esa-sceva/satcom-chunk-collection huggingface dataset:
# Shear Sonic Prediction Based on DELM Optimized by Improved Sparrow Search Algorithm
## 4 Practical Application and Result Analysis
### Feature Selection  
The decision tree-based distributed gradient lifting framework (LightGBM) is used to assess the importance of the features. Compared with extreme gradient boosting (XGBoost), LightGBM uses a gradient-based unilateral sampling algorithm to preprocess sparse data, which speeds up the model training without compromising accuracy [20]. Seven parameters, including DEPTH, CAL, RHOB, NPHI, GR, DT, and RT are input into the LightGBM model to acquire the influence of each parameter on DTS and the order of the characteristic importance. The quantitative scores are shown in Table 2:  
To select the important sample features, the features are added to the ordinary model (DELM) one by one in descending order of importance. The change curve of model errors (RMSE) obtained by the comparison test is shown in Figure 6. When the model involves five inputs, the declining trend of RMSE exhibits a major corner, as seen in Figure 6. As a result, a model with five inputs is used to estimate \"missing\" DTS, which reduces computing complexity and time. The following are the five inputs used in this study: DT, RHOB, CAL, NPHI, and RT logs.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Well Name** & **The** **Index** & \multicolumn{8}{c}{**Petrophysical**} \\  & & **Depth** & **CAL** & **DT** & **GR** & **NPHI** & **RHOB** & **RT** & **DTS** \\  & & **(m)** & **(cm)** & **(us/m)** & **(API)** & **(v/v)** & **(g/cm\({}^{3}\))** & **(ohmm)** & **(us/m)** \\ \hline \multirow{8}{*}{Well M} & Min & 1550.00 & 21.36 & 206.57 & 10.37 & 0.06 & 1.36 & 11.35 & 358.01 \\  & 25\% & 1599.94 & 22.11 & 232.09 & 48.47 & 0.12 & 2.27 & 21.44 & 390.55 \\  & 50\% & 1649.94 & 22.40 & 238.59 & 80.91 & 0.15 & 2.36 & 30.78 & 401.26 \\  & Mean & 1649.93 & 23.17 & 251.91 & 81.72 & 0.21 & 2.37 & 31.52 & 420.57 \\  & 75\% & 1699.94 & 22.87 & 254.12 & 113.83 & 0.22 & 2.55 & 36.97 & 422.96 \\  & Max & 1749.87 & 39.43 & 402.10 & 157.15 & 0.92 & 2.67 & 152.98 & 642.07 \\ \hline \multirow{8}{*}{Well N} & Min & 1880.00 & 21.94 & 187.38 & 14.97 & 0.03 & 1.21 & 6.41 & 341.75 \\  & 25\% & 1910.00 & 23.12 & 231.28 & 53.01 & 0.17 & 2.24 & 16.72 & 392.04 \\ \cline{1-1}  & 50\% & 1940.00 & 24.54 & 237.79 & 86.12 & 0.20 & 2.31 & 27.24 & 399.45 \\ \cline{1-1}  & Mean & 1940.00 & 25.04 & 250.96 & 93.54 & 0.27 & 2.29 & 30.43 & 416.96 \\ \cline{1-1}  & 75\% & 1970.03 & 26.22 & 260.21 & 132.75 & 0.34 & 2.47 & 43.08 & 425.27 \\ \cline{1-1}  & Max & 2000.00 & 41.88 & 407.97 & 212.32 & 0.92 & 2.70 & 70.84 & 651.57 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistical characteristics of Well M and Well N.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Rank** & **Feature** & **Score** \\ \hline
1 & DT & 1078 \\
2 & RHOB & 728 \\
3 & CAL & 491 \\
4 & NPHI & 392 \\
5 & RT & 309 \\
6 & GR & 291 \\
7 & DEPTH & 290 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Importance scores of the parameters based on the LightGBM model.
## 2 Materials and Methods
### Intelligent Inspection System
#### 2.3.3 Profile Inspection  
The contour profile inspection is conducted by comparing the FSS between the GT reference \(I_{g,\beta}(x,y)\) and the \(I_{d,\beta}(x,y)^{SG}\). The comparison is achieved by subtracting the two FSS to obtain a third signal, which describes the variation in the form of the profile in terms of frequency. Finally, the energy is obtained to represent the microscopic distribution of peaks and valleys.  
