esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background and Related Works  
### Overview of Deep Learning  
This section commences by describing the basic structures and operating principles of the three DNNs, VGG [11], GoogLeNet [12], and ResNet [13]. The general principles of the training process used to train such models are then introduced and discussed.  
Neural networks usually have multiple layers, in which each layer is composed of multiple neurons. Deep neural networks (DNNs) mean neural networks having more than two layers; however, they usually have more than ten layers. Depending on the particular composition method, each layer may be either a fully connected layer or a convolutional layer. Ir-respective of the layer type, the neurons compute their outputs using a particular activation function, such as ReLU or sigmoid. Neural networks composed of fully connected layers are referred to as multiple layer perceptrons (MLPs), while those consisting mainly of convolutional layers are called convolutional neural networks (CNN). A deep neural network generally consists of both convolutional layers and fully connected layers, where the former layers account for most of the layers and are arranged at the front end of the model, while the smaller number of fully connected layers are arranged at the back end. The DNN model also may contain some special-purpose layers interspersed between the convolutional and fully connected layers. For example, pooling layers may be used to reduce the dimensions of the sample data, while dropout layers and batch normalization layers may be used to prevent overfitting.  
As described above, three of the most common DNN architectures in use nowadays are VGG, GoogLeNet, and ResNet. The following paragraphs describe each architecture briefly.  
VGG: A deep neural network with the front part of the network consisting of a cyclic arrangement of convolutional layers and pooling layers. This deep neural network exists in several variants, with different numbers of layers in each case. For instance, VGG16 owns 13 convolutional layers and three fully connected layers, and VGG19 has 16 convolutional layers and three fully connected layers.
2. GoogLeNet: GoogLeNet is a deeper neural network, having not only additional convolutional and pooling layers, but also the incorporation of inception modules. In its standard form, GoogLeNet owns 27 layers, where these layers include nine inception modules.
3. ResNet: ResNet inherits some of the features of AlexNet [14] and LeNet [15]. However, ResNet also incorporates additional residual blocks. In these blocks, the input is added to the output. Adding these residual modules makes ResNet possible to deepen the network, which can avoid the vanishing gradient problem. ResNet also owns several versions with different combinations of layers, including ResNet18, consisting of one convolutional layer, eight residual modules, and one fully connected layer.
## 8 Verification of Theoretical Impact Forces

Table 7: Impact simulation conditions. Figure 13: Conditions of impact angle in simulation. In Figure 15, the collision behaviors observed from the top-down view are shown, and damage to the DCP is visible after the vehicle impact. The concrete surface was damaged due to the impact force of the wheels, as shown in Figure 16. However, an essential aspect of the derailment simulations is to determine the load applied to the DCP. Therefore, the simulation results were analyzed to determine the impact forces transmitted to the DCP during the collision. Figure 14: Collision behaviors of the vehicle (front and rear views) [R3500, 300 km/h]. Figure 15: Collision behaviors of the vehicle (top view) [R3500, 300 km/h]. The impact force occurred on the DCP is shown in Figure 17. The black line represents the force data without data processing, and the red line represents the force data processed by a 50 ms moving average. Based on the raw force data, the collision began at 17.4 ms and ended at about 396.7 ms. The average and maximum forces between 17.4 ms and 396.7 ms can be obtained as shown in Table 8. When comparing the present theoretical result and the simulation, relative deviations of 3.14% and 3.94% were obtained for the average and the maximum forces, respectively. This comparison confirms that the present correction method applying suspension stiffness to the Olson model can predict the impact force acting on the DCP by a vehicle derailment well. Simulations for the remaining conditions were conducted and summarized in Table 9. For the six cases, relative deviations less than 8% were obtained between the present theoretical results and their simulation ones. Therefore, it was confirmed that this method considering suspension stiffnesses can predict the impact force taking place on the DCP well. Additionally, it should be noted that the mean and maximum impact forces could be slightly affected by track conditions such as cant deficiency and the friction coefficient between the track and wheel. For DCP design purposes, it is recommended to consider worst-case track conditions similar to those in this study, assuming full cant deficiency and a frictionless wheel-track interface. Figure 16: Damage of the DCP. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Curve Radius** & **Mean Impact Force** & **Maximum Impact Force** \\ \hline Olson model & 350.20 kN & 750.12 kN \\ \
\begin{tabular}{c} [Theory] \\ \end{tabular} & R3500 & 339.2 kN & 779.7 kN \\
\begin{tabular}{c} Relative deviations \\ \end{tabular} & 3.14\% & 3.94\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of theory and simulation. Figure 17: Impact forces occurred on the DCP during collision.
