esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical Model Test  
### Xianglushan Water Transmission Tunnel  
The numerical model is based on a real engineering project, the Xianglushan tunnel in China. This water transmission tunnel was designed for transmitting the water from the Shigu River to the central Yunnan Province, where the water resources are short. The length of the Xianglushan tunnel is 63.426 km. Its cross section is circular, with a diameter of 8.40 m. The surrounding rock mass of the tunnel can be primarily classified into type V of the Chinese code. The site is located within the Xian Shuihe-Diandong seismic zone of the Qinghai-Tibetan region, which contains many problematic areas, including some complicated formations, areas of tectonics and severely faulted zones. There are multiple faults along the tunnel axis, and three of them are the main active faults, named Longpan--Qiaohou, Lijiang--Jianchuan and Heqing--Eryuan, as shown in Figure 2a. For the active faults, the design value of the horizontal displacement for a 100-year period is up to 2.50 m and the maximum value of the vertical displacement is 0.36 m. Among them, the Longpan-Qiaohou fault is the widest and is a normal fault (see Figure 2b). The fault is about N10\({}^{\circ}\) E, NW \(<80^{\circ}\), and the intersection angle between the fault and the tunnel axis is approximately \(50^{\circ}\), which pose a serious threat to the tunnel in the form of displacement.  
Figure 1: (a) Data transfer between FLAC\({}^{\rm 3D}\) and PFC\({}^{\rm 3D}\); (b) the FLAC\({}^{\rm 3D}\)/PFC\({}^{\rm 3D}\) coupling scheme.
## 5 Experimentation, Results, and Analysis
### Significance of Dynamic Context Modeling
#### 5.5.4 Enhancing Generalization  
The flexibility afforded by dynamic context modeling also improves the model's generalization capabilities. When exposed to new, unseen data, the DCAT model can effectively adjust its internal parameters to deal with the unique contextual challenges presented by the new data. This makes the model robust to overfitting and enhances its applicability across diverse datasets and scenarios.
## 3 Recent Advances on IEEE 802.15.4-Based WMSN Approaches
### Overview of Current WMSNs Approaches  
Our study reveals that the proposals here discussed exhibit notable limitations and shortcomings that often restrict the number and scope of current and future applications. This fact opens new opportunities that must converge into a unique and more complete solution. In particular, it must fulfill the greatest possible number of requirements for WMSN technology, but considering important concerns as energy consumption and processing/memory capabilities. To this aim, we pay special attention to the IEEE 802.15.5 standard, a non-beacon approach whose promising features offer a great prospect to the WMSNs for its definite penetration in the consumer market. This standard will be explained in detail in the next section. Table 1 summarizes the main features of the different proposals discussed in this work. In this table we also consider the IEEE 802.15.5 standard and the IEEE 802.15.4e specification, which includes a MAC solution aimed at developing WMSNs.
## 4 Discussion
### Challenges
#### 4.1.9 Collusion Attack Detection  
Most of the existing solutions for insider threats are focused on individual detections. Nevertheless, collaborative attacks can be launched by two or more insiders, which are difficult to detect. The disadvantage of these types of attacks is that the activity of each insider may look benign, but, when combined with other activities, it may result in a malicious action. Therefore, further effort is required to handle collusion attacks [128].
## 5 Example and Validation
### Simulation Validation  
For model validation purposes, an organization's PPS was selected and simulated in AnyLogic 8.8.6 environment, with our vulnerability assessment framework examined specifically through intrusion path analysis. First, we evaluated the PPS using our proposed method, then simulated the intrusion processes, with model validation achieved through a comparative analysis of the assessment and simulation results. The PPS layout is shown in Figure 8a, a 3D simulation view in Figure 8b, and the intrusion behavior workflow in Figure 8c. The PPS's complex network diagram is shown in Figure 9, where dotted lines indicate underpasses. Appendix B Table A2 presents the node/edge configurations, intrusion paths, and protection chains within the complex network. The PPS vulnerability assessment results are provided in Appendix C Table A3.  
