esa-sceva/satcom-chunk-collection huggingface dataset:
# Edge AI for Real-Time Anomaly Detection in Smart Homes
## 4 Experimental Setup
### Evaluation Metrics  
The performance of the anomaly detection models was evaluated based on the following key metrics:  
* **Accuracy, precision, recall, and F1-score**: Used to assess detection performance.
* **Inference time**: Measured the time taken for on-device anomaly monitoring on the Raspberry Pi 4 compared to a cloud-based model execution.
* **Memory usage**: Evaluated the model footprint on edge devices, ensuring feasibility for deployment on resource-limited hardware.
* **Energy consumption**: Measured using power monitoring tools to compare the runtime efficiency of Isolation Forest and LSTM Autoencoder.  
Latency tests showed that the quantized LSTM Autoencoder model achieved inference speeds under 47.3 ms on the Raspberry Pi 4, demonstrating its suitability for real-time edge deployment. Event-triggered execution resulted in a 35% reduction in power usage compared to continuous monitoring across both anomaly detection models, proving its efficiency in resource-limited edge environments.  
With this experimental setup, the following section presents the results obtained from on-device anomaly monitoring, comparing model accuracy, energy efficiency, and computational performance between Isolation Forest and LSTM Autoencoder.
# Modeling of a Coal Transshipment Complex Based on a Queuing Network
## 2 Subject Model
### Description of Structural Elements  
Trains arrive at the receiving yard with six tracks at Toki station, where locomotive uncoupling, then technical and commercial inspections are carried out, and Daltransugol JSC locomotives are coupled to coal trains. According to the regulations, the time required to complete all operations is 60-110 min. Empty cars from Daltransugol JSC arrive at the coal yard of Toki station, where trains are formed and dispatched. Most of the Russian railway network is electrified. There are relatively small areas with diesel locomotives. In particular, such locomotives move coal trains from Toki station to Daltransugol JSC and back.  
Terminal 1 and Terminal 2 are connected to Toki by two routes, with an average running time of 10 min. They include 10 and 7 tracks, respectively, with an effective length of 71-75 gondola cars. The 10 th track of Terminal 1 has two portable complexes for crushing frozen coal, with a capacity of 150 cars per day each.  
Figure 1: Location map and photo of Daltransugol JSC.  
Figure 2: Scheme of the coal transshipment complex.  
The railway cargo yard (hereinafter \"cargo yard\") consists of two unloading complexes, which includes a thrust path, a building for rail car thawing, a car dumper, and a crushing machine (crusher). The car dumper handles two cars at a time, with a maximum output of 34 cars per hour. However, according to data for 2022, the unloading time for a train of 70 gondola cars is 210-220 min or 6 min per pair. Crusher productivity is 3500 tons/hour.  
The conveyor system includes conveyor belts located both outdoors and indoors. In particular, the coal transfer between belts is performed at closed transfer stations to reduce the amount of coal dust. The productivity of one belt averages 4200 tons/hour.  
The coal warehouse (hereinafter \"warehouse\") consists of five open areas with a total capacity of 1.2 million tons. Based on the results of an analysis of open-source satellite images in February 2024, we estimate its filling at 20-25%. Four stacker reclaimers (hereinafter \"SR\", see Figure 3) transfer coal from the conveyor system to the warehouse and back; the productivity of each is up to 7000 tons/hour.  
The berthing cargo yard includes the berth itself for two vessels and two ship-loading machines of the conveyor type. The throughput capacity of each machine is 4200 tons/hour.
# A Novel Fractional Sine Chaotic Map and Its Application to Image Encryption and Watermarking
## 5 Analysis of Image Encryption and Decryption
### Statistical Analysis
#### 5.1.2 Histogram Analysis  
Investigating the image histograms provides information about pixel distribution, and in the cryptosystem, this provides insight into understanding the system's security against statistical attacks. Images with uniformly distributed pixels are known to withstand such attacks. A comparison is carried out between the input and encrypted image histograms. From Figure 11, it can be observed that the input plain image histogram contains a random distribution, while the pixels in the encrypted image are uniformly distributed between \([0,255]\). Thus, our scheme is clearly secure against statistical attacks.
