esa-sceva/satcom-chunk-collection huggingface dataset:
# Voter Authentication Using Enhanced ResNet50 for Facial Recognition
## 1 Introduction
### Proposed System  
The system we propose uses facial recognition technology to guarantee the integrity of the electoral process. Facial recognition is a technology based on facial features. It is used either to verify a person's identity, that is, to ensure that the person is who they claim to be (for access control purposes), or to identify a person within a group of people, in a place or on an image [6].  
The system will be installed in the polling stations and the cameras will be placed in the entrance, also inside the polling station. The cameras at the entrance will capture images of the arrival of the voters. The images captured will be RGB, 150 \(\times\) 150 pixels in size. This RGB format has been chosen because it is widely used in facial recognition systems due to its ability to represent information in colors in a way that is compatible with human vision. This representation provides more details on facial features, which improves differentiation between individuals [7]. The use of this type of image also facilitates the extraction of more detailed features, which can improve the performance of recognition systems, particularly when it comes to distinguishing between individuals with similar appearances in grayscale [8].  
The MTCNN algorithm will be used for efficient face detection. Firstly, it is renowned for its ability to accurately detect faces, even in multi-angle and multi-scale scenarios [9]. Secondly, it is robust to variations in pose, lighting, and occlusion [10]. Third, it improves the accuracy of face detection by simultaneously locating facial regions, identifying facial landmarks, and aligning faces. Fourth, it also has the ability to optimize computational resources by progressively filtering out non-face regions, which improves speed [11].  
In addition, the ResNet50 architecture [12] (see Figure 1), combined with ArcFace loss, will further improve the accuracy of facial recognition. Its usefulness is multiple. Firstly, it allows gradients to circulate more efficiently in the network, thus solving the problem of gradient disappearance, which is common in deep architectures [12]. Secondly, it facilitates the learning of deeper networks, improving the performance of image classification tasks [13]. In addition, thanks to its robustness, it serves as an effective basis for transfer learning in various computer vision tasks [14].
# Hierarchical Active Tracking Control for UAVs via Deep Reinforcement Learning
## 2 Preliminary Considerations
### Proximal Policy Optimization Algorithm  
Policy gradient methods in RL are sensitive to hyperparameters, such as the policy update step. If the policy is updated in an unfavorable direction, the policy will be worse when using the sampled experiences in the next update iteration.  
Trust region policy optimization (TRPO) [24] avoids significant and destructive policy parameter changes with a KL divergence constraint \(\delta\) on the size of the policy update at each iteration:  
\[\begin{array}{ll}\underset{\theta}{\text{maximize}}&\mathbb{E}_{t}\left[ \frac{\pi_{\theta}(a_{t}|s_{t})}{\pi_{\theta_{\text{old}}}(a_{t}|s_{t})}\hat{A} _{t}\right]\\ \text{subject to}&\mathbb{E}_{t}\left[\text{KL}\left[\pi_{\theta_{\text{old}}} \left(\cdot\mid s_{t}\right),\pi_{\theta}(\cdot\mid s_{t})\right]\right]\leq \delta\end{array} \tag{3}\]  
where \(\hat{A}_{t}\) is an estimator of the advantage function at timestep \(t\) and \(\theta_{old}\) represents the parameters of the policy before the update. TRPO approximates Equation (3) by linear objective and quadratic constraints and then solves it with the conjugate gradient algorithm.  
[PERSON] et al. [25] proposed the proximal policy optimization (PPO) algorithm to further simplify TRPO.  
