esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Numerical Method and Validation
The grid used for the calculation of the propeller is displayed in Figure 6. The radius of the propeller was R, and the distance from the front boundary of computational domain to the propeller was 12 R. The distance between the back boundary of the computational domain and the propeller was 24 R, and the distances from the out boundary to the rotation axis of the propeller were 6 R. To test the mesh independence, five grids of the same propeller with different densities were used to calculate aerodynamic characteristics at 10 km altitude when the free-stream velocity was 14.1 m/s and the rotational velocity per minute (\(n\)) was 622 rpm. The information of the five grids is shown in Table 3. Figure 7 shows that the calculation results tended to be stable with the increase in grid density. The thrust coefficient (\(C_{T}\)) and power coefficient (\(C_{P}\)) of Grid 1 and Grid 2 are far from stable \begin{table}
\begin{tabular}{c c c c c} \hline \hline _Span (m)_ & _c (m)_ & _Ma_ & _P (kPa)_ & _Re_ \\ \hline
0.9144 & 0.1524 & 0.05 & 34.475 & \(1.0\times 10^{5}\) \\ \hline \end{tabular}
\end{table} 
Table 1: Test conditions and parameters of the test model. Figure 1: Computation grid of Epller387: (a) Computational domain; (b) Near-wall grids distribution. Figure 2: CFD and experimental results for Eppler 387 at \(Re=1.0\times 10^{5}\): (a) Lift coefficient vs. angle of attack; (b) Drag coefficient vs. angle of attack; (c) Laminar separation location vs. angle of attack. The aerodynamic performances of the propeller calculated by the CFD method were compared with the experimental results in Figure 8. The results show that the thrust coefficient (\(C_{T}\)), power coefficient (\(C_{P}\)), and efficiency (\(\eta\)) calculated by the CFD are close to the experimental results. Therefore, the computational fluid dynamics (CFD) simulation based on the \(\gamma\)-\(Re_{\theta}\) model has sufficient accuracy for the performance calculations of airfoil and propeller at low Reynolds numbers. In this study, it was used to evaluate the performance of the cross-sectional airfoil and propeller during the optimization process.
# Role of Forster Resonance Energy Transfer in Graphene-Based Nanomaterials for Sensing
## 3 Application of Graphene-Based Nanomaterials in Sensing
### Proteins (Cancer Biomarkers) Sensor  
Lately, ovarian cancer (OVC) has posed a high risk to women's health, and late diagnosis can cause death. Protein biomarkers such as cancer antigen 125 (CA125) and human epididymis protein 4 (HE4) are utilized to identify OVC malignancies. Based on the above, [PERSON] and [PERSON] et al. developed \(p\)-phenylenediamine functionalized GQDs through a solvothermal method [68]. Of the two GQDs, one exhibited green emission (515 nm) as a donor and the other exhibited red emission (615 nm) (acceptor). The combination of both GQDs showed better spectral overlapping and exhibited the FRET process (Figure 13). **G11** was taken for the sensing of different proteins, which showed a high fluorescence intensity against a single protein and combined proteins of HE4. The conjugated HE4 with **G11** could detect HE4 through ratiometric sensing with a low detection limit (4.8 pM).
## 2 Materials and Methods
### Network Architecture
#### 2.4.1 Training and Testing Processes  
The proposed pipeline is trained end to end by using the patches from the baseline brain CT image and its corresponding mask as input, targeting the follow-up brain image and its mask. The DVF output of the U-Net architecture has 3 channels and is of the same spatial size as the input patches. Subsequently, the STN applies this DVF to warp the input image with the mask to the predicted image and its mask. To train this patch-based method, we firstly split the provided cases from the training dataset into training and validation subsets in a ratio of 80% to 20%, respectively. Then, we construct the dataset of patches for both training and validation, where from each image of the corresponding set, we extract 500 patches by following the sampling strategy defined before. Notice that this procedure is performed at the image level, hence avoiding data leakage in training, validation, and testing phases. The model is trained with the Adam optimizer [29], with the learning rate of 0.0001 and batch size of 8. The maximum number of training epochs is 150, with the early stopping of 10 epochs applied. The overall loss function is computed following the same  
Figure 2: Illustration of the patch sampling process during training within the lesion expansion area. The procedure is shown for the input CT image and the target follow-up CT image, and the same applies for the input baseline hematoma mask and the target follow-up hematoma mask. Of all patches, 80% are sampled from the HE area, meaning that the center of the patch is located within this region (marked with red contours). The input and target patches are extracted from the same location of the baseline and follow-up CT images (masks). The colors of the patch borders mark each training pair. Zoomed input and target patches show that we are aiming to learn information from the hematoma growth area.  