We conducted 1000 simulated intrusions for each of the system's 41 routes and documented the results. The assessment results obtained using our proposed method are presented in Appendix B Table A2, while the software simulation results are shown in Appendix D Table A4 (route/edge simulations) and A5 (intrusion path simulations). The simulation results showed weaker interception probabilities for nodes n\({}_{10}\), n\({}_{12}\), n\({}_{16}\), n\({}_{20}\), n\({}_{23}\), and n\({}_{27}\), as well as for edges e\({}_{8}\), e\({}_{9}\), e\({}_{23}\), e\({}_{43}\), e\({}_{44}\), and e\({}_{48}\), which were consistent with their  
Figure 8: Structure of an organization’s PPS. The PPS layout is shown in (**a**), a 3D simulation view in (**b**), and the intrusion behavior workflow in (**c**).  
vulnerability rankings in our assessment model. The five paths with the lowest interception probabilities (r\({}_{39}\), r\({}_{38}\), r\({}_{11}\), r\({}_{18}\), r\({}_{10}\)) reflect inadequate protection, corresponding to the top five most vulnerable locations in protection route failures, as indicated in Appendix C from our assessment method.
## 4 Experimental Evaluation
### Application
#### 4.2.1 Stopping Criteria  
The simulation of proposed application models is steady-state and takes a long duration. In practice, it is necessary to define a criterion to stop the simulation. The batch means is one of the best solutions to solve this problem and is widely adopted.  
The batch means divides the simulation parts (called batch) with similar sizes. Next, it computes the average of each batch and constructs a list (called batch means) with theses averages. We can determine the error of the simulation through batch means. The error is calculated with the following equation:  
\[E=\frac{t^{(k-1,1-\alpha)\times S}}{\sqrt{k}} \tag{1}\]  
where \(t\) is the value of the T-Student distribution with degree of freedom _K-1_ and level of confidence (defined by user) equal to _1-\(\alpha\)_, \(k\) is the size of the batch means and \(S\) is the standard derivation of the batch means. We compare the error of the batch means (_E_) with a value defined by the user, called \"max error\". The simulation stops when \(E\) is less than or equal to the max error.
# Bridge Crack Detection Based on SSENets
## 3 Experimental Results and Ablation Study
### Evaluation and Discussion
#### 3.4.3 Computational Efficiency and Complexity of Models  
We use floating-point operations (FLOPs) and running time to measure the efficiency and complexity of the models. As shown in Table 10, compared with [PERSON]'s model, the FLOPs of SENets is increased by 0.4%, the running time is increased by 1%. Compared with Resnet50, which performs best in ResNets, the FLOPs of SSENets is decreased by 38.35% while the running time is decreased by 30.99%.
## 2 Materials and Methods
### Validation and Testing  
The dataset was divided into training, validation, and test sets. The training subset was used to optimize the parameters of the neural network, as described in the previous section, whereas the validation set was used to evaluate the generalizability of the model on data outside the training set and determine the number of training iterations needed. Typically, training stops when the quality metrics on the training and validation datasets start to diverge (the validation performance begins to deteriorate instead of improve). The test set was used to evaluate the quality metrics once the model training process was complete. Dataset splits were chosen based on the typical range of values used commonly for this task. First, the 0.05 of the entire dataset was allocated to the test set, and then the remaining images were split between training and validation following rates of 0.8 and 0.2, respectively. The sizes of the subsets were 760, 190, and 50 images for the training, validation, and test sets, respectively.  
The optimal model obtained by training and internal validation was used to evaluate the quality metrics in the independent test set. The quality metrics used for the testing were the accuracy and the Dice index. The accuracy is defined as the percentage of correctly classified teeth; a tooth is considered correctly classified if the intersection over union (IoU) of the original annotation (ground truth) with a predicted object of the same class is above a threshold (IoU \(>\) 0.5 in this case). On the other hand, the Dice index is a metric that is computed pixel-wise using the following formula:  
\[\text{Dice index}=\frac{1}{\text{n}}\sum_{\text{i=1}}^{\text{n}}\frac{2\text{ TP}_{\text{i}}}{2\text{TP}_{\text{i}}+\text{FP}_{\text{i}}+\text{FN}_{\text{i}}}\]  
TP\({}_{\text{i}}\), FP\({}_{\text{i}}\), and FN\({}_{\text{i}}\) are the true positives, false positives, and false negatives for the i-th class, respectively. The true positives are foreground pixels of class i that are correctly identified, the false negatives are foreground pixels of class i that are not correctly identified, and the false positives are background pixels (class different from i) that have been annotated as class i by the model.
