esa-sceva/satcom-chunk-collection huggingface dataset:
# Recursive Feature Elimination for Improving Learning Points on Hand-Sign Recognition
## 3 Methodology
### Training Steps  
Neural networks are used as the classifier in this research. The type of neural network used is a sequential neural network. The number of input nodes will be the number of features used for training, while the number of output nodes will be ten, corresponding to the digit 0-9. Dataset 1, Dataset 2, and Dataset 3 are used to train three models, totaling nine trained models. Dataset 4 is used to evaluate the performance of the models and not for training. This study's training and evaluation algorithm is depicted in Algorithm 2.  
```
inputs: training, testing, and validation set The selected feature sets Fs15 and Fs10 Initialization: initialize untrained model m1, m2, and m3 output: trained model m1, m2, and m3
1 train m1 using the original training and testing set
2 evaluate m1 performance using the original validation set
3 reduce the number of features in the dataset to follow Fs15
4 train m2 using the Fs15 training and testing set
5 evaluate m2 performance using the Fs15 validation set
6 reduce the number of features in the dataset to follow Fs10
7 train m3 using the Fs10 training and testing set
8 evaluate m3 performance using the Fs10 validation set
```  
**Algorithm 2**Model Training Steps.  
For the model training and evaluation, it will need the training, testing, and validation set. We will also need the feature selection result. Fs15 is the top 15 important features and Fs10 is the top 10 important features. First 3 untrained models \(\mathbf{m1}\), \(\mathbf{m2}\), and \(\mathbf{m3}\) will be initialized. Model \(\mathbf{m1}\) will be trained using the original 21 features, \(\mathbf{m2}\) will use 15 features in Fs15, while \(\mathbf{m3}\) will use 10 features in Fs10. First, we will train and test the model \(\mathbf{m1}\) using the original training and testing data. Then model \(\mathbf{m1}\) will be evaluated using the validation dataset. Next, we will reduce the number of features in the training, testing and validation sets to follow Fs15 features. Then model \(\mathbf{m2}\) will be trained and tested using the already feature-reduced dataset. After that, model \(\mathbf{m2}\) will be evaluated for its performance. The features in the training, testing, and validation will then be reduced once more to conform with Fs10. Finally, model \(\mathbf{m3}\) will be trained, tested, and also validated using the dataset having 10 features only. This procedure will be conducted for Dataset 1, Dataset 2, and Dataset 3 to produce three trained models per dataset.
## 4 Image Semantic Segmentation Algorithm
### Ablation Experiments  
To evaluate the impact of different improvement strategies on the original model, a series of ablation experiments were conducted under identical hyperparameter settings. The baseline model (A) represents the original DeepLabV3+ framework. Model B replaces the backbone network with EfficientNetV2. Model C builds on B by integrating the ECA module after the ASPP. Model D further extends B by incorporating an FPN for multi-scale feature fusion. Finally, Model E combines the FPN and ECA modules, representing the proposed final model. The loss functions and experimental results are illustrated in Figure 10 and summarized in Table 2.  
The results demonstrate that the proposed improvements significantly enhance the performance of the original DeepLabV3+ model (A). Model A achieves a mean Intersection over Union (mIoU) of 96.27%, a mean accuracy (mAcc) of 96.03%, and a parameter count of 41.91 M. Replacing the backbone with EfficientNetV2 in Model B reduces the parameter  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **mAcc/\%** & **mIoU/\%** & **Param/m** \\ \hline Xception & 96.03 & 96.27 & 22.9 \\ ResNet & 96.17 & 96.32 & 11.7 \\ MobileNetV3 & 95.27 & 95.50 & 2.5 \\ ResNeSt & 96.41 & 96.55 & 27.5 \\ EfficientNetV2 & 96.05 & 96.28 & 7.1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of different backbone networks.  
