esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methodology
### Complex Network  
#### 3.3.1 Directed Weighted CN Development Based on AR  
A complex network is a simple graph \(G=(V,\ E)\) consisting of a certain number of node sets \(V\) and edge sets \(E\), where \(V=\{v_{1},v_{2},\ldots,v_{n}\}\) represents the set of all nodes and \(E=\{e_{1},e_{2},\ldots,e_{n}\}\) represents the set of all edges. The complex network can be expressed by the adjacency matrix \(A\) as follows:  
\[A_{ij}=\left\{\begin{array}{ll}a_{ij}\times w_{ij},&i\to j\\ 0,&else\end{array}\right. \tag{4}\]  
where \(a_{ij}\) (\(i,j\in V\)) is equal to \(1\) when event \(i\) triggers event \(j\) and \(0\) otherwise. \(w_{ij}\) denotes the weights of the edges between adjacent nodes \(i\) and \(j\) in the network, i.e., the frequency of event \(j\) triggered by event \(i\). When the adjacency matrix is symmetric, the network is undirected, and conversely, it is a directed network. If all the \(w_{ij}\) are not equal in  
Figure 2: The flowchart of Eclat algorithm.  
the network, the network can be considered a weighted networks, otherwise it is an unweighted network.  
There are diverse interactions between seafarers' unsafe acts and their influencing factors in maritime transportation activities, i.e., the interaction network of seafarers' unsafe acts and their influencing factors belongs to a directed weighted network. In a directed weighted network, nodes, directed edges and edge weights represent seafarers' unsafe acts/influencing factors, interaction between seafarers' unsafe acts/influencing factors and their strengths, respectively. In the present study, based on the complex network theory, the association rule results are mapped into the directed weighted network, and the mapping process is shown in Figure 3. The process consists of three main steps [29]:  
1. Each influencing factor/seafarers' unsafe acts in the association rule represents a node of the network;
2. The LHS and RHS in the association rules represent two adjacent nodes \(i\) and \(j\), respectively, and each association rule forms a directed edge of the network;
3. The confidence value of the association rules is used as the weight \(w_{ij}\) of the edges. To clarify the process, suppose five rules, define R as risk factor, and the confidence values are marked with different colors in Figure 3.
# Hollow-Core Optical Fibers for Telecommunications and Data Transmission
## 12 Applications of HCFs in Networks and Systems  
The current use of HCFs is mostly restricted to technical trials by operators including BT and Comcast. The exception are short data links for HST firms (Section 12.1.3).  
Below, we review some applications of HCFs where their unique properties can substantially improve system performance, sorted by the following advantageous properties:  
* Low latency (Section 12.1);
* Stable latency (Section 12.2);
* Wide bandwidth, low nonlinearity, and high-power capability (Section 12.3).
## 3 Solving Method
### Training Process  
The SAC algorithm is employed to train the proposed encoder-decoder network [60]. SAC is an off-policy algorithm based on the actor-critic framework with maximum entropy. Figure 4 presents the architecture of the critic network in this study. The PyTorch 2.5.1 implementation specifications and corresponding parameters for each network layer are reported in Table A1 of the Appendix A.  
