esa-sceva/satcom-chunk-collection huggingface dataset:
# Three-Dimensional, Time-Dependent Analysis of High- and Low-\(Q\) Free-Electron Laser Oscillators
## 2 Numerical Formulation
### Performance of the MINERVA FEL Code
#### 2.3.1 The Sparc SASE FEL  
The experimental parameters of SPARC [197] are as follows. The electron beam energy was 151.9 MeV, with a bunch charge of 450 pC, and a bunch width of 12.67 ps. The peak current was approximately 53 A for a parabolic temporal bunch profile. The \(x\)- and \(y\)-emitances were 2.5 mm-mrad and 2.9 mm-mrad respectively, and the rms energy spread was 0.02 percent. There were six undulators, each of which were 77 periods in length, with a period of 2.8 cm and an amplitude of 7.88 kG. Each undulator was modeled using Equation (17) with one period for the entrance up-taper and another for the exit down-taper. Furthermore, in the simulation, eight undulators were used to show saturation of the system. The gap between the undulators was 0.4 m in length and the quadrupoles (0.053 m in length with a field gradient of 0.9 kG/cm), forming a strong focusing lattice were located 0.105 m downstream from the exit of the previous undulator. Note that the quadrupole orientations were fixed and did not alternate. The electron beam was matched into the undulator/focusing lattice. The resonance occurred at a wavelength of 491.5 nm. In the experiment, the pulse energies were measured in the gaps between the undulator segments.  
A comparison of the pulse energy as found in the simulation (blue line) and from the experiment (red markers) is shown in Figure 2, where the simulation result is averaged over 20 simulation runs with different noise seeds. This yields convergence to better than 5 percent. Energy conservation in the simulation is maintained to within better than one part in \(10^{4}\). Each marker represents a single measurement that is repeated several times, while the error bar indicates the error in the measurement. The experimental data are courtesy of [PERSON]. Agreement between the simulation and the measured performance is excellent.  
A comparison between the evolution of the relative linewidth as determined from simulation (blue line) and by measurement (red markers, data courtesy of [PERSON]) is shown in Figure 3. Agreement between the simulation and the measured linewidth is within about 35 percent after 15 m. As shown, the predicted linewidths are in substantial agreement with the measurements.
## 2 Single-Camera Trilateration Algorithm
### Camera Position Estimation
In order to determine \(\text{cos}\theta_{\bar{y}}\), we have the following equation in the triangle \(\Delta\mathbf{p}_{0}\mathbf{q}_{i}\mathbf{q}_{j}\) in \(\text{X}_{c}\text{Y}_{c}\text{Z}_{c}\) according to the law of cosine:  
\[\cos\theta_{\bar{y}}=\frac{\rho_{i}^{2}+\rho_{j}^{2}-\rho_{\bar{y}}^{2}}{2\rho _{i}\rho_{j}}, \tag{9}\]  
where \(\rho_{i}=\sqrt{\mathbf{q}_{i}^{T_{c}}\mathbf{q}_{i}}\) denotes the distance between \(\mathbf{p}_{0}\) and \(\mathbf{q}_{i}\), and \(\rho_{\bar{y}}=\sqrt{\left(\
icefrac{{c}}{{q}}-\
icefrac{{c}}{{q}}\right)^{T} \left(\
icefrac{{c}}{{q}}-\
icefrac{{c}}{{q}}\right)}\) denotes the distance between \(\mathbf{q}_{i}\) and \(\mathbf{q}_{j}\). Substituting Equation (4) into Equation (9), we obtain:  
\[\cos\theta_{\bar{y}}=\frac{a_{\bar{y}}}{b_{\bar{y}}}, \tag{10}\]  
where  
\[a_{\bar{y}}=[(\frac{u_{i}-u_{0}}{f_{u}})^{2}+(\frac{v_{i}-v_{0}}{f_{v}})^{2} +1]+[(\frac{u_{j}-u_{0}}{f_{u}})^{2}+(\frac{v_{j}-v_{0}}{f_{v}})^{2}+1]-[(\frac {u_{i}-u_{j}}{f_{u}})^{2}+(\frac{v_{i}-v_{j}}{f_{v}})^{2}],\]  
\[b_{\bar{y}}=2\sqrt{(\frac{u_{i}-u_{0}}{f_{u}})^{2}+(\frac{v_{i}-v_{0}}{f_{v}}) ^{2}+1}\sqrt{(\frac{u_{j}-u_{0}}{f_{u}})^{2}+(\frac{v_{j}-v_{0}}{f_{v}})^{2}+1}.\]  
Since \(\
icefrac{{m}}{{\mathbf{q}}}_{0}=[u_{0},\!v_{0}]^{T}\) and \(\
icefrac{{m}}{{\mathbf{f}}}=[f_{u}f_{v}]^{T}\) are determined through camera calibration and \(\
icefrac{{m}}{{\mathbf{q}}}_{i}\) are obtained by segmenting the visual landmarks from the image, \(\text{cos}\theta_{\bar{y}}\) can be calculated from Equation (10) directly. Based on the above derivation, it is clear that the camera can be positioned by reversing the above steps. That is, first calculate \(\text{cos}\theta_{\bar{y}}\) from Equation (10), next estimate \(r_{i}\) from Equation (6), and then estimate \(\
icefrac{{g}}{{\mathbf{p}}}_{0}\) from Equation (5).
