esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Construction of the APS
#### 3.3.2 Substitute Model  
We set the victim model as the target model \(f\). Due to the unstable efficiency of random or constant initialization, it is essential to devise a heuristic method to initialize the position weight table reasonably and achieve efficient perturbation on keywords. Similarly, the word weights of the attention mechanism should accurately capture each word's extent of influence. Hence, we train a BiGRU model with an attention mechanism as the substitute model \(f^{\prime}\) in order to initialize the position weight table in the AWT. These words can then be perturbed using the word importance. The training data are obtained from the input and output of the target model \(f\). The input text \(X=\left(x_{1},\cdots x_{i},\cdots,x_{n}\right)\) is first encoded by the BiGRU to obtain the hidden state \(H=\left(h_{1},\cdots h_{i},\cdots h_{n}\right)\), with the \(i\)-th hidden state \(h_{i}\) computed as follows:  
\[h_{i}=\text{BiGRU}(x_{i},h_{i-1}). \tag{1}\]  
Then, the attention matrix \(\alpha=\left(\alpha_{1},\cdots,\alpha_{i},\cdots,\alpha_{n}\right)\) is calculated with \(H\), with \(\alpha_{i}\) formulated a  
\[u_{i}=\tanh(W_{\text{word}}\,h_{i}+b_{\text{word}}\ ), \tag{2}\]  
\[\alpha_{i}=\frac{\exp\left(u_{i}^{\top}u_{i}\right)}{\sum_{j=1}^{n}\exp\left( u_{j}^{\top}u_{j}\right)}, \tag{3}\]  
where \(W_{\text{word}}\) is the weight of a one-layer MLP, \(b_{\text{word}}\) represents the bias, and \(u_{i}\) is a hidden state of \(h_{i}\). The final vectors of the input text \(X\) are denoted  
\[s=\sum_{i=1}^{n}\alpha_{i}u_{i}. \tag{4}\]  
After the fully connected layer and softmax layer, the probability distribution \(p\) for classification is obtained. We use the negative log-likelihood of the correct labels as the training loss \(L\), formulated as  
\[p=\text{softmax}(W_{\text{fc}}\cdot s+b_{\text{fc}}), \tag{5}\]  
\[L=-\sum_{k}\log p_{kj}, \tag{6}\]  
where \(W_{\text{fc}}\) represents the weight of fully connected layer, \(b_{\text{fc}}\) is the bias, \(k\) is the amount of input text, and \(j\) is the label of \(X\). The whole structure of \(f^{\prime}\) is shown in Figure 2.  
The substitute model \(f^{\prime}\) is pretrained with only a small amount of training data obtained from the target model \(f\). After each round of the attack, the generated non-adversarial text is utilized for data augmentation to update the parameters of \(f^{\prime}\) with the aim of improving the adversarial transferability.
# Application of Dynamic Time Warping (DTW) in Comparing MRT Signals of Steel Ropes
## 3 Results
### Passive Experiment
Figure 21: DTW-based synchronization of LF signals from the MRT inspection of the NRHD24 rope: (**a**) aligned time series around the wire break symptom at 183 m; (**b**) binary alignment path matrix; (**c**) differential signal \(dLF\) after DTW, showing enhanced signal-to-noise ratio of the diagnostic feature.
## 5 Performance Evaluation of EMOR
### Results
#### 5.2.5 Network Lifetime  
Here we conducted the simulation with the condition that our mobile sink can launch multiple data collection tasks anywhere within the network. The performance of network lifetime can be evaluated in terms of number of dead nodes for which according to [59] [PERSON] et al. have highlighted that network lifetime for WSNs can be defined in terms of the time when 25% of the nodes present in the network have no residual energy left to continue their data delivery tasks. Figure 15 demonstrates the network lifetime performance of different schemes against their simulation time. It can be seen that the number of dead nodes increases as the simulation time increases. In this simulation, we have assumed that the data collection period for mobile sink is 600 seconds and our mobile sink can start the data collection task randomly within the network.  
