esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Solutions and Results
\begin{table}
\begin{tabular}{c c c c c} \hline \(z\) & \(f(z)_{\text{Exact}}\) & \(f(z)_{\text{OHAM}}\) & \(|f(z)_{\text{Exact}}-f_{2}(z)_{\text{OHAM}}\,|\) & \(|f(z)_{\text{Exact}}-f_{7}(z)_{\text{HPM}}\,|\)[56] \\ \hline \(0.1\) & \(-0.09\) & \(-0.08997265186813393\) & \(2.73\times 10^{-5}\) & \(2.9\,\times\,10^{-4}\) \\ \hline  
\[-2\lambda_{1}C_{1}h^{{}^{\prime}}_{0}(\lambda)+\lambda\tau^{2}C_{1}h^{{}^{\prime}}_ {0}(\lambda)+M\tau^{2}C_{1}h^{{}^{\prime}}_{0}(\lambda)+\frac{(2 mC_{1}h^{{}^{ \prime}}_{0}(\lambda)^{2})}{(1+m)}-\lambda\lambda_{1}C_{1}h^{{}^{\prime\prime}}_ {0}(\lambda)\]  
\[-C_{1}h_{0}(\lambda)h^{{}^{\prime\prime}}_{0}(\lambda)-h^{{}^{\prime\prime \prime}}_{0}(\lambda)-C_{1}h^{{}^{\prime\prime\prime}}_{0}(\lambda)+h^{{}^{ \prime\prime\prime}}_{1}(\lambda)=0. \tag{36}\]  
By using the given, boundary conditions from Equation (35) we obtain:  
\[h_{0}(\lambda)=\frac{(-24S+(1+m)\lambda^{2})}{(12(1+m))}, \tag{37}\]  
and similarly with the conditions we obtain \(h_{1}(\lambda)\) from Equation (36),  
\[h_{1}(\lambda)=\frac{1}{(1+m)}\lambda^{2}(-0.125+S(0.5-0.0556\lambda)+0.00023 \lambda^{3}-1.5000\eta_{1}\]  
\[-3\sqrt{(1-\mu)}\eta_{1}+6\sqrt{(1-\mu)}\mu\eta_{1}-3\sqrt{(1-\mu)}\mu^{2} \eta_{1}+0.3333(1-\mu)^{2}5\lambda\eta_{1}+0.02083\lambda^{2}\eta_{1}\]  
\[-\eta\tau^{2}+0.1667\lambda\eta\tau^{2}-0.00694\lambda^{2}\eta\tau^{2}-M\tau^ {2}+0.1667\lambda M\tau^{2}-0.00694\lambda^{2}M\tau^{2}-0.2588\Gamma\tau^{4}\]  
\[+0.0431\Gamma\lambda\tau^{4}-0.001797\Gamma\lambda^{2}\tau^{4}+m(0.375-3\sqrt{ (1-\mu)}-0.00069\lambda^{3}-1.5\eta_{1}\]  
\[-3\sqrt{(1-\mu)}\eta_{1}+6\sqrt{(1-\mu)}\mu(1+\eta_{1})(1+\eta_{1})-\eta\tau^{ 2}-M\tau^{2}-0.25882\Gamma\tau^{4}\]  
\[-3\sqrt{(1-\mu)}\mu^{2}+\lambda(0.3333(1-\mu)^{2.5}+0.3333(1-\mu)^{2.5}\eta_{1 }+0.1667\eta\tau^{2}+0.1667M\tau^{2}\]  
\[-2\lambda_{1}C_{1}h^{{}^{\prime}}_{0}(\lambda)+\lambda\tau^{2}C_{1}h^{{}^{\prime}}_ {0}(\lambda)+M\tau^{2}C_{1}h^{{}^{\prime}}_{0}(\lambda)+\frac{(2 mC_{1}h^{{}^{ \prime}}_{0}(\lambda)^{2})}{(1+m)}-\lambda\lambda_{1}C_{1}h^{{}^{\prime\prime}}_ {0}(\lambda)\]0  
Substituting Equations (37) and (38) in Equation (34), we obtain \(h(\lambda)\). The modified Equations (33) and (34) obtained from OHAM have been solved using boundary conditions, which are provided in Section 2.
