esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Effects of Different Land Use Patterns on \(K_{os}\)
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Depth (cm)** & **Treatments** & **Stable Carbon** & **CA** & **CAI** & **CPI** & **CPMI** \\ \hline \multirow{8}{*}{0–20} & Reference & 14.71 & 0.08 & & \\ \cline{2-6}  & SC & 15.25 \(\pm\) 0.20 d & 0.11 \(\pm\) 0.02 a & 1.37 \(\pm\) 0.23 a & 1.07 \(\pm\) 0.00 d & 147.23 \(\pm\) 24.89 a \\  & MC & 16.18 \(\pm\) 0.08 c & 0.10 \(\pm\) 0.01 a & 1.28 \(\pm\) 0.16 a & 1.13 \(\pm\) 0.02 c & 144.45 \(\pm\) 20.38 a \\  & MSR & 13.64 \(\pm\) 0.44 e & 0.11 \(\pm\) 0.03 a & 1.33 \(\pm\) 0.40 a & 0.96 \(\pm\) 0.05 e & 128.16 \(\pm\) 44.03 a \\  & GB & 20.26 \(\pm\) 0.62 b & 0.09 \(\pm\) 0.00 a & 1.15 \(\pm\) 0.02 a & 1.40 \(\pm\) 0.04 b & 161.18 \(\pm\) 5.89 a \\  & FB & 25.23 \(\pm\) 0.24 a & 0.08 \(\pm\) 0.01 a & 0.96 \(\pm\) 0.07 a & 1.72 \(\pm\) 0.02 a & 165.78 \(\pm\) 13.81 a \\ \hline \multirow{8}{*}{20–40} & SC & 13.40 \(\pm\) 0.78 d & 0.08 \(\pm\) 0.00 a & 1.02 \(\pm\) 0.03 a & 0.92 \(\pm\) 0.06 d & 93.66 \(\pm\) 8.58 a \\  & MC & 14.38 \(\pm\) 0.25 c & 0.06 \(\pm\) 0.01 b & 0.69 \(\pm\) 0.11 b & 0.96 \(\pm\) 0.02 c & 66.82 \(\pm\) 12.29 b \\  & MSR & 12.56 \(\pm\) 0.24 d & 0.06 \(\pm\) 0.00 b & 0.79 \(\pm\) 0.06 b & 0.85 \(\pm\) 0.02 d & 66.73 \(\pm\) 6.46 b \\  & GB & 17.62 \(\pm\) 0.81 b & 0.07 \(\pm\) 0.00 b & 0.81 \(\pm\) 0.02 b & 1.19 \(\pm\) 0.05 b & 96.54 \(\pm\) 2.70 a \\  & FB & 21.12 \(\pm\) 0.89 a & 0.06 \(\pm\) 0.00 b & 0.76 \(\pm\) 0.03 b & 1.42 \(\pm\) 0.06 a & 108.37 \(\pm\) 9.39 a \\ \hline \multirow{8}{*}{40–60} & SC & 12.68 \(\pm\) 0.49 c & 0.05 \(\pm\) 0.00 b & 0.64 \(\pm\) 0.06 b & 0.84 \(\pm\) 0.04 c & 54.53 \(\pm\) 7.19 b \\  & MC & 12.16 \(\pm\) 0.40 c & 0.05 \(\pm\) 0.01 ab & 0.67 \(\pm\) 0.07 ab & 0.81 \(\pm\) 0.03 c & 54.75 \(\pm\) 7.43 b \\ \cline{1-1}  & MSR & 10.57 \(\pm\) 0.74 d & 0.06 \(\pm\) 0.00 ab & 0.70 \(\pm\) 0.06 ab & 0.71 \(\pm\) 0.05 d & 49.63 \(\pm\) 7.85 b \\ \cline{1-1}  & GB & 16.37 \(\pm\) 0.45 b & 0.06 \(\pm\) 0.00 a & 0.78 \(\pm\) 0.05 a & 1.10 \(\pm\) 0.03 b & 86.00 \(\pm\) 8.40 a \\ \cline{1-1}  & FB & 17.68 \(\pm\) 0.20 a & 0.06 \(\pm\) 0.00 a & 0.77 \(\pm\) 0.06 a & 1.19 \(\pm\) 0.02 a & 91.51 \(\pm\) 8.56 a \\ \hline \hline \end{tabular} Note: CA: carbon pool active degree; CAI: carbon pool activity Index; CPI: carbon pool index; CPMI: carbon pool management index. The reference value of labeling organic carbon is 1.09 g\(\cdot\)kg\({}^{-1}\). The experiment treatments included continuous soybean cultivation (SC), continuous maize cultivation (MC), maize–soybean rotation (MSR), grass belt (GB), and forest belt (FB); The lowercase letters represent significant differences among different treatments (\(p<0.05\)). \(n=3\). The same is below.  