Figure 15 presents \(r_{g,\beta}(n)\), its \(M_{g,\beta}(k)\), and the \(LM_{g,\beta}(k)=Ln(ML_{g,\beta}(k)+1)\) of the GTs. As expected, the line profile generates frequencies with zero magnitudes except for \(M_{g,L}(0)=1\). The curve \(M_{g,C}(k)\) and slope \(M_{g,S}(k)\) generate frequencies with similar decreasing signatures.  
Nevertheless, there are differences in the magnitude frequencies that can be known with the difference of \(M_{\delta}(k)=M_{g,C}(k)-M_{g,S}(k)\). The difference is shown in Figure 16, where \(M_{\delta}(0)<0\), \(M_{\delta}(k)\) with \(0<k<40\) have magnitudes higher than zero and \(M_{\delta}(k)\approx 0\) for \(k>40\). This behavior occurs because the GT curve is composed of a slope with less area than the GT slope and some frequencies that form the curve (see Figure 17).  
The form deviation out of tolerance of the \(I_{d,\beta}(x,y)^{SG}\) is determined through the comparison of the FSS of the profile shapes with those of the \(I_{g,\beta}(x,y)\), according to the profile of a line tolerance based in ASME Y14.4 [25] defined by Equation (6).  
\[D_{\Delta,\beta}(k)=\sqrt{(M_{d,\beta}(k)-M_{g,\beta}(k))^{2}} \tag{6}\]  
where \(M_{d,\beta}(k)\) are the Fourier features defined in Equation (5) of the \(I_{d,\beta}(x,y)^{SG}\), which is used for profile analysis. Each vector \(D_{\Delta,\beta}(k)\) comprises more than 300 frequency components, considering that the signatures consist of approximately 600 to 640 elements. Our experiments, guided by Fourier transform properties [11], have revealed the distinctive features of \(D_{\Delta,\beta}(k)\):  
Frequency \(k=0\) is specifically associated with millimetric errors resulting from the calibration piece's placement on the fixture, primarily induced by profile shape deformations. For frequencies \(1>k>250\), these components describe form deviations from the machining process employed to generate a curve, a line, and a slope. Notably, frequencies within the \(1>k>40\) range offer a more detailed characterization of the shape induced by the machining process, providing valuable insights into the shape and quality of milling.  
Similarly, frequencies \(1>k>100\) delineate quality errors in the calibration piece attributable to burrs, defects caused by tool wear, and errors occurring during machining. The interconnected analysis of these frequency components contributes to a comprehensive understanding of the calibration piece's characteristics and manufacturing processes.  
Figure 15: Profile \(r(n)\) of GT line and its \(M(k)\).
# An Overview of Distributed Firewalls and Controllers Intended for Mobile Cloud Computing
## 3 SDN, NFV, and CASB Technologies
### Integrated Approach
#### 3.5.1 Integrating SDN with NFV  
Integrating SDN with NFV introduces potential vulnerabilities, particularly related to hypervisor security. Misconfigurations in hypervisors can impact the functionality and security of SDN controllers, posing risks to the overall network infrastructure. Despite these challenges, the combination of SDN and NFV offers significant advantages. When combined, they enable centralised configuration and the management of routing and forwarding planes through standardised protocols, enhancing network flexibility, scalability, and management capabilities. This integration allows operators to deliver more agile and responsive network services, adapting quickly to changing demands and improving operational efficiency [36; 48].
# Multi-UAV Cooperative Anti-Submarine Search Based on a Rule-Driven MAC Scheme
## 2 Materials and Methods
### Rule-Inspired Path-Selection Strategy  
In this subsection, the association rules used as heuristic information to drive each ant to select the next grid. In the \(t\)-th iteration, the state transition rules of the ants of population \(l\) from grid \((m,n)\) to grid \((o,p)\) are designed as follows:  
\[p_{mnp}^{l}(t)=\left\{\begin{array}{l}\frac{\tau_{op}^{s}(t)\eta_{op}^{s}(t )\eta_{op}^{s-\gamma}(t)\Delta_{mnp}^{s}(t)}{\sum\limits_{o\
eq U_{K}}\frac{ \sigma_{p}^{s}(t)\eta_{op}^{s}(t)\sigma_{qp}^{s-\gamma}(t)\Delta_{mnp}^{s}(t)} },\quad op\in U_{K}\\ 0,\quad otherwise\end{array}\right. \tag{23}\]  
where \(U_{K}\) represents the selectable grid driven by association rules, \(\varphi_{op}(t)\) represents the value of the residual pheromone of other ant sub-groups at grid \((o,p)\), and \(\lambda_{mnp}(t)\) implies that there is a target at grid \((m,n)\). Then, according to association rule \(S_{i}\) and the target probability value existing at grid \((o,p)\), \(\alpha\) represents the importance of the pheromone in grid selection, \(\beta\) represents the relative importance of search revenue, \(\gamma\) represents the inhibitory effect of other population pheromones; the larger \(\gamma\) is, the stronger the relative inhibitory effect. Through this inhibition, UAVs of other groups can avoid repeated searches of the already searched grids of this group. \(\sigma\) represents the enhancement factor for the rule-driven search of grid \((m,n)\), and \(\eta_{op}(t)\) represents heuristic information, which can be defined as:  
\[\eta_{op}(t)=\frac{s_{hp}(t)+s_{lp}(t)}{L_{x}\times L_{y}} \tag{24}\]  
where \(s_{hp}(t)\) and \(s_{lp}(t)\) are the number of high- and low-probability grids that conform to the association rules after the \(t\)-th iteration, respectively.  