The actor in the SAC algorithm maintains the diversity of exploration. A soft target update technique is used to smoothen the alterations during the training process. A dual Q-network is also adopted in SAC. The loss function of SAC is defined as follows [60]:  
\[J_{Q}(\varphi)=\mathrm{E}\bigg{[}\Big{(}Q\big{(}S_{t},A^{t}\big{)}-r\big{(}S_{ t},A^{t}\big{)}-\gamma\mathrm{E}_{S_{t+1}}\Big{[}\mathrm{E}_{\pi_{\theta}} \Big{[}Q_{\bar{\Phi}}\Big{(}S_{t+1},A^{t+1}\big{)}-\alpha\log\pi_{\theta}\Big{(} A^{t+1}\,|\,S_{t+1}\Big{)}\Big{]}\Big{]}\Big{)}^{2}\bigg{]}, \tag{19}\]  
\[J_{\pi}(\theta)=\mathrm{E}_{s\sim D}\big{[}\mathrm{E}_{a\sim\pi_{\theta}}\big{[} \alpha\log\pi_{\theta}(a|s)-Q_{\varphi}(s,a)\big{]}\big{]}, \tag{20}\]  
where \(\varphi\) represents the parameter of the \(Q\) function, \(Q_{\Phi}(s,a)\) is the \(Q\) function, \(A^{t}\) is the action decision at time step \(t\), \(r\) is the reward value, \(\widetilde{\varphi}\) is the parameter of the target network, and \(Q_{\widetilde{\varphi}}\) is the target \(Q\) value function. \(\theta\) is the network parameter of the policy network \(\pi_{\theta}\). \(\alpha\) represents the regularization parameter of entropy. \(D\) is the experience replay pool used to store experience samples. The training process is illustrated in Algorithm 3, where \(\mathrm{Max_{e}}\) is the maximum episode number. _done_ = false indicates \(S_{t+1}\) is not terminal. Learning begins when the number of samples in the experience pool reaches learning size.  
```
Input: Initial SAC parameters \(\theta\), \(\varphi\), \(\widetilde{\varphi}\), experience replay buffer \(D\) Output: Trained encoder-decoder network
1:for number of episode = 1, 2, \(\ldots\), \(\mathrm{Max_{e}}\)do
2: Initial state \(S_{t\gets 0}\)
3:done = False
4:while not done do
5: Get a task action \(a\) based on the network
6: Get a resource action \(a^{\prime}\) based on the heuristic rule MRC
7: Execute \(a\) and \(a^{\prime}\), observe next state \(S_{t+1}\)
8: Get reward \(r\) and update done
9: Store experience sample (\(S_{t}\), \(a\), \(r\), \(S_{t+1}\), done) in \(D\)
10:\(S_{t}\gets\ S_{t+1}\)
11:if size(\(D\)) > learning size do
12: Sample a batch of (\(S_{t}\), \(a\), \(r\), \(S_{t+1}\), \(d\)) from \(D\) randomly
13: Compute the loss as in (18)-(19)
14: Update \(\theta\), \(\varphi\), \(\widetilde{\varphi}\) using Adam
15:endif
16:endfor
```  
**Algorithm 3** Training process based on SAC  
Figure 4: The critic network architecture.
# Analysis of Cyclone Spinning Effect with Different Guide Vane Heights
## 3 Numerical Calculations and Validation of Results  
### Meshing and Mesh Independence Test  
ICEM software built into the Fluent 2022 R1 release was used to mesh the model. The internal structure of the cyclone is complex; therefore, an unstructured tetrahedral mesh is generated, with a maximum size of 3 mm and a guide vane surface of 2.5 mm, while the upstream and downstream areas are standard cylinders with a simple structure and a structured hexahedral mesh with O-type partition and a maximum size of 3 mm. Figure 4 presents a schematic diagram of the meshing.  
The quality of the mesh in the numerical simulation directly affects the accuracy and efficiency of the numerical simulation; the mesh quality of the overall model in this paper is more than 0.5, the minimum angle of the mesh is more than 18\({}^{\circ}\), the overall skewness of the mesh is about 0.4, and the minimum orthogonal mass of the mesh is greater than 0.2.  
A mesh independence test is performed on each mesh size. The number of model meshes generated under different mesh sizes is shown in Table 1.  
