esa-sceva/satcom-chunk-collection huggingface dataset:
# Unsupervised Transformer-Based Anomaly Detection in ECG Signals
## 4 Results and Discussion
### MIT-BIH Arrhythmia Dataset Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**No.** & **No.** & **Hidden** & **F1** & **Accuracy** & **Recall** & **Precision** \\
**Blockes** & **Heads** & **Size** & & & & & \\ \hline
1 & 16 & 32 & 91.6\% & 88.6\% & 97.5\% & 86.3\% \\
1 & 16 & 64 & 91.9\% & 89\% & 98.3\% & 86.3\% \\
1 & 16 & 128 & 91.8\% & 88.7\% & 98.1\% & 86.2\% \\
1 & 16 & 256 & 91.6\% & 88.4\% & 98.4\% & 85.6\% \\
1 & 32 & 64 & 92.1\% & 89.2\% & 98.3\% & 86.6\% \\
1 & 32 & 128 & 92\% & 89.1\% & 98.2\% & 86.6\% \\
1 & 32 & 256 & 91.73\% & 88.6\% & 98.4\% & 85.9\% \\
2 & 16 & 32 & 92.1\% & 89.2\% & 98.2\% & 86.6\% \\
2 & 16 & 64 & 91.8\% & 88.8\% & 98\% & 86.1\% \\
2 & 16 & 128 & 91.8\% & 88.8\% & 98.5\% & 86\% \\
2 & 16 & 256 & 91.71\% & 88.6\% & 98.4\% & 85.8\% \\
2 & 32 & 32 & 92.2\% & 89.4\% & 98\% & 87.1\% \\
2 & 32 & 64 & 92.31\% & 89.5\% & 98.2\% & 87.1\% \\
2 & 32 & 128 & 91.8\% & 88.7\% & 98.4\% & 86\% \\
2 & 32 & 256 & 91.8\% & 88.8\% & 98.6\% & 85.99\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Transformer anomaly detection results for the MIT-BIH test dataset. Figure 9: ROC curve of the MIT-BIH testing set. splitting, but they had the lowest F1 and accuracy with 76.55% and 87.77%, respectively. Our model, compared with state-of-the-art application anomaly detection methods using either supervised or unsupervised deep-learning models, outperforms them. However, this model's performance in anomaly detection still lacks a satisfactory F-score.
## 5 Formal Verification of the 5 GAKA-LCCO Protocol  
To simplify the formal verification of the 5 GAKA-LCCO protocol, we assume that:  
* The parties of the 5 GAKA-LCCO protocol shown in Figure 5 are simplified as the UE, SN, and HN.
* There is a session key \(K_{SN,HN}\) between the SN and the HN, and it is secure.
* \(ngKSI\) and \(ABBA\) do not affect the security of the 5G AKA protocol, so they are ignored here.  
According to these assumptions, the 5 GAKA-LCCO protocol shown in Figure 5 is simplified as follows:  
\begin{tabular}{l l}
1. & \(SN\to UE\): \(RAND_{\text{SN}}||T_{\text{SN}}\). \\
2. & \(UE\to SN\): \(SUCI\). \\
3. & \(SN\to HN\): \(\{RAND_{\text{SN}}||T_{\text{SN}}||SUCI||SNN\}_{K_{\text{SN},HN}}\). \\
4. & \(HN\to SN\): \(\{RAND_{\text{SN}}||K_{\text{SEAF}}||SUPI||RAND||AUTN\}_{K_{\text{SN},HN}}\). \\
5. & \(SN\to UE\): \(RAND||AUTN\). \\ \end{tabular}  
In this section, in order to evaluate the security of the 5 GAKA-LCCO protocol, we mainly employ two formal verification methods, including security proof by using the strand space model [29; 30] and security simulation by the use of the Scyther tool [32; 33]. Moreover, we choose the Dolev-Yao attacker model to check the security of the 5 GAKA-LCCO protocol. In this attacker model, the attacker can completely control the network and conduct a series of attacks.
