esa-sceva/satcom-chunk-collection huggingface dataset:
# Reconstruction and Measurement of Irregular Karst Caves Using BLST along the Shield Metro Line
## 5 Case Study in Jinan Metro
### Detection of Complex Karst Caves and Three-Dimensional Data Acquisition
A half water-filled cave with a volume of 3.15 m\({}^{3}\) was measured at borehole location 24#, at a depth of about 28.6 m; the three-dimensional model of the cave is shown in Figure 12e, recorded as KC5. A water-filled cave with a volume of 3.71 m\({}^{3}\) was measured at borehole location 25#, at a depth of about 22.7 m; the three-dimensional model of the cave is shown in Figure 12f, recorded as KC6. According to the above-mentioned detection results, the exact spatial position of the cave was obtained, including the corresponding horizontal coordinates and depth. Combining the tunnel route design diagram, the relative spatial position relationship between the karst cave and the tunnel was calculated. Figure 13a shows the exact orientation and distance of the karst cave in the circumferential direction of the tunnel; while Figure 13b shows the exact position of the karst cave in the excavation range in front of the tunnel, and the diagram of the vertical section and plane position between the cave and the tunnel. According to the spatial position relationships between karst caves and the tunnel, the distances and orientations between the karst caves and the tunnel were calculated, as shown in Table 4.  
Figure 12: Irregular karst cave model: (**a**) borehole 17#; (**b**) borehole 20#; (**c**) borehole 21#; (**d**) borehole 23#; (**e**) borehole 24#; (**f**) borehole 25#.
# An Automatic ECG Signal Quality Assessment Method Based on Resnet and Self-Attention
## 3 Materials and Methods
### Proposed Self-Attention Module  
Considering that the correlation between individual heartbeats within a regular ECG segment can be used as an auxiliary judgment condition for ECG signal quality assessment, we designed a self-attention module based on the non-local idea for obtaining the dependencies between heartbeats within an ECG segment [46]. As can be seen in Figure 2, there is a clear difference between regular heartbeats and heartbeats with noise interference. Among them, there are obvious similar features between individual heartbeats within the ideal heartbeat fragment. Therefore, the attention module designed in this paper obtains the dependence information between heartbeats within a fragment by calculating the pairwise similarity relationship between channels to obtain the attention graph and then aggregating all features by weighting them to obtain the remote dependence information between channels. For regular ECG signals, there are obvious similarity features between each heartbeat, while, for random noisy signals, these similarity features are not obvious. Examples of unacceptable and acceptable samples are shown in Figure 2.  
Figure 3 illustrates the structure of this self-attention module. Suppose the input feature \(F_{input}\) dimension is \(\text{BxCxW}\), the feature map is reduced to \(1/\text{r}\) of the original number of channels by a \(1\times 1\) convolution to obtain a matrix \(F_{cont1}\). The convolved data is transposed to obtain \(F_{trans}\), the attention matrix \(F_{sa}\) (inter-channel feature similarity matrix) is obtained after the dot product operation, and then the attention matrix is multiplied with \(F_{conv1}\) to obtain the feature \(F_{conv2}\) (with the same dimension as \(F_{conv1}\)) with self-attention information. Finally, a \(1\times 1\) convolution module is employed to obtain the output data \(F_{output}\) with the same dimension as the input data. Therefore, this module can be added to the structure of most deep networks as a flexible attention module.  
Figure 1: Structure diagram of the signal quality assessment model.  
Figure 3: Self-attention module.  
Figure 2: (**a**) Unacceptable sample diagram; (**b**) acceptable sample diagram.  