## 3 Results
### Regulation of Nrf/Kaep1 Signaling Pathway by BAL1  
Nrf2 binds to the negative regulator Keep1, which induces Nrf2 ubiquitination and proteasomal degradation, blocking the allosteric transfer of Nrf2 to the nucleus. As shown in Figure 5a-c, in the experiments where CCC-ESF-1 was stimulated by UVA, a significant gap was found between cytoplasmic and nuclear Nrf2 content, and the protein content of Keep1 was elevated, indicating that UVA stimulation promoted the binding of Keep1 to Nrf2 and blocked the nuclear ectopic of Nrf2, thus reducing the transcription level of antioxidant enzyme genes downstream of Nrf2, which was consistent with the results in Figure 5d-f. Fortunately, BAL1 has a highly significant effect on Nrf2 signaling (\(p<0.001\)), driving nuclear ectopic Nrf2 mainly by blocking the binding of Nrf2 to Keep1. The nuclear displacement ratio (N/C) increased from 0.365 to 1.849, approaching the blank cell state group. Consequently, Heme Oxygen 1 (HO1), NAD(P)H Quinone Dehydrogenase 1 (NQO1), and Glutamate-Cysteine ligase Catalytic Subunit (GCLC) were extremely significantly increased in mRNA expression and protein content (\(p<0.001\)). These results suggest that BAL1 achieves its antioxidant stress effect mainly by regulating the transduction level of Nrf2.  
Figure 4: Changes of major intracellular antioxidant enzymes. (**a**) The enzyme activity and transcription level of Catalase (CAT). (**b**) The enzyme activity and transcription level of Superoxide dismutase (SOD). (**c**) The enzyme activity and transcription level of glutathione peroxidase (GSH-px). Difference analysis between sample group and UVA model group: ns.: \(p>0.05\), *: \(p<0.05\), **: \(p<0.01\), ***: \(p<0.001\).
## 3 Results
### Gram Classification  
Raman spectroscopy was evaluated as a means of Gram bacteria classification. The 5-fold cross-validation distinguished Gram-positive (_L. monocytogenes_ and _S. aureus_) and Gram-negative (_S. enterica_ and _E. coli_) strains at 24 h with high accuracy (0.89), as shown in Figure 3D. The main structural difference between Gram-positive and Gram-negative bacteria is the presence of peptidoglycans on the cell wall. This was observed by [PERSON] et al. (2016), reporting high-intensity peaks at 497 cm\({}^{-1}\) attributed to polysaccharides of the Gram-positive bacteria cells using SERS [18]. Another study did not report significant differences between Gram-positive or Gram-negative bacteria using SERS [19]. However, tip-enhanced Raman spectroscopy (TERS) can differentiate between Gram category bacteria in the 2800-3000 cm\({}^{-1}\) region attributed to methylene stretching vibrations [20]. It is noteworthy to consider that the chemometrics signals may differ for each Raman technique [9]. As observed in Table 2, ORS Raman mainly provided signals distinctive to Gram-positive strains, at 1295-1297 cm\({}^{-1}\) (CH\({}_{2}\) fatty acid deformation). Moreover, signals  
Figure 3: Confusion matrices of SVM 5-fold cross-validation for monocultures: _S. aureus_ (AU), _E. coli_ (EC), _L. monocytogenes_ (L), and _S. enterica_ (S); at three different times: (**A**) 24, (**B**) 18, and (**C**) 8 h; (**D**) confusion matrix of SVM 5-fold cross-validation for Gram-positive (PO)/Gram-negative (NE) at 24 h. Higher numbers show a stronger correlation between the predicted and the actual label.  
