esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Regressions of Different ICMV  
Different ICMV versus \(E_{\text{LWD}}\) and compaction degree are plotted in Figure 7. As expected, R\({}^{2}\) values of empirical method ICMV (CMV and CCV) were lower than the mechanical method ICMV (\(E_{\text{vib}}\)). It means that using mechanical method ICMV to predict ISMV is more accurate than using empirical method ICMV. Besides, R\({}^{2}\) values between ICMV and compaction degree were lower than the R\({}^{2}\) values between ICMV and \(E_{\text{LWD}}\), indicating that ICMV reflected the stiffness of subgrade better than the density of subgrade.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Linear Algorithms**} & \multicolumn{2}{c}{**Nonlinear Algorithms**} \\ \cline{2-3}  & **Machine Learning (ML)** & **Deep Learning (DL)** \\ \hline \multirow{3}{*}{Linear regression} & K-nearest neighbour (KNN) [36], & Artificial neural network \\  & Random forest (RF) [37], Light & (ANN), Recurrent neural \\  & gradient boosting model (LGBM) [29] & network (RNN) [38] \\ \hline \hline \end{tabular} Note: The hyper-parameters of models were automatically determined by GridSearchCV.  
\end{table}
Table 3: Regression and classification algorithm models.  
Figure 6: Structures of (a) ANN (7-56-1) and (b) RNN (7-42-42-1).  
Interestingly, the CCV, which considered the jumping situation by adding first subharmonic and higher-order harmonics, had the worse regression performance than CMV. The reason is that the stiffness of sand subgrade is relatively low, roller jumping phenomenon barely appears during compaction while higher-order harmonics and subharmonics contain little information about subgrade compaction state. In this case, introducing higher-order harmonics and subharmonics cannot improve the prediction accuracy of ICMV, but will probably import other unpredictable interference factors.
## 4 Experiment and Results
### Experiment Results
#### 4.3.2 Portfolio Performance Evaluation
Constructed portfolios based on the LR prediction model performed better than those based on the SVR prediction model in practical trading, as shown in Figure 11, and EQ weights obtained higher returns compared with MCS and MVO. Unfortunately, SRs produced by MCS and MVO weights gradually decreased, even lower than EQ weights. The results seem to indicate that the predicted accuracy contributed a considerable impact on the optimization phase. In this phase, constructed portfolios based on the LSTM model outperformed, as shown in Figure 12, with the highest returns and SRs. On one hand, the returns from EQ, MCS, and MVO were pretty much the same in almost cases. On the other hand, SRs obtained by MCS and MVO were higher compared with EQ weights. It is apparent that the constructed portfolio based on the LSTM  
After evaluating the efficiency of the proposed prediction models in both prediction and practical trading. The results showed that the LSTM prediction model outperformed the proposed prediction models. The efficient prediction is not only for prediction but also support for the optimization phase. These constructed portfolios were selected as efficient portfolios for quantitative trading. As we can see from the results, MCS and MVO weights were slightly different, however, as the number of iterations (scenarios) increases, more computational resources are required. Therefore, MVO weights were selected as the optimal weights for efficient portfolios. The comparison of efficient portfolio performance is given in Figure 13. Returns were gradually increased in both prediction and practical trading. In addition, returns obtained in practical trading are correlated with predicted results. Although SRs showed a tendency to decline, there was only a slight difference between prediction and practical trading. This is evidence that efficient portfolios beat the benchmark S&P 500 in both returns and SRs. Figure 14 shows a summary of the optimal portfolio allocation based on the LSTM prediction model. It showed that the optimal weights of stocks in the portfolio lead to higher active returns and lower volatility relative to the benchmark index. All these differences were statistically significant at approximately 86 and 48 percent higher than the benchmark in terms of return and SR, respectively. Table 4 summarizes the comparison between the efficient portfolios and the benchmark on active returns for each time period. Our constructed portfolios outperformed the benchmark the S&P 500 index. Figure 12: LSTM actual portfolios’ performance. Figure 13: LSTM efficient portfolios’ performances versus the benchmark index.
## 4 Experimental Results
### Discussion
#### 4.7.2 Limitation of the Proposed Method  
Despite having achieved state-of-the-art detection accuracy and real-time detection speed, our approach does not perform well in some cases, as shown in Figure 10. The failure cases are caused by occlusions. In Figure 10a, one person occluded by a tree is not detected. In Figure 10b, one person in crowed is not detected. In Figure 10c, a false detection is generated among the crowd. Thus, occlusions may degrade detection performance, which is a common limitation with any methods.  