At the Reynolds number Re = 140,559, the difference between the average pressures of the inlet and outlet sections of the cyclone with different guide vane heights is compared, as shown in Table 2. It can be concluded from the table that the relative errors of the difference between the average pressure of the two sections of the model with a maximum mesh size of 3 mm and a maximum grid size of 2 mm are 0.21%, 0.17%, 0.19%, 0.16%, and 0.18%, respectively, and the maximum grid size has no effect on the calculation results. When the maximum grid size is selected as 3 mm, the accuracy of the calculation can be guaranteed, and the computing resources are saved, so the model with a maximum grid size of 3 mm and the number of grid elements in each working condition is 4,967,410, 4,951,341, 4,935,829, 4,918,910, and 4,901,197, respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Number of Mesh Elements**} \\ \cline{2-5}
**Guide Vane Height H** & \multicolumn{4}{c}{**Maximum Mesh Size**} \\  & **5 mm** & **4 mm** & **3 mm** & **2 mm** \\ \hline \(H\) = 20 mm & 1,073,011 & 2,095,626 & 4,967,410 & 16,765,008 \\ \(H\) = 25 mm & 1,069,529 & 2,088,847 & 4,951,341 & 16,710,775 \\ \(H\) = 30 mm & 1,066,179 & 2,082,303 & 4,935,829 & 16,658,422 \\ \(H\) = 35 mm & 1,062,524 & 2,075,165 & 4,918,910 & 16,601,321 \\ \(H\) = 40 mm & 1,058,708 & 2,067,692 & 4,901,197 & 16,541,539 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Number of grid cells.  
Figure 4: Schematic diagram of meshing: (**a**) unstructured meshes; (**b**) structured grids.
# Handover-Driven Interference Management for Co-Channel Deployment of Femto- and Macro-Cells
## 6 Performance Evaluation  
### Evaluation Model  
The evaluation model of the simulation was based on the evaluation methodologies of 3 GPP and ITU, and similar to the ones used in prior research works on femtocell [26; 27; 28]. The simulation assumptions are described in the following and summarized in Table 1.
## 3 Simulation Results
### Convergence Speed  
Another important performance index for the iterative reconstruction algorithm is the convergence speed. The convergence of an iterative algorithm represents the speed of the reconstructed image approaching to the original one. In order to study the convergence speed of the algorithm, we define the distance between the reconstructed image and the standard image \(d\) as follows:  
\[d=\left(\begin{array}{c}\sum\limits_{i=1}^{N_{x}}\sum\limits_{j=1}^{N_{y}} \left(A_{i,j}-R_{i,j}\right)^{2}\\ \sum\limits_{i=1}^{N_{x}}\sum\limits_{j=1}^{N_{y}}R_{i,j}^{2}\end{array}\right) ^{1/2} \tag{22}\]  
A smaller \(d\) means that the reconstructed image is closer to the original image thus the reconstructed result is more accurate and vice versa.  
We choose the 90-views limited-angle circular scanning for the study. After each iteration, the obtained reconstructed image is used to calculate the distance \(d\) and the calculated \(d\) for every step of three algorithms is recorded and compared in a line chart in Figure 10. Results show that the reconstructed image of the TV-GPEF is closer to the real one than other two algorithms for every iteration step. Especially for the first several steps, the \(d\) of the TV-GPEF falls even faster than the other two algorithms. After ten iterations, \(d\) for three algorithms converges to a certain value and the value of the TV-GPEF is much smaller than those of the two other algorithms. This means that the TV-GPEF is more accurate than other two algorithms. It can be inferred that the proposed TV-GPEF algorithm has faster convergence speed than the other two algorithms.  
The computation times for the three algorithms are also compared. The time costs of the three algorithms for 120-views, 90-views, and 60-views limited-angle circular scanning are shown and compared in Table 4. Results show that using variable splitting and Barzilai-Borwein based method to solve the TV-based minimization (TV-VB) can greatly reduce the computation time compared to that of TV-GD. TV-GPEF algorithm has slightly higher computational cost compared to that of the TV-based ones due to the compensation procedure for every iteration. However, the convergence speed for TV-GPEF is greatly improved. In fact, TV-GPEF can obtain better results than that of TV-GD and TV-VB after the same computation time.  
