esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theoretical Models
### Multi-Field Coupling Theoretical Model
#### 2.1.2 Temperature Mechanism Analysis  
When the flue gas enters the ESP under a magnetic field, the charged fine particles are not only affected by electric field force and Lorentz force, but also exhibit a certain degree of sensitivity to temperature. The influence of temperature on the capture process of fine particles in the wire-plate ESP is usually achieved by the flue gas density. It should be noted that the flue gas density in a specific area differs at different temperatures, thus leading to changes in the flow field inside the ESP. The above two steps are combined to complete the process of temperature influence on the particle dynamic field. From a quantitative point of view, how the temperature affects the changes in flue gas density and flue gas viscosity is, respectively, given as  
\[\rho_{gk}=M_{gk}/V_{gk} \tag{1}\]  
\[ln\mu_{k}=b_{1k}+b_{2k}lnT_{k}+b_{3k}(lnT_{k})^{2}+b_{4k}(lnT_{k})^{3} \tag{2}\]  
where \(\rho_{gk}\) is density of flue gas (kg/m\({}^{3}\)), \(M_{gk}\) is air quality (kg), \(V_{gk}\) is air volume (m\({}^{3}\)), \(\mu_{k}\) is the flue gas dynamic viscosity coefficient (kg/(m-s)), \(b_{ik}\) is the correlation coefficient, and \(T_{k}\) is temperature (\(K\)).  
As a result of the irregular thermal motion and mutual collisions between ions and particles, the change in particle charge quantity decreases with increasing temperature, leading to a reduction in the electric field force. Consequently, temperature influences the diffusion of charge and, in turn, affects the electromagnetic field.  
Moreover, in the wire-plate ESP, the total charge of the particles can be expressed as \(Q_{p}=Q_{sat}+Q_{diff}\), where \(Q_{sat}\) is the electric field charge and \(Q_{diff}\) is the diffusion charge, and \(Q_{diff}\) is written as [30]  
\[Q_{diff}=\frac{2\pi\epsilon_{0}K_{0}TD_{i}}{q_{e}}\ln\biggl{(}1+\frac{q_{e} ^{2}D_{i}N_{0}t}{2\epsilon_{0}\sqrt{2m_{ion}\tau K_{0}T}}\biggr{)} \tag{3}\]  
Figure 1: Analysis mechanism of a wire-plate ESP under magnetic field.  
in which \(\varepsilon_{0}\) is the vacuum permittivity, \(K_{0}\) is the Boltzmann constant (1.38\(\cdot\)10\({}^{-23}\) J/K), \(D_{i}\) is the particle size (\(\upmu\)m), \(q_{e}\) is the electron charge (1.6\(\cdot\)10\({}^{-19}\) C), \(N_{0}\) is average ion number density per unit volume, \(t\) is the time for the particles to move in an electric field (s), and \(m_{ion}\) is the ionic mass (kg).  
The temperature field in ESP is assumed to be uniformly distributed and does not change with time, namely, it does not involve solving the temperature field equations.
# ICRSSD: Identification and Classification for Railway Structured Sensitive Data
## 2 Methods  
As shown in Figure 1, the overall framework consists of 3 modules: the sensitive data identification, the sensitive data classification, and the rule base.  
Sensitive data identification. Based on different data characteristics, we further divide structured data into canonical data and semi-canonical data to enhance accuracy. Furthermore, canonical sensitive data identification includes 4 steps: information entropy and maximum discrete entropy calculations to obtain the initial sensitivity; cluster analysis to obtain sensitive and suspicious sensitive attribute sets; association rule mining to obtain strong association relationships of attributes across system scope; and attribute sensitivity updating to obtain the final sensitivity. Semi-canonical sensitive data identification includes 2 collaborative parts: regular expression identification and keyword identification.  
Sensitive data classification. To improve the flexibility and accuracy of classification, we adopt a dynamic and multi-granularity classified strategy to classify the railway data into three levels. It consists of 3 steps:clustering-based dynamic classification; threshold-based static classification; and rule-based classification adjustment. Dynamicity refers to the use of clustering-based dynamic classification for attributes, which allows the model to adapt to changes in business data without the need for manual threshold adjustment. Multi-granularity reflects the classified requirements specific to the railway domain, encompassing attribute-level, table-level, and system-level classification to support fine-grained and multi-layered sensitivity management. Rule-based adjustment serves as a supplementary mechanism for attribute classification.  