**Remark 3**.: _It should be noted that the rule-inspired path selection strategy (23) takes association rules as heuristic information and to design the heuristic function (24). This selection strategy not only depends on the pheromone concentration of the next grid to select the path but also selects the path according to the association rules presented by the target and the grid that conforms to the association rules._
## 2 Materials and Methods
### The Evaluation of the Feature Vector and the Classifier
#### 2.3.2 The Intelligent Fault Classification Method  
The fault classification method is another significant determinant factor for the timeliness and accuracy of fault diagnosis. Thus, it is crucial in designing a suitable fault classification method according to the specificity of the faults and the feature vector.  
For the REB fault diagnosis discussed in this study, the unique feature vector DFV not only had a very simple structure but also made the corresponding relation between the feature vectors and REB faults absolutely clear. In this case, the general classifiers all could get the satisfactory accuracy rate for the fault classification, such as BP, RBF, PNN, SVM and so on. By comparison, PNN is easy to train, has a better real-time performance, has a simpler structure and the calculation of PNN is simpler and faster [23]. Most importantly, the PNN was proved to be very suitable for classification problems of this type [23]. Consequently, this paper chose the PNN to design the intelligent fault classification method.  
The probabilistic neural network (PNN) based on the Parzen probabilistic density function and the Bayesian classification rule was proposed by [PERSON] [24]. The PNN had four layers, namely the input layer, the pattern layer, the summation layer and the output layer, and its basic structure is illustrated in Figure 10. As found in many studies [22; 23], the PNN is not only easy to train but also very excellent in timeliness. Furthermore, in the case of sufficient training samples, the PNN could get the optimal classification results. This is only a brief introduction of the PNN theory, and the detailed explanation was given by [PERSON] (1990) [24].
# Seismic Fragility Analysis of the Transmission Tower-Line System Considering Bol Slippage
## 3 Limit State Determination  
### Method  
Before the seismic fragility analysis, the limit states of the structure under different load strengths should be determined. The limit performance index of the TTLS is calculated using the pushover analysis method, where geometric nonlinearities are considered. To accurately evaluate the limit performance index of the tower, the pushover analysis conducted in this paper utilized three types of load application methods: uniform lateral load distribution, inverted triangle lateral load distribution, and height equivalent load distribution.  
Due to the lattice structure of transmission towers consisting of numerous members and joints, the transmission tower is usually divided into different sections along the height in the analysis, and each section is defined as a layer, as shown in Figure 5[27]. The uniform lateral load distribution means that the horizontal lateral force of each layer on the structure is proportional to the representative value of gravity. The calculation formula for the horizontal force loading coefficient \(\delta_{i}\) of the structure on the \(i\)th layer is as follows:  
\[\delta_{i}=G_{i}/\sum_{i=1}^{N_{c}}G_{i} \tag{4}\]  
where \(\delta_{i}\) is the ratio of the horizontal load increment on the \(i\)th layer to the total shear increment at the bottom; \(G_{i}\) is the representative value of the gravity on the \(i\)th layer; \(N_{c}\) is the total number of layers in the structure.  