It is necessary to solve the challenges of detecting occluded pedestrians. This common problem has already been explored [46; 47]. We can regard pedestrians as a combination of different body parts. For each body part, CNN learns features and generate a score, respectively. If the body part is occluded, the score will be lower, otherwise, the score will be higher. During training, the features of each body part will be integrated. The integrated feature will be used for pedestrian classification and localization. The issues to solve in future are how many parts of a body should be used and how to effectively integrate the features of each body part, with low resolution IR images. In addition, we can take advantage of supervised learning to optimize the occlusion problem from the loss function perspective. We can consider designing the loss function to force proposals away from the second largest ground-truth bounding boxes that overlap with it. The objective is to make the proposal close to real objects and away from the false objects, thereby reducing the false detection rate due to occlusions.
# Sequence Deep Learning for Seismic Ground Response Modeling: 1D-CNN, LSTM, and Transformer Approach
## 3 Prediction Models
### One-Dimensional Convolutional Neural Network (CNN)-Based Model  
#### 3.2.1 Overview  
Convolutional neural networks (CNNs) have been widely used in image processing. CNNs learn the local spatial pattern in input data through convolution kernels, which sweep across the 2D data to extract meaningful adjacent features [43]. While commonly associated with 2D data, CNNs can also be applied to 1D sequential data [26]. One-dimensional CNNs (1D-CNNs) are designed for tasks involving time series and signal processing. In 1D CNNs, the convolutional kernel operates along a single dimension to capture local dependencies and patterns within input sequences.  
The convolutional kernel (see Figure 4) slides over the input sequence, performing element-wise multiplications to capture relationships between adjacent elements. As the network deepens, the convolutional layers learn more complex and abstract representations of the input sequences. The hierarchical structure of 1D CNNs allows them to capture sequential dependencies, and understand underlying patterns and relationships. Pooling layers downsample the feature maps, reducing dimensions while preserving key features, thus focusing on the most informative aspects of the input sequences. More technical details can be found in [PERSON] et al. and [PERSON] et al. [26; 43].
# Modeling and Analysis of Noise Emission Using Data from Flight Simulators
## 3 Comparative Analysis
### Parameters for Single-Factor Impact Analysis
This effect is mainly caused by the increased lateral attenuation. Based on this factor, it is apparent that the distance from the noise source to the observer (i.e., the climb gradient/trajectory) has a greater impact on the overall noise exposure, compared to the power of the noise source (i.e., engine thrust setting). The following Table 2 compiles the \"over 85 dB noise contours\" and their area, along with the total mean value of the SEL metric for the entire grid of observers. \begin{table}
\begin{tabular}{c c c c} \hline \(S_{\geq 85}^{SEL}=10.70\) [km\({}^{2}\)] & \(S_{\geq 85}^{SEL}=8.92\) [km\({}^{2}\)] & \(S_{\geq 85}^{SEL}=11.09\) [km\({}^{2}\)] & \(S_{\geq 85}^{SEL}=9.87\) [km\({}^{2}\)] \\ \hline \(SEL_{mean}=76.2\) [dB] & \(SEL_{mean}=74.8\) [dB] & \(SEL_{mean}=75.9\) [dB] & \(SEL_{mean}=74.5\) [dB] \\ \hline \end{tabular}
\end{table} 
Table 2: \(S_{\geq 85}^{SEL}\) contours and area. Figure 7: Values of SEL [dB] for observers \(O_{1},\ O_{2},\ O_{3}\), respectively. It should be noted that the reduced engine power has caused two significant changes in the shape of the analyzed areas. For both procedures, they have been elongated due to flying at a lower trajectory, but more importantly, they have been significantly narrowed, which correlates with the engine power setting. Therefore, it can be inferred that while the distance component has the greatest impact on the length of the noise footprint, the engine power component influences the transverse dimension of the area. In this example, although some observers noted increased levels of noise exposure, the overall surface area exposed to the highest \(SEL\) was decreased by 19.9% for NADP1 and 12.4% for NADP2, alongside with the mean value of \(SEL\) for the entire grid of observers. Figure 8 illustrates the relative change in carbon dioxide emitted with the reduced thrust. As expected, thrust reduction in combination with an appropriately chosen noise-abatement procedure allows for \(CO_{2}\) reductions of nearly 10% for the take-off and initial climb phases. Moreover, the blue NADP2 procedure results in a greater \(CO_{2}\) reduction in comparison with the red NADP1 procedure.