# A Review on Styrene Substitutes in Thermosets and Their Composites
## 2 Traditionally Crosslinkable Monomers  
Most of traditionally crosslinkable monomers are industrially available and hence easily utilized as styrene replacements in resins and composites in a large scale. Although these RDs are mostly derived from fossil fuels and toxic as styrene, they still have advantages over styrene such as high boiling point and polymerization efficiency. Their products also show superior properties compared to styrene analogues.
# Post-Flood UAV-Based Free Space Optics Recovery Communications with Spatial Mode Diversity
## 4 Methods
The MEMS switch is controlled dynamically using an algorithm designed LabVIEW software by National Instruments (Austin, TX, USA), for automated switching between the two channels based on the comparison of the current SINR to the threshold SNR value, \(SINR\gamma\). While the received SINR of the primary channel is equal to greater than \(SINR\gamma\), the received data from the primary channel are forwarded to the composite channel by the MEMS switch. On the other hand, when the received SINR of the primary channel is less than \(SINR\gamma\), the received data from the backup channel is forwarded to the composite channel by the MEMS switch. A key parameter for indicating the strength of the atmospheric turbulence is the refractive index structure parameter, \(C_{n}^{2}\), which was evaluated by transmitting the concerned mode and then calculating the scintillation index at the receiver using the intensity profile measurements from a charge-coupled-device camera. The scintillation index is given by the Rytov variance [62]:  
\[\sigma^{2}\approx\frac{\left\langle I^{2}\right\rangle-\left\langle I\right\rangle ^{2}}{\left\langle I\right\rangle^{2}} \tag{14}\]  
where \(I\) is the on-axis beam intensity and the angle brackets \(<\) denote the ensemble average. The approach for measuring the scintillation index is similar to that in [61]. The refractive index structure parameter \(C_{n}^{2}\) is then evaluated by substituting the scintillation index into:  
\[\sigma^{2}=1.23C_{n}^{2}k^{2/6}L^{11/6} \tag{15}\]  
where wave number \(k=2\pi/\lambda\) and \(L\) is the link distance. Using Equation (13) to Equation (14), the \(C_{n}^{2}\) values were computed for various weather conditions based on the data transmission experiments using the designed FSO transceiver. The turbulence strength and turbulence fluctuation are indicated by \(C_{n}^{2}\) and the Rytov variance \(\sigma^{2}\), respectively. The values of \(C_{n}^{2}\) in the atmosphere are typically in the range from \(10^{-17}\) m\({}^{-2/3}\) for weaker turbulence strength to \(10^{-13}\) m\({}^{-2/3}\) for stronger turbulence strength. The value of \(\sigma^{2}\) is typically related to the refractive index inhomogeneities \(C_{n}^{2}\), optical wavelength \(\lambda\), and the propagating distance \(L\). A higher value of \(\sigma^{2}\) generally represents stronger turbulence fluctuation [62]. Weak turbulence is associated with \(\sigma^{2}<1\), and moderate fluctuation conditions are characterized by \(\sigma^{2}\approx 1\). Strong fluctuations are associated with \(\sigma^{2}>1\)[62].
# Periodic Surface Defect Detection in Steel Plates Based on Deep Learning
## 3 Periodic Defect Detection Algorithm Based on CNN + LSTM
### The Improved Algorithm Based on CNN + LSTM + Attention
#### 3.3.2 The Principle of an Improved Algorithm with the Attention Mechanism  
The attention mechanism was implemented in the encoder-decoder framework throughout the network [19]. The encoder-decoder structure without the attention mechanism usually takes the last state of the encoder as the input to the decoder; the subsequent decoder process has nothing to do with the previous input, and is only related to the last inputted state. The encoder-decoder structure with the attention mechanism has different input states of the decoder at different times.  
Figure 11 shows the structure of the implementation of the attention mechanism. The specific steps are as follows:  
1. The similarity \(a_{0}^{1}\) between h\({}^{1}\) (the hidden layer output vector of LSTM at the current time) and \(Z^{0}\) (the initial vector, the hidden state of the decoder) is calculated by the matching module \"match\".
2. The current output \(Z^{0}\) needs to be matched with each input (h\({}^{1}\)-h\({}^{4}\)) to obtain the similarity between the current output \(Z^{0}\) and all inputs (\(a_{0}^{1}\)-\(a_{0}^{4}\)).