Equations (6)-(9) show the computational principle of the self-attention module. \(x\) represents the feature matrix of the input self-attention module, \(W_{z}\) and \(W_{g}\) can here be considered as a convolution operation to change the number of data channels, and \(f\big{(}x_{i},x_{j}\big{)}\) represents the dot product operation to obtain the self-attention matrix. We set the normalization factor as \(C(x)=N\), where \(N\) is the number of positions in \(x\).  
\[z_{i}=W_{z}y_{i} \tag{6}\]  
\[y_{i}=\frac{1}{C(x)}{\sum_{\forall j}}f\big{(}x_{i},x_{j}\big{)}g\big{(}x_{j} \big{)} \tag{7}\]  
\[g\big{(}x_{j}\big{)}=W_{g}x_{j} \tag{8}\]  
\[f\big{(}x_{i},x_{j}\big{)}=\theta(x_{i})^{T}\varphi\big{(}x_{j}\big{)} \tag{9}\]
# Super-Resolution Reconstruction of an Array Lidar Range Profile
## 2 Methods
### Improved POCS Method
#### 2.2.1 Motion Estimation by the Lucas-Kanade Optical Flow Method  
The optical flow method estimates the motion state of the target in the image by using the temporal and spatial variations in the distance values in the range profiles of two adjacent frames and their corresponding distance values to obtain the motion vector field. The LK optical flow method is based on the optical flow field and assumes that the optical flow vectors are constant in the region centered on a single point. Set the distance value of point \((x,y)\) at time \(t\) as \(I(x,y)\). This point reaches point \((x+dx,y+dy)\) at \(t+dt\) time, and the corresponding distance value is \(I(x+dx,y+dy)\). According to the image consistency hypothesis, when \(dt\to 0\), the distance value of each pixel of the range profile remains unchanged, that is:  
\[I(x,y,t)=I(x+dx,y+dy,t+dt) \tag{5}\]  
We expanded the right-hand side of Equation (5) by a Taylor series expansion, as in:  
\[\frac{\partial I}{\partial x}\frac{dx}{dt}+\frac{\partial I}{\partial y}\frac {dy}{dt}+\frac{\partial I}{\partial t}=I_{x}\frac{dx}{dt}+I_{y}\frac{dy}{dt}+I _{t}=0 \tag{6}\]  
Let \(u=\frac{dx}{dt}\) and \(v=\frac{dy}{dt}\) be expressed in vector form, as in:  
\[\
abla I\cdot U+I_{t}=0 \tag{7}\]  
\(\
abla I=[I_{x},I_{y}]\) denotes the direction of the gradient from the image and \(U=[u,v]^{T}\) indicates the optical flow vector. We used the least square method to solve \(U=[u,v]^{T}\).
# Boosting the Performance of CDCL-Based SAT Solvers by Exploiting Backbones and Backdoors
## 3 Related Work
### MapleLCMDistClronoBT-DL-v3 and LSTech
This value of _stthreshold_ is set at 300, but if the SLS solution does not improve, it increases; otherwise, it decreases, keeping the value above 300 (lines 24-27). If the SLS solver fails to find a satisfying assignment within certain limits, then the solver goes back to where it was interrupted (line 28). ```
Input:\(\phi\) is a CNF formula with variables \(x\in X\) Output:\(\textit{satisfiable}\) and \(\textit{solution}\), otherwise unsatisfiable
1 Initialize() whilenotAllvariablesAssienced()do
2\((\phi,\textit{status},\textit{propagations})\leftarrow\)BooleanConstraintPropagation(\(\phi\),\(\textit{solution}\))// returns the status true ifthere is a conflict, false otherwise, and the number of propagations ifstatus is out/leftthen
3\(\textit{call}\textit{lifts}\leftarrow\textit{Control}\textit{lifts}+1\) (\(\textit{block}\), learntClauseYars,\(\textit{conf}\textit{slideYars})\leftarrow\)ConflictAnalysis()// build the learnt clause \(\phi\)\(\phi\)\(\phi\)\(\psi\) learntClause and return the new decision level AfterConflictAnalysis( learntClauseYars,\(\textit{conf}\textit{lideYars}\)) iflevel is equal to 0 then returnunsatisfiable
4 else
5\(\textit{solution}\leftarrow\)Backtrack(\(\textit{block}\),\(\textit{solution}\))// backtrack to the new decision level decisionLevel \(\leftarrow\)\(\textit{block}\) OnUnassiscn(\(x\))// called when variable x is unassigned by backtracking or restart
6
7 elseifrestart condition is triggeredthen
8\(\textit{solution}\leftarrow\)Backtrack(\(0\),\(\textit{solution}\))// backtrack to the new decision level OnUnassiscn(\(x\))// called when variable x is unassigned by backtracking or restart