# Roto-Translational Control of Spacecraft in Low Earth Orbit Using Environmental Forces and Torques
## 2 Spacecraft Dynamics
### Aerodynamic Forces and Torques  
A spacecraft in LEO experiences aerodynamic drag and lift forces due to the interaction with the atmosphere. The DMD surfaces, which are significantly larger than the faces of the CubeSat, contribute to the total aerodynamic drag and lift forces as  
\[\mathbf{F}_{D,j} \triangleq-\frac{\rho w_{b}L_{j}C_{D,j}}{2}\mathbf{V}_{\perp,j}^{2} \frac{\mathbf{V}_{\mathbf{r}}}{\|\mathbf{V}_{\mathbf{r}}\|}, \tag{16}\] \[\mathbf{F}_{L,j} \triangleq-\frac{\rho w_{b}L_{j}C_{L,j}}{2}\mathbf{V}_{\perp,j}^{2} \bigg{(}\frac{\mathbf{V}_{\mathbf{r}}}{\|\mathbf{V}_{\mathbf{r}}\|}\times\mathbf{n}_{j}\times\frac{ \mathbf{V}_{\mathbf{r}}}{\|\mathbf{V}_{\mathbf{r}}\|}\bigg{)}, \tag{17}\]  
where the subscript \(j\) indicates the \(j\)th DMD surface, \(\rho\in\mathbb{R}\) is the atmospheric density, \(C_{D,j}\), \(C_{L,j}\), \(w_{b}\), \(L_{j}\in\mathbb{R}\) are the drag, lift coefficients, width and length of the \(j\)th DMD surface, respectively. The vector \(\mathbf{V}_{\mathbf{r}}\in\mathbb{R}^{3}\) represents the spacecraft-atmosphere relative velocity vector, \(\mathbf{n}_{j}\in\mathbb{R}^{3}\) is the unit vector that represents the direction normal to the \(j\)th DMD surface and \(\mathbf{V}_{\perp,j}\triangleq\mathbf{V}_{\mathbf{r}}\cdot\mathbf{n}_{j}\in\mathbb{R}\).  
**Assumption 2**.: _The spacecraft are assumed to be flying with the DMD surfaces installed on the anti-ram face. Other attitude configurations are considered beyond the scope of this paper._  
The torques produced by aerodynamic drag and lift are given by  
\[\mathbf{\tau}_{k}\triangleq\sum_{j=1}^{4}R_{j}^{\times}\mathbf{F}_{k,j},\ \ k=D,L, \tag{18}\]  
where \(\mathbf{R}_{j}\triangleq\mathbf{r}_{c}+\mathbf{r}_{j}\in\mathbb{R}^{3}\), \(\mathbf{r}_{c}\triangleq[c_{1}\ c_{2}\ c_{3}]^{T}\in\mathbb{R}^{3}\) is the vector that goes from the spacecraft CoM to the geometric center of the anti-ram face of the CubeSat (\(O^{\prime}\)), and \(\ \mathbf{r}_{j}\in\mathbb{R}^{3}\) is the vector that goes from \(O^{\prime}\) to the center of pressure of the \(j\)th DMD surface, see Figure 3.
# Analysis of Numerical Artifacts Using Tetrahedral Meshes in Low Frequency Numerical Dosimetry
## 3 Tetrahedral Mesh Quality  
A good mesh quality is an important prerequisite to ensure numerical results in agreement with the reference solution. The best quality mesh is achieved when it is uniformly composed by regular polygons in two-dimensional space and regular polyhedra in three-dimensional space. The mesh quality issue does not arise when voxelized-human models are used in numerical dosimetry because the meshes are discretized with regular cubic elements. On the other hand, the purpose of tetrahedral meshes is to reproduce curved boundaries. Therefore, it is common to find tetrahedra with much longer edges than others in the same mesh discretization. From a geometric point of view this guarantees a better reproduction of the object shape, however, from a computational point of view this affects the numerical accuracy.  