\end{table} 
Table 3: Effects of different land use patterns on carbon pool management index (CPMI) of soil humus layers.
## 2 Dataset and Methodology
### Evaluation  
The validation methods can be classified into two main categories.  
1. K-fold cross-validation involves splitting the dataset into k parts. While one part is reserved for testing, the other will be used in training. This is repeated k times, and a different part is used each time. This method reports higher accuracy but struggles with generalization.
2. Leave-One-Subject-Out (LOSO) cross-validation is a variant of k-fold cross-validation, but one subject is left out of the entire fold for testing. The approach employs a testing subset of size \(p\) and a training subset of size \(n-p\), where \(p\) retains all samples from a specific subject together. This process is iterated \(n\) times, ensuring that a given subject is not simultaneously present in both testing and training sets. This approach facilitates model generalization by incorporating data from diverse subjects.  
All of the trained models are validated using k-fold and LOSO cross-validation. For k-fold, the k value was set to 8, meaning each time, 7 of 8 of the data points were used for training, and 1 of 8 was used for testing. This value was set to match the training data for LOSO for standardized comparison. For LOSO, each time, seven of the eight subjects' data were used for training, and the remaining one was used for testing.  
Accuracy, precision, and recall were used to calculate the performance metrics of all the approaches. While accuracy evaluates correct classifications, precision and recall measure performance in the predicted classes.
# Stress-Dilatancy Behavior of Alluvial Sands
## 5 Elasticity Parameters  
Assuming the elastic behavior of sand at the onset of shear, the elastic parameters can be calculated using the formulae  
\[G^{*}=\delta q/3\delta\epsilon_{q}, \tag{19}\]  
\[K^{*}=\delta p^{\prime}/\delta\epsilon_{v}, \tag{20}\]  
\[v^{*}=\frac{3K^{*}-2G^{*}}{2(3K^{*}+G^{*})}. \tag{21}\]  
A superscript index (*) has been introduced to distinguish elastic parameters for small strains (\(\overline{\epsilon}_{a}=0.1\%\)), calculated from Equations (16)-(18), from elastic parameters for very small strains obtained by analyzing the propagation of the shear and compression wave velocities in the sample (Table 3). The values \(G^{*}\) and \(K^{*}\) are usually smaller than \(G\) and \(K\)[15].  
The differences can be expressed by  
\[\alpha_{G}=G/G^{*}, \tag{22}\]  
\[\alpha_{K}=K/K^{*}. \tag{23}\]  
The elastic parameters for small and very small strains are presented in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Test** & **TXCD 2/17.5** & \multicolumn{3}{c}{**TXCD 5/22.5**} & \multicolumn{3}{c}{**TXCD 7/14.5**} & \multicolumn{3}{c}{**TXCD 20/7**} \\ \hline \(\sigma_{3}^{\prime}\) (kPa) & 170 & 370 & 220 & 420 & 720 & 75 & 150 & 350 & 70 & 470 \\ \hline \(\Delta\epsilon_{a}\) (\%) & 0.095 & 0.384 & 0.209 & 0.003 & 0.222 & 0.002 & 0.808 & 0.529 & 0.001 & 0.007 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The \(\Delta\epsilon_{a}\) values for the analyzed tests.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Test** & **TXCD 2/17.5** & \multicolumn{3}{c}{**TXCD 5/22.5**} & \multicolumn{3}{c}{**TXCD 7/14.5**} & \multicolumn{3}{c}{**TXCD 20/7**} \\ \hline \(\sigma_{3}^{\prime}\) (kPa) & 170 & 370 & 220 & 420 & 720 & 75 & 150 & 350 & 70 & 470 \\ \hline \(\Delta\epsilon_{a}\) (\%) & 0.095 & 0.384 & 0.209 & 0.003 & 0.222 & 0.002 & 0.808 & 0.529 & 0.001 & 0.007 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Elastic parameters for tested sands.  