# Static Wind Reliability Analysis of Long-Span Highway Cable-Stayed Bridge in Service
## 3 Functional Function and Parameter Distribution for Aerostatic Instability Analysis
### Probability Distribution Function of Wind Speed at Bridge Site  
According to the statements in Wind-resistant Design Specification for Highway Bridges (JTG/T 3360-01-2018) in China: \"When the weather station in the area where the bridge is located has sufficient unmeasured data of continuous wind speed, the probability distribution type of annual maximum wind speed of the local weather station can be adopted.\" [22], the wind speed data from local meteorological stations are used, and extreme value type-I distribution is used to fit the probability distribution function of wind speed at the bridge site in this research.  
Based on the existing wind speed measurement data provided by Dinghai Meteorological Station [26] which is close to Xiangshan Harbor Cable-stayed Bridge, the wind speed data used for fitting in this paper is selected including the 10 min annual average maximum wind speed for 30 years from 1987 to 2016. The detailed wind speed data is shown in Ref. [27].  
The cumulative probability distribution function of wind speed described by Extreme value type-I distribution can be expressed as:  
\[F(x)=\exp\{-\exp[-a(x-b)]\} \tag{15}\]  
where \(a\) and \(b\) can be inferred by the moment method in accordance with the relationship between mathematical expectation \(E_{x}\) (or called average value), variance and these two parameters \(a\) and \(b\), namely  
\[\begin{cases}E_{x}=\frac{\gamma}{a}+b\\ \sigma=\frac{\pi}{\sqrt{6}a}\end{cases} \tag{16}\]  
which \(\gamma\) is [PERSON]'s constant, \(\gamma\approx 0.57722\). Then the parameters \(a\) and \(b\) can be calculated on basis of the average value and average square error of the wind speed. Based on the selected wind speed data at the meteorological station, the cumulative probability distribution function of wind speed at the meteorological station is determined as follows:  
\[F(x)=\exp\{-\exp[-0.2243\cdot(x-7.65541)]\} \tag{17}\]  
However, the height of the main girder is different from the corresponding height of the wind speed data at the meteorological station, so the calculated reference wind speed should be transformed into the wind speed at the bridge deck of the main girder. The wind speed relationship at different heights is as follows:  
\[\frac{U_{Z2}}{U_{Z1}}=\left(\frac{Z_{2}}{Z_{1}}\right)^{\alpha_{0}} \tag{18}\]  
where \(U_{Z2}\), \(U_{Z1}\), and \(\alpha\) stands for the wind speed value at height \(Z_{2}\), at height \(Z_{1}\) and the impact factor of surface roughness, respectively. Then the parameters \(a\) and \(b\) can be redetermined, thus the cumulative probability distribution function of wind speed at the bridge deck is rewritten as:  
\[F(x)=\exp\{-\exp[-0.184\cdot(x-9.361)]\} \tag{19}\]
## 4 Characteristics of Different Time Distribution Systems
### Network Time Protocol
#### 4.1.5 Application  
NTP is widely used by millions of hosts on the Internet to synchronize their clocks, ensuring accurate timekeeping essential for the proper functioning of many network applications [79] inside the substations and control centers.  
When data connections are available and synchronization precision on the order of a few milliseconds is sufficient, NTP can be used to synchronize remote devices. In power systems, these devices typically handle noncritical measurements (e.g., weather data) or lower-speed events (e.g., market pricing signals or physical security notifications) [80].  
Due to the accuracy, reliability, and security demands, Precision Time Protocol is usually adopted for time synchronization in modern power system applications listed in Section 2[81].
# A New Internet of Things Hybrid VLC/RF System for m-Health in an Underground Mining Industry
## 4 Network Architecture  
As shown in Figure 2, an IoT approach is provided by connecting multiple wearable sensors placed in miners that connect to the Internet. These wearable sensors collect and share primary vital sign data for use and evaluation by health-related organizations. Wearable sensors must be connected to a network so that the collected data can be saved and shared. For this purpose, VLC lamps and RF receivers are used. VLC lamps that use different frequencies are responsible for sending an identifier for the correct positioning of the miner, collecting the health data of the miners, and providing connectivity to the network and the Internet, an uplink based on RF technology is used. The monitoring center may have the ability to make immediate decisions or send the data over the Internet for further analysis to storage in a database using a standard recognized as HL7 [35].