count to 26.04 M while improving mIoU and mAcc to 95.79% and 96.07%, respectively, substantially reducing model complexity. By incorporating the ECA module in Model C, the parameter count slightly increases to 26.51 M, but mIoU and mAcc are further improved to 96.13% and 96.50%, respectively, indicating the ECA module's effectiveness in enhancing feature representation. Model D introduces the FPN to Model B for multi-scale feature fusion, resulting in a parameter count of 28.02 M, with mIoU and mAcc increasing to 96.49% and 97.34%, respectively, highlighting the importance of multi-scale feature integration in improving model performance. The final model (E), which combines the FPN and ECA modules, achieves the best performance with a parameter count of 28.51 M, mIoU of 97.19%, and mAcc of 97.88%. These results validate the complementary effects of feature enhancement and multi-scale fusion, maximizing the overall performance of the model.
## Materials and Methods
### Methods
#### 2.5.2 Minimum Hydrological Response Unit  
The result of groundwater depth is spatially presented as raster data, which is interpolated according to the cokriging interpolation; however, the division unit of groundwater depth is not similar to the regular shape of the grid in the actual situation due to the spatial difference of multiple factors and the irregularity of land use types; therefore, in this paper, we borrow the concept of MHRU to transform raster data into vector data, so that the subsequent spatial analysis would be more suitable for the actual situation in the study area.  
The minimum hydrological response unit (MHRU) is based on the hydrological response unit (HRU), which is the basic unit of simulation in the SWAT (Soil and Water Assessment Tool) model [45; 46]. The minimum response unit is defined by land use, soil type, and slope based on HRU. Combined with the moderate index method, the adaptive index threshold system is constructed, and the sub-basin can be better divided by hydrological analysis based on DEM. Based on the generation of the sub-basin, the hydrological response unit is determined and relevant research is carried out at this scale [47; 48; 49].  
The basin calculation can be achieved using hydrological analysis in ArcGIS. The D8 single flow algorithm principle defines the eight directions around each grid unit of the DEM [50; 51]. Assuming that the rain falls on a certain grid in the terrain, the water flow of the modified grid will flow to the grid with the lowest of the eight grid tops. If the maximum descending direction of multiple cell grids stays the same, then the adjacent cell range should be expanded until the steepest descent direction is found [52; 53; 54]; therefore, it is feasible to use the hydrological response unit to express the variation of groundwater depth.  
The HRU is delineated using the hydrological analysis tool in ArcGIS. The watershed delineated by DEM can only represent one feature of the terrain. There are a variety of land types in the same watershed, and the groundwater content of the storage is also different; therefore, the land cover data are used to divide the watershed twice, and small patches of the boundary are combined to obtain the final MHRU. The land cover and watershed division of each MHRU, which can evaluate the groundwater depth and analyze the spatial and temporal differentiation of groundwater depth, are different and have unique characteristics.  
First, we assign a value to the divided MHRU. Then, through the partition module in the analysis tool, the average groundwater depth of each hydrological response unit in the space can be statistically analyzed, and then space depth can be analyzed through the attribute connection.
# Efficient V2V Communications by Clustering-Based Collaborative Caching +
## 3 The Proposed Method
### Cluster Construction and Maintenance  
#### 3.1.1 Detecting Neighbors by Exchanging Beacons  
Each vehicle periodically broadcasts a beacon to alert nearby vehicles of its presence. Before broadcasting a beacon, the sender removes from its neighbor list all neighbor vehicles for which their information has not been updated within \(t_{\text{neighbor}}\). Each beacon contains the senders' vehicle ID, position, speed, the number of neighbor vehicles, a flag indicating whether the sender is a CH, the ID of the associated CH if the sender is a CM, and the connection expired predicted (CEP) CH ID if the sender vehicle is a CH. When a vehicle receives a beacon, it records the sender as its neighbor vehicle.
