esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The purpose of this paper is to present a new and accurate, fully explicit finite-difference time-domain method for modeling nonlinear electromagnetics. The approach relies on a stable algorithm based on a general vector auxiliary differential equation in order to solve the curl Maxwell's equation in a frequency-dependent and nonlinear medium. The energy conservation and stability of the presented scheme are theoretically proved. The algorithms presented here can accurately describe laser pulse interaction with metals and nonlinear dielectric media interfaces where Kerr and Raman effects, as well as multiphoton ionization and metal dispersion, occur simultaneously. The approach is finally illustrated by simulating the nonlinear propagation of an ultrafast laser pulse through a dielectric medium transiently turning to inhomogeneous metal-like states by local free-electron plasma formation. This free carrier generation can also be localized in the dielectric region surrounding nanovoids and embedded metallic nanoparticles, and may trigger collective effects depending on the distance between them. The proposed numerical approach can also be applied to deal with full-wave electromagnetic simulations of optical guided systems where nonlinear effects play an important role and cannot be neglected.  
Keywords:finite-difference time-domain method (FDTD); nonlinear propagation; Raman effect simulation; Kerr effect simulation; light propagation in a photoionizable media; plasma; Maxwell equations solver; laser pulse interaction; general vector auxiliary differential equation (GVADE) +
Footnote †: journal: Applied Sciences
# Ductile Fracture Behavior of Mild and High-Tensile Strength Shipbuilding Steeds
## 3 Experiments  
The experimental data for one normal strength and two high-strength steel grades, which are widely used maritime industry, are used to demonstrate the calibration procedure and validate the model. The target steels are:  
* A grade (KR) normal-strength (mild) steel plate (6 mm);
* AH36 grade (ASTM A131) high-strength steel plate (6 mm);
* DH36 grade (LR) high-strength steel plate (7 mm).  
[PERSON] et al. [32] published the test results for grade DH36 steel and [PERSON] et al. [28] presented the data for AH36. The tests for A grade are reported in the present study. The base plates with the thicknesses given above are procured from three different Korean steel producers. According to the mill test certificates, Table 1 lists the chemical composition of the steel plates considered. The test specimens are cut from the mid-layer of the plates given above using the CNC wire-cutting method perpendicular to the plate rolling direction.  
Figure 7: Shape of the cut-off region of the proposed fracture model for \(C_{1}=0.6\).  
Standard flat dog-bone specimens with a 50-mm-long, 12.5-mm-wide and 2-mm-thick gauge section are used to obtain the flow stress before the onset of diffuse necking. The loading speed is 1 mm/min. In general, marine structural steels display planar-isotropic characteristics, as reported by [PERSON] et al. [36]. For simplicity, the mechanical properties of the tested steel grades are assumed to be isotropic. Figure 8 presents the measured true stress versus logarithmic plastic strain curves for the three steel grades up to diffuse necking.  
Uniaxial tension tests are performed on central hole and notched specimens. These tension specimens are also 2-mm thick. The first type of flat specimen, designated as NT20, has a circular cut-out with a notch radius of 20 mm. The gauge section width is 10 mm. The second type of notched specimen, designated as PST, has a smaller notch radius and but larger gauge width. The central hole specimen (CH) features a 6-mm hole at the center. The shear specimen is adopted from the design proposed by [PERSON] et al. [38]. Its thickness is 4 mm. Figure 9 shows the scantlings of the test specimens. The tests are performed with a speed of 0.5 mm/min. The reported displacement correspond to the extensometer measurements at 50-mm gauge length. The tests are conducted three times and the repeatability of the results is confirmed.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Grade** & **C** & **Si** & **Mn** & **P** & **S** & **Cu** & **Cr** & **Ni** & **Mo** & **Al** & **Nb** & **V** & **Ti** \\ \hline A & 0.1395 & 0.183 & 0.511 & 0.0101 & 0.0042 & - & - & - & - & - & - & - & - \\ AH36 & 0.16 & 0.34 & 1.4 & 0.019 & 0.005 & 0.03 & 0.02 & 0.01 & 0.003 & 0 & 0.002 & 0.003 & 0.001 \\ DH36 & 0.1254 & 0.272 & 1.495 & 0.0114 & 0.0038 & 0.085 & 0.05 & 0.09 & 0 & 0.042 & 0.029 & 0.002 & 0.016 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical composition of the tested steels (wt.%).  