In POFA, the node dies faster than any other scheme due to the reason that POFA focuses more on the reliability of data transmission at the expense of energy consumption, so it reaches the 25% dead node mark faster than any other scheme. It is evident from the graph that the performance of EMOR is better than all schemes including DCBONC. The main reason is the calculation of optimal paths for nodes in advance and employment of flexible routing protocol. The slight difference between DCBONC and real-time EMOR can be explained by the fact that decision variables for calculation of optimal paths in EMOR are more effective than that of DCBONC. In DCBONC, only TF-parameter is used in calculation of optimal paths whereas in EMOR, we use the TF parameter, residual energy, buffer size, and link quality factor to achieve better network lifetime. Moreover, the energy consumed  
Figure 14: Average end-end delay against packets received.  
per node is less in EMOR in comparison to other schemes due to the fact the EMOR can change the data forwarding paths adaptively, especially in the low connectivity areas of the network.
# Maximum Transmit Power for UE in an LTE Small Cell Uplink
## 3 Network and Propagation Model
### HetNet Deployment with Uniformly Distributed Small Cells  
In this section, the HetNet deployment with uniformly distributed small cells within a macro cell was considered. The deployment was considered to be a HetNet environment as the macro and small cells co-exist. The small cells were deployed in the central macro cell, mentioned as the reference case in Section 3.1. The central small cell was placed in the center of the macro cell and was surrounded by a two interfering rings of small cells. Figure 2 depicts the deployment scenario. The focus was on the small cell uplink TPC parameters, in particular the \(P_{\rm max}\) limit.  
For such deployment, the central small cell is prone to interference offered by the small cells in the interfering rings around the central small cell as well as the macro cell interference. It is important to mention here that the impact of interference from the macro cell was considered for the macro cell containing the small cells, as well as the neighboring macro cells. The interference from the neighboring macro cells was expected to be lower, as the distance between the central small cell eNodeB and neighboring macro cell boundary was \(5R\).
## 3 Proposed Stress-Strength Method
### Numerical Application of the Proposed Method
#### 3.3.2 Stress-Strength Analysis  
The steps to determine the normal distribution parameters of the strength are as follows:  
_Step 10_.: By using the tensile strength \(S_{e}=119.89\) MPa value as the mean of the normal distribution \(S\) and its estimated standard deviation \(\sigma_{dev}\)\(s=11.98\) value, the normal strength distribution parameters are \(N_{S}\)(119.89, 11.98).  
_Step 11_.: Based on the stress \(N_{s}\)(184.03, 18.40) and the strength \(N_{S}\)(119.89, 11.98) distributions parameters, from Equation (17), \(Z=-0.0013\).  
_Step 12_.: From the \(Z\) value, the reliability of the stress-strength function for the W beam 33X291, is \(R(t)=1\%\).  
In Table 5, the weigh-cost analysis for the W beams 30X357 and 33X318 is given.  
Figure 4: \(\varepsilon N\) curve.  
Consequently, as a result of the application case given in Section 3.3, from the second column of Table 5, the W beam weight-cost that better fulfills the LRFD and AISC selection methods is the W33X291. Note that even though the performance of the W beam W30X357 fulfills the LRFD and AISC methodologies, among the three alternatives is the one that presents higher reliability. Therefore, designers can select the one that they consider the best choice for their objective. In our case, we recommend the beam with higher reliability. Finally, notice when we use less modified factors the designed reliability will be higher, this because the higher the \(S_{e}\) value, the higher the reliability.
# Pavement Disease Visual Detection by Structure Perception and Feature Attention Network
## 3 Structure Perception and Feature Attention Network
### Loss Function  
In general, YOLOv8n employs the complete intersection over union (CIoU) [38] as the default loss function, which is found to have some limitations when dealing with pavement disease detection. Though the penalty term on the shapes of predicted bounding boxes has been considered, it may increase the computational cost and complexity during the training process. In some cases like small pavement disease detection or mis-aligned predictions, its performance is not as stable as expected.  