## 4 Discussion
### Initial Slope of the Melting Curve of PuO\({}_{2}\)  
The initial melting slope is an important thermodynamic characteristic of a substance because (i) it relates the melting point with the latent heat of fusion, \(L_{m}\), and volume change at melt, \(\Delta V_{m}\), via the Clausius-Clapeyron formula \(dT_{m}/dP=T_{m}\,\Delta V_{m}/L_{m}\), hence it is important for the validation of the thermodynamic modeling using, e.g., [PERSON]'s approach, and (ii) it is related to the parameters \(a\) and \(b\) of the Simon-Glatzel (SG) melting equation \(T_{m}(P)=T_{m}(0)(1+P/a)^{b}\), via \(dT_{m}/dP|_{P=0}=T_{m}(0)\,b/a\), hence it is important for the validation of the melting curve as a whole and of the corresponding SG equation.  
Theoretical calculations and thermal expansion estimates reveal that at \(T\)\(\sim\)3000 K, the lattice constant of the fluorite structure of PuO\({}_{2}\) is \(\sim\)5.6 A [24]. We therefore ran melting simulations for a set of three supercells having a lattice constant of \(\sim\)5.6 A to determine the low-\(P\) segment of the melting point of PuO\({}_{2}\) in order to obtain its initial slope to estimate the uncertainty in \(T\) associated with a potential uncertainty in \(P\). Our results are summarized in Table 2.  
Figure 12: The same as in Figure 4 for the 768-atom PuO\({}_{2}\) system.  
Figure 13: The same as in Figure 3 for the 768-atom PuO\({}_{2}\) system. Only the running average is shown.  
The three melting points of PuO\({}_{2}\) are shown in Figure 14. Taking into account the corresponding uncertainties, the resulting analytic form that describes them best is  
\[T_{m}(P)\;=\;3045.9\;+\;92.5\;P\;-\;25.9\;P^{2}, \tag{1}\]  
where \(P\) stands for pressure, which can be considered as the low-\(P\) (\(-2\) GPa \(\leq P\leq 1\) GPa) melting curve of PuO\({}_{2}\). Thus, the ab initio ambient melting point of plutonim oxide that results from our QMD simulations is \(T_{m}\approx 3046\) K, which is in excellent agreement with the most recent experimental studies: \(3017\pm 28\) K [6] and \(3050\pm 59\) K [7]. The initial slope of the PuO\({}_{2}\) melting curve given by Equation (1), 92.5 K/GPa, is consistent with the known value of this slope for UO\({}_{2}\), the other endpoint of MOX: 92.9 \(\pm\) 17.0 K/GPa [28]. We note that the ambient melting points of PuO\({}_{2}\) and UO\({}_{2}\) appear to be close to each other as well: \(\sim\)3050 K and \(\sim\)3150 K (e.g., 3147 K [28]), respectively.  
Interpolating the corresponding values in Table 2 gives the following values of the lattice constant and density at the ambient melting point of PuO\({}_{2}\) of 3050 K which we adopt based on our QMD simulations: 5.590 A and 10.50 g/cm\({}^{3}\). All these values correspond to the last line in Table 1.
## 4 Results and Analysis
### Simulation Results of Arch Culvert Construction
#### 4.1.3 Structural Stress and Deformation  
Figures 17 and 18 show the vertical and horizontal deformation distribution of the steel corrugated pipe structure after construction. Because the vertical load of the vault at the right end of the arch culvert is larger and the soil is thicker. The vertical deformation of the arch culvert structure on the right side is larger. The 10-hole and 11-hole have the maximum vertical deformation of 23 mm. The maximum horizontal displacement of about 9 mm appears at the waist of the 4-hole to 6-hole. The horizontal displacement is related to the constraints of the soil, and the left culvert structure is covered with thin soil. The soil constraints of the left and right arch structures are small. Therefore, the horizontal deformation of the middle and left culvert structure is larger, which is similar to the deformation results of Wadi [31].  