PPO denotes the probability ratio as \(r_{t}(\theta)\):  
\[r_{t}(\theta)=\frac{\pi_{\theta}(a_{t}\mid s_{t})}{\pi_{\theta_{old}}(a_{t} \mid s_{t})} \tag{4}\]  
and proposes a new objective as follows:  
\[\begin{array}{ll}\mathcal{L}^{\text{Clip}}(\theta)&=\mathbb{E}_{t}\left[ \min\left(r_{t}(\theta)5\hat{A}_{t},\text{clip}_{obj}\right)\right]\\ \text{clip}_{obj}&=\text{clip}(r_{t}(\theta),1-\epsilon,1+\epsilon)\hat{A}_{ t}\end{array} \tag{5}\]  
where \(\epsilon\) is a hyperparameter that clips the moving \(r_{t}\) in the range \([1-\epsilon,1+\epsilon]\).  
The minimum of the clipped objective clip\({}_{obj}\) is taken as the final objective, which restricts large policy updates. PPO has been demonstrated to work well on a collection of benchmark tasks, including simulated robotic locomotion and Atari game playing [25], and is one of the most powerful model-free deep RL algorithms.
## 3 System Model
### The Time Complexity of EBRA-NLE  
In our proposed EBRA-NLE, \(N\) is the number of sensor nodes in the network, \(FN(i)\) is the set of forward neighboring node of node \(i\). \(n\) is the number of \(FN(i)\), that is, \(\ n=\left|FN(i)\right|\). In addition, the number of forward neighbors of each node is different, so \(n\) is usually an indefinite value much less than \(N\). In order to avoid confusion between \(N\) and \(n\), here set \(b\gets n\).  
At first, calculate the three indexes values of \(N\) nodes' forward neighbors and convert them into corresponding membership function through the sine mapping function, which generally requires calculation complexity of \(O(N)(3*b*N)\). Therefore, the time complexity required to calculate the belief function of three indexes by the membership function is also \(O(N)\). In the stage of data fusion by adopting DS rules, three attribute indexes need to be fused twice. For proposition \(\{A_{1},\ldots,A_{b}\}\) in each fusion, the complexity of conflict hypothesis \(Ch\) is \(O(1)(2*b*(b-1))\) and the complexity of the allocation for the fused belief function is \(O(N)(2*3*b*N)\). For example, the belief function of \(A_{1}\) in one fusion process is:\(m(A_{1})=\frac{m_{1}(A_{1})m_{2}(A_{1})+m_{1}(A_{1})m_{2}(\theta)+m_{2}(A_{1 })m_{1}(\theta)}{1-Ch}\). Therefore, the time complexity of DS data fusion is \(O(N)\).  
To sum up, the time complexity of our proposed algorithm is usually \(O(N)\). In the worst case, the number of forward neighbors of each node is nearly \(N-1\), and then the worst time complexity is \(O(N^{2})\).
## 3 Test Section of Asphalt Concrete for Ballastless Track Substructure
### Design of the Asphalt Concrete  
According to the above analysis of the working conditions of the asphalt concrete for the high-speed railway substructure, asphalt concrete materials should have a good waterproofing performance, bearing capacity, low-temperature cracking resistance, water damage resistance, and fatigue resistance. Therefore, in the design process of the asphalt concrete for this project, many factors such as the selection of the asphalt binders and aggregates and the gradation optimization method had been taken into account comprehensively. Moreover, the dense gradation asphalt concrete AC-16, with the  
Figure 1: Schematic diagram of the asphalt concrete substructure in the test section.  
nominal maximum aggregate size of 16 mm, was selected as the design target, and the construction method of hot mixing and hot paving was adopted to achieve the above functionalities.  