# GenConViT: Deepfake Video Detection Using Generative Convolutional Vision Transformer
## 4 Evaluation
### Experimental Results and Discussion  
In this section, we present the experimental results and discuss the performance of our proposed GenConViT model.  
Table 4 summarizes the accuracy of the proposed GenConViT model, as well as its internal networks A and B, on the evaluation datasets. The table also shows its accuracy for the real and fake samples separately, to show potential discrepancies in its performance. Note that the TIMIT dataset does not contain any real videos.  
The results demonstrate that GenConViT delivers strong performance across various datasets. The individual networks A and B also both demonstrate decent performances, but are outperformed by their combination. While networks A and B individually achieve 94% accuracy on average, the full GenConViT ensemble improves upon the average accuracy of network A by 2.2% and network B by 1.79%, indicating the benefit of using both models for detection. From Table 4, we can also see that the model has an average 5% increase in accuracy on the Celeb-DF (v2) dataset. Additionally, its measured performance is similar for both real and fake videos, except for Celeb-DF (v2), for which it has a worse accuracy in detecting real videos compared to fake videos.  
For completeness, Tables 5 and 6 present the AUC value and F1 score, respectively. To further investigate GenConViT's performance, Figure 3 presents the resulting ROC curve. Both networks A and B lead to relatively similar results.  
Overall, the proposed GenConViT model has an average accuracy of 95.8% and an AUC value of 99.3% across the tested datasets. These results highlight our model's  
It is worth noting that the testing subdatasets of the DFDC and FF++ are usually not shared in the scientific literature. Hence, the exact datasets evaluated may differ, although comparing the reported values gives an idea of how their performances compare. Table 1 lists the datasets used by other deepfake detection models for training and evaluation. Note again that Table 1 shows that our dataset is trained and evaluated using the most datasets.  
From Table 7 (DFDC dataset), we can observe that previous approaches achieved accuracy values ranging from 91.5% to 98.24% on the DFDC dataset. Notable models included [PERSON] [18] (91.69%), [PERSON] [84] (92.02%), and [PERSON] [69] (97.2%). Some models excelled in additional metrics, such as the STDT [28] model, with its 97.44% accuracy, 99.1% AUC, and 98.48% F1 score. In comparison to previous approaches, our proposed model demonstrated an excellent performance, achieving an average accuracy of 98.5%, an AUC of 99.9%, and an F1 score of 99.1%.
# Drone Arc Routing Problems and Metaheuristic Solution Approach
## 5 Computational Results
### Test Problems Generated for DARP  
Seven differently sized test problems were generated for the DARP-DD. These test problems are used to test the performance of the proposed metaheuristic algorithm. The graphs are disconnected and consist of more than one component. The information about the test problems is presented in Table 4.  
In Table 4, the first column presents the name of the test problem. Since the graphs to be used in the DARP-DD are disconnected, the number of components on the graph is given in the second column. The third column contains the number of nodes on the graph. The number of required and non-required edges on the graph is shown in the last two columns. The required edges on the graph can be curved or linear. The number of non-required edges in the graph is calculated by the number of nodes. The disconnected graph is converted to complete graph since the drone can directly traverse between all nodes on the graph. Since all node pairs are connected in a complete graph, this graph will have \((n\times(n-1))/2\) non-required edges with \(n\) nodes. The largest-sized problem generated is DARP-7, which has 34 components, 200 required edges, and 18,915 non-required edges. There is detailed information about the test problems shown in Table 4 at [[https://dataandresults.wixsite.com/darp](https://dataandresults.wixsite.com/darp)]([https://dataandresults.wixsite.com/darp](https://dataandresults.wixsite.com/darp)), accessed on 20 June 2023.  