logic as in [14]: \(L=L_{\text{seg}}+L_{\text{sim}}+L_{\text{smooth}}\), where \(L_{\text{seg}}\), \(L_{\text{sim}}\), and \(L_{\text{smooth}}\) are individual losses for each part of the pipeline. \(L_{\text{seg}}\) is the hematoma mask loss, which, in our case, is the Dice loss between the predicted hematoma mask (FU1 mask-pred) and the ground-truth follow-up mask (FU1 mask) (Figure 3, loss shown in green). \(L_{\text{sim}}\) is the image similarity loss, which is the \(L_{1}\) between the predicted follow-up image (FU1-pred) and ground-truth follow-up image (FU1) (Figure 3, loss shown in yellow). These two losses are used to learn the DVF, and minimizing them encourages the predicted image and hematoma mask to be similar to their corresponding ground truth. Finally, \(L_{\text{smooth}}\) (Figure 3, loss shown in magenta) is used to minimize the discrepancy between the transformed image and the target image, i.e., it penalizes the predicted DVF being too complex or noisy, hence helping to avoid producing unrealistic artifacts on the output images. It is a L2 norm regularization term applied to the spatial gradient of the DVF.
## 2 Methodology and Data
### CART Decision Tree  
Classification and Regression Trees (CARTs) are a widely used machine learning algorithm for classification and regression analysis, first introduced by [PERSON], [PERSON], [PERSON], and [PERSON] in 1984 [45]. The CART is a non-parametric decision tree method that uses a recursive partitioning strategy to create a tree-based model.  
The algorithm begins with the entire dataset and then selects the variable and its threshold that best separates the data into two subsets based on the impurity of the resulting subsets. The impurity can be measured using different metrics; the mostly used impurity measures are the Gini index or entropy. The whole tree structure is then created by repeatedly splitting the data into two smaller subsets based on the values of the input variables. The splitting process is repeated until a stopping criterion is met, such as a minimum number of observations per leaf or a maximum tree depth [46; 47]. In this study, we used a maximum tree depth of 5; minimum records in the parent branch of 2%; minimum records in the child branch of 1%; and a minimum change in impurity of 0.0001 as the stopping criteria. The impurity is measured using the Gini index, which measures the probability of misclassifying a randomly chosen data case from a given node. It is calculated as follows:  
\[\text{Gini Index}=1-\sum{p_{i}}^{2}\]  
where \(p_{i}\) is the proportion of samples in class \(i\) at the node. The Gini index reaches its minimum when all samples at the tree node belong to the same class of the target variable.  
Entropy measures the amount of information or uncertainty in a given data set. It is calculated using  
\[\text{Entropy}=-\sum{p_{i}}\cdot\log{p_{i}}\]  
where \(p_{i}\) is the proportion of samples in class \(i\) at the node. Entropy reaches its minimum when all samples at the node belong to the same class.  
The Gini index and entropy are the most commonly used impurity measures in the CART algorithm. Some studies have shown that the Gini index tends to perform slightly better in practice for classification tasks, while entropy may be more sensitive to changes in the data distribution [37; 38].  
The CART method can handle both categorical and continuous input variables, which is considered one of its main advantages. CART modelling results are unaffected by either the collinearity between the input variables or the occurrence of outliers in the dataset. It can handle the missing data by setting the surrogate input variables for each variable. As the main disadvantage of the method, we mention the problem of overfitting, where the tree is too complex and fits the noise in the training data instead of the general patterns [45]. To solve this problem, the CART could be pruned after its building to reduce overfitting and improve generalisation performance. Pruning means removing such branches of the tree that do not significantly improve the overall accuracy of the tree [45; 48]. To avoid this problem of overfitting, the CART in this study was pruned.
