esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Activation by Ultrasound  
A limiting factor for heterogeneous reactions, either solid-liquid or liquid-liquid, is how to enhance mass transfer efficiently, thus favoring mixing. A priori, this mechanical effect can be achieved under vigorous stirring and/or heating. A parallel activation that often works under milder conditions is provided by ultrasonic waves, even using a conventional cleaning bath [37]. By virtue of cavitational implosion in liquids, high local energies are attained within the bubble and at the bubble interface, which globally increases surface area and exposes a fresh layer of the catalyst to the reacting partners. Accordingly, we have also investigated the use of ultrasonic radiation to activate the formation of ketal **11** from diol **9** using the most efficient catalyst, **HT-S** (Table 9). The reaction was carried out in a sealed vial under the same conditions, at 25 \({}^{\circ}\)C and under sonication. Since the acoustic radiation is not uniformly distributed and its energy is attenuated with distance from the emitter, we turned to a new class of cheap sonoreactors, which involve microstructured plastic bags (commercialized as BuBble(r) bag, Figure 12). It has been reported that such bags enhance cavitation and, therefore, considerably accelerate reactions involving the use of heterogeneous catalysts [38].  
Figure 11: Recycling experiment with catalyst **HT-S**.  
\begin{table}
\begin{tabular}{c c c} \hline
**Entry** & **Time** & **Yield** \\ \hline
1 & 1.8 h & 92\% \\
2 a  & 1.8 h & 93\% \\
3 b  & 1.0 h & 95\% \\ \hline \end{tabular}
\end{table}
Table 9: Formation of **11** through activation by ultrasonication.  
Results show a significant reduction in the reaction time by applying sonication to the reaction mixture in the vial (entry 1), maintaining excellent yields. This means that the transformation is robust enough and sensitive to the mechanical action of sound waves. As shown above, this reaction required one day when it was magnetically stirred at 25 \({}^{\circ}\)C. On the other hand, placing the vial inside a BuBble bag drove to a similar result (entry 2). However, when the bag was filled with water, the reaction was accelerated, and actionide **11** was formed in only one hour with excellent yield. This result is comparable to that observed in the reaction of the same diol at 60 \({}^{\circ}\)C (Table 8, entry 3). Overall, the cavitation intensifying bag appears to behave similarly to membrane emulsification, i.e., capable of making small droplets or smaller particles in the present case at low energy densities [39].
## 4 Numerical Simulation of Excavation and Support
### Peripheral Displacement of Tunnel
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Distance Between Monitoring Point and Tunnel Face/m** & \(\mathbf{-2}\) **D** & \(\mathbf{-1}\) **D** & \(\mathbf{-0.5}\) **D** & **0** & **1 D** & **2 D** & **4 D** \\ \hline Vault settlement at monitoring point/cm & 0.0267 & 0.1662 & 0.4334 & 2.950 & 4.711 & 4.987 & 4.998 \\ Percentage of total displacement/\% & 0.534 & 3.324 & 8.668 & 59.04 & 94.22 & 99.74 & 99.96 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Displacement at monitoring points of tunnel in trial propulsion stage. Figure 9: Curve of the displacement release rate around the tunnel face. excavation and support. Equations (14) and (15) and their fitting curves (Figure 9) are very useful for estimating the reserved deformation and the effect of supporting structures. The above theoretical analysis and numerical tunnel simulation reveal the changing law of the surrounding rock state during the process of excavation and support of a soft rock tunnel; however, it is based on an ideal state, which is different from an actual situation. When this is applied to practical engineering, we should also consider aspects of the actual engineering construction, such as the inhomogeneity of the rock mass, tectonic crustal stress, etc. The inhomogeneity of surrounding rock can be obtained by in-situ geological observation and advanced geological prediction. In this project, the occurrence and strike of phyllite should be considered. Except for some sections, most sections are fractured, the attitude of the bed is complicated, irregularity is obvious, and the inhomogeneity is clear. In terms of tectonic crustal stress, the project is located in a complex fault zone. The tectonic crustal stress is very complex, which has the greatest impact on the project. Its direct impact is the irregularity of deformation of the rock mass. Therefore, it was necessary to improve the above research results combined with field monitoring data.