Figure 5: Architecture of CBAM.  
In order to address the problem, the GIoU loss function [35] is adopted in SPFAN. Compared with other loss functions, GIoU is computationally simpler and more intuitive. It can more accurately measure the positional relationship between the prediction and ground truth by simply subtracting an extra area from the intersection over union (IoU). Such a simplified computation allows SPFAN to converge faster in the training process and stably handle pavement diseases with various shapes and sizes in the inference stage, improving the efficiency and efficacy of the detection model. As verified in experiments, GIoU achieves better detection performance to other loss functions. GIoU can be calculated as (3):  
\[\text{GIoU}=1-\text{IoU}+\frac{|C-(A\cup B)|}{|C|}, \tag{3}\]  
where IoU means the ratio of intersection to union, which can measure the ratio between the prediction and ground truth in object detection tasks. Formally, IoU can be computed as (4):  
\[\text{IoU}=\frac{|A\cap B|}{|A\cup B|}, \tag{4}\]  
where \(A\) represents the prediction, \(B\) denotes the ground truth, and \(C\) signifies the smallest rectangular that surrounds \(A\) and \(B\), as visualized in Figure 6.
## 2 Proposed Method
### Optimized Fusion Solution  
From above, we have obtained the fuzzy estimates of spectral basis \(\mathbf{D}\) and coefficients \(\mathbf{C}\). However, the fused images generated from the fuzzy estimates \(\mathbf{D}\), \(\mathbf{C}\) and the fusion quality are unsatisfactory. Therefore, based on Equation (4), we can optimize the spectral basis D by solving the optimization procedure of the following equation.  
\[\min_{\mathbf{D}}\|\mathbf{H}-\mathbf{D}\mathbf{C}\mathbf{B}\|_{F}^{2} \tag{10}\]  
In Equation (10), we assume that \(\mathbf{X}=\mathbf{C}\mathbf{B}\); then, Equation (10) can be resolved as  
\[\min_{\mathbf{D}}\|\mathbf{H}-\mathbf{D}\mathbf{X}\|_{F}^{2} \tag{11}\]  
We extend the multiplicative update rule in [21] to obtain an approximate solution for solving Equation (11), i.e.,  
\[\mathbf{D}=\mathbf{D}.\times\left(\mathbf{H}\mathbf{X}^{T}\right)./\left( \mathbf{D}\mathbf{X}\mathbf{X}^{T}\right) \tag{12}\]  
After obtaining the optimized spectral base \(\mathbf{D}\), we can obtain the HR-HSI fuzzy estimation matrix \(\mathbf{Z}_{1}\) as  
\[\mathbf{Z}_{1}=\mathbf{D}\mathbf{C} \tag{13}\]  
Obtaining the HR-HSI estimation result \(\mathbf{Z}_{1}\), fusion quality still has room for further improvement. Combining Equation (2), we can obtain  
\[\mathbf{M}=\mathbf{R}\mathbf{Z}_{1} \tag{14}\]  
Combining the known matrix \(\mathbf{M}\) and \(\mathbf{R}\) to further optimize the fusion result \(Z_{1}\),  
\[\min_{\mathbf{Z}_{1}}\|\mathbf{M}-\mathbf{R}\mathbf{Z}_{1}\|_{F}^{2} \tag{15}\]  
Similarly, we use the extended multiplication iteration rule to solve Equation (15), and we can obtain  
\[\mathbf{Z}_{1}=\mathbf{Z}_{1}.\times\left(\mathbf{R}^{T}\mathbf{M}\right)./ \left(\mathbf{R}^{T}\mathbf{R}\mathbf{Z}_{1}\right) \tag{16}\]After iterative optimization, we can obtain a more accurate fused image \(\mathbf{Z}=\mathbf{Z}_{1}\). Finally, our proposed method can be described by Algorithm 2, where \(\mathbf{q}\) represents the parameter to be adjusted.  