of Gram-negative strains, at 1086-1087 cm\({}^{-1}\) (DNA phosphate backbones), enabled the model to predict them accurately. Thus, this system could also classify bacteria at this level.
# A Hybrid Route Selection Scheme for 5G Network Scenarios: An Experimental Approach
## 6 Conclusions and Future Work  
This paper proposes CenTri, which is a hybrid route selection scheme that uses white spaces to offload traffic from macrocell to small-cell base stations with heterogeneous nodes in 5G network scenarios. It caters to important characteristics of 5G network scenarios, including the dynamicity of channel availability, heterogeneity, and ultra-densification. In this paper, device-to-device (D2D) communication uses traditional reinforcement learning (TRL) and dynamic reinforcement learning (DRL) approaches. While TRL uses a constant learning rate, DRL uses a dynamic learning rate that changes with primary user (PU) activity levels. Our work was tested in a testbed with eleven USRP/GNU radio units. Each USRP unit was embedded with a mini-computer called RP3 to provide more realistic scenarios. Compared to TRL, experimental results show improvement in different quality of service (QoS) metrics, including a higher packet delivery ratio, throughput, lower end-to-end delay, and the number of route breakages. Routes with higher intermediate nodes also achieved higher end-to-end delay but a lower packet delivery ratio and throughput.  
In the future, CenTri will require more testing with a higher number of routes and intermediate nodes. Better processing units can relax the assumptions made, including the processing delay. Moreover, a cross-layer design for studying the physical data link and network layers will help to provide a more realistic testing environment.  
Figure 12: Cumulative number of route breakages between TRL with \(\alpha=0.5\) and the DRL mechanism at a different PU-OFF time.  
**Author Contributions:** Conceptualization, M.K.C., K.-L.A.Y., M.H.L. and Y.-W.C.; methodology, M.K.C., K.-L.A.Y., M.H.L. and Y.-W.C.; investigation, M.K.C.; resources, M.K.C., K.-L.A.Y., M.H.L. and Y.-W.C.; data curation, M.K.C.; writing--original draft preparation, M.K.C.; writing--review and editing, K.-L.A.Y., M.H.L. and Y.-W.C.; visualization, M.K.C.; project administration, K.-L.A.Y. and Y.-W.C.; funding acquisition, K.-L.A.Y. and Y.-W.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by a publication fund under the Research Creativity and Management Office, Universiti Sains Malaysia. This research was also supported by Universiti Tunku Abdul Rahman.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data sharing not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Deep Learning-Based Mobile Application for Monkeypox Detection
### DL Approaches  
#### 1.3.1 Cnn  
Utilizing deep learning (DL) techniques enables the automated acquisition of complex features necessary for visual pattern recognition. Convolutional neural networks (CNNs), a specific type of DL approach, have been extensively employed in various computer vision tasks, encompassing facial expression recognition, text recognition, face recognition, gender classification, age classification, and action recognition. Moreover, CNNs have exhibited remarkable performance in biomedical applications, specifically in the domains of pattern recognition and computer vision. Different CNN architectures employ different combinations of convolutional, pooling, and fully connected layers. The convolutional process involves the conversion of input data into filters [21; 32]. The convolution process is visually depicted in Figure 2.  