The algorithm can determine the number of drones that will service the required edges in the test problems. However, there is a need for the maximum number of drones to be used. The maximum number of drones to be used in the test problem can be calculated by the equation given below.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Test Problems**} & **Number of** & **Number of** & **Number of** & **Number of** \\  & **Components** & **Nodes** & **Required Edges** & **Non-Required Edges** \\ \hline DARP-1 & 5 & 19 & 17 & 171 \\ DARP-2 & 6 & 29 & 28 & 406 \\ DARP-3 & 10 & 46 & 44 & 1035 \\ DARP-4 & 11 & 64 & 64 & 2016 \\ DARP-5 & 19 & 87 & 84 & 3741 \\ DARP-6 & 17 & 98 & 100 & 4753 \\ DARP-7 & 34 & 195 & 200 & 18,915 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Generated test problems.  
\[k_{max}=\left(\left(\sum_{(i,j)\in E_{R}}d_{ij}\right)+(n-1)\times d_{max}\right)/Q \tag{4}\]  
The maximum number of drones used in the solution of the problem is calculated by considering the total energy demand values of the required edges, the maximum energy demand value between pairs of nodes (while deadheading) on the graph, the smallest number of connections between components (number of components--1), and the drone energy capacity. The value of \(k_{max}\) is rounded up if the number of drones is not an integer.
# Multi-Population Parallel Wolf Pack Algorithm for Task Assignment of UAV Swarm
## 4 Experiments of Task Assignment for UAV Swarm Using MPPWPA
### The Results and Analyses of Simulation Experiments
#### 4.2.3 Simulation Results in Scenario 3  
According to the analysis of the optimization performance in the first two scenarios, the proposed MPPWPA was effective in solving the task assignment problem and had good robustness to control parameters. In order to verify its effectiveness for problems with higher dimensional variables, we applied it in scenario 3, where the solution space was 150-dimensional. As shown in Figure 20 and Table 10, MPPWPA exhibited satisfying performance, especially in terms of convergence time, compared with the PSO, GA, ABC and WPA. Through the statistical data, it was found that the convergence time of the MPPWPA was 62% less than that of the WPA, which is of great significance to the research of UAV swarm task assignment.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline _Num_ & \multicolumn{2}{c}{**2**} & \multicolumn{2}{c}{**4**} & \multicolumn{2}{c}{**8**} & \multicolumn{2}{c}{**16**} \\ \hline _Pm_ & **Ave.** & **Std.** & **Ave.** & **Std.** & **Ave.** & **Std.** & **Ave.** & **Std.** \\ \hline \(1\) & 259.7 & 15.5 & 251.7 & 16.5 & 263.9 & 16.2 & 254.9 & 20.6 \\ 0.8 & 252.6 & 15.1 & 257.1 & 19.3 & **250.6** & 13.6 & 257.2 & 13.5 \\ 0.6 & 257.4 & 20.9 & **248.27** & 14.1 & **250.6** & 13.0 & 254.2 & 14.0 \\ 0.4 & **249.5** & 14.4 & 255.9 & 15.1 & 254.1 & 18.1 & **251.9** & 15.3 \\ 0.2 & 252.4 & 14.5 & 254.8 & 18.4 & 260.2 & 14.5 & 252.7 & 14.9 \\ 0 & 259.9 & 15.2 & 251.7 & 13.8 & 261.7 & 21.3 & 258.5 & 15.1 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Objective function value with different \(P_{m}\) in scenario 2.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline _Num_ & \multicolumn{2}{c}{**2**} & \multicolumn{2}{c}{**4**} & \multicolumn{2}{c}{**8**} & \multicolumn{2}{c}{**16**} \\ \hline _Pm_ & **Ave.** & **Std.** & **Ave.** & **Std.** & **Ave.** & **Std.** & **Ave.** & **Std.** \\ \hline \(1\) & 108.5 & 41.2 & 70.3 & 36.1 & 64.1 & 19.7 & 65.2 & 15.4 \\ 0.8 & 109.2 & 47.0 & 72.1 & 24.2 & 63.7 & 22.5 & 64.9 & 12.3 \\ 0.6 & **101.1** & 37.9 & 68.3 & 21.1 & 69.2 & 28.8 & 57.1 & 10.0 \\ 0.4 & 109.0 & 29.8 & **67.4** & 27.9 & **53.8** & 8.4 & 47.1 & 6.9 \\ 0.2 & 110.5 & 40.87 & 78.1 & 23.5 & 54.4 & 16.8 & **45.0** & 9.8 \\ 0 & 110.1 & 42.9 & 80.8 & 17.5 & 66.7 & 8.13 & 65.7 & 1.4 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Convergence time with different \(P_{m}\) in scenario 2.