According to the specifications [32], when the backfill is completed, the shape deformation of the structure section should not exceed 2%. Additionally, the settlement deformation of steel corrugated pipes with vector heights of 6.205 m and 7.770 m would be less than 124.4 mm and 155.4 mm, respectively. The calculation results confirmed that the deformation did not exceed 2%.  
Figure 19 shows the maximum principal stress profile of steel corrugated pipe after construction. The maximum principal stress of the structure is 0.86 MPa at the waist of  
Figure 16: Post-construction pore pressure contour (unit kPa).  
Figure 17: Post-construction vertical deformation contour of structure (unit: mm).  
Figure 18: Post-construction horizontal deformation contour of structure (unit: mm).  
the structure. Because the strength and stiffness of the material around the pipe suddenly change at the boundary between the protected masonry and the backfill. Appropriate protective measures should be taken during actual construction. When the thickness of low alloy high strength structural steel Q345 is less than 16 mm, the design values for compressive strength and bending strength are 305 MPa, while the design value for shear strength is 175 MPa. The results confirm that the material strength meets the design requirements.
## 3 Countermeasure to Suppression Attack
### HED: Heuristic-Based Detection Scheme
At the end of first \(\omega\), \(n_{a}\) observes the actual increment of sequence numbers based on its multicast trace table, \(inc_{seq}=(MT_{a}[m].ls-MT_{a}[m].fs)\), calculates the most recent increment rate of sequence numbers based on Equation (2), updates increment rate of the minimum sequence number based on Equation (3), and then heuristically calculates the increment threshold of sequence numbers, \(th_{seq}=(MT_{a}[m].t_{lp}-MT_{a}[m].t_{fp})\times R_{inc}\). If \(inc_{seq}>th_{seq}\), the multicast operations of \(n_{m}\) are suspected as the multicasting misbehavior, the number of detected multicasting misbehaviors of \(n_{m}\), \(c_{mis}^{m}\), is increased by one, and the observation window of \(n_{m}\), \(\omega^{m}\), is reduced by half, \(\frac{\omega^{m}}{2}\). Additionally, the observation window of suspected node becomes shorter, and the multicast operations of a malicious node can be observed more often, and can result in more detections of multicasting misbehaviors. Thus, the smaller the observation window is, the more often the multicasting misbehaviors of malicious node can be detected. Both major operations of MPL protocol and HED scheme are summarized in Figure 5.  
Figure 4: Example of adaptively adjusted observation window \(\omega\) based on the detected multicasting misbehavior.
## 3 Robot Motion Characteristics Analysis  
### Kinematic Model Analysis  
#### 3.1.1 Motion Analysis of the Entire Robot Mode  
The complete motion of the robot is studied in a non-ideal state where the geometric center does not coincide with the center of mass in the wheeled movement mode. We assume that there is no relative sliding between the ground and the wheel and that the transformable wheel is in rigid contact with the ground without structural transformation. Figure 5 shows the kinematic model of the robot in the global coordinate system.  
We define \(q=(x_{D},y_{D},\theta)^{\mathsf{T}}\) as the robot's pose, \((x_{C},y_{C})\) as the coordinate of the robot geometric center \(C\) in the global coordinate system, \((x_{D},y_{D})\) as the coordinate of the robot center of mass \(D\) in the global coordinate system, \(\dot{x_{r}}\) and \(\dot{y_{r}}\) as the two components of the velocity in the local coordinate system, \(\theta\) as the directional angle of the robot, \(d\) as the distance between the geometric center \(C\) and the center of mass \(D\), \(R\) as the radius of the wheel, \(a\) as the width of the body, and \(w_{l}\) and \(w_{r}\) as the rotational speeds of the left and right wheels of the wheel [20,21].  
