimal cuts for an appropriate multistate flow network (MFN) from figures 2-4. The arcs in networks 1,2,3,5,6,7 are ordered firstly according to the order of their beginning nodes and secondly on the order of their ending nodes. Only in the network Id 4 from Fig. 3 the order of the arcs is another, and this order is visible in Fig. 3.

The folder 'd_c_MCs lists' contains 126 files and the folder 'd_c_MCs lists2' contains 315 files in the csv format. Each of these files has the name of the form i_j_(d,c)-MCs, where i denotes the MFN ID, j - the number of the variant of the greatest state vector, d - demand level, and c- cost constraint. Each of these files contains all (d,c)-MCs determined for given MFN ID - i, and the greatest state vector of the number of the variant - j.

The file 'arr_tib.csv' contains experimental results of numerical experiments conducted on a computer with Intel(R) Core(TM) i7-8750H CPU and 16 GB of RAM. This file is a result of the file 'Params_all.csv' containing the raw data from the numerical experiments, where computational times are expressed in nanoseconds.

Column 1 contains the consecutive numbers of cases;Column 2 'i' - MFN ID; Column 3 'j' - the number of the variant of the greatest state vector;Column 4 'm' - the number of edges in a given MFN;Column 5 'p' - the number of all MCS of this MFN;Column 6 'W' - coordinates of the greatest SSV W;Column 7 'd' - given level d of multistate minimal cuts; Column 8 'c' - the total budget c;Column 9 'q1/p' - the ratio of the number of elements of set Q_1 defined by (19) in Lemma 8 in the related article to p;Column 10 'q2/p' - the ratio of the number of elements of set Q_2 defined by (20) in Lemma 8 in the related article to p; Column 11 '#(d,c)-MCs' - the number of all (d,c)-MCs; Column 12 'T_F_K' - expressed in seconds the mean computational time T_{FK} of the algorithm described in the article M. Forghani-elahabad, N. Kagan, Assessing Reliability of Multistate Flow Networks Under Cost Constraint in Terms of Minimal Cuts Int. J. Reliab. Qual. Saf. Eng.2019,26, 1950025; Column 13 'T_K' - expressed in seconds the mean computational time of the main algorithm from the presented algorithm;Column 14 'R" - the ratio T_F_K/T_K;Column 15 'T_aux' - expressed in seconds the mean computational time of the introductory algorithm from the presented algorithm;Column 16 'T_K_t' - expressed in seconds the total mean computational time of the presented algorithm;Column 17 'R_p' - the ratio T_F_K/T_K_t

Computational times in columns 12, 13, 15, and 16 in file 'arr_tib.csv' are expressed in seconds and are results of the use of the microbenchmark procedure from the microbenchmark library implemented in R.

There are 4 scripts in R. Script 'Init_params.r.r' enables us to load the list of MCs from the folder 'MCs lists' and initialize other parameters used in numerical experiments. Sometimes to establish some parameters it is necessary to compile some procedures from the script 'kozyra_d_c_MCs.r' which contains R implementations of presented algorithms. The script 'Forghani.r' contains R implementation of the algorithm described in Forghani-elahabad, M. and N. Kagan (2019a). Assessing reliability of multistate flow networks under cost constraint in terms of minimal cuts. Int. J. Reliab. Qual. Saf. Eng. 26 (5), 1-18. The script 'tests.r' contains code that enables us to execute numerical experiments described in the last section of the article to compare the time complexity of the presented algorithm and the algorithm described in Forghani-elahabad, M. and N. Kagan (2019a). Assessing reliability of multistate flow networks under cost constraint in terms of minimal cuts. Int. J. Reliab. Qual. Saf. Eng. 26 (5), 1-18.

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To protect the equipment in a building struck directly by lightning, surge protective devices (SPDs) are installed between the N-phase and ground lines at the power utility substation of a TT system. In this case, when operating the SPD, the wiring system of the low-voltage distribution line will be a TN system. Although the overvoltage between these lines at the power utility substation can be limited during SPD operation, an overvoltage between these lines is induced at each floor distribution board connected thereafter.

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This dataset was prepared to estimate the winding temperature of a BLDC motor for a variable load and speed profile. It contains two files. The first one is the measurement results for the motor without cooling, while the second one is the measurement results after installing an additional cooling fan on the shaft. The data included in the files are time stamp, winding temperature, casing temperature, speed, current, power loss, mean and standard deviation of the measured quantities for 14400 data records.

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