Figure 8: Experimentally obtained true stress-equivalent plastic strain data.
## 3 Principle and Processing Flow of Atmospheric Correction Algorithm  
Assuming that the target surface observed by the satellite is a uniform Lambertian surface, the observed Top-of-Atmosphere (TOA) radiance can be expressed using Equation (1):  
\[L(\theta_{s},\theta_{v},\varphi)=T_{g}(\theta_{s},\theta_{v})\cdot[L_{path}( \theta_{s},\theta_{v},\varphi)+\frac{\rho_{1}F_{s}\mu_{s}}{\pi(1-\rho_{1}S)}T (\theta_{s})T(\theta_{v})] \tag{1}\]  
where \(\theta_{s}\) is the solar zenith angle (SZA), \(\theta_{v}\) is the viewing zenith angle (VZA), \(\varphi\) is the relative azimuth angle (RAA), and \(\mu_{s}\) is the cosine of the SZA. \(F_{s}\) is the extraterrestrial solar irradiance, representing the solar irradiance incident vertically outside the atmosphere,  
Figure 1: Schematic of synchronized detection between PSAC and MSC.  
and it varies with wavelength. \(L_{path}\) is the atmospheric path radiance, \(T_{g}\) is the direct transmittance, and \(T(\theta_{v})\) and \(T(\theta_{s})\) represent the upward and downward diffuse transmittance, respectively. \(L_{path}\), \(T_{g}\), \(T(\theta_{v})\), and \(T(\theta_{s})\) are all atmospheric radiances, which can be calculated using an atmospheric radiative transfer model.  
Based on this theoretical foundation, the atmospheric correction process integrates multiple computational steps to accurately retrieve surface reflectance. Figure 2 illustrates the atmospheric correction workflow proposed in this study. The subsequent sections will provide a detailed explanation of the processing steps outlined in the flowchart, including data matching, aerosol and water vapor retrieval, the construction of atmospheric correction lookup tables, atmospheric radiative correction, adjacency effect correction, and BRDF correction.
## 1 Introduction
proposed a dynamic decision-making method for monitoring weapons' equipment health state based on big data, in which the K-nearest neighbor algorithm was introduced to calculate the similarity for case reasoning and fact feature set matching was used for rule reasoning [11]. [PERSON] et al. studied the dual reasoning mechanism of rules and cases and established an expert system to diagnose the fault of a belt conveyor drive system; the accuracy rate was higher than that of the diagnostic system supported by a single reasoning mechanism algorithm [12]. [PERSON] et al. screened out the automobile radiators related to the design requirements from a case database by using the fuzzy evaluation algorithm and then modified the most similar cases according to case reasoning technology to make the cases meet the final design requirements [13]. [PERSON] et al. studied the classification of case attributes, proposed a tree-based semantic similarity measurement method, and established an expert system for aero-engine fault diagnosis based on case reasoning [14]. [PERSON] et al. provided an intelligent food quality assurance system composed of a case-based reasoning (CBR) engine and a fuzzy rule-based reasoning (FBR) engine; some quality assurance operations were obtained from the CBR engine, and the best storage conditions for the inventory were given by the FBR engine [15]. [PERSON] et al. developed a decision system to provide accurate irrigation strategies that maximize cotton yield based on integrating the cotton model, CSM-CROPGRO, from the DSSAT model with reinforcement learning algorithms [16]. Currently, post-processing analyses of the flow field simulation still rely on the researchers' experience, so it is difficult to obtain information from post-processing automatically. In this paper, we aimed to analyze the simulation result data automatically. The simulation result data of a cleaning chamber flow field were transformed into image data, the key points were defined and identified, the regions were divided, and finally, the characteristics of airflow in the cleaning chamber were analyzed based on the object detection algorithm, the knowledge base, and the reasoning mechanism.
## 3 GPU Parallelization of Metaphorless Optimization Algorithms
### A Novel GPU-Based Parallelization of the MaGI Algorithm  
This section introduces a novel GPU-based parallelization of the MaGI algorithm following the methodology outlined in this study, as illustrated by Algorithm 25.  