```
0: H M R B S q  Get D by Equations (6) and (7)  Get C by Algorithm 2 for k = 1:K do  Update D by Equation (12) endfor \(\mathbf{Z_{1}}=\mathbf{D}\) for k = 1:K do  Update \(\mathbf{Z_{1}}\) by Equation (16) endfor \(\mathbf{Z}=\mathbf{Z_{1}}\) return Z
```  
**Algorithm 2** FSF Algorithm
## 4 Experiments  
### Case Western Reserve University (CWRU) Bearing Experiment Data Analysis  
The CWRU's bearing dataset has been extensively utilized by researchers to validate various diagnostic methods. For the purposes of this section, the sampling frequency of the system chosen to collect the bearing vibration acceleration data was 48 kHz, the motor speed was 1797 rpm, and the load sizes applied to the bearings in the experiment were 735 N, 1470 N, and 2205 N. The bearings exhibited four health states under each load: normal condition (N), inner ring failure (IN), outer ring failure (OF), and roller failure (RF). Each failure state of the bearing included three different damage levels in addition to the healthy state: 0.178 mm, 0.356 mm, and 0.533 mm. Two of the three different load conditions were chosen as the source domain dataset and the target domain dataset. A total of 2000 samples were randomly chosen from the sample data associated with each health state to serve as the training and testing sample sets for the two domains. The total quantity of samples for training and testing was 40,000. The data setup of the experimental operation is summarized in Table 2, while the dataset under each load condition is presented in Table 3. Figure 4 illustrates the dataset for the 735 N load condition under normal conditions, along with the time and frequency domain images of the signals for each fault condition, with a damage level of 0.178 mm.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Condition** & **Rotation Speed (rpm)** & **Load** & **Sample Size** & **Sample Length** \\ \hline A & 1797 & 735 N & 40,000 & 1024 \\ B & 1797 & 1470 N & 40,000 & 1024 \\ C & 1797 & 2205 N & 40,000 & 1024 \\ \hline \end{tabular}
\end{table}
Table 2: Dataset descriptions of different operating conditions.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Fault Type** & **Degree of Damage** & **Sample Size** & **Label** \\ \hline N & None & 2000 & 9 \\ \hline  & 0.178 mm & 2000 & 8 \\ IF & 0.356 mm & 2000 & 7 \\  & 0.533 mm & 2000 & 6 \\ \hline  & 0.178 mm & 2000 & 5 \\ OF & 0.356 mm & 2000 & 4 \\  & 0.533 mm & 2000 & 3 \\ \hline  & 0.178 mm & 2000 & 2 \\ RF & 0.356 mm & 2000 & 1 \\  & 0.533 mm & 2000 & 0 \\ \hline \end{tabular}
\end{table}
Table 3: Dataset settings.
# Fast and Accurate Visual Tracking with Group Convolution and Pixel-Level Correlation
## 3 Proposed Method  
In this section, we describe the network framework in detail. As shown in Figure 1, our model mainly consists of a Siamese network backbone and two sub-network detection heads for the bounding box classification and regression. The Siamese backbone network is fine-tuned from ResNet50, inspired by the transformer structure, reducing the use of activation functions and normalization, and instead using channel attention [18] and spatial attention [19] modules in the classification and regression sub-networks to make the network more accurate in extracting features. Moreover, to improve the inference speed, a group convolution and 1 \(\times\) 1 convolution are used for the dimensionality reduction in the feature fusion stage; both of them accelerate the computation speed and reduce the inference time. The cross-correlation operation no longer uses depth-wise correlation [3]; template features and search features are correlated in a pixel-level matching model, which can effectively reduce background clutter and allow the model to refine the object boundary ranges and focus more on the target.
## 2 Materials and Methods
### Scorecard Model
#### 2.3.4 Feature Selection  
With the initial 11 features extracted from vibration data, it is necessary to further filter out the ones that are meaningless to the model. Here, the screening is conducted based on the predictive ability and linear correlation of the features.  