3. Normalize all similarities with the softmax function so that the sum of all similarities (i.e., weights) is 1.  
Figure 10: Abstract model of the attention mechanism.  
Calculate the weighted sum of each input (h\({}^{1}\)-h\({}^{4}\)) and its normalized weight (\(a_{0}^{1}\)-\(a_{0}^{4}\)), as the next input c\({}^{0}\).
5. Using c\({}^{0}\) as the input of LSTM of the next timestep, the output hidden state z\({}^{1}\) of the next timestep is determined by c\({}^{0}\) and z\({}^{0}\).
6. Then, replace z\({}^{0}\) with z\({}^{1}\) and repeat steps (1)-(5) until the timesteps end.  
By introducing the above attention mechanism into the CNN + LSTM network presented in Section 3, an improved algorithm was obtained, and its structure is shown in Figure 12. First, the CNN extracts the feature of the periodic defect image. Then, the extracted feature vector is inputted into the LSTM with the attention mechanism. Finally, a value is outputted in the last output position. O is the final output, which represents whether there is a periodic defect in this image sequence or not.  
Figure 11: The structure of the implementation of the attention mechanism.  
Figure 12: The network structure of the improved algorithm with the attention mechanism.
## 4 Analysis and Discussion of Results
### Test No. 2: Positioning Error  
Test no. 2 was used to evaluate the positioning accuracy and assess the method and strategy of Figure 11; therefore, it required five positions to be selected. The robot was automatically positioned at 35% speed and with the payload attached. The variations in the positions were recorded before and after disabling the motor driver. Table 4 presents the mean square error influenced by test no. 2 in relation to the five selected positions.  
The results obtained in test no. 2, for which the errors are shown in Table 4, present a relatively low and acceptable error, being the result of pinion and toothed ring wear, errors in measurement and conversion, action and response time to the motor, and the inertia caused by the weight of the payload and the robotic arm.
# The Development of Teleoperated Driving to Cooperate with the Autonomous Driving Experience
###### Abstract  
Autonomous vehicles are increasingly being adopted, with manufacturers competing to enhance automation capabilities. While full automation eliminates human input, lower levels still require driver intervention under specific conditions. This study presents the design and development of a prototype vehicle featuring both low- and high-level control systems, integrated with a 5G-based teleoperation interface that enables seamless switching between autonomous and remote-control modes. The system includes a malfunction surveillance unit that monitors communication latency and obstacle conditions, triggering a hardware-based emergency braking mechanism when safety thresholds are exceeded. Field experiments conducted over four test phases around Chulalongkorn University demonstrated stable performance under both driving modes. Mean lateral deviations ranged from 0.19 m to 0.33 m, with maximum deviations up to 0.88 m. Average end-to-end latency was 109.7 ms, with worst-case spikes of 316.6 ms. The emergency fallback system successfully identified all predefined fault conditions and responded with timely braking. Latency-aware stopping analysis showed an increase in braking distance from 1.42 m to 2.37 m at 3 m/s. In scenarios with extreme latency (>500 ms), the system required operator steering input or fallback to autonomous mode to avoid obstacles. These results confirm the platform's effectiveness in real-world teleoperation over public 5G networks and its potential scalability for broader deployment.  