9
10
11 elseif\(\textit{conf}\textit{lide}<50,000\)then
12\(\textit{branchMode}\leftarrow\textit{Dist}\)
13
14 else
15 if\(\textit{approgations}\leq\textit{threshold}\)// threshold value initialized to 30,000,000 then
16\(\textit{branchMode}\leftarrow\textit{LRB}\)
17
18 else
19\(\textit{branchMode}\leftarrow\textit{VSIDS}\)
20
21 if\(\textit{branchMode}\) has switched to a new heuristicthen
22\(\textit{threshold}\leftarrow\textit{threshold}+\textit{threshold}\times 0.1\) #propagations \(\leftarrow\) 0// #propagations is reset to zero each time branchMode has switched to a new heuristic \(x\leftarrow\)PickRranchingVariable()// the decision heuristic is based on branchMode OnAssiscn()// called when variable x is assigned by branching or propagations decisionLevel \(\leftarrow\) decisionLevel \(+1\)// increment decision level due to new decision \(\textit{solution}\leftarrow\textit{solution}\cup(x\textit{value})\) return(\(\textit{satisfiable}\),\(\textit{solution}\))
```  
**Algorithm 1**Pseudocode for top-level implementation of MapleLCMDistChronoBT-DL-v3.
## 1 Motivation
However, we still rely on Web of Science, as a visual inspection revealed that only a few recent papers can be found with Google Scholar, but not in Web of Science. Furthermore, an in-depth comparison of academic databases is a complex endeavor, which is beyond the scope of this paper. However, we are aware of this limitation regarding the presented review. In the second step of a bibliometric analysis, one must specify the set of search strings to be applied to the used academic database (cf. Figure 1, 2). In the context, we applied the following search strings to Web of Science: _predictive maintenance_, _condition monitoring_, _smart maintenance_, _lean maintenance_, _reliability-centered maintenance_, and _predictive analytics_. Those search strings were defined after an open search on various maintenance techniques. All those techniques can be also applied in the field of _Engineering Asset Management_ ([13; 14]). Concerning the 12 functions of general asset management, as introduced by [15], we are mainly interested in Risk Management, Condition Monitoring, Asset Usage Life Cycle, Performance Measures, and Information Systems. For example, the integration of a maintenance method into asset management, e.g., by using condition monitoring [16], represents one typical workflow that is relevant for us. After applying the search strings to Web of Science, we exported the results (cf. Figure 1, 3) to apply several preprocessing steps (cf. Figure 1, 4-5) to remove unfitting results (e.g., maintenance in a medical context) from the extracted data. Following this, the cleansed data was fed into a software for bibliometric analysis. As depicted in Figure 1, the performed calculations and related interpretations comprise five steps (i.e., Steps 5-10 in Figure 1). The obtained results, in turn, provide a valuable overview for state of the art maintenance systems in general and predictive maintenance in particular. Additionally, we were able to identify fundamental (i.e., distinguished) papers in this field. Moreover, to the best of our knowledge, the work at hand is the first one that applied bibliometric analysis in reviewing the research field of equipment maintenance systems. In addition, our results underlie that bibliometric analyses are able to gain valuable insights. The remainder of this paper is structured as follows: Section 2 provides deeper insight into the methodology applied, i.e., the bibliometric analysis. The result set obtained from the search strings applied to Web of Science are discussed in Section 3. In Section 4, results of the bibliometric analysis are presented. In turn, Section 5 introduces a novel measure to rank single articles in a multi-dimensional way. Section 6 predicts research trends based on the past development of related topics, whereas Section 7 discusses threats to validity. Finally, Section 8 concludes the paper and gives an outlook. Figure 1: Workflow of a Bibliometric Analysis.