# Safety Performance Evaluation of Ultra-High-Performance Concrete Semi-Assembled Barrier
## 5 Analysis of the Crash Simulation Results
### Impact Force  
In this study, the collision forces of the three vehicles colliding with a newly assembled barrier during the simulation process were extracted (Figure 5). It can be seen from the figure that the peak value of the collision force of the car to the barrier was larger than that of the first collision of the bus and the truck. This is because of the small height of the car. The body directly collides with the barrier, and the stiffness of the body material is large, resulting in a large collision force. The first collision between the bus and truck was caused by a crash between the tire and barrier. The tire has a specific elasticity, and the climbing phenomenon can consume some of the energy, which reduces the collision force between the vehicle and the barrier to some extent.  
Figure 4: A 10-ton bus-barrier coupling model.  
Figure 5: Time history of the barrier collision force.  
The Design Guidelines for Highway Safety Facilities (JTG D81-2017) [26] provide a vehicle collision load formula (Equation (11)) to calculate the theoretical collision force, which has been used in several countries to obtain the design load using the predicted lateral impact force [39]. In the United States, the predicted lateral impact force by this method has been compared with the magnitude of the measured impact force in the crash test, and the prediction accuracy of the formula was found to be \(\pm\)20%. The relevant parameters of the three models are listed in Table 5.  
\[F_{\text{Xmax}}=\frac{\pi}{2}\cdot\frac{mv_{1}^{2}\text{sin}^{2}\theta}{2000[ \text{C}\text{sin}\theta-b(1-\cos\theta)+D]} \tag{11}\]  
In which:  
\(F_{Xmax}\)--Maximum lateral force acting on the barrier by a vehicle (kN);  
\(m\)--Mass of vehicle (kg);  
\(v_{1}\)--Velocity of vehicle (m/s);  
\(\theta\)--Collision angle of vehicle (\({}^{\circ}\));  
\(C\)--Distance of the vehicle's center of gravity from the front bumper (m);  
\(b\)--Half of the vehicle width (m);  
\(D\)--Maximum lateral dynamic deformation value of the barrier (m), \(D=0\) for concrete barrier.  
According to the calculations in Equation (11) and Table 5, the theoretical collision forces of the three models are 189, 333, and 313 kN, respectively. Considering that the magnitude of the collision force changes dynamically when a car collides with a barrier, the peak dynamic load generally exceeds the static design load [40]. In this study, the 25 ms average of the collision forces for the three models was extracted (Figure 5). The peaks of the 25 ms collision force F\({}_{25\text{ms}}\) of the three models were 182, 307, and 324 kN, respectively. The errors compared to the theoretical collision force were 3.8%, 7.8%, and 3.4%, respectively, which were all within 20%. Therefore, it is considered reliable to use the peak of the 25 ms collision force as a reference value for the static load design.
## 3 Modeling and Data Sources  
The research framework of this study, as illustrated in Figure 2, begins with the collection of fire incident data and meteorological factors. This is followed by a detailed analysis of the relationship between fire data and meteorological factors, with a focus on the correlation between meteorological factors and the occurrence of forest fires. Upon establishing this correlation, a stepwise regression model is constructed, thoroughly fitted, and predicted. The model is validated through uncertainty analysis to enhance the reliability of the research. The third step involves building an ARIMA model based on fire frequency and affected area data, confirming the model order through ADF testing, selecting the optimal model according to AIC, SC, and HQC criteria, and conducting fire prediction after passing the residual test. Finally, the stepwise regression model and ARIMA model are compared using the RMSE mean square error test, with results indicating the ARIMA model's superior performance in fire prediction.
## 5 Simulation Results
### Verification of Identification Model  
#### 5.1.1 Power Generation System  
The power generation system is affected only by the fuel, so it can be considered as a SISO system. According to the nonlinear analysis in Section 2, the nonlinearity of the power generation system is weak, so the conventional recursive least squares method is used to identify the model of the power generation system. The input sequence shown in Figure 5(a) is applied to the MGT-CCP system to obtain the output data shown in Figure 6b. The model of the power generation system was identified using the first 3000 s of data. The identified input and output models are as follows:  
\[y_{t}-1.5691y_{t-1}+0.5895y_{t-2}=176940u_{t-1}-42160u_{t-2} \tag{23}\]  
The accuracy of the identification model is verified using the last 3000 s of the data of Figure 6. The verification results are shown in Figure 7. It can be seen that the identified linear model has high accuracy, which also indicates that the nonlinearity of the power supply system is very weak, which is consistent with the nonlinear analysis results in the second section.