Rule base. With the support of the rule base, we can identify semi-canonical data and adjust the classification of attributes to improve the accuracy of identification and classification. The rule base consists of two parts: identification rules and classification rules,  
Figure 1: Structured sensitive data identification and classification framework diagram.  
while the identification rules include regular expressions and keyword lists. The rule base is constructed by experts in the relevant fields. But when constructing regular expressions, we can leverage large language models for assisted generation to improve the accuracy and efficiency of regular expression generation.
## 4 Discussion  
The analysis of the results of studies of dry nanostructuring burnishing of AISI 52100 hardened steel with a flat cylindrical indenter inclined at an angle of 0.5 to 2.5 degrees to the treated surface made it possible to reveal the features of change and the relationships among contact pressure, friction coefficient, and plastic deformation with the formed microstructure, microhardness, and roughness of the modified surface layer.  
Modeling was performed using ABAQUS software, which is most often used for burnishing simulation. The problem was solved in a two-dimensional approximation in a dynamic coupled thermomechanical formulation, which made it possible to study in sufficient detail the stress and strain state in the contact area and the effect of elevated temperatures on it due to the formation of heat during plastic deformation and friction sliding. The presented finite element model is the development of the model proposed earlier by the authors [12]. The indenter was modeled with a rigid body, and the elastoplastic Johnson-Cook model was used to describe the behavior of steel. Significant variations in the values of the parameters of this model, which are reported in the literature, are due to the influence of steel pretreatment on its strength properties and have a great influence on the values of stresses and strains obtained in the calculations.  
Finite element modeling of the process in the ABAQUS/Explicit package made it possible to establish the optimal value of the burnishing force of 250 N to provide a contact hydrostatic pressure of more than 3.5 GPa and an accumulated plastic strain \(\varepsilon=1.2\) at an indenter tilt angle of 2 degrees. In this case, the temperature in the contact zone reaches 405 \({}^{\circ}\)C and, according to previous studies, also contributes to the grinding of the initial structure into nanocrystallites.  
A detailed study of stress distributions in the contact zone showed that during the movement of an inclined cylindrical indenter, tensile normal stresses do not form behind it, which takes place for a hemispherical indenter and could lead to cracking of the surface layers of the steel under machining. According to the literature review, the strengthening of AISI 52100 (100 Gr6) quenched steel by rolling the surface with a ball with a diameter of 6 mm under a hydrostatic pressure of 400 bar gives the value 62.4 \(\pm\) 0.36 HRC [29]. It is also shown that the microhardness after treatment reaches a value of 763 HV. In our case, we get a microhardness value of 1508.2 HV\({}_{0.05}\), which proves the advantages of the proposed approach.  
Figure 20: Change in the arithmetic mean deviation of the surface profile \(Ra\) depending on the tilt angle of the cylindrical indenter \(\alpha\).
## 4 Case Studies
### Uncertainty Analysis of Series-Parallel Systems  
#### 4.1.1 Case Description  
Figure 4 shows a series-parallel system consisting of four components C1, C2, C3, and C4. Four cases with different failure uncertainty levels of components are given:  
\[\begin{array}{llll}\text{Case 1}:&\text{C1}:&\mu_{\lambda_{1}}=-3.5066,\ \delta_{\lambda_{1}}=0;&\text{C2}:&\mu_{\lambda_{2}}=-3.5066,\ \delta_{\lambda_{2}}=0;\\ &\text{C3}:&\mu_{\lambda_{3}}=-3.5066,\ \delta_{\lambda_{3}}=0;&\text{C4}:&\mu_{ \lambda_{4}}=-3.5066,\ \delta_{\lambda_{4}}=0;\\ &\text{C2}:&\text{C1}:&\mu_{\lambda_{1}}=-3.5066,\ \delta_{\lambda_{1}}=0.05;& \text{C2}:&\mu_{\lambda_{2}}=-3.5066,\ \delta_{\lambda_{2}}=0.05;\\ &\text{C3}:&\mu_{\lambda_{3}}=-3.5066,\ \delta_{\lambda_{3}}=0.05;&\text{C4}:&\mu_{ \lambda_{4}}=-3.5066,\ \delta_{\lambda_{4}}=0.05;\\ &\text{C3}:&\mu_{\lambda_{1}}=-3.5066,\ \delta_{\lambda_{1}}=0.05;&\text{C2}:&\mu_{ \lambda_{2}}=-3.5066,\ \delta_{\lambda_{2}}=0.1;\\ &\text{C3}:&\mu_{\lambda_{3}}=-3.5066,\ \delta_{\lambda_{3}}=0.05;&\text{C4}:&\mu_{ \lambda_{4}}=-3.5066,\ \delta_{\lambda_{4}}=0.1;\\ &\text{C4}:&\text{C1}:&\mu_{\lambda_{1}}=-3.5066,\ \delta_{\lambda_{1}}=0.05;& \text{C2}:&\mu_{\lambda_{2}}=-3.5066,\ \delta_{\lambda_{2}}=0.025;\\ &\text{C3}:&\mu_{\lambda_{3}}=-3.5066,\ \delta_{\lambda_{3}}=0.05;&\text{C4}:&\mu_{ \lambda_{4}}=-3.5066,\ \delta_{\lambda_{4}}=0.025.\\ \end{array}\]  
In these four cases, the failure rate of each component obeys a lognormal distribution, expressed with two parameters of the mean \(\mu_{\lambda_{i}}\) and standard deviation \(\delta_{\lambda_{i}}\). To discuss the impact of component failure rate uncertainty on system reliability, the mean values of four  
Figure 4: A series-parallel system.  