autonomous vehicle control; 5G teleoperation; control mode transition; latency-aware emergency system; dual-mode electric vehicle; Robot Operating System (ROS)
## 2 Preliminaries and Problem Formulation
### Basic Concepts of Algebraic Graph Theory and Relevant Notions  
Let \(\mathrm{G}=\{\mathcal{V},\mathcal{E}\}\) describe an undirected graph, where \(\mathcal{V}=\{v_{1},v_{2},\cdots v_{n}\}\) denotes the set of nodes. \(\mathcal{E}\subseteq\big{\{}\big{(}v_{i},v_{j}\big{)}:v_{i},v_{j}\in\mathcal{ V};i\
eq j\big{\}}\) denotes the set of edges, which belong to the graph. Define \(\mathrm{A}=\big{[}a_{ij}\big{]}\in\mathbb{R}^{N\times N}\) for the adjacency matrix; in addition, the elements of the weighted adjacency matrix are non-negative. An edge of \(\mathrm{G}\) is denoted by \(\epsilon_{ij}=\big{(}v_{i},v_{j}\big{)}\), representing the information flow from agent \(\mathrm{i}\) to agent \(\mathrm{j}\). The graph is called undirected if and only if the edges \(\epsilon_{ij}\) and \(\epsilon_{ji}\) are equivalent, which means that the information can be transmitted between them; on the contrary, we call a graph \(\mathrm{a}\) directed graph if the edges \(\epsilon_{ij}\) and \(\epsilon_{ji}\) are different, and the flow of information has a direction. In this article, the graph is an undirected graph if there is no special statement. The elements of adjacency matrix \(\mathrm{A}=\big{\{}a_{ij}\big{\}}\) satisfy that the edge \(\epsilon_{ij}\in\mathcal{E}\), then \(a_{ij}=1\), otherwise \(a_{ij}=0\). The degree matrix \(\mathrm{D}=diag(d_{1},d_{2},d_{3},\cdots,d_{n})\in\mathbb{R}^{N\times N}\) is a diagonal matrix where \(d_{i}=\sum_{j=1}^{N}a_{ij}\), \(N\) is the number of the follower agents. The Laplacian matrix of the graph \(\mathrm{G}\) is defined as \(\mathrm{L}=\mathrm{D}-\mathrm{A}\).  
An undirected graph is said to be a connected graph if there is at least one path between any two nodes of the graph. For a leader-follower system, the leader agent can only send the information to its neighbor agents, but does not receive information from anyone. Furthermore, we define the leader adjacency matrix as \(\mathrm{F}=diag(b_{1},b_{2},b_{3},\cdots,b_{n})\in\mathbb{R}^{N\times N}\), corresponding to the adjacency matrix; the leader adjacency matrix is also a diagonal matrix. If the leader's information is available to the agent \(\mathrm{i}\), \(b_{i}=1\), and \(b_{i}=0\), otherwise. Then, we have the definition of graph \(\overline{\mathrm{G}}\), which consists of the leader agent, the edges between the leader agent and its follower agents and the graph \(\mathrm{G}\).  
**Lemma 1**.: _The graph \(G\) is connected if and only if Laplacian \(L\) of \(G\) has a simple zero eigenvalue._  
**Lemma 2**.: _For a leader-following system, if the undirected graph \(\overline{\mathrm{G}}\) is connected, then the matrix \(H=L+F\) is symmetric positive definite._
###### Abstract  
The aim of the study is to identify the relationships between anthropometric variables and knee joint extensor peak torque, knee joint flexor peak torque, and conventional hamstring-to-quadriceps ratio in Polish first-division soccer players. The study examined 37 soccer players aged 19 to 30 years (body mass: 76.8 \(\pm\) 7.2 kg, body height 1.82 \(\pm\) 0.06 m). Muscle torques of the knee joint were measured under isometric conditions using a Biodex 4 Pro dynamometer. The anthropometric variables such as body part lengths, breadths, and girths and skinfeld thickness were measured. The strongest relationships of knee joint extensors were observed with body mass and variables describing skeleton size and lower-limb muscles. Regarding knee flexor torque, a significant relationship was found only with body mass. However, no significant relationships were observed between the conventional hamstring-to-quadriceps ratio and the anthropometric variables studied. The regression analysis results identified body height, body mass, and thigh and calf girth as the features most associated with knee joint torque. However, anthropometric measurements do not provide full information about the torque proportions of antagonist muscle groups, which is very important for injury prevention. Therefore, measurements using special biomechanical equipment are also necessary for the comprehensive analyses and control of the effects of sports training.  
anthropometric variables; hamstring-to-quadriceps ratio; H/Q ratio; injury prevention; isometric; lower limb; measurement acquisitions and techniques; strength and conditioning; sports training; strength abilities +
Footnote †: journal: Applied Sciences
## 6 Algorithms and Frameworks in Federated Learning
### Federated Stochastic Gradient  
In decentralized learning, Federated Stochastic Gradient (FSG) is a key technique, combining federated learning (FL) with stochastic gradient methods. It enhances the computational efficiency and supports resilient model training across distributed devices.  
Federated Stochastic Gradient Descent (FedSGD) adapts the classical Stochastic Gradient Descent (SGD) for FL, addressing privacy and data distribution issues. In FedSGD, each device performs local SGD computations and sends updates to a central server, which aggregates them to refine the global model [84; 85]. This approach avoids centralizing the data while improving the model performance.