Information Value (_IV_) is used to evaluate the predictive ability of a feature variable to the stability label. Equations are as follows:  
\[IV_{i}=\left(\frac{y_{i}}{y_{T}}-\frac{n_{i}}{n_{T}}\right)\times WOE_{i} \tag{13}\]  
\[IV=\sum_{i}^{n}IV_{i} \tag{14}\]  
\(IV_{i}\) is the information value in the \(i\)th bin of the feature variable, and the \(IV\) value corresponds to the feature and is the sum of the \(IV_{i}\) values of all bins. The larger the \(IV\) value, the greater the difference in the WOE values in the bins of the feature; i.e., the stronger the ability to predict the label. Common thresholds for feature screening using \(IV\) values are shown in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline
**IV** & **Predictive Ability** \\ \hline \(<\)0.02 & None \\
0.02–0.1 & Low \\
0.1–0.3 & Mid \\
0.3–0.5 & High \\ \(>\)0.5 & Highest \\ \hline \end{tabular}
\end{table}
Table 1: Thresholds of IV.  
Linear correlation is also considered for feature selection, including the correlation between features and labels as well as the pairwise correlation among features. As (15), the Pearson coefficient is adopted to evaluate the linear correlation of variables. The range of the coefficient is [\(-\)1, 1]. A large absolute value of the coefficient means a strong linear correlation between two features.  
\[r_{X,Y}=\frac{cov(X,Y)}{\delta_{X}\delta_{Y}}=\frac{E\big{(}\big{(}X-\overline{ X}\big{)}\big{(}Y-\overline{Y}\big{)}\big{)}}{\sqrt{\sum_{i=1}^{n}\big{(}X_{i}- \overline{X}\big{)}^{2}}\sqrt{\sum_{i=1}^{n}\big{(}Y_{i}-\overline{Y}\big{)}^{ 2}}} \tag{15}\]  
For feature selection, the retained features have large \(IV\) values, strong correlations with labels, and weak correlations with each other.
# Design and Control of an Ultra-Low-Cost Logistic Delivery Fixed-Wing UAV
## 2 Overall UAV Design
### Wing Design
#### 2.4.2 Relationship between Wing Parameters and Performance  
Wing parameters are crucial determinants of wing performance, with varying parameters exerting distinct impacts. Referring to aircrafts within the same Reynolds number range, the wing loading is preliminarily set at 65 g/dm\({}^{2}\), from which the wing area is deduced to be 3400 cm\({}^{2}\), and the wingspan is 150.22 cm. A single wing side is chosen to be 70 cm, resulting in a wingspan of 148.7 cm (with the fuselage width being 8.7 cm), an aspect ratio of 6.87, and a taper ratio selected at 1.5, meaning that the wing root is 276 mm, and the wingtip is 184 mm, with a sweep angle of 3\({}^{\circ}\). Given the wing's mounting position near the topmost part of the fuselage, the stability is enhanced; thus, we do not set an opposite angle. The aileron area is initially set to 20% of the wing area. Considering folding mechanisms, the final aileron length is 420 mm, with a chord length of 68 mm, accounting for approximately 17.8% of the wing area. The principal parameters are detailed in Table 3.  
The wing design parameters articulated in this study, as enumerated in Table 4, exert several significant effects.  
A wingspan of 1487 mm was selected, based on an integrated consideration of the UAV's design requisites and flight performance, to ensure adequate lift while minimizing the drag and weight.  
The UAV design employs a chord length of 233 mm, as determined by balancing the UAV's load demands and structural integrity, thus providing sufficient lift while ensuring the wing's strength and rigidity.  
An aspect ratio of 6.87 was chosen, aiming to optimize the UAV's flight velocity and efficiency, thereby enhancing the lift-to-drag ratio and glide ratio while controlling wing deformation and buckling.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Tail Arm/mm** & **Tail Capacity** & **Area/mm\({}^{2}\)** & **Sweep Angle/\({}^{\circ}\)** \\ \hline Horizontal tail & 550 & 0.67 & 85,000 & 6 \\ Vertical tail & 0.05 & 22,000 \(\times\) 2 & 25 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Main parameters of the tail wing.  