cases were set to be the same value and the standard deviation was set to different values in the four cases. In particular, \(\delta_{\lambda_{i}}=0\) was set in case 1, which means that the failure rate of each component was a constant value and no uncertainty of reliability was considered in this case.
## 4 Reliability Comparison and Evaluation
For the integrated wired/wireless sensor networks:  
\[L_{i}=(1-w^{j}) \tag{1}\]1  
where \(x\) is the probability that a node cannot forward to a next hop, \(j\) is the maximum jump factor, and \(d\) is the probability that a wired link is healthy. We have \((1-d)\) is the probability of having a faulty wired link. A sensor will not be able to forward its messages through one direction if and only if the wired segment is broken and all next \(j\) sensors are faulty. Otherwise, the sensor can forward its information. To evaluate the three architectures, let us assume the following scenario. We have an underwater pipeline that needs to be monitored using 500 sensors distributed uniformly. If a wired network is used to connect the sensors, let us assume that the health probability of each wired link between each neighbor pair of sensors is 0.99. If a wireless sensor network is used, let us assume that the probability that a wireless node can forward a message is 0.85, and the jump factor is 3. For the wired/wireless network, we assume having the same corresponding values as the ones used for the wired and wireless networks. Using Equation (8) where \(w\) is replaced by Equation (9) for the wired network, Equation (10) for the wireless network, and Equation (11) for the integrated wired/wireless network, the expectation for the network having the ability to transfer its sensed information to the NCC is shown (see Figure 8). \[L_{i}=(1-w^{j}) \tag{1}\]2 Transmission reliability for different architecture scenarios. \[L_{i}=(1-w^{j}) \tag{1}\]3 Impact of increasing the network length and the number of sensors. On the other hand, to evaluate the impact of increasing the number of sensors on the networks and the total distance of the network, we will consider the previous example with varying numbers of sensors. The results are shown in Figure 9 for networks with 100 to 1,000 sensors. As we can see from the figure, the connectivity reliability of both wired and wireless sensor networks are significantly reduced with the increasing size of the network while the connectivity reliability was not reduced much in the integrated wired/wireless network.
# Decoding Urban Intelligence: Clustering and Feature Importance in Smart Cities
## 3 Background
### Linear SVM  
Linear SVMs [24] aim to create a robust classifier by finding the hyperplane that best separates the classes, ensuring maximum margin, and minimizing classification errorson new data. The goal of a linear SVM is to find a hyperplane defined by \(\mathbf{w}\cdot\mathbf{x}-b=0\), where \(\mathbf{w}\) is the normal vector to the hyperplane and \(b\) is the bias term. The objective is to maximize the margin \(\frac{2}{\|\mathbf{w}\|}\), which is the distance between the hyperplane and the nearest data point of each class.  
This can be formulated as an optimization problem  
\[\min_{\mathbf{w},b}\frac{1}{2}\|\mathbf{w}\|^{2}\]  
subject to the following constraints:  
\[y_{i}(\mathbf{w}\cdot\mathbf{x}_{i}-b)\geq 1\quad\forall i\]  
Here, \(\mathbf{x}_{i}\) represents the input vectors, and \(y_{i}\in\{-1,1\}\) are the corresponding class labels.  