Usually, it is assumed that the elastic parameters of sand depend on the void ratio (\(e\)) and effective mean stress (\(p^{\prime}\)) [16]. In this paper, it is assumed that the elasticity parameters at very small strains can be approximated by the following formulae:  
\[G_{appr}=A_{G}\ p_{a}\ \frac{(e_{G}-e)^{2}}{1+e}\bigg{(}\frac{p^{\prime}}{p_{a}} \bigg{)}^{a_{G}}, \tag{24}\]  
\[K_{appr}=A_{K}\ p_{a}\ \frac{(e_{K}-e)^{2}}{1+e}\bigg{(}\frac{p^{\prime}}{p_{a}} \bigg{)}^{a_{K}}, \tag{25}\]  
\[v_{appr}=\frac{3K_{appr}-2G_{appr}}{2\big{(}3K_{appr}+G_{appr}\big{)}}, \tag{26}\]  
where \(A_{G}=220\), \(A_{K}=520\), \(e_{G}=e_{K}=2.87\), \(a_{G}=0.55\), \(a_{K}=0.75\), and the atmospheric pressure \(p_{a}=101\) kPa. The parameters \(e_{G}\) and \(a_{G}\) are very similar to those given by [PERSON] and Richart [16] (\(e_{G}=2.97\) and \(a_{G}=0.5\)). \(G\), \(K\), \(G_{appr}\), and \(K_{appr}\) are shown in Figure 3.  
The parameters \(G=G_{appr}\) and \(K=K_{appr}\) were used for further analysis (Figure 3).  
Figure 3: The shear and bulk moduli of the tested sand: (**a**) \(G_{appr}-G\); (**b**) \(K_{appr}-K\).
## 3 Design and Simulation of BP-PID-Smith Based Controller for pH Regulation System
### Design of PID-Smith Prediction Compensator  
When the model cannot be matched exactly, at this time \(G_{p}^{*}(s)\
eq G_{p}(s)\), \(\tau_{1}\
eq\tau_{2}\), the overall closed-loop transfer function of the system is:  
\[\frac{Y(s)}{X(s)}=\frac{G_{c}(s)G_{p}(s)e^{-\tau_{1}s}}{1+G_{c}(s)\left[G_{p}^{* }(s)+G_{p}(s)e^{-\tau_{1}s}-G_{p}^{*}(s)e^{-\tau_{2}s}\right]} \tag{27}\]  
From the above equation, it can be seen that when the deviation between the actual model and the theoretical model is large, there is a lag term in the characteristic equation of the system, which makes the Smith controller unable to correct the error between the actual model and the theoretical model even though, which may eventually cause the output signal of the system to oscillate and diverge. Therefore, [PERSON]'s prediction compensation is not suitable for the case where the theoretical model has a large deviation from the actual model.  
The PID-Smith prediction control adds a suitable PID compensation controller to avoid the time lag term in the closed-loop characteristic equation and the model mismatch, which eventually leads to the oscillation and divergence of the output signal, thus reducing the effect of the time lag term on the system, and the structure of the PID-Smith prediction compensator is shown in Figure 6.  
Where \(G_{c2}(s)\) and \(G_{p}^{*}(s)\) together form the compensation controller, \(G_{p}(s)e^{-\tau s}\) is the system with delay model, and \(G_{c1}(s)\) is the main controller model.  
From Figure 6, the closed-loop transfer function of the system is:  
\[\frac{Y(s)}{X(s)}=\frac{G_{c1}(s)G_{p}(s)e^{-\tau s}}{1+G_{c1}(s)G_{p}^{*}(s) \frac{1+G_{p}(s)G_{c2}(s)e^{-\tau s}}{1+G_{c2}(s)e^{-\tau s}G_{p}^{*}(s)}} \tag{28}\]  
Figure 5: Simulation model of pH regulation system based on [PERSON]’s prediction compensation.  
Figure 6: Structure of PID-Smith predictive compensator.  