Finally, a performance grade of a PG 76-28 SBS (styrene-butadiene-styrene) + TPS (tough polystyrene)-modified asphalt binder provided by Jiangsu Baoli International Investment Co., Ltd. (Jiangyin, China) was selected. The basic properties of this composite modified asphalt binder are shown in Table 1, based on Chinese test protocols [23]. The aggregates were provided by the China Academy Railway Institute (Beijing, China). The final mixture gradation is shown in Figure 2. The volume parameters and stability and flow values of the Marshall specimens with different asphalt-aggregate ratios (4.0%, 4.5%, 5.0%, 5.5%, and 6.0%) were measured, and the test results are shown in Table 2. According to the Chinese technical specifications [24], the optimum asphalt-aggregate ratio was determined as 5.1%.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameters (Unit)** & **Test Results** & **Technical Requirements** & **Test Methods** \\ \hline Penetration @ 25 \({}^{\circ}\)C (0.1 mm) & 72.9 & 60–80 & T 0604 \\ Penetration index & 1.6 & \(\geq\)0.5 & T 0604 \\ Ductility @ 5 \({}^{\circ}\)C (cm) & 50.1 & \(\geq\)30 & T 0605 \\ Softening point (\({}^{\circ}\)C) & 90.5 & \(\geq\)80 & T 0606 \\ Fraas breaking point (\({}^{\circ}\)C) & \(-\)28 & \(\leq\)\(-\)25 & T 0613 \\ Dynamic viscosity @ 175 \({}^{\circ}\)C (Pa-s) & 1.12 & \(\leq\)1.5 & T 0625 \\ Flash point (\({}^{\circ}\)C) & 280 & \(\geq\)260 & T 0611 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic properties of the styrene-butadiene-styrene (SBS) + tough polystyrene (TPS)-modified asphalt binder.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Asphal-** & **Bulk Density** & **Void Ratio** & **VMA** & **VFA** & **Stability** & **Flow Values** \\
**Aggregate Ratio (\%)** & **(g/cm\({}^{3}\))** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(kN)** & **(dmm)** \\ \hline
4.0 & 2.473 & 7.1 & 14.71 & 51.99 & 14.57 & 3.77 \\
4.5 & 2.506 & 5.1 & 14.00 & 63
## 3 Results and Discussion
### Optical Microscopy Analysis  
The results obtained with the use of optical microscopy have been developed separately for cotton and polyester fibres because these fibres were individually selected from the tested fabric samples.  
An observation of microscopic images of cotton fibres taken from fabrics exposed to heat radiation for 20 s did not reveal any visible changes in the fibre structure. Larger damaged areas were noticed after the application of heat radiation for 30 s and 40 s. After prolonged exposure to heat radiation, cotton fibres became more fragile and, in consequence, torn and jagged, especially on the ends. Nevertheless, for cotton fibres, long-term and thorough observation was required to detect the previously mentioned changes. The main features were noticed only in polarised light, but they could have been mistaken for naturally occurring blackening within cotton fibres (Figure 5a,b).  
Figure 5: Polarised light microscopy images of cotton fibre of Sample 2 (CO 79/PES 21) and polyester fibre of Sample 4 (CO 70/PES 30): Cotton fibre (**a**) before exposure and (**b**) after radiant heat exposure (10 kW\(\cdot\)m\({}^{-2}\), 40 s); polyester fibre (**c**) before exposure and (**d**) after radiant heat exposure (10 kW\(\cdot\)m\({}^{-2}\), 40 s).  
The first thermal changes to polyester fibres were visible after an exposure time of 20 s, in the form of small knobs within the fibres with bold on their endings, as well as contractions resulting from melting that were very well outlined in both white and polarised light. Additionally, here and there, narrowings were noticeable in the structure of the fibres; however, the presence of thickened areas connected with melting dominated. The fibres exposed to heat radiation for 30 s and 40 s also underwent significant changes in their physical microstructure. The characteristic coloured lines parallel to the fibre axis (Figure 5c) illustrate the overall orientation of the PES fibres before their exposition to heat radiation. The high temperature caused local changes in the fibre physical microstructure, which is reflected in the disturbance of the regular line pattern in the image Figure 5d (place indicated by an arrow).  
It should be emphasised, however, that in the case of _Sample 3_, the result of observation was not so obvious because fibres' thickening was observed already in the starting material, indicating a previously applied technological process during which high temperature was used.