# Password Cracking with Brute Force Algorithm and Dictionary Attack Using Parallel Programming
## 1 Introduction
The objective of this study is to determine the most efficient and effective approach for password cracking by evaluating the performance of various hardware configurations for parallel brute force and dictionary attacks. Furthermore, the objective aims to investigate the impact of character sets on password-cracking performance, specifically the inclusion of special characters. The results of this study demonstrate the significant impact that hardware configurations, such as multiple cores or powerful GPUs, can have on the efficiency of password-cracking techniques. By leveraging these advanced hardware tools, security professionals can achieve a dramatic increase in password-cracking speed and accuracy, which is critical in the fight against cyber threats. These findings underscore the importance of ongoing research and development in the field of hardware configurations for password cracking and suggest that future advances in this area could have a transformative effect on the field of cybersecurity. The rest of this work is structured in the following manner. In Section 2, a review of 16 related papers of literature is presented. The proposed techniques are given in Section 3. The proposed techniques used are as follows: implementing parallel processing in brute force and dictionary attacks using Python language, implementing the dictionary attack with OpenMP in C++, and implementing brute force and dictionary attacks using Hashcat. In Section 4, there are research studies that encompass both the experimental design and the optimization technique or method used to search for parameters. In Section 5, results and discussion can be found. The analysis and comparison with other research studies are presented in Section 6. Finally, Section 7 contains the conclusion and suggestions derived from the implementation.
# Analysis of Vertical Earth Pressure Acting on Box Culverts through Centrifuge Model Test
## 5 Variability Analysis of Vertical Earth Pressure Acting on Box Culvert  
### Estimation of Bias Factor  
The measured vertical earth pressure (\(\sigma_{v,measured.}\)) shown in Figures 9 and 10 under the loading and unloading conditions and the theoretical vertical earth pressure (\(\sigma_{v,thro.}\)) computed using Equation (1) were compared using Equation (2). The ratio \(\lambda_{\sigma}\) represents the same concept as the bias factor (ratio of measured and nominal value) in load and resistance factor design (LRFD), and it can be used to judge the uncertainty of the measured vertical earth pressure and the appropriateness of the theoretical vertical earth pressure.  
\[\lambda_{\sigma}\text{= }\sigma_{v,measured}\text{/}\sigma_{v,thro.} \tag{2}\]  
When \(\lambda_{\sigma}\) is close to 1.0, the measured and theoretical values are similar, meaning that the test processes are accurate, and appropriate theoretical values are obtained. Under the assumption that the measured earth pressure is accurate, if \(\lambda_{\sigma}\) is less than 1.0, the theoretical earth pressure is overestimated; if \(\lambda_{\sigma}\) is greater than 1.0, it means that the theoretical earth pressure is underestimated. In this case, the theoretical earth pressure should be corrected appropriately. Similarly, under the assumption that the theoretical earth pressure is correct, if \(\lambda_{\sigma}\) is less than 1.0, the measured vertical earth pressure is lower than the actual value; if \(\lambda_{\sigma}\) is greater than 1.0, the measured vertical earth pressure is greater than the actual value. These scenarios may occur owing to the inherent variability of the ground or owing to measurement errors.  
Figure 11 shows the bias factor calculated using Equation (2). Most of the bias factors exceed 1.0, and they tend to decrease as the gravitational acceleration increases. In addition, as the cover depth increases, the data scatter increases, and it is considered that an error is introduced in in the measurement process or an inappropriate theoretical value is computed owing to the uncertainty of the soil above the box culvert.  
Figure 10: Theoretical vs. measured vertical earth pressure under the (**a**) loading and (**b**) unloading conditions.
# Isoline Tracking in Particle-Based Fluids Using Level-Set Learning Representation
## 4 Experiments
### Experimental Environment  
The SPH fluid simulation environment was implemented using Python version 3.9.7, and for training, Pytorch version 1.11.0 and CUDA were utilized. The computer environment used was Windows 11, AMD CPU 5800X, NVIDIA GPU RTX 3080 with 12 GB VRAM, and 64 GB RAM.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline
**Excluded Feature** & \(\mathbf{x}\) & \(\mathbf{y}\) & \(\mathbf{p}\) & \(\mathbf{p}\) & \(\mathbf{N}\) & None \\ \hline \(Trainingloss\) & \(7.3320\times 10^{-6}\) & \(1.2251\times 10^{-5}\) & \(4.1759\times 10^{-5}\) & \(0.0005\) & \(8.5889\times 10^{-6}\) & \(9.0171\times 10^{-6}\) \\ \(Validationloss\) & \(1.0543\times 10^{-5}\) & \(1.7174\times 10^{-5}\) & \(5.9174\times 10^{-5}\) & \(0.0007\) & \(1.2354\times 10^{-5}\) & \(1.2686\times 10^{-5}\) \\ \hline \end{tabular}
\end{table}
Table 2: Training loss and validation loss after removing a specific feature.  