The inverted triangle lateral load distribution means that the acceleration of each layer is linearly distributed along the height direction. The calculation formula for the horizontal force loading coefficient \(\delta_{i}\) of the structure is as follows:  
\[\delta_{i}=G_{i}H_{i}/\sum_{i=1}^{N}G_{i}H_{i} \tag{5}\]  
where \(H_{i}\) is the \(i\)th height of the structure.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Case No.** & **Seismic Wave** & **Recording Station** & **Magnitude (Moment Magnitude)** \\ \hline SW11 & Borah Peak\_ID-01 & ANL-767 Reactor Plant (Bsmt) & 6.88 \\ SW12 & Taiwan SMART1(45) & SMART1 C00 & 7.3 \\ SW13 & Superstition Hills-02 & Kornbloom Road (temp) & 6.54 \\ SW14 & Spitak\_Armenia & Gukasian & 6.77 \\ SW15 & Loma Prieta & APEEL 10-Skyline & 6.93 \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._  
Figure 4: Acceleration response spectrum comparison.  
The height equivalent load distribution considers the variation in acceleration along the height of each layer. The calculation formula for the horizontal force loading coefficient \(\delta_{i}\) is as follows:  
\[\delta_{i}=G_{i}H_{i}^{k}/\sum_{i=1}^{N}G_{i}H_{i}^{k} \tag{6}\]  
where \(k\) is the calculation coefficient, whose value is related to the elastic period of the basic vibration mode of the structure. When the basic period \(T\) is less than 0.5 s, \(k=1.0\); when \(T\) is greater than 2.5 s, \(k=2.0\); when \(T\) is between 0.5 s and 2.5 s, \(k\) is calculated by the linear interpolation method.
## 2 Materials and Methods  
### Sample Collection and Culture Conditions  
The centric diatom, _Cyclotella meneghiniana_ (homotypic synonym: _Stephancyclus meneghiniana_), was collected from the Rongjiang River (23\({}^{\circ}\)17\({}^{\prime}\) N, 116\({}^{\circ}\)47\({}^{\prime}\) E) on the southeast coast of China; it was isolated and cultured in F/2 medium at 15 \({}^{\circ}\)C [42]. Before the isolation, the medium was autoclaved at 121 \({}^{\circ}\)C for 20 min and cultivated in a 250 mL Erlenmeyer flask under illumination from the fluorescent lamp (40 umol m\({}^{-2}\) s\({}^{-1}\)) with light: dark cycles of 12 h:12 h until reaching the exponential stage. Subsequently, algal cells were introduced into 1000 mL conical containers filled with 400 mL of sterile medium, maintaining a salinity of 24.5%, and cultivated at 24 \({}^{\circ}\)C, mirroring the conditions of axenic culture. The collection of _C. meneghiniana_ cells occurred during their mid-exponential growth stage, involving a 20 min centrifugation at 2000\(\times\)\(g\) (Eppendorf Centrifuge 5810R, Hamburg, HH, Germany), followed by a 48 h freeze-drying at -70 \({}^{\circ}\)C prior to experimental use.  
Investigating improved growth conditions and nutrient environments for cell pigment generation, _C. meneghiniana_ was grown at 24 \({}^{\circ}\)C for 12 days under three distinct light intensities and nutrient levels. A total of eight different nutrient therapies were sustained, with the specifics provided in Table S1. The levels of vitamins remained consistent across all experimental environments. The various light intensity treatments are categorized as LL (low light, 45 umol photons m\({}^{-2}\) s\({}^{-1}\)), ML (medium light, 110 umol photons m\({}^{-2}\) s\({}^{-1}\)), HL (high light, 170 umol photons m\({}^{-2}\) s\({}^{-1}\)), nutrient concentrations are denoted as C (Control, 882.457 mM NaNO\({}_{3}\), 36.235 mM NaH\({}_{2}\)PO\({}_{4}\), 105.559 mM Na\({}_{2}\)SiO\({}_{3}\), 11.686 mM Na\({}_{2}\)EDTA, 11.654 mM FeCl\({}_{3}\), 909.504 mM MnCl\({}_{2}\), 76.506 mM ZnSO\({}_{4}\), 42.029 mM CoCl\({}_{2}\), 39.249 mM CuSO\({}_{4}\), 26.038 mM Na\({}_{2}\)MoO\({}_{4}\), 11.86 mM VB\({}_{1}\), 4.09 mM VB\({}_{7}\), 2.21 mM VB\({}_{12}\)), HN (High nitrate, 1588.422 mM NaNO\({}_{3}\)), MN (Mediumnitrate, 1176.609 mM NaNO\({}_{3}\)), HP (High phosphate, 253.642 mM NaH\({}_{2}\)PO\({}_{4}\)), MP (Mediumphosphate, 144.938 mM NaH\({}_{2}\)PO\({}_{4}\)), HS (High silicate, 316.678 mM Na\({}_{2}\)SiO\({}_{3}\)), HM (High trace metals, 80.593 mM Na\({}_{2}\)EDTA, 96.193 mM FeCl\({}_{3}\), 1263.2 mM MnCl\({}_{2}\), 208.652 mM ZnSO\({}_{4}\), 126.088 mM CoCl\({}_{2}\), 120.149 mM CuSO\({}_{4}\), 103.327 mM Na\({}_{2}\)MoO\({}_{4}\)), LC (lower concentration than F/2 medium, 588.305 mM NaNO\({}_{3}\), 21.741 mM NaH\({}_{2}\)PO\({}_{4}\), 52.78 mM Na\({}_{2}\)SiO\({}_{3}\), 5.722 mM Na\({}_{2}\)EDTA, 4.477 mM FeCl\({}_{3}\), 429.48 mM MnCl\({}_{2}\), 21.015 mM CoCl\({}_{2}\), 17.622 mM CuSO\({}_{4}\), and 13.226 mM Na\({}_{2}\)MoO\({}_{4}\)).