## 7 Performance Evaluation
### Total Energy Consumption  
Based on the results from previous subsections, we compute total energy consumption to show that our proposed schemes are more energy-efficient than Secure Deluge. In each hop, one node sends a code image which is received by the other node while only one computation is performed in the receiver node only. Figure 17 shows total energy consumption including communication and computation. SPA, MBSA and BFBSA consumes less energy than Secure Deluge by approximately 12.9%, 44.7% and 38.8%. This benefit become larger when the hop count increases and the link quality varies more dynamically. It is important to note that BFBSA consumes less energy than Secure Deluge despite high computation overhead because communication spends much more energy than computation.
## 4 Results and Discussion
It is a graph presen  
Figure 8: TRL and VLL analysis of CLBEDC-SND approach. Figure 9: Precision-recall analysis of CLBEDC-SND approach. a classification model at all classification thresholds. It is a probability curve and tells how much the model is capable of distinguishing between classes. For ensuring the enhanced emotion classification results of the CLBEDC-SND model, a detailed comparative analysis is shown in Table 5 and Figure 11[24]. The obtained results revealed that the CLBEDC-SND method has accomplished improved results under all measures. For example, with respect to \(accu_{y}\), the CLBEDC-SND method has reached increased \(accu_{y}\) of 98.72% whereas the LRA-DNN, DNN, CNN, and ANN models have offered decreased \(accu_{y}\) of 94.48%, 92.08%, 89.93%, and 88.05%, respectively. In the meantime, with respect to \(pren_{c}\), the CLBEDC-SND technique has gained increased \(pren_{c}\) of 95.49% whereas the LRA-DNN, DNN, CNN, and ANN approaches have presented decreased \(pren_{c}\) of 87.96%, 86.74%, 80.47%, and 78.43% correspondingly. Furthermore, with respect to \(reca_{l}\), the CLBEDC-SND method has attained increased \(reca_{l}\) of 95.53% whereas the LRA-DNN, DNN, CNN, and ANN models have presented decreased \(reca_{l}\) of 91.70%, 90.53%, 87.59%, and 84.75%, correspondingly. Next, with respect to \(F_{score}\), the CLBEDC-SND technique has attained increased \(F_{score}\) of 99.25% whereas the LRA-DNN, DNN, CNN, and ANN methodologies have rendered decreased \(F_{score}\) of 91.67%, 88.39%, 86.10%, and 83.93%, correspondingly. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Accuracy** & **Precision** & **Recall** & **F-Score** \\ \hline CLBEDC-SND & 98.72 & 95.49 & 95.53 & 99.25 \\ \hline LRA-DNN & 94.48 & 87.96 & 91.70 & 91.67 \\ \hline Depp Neural Network (DNN) Model & 92.08 & 86.74 & 90.53 & 88.39 \\ \hline CNN Model & 89.93 & 80.47 & 87.59 & 86.10 \\ \hline Artificial Neural Network (ANN) & 88.05 & 78.43 & 84.75 & 83.93 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparative analysis of CLBEDC-SND approach with existing algorithms. Figure 10: ROC curve analysis of CLBEDC-SND approach. From these experimental evaluations, it is concluded that the CLBEDC-SND model has offered superior emotion classification results over other models. The enhanced performance of the CLBEDC-SND model is due to the inclusion of fuzzy based sentiment scoring and SFLO based optimal parameter tuning process. Therefore, the proposed model can be employed in real time social networking sites such as Facebook, Twitter, Instagram, etc. for emotion classification.
