**The law of conservation of energy has not been disproven. However, an inability to disprove this law is not proof of its validity. This article introduces a model, with the use of Ansys and MapleSim simulations, of how to create energy with a lagging current and a quasi-sinusoidal variance in a stator’s capacitance. That is, the stator’s mean capacitance is larger than its median, so that the top half of its waveform is larger than its bottom half.**

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One of the weak points of most of denoising algoritms (deep learning based ones) is the training data. Due to no or very limited amount of groundtruth data available, these algorithms are often evaluated using synthetic noise models such as Additive Zero-Mean Gaussian noise. The downside of this approach is that these simple model do not represent noise present in natural imagery. For evaluation of denoising algorithms’ performance in poor light conditions, we need either representative models or real noisy images paired with those we can consider as groundtruth.

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This dataset contains the following simulated information of the IEEE 14 bus system: The 24-hour load profile of all the nodes of the IEE 14 bus circuit, voltages and currents measured by the PMUs placed at optimal locations which minimizes the variance on the estimated voltage states, voltages and currents measured by the PMUs placed at sub-optimal locations.

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The MPSC-rPPG dataset comprises photoplethysmograph (rPPG) data with the PPG ground truth, making it a perfect dataset to evaluate various algorithms for extracting PPG, measuring heart rate, heart rate variability from video. The dataset contains facial videos and Blood Volume Pulse (BVP) data captured concurrently.

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This is the protein PDB dataset for the article "Novel Algorithm for Improved Protein Classification Using Graph Similarity". This dataset consists of 9 classes of proteins.

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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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Many companies, e.g., Facebook and YouTube, use the REST architecture and provide REST APIs to their clients. Likeany other software systems, REST APIs need maintenance and must evolve to improve and stay relevant. Antipatterns—poor designpractices—hinder this maintenance and evolution. Although the literature defines many antipatterns and proposes approaches for their(automatic) detection, theircorrectiondid not receive much attention.

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A Course on Literature Searching/Compiling/Understanding for Support of Research/Projects

Unzip file to see supplementary materials.

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