# Explainable Ensemble Learning Model for Residual Strength Forecasting of Defective Pipelines
## 2 Individual Methods
### Construction of Residual Strength Prediction Model  
This study proposes the BO-XGBoost method for predicting the residual strength of defective pipelines, with the prediction framework illustrated in Figure 10. The framework consists of data collection and processing, model construction, performance evaluation, and interpretability analysis. Regarding data collection, the seven types of correlated parameters are relatively limited in features, which neither compromises the model's generalization capability nor significantly increases computational costs. Therefore, the seven factors chosen as input features for the model include the pipe outer diameter \(D\), pipe wall thickness \(t\), defect length \(L\), defect depth \(d\), defect width \(w\), pipe yield strength \(\sigma_{y}\), and ultimate tensile strength \(\sigma_{u}\). At the same time, the failure pressure \(P_{f}\) is considered the output parameter of the model. During data processing, stratified sampling is applied to divide the dataset into training set and test set, ensuring the objectivity and reliability of the prediction results. To eliminate the dimensional differences among features, ensure comparability across different features, and enhance the performance and accuracy of the model, it is necessary to normalize the input data. In this study, the input data were normalized to the range of [0, 1]. Additionally, to ensure the objectivity of the prediction results, avoid overfitting, and enhance the generalization capability of the model, 6-fold cross-validation was employed for data preprocessing. For model construction, the predictive performance of six mainstream models is evaluated using metrics such as RE, MAPE, R\({}^{2}\), and RMSE to identify the optimal model. Finally, SHAP analysis is conducted to investigate the model's  
Figure 9: Comparison chart of MAPE obtained by random sampling and stratified sampling.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Metric** & **Formulas** & **Best Performance Value** \\ \hline RE & \(RE(\%)=\frac{\hat{y_{i}}-\hat{y_{i}}}{y_{i}}\times 100\%\) & Closest value to 0 \\ R\({}^{2}\) & \(R^{2}=1-\sum\limits_{i=1}^{N}\left(y_{i}-\hat{y_{i}}\right)^{2}/\sum\limits_{i= 1}^{N}\left(y_{i}-\overline{y_{i}}\right)^{2}\) & Closest value to 1 \\ MAPE & \(MAPE(\%)=\frac{1}{N}\sum\limits_{i=1}^{N}\left|\frac{y_{i}-\hat{y_{i}}}{y_{i}} \right|\times 100\%\) & Minimum \\ RMSE & \(RMSE=\sqrt{\frac{1}{N}\times\sum\limits_{i=1}^{N}\left(\hat{y_{i}}-y_{i}\right) ^{2}}\) & Minimum \\ \end{tabular}
\end{table}
Table 3: Performance measures.  
interpretability, quantifying the influence of input variables on the output and enhancing model transparency. The development of this model contributes to improving the integrity management of oil and gas pipelines and offers decision-making support for the intelligent management of oil and gas pipelines.
## 4 Discussion
These observations highlight that minimizing unnecessary radiation exposure to the PVS may offer short-term cognitive benefits for patients undergoing radiotherapy. Moreover, the ability to reliably segment the PVS through robust ventricle segmentation models holds significant value for research, particularly in advancing predictive models of treatment-related side effects. While this study primarily focused on radiotherapy planning, the developed model could also assist in monitoring ventricular size, potentially aiding hydrocephalus management in a similar manner to the work by [9,10]. Although we did not explicitly validate volume tracking, the model's accuracy in segmenting these structures suggests it could be useful for longitudinal tracking. Future work could further explore this potential, especially with longitudinal datasets, to assess its efficacy in volume monitoring and for hydrocephalus. The nnU-Net model has demonstrated strong performance in this study, with accuracy and consistency in both research and clinical applications. However, successful integration into clinical workflows requires addressing several challenges. The process typically involves a two-step framework: commissioning, which includes training, validation, and testing (already completed in this study), and implementation and quality assurance, which focuses on embedding the model into clinical workflows [36]. The model's fast inference time (under 3 min) shows promise in improving workflow efficiency. Crucially, the superior segmentation accuracy and clinical acceptability of nnU-Net can translate directly into time savings during treatment planning by reducing the need for manual corrections. This is particularly valuable in the case of complex structures like the PVS, where manual delineation is time-consuming. To ensure reliable clinical use, the model's outputs must meet clinical standards, and outliers should be addressed through continuous quality assurance. Additionally, training clinical users on the model's capabilities and limitations is essential. With these considerations in place, the insights gained from this study can be applied to integrate nnU-Net into clinical practice, offering significant benefits in terms of segmentation accuracy, consistency, and overall workflow efficiency in clinical radiotherapy.