The characteristic equation of the system is:  
\[D(s)=1+G_{c1}(s)G_{p}^{*}(s)\frac{1+G_{p}(s)G_{c2}(s)e^{-\tau s}}{1+G_{c2}(s)e^{- \tau s}G_{p}^{*}(s)}=0 \tag{29}\]  
If the mode of \(G_{c2}(s)\) is chosen to be small enough, then:  
\[1+G_{p}(s)G_{c2}(s)e^{-\tau s}\approx 1 \tag{30}\]  
\[1+G_{c2}(s)e^{-\tau s}G_{p}^{*}(s)\approx 1 \tag{31}\]  
At this point the system characteristic equation simplifies to  
\[D(s)=1+G_{c1}(s)G_{p}^{*}(s)=0 \tag{32}\]  
From the above equation, it can be seen that the system stability is not affected by the time lag of the compensating controller and the controlled object, and the system characteristic equation does not contain the time lag term.  
The simulation model of the pH regulation system based on PID-Smith prediction compensation is shown in Figure 7.
## 4 Discussions  
In this study, the accuracy of the stance duration, vGRF and COP trajectory measurements taken from a WBB was assessed by comparison with a laboratory-grade force plate.  
For the stance duration, the differences ranged from \(-31\) to \(24\) ms. These results were equivalent to a foot switch system [25], which can measure IC and end of contact accurately using force sensors placed on the heel and toes of the sole of a foot. The WBB was validated for temporal gait analysis on the basis of the measurement of kinetic parameters. However, the WBB lacks the accuracy to be considered a replacement for the force plates. These differences may be caused by the response delay of the WBB to the dynamic changes in the vGRF upon IC and TO in a walking stance. This insight is supported by the BAP results, which showed large measurement error values when small forces were applied, particularly in straight walking, as shown in Figure 4.  
Meanwhile, for the assessment of the vGRF over \(400\) N, the BAPs revealed a random spread in the error. The result suggests that differences between the measurement devices do not depend on the magnitude of the vGRF in midstance while walking. More importantly, Pearson's correlation coefficient and %RMSE values for walking in both a straight line and along a curved path were equivalent to those of commercial instrumented insoles [26], which are used as an alternative to the force plates. These results indicate that the WBB is an appropriate tool to measure the vGRF while walking.  
When evaluating the accuracy of the COP trajectory, the RMSE values in the \(x\) (anterior-posterior) and \(y\) (mediolateral) directions, as measured using the WBB, were equivalent to those of the commercial insoles [27], but these values were not equivalent to those of a laboratory-grade device. These errors are thought to be caused by two factors. One is an effect of horizontal GRF, which was difficult to measure using the WBB because it was applied in a different direction to the measurement surface than it was in the force plate, and it had a different height from the floor. The other factor was the inconsistent sampling rate of the WBB. It was particularly difficult for the WBB to trace the force after heel contact and before the end of foot contact, which caused the values of the vGRF to change sharply. The COP trajectory accuracies were reduced in this phase because calculating the COP trajectory depends on the force value.
# A Robust Scheduling Framework for Re-Manufacturing Activities of Turbine Blades
## 4 Robust Scheduling Framework
### Shop Scheduling Model  
Grounding on the description of the re-manufacturing process in Section 3.1, several batches of turbine blades are repaired in the same shop, different batches may need different sets of operations, sharing the same resource (e.g., workers, machines). The scheduling of batches of turbine blades can be modeled through a resource constrained project scheduling problem (RCPSP) with limited renewable resources [22]. The structure of the processes to be operated is defined by precedence relations among the activities, the associated processing times can be modeled through random variables to consider the associated uncertainty. The aim of the scheduling approach is the minimization of the makespan, supporting the optimized utilization of the resources [23].  
Each activity represents a processing operation of a whole batch of blades in a specific process phase. Activities cannot be interrupted, hence, a non-preemptive schedule is pursued. The set of precedence relations are usually given as a directed acyclic graph, where nodes represent activities while an edge \((u,v)\) models a precedence relation enforcing \(u\) to finish before \(v\) is allowed to start. The graph contains two dummy activities, the source node S and the sink node T, modeling the start and finish of the whole set of activities. A graph representing the processing of \(k\) batches of blades is reported in Figure 6. A set of batches \((1,2,3,\dots,k)\) of turbine blades which come from different of customer orders and stages need to be re-manufactured, each horizontal chain from source node S to sink node T represents the processing steps of one batch of turbine blades. Different batches undergo different process steps while with many equal operations \((A,B,C,D,\dots)\) consuming same resources.  