To solve this problem, we typically use Lagrange multipliers, leading to the dual form of the problem  
\[\max_{\alpha}\sum_{i=1}^{n}\alpha_{i}-\frac{1}{2}\sum_{i=1}^{n}\sum_{j=1}^{n} \alpha_{i}\alpha_{j}y_{i}y_{j}\mathbf{x}_{i}\cdot\mathbf{x}_{j}\]  
subject to  
\[\sum_{i=1}^{n}\alpha_{i}y_{i}=0,\quad\alpha_{i}\geq 0\quad\forall i\]  
The solution to the dual problem gives the values of \(\alpha_{i}\), which are then used to find the optimal hyperplane parameters \(\mathbf{w}\) and \(b\). The final decision function for classifying a new point \(\mathbf{x}\) is as follows:  
\[f(\mathbf{x})=\text{sign}(\mathbf{w}\cdot\mathbf{x}-b)\]
# Mechanical Design and Testing of Mobile Monitoring Units for Security Systems
## 4 Discussion
And finally, as was proven by the simulations and experiments performed, the mobile monitoring units are able to withstand demanding exterior conditions, involving wind and different operational temperatures. Also, for the correct functioning of the whole system, the possibility of sensor signal pre-processing and the proper settings of the sensor alarm ranges, frequencies, velocities and magnitudes (resulting from dynamic impacts) were determined. Thanks to the mechanical design and functional principle of the mobile monitoring units, which require no galvanic electrical interconnection or cable communication and can be positioned at perimeters as self-standing units, the installation of the whole security system is relatively quick. Furthermore, the mobile monitoring units do not necessarily have to create a closed perimeter; therefore, they can also be used as gates, or to secure areas where a part of the perimeter is secured by other means--for example, by natural obstacles or inaccessible terrain, or by different kinds of objects or manmade obstacles. There is also no need to build fences, and the developed monitoring security system does not induce disturbing sensations for the protected people. Furthermore, thanks to the sensory equipment employed, there is no need to apply tags to the people; in this way, the security system helps to increase the psychological well-being of the protected people, and, thanks to the limited visual impact on the ambient environment, the psychical comfort of the protected people is also increased. As the work on the mobile monitoring security system is almost finished, future work will be focused on the testing of the whole mobile monitoring system under different environmental and terrain conditions. In the future, there will also be a possibility to use the developed mobile monitoring units for other types of applications, for example, environmental monitoring or observations, in which mobile monitoring units can be helpful.
## 3 Methodology
### Optimal Placement Problem  
Optimal placement problem of control devices remains a serious issue. Sometimes, disarrangement of control device not only cannot be effective, also can be associated with technical and economic losses. Likewise, if equipment is not fit in an optimal location, its effectiveness decreases and is not technically feasible. When the objective function is set to minimize voltage deviation and number of installed ATCTr, in this scenario, voltage control efficiency depends on the placement of ATCTr [32; 33]. Moreover, optimal scheduling of devices depends on the placement of the devices. Therefore, optimum placement of ATCTs can reduce voltage deviation.  
Multiobjective optimization using GA with the objective function of voltage deviation was applied to solve the optimization problem. The proposed method aims to hence perform a power flow analysis, to calculate voltage magnitudes at different buses. GA randomly in each process locates ATCTr in different nodes with different alignments and configurations. These processes are repeatedly carried out until the comparison between all genes is made. Finally, the best gene (optimal placement) with least voltage deviation is specified from comparing the last population with the best gene from the new population. In order to take into account, the optimal placement of ATCTs, a string of N bits (representing the total N nodes) was used to decide the location nodes at which to introduce an ATCTr, as shown below:  
\[\mathbb{P}=(a_{N},\ a_{N-1},\ldots,\ a_{1}),\ (a_{i}\in 1,0) \tag{8}\]  
where, \(\mathbb{P}\) represents the placement of installed ATCTs in overall nodes. Here, \"0\" represents a node with no ATCTr, whereas \"1\" represents a node with ATCTr-installed bus. Figure 2 and Table 2 represent an example of coding used for multiobjective optimization; the placement of installed ATCTs in distribution network is demarcated with circles.  
Comprehensively, the stages of the proposed methodology are demonstrated in the flowchart shown in Figure 3.