# Data-Based Modelling of Chemical Oxygen Demand for Industrial Wastewater Treatment
## 2 Materials and Methods
### Wastewater Treatment Plant  
Data from a certain wastewater treatment plant related to a paper manufacturing plant were utilised in this study. A simplified schematic of the wastewater treatment plant in question is depicted in Figure 1. The wastewater leading to the WWTP originates from multiple sources. These sources include paper machines and the debarking process. The wastewaters from paper machines flow to the wastewater tank as individual streams. This tank also includes the wastewater from debarking. In addition, one wastewater stream (paper machine filtrate) enters the pumping station after primary clarification. The positions where online COD is measured are indicated in Figure 1.  
The wastewater purification process at the plant consists of primary clarification as a primary treatment method and activated sludge process as a secondary treatment. The primary clarifier plays a crucial role in removing pollutants from the wastewater. It operates  
Figure 1: Simplified schematic of the studied wastewater treatment plant and activated sludge process.  
by allowing the settling of heavier or more readily separable solids at the bottom of the clarifier, forming a sludge layer, while the clarified water is collected from the top. The primary clarification helps improve the overall efficiency and cost-effectiveness of the process. After the primary clarifier, the paper machine filtrate stream is mixed with the rest of the wastewater. This combined wastewater is then pumped into a tank to wait for aeration. At this point, the wastewater quality measurements that are important for assessing the effectiveness of the treatment process and monitoring the performance of the plant are taken. Parameters such as COD, temperature, pH and other indicators provide valuable information about the overall condition of the wastewater going into aeration. The wastewater is then divided into two streams for the rest of the wastewater treatment. Next, the wastewater streams are sent to biological wastewater treatment, where the wastewater is mixed with air in a tank during the aeration process. The continuous circulation of air promotes the degradation of organic matter present in wastewater through the action of microorganisms. Following the biological treatment (aeration) stage, the wastewater undergoes the final treatment step in a secondary clarifier. In this step, the remaining pollutants and sludge are separated and removed from the wastewater, further improving its quality. The sludge is collected and further processed for disposal or potential reuse. Once the wastewater has undergone all the mentioned treatment steps, it is considered sufficiently treated and ready for discharge into the river. This final step ensures that the purified wastewater meets regulatory requirements and minimises its impact on the receiving water body.
# A Look at the Importance of Chirality in Drug Activity: Some Significative Examples
## 8 Proton Pump Inhibitors (PPIs)  
Nowadays, proton pump inhibitors (PPIs) are among the most prescribed classes of drugs used to treat several gastrointestinal conditions, such as gastroesophageal reflux or Barrett's esophagus, as well as laryngopharyngeal reflux [113]. Omeprazole (**40**), the first PPI studied, contains a stereogenic sulfur atom. It represents a pro-drug, which is rendered achiral via reduction or oxidation of the sulfoxide. Omeprazole, represents a case in which the single-enantiomer offered little clinical advantage over the racemate; however, it was introduced into the market as a single enantiomer as a patent protection strategy of the pharmaceutical companies against the generic competitors [114]. Esomeprazole is the \(S\)-isomer of omeprazole with favorable pharmacokinetic profile relative to omeprazole [115]. After some years, another PPI, namely lansoprazole (**41**), underwent the \"chiral switch\" as well; in 2009, Takeda launched the single dextrorotatory enantiomer, namely the (\(R\))-lansoprazole, or dexlassoprazole, on the USA market. Additionally, in this case, this enantiomer has not been proved to be superior to the racemate in terms of efficacy in the pre-approval studies [116].