Figure 4: NACA 4412 airfoil integral trapezoid wing.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**L/mm** & **b0/mm** & **b1/mm** & **MAC/mm** & \(\mathbf{\lambda}\) & **av** & **Sweep Angle/\({}^{\circ}\)** \\ \hline
1487 & 276 & 184 & 233 & 6.87 & 1.5 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Main parameters of the wing.  
A taper ratio [46] of 1.50 was selected, considering the balance between the UAV's aerodynamic performance and stability, capable for lowering the drag coefficient while maintaining adequate lateral stability.  
To accommodate the UAV's flight speed and control requirements, a sweep angle of 3\({}^{\circ}\) was chosen, effectively reducing induced drag while ensuring sufficient lateral stability and maneuverability.
## 3 Results and Discussion
### Electromagnetic Simulation and Analysis for Dielectric Heating Applications  
This section explores the electromagnetic simulation and analysis critical to optimizing dielectric heating applications for biological tissues. The investigations include the frequency-dependent behavior of the horn antenna, the distribution of power intensity across fish tissues, and the evaluation of Specific Absorption Rate (SAR) to assess energy absorption and safety. Each analysis aims to ensure effective energy delivery to target tissues while maintaining compliance with safety standards.  
The Frequency Response of Horn Antenna subsection examines the antenna's performance across different frequencies, focusing on its radiation pattern, reflection coefficient, and directivity. Following this, the Frequency Response of Power Intensity delves into the simulated power distribution across fish tissues at varying distances, emphasizing its impact on heating uniformity. Finally, the Simulated Specific Absorption Rate (SAR) analysis evaluates SAR values under ICNIRP safety guidelines to identify configurations that balance efficient energy transfer and safety. Together, these analyses provide a comprehensive understanding of the parameters influencing the dielectric heating process.
# Machine Learning for Predicting Neutron Effective Dose
## 2 Methodology
### Evaluation and Cross Validation  
Table 2 shows a sample of the cross-validation scores of the ML models for the mean square error, mean absolute error, and R\({}^{2}\), employing categorical labeled data for the EXTR-C, XGB-C, and GB-C ML models and numerical data for the EXTR, XGB, and GB-ML models.  
Figure 1 illustrates a sample of R\({}^{2}\) values for the categorically labeled data models. In addition, the three models were compared in terms of k-fold accuracy for both the training and testing sets. The box plot in Figure 1 displays the results of the k-fold accuracy analysis with five cross-validations, showing the performance of all models for both types of data fitting, including the training and testing sets. In general, all three models performed well and demonstrated better results when fitting the categorical data. K-fold analysis can be used with large or small size data. However, while the hyperparameter tuning of any of the three models may lead to improved results, hyperparameters tuning except for k-fold analysis were avoided in this study, as it was found to potentially cause overfitting of the results. Future studies involving larger data sets should consider the use of hyperparameter tuning techniques.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**ML Model** & **Mean Square Error** & **Mean Absolute Error** & **R\({}^{2}\)** \\ \hline EXTR-C & 3.42 & 0.88 & 0.992 \\ \hline XGB-C & 1.57 & 0.01 & 0.996 \\ \hline GB-C & 5.41 & 1.32 & 0.987 \\ \hline EXTR & 6.54 & 1.24 & 0.984 \\ \hline XGB & 2.30 & 0.65 & 0.994 \\ \hline GB & 3.82 & 1.14 & 0.990 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Cross-validation scores (fractional unit) of the ML models for mean square error, mean absolute error, and R\({}^{2}\), employing categorical labeled data (for the EXTR-C, XGB-C, and GB-C ML models) and numerical data (for the EXTR, XGB, and GB ML models).