Since the MaGI algorithm employs two sequential metaphorless heuristics in sequence, its approach to parallel implementation closely resembles that of BWP.  
```
1:/*Initialization*/
2:Initialize\(numVar\), \(popSize\) and \(maxIter\);\(\triangleright\)Host
3:\(X\leftarrow\textsc{Generate\_initial\_pop\_kernel}()\);
4:Evaluate_fitness_kernel(\(X\));
5:\(i\gets 1\);
6:/*Main loop*/
7:while\(i\leq maxIter\)do\(\triangleright\)Host
8:/*Leverage the Jaya heuristic*/
9:Determine\(X_{best,i}\) and \(X_{worst,i}\);
10:\(X_{i}^{new}\leftarrow\textsc{Jaya-driven\_population\_UPDATE\_kernel}(X_{i})\);
11:Evaluate_fitness_kernel(\(X_{i}^{new}\));
12:Greedy_selection_kernel(\(X_{i}\), \(X_{i}^{new}\));
13:Determine\(X_{best}\) and \(X_{worst,i}\);
14:/*Leverage the Rao-2 based heuristic*/
15:\(t_{i}\leftarrow\textsc{Select\_random\_solutions\_kernel}()\);
16:\(X_{i}^{new}\leftarrow\textsc{MaGI-Rao2-based\_population\_UPDATE\_kernel}(X_{i})\);
17:Evaluate_fitness_kernel(\(X_{i}^{new}\));
18:Greedy_selection_kernel(\(X_{i}\), \(X_{i}^{new}\));
19:\(i\gets i+1\);\(\triangleright\)Host
20:endwhile
21:Output the best solution found and terminate.\(\triangleright\)Host and device
```  
**Algorithm 25** GPU-based parallel MaGI algorithm  
In accordance with the original algorithm (Algorithm 8), the first phase of the main loop of the GPU-based parallel MaGI algorithm involves employing the Jaya heuristic to generate an updated population, followed by further refinement using a Rao-2-based heuristic. This last heuristic requires the identification of random solutions from the population that have to be precomputed for all candidate solutions before population updates take place (line 15 of Algorithm 25), in accordance with the parallelization strategy presented for the original Rao-2 algorithm.  
The GPU-based parallel implementation of the modified version of the Rao-2 heuristic is presented in Algorithm 26.  
```
1:functionMAGI-Rao2-based_population_update_kernel(X)
2:/*Device code */
3: Determine row using \(x\) dimension of \(blockDim\), \(blockIdx\), and \(threadIdx\);
4: Determine \(col\) using \(y\) dimension of \(blockDim\), \(blockIdx\), and \(threadIdx\);
5:ifrow\(\leq\) popSize and\(col\leq numVar\)then
6:if\(f(X)[row]\) is better than \(f(X)[t[row]]\)then\(\triangleright\)Equation (14)
7:\(X^{new}[row,col]\gets X[row,col]+rand()\times(X_{best}[col]-abs(X_{n worst}[col]))+rand()\times(X[row,col]-X[t[row],col])\);
8:else
9:\(X^{new}[row,col]\gets X[row,col]+rand()\times(X_{best}[col]-abs(X_{n worst}[col]))+rand()\times(X[t[row],col]-X[row,col])\);
10:endif
11:Check_boundary_integrity_function(\(X^{new}\)).
12:endif
13:endfunction
```  
**Algorithm 26** Kernel for population update (MaGI)
## 6 Results and Discussion
### Convergence Analysis  
Convergence analyses regarding the time-step and mesh density are performed for the block rocking on a rigid base. The rigid base is obtained by setting the beam Young's modulus to a very high value, in this case taken to be equal to \(10^{24}\) Pa. The block's initial conditions are set to be equal to the experimental ones for the B6M block (which will be presented in the following section). In Figure 10, the results of four simulations with different time-steps ranging from \(10^{-3}\) s to \(10^{-6}\) s are shown. It is observed that convergent behaviour is displayed as the time-step is decreased. With time-steps \(10^{-4}\) s and smaller, no significant change in the block behaviour (in terms of block rotation) is observed. The time-step \(10^{-3}\) s appears to be too large, causing the results to differ from the smaller time-steps after approximately 1.5 s. A time-step of \(10^{-5}\) s is chosen for the following analyses.  