## 3 Results
### FT-IR Analysis  
FT-IR spectroscopy is effectively applied to study the chemical composition of materials in terms of functional group characterization [33; 34]. All FT-IR results of samples are displayed in Figure 1b. In the spectrum of Ca/AC, the band at 3730 cm\({}^{-1}\) (Si-OH) was almost absent due to the fact that Bronsted acid sites of AC were neutralized by Ca(OH)\({}_{2}\). Furthermore, in the spectra of the three modified samples, new peaks attributable to M-OH (M = Ca, Na) appeared at 1500-1400 cm\({}^{-1}\), which could be ascribed to the intense interactions of water molecules with the cations [35]. CaNa/AC and Na/AC bands at 1040 cm\({}^{-1}\) (Si-O-Si) shifted to a lower frequency (red-shifted), indicating the generation of stronger basic sites in the samples, which is conducive to enhancing the catalytic activity [36]. However, due to the intense base solution erosion, the absence of the two peaks at 3730 cm\({}^{-1}\) and 3640 cm\({}^{-1}\) and the significant decrease in the intensity of the peak at 1040 cm\({}^{-1}\) in the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample** & **Basic Strength (H\_)** & **Basicity (mmol of HCI g\({}^{-1}\))** & **Conversion (\%)** \\ \hline AC & H\_ \(<\) 7.2 & - & - \\ Ca/AC & 7.2 \(<\) H\_ \(<\) 9.8 & 1.4 & 7.3 \\ Na/AC & 12.2 \(<\) H\_ \(<\) 15.0 & 3.3 & 58.6 \\ CaNa/AC & 15.0 \(<\) H\_ \(<\) 18.4 & 5.0 & 81.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic strength, basicity, and catalytic activity of AC, Ca/AC, Na/AC, and CaNa/AC.  
Figure 1: (**a**) CO\({}_{2}\)-TPD profiles of Ca/AC, Na/AC, and CaNa/AC; (**b**) FT-IR spectra of AC, CA/AC, Na/AC, and CaNa/AC.  
spectrum of Na/AC suggested the obvious degradation of the mineral structure [37; 38]. In addition, the characteristic montmorillonite bands at 1040 cm\({}^{-1}\) and 3640 cm\({}^{-1}\) suggested that the montmorillonite structure of CaNa/AC was more intact than that of Na/AC because of the addition of Ca, which is beneficial for enhancing the recycling performance of the catalyst.
# Cable Force Identification of Two Bending Cable Networks with Arbitrary Boundary Conditions
## 2 Formulation of Analytical Solution
### Governing Equation of Free Vibration System
;\;i=1,2 \tag{7}\]  
\[\overline{v}_{21}(l_{21})-\overline{v}_{11}(l_{11})=T_{c}/(K_{c}\sin\theta_{c}) \tag{8}\]  
For the force equilibrium at nodes N\({}_{1}\) and N\({}_{2}\), as shown in Figure 4, the total vertically projected shears, tensions, and axial force in the cross-tie must equal zero. \[-V_{11}-V_{12}+T_{1}\sin(\theta_{11})-T_{1}\sin(\theta_{12})+T_{c}\sin\theta_{c }=0 \tag{9}\]  
\[-V_{21}-V_{22}+T_{2}\sin(\theta_{21})-T_{2}\sin(\theta_{22})-T_{c}\sin\theta_{c }=0 \tag{10}\]  
where \(V_{ij}=-EI_{i}\overline{v}^{\prime\prime}{}_{ij}(l_{ij})\); \(\theta_{ij}\) is small deformation, and then \(\sin\theta_{ij}\approx\tan\theta_{ij}\approx v^{\prime}{}_{ij}\big{(}l_{ij} \big{)}\). In Figure 4, symbols \(T_{i}\), \(V_{ij}\), \(M_{ij,}\), and \(\theta_{ij}\) are the tensions, shears, moments, and slopes of the four segments at location \(l_{ij}\), respectively. The conditions in Equation (7)-(10) are substituted into Equation (6) to solve the 8 unknowns. These equations are written in matrix form, as shown in Equation (11). More details of the matrix form are shown in Appendix B.  