Figure 10: The line chart of the distance between the reconstructed image and the original image for each iteration from the TV-GD, TV-VB and TV-GPEF algorithms.  
From the above discussion, it can be indicated that the TV-GPEF algorithm precedes the other two algorithms in terms of convergence.
## 4 Data-Driven CIM Construction
### Data-Driven CIM Construction Process  
The construction of CIM is a systematic process involving several key steps. First, the project objectives and requirements are defined, and specific application scenarios such as urban planning, smart transportation, and environmental monitoring are determined. Following this, data collection is carried out via multiple channels, including government departments, enterprises, sensor networks, and satellite imagery, ensuring data legitimacy and credibility [53; 55]. After data collection, the data undergo processing and cleaning,  
Figure 3: Data-driven CIM development framework.  
including format conversion, error correction, deduplication, and completion, to ensure its quality. Next, data from various sources are integrated into a unified data model, and through matching and merging, a comprehensive urban information model is formed and stored in a secure system [36; 57; 56].  
Data modeling and analysis are central to CIM construction, utilizing digital city models to analyze aspects such as traffic flow, environmental changes, and building structures. Subsequently, visualization tools are employed to transform complex data into easy-to-understand graphics and charts, aiding decision makers and the public in better understanding the information. Finally, CIM's outputs are applied to urban management and decision making, such as optimizing the traffic flow and monitoring the air quality [57]. Ongoing maintenance and updates are essential to ensure the accuracy, timeliness, and stability of data and models. Through this series of steps, CIM provides strong support for urban planning and management, driving the intelligent development of cities [39; 58; 59].
## 3 Multi-Objective Optimization
### Experiments  
Unfortunately, because force's measurement has not successfully conducted, the aim of this section is to only evaluate the validity of the design. It is worth to mention that there always have differences and errors during manufacturing process, which effects to machine's performance. A prototype was fabricated based on the responses in case 1. Although the electromagnetic force of this model was higher than the limitation, the power and power density had a better tolerance margin (about 20% of tolerance for average power and power density). Figure 10 shows the components and assembly of the prototype. The test bench is the same as the one used for previous machines in [8; 9].  
During the manufacturing process, two important changes were made. Firstly, the material of the inner iron frame is carbon steel S20C instead of stainless steel. Secondly, during optimization stage, fill factor was 50% with 396 turns per slot. However, fill factor was possibly increased to approximately 55.6% by manufacturer, hence the number of turns per slot is 440. Consequently, output power,  
Figure 10: Prototype.  
power density, and electromagnetic force were all increased together. The machine's performance following these different changes is summarized in Table 5.  
Figure 11 shows the no-load voltage back EMF calculated by simulation and the corresponding experiment when the vibration speed is 0.25 m/s. Because of the noise and great shaking in the testing system caused by the high rotation speed of the sub-motor, the measured maximum, and RMS back EMF from the experiment (355.7 and 190.6 V) were slightly higher than the analyzed results (330.1 and 189.6 V).  
A comparison of the simulated and experimental output power under the full load condition is presented in Figure 12. When the vibration speed was 0.250 m/s, the maximum and average output power obtained in the experiment were 426.5 and 130.6 W, which were slightly lower than the FEM analysis. Unfortunately, it was not possible to measure the electromagnetic force.  
Figure 11: No-load voltage back EMF (\(v_{rms}=0.25\) m/s).  
Figure 12: Output power under full load condition (\(v_{rms}=0.25\) m/s).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Item**} & **SUS** & \multicolumn{2}{c}{**S20C**} \\ \cline{2-4}  & \multicolumn{2}{c}{**396 Turns/Slot**} & \multicolumn{2}{c}{**440 Turns/Slot**} \\ \hline Max. output power, \(\mathrm{P_{m}}\) (W) & 350.5 & 401.5 & 435.4 \\ Ave. output power, \(\mathrm{P_{av}}\) (W) & 127.7 & 152.0 & 166.4 \\ Max. power density, \(\mathrm{P_{d\_m}}\) (W) & 0.337 & 0.386 & 0.419 \\ Ave. power density, \(\mathrm{P_{d\_av}}\) (W) & 0.123 & 0.146 & 0.160 \\ Max. electromagnetic force, \(\mathrm{F_{m}}\) (N) & 2683.3 & 3131.6 & 3223.3 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Machine’s performance following changes.