## 5 Dynamic Loading Mitigation of Offshore Platform Using TMD
### Dynamic Responses and Discussion  
This section comparatively evaluates the dynamic performance of an offshore platform under two excitation patterns (Figure 9), aligned and misaligned typhoon-wave loads. The effectiveness of the TMD in mitigating acceleration, velocity, deck displacement, and base shear is quantitatively assessed. The TMD is positioned at the mid-span of the top deck. The numerical model utilizes a right-hand Cartesian coordinate system as the global reference frame, where the \(Z\)-axis is vertically upward, and the origin is defined at sea level. Notably, the maximum pile top force observed in the simulation did not trigger irreversible plastic deformation, confirming the structural integrity under the applied loads. The simulation spans 0-700 s, with results from the 100-200 s interval selected for presentation.
## 5 Simulation and Data Analysis
### Simulation Study

Table 2: Biases and MSEs corresponding to different estimates for \(a_{1}\). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & & \multicolumn{2}{c}{**MLE**} & \multicolumn{2}{c}{**BE**} \\ \cline{4-8} \multicolumn{1}{c}{(\(\alpha\), \(\beta\))} & \multicolumn{1}{c}{**pc**} & \multicolumn{1}{c}{\(n\)} & \multicolumn{1}{c}{**OPT**} & \multicolumn{1}{c}{**EM**} & \multicolumn{1}{c}{**Prior 1**} & \multicolumn{1}{c}{**Prior 2**} \\ \hline (0.5, 0.5) & 0.19 & 30 & bias & 0.0249 & 0.0249 & \(-\)0.0860 & 0.0123 \\  & & & mse & 0.0617 & 0.0622 & 0.0993 & 0.0542 \\  & & 50 & bias & 0.0182 & 0.0182 & \(-\)0.0969 & \(-\)0.0024 \\  & & & mse & 0.0361 & 0.0361 & 0.1002 & 0.0352 \\  & & 100 & bias & 0.0139 & 0.0138 & \(-\)0.0994 & \(-\)0.0061 \\  & & & mse & 0.0156 & 0.0156 & 0.0936 & 0.0164 \\ \hline (0.5, 1) & 0.14 & 30 & bias & 0.0270 & 0.0267 & \(-\)0.0829 & 0.0066 \\  & & & mse & 0.0636 & 0.0641 & 0.1024 & 0.0537 \\  & & 50 & bias & 0.0183 & 0.0182 & \(-\)0.0963 & 0.0011 \\  & & & mse & 0.0354 & 0.0355 & 0.1008 & 0.0322 \\  & & 100 & bias & 0.0132 & 0.0129 & \(-\)0.1296 & \(-\)0.0044 \\  & & & mse & 0.0153 & 0.0153 & 0.1324 & 0.0169 \\ \hline (1, 0.5) & 0.28 & 30 & bias & 0.0275 & 0.0275 & \(-\)0.1150 & 0.0015 \\  & & & mse & 0.0636 & 0.0636 & 0.1078 & 0.0593 \\  & & 50 & bias & 0.0188 & 0.0185 & \(-\)0.1198 & \(-\)0.0171 \\  & & & mse & 0.0369 & 0.0368 & 0.1016 & 0.0313 \\  & & 100 & bias & 0.0145 & 0.0143 & \(-\)0.1562 & \(-\)0.0203 \\  & & & mse & 0.0156 & 0.0155 & 0.1372 & 0.0182 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Biases and MSEs corresponding to different estimates for \(a_{2}\).
# Intersection-Based Link-Adaptive Beaconless Forwarding in Urban Vehicular Ad-Hoc Networks
## 3 Intersection-Based Link-Adaptive Beaconless Forwarding for City Scenarios
### VANET Model  
The vehicles are assumed to be moving in four different directions (east, west, north and south) in an urban environment. The WAVE architecture comprises of IEEE 802.11p and IEEE 1609 standards tosupport the lower two (PHY and MAC) and higher layers, respectively, is used as the communication model. Here, V2V communication is used to forward the data packet from a source vehicle until the last-hop vehicle, whereas V2I communication is used to forward the packet from the last-hop vehicle to destination (static infrastructure). Each vehicle is assumed to be equipped with GPS and digital maps. The GPS feature enables the vehicle to know its location information, and digital maps further reveal the location information of the destination and the intersections. Apart from this, each vehicle receiving the data packet knows the position information of the previous relay vehicle, which is stored in the header of data packet. Also, for all simulation scenarios, during data transmission, it is assumed that there exists at least one data packet that has reached destination traversing more than one hop. Finally, for the forwarding angle calculations, the line connecting the relaying vehicle and the destination is considered as x-axis. Also, the terms vehicle and node will be used interchangeably and carry the same meaning.