Suppose a square neuron layer is followed by a convolutional layer. If we use a \(m\times m\) filter \(w\), our convolutional layer output will be of the size \((N-m+1)\times(N-m+1)\). To calculate the pre-nonlinearity input to a unit \(x^{l}_{ij}\) in our layer, we must sum up the contributions (weighted by the filter components) from the previous layer cells.  
\[x^{l}_{ij}=\sum_{a=0}^{m-1}\sum_{b=0}^{m-1}w_{ab}y^{l-1}_{(i+a)(j+b)} \tag{1}\]  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Research Paper** & **Network Used and Accuracy Value (\%)** & **Dataset Used** & **Additional Evaluation Measurement** \\ \hline [PERSON] et al. [21] & ResNet18 (86.8\%), GoogleNet (82.22\%), EfficientNetb0 (91.11\%), NasnetMobile (86.67\%), ShuffleNet (80.00\%), MobileNetv2 (91.11\%) & Prepared & 1-fold cross-validation \\ \hline [PERSON] et al. [12] & VGGNet (71\%), MobileNet (94.4\%) & MSLD &
\begin{tabular}{l} Precision, Recall, \\ F1-score and \\ Specificity \\ \end{tabular} \\ \hline [PERSON] et al. [25] & VGG19-CBAM-Dense (71.86\%), Xception & & \\  & CBAM-Dense (83.89\%), DenseNet121-CBAM-Dense (78.27\%), MobileNetV2-CBAM-Dense (74.07\%), EfficientNetB3 CBAMDense (81.43\%), Ensemble (79.26\%), ShuffleNet (80.00\%) & 4-fold cross-validation \\ \hline [PERSON] et al. [31] & Xception (96.83\%), Resnet50 (96.93\%), DenseNet201 (97.16\%), InceptionV3 (96.62\%), VGG19 (96.97\%), InceptionResnet (96.65\%) & Prepared & – \\ \hline [PERSON] et al. [23] & ResNet-18 (98.25\%), ResNet50 (96.49\%), VGG-16 (92.98\%), DenseNet-161 (96.49\%), EfficientNetB7 (94.74\%), MobileNetV2 (96.49\%), EfficientNetV2 (96.49\%), MobileNetV3 (75.44\%), DenseNet-50 (92.98\%), ShuffleNetV2 (78.95\%), ConvNeXt (96.49\%) & Prepared & – \\ \hline [PERSON] et al. [9] & VGG16 (0.93\%), InceptionResNetV2 (0.98\%), ResNet50 (0.72\%), ResNet101 (0.72\%), MobileNetV2 (0.99\%), VGG19 (0.90\%) & Prepared & Precision \\ \hline [PERSON] et al. [22] & ResNet50 (0.72\%), Inception-V3 (0.71\%), DenseNet21 (0.78\%), MnasNet-AI (0.72\%), MobileNet-V2 (0.77\%), ShuffleNet-V2 (0.79\%), SqueezeNet (0.65\%) & Prepared & 5-fold cross-validation \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the different approaches in previous studies.
# End Face Attitude Detection of Special Steel Bars Based on Improved DBSCAN
## 3 Proposed End Face Attitude Detection Algorithm for Special Steel Bars
### Acquisition of the Special Steel Bar End Face Attitude
#### 3.4.2 Fast Calculation Method of the Special Steel Bar End Face Attitude  
From the circle detection results, it can be concluded that the proposed algorithm can accurately detect all circular targets in the image, providing a new method for detecting circles. However, we only need the circumcircle parameters when calculating the end face attitude. When the acquisition system captures the image each time, the distance between the camera and the end face is basically the same, so in the image, the steel bar circumcircle should fall within a circle ring. We establish a circle ring template centered on the center of the circumscribed circle and with a radius in the range of \(\pm 30\) pixels based on the first set of accurate circumscribed circle parameters we have obtained. As shown in Figure 4, the fast template is applied to the image after canny edge detection, so that the edge information  
Figure 7: Simulation results of steel bar end face attitude detection. (**a**) Results of circle detection. (**b**) Extraction results of the steel bar end face. (**c**) Calculation results of the end face attitude.  
within the template range is retained and other edge information is removed. Through this approach, a vast amount of edge information that has no relationship to the steel bar's circumscribed circle is removed. This significantly reduces the amount of calculation, enhances the efficiency of end face pose detection, and ultimately improves the speed of end face pose detection. This paper analyzes the calculation time for 1800 images. The average time consumption of each image is 2.254 s, and the maximum time consumption is 3.656 s, which fully meets the timing requirements of the current production line.