## 4 Conclusions  
A novel magnetic molecularly imprinted polymer (MIP)-coated stir bar was prepared in this study and combined with high-performance liquid chromatography (HPLC) for the detection and analysis of trace amounts of bisphenol A (BPA) in real water samples. Under optimal conditions, the maximum adsorption capacity of BPA-MIPs-SBSE was twice that of BPA-NIPs-SBSE. This indicates that BPA-MIPs-SBSE exhibits superior binding affinity and selective adsorption characteristics compared to non-imprinted coatings. In conclusion, the method proposed in this study is simple, highly sensitive, selective, environmentally friendly, and particularly suitable for the determination of trace amounts of BPA in complex samples.  
[PERSON], [PERSON], Z.C., P.Y., J.S. and J.K. contributed to the conception and design of the study; data curation, [PERSON]; formal analysis, [PERSON], Z.W. and P.Y.; funding acquisition, J.K. and P.Y.; investigation, J.K.; methodology, J.W. and J.K.; project administration, Z.C.; supervision, J.S., Z.C. and P.Y.; writing--original draft, [PERSON] and Z.W.; writing--review and editing, J.S., Z.C., P.Y. and J.K. All authors have read and agreed to the published version of the manuscript.  
This work was jointly supported by the National Key Research and Development Program of China (Grant No. 2022 YFD3203701), The National Natural Science Foundation of China (Grant No. 51608148), Heilongjiang Province Postdoctoral Fund (Grant No. LBH-Z22142), the Heilongjiang Touyan Innovation Team Program (Grant No. HIT-SE-01), the Science Foundation of Heilongjiang Academy of Science (No. KY2022 ZR04), the Science Foundation of Heilongjiang Provincial Institute (CZKYF2023-1-B036), and the Central Government Guides Local Science and Technology Development Projects (No. ZY20B15).  
The authors declare no conflict of interest.  
The list of symbols in the manuscript.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Samples**} & \multicolumn{2}{c}{**Spiking**} & \multicolumn{2}{c}{**Recovery**} & \multirow{2}{*}{**Recovery Rate**} & \multirow{2}{*}{**RSD**} \\  & **Concentration (\(\mu\)g/L)** & & & **(\%)** \\ \hline \multirow{3}{*}{Deionized water} & 500 & 465.5 & 93 & 4.5 \\  & 2000 & 1768.8 & 88 & 5.7 \\ \multirow{3}{*}{Tap water} & 500 & 434 & 87 & 5.5 \\  & 2000 & 1677.8 & 84 & 6.4 \\ \multirow{3}{*}{Bottled water} & 500 & 397.6 & 80 & 5.0 \\  & 2000 & 1516.8 & 76 & 8.2 \\ \multirow{3}{*}{Reservoir water} & 500 & 379 & 76 & 8.3 \\  & 2000 & 1410.6 & 71 & 8.7 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The average recovery rates of spiked real water samples.
## 4 Hardware-in-the-Loop Real-Time Simulation  
The proposed control strategy was validated through a real-time simulation on a hardware-in-the-loop (HIL) platform, which mainly includes the real-time host PC and the controller. Based on the actual vehicle parameters, the dynamics model of a four-module six-axle SRT in the host PC was established using SIMPACK Linux. The model consists of four modules, with two independent steering running gears for the first and fourth modules and one independent steering running gear for the middle two modules. The first and sixth axles can provide driving torque. The running gear comprises an axle, two wheels, and a steering mechanism. The steering mechanism can be simplified as a four-bar linkage. Furthermore, the tire adopts a magic formula empirical model. Through the real-time module of SIMPACK Linux, it is possible to synchronize the passing speed of simulated time with real time, thereby achieving the effect of the actual operation condition of the vehicle. Table 1 shows the basic parameters of the vehicle dynamics model [34].  