Figure 5: Repairing process example for a batch with rework.  
The set of renewable resources represents production resources in the shop (e.g., experienced workers, machines). Each resource has a maximum capacity per time slot while each activity is associated to a requested amount of each resource for each time slot (greater or equal to zero). The dummy source and sink activities require no resource.  
With respect to the objective to be pursued, the minimization of the makespan (i.e., the completion time of the dummy sink activity) is addressed in this paper. Nevertheless, pursuing a robust approach entails the need of addressing the uncertainty affecting the scheduling problem in the objective function. The following subsections are going to address this aspect.
## 4 Experiments and Results
### Baselines  
In order to fairly evaluate the performance of the proposed method, we selected the following models as baselines, which are all unsupervised methods.  
**AE**[35]: The baseline system in DCASE 2020 workshop [35], which is based on an autoencoder.  
**EGBAD**[38]: An efficient anomaly detection model based on GAN. This model avoids the problem of expensive optimization steps required for traditional GAN during testing by training the generator, discriminator and encoder simultaneously, thereby significantly improving detection efficiency and achieving better performance.  
**IDNN**[39]: An abnormal sound detection method based on an interpolation deep neural network. Different from an autoencoder, it removes the continuous spectrogram of the center frame through input features and predicts the interpolation result of the missing frame as output, thus avoiding the problem of the unstable prediction of edge frames.  
**ANP**[40]: An unsupervised abnormal sound detection model based on the attentive neural process (ANP), which reconstructs the latent representation by dynamically selecting context and target areas of the spectrogram without predefined masks. The model uses attention mechanism and iterative optimization strategy to focus on the complex areas as the basis for abnormal scoring.  
**PAE**[41]: A transformer-based unsupervised abnormal sound detection model. PAE captures inter-frame context information through the self-attention mechanism and combines it with the mask prediction strategy to improve the modeling ability of long sequences. It outperforms traditional methods on the DCASE2020 Task 2 dataset.  
It is worth mentioning that all models keep the consistency of training data and test data when comparing, and the settings during training are the same with those mentioned in their papers.
## 1 Introduction
[34] performed all-electron spin-polarized relativistic calculations of the electronic and thermodynamical properties of stoichiometric UN and UN\({}_{2}\) compounds. [PERSON] and [PERSON] [39] studied the electronic properties and phonon-dispersion relations of uranium mononitride using the pseudopotential density-functional-theory (DFT) method. We note that uranium mononitride is an antiferromagnetic compound (anti-parallel spins on the uranium atoms in 001 planes) with substantial magnetic moments on the uranium atoms (\(\sim\)0.75 \(\mu_{\text{B}}\)) [3]. The spin-polarized uranium 5f electrons in this compound have been treated in different ways in the past. [PERSON] [30] and others [35; 36] employed DFT, while stronger 5f-electron correlations were incorporated by going beyond DFT, applying either electron self-interaction-corrected theory (SIC-DFT) [37] or an on-site Coulomb repulsion term (effective Hubbard U; DFT+U) [38; 42; 43]. The principal failure of the DFT model is that it favors a ferromagnetic state over the observed anti-ferromagnetic one. However, the magnetic properties of UN are not the primary focus of the present study, but rather other ground-state properties that are derived from the energetics of the compound. Moreover, we are specifically interested in the thermodynamical behavior at elevated temperatures near the melting point (\(\sim\)3000 K). These temperatures are far above the magnetic ordering of uranium mononitride (Neel temperature is 53 K [3]) and therefore we have chosen, similar to [35; 36], to restrict ourselves to a ferromagnetic configuration for most of our calculations. Another motivation for this simplification is that total energy and bonding properties of ferromagnetic and anti-ferromagnetic UN are nearly indistinguishable, as we shall show below. The basic idea of the thermodynamical modeling is founded on the CALPHAD (CALculation of PHAse Diagrams) methodology that involves calculating phase equilibria and phase diagrams from experimental inputs [45; 46; 47]. Therefore, we employ this method to extract the relevant thermodynamical properties derived from experimental characterizations for comparison with our theory. A general description of the CALPHAD method and its application to the UN system will be presented in Section 2.3. Due to its potential as a fuel material, knowledge of the thermal behavior is essential. Understanding thermodynamical properties from first-principles theory, including enthalpy of formation, heat capacity, and free energy, is important for reliably determining the phase stability and phase diagram. For the calculation of these properties we go beyond previous first-principles modeling, that are limited to a quasi-harmonic treatment of the lattice vibrations, and here include anharmonic effects via the self-consistent ab initio lattice dynamics (SCAILD) method [48]. This approach is computationally efficient, and it accounts for phonon-phonon interactions that may produce strong anharmonic effects. The phonon-phonon coupling was shown to be important for uranium metal [49] but have heretofore been ignored in first-principles modeling for UN.