## 1 Introduction  
### Overview  
With the rapid growth of networking technologies and the increasing number of cyber threats, ensuring effective cybersecurity has become a paramount concern. One crucial aspect of cybersecurity is the detection and prevention of unauthorized access and malicious activities within computer networks. Intrusion Detection Systems (IDS) play a vital role in monitoring network traffic and identifying potential security breaches. Traditional IDS methods rely heavily on signature-based approaches, which are limited in their ability to detect novel and sophisticated attacks. To overcome these limitations, researchers and practitioners have started to explore the integration of machine learning techniques into IDS design. Machine learning (ML) has demonstrated remarkable success in various domains, including natural language processing, computer vision, and pattern recognition. Leveraging ML algorithms in the realm of networking cybersecurity offers promising opportunities to enhance the accuracy and efficiency of intrusion detection systems. ML-based IDS models can learn from large volumes of network data, detect anomalous patterns, and adapt to evolving attack strategies [1; 2; 3]. This approach holds the potential to improve the overall security posture by reducing false positives and detectingpreviously unknown attacks. The design of IDS exploiting machine learning for networking cybersecurity involves several key components. Firstly, a robust and comprehensive dataset is required for training and evaluating the ML models. The proposed datasets encompass a wide range of network traffic patterns, including both normal and malicious activities, to enable effective learning. Secondly, suitable feature selection and extraction techniques are crucial to capture relevant information from the network data [4]. These features serve as input to the ML algorithms, enabling them to discern normal traffic from potential intrusions. Furthermore, the choice of ML algorithms plays a vital role in IDS design. Various algorithms, such as support vector machines, random forests, and deep learning architectures, have been investigated for intrusion detection. Each algorithm has its strengths and weaknesses, and the selection depends on factors such as the complexity of the problem, the availability of data, and the desired trade-offs between detection accuracy and computational efficiency. In this paper, we explore the design and implementation of intrusion detection systems that exploit machine learning techniques for networking cybersecurity. We examine various machine learning algorithms and methodologies that have been applied to IDS for both binary and multi-classification approaches. We analyze their strengths and weaknesses and discuss their suitability for different types of network environments and attack scenarios.
# Semantic Segmentation with High-Resolution Sentinel-1 SAR Data
## 2 Related Works
### Common Problems for SAR Image Segmentation  
Main problems encountered in this area are as follows:  
1. SAR images have a low resolution for segmentation tasks;
2. SAR image noise, backscattering and geometric distortion;
3. No easy access to a free, labeled dataset, though some SAR images can be accessed through paid services;
4. The numbers of training and test images in the datasets used in experiments are low due to the fact that the labeled dataset is not open source.
## 3 Calculation of Lightning Current Distribution Characteristics  
### The Influence of Lightning Peak Current  
ATP-EMTP is utilized to establish the transmission tower line model. Figure 4a depicts its relative position. The lightning current is modeled using a double exponential model with a waveform parameter of 2.6/50 us. The lightning stroke point occurs at the A-phase conductor in the center of the T\({}_{0}\) and T\({}_{\rm R1}\) spans. With a soil resistivity of \(\rho\) = 1000 \(\Omega\) m, the simulation results indicate that the lightning withstand level of shielding failure for a  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter Name** & **Numerical Value** \\ \hline Outside diameter/mm & 429 \\ \hline Steel thickness/mm & 9.5 \\ \hline Insulation thickness/mm & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Pipeline parameters.  
Figure 4: Simulation diagram of tower grounding grid and pipeline. (**a**) Schematic diagram of lightning conductor. (**b**) Top view of tower grounding grid and natural gas pipeline.  
Figure 3: Natural gas pipeline profile and construction site. (**a**) Natural gas pipeline section diagram. (**b**) Natural gas pipeline construction site.  
500 kV overhead line is 24.18 kA. It is observed that when the amplitude of the lightning current is below 24.18 kA, the majority of the lightning current flows through the overhead conductor. Keeping other simulation parameters constant, the lightning peak current (I\({}_{0}\)) is varied to 5 kA, 30 kA, 35 kA, 40 kA, 45 kA, and 50 kA to assess its impact on the lightning current distribution after line insulator flashover. Figure 5 illustrates the lightning current distribution at the T\({}_{0}\) and T\({}_{\rm R1}\) towers.  
According to the results shown in Figure 5, it is observed that when the amplitude of the lightning current exceeds 24.18 kA, the line insulators of T0 and TR1 towers experience flashover. This occurs because the lightning strike point is located at the center of the overhead wire span between T\({}_{0}\) and T\({}_{\rm R1}\) towers. As a result, the lightning current is dispersed into the ground through the T\({}_{0}\) and T\({}_{\rm R1}\) tower grounding grid. Moreover, as the amplitude of the lightning current increases, the current flowing through the T\({}_{0}\) and T\({}_{\rm R1}\) tower grounding grid also gradually increases. Consequently, the overvoltage issue of the T\({}_{0}\) and T\({}_{\rm R1}\) tower grounding current affecting the adjacent pipeline needs to be taken into consideration.