# Design of an Isolated Bidirectional Symmetric Resonant Converter
## 5 The Structure of the Digital Signal Control System  
Figure 13 shows the control system structure of the developed isolated bidirectional symmetric resonant converter. The digital signal processor (DSP) IC TMS320F28335 of Texas Instruments was employed as the control core of this bidirectional converter system to manipulate the bidirection power flow approach. In comparison with the analog scheme, the digital signal approach can effectively reduce the amount of circuit component and thus decrease the size and cost accordingly.
## 3 Material and Methods
### Evaluation Metrics
\begin{table}
\begin{tabular}{c c} \hline \hline
**Metric** & **Formula** & **Description** \\ \hline Accuracy & \(\frac{TP+TN}{TP+TN+FP+FN}\) & Correctly classified instances divided by the total number of instances \\ \hline Precision & \(\frac{TP}{TP+FP}\) & True positive cases divided by the predicted positive cases \\ \hline Sensitivity (recall) & \(\frac{TP}{TP+FN}\) & Positive predicted cases divided by total confirmed cases \\ \hline Specificity & \(\frac{TN}{FP+TN}\) & Negative predicted cases divided by the total discarded cases \\ \hline AUROC & - & Probability of how well a diagnostic test can distinguish between two groups \\ \hline F1 score & \(2\times\frac{Precision\times Recall}{Precision+Recall}\) & Harmonic mean of the precision and the recall \\ \hline Cohen’s Kappa & \(\frac{P_{s}-P_{s}}{1-P_{s}}\) & Agreement between predictions and true labels, adjusted for chance \\ \(P_{s}\): Observed agreement, \(P_{s}\): Expected agreement by chance & \\ \hline LR+ & \(\frac{Sensitivity}{1-Specification}\) & How much the odds of a condition increase when the result is positive \\ \hline LR- & \(\frac{1-Sensitivity}{Specificity}\) & How much the odds of a condition decrease when the result is negative \\ \hline DOR & \(\frac{LR+}{LR-}\) & Overall assessment of the diagnostic test performance \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Metrics used for diagnosis.
# An Approach for Selecting the Most Explanatory Features for Facial Expression Recognition
## 1 Introduction  
Computers are quickly becoming a ubiquitous part of our lives, and we spend a large amount of time interacting with computers of one type or another; however, the devices that we use are indifferent to our affective states, as they are emotionally blind. Successful human-human communication relies on the ability to read affective and emotional signals. Human-computer interaction, which does not consider the affective states of its users, loses a large part of the information available from the interaction.  
At present, expression recognition can be achieved using neural networks. A neural network behaves like a black-box model; therefore, it is difficult to understand how it performs its classifications. Recent works have attempted to create attention maps to understand the results obtained by neural networks [1; 2]; however, these maps do not produce reasonable interpretations of the types of features learned by the neural network.  
Other solutions, such as sparse representation, help in studying the classification performance. In this context, a trustworthy intelligent system must be able to explain its decisions and actions to human users through the use of techniques that produce more understandable models, while maintaining high performance levels [3].  
Thus, in this paper, we analyze the importance of each feature used for facial expression recognition. Feature-relevance techniques seem to be some of the most-used schemesfor post hoc explanation [4, 5], as they can provide an explicit description of the inner behavior of the model, contributing to the goal of designing an explainable intelligent system.  
In our work, we focus on determining which feature sub-spaces are the most advantageous, where sparse representation (SR) was used to determine the features.  
The remainder of this paper is organized as follows: Section 2 introduces the related work. Section 3 describes the methodology, local and geometric features, expression classification, and combination framework. Section 4 describes the specific experimental process. Section 5 analyzes and discusses the results. The research is summarized, and our conclusions are offered in Section 6.