## 3 Antenna Optimization: the CRO-SL Algorithm  
A multi-method ensemble CRO-SL is the algorithm used to optimized the proposed antenna. This algorithm is an advanced version of a basic original version of the CRO [18]. The CRO is an evolutionary-type algorithm in which the search operators are based on the processes occurring in a coral reefs, including reproduction, fight for space, or depredation [19]. The pseudocode of the original CRO is shown below, with the different CRO phases (ree initialization and reef formation), along with all the operators applied to guide the search.  
The CRO-SL (Coral Reef Optimization with Substrate Layers) is an improved version of the CRO [19]. It consists of a multi-method ensemble for optimization [27], with extremely good search capabilities for optimization tasks. The CRO-SL has the same algorithmic structure than the basic CRO, but several substrate layers are defined in the algorithm, each one implementing a different search procedure or strategy. In fact, the CRO-SL is an ensemble approach which promotes competitive co-evolution, where each substrate layer may represent different processes (different models, search operators, problem's parameters, etc.), though the multi-method version, in which the  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Variable** & **Range** \\ \hline \multicolumn{2}{c}{**Antenna top view**} \\ \hline U\_T & [1,5] \\ U\_Patch\_h & [1,Lp] \\ U\_Patch\_AY & [0.5,(Wp-2 Circle\_R)/2] \\ U\_Patch\_AX & [0.5,Lp/2-U\_Patch\_W-Circle\_R] \\ U\_Ah & [1,(Wp-7 Mean\_A\_8 Mean\_W-2U\_T)/2] \\ U\_L & [0.5,Wp] \\ U\_Patch\_W & [1,5] \\ Circle\_R & [11,Wp/2] \\ Circle\_Angle & [0.5,180] \\ Circle\_S & [3,5] \\ Circle\_CT & [1,5] \\ Circle\_T & [3,5] \\ Lp=60 mm; Wp=90 mm & \\ \hline \multicolumn{2}{c}{**Antenna ground plane**} \\ \hline Mean\_W & [1,2.7] \\ Mean\_A & [1,10] \\ Mean\_h & [1,Lp] \\ U\_AV & [1,(Lp-Mean\_h-2U\_T)/2] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Definition of the variables to optimize in the design of the proposed antenna (see Figure 1 for details).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithm Step** & **Pseudo-Code for the CRO Algorithm** \\ \hline
1 & **Require:** CRO algorithm parameters \\
2 & **Ensure:** An optimal feasible individual (best antenna design) \\
3 & Initialize the algorithm and CRO parameters \\
4 & **for** each iteration of the simulation **do** \\
5 & Update values of CRO parameters: predation probability, etc. \\
6 & Broadcast spawning and Brooding operators \\
7 & Settlement of new corals \\
8 & Predation process \\
9 & Evaluate the new population in the coral reef \\
10 & **end for** \\
11 & **Return:** the best individual (final solution) from the reef \\ \hline \hline \end{tabular}
\end{table}
Table 2: Description of the pseudo-code for the Coral Reefs Optimization (CRO) algorithm.  
substrate layers represent different search operators, has been the most successful version. Details on the overall CRO-SL algorithm and the mechanisms to include substrate layers are well-reported in reference [19]. The main steps of the CRO algorithm have been detailed in Table 2.