## 3 Results
### Mechanical Characterization of the Polyurea
The blast mitigation and ballistic protection characteristics of a polyurea are determined by its hard and soft domains, respectively [13]. The content of the hard segment of a polyurea in combination with the degree of phase separation (hard and soft phase) determines the mechanical properties of a polyurea system [29]. In the FTIR spectrum of both polyureas, presented in Figure 2, two peaks are present in the range of 1600-1700 cm\({}^{-1}\) attributed to the C=O stretching vibrations, with two nearby N-H in the urea group, which is conventionally called \"ordered\" bonding, that forms the hard phase (-NH-CO-NH-) of the polyurea. In addition to the chemical bonds present in both polyureas that contribute to the blast mitigation ability, the presence of titanium in polyurea B may have helped in the substantial increase of the tensile elongation of this material compared to polyurea A since titanium nanoparticles are commonly used in composites to improve the mechanical properties of materials. Figure 8: Comparison between the tensile properties of a specimen of polyureas A and B. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Sample**} & **Tensile Strength** & **Maximum** & **Young Modulus** \\  & **(MPa)** & **Elongation (\%)** & **(GPa)** \\ \hline Polyurea A & 16.80 \(\pm\) 1.84 & 113.61 \(\pm\) 15.82 & 0.17 \(\pm\) 0.03 \\ Polyurea B & 31.23 \(\pm\) 2.04 & 2920.86 \(\pm\) 43.98 & 0.03 \(\pm\) 0.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Tensile test results. As mentioned beforehand, the polyurea is an elastomer that, when subjected to blast and impact loads, tends to exhibit high strain to failure, thereby absorbing or dissipating the energy arising from dynamic loads. The characterization performed on both polyureas showed, especially the mechanical tests, demonstrated that polyurea B has more potential to be used as a coating in buildings for blast protection than polyurea A. Since this polyurea presents an exceptional capacity to absorb high deformations loads, which is a crucial characteristic to dissipate the energy generated during explosions and subsequent fragments.
# Device-Free Crowd Size Estimation Using Wireless Sensing on Subway Platforms
## 4 Materials and Methods  
Figure 4 presents two block diagrams of the implemented methods that will be compared in the result section. In (a), the baseline implementation is depicted, using a single model to estimate a people count based on the mean attenuation of a single virtual sensor network. In (b), we propose to expand on this by splitting the people count estimation into two separate models based on whether a rail vehicle is present or not. To be able to switch between these model outputs, we use a logistic regression model to predict the presence of a rail vehicle. All three of the models in the proposed solution (b) use a mean attenuation from a different virtual sensor network, selected to optimize the accuracy of the model.
# A Survey of Visible-Light-Communication-Based Indoor Positioning Systems
## 4 Techniques of VLC-Based Indoor Positioning Systems
### Positioning Techniques
#### 4.2.2 TOA and TDOA  
TOA is a distance estimation technique that measures the time it takes for a signal to travel from the transmitter to the receiver. Due to the high speed of light, achieving accurate distance measurements using TOA-based methods requires the use of highly accurate time measurement instruments. The research presented in [59] explores TOA-based techniques and establishes theoretical accuracy limits based on the Cramer-Rao Bound (CRB). It is important to note that TOA-based methods typically assume perfect synchronization between the transmitters and receiver for accurate time measurement, which can be challenging to achieve in practice. In comparison, time difference of arrival (TDOA)-based methods are generally preferred because they only require time synchronization between the transmitters.  