In Figure 11, results from simulations with different beam FEM meshes, composed of 3, 6 and 12 elements, are shown. No difference is observed in the results between the meshes. We point out that due to the code architecture, the block should not touch the two elements at the ends, and therefore, the mesh of 6 elements is used in further analyses.  
Figure 10: Analysis of the influence of time-step size on the rocking response.
## 4 Methodology
### Feature Selection  
In large-scale, high-dimensional Industrial IoT datasets such as TON_IoT, feature selection plays a crucial role in improving the performance of intrusion detection systems. By eliminating redundant or irrelevant features and retaining those strongly correlated with the target variable, the model's detection accuracy can be enhanced while significantly reducing training time and computational overhead. To address the \"curse of dimensionality\" associated with high-dimensional data, this study adopts a hybrid feature selection approach. First, the raw data undergo a coarse filtering stage, followed by a more refined selection process using logistic regression and Forward Feature Selection to identify the most representative feature subset [33]. Before finalizing this hybrid approach, we conducted a comparative evaluation of ten feature selection methods, including correlation analysis, feature importance ranking using random forest, logistic regression and Ridge Classifier, as well as forward selection and backward selection combined with these classifiers. Logistic regression with Forward Feature Selection consistently outperformed the alternatives in balancing detection accuracy and computational efficiency. This method iteratively evaluates the contribution of each new feature to classification performance, retaining only the minimal subset that achieves optimal accuracy.  
The original TON_IoT dataset contains 44 features. After applying the proposed selection strategy, 20 features were identified as having strong correlations with the target class (i.e., normal vs. attack types), demonstrating high utility for anomaly detection tasks. This reduced feature subset was subsequently used for training and testing the DOA-BP model. The selected feature count is summarized in Table 2.
# Finite Element Analysis of Proposed Self-Locking Joint for Modular Steel Structures
## 5 Parametric Study on the Proposed Connection
### Effect of Coefficient of Friction, \(\mu_{f}\)  
To study the effect of the coefficient of friction on the behavior of the joint, three models were developed, namely Cp8, Cp1, and Cp9, to consider the coefficient of friction of 0.19, 0.23, and 0.27, respectively. This coefficient of friction is imposed between the parts of connection, bolts, beams, and columns in finite element analysis. The above values of \(\mu\) were selected for different surface treatment ranges, from painted to almost black steel surface [31,34]. From Figure 10b, varying values of \(\mu_{f}\) did not significantly affect the initial rotational stiffness of joints, while an insignificant increase in moment carrying capacity was noticed for \(\mu_{f}=0.27\).
## 4 Results and Discussion
### Performance Prediction of Case CCKEF System under Various Conditions
#### 4.2.4 Different Exhaust Air Volumes
By contrast, it was better for the case CCKEF system to operate at the optimum airflow according to the simultaneous coefficient of stoves (scenario b: also called demand control). The demand control of the CCKEF system can not only provide a comfortable indoor environment and eligible purifi  
Figure 14: Indoor temperature, air velocity, pollutant concentration and system purification efficiency under different exhaust air volume. (**a**) Air temperature; (**b**) Air velocity; (**c**) C6H6 concentration; (**d**) Purification efficiency. cation efficiency but also reduce the system energy consumption by 3.75% compared with the other two scenarios.
## 3 Results and Discussion
### New Design of Joint  
In order to calculate the reinforcement splice length and width of the transverse joint of the precast modular bridge, an optimal cross section was first selected with respect to each variable, height, and reinforcement. For the purpose of comparison, the original cross section based on Code 2010 (the strength design method) and the cross section in which the number of reinforcements and diameter were varied based on the limit state design method were selected, respectively. The flexural performance (i.e., safety factor) of the original design that corresponded to 1.140 was used as the criterion. Table 6 summarizes the safety factor relative to variables. When six D16 reinforcements were used, the height of the cross section was decreased by 5 mm, and the amount of reinforcement was decreased by 14.3% while securing a flexural performance similar to the original one. In the case of nine D13 reinforcements, the amount of reinforcement was decreased by 18.0% without a change in height.  