## 5 Results and Discussion
### Leyte Province Results
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Soil Profile** & **No. of** & **Match Rate** & \multirow{2}{*}{**R\({}^{2}\)**} & \multirow{2}{*}{**r**} & \multirow{2}{*}{**RMSE**} \\
**Type** & **Samples** & & **(\%)** & & \\ \hline \(S_{C}\) & 5 & 100 & 0.328 & \(-\)0.573 & 29.335 \\ \(S_{D}\) & 15 & 73.33 & 0.327 & 0.573 & 51.493 \\ \(S_{E}\) & 7 & 85.71 & 0.294 & 0.572 & 51.493 \\ All & 27 & 81.48 & 0.806 & 0.898 & 50.026 \\ \hline \end{tabular}
\end{table} 
Table 7: Match Rate, R\({}^{2}\), r, and RMSE values for all soil profile types in Leyte Province. However, some data points deviate from this trend, as shown in Figure 12, particularly those from lowland areas. These sites have deep layers of sediments, resulting in longer site periods despite relatively high Vs30 values. The soft sediments extend the site period while underlying hard bedrock or stiff soil layers contribute to higher Vs30 values [14]. The inversion efficiently estimates Vs30 for shorter period sites (site periods less than 1), where most data points align closely with the trendline. Overall, the estimated values accurately reflect the expected relationship between period and Vs30.
## 4 Results and Discussions  
The selected datasets from the four typhoons and the three hurricanes summarized in the preceding section were investigated in the analytical framework presented in Section 2. Specifically, both the datasets in typhoons and hurricanes were converted to the standard exposure with a roughness length  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Tropical** \\ **Cyclones** \\ \end{tabular} } & \multirow{2}{*}{**Sites**} & \multirow{2}{*}{**Height (m)**} & \multirow{2}{*}{\begin{tabular}{c} **Anemometer** \\ **Types** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Number of** \\ **Runs** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Average of the** \\ **10 min Mean Wind** \\ **Speed (m/s)** \\ \end{tabular} } \\ \hline \multirow{4}{*}{
\begin{tabular}{c} **Chanchu** \\ \end{tabular} } & \multirow{4}{*}{RBT} & 10 & Sonic & 26 & 19.19 \\  & & & Propeller & 15 & 18.87 \\  & & 30 & Sonic & 37 & 21.96 \\  & & 60 & Propeller & 58 & 22.19 \\ \cline{2-6}  & \multirow{4}{*}{OT} & 5 & Sonic & 21 & 24.69 \\  & & 10 & Sonic & 45 & 24.28 \\ \hline \multirow{2}{*}{Prapiroon} & \multirow{2}{*}{BT} & \multirow{2}{*}{10} & Sonic & 18 & 20.13 \\  & & & Propeller & 4 & 22.69 \\ \hline \multirow{2}{*}{Nuri} & \multirow{2}{*}{MFB} & 30 & Sonic & 8 & 18.25 \\ \cline{2-6}  & & 10 & Sonic & 83 & 24.26 \\  & & 60 & Sonic & 100 & 25.11 \\ \hline \multirow{4}{*}{Hagupit} & \multirow{4}{*}{ST} & \multirow{4}{*}{5} & Sonic & 38 & 28.53 \\  & & & Propeller & 18 & 20.34 \\  & & & Propeller & 27 & 20.34 \\ \cline{2-6}  & & & Sonic & 61 & 21.30 \\  & & & Propeller & 58 & 20.93 \\ \hline \multirow{4}{*}{Katrina} & \multirow{4}{*}{T1} & 5 & Propeller & 16 & 21.98 \\  & & 10 & Propeller & 43 & 22.45 \\ \cline{2-6}  & & 5 & Propeller & 34 & 21.50 \\  & & 10 & Propeller & 40 & 24.42 \\ \cline{2-6}  & & 5 & Propeller & 5 & 23.02 \\  & & 10 & Propeller & 14 & 22.46 \\ \hline \multirow{4}{*}{Rita} & \multirow{4}{*}{T0} & 5 & Propeller & 46 & 21.05 \\  & & 10 & Propeller & 60 & 22.17 \\ \cline{2-6}  & & 13 & Propeller & 15 & 18.85 \\ \cline{2-6}  & & 5 & Propeller & 19 & 18.86 \\  & & 10 & Propeller & 32 & 19.55 \\ \hline \multirow{4}{*}{Wilima} & \multirow{4}{*}{T0} & 5 & Propeller & 13 & 21.08 \\  & & 10 & Propeller & 19 & 22.41 \\ \cline{2-6}  & & 5 & Propeller & 23 & 23.65 \\ \cline{2-6}  & & 10 & Propeller & 37 & 26.17 \\ \cline{2-6}  & & 5 & Propeller & 16 & 21.34 \\  & & 10 & Propeller & 18 & 23.66 \\ \cline{2-6}  & & 5 & Propeller & 9 & 21.36 \\ \cline{2-6}  & & 10 & Propeller & 14 & 22.63 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Datasets analyzed in this study. Reproduced with permission from [PERSON] et al. (2016), Elsevier, 2019.  