Unlike the direct time measurement approach, the visible light indoor localization scheme proposed in [60], which is based on TDOA, measures the phase differences in the received signals from different transmitters (LEDs). In general, variations in the distance traveled by the light result in phase differences in the received signals. By achieving time synchronization among the transmitters, it becomes possible to calculate the distances between the LEDs and the receiver based on the observed phase differences. In [61], an enhancement to the aforementioned scheme is presented to tackle the impact of noise impairments. Considering that the received optical signal at the receiver is subject to additive white Gaussian noise (AWGN), error mitigation and reduction are achieved through the use of average filtering and maximum likelihood methods.  
In [62], a 2D localization method based on estimating the TDOA from a pair of LEDs to a receiver is introduced. Instead of directly calculating the phase difference of the signals, the concept is to measure the peak-to-peak amplitude of the combined signals at the receiver. This is achieved by transmitting two sinusoidal signals of known frequencies from the LED pair. When the signals add constructively (in phase), the peak-to-peak amplitude reaches its maximum value, while destructive interference (out of phase) results in the minimum value. Using this technique, a trilateration-based localization scheme can be developed to determine the relative distances to the LEDs without having to explicitly calculate the phase differences between the received signals from different LEDs.  
In [63], a low-complexity TDOA-based system is proposed for 3D localization using LEDs. Similar to [60], the distances between the LEDs and receiver are derived by measuring the phase differences between the received signals. Under the assumption that all the LEDs have the same height, a set of simultaneous linear equations can be constructed and solved to determine the coordinates of the receiver.
## 3 Algorithm for Triangle-Free Graphs  
Our algorithm works in two phases and uses the local structure of the graph. Below, we present the pseudocode of the algorithm:  
```
(Phase 1) for\(v\in V(C)\) with \(\deg(v)=2\) in parallel do  select vertex \(\beta_{1}(v)\) with the smallest ID from the set \(\{w\in N[v]:\deg(w)=\min(\deg(N[v]))\}\) \(D_{1}:=D_{1}\cup\{\beta_{1}(v)\}\) \(\beta(v)=\beta_{1}(v)\) endfor (Phase 2) for\(v\in V\setminus N[D_{1}]\) in parallel do  select vertex \(\beta_{2}(v)\) with the smallest ID from the set \(\{w\in N[v]:\deg(w)=\max(\deg(N[v]))\}\) \(\beta(v)=\beta_{2}(v)\) if\(\exists u\in V(G)\) such that \(N[\beta(v)]\cap(V\setminus N[D_{1}])\subset N[u]\cap(V\setminus N[D_{1}])\)then  select vertex \(\gamma(v)\) with the smallest ID from this set \(D_{2}^{*}:=D_{2}^{*}\cup\{\gamma(v)\}\) else \(D_{2}^{*}:=D_{2}^{*\prime\prime}\cup\{\beta(v)\}\) endif endfor \(D_{2}:=D_{1}^{*}\cup D_{2}^{*\prime\prime}\) return\(D^{*}:=D_{1}\cup D_{2}\)
```  
In the first phase, each vertex of degree 2 selects its representative for the dominating set. In the second phase, each vertex \(v\) not dominated in phase 1 first selects a vertex \(\beta_{2}(v)\) with the maximum degree from its neighborhood. Then, it checks whether there exists another vertex (\(\gamma(v)\)) that would dominate all vertices not dominated in the first phase but dominated by \(\beta_{2}(v)\). If such a vertex exists (possibly one that dominates more vertices not dominated in the first phase than \(\beta_{2}(v)\)), the algorithm selects this vertex. If both \(\beta_{2}(v)\) and \(\gamma(v)\) dominate the same set of vertices, the algorithm selects the one with the smallest ID. This approach ensures that when multiple vertices could dominate the same local neighborhood, only one of them is selected for the dominating set. This optimization allows us to achieve a better bound on the approximation ratio of the algorithm.