# Enhancing Machine Learning Models Through PCA, SMOTE-ENN, and Stochastic Weighted Averaging
## 3 Experimental Results and Discussions
### Stochastic Weighted Averaging (SWA) and Final Ensemble Model
#### 3.6.1 Model Training and Performance Before SWA  
The ensemble model was initially evaluated without SWA. The Stacking classifier, using XGBoost as the meta-model, and the Voting classifier, which employed a soft voting mechanism, were applied to the test data. The accuracy and mean absolute error (MAE) were calculated to assess the model's performance. The model achieved an accuracy of 0.9197 and an MAE of 0.0803, indicating strong baseline performance with reasonable accuracy and minimal error.
## 5 Simulation Results
### Selection of Proposed Voltage Control Coefficients  
To make the selection of surface parameters more reasonable, we provide an illustration that shows how the output voltage error and inductor current is impacted by the selection of control parameters. It needs to be acknowledged that there is not yet a quantitative method in the literature for determining the ideal value of the controller coefficients. As a result, a few trial-and-error stages are needed to identify the ideal values of the coefficients for an acceptable performance of state variables. For this reason, the gamma coefficient (\(\gamma\)) is selected, and its effect on both the output voltage and inductor current is explored. The \(\gamma\) parameter is changed with the values of 5, 10, and 25, and the results obtained are plotted in Figure 7. Obviously, as the value of \(\gamma\) is increased, the output voltage converges to its reference value in a shorter time, which is a desirable dynamic response. On the other hand, a higher coefficient value creates a greater ripple in the inductor current, as seen in the same figure, because the switching frequency depends on the coefficients of the surface used, as shown in (11). When these parameters change, the switching frequency also changes, which triggers ripples in the inductor current. Hence, the control coefficients should be chosen with care in order to maintain a balance between the output voltage and inductor current. Taking into consideration the mentioned requirements, the coefficients of the surface proposed in the study are established as \(\lambda=10\), \(\beta=2000\), \(\gamma=10\).  
Figure 6: Block diagram of the system.
This is undoubtedly observed from the avalanche effect of TEA. Moreover, the time taken for encryption and decryption is high, leading to reduced efficiency of TEA. Although many versions of TEA have been proposed over these years [20; 21; 22; 23], none of them have given concrete solutions to the above problems. This paper proposes an algorithm named NTSA (novel tiny symmetric encryption algorithm) that improves the security features of TEA by introducing more key confusions. Most of the works with TEA and its variations has focused only on decreasing the delay in delivery. Very few researches have been done on key alteration as a method to enhance the security of the transmission algorithm. In the proposed method we introduce multiple key alterations dynamically and secure the key from intruders. Since the key is computed dynamically, the key values are changed during the execution time and cannot be pre-computed. Furthermore, our proposed algorithm (NTSA) takes less time for encryption and decryption compared to TEA and thus provides both better security and efficiency for all the modern applications in IoT networks. The rest of the research is organized into four sections as follows. Section 2 discusses different existing encryption mechanisms for the secure transmission of data through IoT networks. This section further explains the implementation of the TEA algorithm in detail. Few variations of the TEA algorithm are also discussed in this section. Our proposed NTSA is explained in the Section 3. Section 4 presents the experimental results of the NTSA algorithm and we conclude in Section 5 and express some potential future works.
# Prediction of Unconfined Compressive Strength in Cement-Treated Soils: A Machine Learning Approach
## 2 Materials and Methods
### Machine Learning Methodology
#### 2.3.3 Nested Cross-Validation Framework  
Given the relatively small dataset and the considerable dispersion of UCS values, a simple train-test split was deemed insufficient for estimating model performance reliably [75; 76; 77]. A nested cross-validation (NCV) approach was therefore employed to serve a dual critical purpose: providing unbiased estimates of model generalization performance while simultaneously generating the prediction error data necessary for uncertainty quantification.  
The NCV procedure involves two nested loops with fundamentally different objectives that work synergistically to support the uncertainty quantification framework. The outer loop partitions the dataset into distinct training and test sets, ensuring that the evaluation of the final model is conducted on unseen data. Crucially, these outer fold predictions also serve as the foundation for training the uncertainty model, as each data point appears exactly once as a validation sample, providing clean out-of-sample predictions and their corresponding absolute errors.  
The inner loop further subdivides the training data to optimize model parameters and select the best-performing model configuration within each outer fold. Hyperparameter optimization is essential for configuring machine learning algorithms, with approaches ranging from simple grid search to advanced techniques like Bayesian optimization or Hyperband [78]. This separation of model tuning and performance evaluation is essential both for avoiding selection bias in performance estimates [79; 80; 81; 82; 83] and for ensuring that the prediction errors used for uncertainty quantification are genuinely representative of model behavior on unseen data.  