# Phishing Node Detection in Ethereum Transaction Network Using Graph Convolutional Networks
## 4 Proposed BM-GCN Model
### Representing the Features of the Graph
#### 4.1.2 Time Variance Graph  
Intuitively, the interval of transaction time, which shows the changes in trading time between two nodes, can be used effectively to describe the transaction relationship between nodes. To introduce the time feature of transactions between nodes, we use the variance of the time of the transactions to construct the second inter-node graph. Let \(v_{ij}\) denote the variance in the transaction time from node \(i\) to \(j\); the mean value of the transaction time from \(i\) to \(j\) is \(\overline{t_{ij}}\), the total number of transactions from \(i\) to \(j\) is \(n_{i,j}\), and the time of \(k\)-th transaction is \(\tau_{k}\). The graph of the time variance is constructed as follows:  
\[G_{v}(V,E)\quad weight=TransactionTimeVariance \tag{3}\]  
\[v_{i,j}=\frac{\sum_{k=1}^{n_{ij}}{(\tau_{k}-\overline{t_{ij}})^{2}}}{n_{i,j}} \tag{4}\]  
\[A_{v}=\left(\begin{array}{cccccc}0&v_{0,1}&v_{0,2}&\cdots&v_{0,N-1}\\ v_{1,0}&0&\cdots&\cdots&v_{1,N-1}\\ v_{2,0}&v_{2,1}&0&\cdots&v_{2,N-1}\\ \vdots&\vdots&\vdots&\ddots&\vdots\\ v_{N-1,0}&\cdots&\cdots&\cdots&0\end{array}\right) \tag{5}\]
## 3 Testing Results and Analysis
### Compaction Test  
As per ASTM D698 and ASTM D1557, the improved and standard soil compaction tests were conducted to determine the compaction curves, maximum dry density \(\gamma_{\rm dmax}\), and OMC. The present study distinguished clods with varying grain sizes and cement content to conduct improved and standard soil compaction tests. Curves of soil graduations for soil that passed through the sieves (\(<\)\(\#4\), \(\#4\)-\(3/4^{\prime\prime}\), or larger than \(3/4^{\prime\prime}\)) were adopted to simulate clod size for tests involving the varying cement content levels of 0%, 4%, and 8%. Figure 6 presents the compaction curve results, with x- and y-axis denoting soil water content and dry density, respectively. Figure 6 indicates that larger clod grain sizes led to the rightward and downward movement of the compaction curves. The improved soil compaction curve was positioned to the upper-left side of the standard soil compaction curves. Figure 6 indicates that an increase in added cement caused the maximum dry density of both improved soil and standard soil compaction curves to move downward, indicating an increase in OMC and decrease in maximum dry density. The improved soil compaction test results revealed that the mudstone specimen OMC and \(\gamma_{\rm dmax}\) were 10.1% and 19.9 kN/m\({}^{3}\), respectively. When the cement addition C was 4% and 8%, the OMC levels were 10.8% and 11.6%, respectively, and the maximum dry density levels were 19.6 kN/m\({}^{3}\) and 19.5 kN/m\({}^{3}\), respectively. For subsequent specimen preparation, compaction test results were employed as the basis, and a line was drawn approximately parallel to the zero air void curve (ZAVC) along the compaction curve on the wet side; this line represents the S\({}_{\rm r}\) = 91% curve (Figure 6). The curve was used as the basis for controlling moisture content for each dry density level during specimen preparation. The relevant moisture content levels for dry density levels of 17.5, 18.0, 18.5, 19.0, 19.5, and 20.0 kN/m\({}^{3}\) were  
Figure 4: Particle size distribution curves of mudstone original soils and soils passing through \(\#4\).  
Figure 5: Testing soils in the paper located within the plastic chart according to the United Soils Classification System.  
16.2%, 15.8%, 15.2%, 14.0%, 12.3%, and 10.1%, respectively. The moisture content controls for mudstone-cement specimens were identical.