# Hospital Readmission and Length-of-Stay Prediction Using an Optimized Hybrid Deep Model
## 4 Experimental Results
### Performance Analysis
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Accuracy** & **Sensitivity** & **Specificity** & **F1-Measure** & **Precision** \\  & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline GAOCNN & 99.4 & 99.4 & 99.8 & 99.4 & 99.4 \\ VGG16 & 14.1 & 14.6 & 20.5 & 14.6 & 14.6 \\ ResNet & 12.7 & 12.7 & 17.8 & 12.7 & 12.7 \\ LR & 92.1 & 92.1 & 98.8 & 92.1 & 92.3 \\ RF & 89.3 & 89.3 & 95.6 & 89.2 & 89.1 \\ XGB & 91.4 & 91.4 & 98.4 & 91.4 & 91.3 \\ SVM & 84.7 & 84.7 & 92.8 & 84.7 & 84.8 \\ CNN + LR & 70.3 & 70.3 & 89.9 & 70.2 & 70.6 \\ CNN + RF & 87.3 & 87.3 & 96.1 & 87.3 & 87.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of the LOS prediction using different models for COVID-19 patients. For better observation of the performed prediction tasks using GAOCNN, we compute the model's normalized confusion matrix [54]. For readmission prediction in diabetic patients, the confusion matrix is shown in Figure 7. The result shows that there is just a 3% chance of incorrect prediction for the patients who are readmitted within 30 days. Furthermore, for the patients who are readmitted after 30 days, the error rate is 5%. For the LOS, the normalized confusion matrix of the prediction in diabetic, COVID-19, and ICU patients is shown in Figures 7-9, respectively. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Accuracy** & **Sensitivity** & **Specificity** & **F1-Measure** & **Precision** \\  & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline GAOCNN & 94.1 & 94.0 & 98.8 & 94.2 & 94.5 \\ VGG16 & 10.1 & 10.1 & 20.6 & 10.1 & 10.1 \\ ResNet & 8.7 & 28.7 & 8.9 & 17.7 & 17.7 \\ GoogLeNet & 17.7 & 15.9 & 42.6 & 25.2 & 60.1 \\ LR & 43.9 & 43.9 & 65.1 & 38.4 & 36.2 \\ RF & 76.1 & 76.1 & 89.6 & 76.1 & 76.0 \\ XGB & 83.5 & 83.5 & 93.7 & 83.3 & 83.2 \\ SVM & 56.0 & 59.4 & 83.3 & 56.1 & 56.0 \\ CNN + LR & 43.6 & 43.6 & 72.7 & 42.4 & 41.8 \\ CNN + RF & 80.9 & 80.9 & 90.6 & 80.9 & 81.0 \\ CNN + XGB & 83.2 & 83.2 & 96.5 & 83.1 & 82.9 \\ CNN + SVM & 39.8 & 39.8 & 59.0 & 39.3 & 39.6 \\ SGANs & 56.1 & 45.7 & 92.6 & 54.5 & 67.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results of LOS prediction using different models for ICU patients. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Accuracy** & **Sensitivity** & **Specificity** & **F1-Measure** & **Precision** \\  & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline CNN + XGB & 87.7 & 87.7 & 96.2 & 87.8 & 88.6 \\ CNN + SVM & 81.3 & 81.3 & 92.5 & 81.3 & 81.8 \\ SGANs & 93.5 & 93.3 & 98.8 & 93.6 & 93.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._  
Figure 7: The normalized confusion matrix; (**a**) readmission prediction in diabetic patients, (**b**) LOS prediction in diabetic patients.
# Microbial Bioremediation and Biodegradation of Petroleum Products--A Mini Review
## 3 Microbial Diversity and Ecology
The comparison of the selective high-throughput sequencing methods is shown in Table 1.  