## 2 Related Works
In [17], the suggested protocol used the shuffling frog-leaping algorithm (SFLA) to pick relay nodes and CHs by optimizing two fuzzy rule tables. Firstly, FMSFLA designed fuzzy rules based on the parameters residual energy, distance to the BS, and the number of neighboring nodes to choose candidate CHs and ensure an appropriate distribution of CHs by specifying the overlap threshold between CHs. In the second phase, FMSFLA selected the relay nodes named parents from the candidate nodes based on the following parameters: energy, distance between each node, and the mean load of the selected route between the candidate CH node n and the BS. Simulation results demonstrated that FMSFLA can achieve a consistent network workload, lower energy consumption, and extend the network lifetime, but it requires off-line optimization. [PERSON] et al. [35] developed a fuzzy-based protocol by using an updated SFLA algorithm. They designed a new encoding scheme that utilized three fuzzy inference rules (FIS) to compute the odds of becoming a CH, the specific CH of a non-CH, and identifying an NH (next-hop node). EFC-ISFLA introduced the historical selection of nodes as CHs and defined an overlay boundary that sets the minimum allowable distance between adjacent CHs. In contrast to previous research [17], the improved SFLA (ISFLA) combined multiple OBL strategies to enhance the synergy between search algorithm advancements and exploratory capabilities. The comparison results showed that the protocol outperformed its competitors in terms of network stability, network lifetime, data transmission, and the correctness of test functions. Nevertheless, the superior performance of the protocol is accompanied by a significant increase in computation time, a more enormous load on the base station, and an increase in cost. In [36], the proposed designed Type-2 fuzzy logic system for CH selection was combined with a BFO (bacterial foraging optimization) routing algorithm. The node degree, node centrality, and energy are utilized as three fuzzy inputs to calculate the node's probability of becoming CH. However, the generated 27 rules still exhibit low accuracy in solving the objective function.
# Prediction of Transient Temperature Distributions for Laser Welding of Dissimilar Metals
###### Abstract  
Distribution of temperature during the welding process is essential for predicting and realizing some important welding features such as microstructure of the welds, heat-affected zone (HAZ), residual stresses, and their effects. In this paper, a numerical model was developed using COMSOL Multiphysics of dissimilar laser welding (but joint) of AISI 316L and Ti6 Al4V thin sheet of 2.5 mm thickness. A continuous mode (CW) fiber laser heat source of 300 W laser power was used for the present study. A time-dependent prediction of temperature distributions was attempted. The heat source was assumed as a Hermit-Gaussian analytical function with a moving velocity of 120 mm/min. Both convective and radiant heat loss and phase change of the materials were considered for the analysis. In addition, variation of temperature-dependent material properties was also considered. The maximum and minimum temperature for the two materials at different times and the temperature in the different penetration depths were also predicted. It was found that the average temperature that can be achieved in the bottom-most surface near the weld line was more than 2400 K, which justifies the penetration. Averages of maximum temperatures on the weld line at different times at the laser spot irradiation were identified near 3000 K.The temperature fluctuation near the weld line was minimal and decreased more in the traverse direction. Scanning with a displaced laser relative to the interface toward the Ti6 Al4V side reduces the maximum temperature at the interface and the HAZ of the 316L side. All of these predictions agree well with the experimental results reported in current literature studies.  
numerical model; dissimilar; time-dependent; laser heat source; thermal properties +
Footnote †: journal: Physics Letters B