The control strategy was implemented in the NVIDIA Jetson Orin NX module, selected as the controller hardware. Table 2 shows the basic parameters of the MPC algorithm in this paper. The controller communicates with the real-time simulation host PC through TCP/IP communication and retains the CAN communication function to adapt to different forms of vehicle networks. Figure 7 shows a schematic diagram of real-time simulation through the HIL platform. The input of the dynamics model is the wheel steering angle of each axle and the driving torque of the first and sixth axles. The output of the dynamics model is vehicle status and target path information. After receiving the output information of the dynamics model, the controller first calculates the path-tracking target then calculates the required CG generalized force of each module and redistributes it. Finally, the controller allocates the wheel status of each module and outputs it to the real-time simulation host PC.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Title** & **Value** & **Unit** \\ \hline Mass (the first and the fourth module) & 12,685 & kg \\ Mass (the second and the third module) & 11,893 & kg \\ Yaw moment of inertia (the first and the fourth module) & 57,157 & kg \(\cdot\)m\({}^{2}\) \\ Yaw moment of inertia (the second and the third module) & 50,272 & kg \(\cdot\)m\({}^{2}\) \\ Total length & 35 & m \\ Width & 2.55 & m \\ Distance between the first and the second axle & 4.705 & m \\ Distance between the second and the third axle & 7.477 & m \\ Distance between the third and the fourth axle & 5.355 & m \\ Track & 2.36 & m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic parameters of the vehicle.
## 4 Simulations and Experiments
### Experiments
#### 4.2.1 Experimental Setup
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Parameters** & **Roll** & **Pitch** \\ \hline State transition matrix \(A_{0}\) & 1 & 1 \\ Measurement transition matrix \(H_{0}\) & 1 & 1 \\ Measurement transition matrix \(H_{0}\) & 1 & 1 \\ Process noise \(Q_{0}\)\((deg)\) & 0.001 & 0.001 & 0.001 \\ Measurement noise \(R_{0}\)\((deg)\) & 0.001 & 0.001 & 0.001 \\ Adjustable parameter \(\delta_{j}\) & 1 & 1 & 1 \\ Random process noise \(\omega\)\((deg)\) & 0.05 & 0.05 & 0.01 \\ Adjustable parameter \(a_{j}\) & 0.8 & 0.8 & 0.8 \\ Adjustable parameter \(b_{j}\) & 0.1 & 0.1 & 0.1 \\ Adjustable parameter \(c_{j}\) & 0.1 & 0.1 & 0.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Parameters of attitude estimation for suppressing maneuvering acceleration interference. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Parameters** & **Roll** & **Pitch** \\ \hline State transition matrix \(A_{0}\) & 1 & 1 \\ Measurement transition matrix \(H_{0}\) & 1 & 1 \\ Process noise \(Q_{0}\)\((deg)\) & 0.001 & 0.001 & 0.001 \\ Measurement noise \(R_{0}\)\((deg)\) & 0.001 & 0.001 & 0.001 \\ Adjustable parameter \(\delta_{j}\) & 1 & 1 & 1 \\ Random process noise \(\omega\)\((deg)\) & 0.05 & 0.05 & 0.01 \\ Adjustable parameter \(a_{j}\) & 0.8 & 0.8 & 0.8 \\ Adjustable parameter \(b_{j}\) & 0.1 & 0.1 & 0.1 \\ Adjustable parameter \(c_{j}\) & 0.1 & 0.1 & 0.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Parameters of data fusion method based on gyro and accelerometer. Figure 9: Experimental device. \[\left[ \begin{array}{cc}c_{y1}&c_{y2}&c_{y3}\\ \end{array} \right]^{T}=A_{z,\psi}\cdot A_{x,\gamma}\cdot A_{y,\theta 0^{\circ}}\cdot A_{y,\theta}\cdot\left[ \begin{array}{cc}0&\phi_{3}&0\\ \end{array} \right]^{T}. \tag{32}\]  
The 3-axis angular attitude in the geographic coordinate system is  
\[\left[ \begin{array}{cc}\gamma&\theta&\psi\\ \end{array} \right]^{T}=\left[ \begin{array}{cc}a_{1}+b_{1}+c_{1}&a_{2}+b_{2}+ c_{2}&a_{3}+b_{3}+c_{3}\\ \end{array} \right]^{T}. \tag{33}\]  
In this study, RMSE calculated by Equation (27) is applied to compare the accuracy of signal processing algorithms. Where \(y_{k}\) is reference angular attitude calculated by encoder, \(y_{k}\) is angular attitude calculated by corresponding algorithm and \(m\) is number of sampling points.