\[\mathbf{RC}=0 \tag{11}\]  
For a nontrivial solution of \(\mathbf{C}\), the determinant of matrix \(\mathbf{R}\) must be equal to zero. The analytical expression of the characteristic equation of the two-bending-cable network can be obtained by calculating the determinant of matrix \(\mathbf{R}\). The solution is shown in Equation (12)  
\[F(\omega)=\left\{ \begin{array}{l}\frac{\phi_{1}\sqrt{T_{c}^{2}+4\pi i\alpha^ {2}E_{1}}}{K_{c}\sin^{2}\theta_{c}}\sin(\phi_{1}L_{1})\sin(\phi_{2}L_{2})\\ -\left\{\begin{array}{l}\frac{\phi_{1}\sin(\phi_{11})\sinh[\phi_{11}(L_{1}-l _{11})]}{\sinh[\phi_{11}(L_{1})]}\sin(\phi_{1}L_{1})\sin(\phi_{2}L_{2})\\ -\sin(\phi_{1}l_{11})\sin[\phi_{1}(L_{1}-l_{11})]\sin(\phi_{2}L_{2})\\ \end{array}\right\}=0\\ -\frac{\phi_{1}\sqrt{T_{c}^{2}+4\pi i\alpha^{2}E_{1}}}{\phi_{2}\sqrt{T_{c}^{2} +4\pi i\alpha^{2}E_{1}}}\left\{\begin{array}{l}\frac{\phi_{2}\sin(\phi_{11} )\sinh[\phi_{2}(L_{1}-l_{12})]}{\sinh[\phi_{1}L_{1})}\sin(\phi_{2}L_{2})\\ -\sin(\phi_{2}l_{21})\sin[\phi_{2}(L_{2}-l_{21})]\sin(\phi_{1}L_{1})\\ \end{array}\right\}\end{array} \right\}=0 \tag{12}\]  
Figure 4: Free body diagram of a cable system. (**a**) At node N\({}_{1}\), (**b**) At node N\({}_{2}\).
# Strategies and Challenges in Detecting XSS Vulnerabilities Using an Innovative Cookie Collector
## 1 Introduction
### Limitations of Traditional Detection Methods
#### 1.2.3 Static Analysis Shortcomings  
_Framework complexity:_ modern JavaScript frameworks like React, Angular, and Vue introduce complexities that traditional static analyzers fail to properly model, creating blind spots in detection.  
_Event-driven architecture challenges:_ traditional tools struggle with the event-driven nature of modern web applications, missing XSS vectors triggered through complex event chains.  
_Template engine vulnerabilities:_ as highlighted by [PERSON] et al., traditional static analysis often fails to detect XSS vulnerabilities arising from template engines that dynamically generate code [13].
## 5 Proposed mMTC Device Grouping Algorithms
### Reinforcement Learning-Based Device Grouping Algorithm  
In the context of the B5G network, with mMTC as one of the key enablers, it is increasingly evident that the proliferation of smart devices will be exponential. Given this observation, we concluded that any proposed model should be scalable. Although our two previously proposed methods showed impressive results, their performance will decrease as the number of devices grows due to the computational complexity, as the time complexity of BFD is \(0(nlogn)\)[10, 11].  
This work addresses the problems using a reinforcement learning (RL) model, leveraging its well-documented efficacy in tackling a broad spectrum of combinatorial optimization problems. Studies such as those by [45, 46, 47], along with earlier works by [48], underscore RL's ability to deliver outstanding outcomes in diverse combinatorial optimization settings. We reformulate our problem as a constrained Markov decision process (MDP) and introduce an attention-based RL model that not only adapts to complex, dynamic environments but also offers a more sustainable grouping strategy compared to previous models. RL's inherent flexibility and adaptability allow it to interact seamlessly with dynamic environments, optimizing decisions to maximize long-term benefits without adding complexity to the model.  
The problem has been mathematically reformulated as a sequential decision-making problem in recent research studies [49, 50]. In this reformulation, the agent is a recurring neural network (RNN) that performs actions to group MTC devices based on a specific policy \(\pi(s)\). The agent receives feedback called a \"Reward\" for its actions. Considering the time constraint of the application and the weight of the device, the agent's primary objective is to find the optimal policy \(\pi(*)\) that guides the device grouping actions, minimizing the number of groups created and maximizing the overall reward \(\mathbf{R}\).