Sequencing is complete, as seen in Figure 4; they are then analyzed using bioinformatic methods. Gas chromatography-flame ionization detection (GC-FID) is the most frequently used procedure to detect residual PHs during microbial degradation. In gas chromatography, nitrogen, hydrogen, or helium are inert gas carriers [21]. Carrier gas keeps the aqueous liquids or gas mixtures measured in a capillary column on a detector with boiling points below 400 C. In the case of complex mixtures, this enables better resolution of the components. This method monitors the substance of TPHs in the C10 to C40 range (n-alkanes) from solids such as waste and soil. The GC-FID with detection limits of 10 mg TPHs per kg soil is used \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Technique**} & \multirow{2}{*}{**Read Length**} & \multirow{2}{*}{**Precision**} & \multirow{2}{*}{**Running Time**} & \multicolumn{2}{c}{**Cost Per 1**} & \multirow{2}{*}{**Merits**} & \multirow{2}{*}{**Demerits**} \\  & & & & & **Pairs in US\$ & & \\ \hline Synthesis & 50–500 bp: & & & & High & concentrations of \\ sequencing & HiSeq 2500 & 99.9\% & 1–11 days & 0.005–0.15 & High sequence & DNA require \\  & & & & & yield potential & high-cost \\ Ion & & & & & & equipment \\ semiconductor & 400 bp & 98\% & 2 h & 1 & Fast, high-cost & Homopolymer \\ (Ion Torrent) & & & & & equipment & errors \\ Pyrosequencing & 700 bp & 99.9\% & 24 h & 10 & Fast, long read & Homopolymer \\ Chain & & & & & size & errors, runs are costly \\ termination & 400–900 bp & 99.9\% & 20 min to 3 h & 2400 & Useful for many & Time-consuming \\  & (Sanger) & & & & applications, long & step of PCR, \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of selective high-throughput sequencing methods. Adapted from [PERSON] & Scalvenzi [21]. for both qualitative and quantitative applications. Another technique that mixes GC with mass spectroscopy (MS) is GC-MS [30]. Due to its flexibility in calculating PAH and TPH, the MS is considered a ubiquitous detector. Another analytical approach to characterize PHs is infrared spectroscopy (IR). The method is such that a spectrum is formed at the point where it holds energy in the IR region of the electromagnetic spectrum with bending and stretching vibrations associated with a molecule [22; 29]. Hydrocarbon derivative spectra mainly derive from overtones or combinations of C-H stretching vibrations of the aromatic C-H or terminal CH3 and saturated CH2 functional groups [21; 30]. Therefore, IR-based detection is beneficial in elucidating stem PHs and remaining functional groups during microbial degradation. In the biopile system of contaminated crude oil desert soils, Fourier transforms infrared spectroscopy (FTIR) and gravimetric methods have also been used to measure TPHs [31].
## 3 Model and Assumptions
### Communication  
Communication links between two sensor nodes are established based on the distance between the nodes. Two sensor nodes \(m\) and \(n\) are able to communicate with each other if the distance between the nodes is no more than their communication radius, and as noted above, this communication is bidirectional. From our empirical analysis, we note that the sensor network formed using the Gaussian deployment model, with parameters as listed in the previous paragraph, yields a connected (undirected) graph in the sensor field. We also note that while there may exist a communication link between two nodes \(m\) and \(n\), whether or not the nodes actually exchange messages with each other depends on each node's determination of the other node's authenticity status and will be discussed in detail in Section 5.
## 2 Materials and Methods
### Monitoring Devices  
The IQM device is an online delivery monitoring system, checking in real time the accuracy of the radiation fluence. The device consists of a large area transmission chamber, designed to be mounted on the linac head. An electric field gradient is generated by the inclined chamber electrode plates, providing a non-uniform charge collection linearly varying in the direction parallel to the multi-leaf collimator's motion. During the irradiation, a spatially sensitive dose-area product is generated and reported in arbitrary units (counts). A picture of an IQM detector mounted on a linac head is shown in Figure 2.  
The IQM software allows an easy comparison of the acquired measurements with reference data to detect possible deviations from nominal parameters. The system has also an integrated inclinometer to measure collimator and gantry angles. Details of the detector described by several authors can be found in reference [40].  
The SoftDiso system is a piece of online monitoring software reconstructing the dose by using the transmission signal measured by an EPID device. The reconstructed dose is compared with the  
Figure 1: A scheme of measurement setup is displayed.  