## 4 Discussion
### Influence of Hydrogen Peroxide/Organic Carbon Molar Ratio  
It is known that the addition of H\({}_{2}\)O\({}_{2}\) leads to an improvement in the rate of oxidation of organic compounds due to the formation on the surface of the TiO\({}_{2}\) photocatalyst of HO- radicals, according to the following reaction.  
\[\text{TiO}_{2}\left(\text{e}^{-}\right)+\text{H}_{2}\text{O}_{2}\longrightarrow \text{TiO}_{2}+\text{OH}^{-}+\cdot\text{OH} \tag{26}\]  
The rate of degradation of the organic compound increases with increasing concentration of H\({}_{2}\)O\({}_{2}\) due to the generation of an increasing number of HO- radicals, the reaction being favored by an acidic pH of the working solution. It has been found that this direct dependence is only valid up to a certain concentration of H\({}_{2}\)O\({}_{2}\), after which the reaction rate begins to decrease. This decrease is due to side reactions that occur at the surface of the TiO\({}_{2}\) catalyst, such as the following [33].  
\[2\text{H}_{2}\text{O}_{2}\longrightarrow\text{H}_{2}\text{O}+\text{O}_{2} \tag{27}\]  
\[\cdot\text{OH}+\text{H}_{2}\text{O}_{2}\longrightarrow\text{HO}_{2}\cdot+ \text{H}_{2}\text{O} \tag{28}\]  
\[\cdot\text{OH}+\text{HO}_{2}\cdot\longrightarrow\text{O}_{2}+\text{H}_{2} \text{O} \tag{29}\]  
Given that there is a high concentration of H\({}_{2}\)O\({}_{2}\) in the system, it will participate in a competition with the organic compound in the reaction with HO- radicals, according to the reaction (28). To avoid these side reactions, the addition of H\({}_{2}\)O\({}_{2}\) must be optimized to obtain the maximum rate of degradation of the organic compound. The optimum H\({}_{2}\)O\({}_{2}\) concentration depends on the nature and concentration of the organic compound to be degraded. Resulting from the data obtained in this work, a stoichiometric hydrogen peroxide/organic carbon molar ratio is favorable for the degradation of the initial organic compound, namely 2,4-DCF.
## 4 Parametric Study
### Effect of the Embedment Depth  
In this section, the impact of the embedment depth on the mechanical properties of our model is studied. In total, three embedment depths are considered: 50 mm, 70 mm, and the initial embedment presented in the previous section of 100 mm. For each model, the same degrees of corrosion were considered, and the evolution of the maximum pull-out load for each simulation as a function of the corrosion degree for the three embedment depths studied is presented in Figure 10.  
Figure 9: Representation of the concrete cone failure in the numerical pull-out test (**a**), tensile damage in the numerical model (**b**).  
We can notice in Figure 10 that the greater the embedment depth is, the greater the pulling-out load is. This result has been reported in the literature [19, 27], and can be explained as follows: the embedment depth is directly linked to the size of the surface in contact with the concrete. This means that there is more friction as the embedment depth is increased, which makes it harder to pull-out the reinforcement, even if it is corroded, as can be seen in Figure 10. We can notice that the pulling load is twice as bigger for an embedment depth of 100 mm than for an embedment depth of 50 mm. Finally, we can also note that there is an increase in the maximum pulling load for lower degrees of corrosion and a significant drop after about 1.5% corrosion, and that this drop happens at around the same corrosion degree for the three models. We can conclude that the embedment depth only affects the load necessary to pull-out the rebar.
## 5 Conclusions  
A novel scheme, CPM-GFDM, has been introduced for frequency-selective wireless channels. In this scheme, the memoryless mapping schemes such as QAM and PSK are replaced with CPM mapping that has memory. At the GFDM receiver, this memory is exploited to improve the error performance of the CPM-GFDM scheme. Using extensive simulations and two types of receivers, i.e., matched filter and zero-forcing, it has been shown that there are several values of the modulation index \(h\) for which the proposed CPM-GFDM system outperforms the conventional GFDM system. The performance of the proposed system is particularly better than a conventional scheme when using higher modulation schemes, i.e., when sending two bits or three bits at a time. Hence, the proposed scheme is a strong candidate for high-speed wireless communication.  
The author would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code: (22 UQU4300148 DSR02). The author would also like to thank the College of Computer and Information Systems, Umm Al-Qura University, Makkah, Saudi Arabia.  
The author declares no conflict of interest.
## 2 Methodology
### Event Filtering and Expert Knowledge Extraction Modules: Structure and Operation
#### 2.2.2 Automatic Expert Knowledge Extraction Module
Together, these two modules form the backbone of the acquisition and treatment of the data recollected. Their carefully engineered modular architectures not only provide clear operational benefits but also ensure that each stage of processing, whether filtering anomalies or extracting expert knowledge, can be independently optimized and updated, as needed, in the future. This design was essential to handle the diversity of sensor data and the complexity of expert decision patterns in a robust, scalable manner.