This dual-purpose design ensures that hyperparameter tuning is conducted independently within inner folds, while both performance estimation and uncertainty training data generation are carried out on separate outer folds. This approach prevents data leakage in both the primary prediction task and the uncertainty quantification task, ensuring that the final system can reliably estimate both central predictions and confidence bounds for new soil-cement combinations.  
The NCV procedure was implemented with a 5-fold outer cross-validation loop for performance estimation and uncertainty data generation, combined with a 5-fold inner cross-validation loop for hyperparameter tuning. This nested approach allowed for comprehensive evaluation of each model while generating a complete set of 185 out-of-sample predictions and their associated prediction errors, forming the training dataset for the uncertainty quantification model.
# Kriging Interpolation in Modelling Tropospheric Wet Delay
## 1 Introduction
[34] evaluated three semivariogram models of the Kriging interpolation, a spherical, an exponential and a Gaussian model and concluded that no difference can be discerned between the models. [PERSON] et al. [35] implemented the Kriging interpolation in generating the slant tropospheric delays to mitigate the large-scale interferometric synthetic aperture radar (InSAR) tropospheric delay noise. They concluded that the Kriging interpolation can be effectively used if the spatial resolution of reference stations is dense enough. Although the Kriging interpolation has been proven to be useful and capable in predicting the tropospheric delay, further investigation is still required for fully understanding the properties of this method because the existing research has not addressed all aspects of the Kriging interpolation. For instance, at least 1-year long dataset need to be considered to analyze the seasonal effects on predicting the tropospheric delay since the water vapor content is a period-varying amount depending on temperature and precipitation. However, the previous studies only processed 1-day or 1-week long data, which cannot reflect the behaviors of the predicted tropospheric delay in a long period. Besides, the previous studies only discussed the performances of the Kriging interpolation inside the reference network. However, users may confront the situation in practice that they are on edge or even outside the network. Thus, it is worthwhile to investigate the different situations to fulfil the user's needs. It is known that the dense network can obtain higher prediction accuracy than that of the sparse network, but to what extent the dense network can achieve is still unknown. By considering the concerns mentioned above, this article is organized as follows. Section 2 introduces a widely used GNSS positioning model and presents the strategy of implementing the Kriging in generating the zenith wet delay predictions at certain locations. A sparse GNSS network with 8 stations and a dense network with 19 stations are chosen as the reference in Section 3 from the continuously operating reference stations (CORS) of the Netherlands, and 15 other stations are chosen as the user. An entire year's data was processed to verify the performances of the Kriging interpolation by comparing the predicted zenith wet delays with the estimated zenith wet delays at each user station. The user stations are divided into three categories: 5 stations inside the networks, 5 stations on the edge of the networks and 5 stations outside the networks. Section 4 contains conclusions and discussions.
# A Traffic Information Detection Method at Single Intersection Based on Wi-Fi Data
## 2 Theoretical and Technical Foundations  
The realization of this system is based on the collection of Wi-Fi data at a single intersection. If the Wi-Fi collection device is rationally arranged at the intersection, the Wi-Fi data of the mobile devices carried by the passengers in the vehicle on the road can be collected, and the traffic information of the intersection can be further detected.
# Digital-Twin-Based System for Foam Cleaning Robots in Spent Fuel Pools
## 5 Obstacle Detection and Localization
### Obstacle Avoidance Operations  
After obstacle detection and target localization, the obtained position coordinates are transmitted to the robot, allowing it to quickly assess whether obstacles affect its standard navigation on the current path. When obstacles are detected on or near the path and hinder the robot's standard navigation, appropriate obstacle avoidance measures are taken.  
Furthermore, ultrasonic sensors are employed for obstacle avoidance if the visual system is affected by lighting conditions or obstruction. In this study, the HC-SR04 ultrasonic module was utilized, with a range of 0.02-4 m, an accuracy of \(\pm\)3 mm, and an operating temperature range of 0-50.  
The principle of ultrasonic sensor ranging relies on the speed of sound propagation in the air, calculating the distance information of obstacles by measuring the time interval between the moment the source sound wave is transmitted and the moment the echo signal is received, as expressed in Equation (21).  
\[S=\frac{1}{2T}*340\ \mathrm{m/s} \tag{21}\]  
In Equation (21), \(S\) represents the distance between the sensor and the obstacle, and \(T\) denotes the echo duration, the duration of the signal level. Based on the distance obtained from Equation (21), and with a set distance threshold of 3 m, the robot will rotate clockwise by 90 degrees to continue its cleaning operation when the distance is less than this threshold.  
In conclusion, the combination of visual and ultrasonic obstacle avoidance could provide robust obstacle avoidance capabilities, ensuring that the cleaning robot could navigate through complex environments effectively.