Figure 5: Graphs of training and validation loss for the experiments based on Table 2.  
The dataset comprised 5,672,400 particles (3912 frames \(\times\) 1450 particles), with 70% used for training and 30% for validation. Four models were set as comparison groups: LR, LSTM, learning representation (1-layer), and our method, where LR is the linear regression model from Lazypredict.  
The learning representation (1-layer) was trained with a learning rate of 0.0001, \(\frac{1}{2}\) after 50,000 epochs, and applied early stopping at 100,000 epochs. The optimizer used was Adam, with the layer configuration being linear with 5 inputs, 32 in the hidden layer, and 1 output. The loss function used was MSE, and the activation function was LeakyReLU to include negative values in the learning process.  
The LSTM was trained with a learning rate of 0.0001 and applied early stopping at 500 epochs. The optimizer used was Adam, with the layer configuration set to 6 inputs for LSTM, 1000 in the hidden layers, and a linear layer with 1000 inputs, with the output set to 1. The loss function used was MSE, and the number of input frames was set to 3.  
The method proposed in this paper was trained with a learning rate of 0.0001, \(\frac{1}{2}\) after 50,000 epochs, and applied early stopping at 100,000 epochs. The optimizer used was Adam, with the layer configuration being linear with 5 inputs, 64 and 32 in the hidden layers, and the output set to 1. The activation function was LeakyReLU, used to include negative values in the learning process. Dropout was not used in any of the models.  
Experiments were conducted in a total of five scenes for the comparison of each model and to verify the performance of the proposed model. Information will be explained in detail in the following section.
# Renyi Entropy-Based Adaptive Integration Method for 5G-Based Passive Radar Drone Detection
## 1 Introduction
Then, the range resolution will be limited and variable, which renders inferior the properties of the passive radar. Only when the network resources are fully used is it possible, for example, to detect small objects such as drones. Thus, there is a clear need for an algorithm that allows the selection of time intervals with a dense allocation of resources in time and frequency. Thanks to this, it will be possible to make the results of radar processing independent of the momentary drops in the energy of the signal emitted by the 5G network. Content-dependent transmission is nothing new, as it is found in other systems such as WiFi [18; 19; 20] and FM radio [21; 22]. In [21], an approach for FM transmitter selection is proposed. It is based on instantaneous effective bandwidth measurement. It is evaluated in this paper in regard to using 5G networks as illuminators of opportunity for passive radar, as a reference method. In other works so far, especially those describing the possibilities of using the 5G network for target detection, no studies show how to adaptively select the time interval for signal integration. Usually, the work comes down to manual signal part selection with suitable parameters from the point of view of radiolocation. In [15; 16; 17], 5G signals with a full amount of content were employed, and the content dependency issue was not addressed. In [9], this topic was highlighted but was not solved. Thus, this paper is the first (to the best of the authors' knowledge) to address and solve this issue in a 5G-based passive radar system. Future 5G-network-based passive radars could be used to secure critical infrastructure, so adaptive algorithms should be provided to decide on the right moment of integration.
# Seismic Response Analysis of Buried Nuclear Power Plant Structures in Non-Bedrock Sites
## 1 Introduction
### Comparison Between Numerical Simulation and Experimental Results  
A simplified analysis model of a site with a liquefiable interlayer and a nuclear power structure was established. The numerical simulation method and platform introduced in this chapter were applied to analyze the seismic response of the model. The effectiveness of the numerical simulation method proposed in this study was verified by comparing the results with existing centrifuge test data, including the soil excess pore pressure ratio, site acceleration, structural acceleration, and the dynamic strain of the structure.