Different metrics can be used to assess the mesh quality. In this paper, the quality \(q\) of tetrahedral meshes is evaluated by using the Normalized Shape Ratio, as described in [29], obtained as the ratio between the radius \(r\) of the sphere inscribed in and the radius \(R\) of the sphere circumscribed to the tetrahedron (see Figure 2):  
\[q=3\frac{r}{R} \tag{2}\]  
The factor 3 is used to normalize the value of \(q\) in the range \((0\,;\,1]\). For a regular tetrahedron, \(q\) equals 1; therefore, a good-quality mesh should have most of tetrahedra with quality index close to 1.
## 4 Lunar Antenna Types  
Within this section, we offer a comprehensive overview of the diverse types of antennas employed in lunar applications. Our diligent efforts to conduct a thorough literature review are evident, despite the challenges posed by the limited availability of articles concerning lunar antennas. These antennas are classified into planar antennas, reflector antennas, low-frequency antennas, and wire antennas. Additionally, considering their applications, these antennas are further divided into two sub-categories: mission-oriented antennas, utilized in active lunar missions, and research-focused antennas, exclusively employed for academic investigations. Our objective is to furnish a comprehensive synthesis of the encountered antennas, organized by their respective types. Antennas designed for specific missions should offer advantages such as a compact profile, easy installation, low power requirements, and durability, all while being shielded by a protective radome. Figure 3 shows this summary.  
Figure 2: Schematic of the lunar environment [41].
## 2 Materials and Methods
### Methodology
#### 2.3.7 Accuracy Assessment  
The spatial identity of the crop map was evaluated with test samples that would calculate the overall accuracy (OA), precision (UA), recall (PA), and Weighted F1 Score [53]. When the sample points are unbalanced, it is not suitable to use the same weight to calculate the F1 Score, and the number of each class should be used as the weight value [17].  
The OA indicates the proportion of correctly predicted samples by the model out of all samples. Precision shows the proportion between the number of samples correctly predicted by the model and the total number of samples predicted by the model for a specific category. Recall represents the ratio of correct samples predicted by the model in the actual samples of a certain class. F1 Score reflects the relationship between the precision and recall of the model. The Kappa coefficient signifies the balance of the confusion matrix and is employed for assessing consistency [54]. R2 and RMSE were used to calculate statistical correlations.  
\[\text{overall accuracy}=\frac{\text{TP}+\text{FN}}{\text{TP}+\text{FP}+\text{ FN}+\text{TN}} \tag{7}\]  
\[\text{precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{8}\]  
\[\text{recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{9}\]  
\[\text{FI Score}_{\text{class}}=2\times\frac{\text{precision}\times\text{ recall}}{\text{precision}+\text{recall}} \tag{10}\]  
\[\text{Kappa coefficient}=\frac{\text{P}_{\text{o}}-\text{P}_{\text{e}}}{1- \text{P}_{\text{e}}} \tag{11}\]  
Figure 7: Architecture of the ensemble classifier.  
\[\text{R}^{2}=1-\frac{\frac{\sum\limits_{i=1}^{\text{m}}{(y_{i}-\hat{y}_{i})}^{2}} {\frac{\text{m}}{\text{m}}}}{\frac{\sum\limits_{i=1}^{\text{m}}{(y_{i}-\hat{y}_ {i})}^{2}}{\text{m}}} \tag{12}\]  
\[\text{RMSE}=\sqrt{\frac{1}{\text{m}}\sum\limits_{i=1}^{\text{m}}{(y_{i}-\hat{ y}_{i})}^{2}} \tag{13}\]  
The meanings of the parameters in Table 5 are shown below: TP indicates the correct classification of the positive class, FN represents the misclassification of the positive class as the negative class, FP signifies the misclassification of the negative class as the positive class, and TN indicates the correct classification of the negative class. \(P_{0}\) represents the overall accuracy, and \(P_{\text{e}}\) is the ratio of the total number of cells in the actual cluster multiplied by the total number of cells on the diagonal of the confusion matrix to the square of the number of samples. \(y_{\text{i}}\) indicates true value, \(\hat{y}_{\text{i}}\) denotes the average of true values, and \(\hat{y}_{\text{i}}\) represents predicted value.