# Virtual Pseudonym-Changing and Dynamic Grouping Policy for Privacy Preservation in VANETs
## 9 Analysis and Discussion
### Cost of Computation  
The cost of computation includes computation latency and vehicle communication latency. The computation latency consists of the time required for group formation, vehicle joining, and other related computations for applying privacy schemes. The communication latency is the time taken by vehicles to communicate with each other for setting up the privacy protection scheme. Figure 26 shows the comparative results of the proposed scheme DGVP and existing schemes CPS and CLPS regarding average computation cost. The CPS requires extra time for distance calculation with RSU, trip time calculation, and pseudonym calculation, which make its computation cost higher than both DGVP and CLPS. The CLPS has a lower computation due to the reduced number of cooperative vehicles in the region. DGVP has modest increase in computation time but provides robust privacy protection compared with CPS and CLPS. The average communication latency at different traffic densities is shown in Figure 27. The communication cost of the proposed scheme DGVP is higher than CLPS but lower than CPS. This is because of communication among the vehicles for the virtual pseudonym process and group communication. The cost of communication of CPS is high due to the communication of vehicles with road infrastructure as well as among the vehicles for the anonymization process. Our proposed scheme achieves improved results regarding vehicle location privacy protection at the cost of a small increase in computation and communication latency.  
Figure 26: Computation Latency (MS) at different vehicle traffic density.
## 3 Model Selection
### Methodology Selection and Rationale  
This study employs a variety of machine learning models to predict student academic performance, each of which has unique advantages that enable it to excel in different tasks and datasets. The linear regression model [21] is simple in structure, computationally efficient, and highly interpretable, making it suitable for small datasets. Ridge regression [22] effectively prevents overfitting through L2 regularization, improving generalization, and is particularly useful for addressing multicollinearity issues. Lasso regression uses L1 regularization for automatic feature selection [23], making it suitable for high-dimensional data and enhancing model interpretability. Decision tree regression [24] captures nonlinear relationships, is robust to outliers and missing values, and is easy to visualize. Random forests [25], by integrating multiple decision trees, improve predictive accuracy, assess feature importance, and reduce the risk of overfitting. Support vector machines [26; 27] perform exceptionally well with high-dimensional and small sample data, offering strong classification performance and robustness, capable of handling both linear and nonlinear data. Gradient boosting trees [28] iteratively build decision trees, improving model performance and handling complex data and nonlinear relationships. K-nearest neighbors (KNN) [29; 30] is intuitive, simple, does not require assumptions about data distribution, and is robust to noise and outliers. AdaBoost [31; 32] significantly enhances classification performance by adjusting sample weights, with strong adaptability and ease of implementation. Through the multi-model fusion framework, this study fully leverages the strengths of each model, significantly improving prediction accuracy and reliability, providing effective decision support for student education management.
# Transmission Power Control in Wireless Sensor Networks Using Fuzzy Adaptive Data Rate
## 1 Introduction
These studies take RSSI as one of the feedback parameters for balancing power consumption and communication quality. However, RSSI is not a reliable parameter for evaluating communication quality because it may be interfered with by noise. The control algorithms also adopt PER as feedback to avoid excessive errors, but it only reacts after errors occur. Considering the effect of noise, this study employs SNR as a feedback parameter. SNR is calculated by signal strength and noise, and it ameliorates the disadvantages when using RSSI and PER as the feedback parameters. The transmission power and data rate can be adjusted in advance of the errors occurring, preventing the rapid PER rises. In addition, the algorithm proposed in this study adopts mixed control, altering the transmission power and data rate simultaneously to obtain lower power consumption and better response time. Therefore, to extend battery life and maintain communication quality, an ADR and TPC mixed control algorithm using fuzzy systems is proposed in this study. The control algorithm can lower the power consumption while maintaining communication quality. The error count and error interval perform the inputs of a fuzzy system, and the corresponding fuzzy system output is Guard that is utilized for limiting the upper bounds of data rate and transmission power. The long-term experimental results are introduced to prove that the control algorithm can overcome environmental interference and achieve a low-power performance. The sensor nodes have reliable communication under an ultra-low-power consumption. If it is powered by batteries of 1200 mAh, the power consumption of the proposed approach is improved by 73%. The test results also indicate that the PER of all the nodes of the experimental group is approximately 1%. The contribution of this paper is to apply the Fuzzy Logic Control into WSN to decrease the impact of interference on environment and maintain the link quality with low-power consumption. Moreover, the proposed scheme has been implemented in real-world settings to verify its effectiveness and reliability. The rest of this paper is as follows. Section 2 introduces the system implementation, including the system architecture and implementation of TDMA. The corresponding fuzzy control algorithm and analysis of power consumption and performance in WSNs is introduced in Section 3, and Section 4 introduces the experimental results and discussions. Finally, the conclusion is given.