## 3 Results  
### Distribution of WUE, GPP, ET, and Their Trends  
The distributions of the average annual WUE, GPP, and ET in the STC from 1982 to 2018 are illustrated in Figure 1. In the entire vegetation coverage area, the annual average value of WUE ranged from 0 to 7.72 g C/m\({}^{2}\)/mm, and approximately 90% of the vegetation area had a WUE higher than 1 g C/m\({}^{2}\)/mm, with 14% of the vegetation area presenting a WUE above 2 g C/m\({}^{2}\)/mm. In the vegetated area, the annual mean GPP value ranged from 0 to 2944 g C/m\({}^{2}\)/year. The spatial distribution pattern was consistent with the annual mean WUE (Figure 1a,b). In the same area, the annual mean ET value ranged from 0 to 1703 mm/year; 51.40% of the vegetation area had an annual mean ET value higher than 1000 mm/year, and only 1% of the vegetation area had an annual mean ET value lower than 500 mm/year.  
Among the six major landscapes (Figure 1), EBF had the highest annual average WUE at 1.89 g C/m\({}^{2}\)/mm, followed by DBF at 1.74 g C/m\({}^{2}\)/mm, and ECF at 1.70 g C/m\({}^{2}\)/mm. Croplands had the lowest annual average WUE of 1.33 g C/m\({}^{2}\)/mm. The annual mean GPP was similar to that of the WUE in different landscape types, with EBF having the highest annual mean GPP (1954 g C/m\({}^{2}\)/year) and cropland having the lowest (1303 g C/m\({}^{2}\)/year). The annual mean ET values in different landscapes were almost consistent between 910 and 1050 mm/year.  
The spatial distributions of the variations in annual WUE, GPP, and ET from 1982 to 2018 are illustrated in Figure 2. Trends for WUE ranged between \(-\)0.23 and 0.14 g C/m\({}^{2}\)/mm/year. The changing trends in GPP and WUE showed similar spatial distribution patterns, but were quite different from those of ET (Figure 2c). For the six landscapes, WUE decreased over the study period, with the strongest downward trend in EBF (\(-\)0.0037 g C/m\({}^{2}\)/mm/year) and a weaker trend in grassland (\(-\)0.0001 g C/m\({}^{2}\)/mm/year). In addition, cropland showed an increasing trend (0.0007 g C/m\({}^{2}\)/mm/year). The trend of GPP was between \(-\)110 and 95 g C/m\({}^{2}\)/year\({}^{2}\) (Figure 2b), with the GPP of DBF, ECF, shrub, grassland, and cropland increasing to varying degrees, and the GPP of EBF decreasing slightly. The trend of ET was between \(-\)10 and 11 mm/year\({}^{2}\) (Figure 2c). At the same time, the change in ET showed an increasing trend for all landscapes.  
Figure 1: Spatial patterns of (**a**) mean annual water use efficiency (WUE), (**b**) Gross Primary Productivity (GPP), and (**c**) evapotranspiration (ET) in subtropical China. The right insets indicate the mean annual WUE, GPP, and ET of different landscapes, with whiskers indicating the standard deviations of all grid cell values. Other abbreviations: ECF, evergreen coniferous forest; EBF, evergreen broadleaf forest; DBF, deciduous broadleaf forest.  