Assuming that the robot velocity is \(v\) and the robot component on \(O_{\mathsf{xy}}\) is  
\[\left\{\begin{array}{l}\dot{x_{C}}=v\cos\theta\\ \dot{y_{C}}=v\sin\theta\end{array}\right. \tag{1}\]  
then according to the geometric relationship, the positions of the geometric center \(C\) of the robot and the center of mass \(D\) are  
\[\left\{\begin{array}{l}x_{D}=x_{C}+d\cos\theta\\ y_{D}=y_{C}+d\sin\theta\end{array}\right. \tag{2}\]  
Derivative of Equation (2):  
\[\left\{\begin{array}{l}\dot{x_{D}}=\dot{x_{C}}-d\dot{\theta}\sin\theta\\ \dot{y_{D}}=\dot{y_{C}}+d\dot{\theta}\cos\theta\end{array}\right. \tag{3}\]  
The robot is then subjected to the non-holonomic constraint equation, which is obtained as:  
\[\dot{x_{D}}\sin\theta-\dot{y_{D}}\cos\theta+d\dot{\theta}=0 \tag{4}\]  
Figure 5: Robot kinematic model.  
where \(v=\frac{R}{2}(w_{l}+w_{r})\) and \(\dot{\theta}=\frac{w_{r}R-w_{l}R}{a}\).  
According to the robot motion conditions, we obtain the kinematic equation of the robot in its no-slip condition:  
\[\begin{bmatrix}\dot{x_{D}}\\ y_{D}\\ \dot{\theta}\end{bmatrix}=\begin{bmatrix}\frac{R}{2}\cos\theta-\frac{Rd}{a} \sin\theta&\frac{R}{2}\cos\theta-\frac{Rd}{a}\sin\theta\\ \frac{R}{a}\sin\theta+\frac{Rd}{a}\cos\theta&\frac{R}{2}\sin\theta-\frac{Rd}{a} \cos\theta\\ \frac{R}{a}&-\frac{R}{a}\end{bmatrix}\begin{bmatrix}w_{r}\\ w_{l}\end{bmatrix} \tag{5}\]
# A Deep-Learning-Based Secure Routing Protocol to Avoid Blackhole Attacks in VANETs +
## 4 The Proposed Deep Learning Framework
### Deep Learning for Routing  
In the routing process, we proposed the architecture of the DNN model that is employed twice for the purposes of blackhole node detection and finding the optimal weight that minimizes the routing fitness function value, as detailed below:  
* **Deep learning for blackhole detection:** To solve problem II, we utilize a DNN model for classifying blackhole nodes and normal nodes in VANETs. This makes it possible to choose the flexibility between secure routing and normal routing strategies based on the types of nodes that were identified. Our primary objective is to enhance the security and efficiency of routing in VANETs by detecting and avoiding blackhole attacks. Based on the behavioral characteristics of the blackhole attack mentioned in Section 2, we can define the input parameter to train the DNN model. The input parameters are node identifier (\(\text{N}_{\text{ID}}\)), destination sequence number (\(\text{D}_{\text{geq}}\)), hop counts (H), remaining energy (E), and the node's position (Pos). The DNN model we employ is a feed-forward neural network with \(1\times 5\) dimensional input layers, several hidden layers with numerous hidden neurons connected with symmetric weight, and \(1\times 2\) dimensional output layers to classify nodes in real-time as either blackhole nodes (\(B_{n}\)) or normal nodes (\(N_{n}\)), which can be seen in Figure 8. Table 7 presents the structure of the layers employed in the DNN model.
* **Deep learning for finding optimal fitness function values for routing:** To solve problem III, we also utilize a DNN model to find the optimal weight that minimizes the routing fitness function values the same way as in clustering but with different parameters of the input layers. Three factors such as the remaining energy (E), distance (Dist), and hop count (H) of the node are considered for input parameters. To determine the ideal weight values for \(x_{1}\), \(x_{2}\), and \(x_{3}\), we employ a feed-forward neural network with \(1\times 3\) dimensional input layers, several hidden layers with numerous hidden neurons connected with symmetric weight, and \(1\times 3\) dimensional output layers, as shown in Figure 9. Table 8 presents the structure of the layers employed in the DNN model, which enhances the system's performance.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Size** & **Activation Function** \\ \hline Input & 10 & - \\ Layer 1 & 40 & ELU \\ Layer 2 & 100 & ELU \\ Layer 3 & 80 & ELU \\ Output & 2 & LINEAR \\ \hline \hline \end{tabular}
\end{table}
Table 7: The layer structure of the DNN model for node classification.  