The differences in the joint width based on the change in the splice length of the reinforcement were compared with respect to the selected optimal cross section. For the ease of construction, the width of the transverse joint was set as twice the splice length considering the clearance. Table 7 summarizes the examined splice length and width of the transverse joint. In a manner similar to the original design, when a lap splice was applied, the width of the transverse joint decreased by 21.1% in the case of D16-6 EA, and by 36.8% in the case of D13-9 EA.  
The application of a loop (U-bar) that was unavailable in the original design due to the limitation in the height of the cross section was enabled by Code 2015, and the minimum thicknesses corresponded to 196 and 178 mm when D16 reinforcement and D13 reinforcement, respectively, were used. Accordingly, the width of the joint decreased by 26.3% in the case of D16-6 EA and by 47.4% in the case of D13-9 EA.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Height (mm)**} & **Code 2010** & **Code 2015** \\ \cline{2-4}  & **D16–7 EA (1390.2 mm2)** & **D16–6 EA (1191.6 mm2)** & **D13–9 EA (1140.3 mm2)** \\ \hline
220 & 1.140 & 1.187 & 1.150 \\
215 & 1.105 & 1.149 & 1.113 \\
210 & 1.071 & 1.110 & 1.076 \\
205 & 1.036 & 1.072 & 1.039 \\
200 & 1.002 & 1.033 & 1.002 \\
195 & 0.967 & 0.995 & 0.966 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Safety factor by height and reinforcement amount for the cross section.  
Figure 7: Changes in the safety factor based on the amount of reinforcement.  
The induced optimal width of the transverse joint was expected to reduce the volume of site work for concrete. This in turn was expected to be more conducive to the construction of the modular bridge and to increase the efficiency of cross-sectional performance in the top flange section prior to the attachment of the section to the bridge [15]. Additionally, the small optimal joint is potentially sufficient to transfer shear and moment between adjacent flange modules [5; 11].
# An Efficient Model-Based Clustering via Joint Multiple Sink Placement for WSNs
## 3 Preliminaries
### Problem Definition  
A typical WSN consists of N+M nodes, where N sensor and actuator nodes are deployed randomly in an area with different communication ranges and M stationary sinks. Sensor nodes can monitor environmental factors as well as generate data. Actuator nodes or end-users can use the generated data. The actuator nodes can convey data from SNs via intermediary nodes or end-users to the BS in order to execute a physical parameter. In general, each node can transfer data to the sink in direct or indirect communication. Therefore, the WSN serves as a link between the physical and digital information worlds by gathering data from its surroundings and transmitting it to digital devices like computers.  
Direct communication is the process of allowing nodes to communicate directly with the sink. It would only be accessible to nodes with a communication range more extensive than their distance from the sink (within range of the sink). Indirect communication is available for all nodes. It is based on assigning intermediate nodes to convey data to a sink.  
Furthermore, indirect communication can be visualized in two cases. Firstly, the node's packets are usually sent to neighbour nodes based on their communication range (within range of neighbouring nodes) until they reach the sink. Thus, the nearest node to the sink will receive different requests, which causes it to deplete its energy faster (hole energy issue), as shown in Figure 1. In the simplest scenario, each node sends packets to the neighbouring nodes, which may be along the longest path. Consequently, this kind of scenario can eventually cause many nodes to run out of energy. In the second case, the nodes are grouped into clusters, and each specified CH node transmits data from its cluster members to the sink; this reduces their energy consumption but has a heavy toll on CHs.  
Figure 2 illustrates this scenario. Furthermore, all nodes have to relate to a single CH by communicating outside the conventional radio range. As a result of the formal scheme, service quality and performance suffer a severe degradation. For this purpose, the nodes are either clustered physically or virtually, which enables a comprehensive visualisation of the study environment and better control of environmental variables. Moreover, a specific node is designated as the Cluster Head based on a selection factor, in addition to placing a sink in the cluster's centre, which aims to minimize energy consumption and lengthen the network's lifetime.  
The network is depicted as a graph G (S, A), where S = [n\({}_{1}\),n\({}_{2}\),...,n\({}_{\text{N+M}}\)] nodes, and A represents wireless connections. Table 3 describes the notations used in the remainder of this paper.  
Figure 1: Data transmission using neighbouring nodes.