Figure 2: _Cont._
## 3 Position Tracking Controller Design
### Controller Design  
The tracking error \(e=\begin{bmatrix}e_{I}&e_{\theta}&e_{\omega}&e_{\alpha}&e_{\beta}\end{bmatrix}^{T}\) is defined as  
\[\begin{split} e_{I}&=\int_{0}^{t}e_{1}d\tau\\ e_{\theta}&=\theta_{r}-\theta\\ e_{\omega}&=\omega_{r}-\omega\\ e_{\alpha}&=i_{\alpha_{r}}-i_{\alpha}\\ e_{\beta}&=i_{\beta_{r}}-i_{\beta}\end{split} \tag{9}\]  
where the integrator \(e_{I}\) is used to suppress the steady-state error. From (2) and (9), the tracking error dynamics can be obtained as  
\[\begin{split}\dot{e}_{I}&=e_{\theta}\\ e_{\theta}&=e_{\omega}\\ \dot{e}_{\omega}&=\frac{3}{2}\frac{p\[PERSON]}{J}\tau_{r}- \left[-\frac{3}{2}\frac{p\[PERSON]}{J}i_{\alpha}\sin(p\theta)+\frac{3}{2}\frac{p \[PERSON]}{J}i_{\beta}\cos(p\theta)\right]-\frac{B}{J}e_{\omega}\\ e_{\alpha}&=-\frac{R}{L}e_{\alpha}+\frac{p\[PERSON]}{L} \omega_{r}\sin(p\theta_{r})+\frac{1}{L}v_{\alpha_{r}}-\frac{p\[PERSON]}{L}\omega \sin(p\theta)-\frac{1}{L}v_{\alpha}\\ \dot{e}_{\beta}&=-\frac{R}{L}e_{\beta}-\frac{p\[PERSON]}{L} \omega_{r}\cos(p\theta_{r})+\frac{1}{L}v_{\beta_{r}}+\frac{p\[PERSON]}{L}\omega \cos(p\theta)-\frac{1}{L}v_{\beta}.\end{split} \tag{10}\]  
The desired torque \(\tau_{r}\) and the desired voltages \(v_{\alpha_{r}}\), and \(v_{\beta_{r}}\) are then designed to make the tracking error dynamics (10) be the linear system as  
\[\begin{split}\tau_{r}&=\frac{2}{3}\frac{J}{p\[PERSON]} \left[-\frac{3}{2}\frac{p\[PERSON]}{J}i_{\alpha}\sin(p\theta)+\frac{3}{2}\frac{p \[PERSON]}{J}i_{\beta}\cos(p\theta)+k_{\alpha}e_{\alpha}+k_{\beta}e_{\beta}\right] \\ v_{\alpha}&=p\[PERSON]\omega_{r}\sin(p\theta_{r})-p\[PERSON] \omega\sin(p\theta)+v_{\alpha_{r}}-Lu_{\alpha}\\ v_{\beta}&=-p\[PERSON]\omega_{r}\cos(p\theta_{r})+p\[PERSON] \omega\cos(p\theta)+v_{\beta_{r}}-Lu_{\beta}\end{split} \tag{11}\]  
where \(u_{\alpha}\) and \(u_{\beta}\) are yet to be defined. With (11), the tracking error dynamics (10) become  
\[\begin{split}\dot{e}_{I}&=e_{\theta}\\ \dot{e}_{\theta}&=e_{\omega}\\ e_{\omega}&=k_{\alpha}e_{\alpha}+k_{\beta}e_{\beta}- \frac{B}{J}e_{\omega}\\ \dot{e}_{\alpha}&=-\frac{R}{L}e_{\alpha}+u_{\alpha}\\ e_{\beta}&=-\frac{R}{L}e_{\beta}+u_{\beta}.\end{split} \tag{12}\]The tracking error dynamics (12) can be rewritten as  
\[\underbrace{\begin{bmatrix}e_{l}\\ e_{\theta}\\ e_{\omega}\\ e_{\theta}\end{bmatrix}}_{\hat{e}}=\underbrace{\begin{bmatrix}0&1&0&0&0\\ 0&0&1&0&0\\ 0&0&-\frac{B}{J}&k_{\alpha}&k_{\beta}\\ 0&0&0&-\frac{R}{L}&0\\ 0&0&0&0&-\frac{R}{L}\end{bmatrix}}_{A_{e}}\underbrace{\begin{bmatrix}e_{l}\\ e_{\omega}\\ e_{\alpha}\\ e_{\beta}\end{bmatrix}}_{\hat{e}}+\underbrace{\begin{bmatrix}0&0\\ 0&0\\ 1&0\\ 0&1\end{bmatrix}}_{B_{e}}\underbrace{\begin{bmatrix}u_{\alpha}\\ u_{\beta}\\ u_{\alpha}\end{bmatrix}}_{u} \tag{13}\]  
The control input \(u\) is designed via the state feedback as  
\[u=-K_{e}e \tag{14}\]  
where \(K_{e}\) is the control gain matrix. If \(K_{e}\) is chosen such that \(A_{e}-B_{e}K_{e}\) is Hurwitz, then the origin of the error dynamics (13) is exponentially stable.