Figure 8: The structure of the DNN model for node classification.  
Figure 9: The structure of the DNN model for routing.
# A Sentiment-Aware Contextual Model for Real-Time Disaster Prediction Using Twitter Data
## 3 Material and Methods
### Bilstm with Attention  
A regular LSTM unit consists of a cell, an input gate, an output gate and a forget gate. The cell can memorize values over arbitrary time periods, and the three gates regulate information flow into and out of the cell to keep what matters and forget what does not. The BiLSTM consists of a forward and a backward LSTM that process an input token vector from both directions. By looking at past and future words, a BiLSTM network can potentially capture the more semantic meaning of a sentence. In our study, the word embeddings \(\mathbf{e}\) produced from module I are fed into a standard BiLSTM layer to generate a list of hidden states \(\mathbf{h}=\{h_{i}|i=1,...,n\}\), where \(h_{i}\) is given by Equation set (1).  
\[\begin{split}\overleftarrow{h_{i}}&=\overleftarrow{ \text{LSTM}}(e_{i},\overleftarrow{h_{i-1}})\\ \overrightarrow{h_{i}}&=\overrightarrow{\text{LSTM }}(e_{i},\overrightarrow{h_{i-1}})\\ h_{i}&=[\overleftarrow{h_{i}};\overrightarrow{h _{i}}]\end{split} \tag{1}\]  
where \([\cdot]\) is a concatenation operation. The structure is shown in Figure 5.  
Figure 4: Module I: SentiBERT.  
In a Tweet, each word influences the disaster polarity differently. Using an attention mechanism can help the model learn to assign different weights to different words so that the more influential words are given higher weights. For a hidden state \(h_{i}\), its attention \(a_{i}\) is given in the Equation set (2).  
\[\begin{split}& u_{i}=\tanh(W\cdot h_{i}+b)\\ & a_{i}=\frac{e^{u_{i}^{\top}\cdot u_{w}}}{\sum_{i}e^{u_{i}^{ \top}\cdot u_{w}}},\end{split} \tag{2}\]  
where \(W\) denotes a weight matrix, \(b\) denotes the bias, and \(u_{w}\) a global context vector, and all three are learned during training. The output of module II is a concatenation of attentive hidden states \(\mathbf{H}^{\prime}=[a_{1}h_{1};...;a_{n}h_{n}]\).
# rStaple: A Robust Complementary Learning Method for Real-Time Object Tracking
###### Abstract  
Object tracking is a challenging research task because of drastic appearance changes of the target and a lack of training samples. Most online learning trackers are hampered by complications, e.g., drifting problem under occlusion, being out of view, or fast motion. In this paper, a real-time object tracking algorithm termed \"robust sum of template and pixel-wise learners\" (rStaple) is proposed to address those problems. It combines multi-feature correlation filters with a color histogram. Firstly, we extract a combination of specific features from the searching area around the target and then merge feature channels to train a translation correlation filter online. Secondly, the target state is determined by a discriminating mechanism, wherein the model update procedure stops when the target is occluded or out of view, and re-activated when the target re-appears. In addition, by calculating the color histogram score in the searching area, a significant enhancement is adopted for the score map. The target position can be estimated by combining the enhanced color histogram score with the correlation filter response map. Finally, a scale filter is trained for multi-scale detection to obtain the final tracking result. Extensive experimental results on a large benchmark dataset demonstrates that the proposed rStaple is superior to several state-of-the-art algorithms in terms of accuracy and efficiency.  
object tracking; feature fusion; adaptive model update; real-time +
Footnote †: journal: Applied Sciences