of 0.03 m at 10 m height and an observation time scale of 10 min. The latitude of the standard terrain is assumed to be 25\({}^{\circ}\).
# A New Maximum Likelihood Estimator Formulated in Pole-Residue Modal Model
## 3 Simulated Data Analysis  
In order to validate the proposed implementation of the MLE-MM, two examples are analysed. The first consists of a simulated EMA of a five-DOF system and the second corresponds to a lattice tower structure with two pairs of closely spaced modes. In these analyses, the modal parameters together with their uncertainty bounds were estimated using the new MLE-PMM introduced in Section 2.2. The results obtained for both examples are presented in following subsections.
## 6 Discussion  
Our results demonstrate the feasibility of automatically classifying neonatal behavioral states using a multimodal approach that combines video-derived motion features and pulse rate variability (PRV). The model achieved high overall accuracy and performed well across all behavioral categories, including subtle transitions between sleep phases. These findings align with and extend earlier observations on the clinical value of PRV features in assessing infant sleep and wakefulness.  
Importantly, this study highlights the potential for clinical integration. Current behavioral assessments in neonatal intensive care units (NICUs) often rely on manual observation, which is time-consuming, subjective, and limited in temporal resolution. Our automated system, by contrast, enables continuous, real-time monitoring of behavioral states with no disruption to the infant. This aligns well with the goals of individualized care frameworks such as the NIDCAP, which emphasize sensitive, developmentally appropriate interactions based on the infant's behavioral cues.  
The ability to reliably detect whether an infant is asleep, drowsy, or awake can support several clinical applications. For instance, caregivers could time interventions to coincide with optimal states of receptivity or avoid disrupting deep sleep phases that are critical for neurodevelopment. Moreover, quantitative metrics derived from behavioral state distributions over time may serve as early indicators of neurological maturation or dysregulation.  
The added value of PRV features was particularly evident in sleep phases, where motion is minimal and visual signals alone may be ambiguous. This supports the inclusion of physiological data in future monitoring systems, especially in contexts where fine-grained state differentiation is clinically relevant. At the same time, the visual modality alone provided strong performance during more active states, which opens the possibility of tailoring sensing strategies to the infant's condition or care environment.
# 5G Network Deployment Planning Using Metaheuristic Approaches
## 3 Methodology
### Metaheuristic Algorithms  
PSO emulates bird flocking behavior to optimize 5G base station deployment, treating station locations as particles and iteratively adjusting them based on local and global best solutions to minimize deployment cost while maximizing coverage and resource utilization [53]. The GA addresses conflicting objectives in deployment by balancing factors like coverage, cost, and user satisfaction, representing base station configurations as chromosomes and evolving them through genetic operations for decision-makers to choose from [54]. SA gradually refines base station configurations in a large search space, striking a balance between exploration and exploitation to achieve efficient deployment by escaping local optima [55]. The GWO algorithm, inspired by wolf social hierarchy, iteratively refines base station layouts through hunting behavior, balancing coverage, and cost factors efficiently via alpha dominance and collaborative hunting strategies, facilitating effective 5G deployment planning [56].  
Figure 2 illustrates the common steps involved in the metaheuristic optimization process, combining core concepts from the PSO, GA, SA, and GWO algorithms [53, 54, 55, 56].