Figure 2: **Left. The Integral Quality Monitor detector (IQM) system mounted on the linac head. Right. Alderson RANDO phantom with two silicon prostheses to simulate the female anatomy.**planned dose through an R-value, defined as the ratio of the measured to calculated doses at the isocenter point (Diso/DTPS) [32; 33]. To check the inter-session patient position reproducibility and to identify possible anatomical variations over the whole treatment, a 2D gamma-index analysis tool compares an EPID reference image (usually acquired during the first/second session) with treatment images by using global gamma passing rate (\(\gamma_{\text{PR}}\)) and gamma mean value (\(\gamma_{\text{mean}}\)) indexes.
## 6 Simulation Results and Discussions
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**S. No.** & **Anchor Node** & **Virtual Node-1** & **Virtual Node-2** & **Virtual Node-3** & **Virtual Node-4** & **Virtual Node-5** & **Virtual Node-6** \\ \hline TN-1 & 4.729 & 7.985 & 9.856 & 8.772 & 6.018 & 1.577 & 3.367 \\ TN-2 & 4.139 & 4.323 & 7.688 & 8.134 & 8.316 & 5.441 & 1.365 \\ TN-3 & 7.510 & 8.355 & 3.779 & 7.192 & 10.886 & 13.368 & 12.718 \\ TN-4 & 3.931 & 6.464 & 7.867 & 7.640 & 5.321 & 2.525 & 3.192 \\ TN-5 & 8.458 & 4.030 & 8.776 & 12.685 & 13.341 & 11.129 & 6.457 \\ TN-6 & 4.965 & 4.222 & 1.745 & 6.324 & 9.246 & 9.854 & 7.953 \\ TN-7 & 4.733 & 7.308 & 9.566 & 9.254 & 6.621 & 2.354 & 2.589 \\ TN-8 & 5.270 & 0.785 & 4.340 & 7.893 & 9.250 & 8.389 & 5.406 \\ TN-9 & 8.533 & 12.497 & 10.032 & 6.339 & 3.873 & 8.570 & 12.363 \\ TN-10 & 6.453 & 8.393 & 11.631 & 10.231 & 9.030 & 4.347 & 1.784 \\ TN-11 & 6.354 & 6.268 & 9.177 & 10.846 & 10.592 & 6.973 & 2.082 \\ TN-12 & 5.699 & 4.595 & 0.9851 & 5.631 & 9.0543 & 10.295 & 8.787 \\ TN-13 & 7.303 & 12.674 & 10.971 & 6.131 & 2.422 & 6.640 & 10.730 \\ TN-14 & 7.284 & 11.681 & 11.222 & 6.837 & 2.698 & 6.369 & 10.722 \\ TN-15 & 4.493 & 4.132 & 7.995 & 9.758 & 9.046 & 6.052 & 1.576 \\ TN-16 & 4.764 & 7.591 & 9.557 & 9.010 & 6.343 & 2.028 & 2.893 \\ TN-17 & 8.208 & 4.642 & 9.757 & 12.534 & 12.685 & 9.636 & 4.797 \\ TN-18 & 3.428 & 2.189 & 2.194 & 5.394 & 7.747 & 8.225 & 6.786 \\ TN-19 & 8.253 & 13.861 & 9.874 & 5.079 & 4.091 & 8.979 & 12.438 \\ TN-20 & 5.467 & 8.793 & 4.997 & 0.563 & 5.384 & 9.057 & 10.466 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Distance between target nodes and anchors deployed in the area. Figure 9: Localization using BBO algorithm for five distinct movements. Figure 10: Localization using PSO algorithm for five distinct movements. Figure 11: Localization using FA algorithm for five distinct movements. Figure 12: Localization using HPSO algorithm for five distinct movements. Figure 13: Localization using mLebTLBO algorithm for five distinct movements. These optimization methods were simulated for five distinct movements after a specified interval, as shown in the Figures 9-13, and the entire process repeats itself for each interval. For each optimization strategy, the '+' sign in the fifth movement represents the best predicted localized nodes. As shown in Figure 13, mLebTLBO has a low localization error when contrasted to alternative meta-heuristic strategies. Table 5 outlines the findings from the mobility of target nodes in the first five locations, demonstrating an enhancement in mean localization error. Localization is done with the use of an individual anchor in this work, which saves energy because no three real anchors are used. The lower localization error is achieved at the cost of higher computational complexity because of the complex mLebTLBO algorithm.