# The Extraction of Maximal-Sum Principal Submatrix and Its Applications
## 2 Related Work
### Best Submatrix Extraction  
There are many applications related to extracting the best \(k\times k\) submatrix from a matrix A. Due to different purposes of application, the definition of the term \"best\" also varies. The best submatrix means that it has the maximum volume [8], the largest logarithm of the determinant or the minimal trace of the inverse of the selected principal submatrix [6], the max-algebraic permanent [9], etc.  
In many cases, the best submatrix means to find the submatrix that has the maximum sum. The maximal-sum submatrix (MSS) can be a principal submatrix or not a principal submatrix [10; 11]. The extracted submatrix can be a rectangle [5; 12], which means that both the extracted rows and columns are continuous. The max-sum submatrix can also be defined as a rectangular submatrix, in which both the rows and the columns are non-necessarily contiguous [1; 2].  
The problems discussed in the existing literature mentioned above are both related and different from the problem we want to solve in this paper, but none of them is completely consistent with the problem we are solving. Similarities and differences of these problems between the mentioned literature and ours are listed in Table 1. Compared to the best \(k\times k\) submatrix extraction, the submatrix to be extracted is a square matrix, and the calculated submatrix features are different. The characteristics of existing methods for calculation are the maximum volume [8], the largest logarithm of the determinant and the minimal trace of the inverse of the selected principal submatrix, but we focus on the sum of all elements of the submatrix.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref.** & **Object** & **Connectivity** & **Principal Submatrix or Not** & **Submatrix Order** & **Accurate or** \\ \hline
[1] & The sum of the entries of submatrix & Discrete & N & Unfixed & Accurate \\
[2] & The sum of the entries of submatrix & Discrete & N & Unfixed & Approximate \\
[3; 4; 5] & The sum of the entries of submatrix & Consecutive & N & Unfixed & Accurate \\
[6] & The determinant of submatrix \({}^{+}\) & Discrete & Y & Fixed & Approximate \\
[7] & The sum of the entries of submatrix \({}^{+}\) & Discrete & Y & Fixed & Approximate \\
[8] & The maximum volume submatrix & Discrete & Y & Fixed & Approximate \\ Ours & The sum of the entries of submatrix & Discrete & Y & Fixed & Accurate \\ \hline \hline \end{tabular}  
* This is equivalent to the largest logarithm of the determinant of submatrix in [6]; \({}^{+}\) The maximum edge-weighted subgraph problem in [7] is equivalent to the problem of maximal sum of the entries of submatrix.  
\end{table}
Table 1: Similarities and differences of the problems between the mentioned literature and ours.  
Compared to the MSS extraction, the commonality of all methods is to find the optimal solution based on the sum of all elements of the submatrix, but most methods do not specify the size of the submatrix, and we, in this paper, focus on the \(k\)-order principal submatrix.
## 5 Taxonomy of Soft Computing Techniques Aware Clustering-Based Routing Protocols in VANETs
### Firefly Algorithm  
Firefly Algorithm (FA) imitates the characteristics and twinkling behavior of tropic firefly swarms. FA has two essential resources, which makes it better than other computational algorithms. The exceptional characteristics of the FA are local attractions and automatic regrouping. Since light strength and distance are proportionate, the attraction among the fireflies tends to be global or regional based on the absorption coefficient. This allows both global and regional modes to be visited. FA can sub-split and regroups based on the neighboring attractiveness, this merit of FA becomes more acceptable for clustering issues [32].  
[PERSON] et al. [33] proposed a Reputation-based Weighted Clustering Protocol (RWCP). To stabilize the VANET topology, the RWCP is enclosed by considering the path of vehicles, location, speed, neighboring vehicles, and the status of each node. The work uses a multi-objective issue that takes the considerations of the RWCP as the feedback and intends to provide an improved cluster lifespan, enhanced packet distribution ratio, and decreased overhead of the cluster. An evolutionary approach, the multi-objective firefly algorithm (MOFA), is used to optimize the factors of the RWCP. The suggested objective function uses an algorithm to initialize the fireflies, and random walk to find the best solution and sort for best estimation techniques. The TETCOS NetSim simulator and MOFA structure have been used to refine simulations. With similar evolutionary optimization methods, the results are tested. The simulation results indicate that the suggested Mean Cluster Lifespan, PDR, and Control Packer Overhead technique performs well.  
The proposed algorithm enhances the lifetime of the cluster, PDR, and reduces the overhead. Still, improvements can be carried out by reducing computational time and to cater for areas with more vehicular density.