# Current Sensorless MPPT Control for PV Systems Based on Robust Observer
## 3 Problem Formulation
### Bounded System Model Representation  
In order to apply the AEM, the selected model needs to be described in a particular way satisfying the quasi-Lipschitz condition. This section focuses on the analysis of system model characteristics and its representation in such bounded form.  
The PV system model is represented in the following form  
\[\dot{x}=Ax+g(x)u+\phi \tag{11}\]  
\[y=h(x)+w\]  
where  
\[A=\begin{bmatrix}0&-\frac{1}{\Gamma}\\ \frac{1}{C}&-\frac{1}{R_{1}C}\end{bmatrix}\,\ \ g(x)=\begin{bmatrix}\frac{b_{ 0}}{\Gamma}\\ \frac{-b_{1}}{C}\end{bmatrix}\]  
\[\phi=\begin{bmatrix}\gamma_{1}V_{pv}\\ -\gamma_{2}+\gamma_{3}\end{bmatrix}\]  
\[\gamma_{1}=\frac{\Delta L}{L(L+\Delta L)}\ \,\ \ \gamma_{2}=\frac{\Delta C}{C(C+ \Delta C)}\]  
\[\gamma_{3}=\frac{R\Delta C+C\Delta R+\Delta R\Delta C}{RC(RC+R\Delta C+C\Delta R +\Delta R\Delta C)}\]  
and \(\Delta(\cdot)\) represents a parametric uncertainty in the component.  
The term \(\phi\) is required to be bounded as stated in (9), then  
\[\left\|\phi\right\|^{2} =\left(\gamma_{1}V_{pv}\right)^{2}+(-\gamma_{2}+\gamma_{3})^{2}\] \[=\left(\gamma_{1}^{2}V_{pv}^{2}+\gamma_{2}^{2}+\gamma_{3}^{2}-2 \gamma_{2}\gamma_{3}\right) \tag{12}\]  
Introducing an upper bound \(V_{pv}=V_{max}\) corresponding to the maximum PV array voltage, the following inequality is obtained  
\[\left\|\phi\right\|^{2}\leq\underbrace{\left(\gamma_{1}^{2}V_{max}^{2}+\gamma _{2}^{2}+\gamma_{3}^{2}-2\gamma_{2}\gamma_{3}\right)}_{\eta_{0}} \tag{13}\]  
The considered output's external disturbance is assumed to be bounded by a constant \(d_{0}\) as  
\[\left\|w\right\|^{2}\leq d_{0} \tag{14}\]  
Note that (13) and (14) are not quasi-Lipschitz bounded but Lipschitz bounded instead. Hence, the considered PV system with parametric uncertainty fulfills the required condition for AEM.
## 4 Results and Discussion  
This section will elaborate on ECG beat segmentation and detection, training and validation, ECG biometric authentication experiments, and comparison with other methods.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**MIT-BIH**} & \multicolumn{2}{c}{**ECG-ID**} \\ \hline \multirow{2}{*}{\#} & \multirow{2}{*}{**Layer**} & \multicolumn{2}{c}{**Number of**} & \multirow{2}{*}{**Output**} & \multirow{2}{*}{**Number of**} & \multirow{2}{*}{**Output**} & \multirow{2}{*}{**Number of**} \\  & & **Filters, Size,** & & & & \\  & & **Stride** & & & & \\ \hline
1 & inputlayer & & (256, 1) & 0 & (256, 1) & 0 \\
2 & convolution1d\_1 & 32, 5, 4 & (64, 32) & 192 & (64, 32) & 192 \\
3 & batch\_normalization\_1 & & (64, 32) & 128 & (64, 32) & 128 \\
4 & activation\_1 & & (64, 32) & 0 & (64, 32) & 0 \\
5 & convolution1d\_2 & 32, 5, 1 & (64, 32) & 5152 & (64, 32) & 5152 \\
6 & convolution1d\_3 & 16, 1, 1 & (64, 16) & 528 & (64, 16) & 528 \\
7 & convolution1d\_4 & 32, 5, 4 & (16, 32) & 2592 & (16, 32) & 2592 \\
8 & batch\_normalization\_2 & & (16, 32) & 128 & (16, 32) & 128 \\
9 & activation\_2 & & (16, 32) & 0 & (16, 32) & 0 \\
10 & max\_pooling1d\_1 & & (16, 32) & 0 & (16, 32) & 0 \\
11 & add\_1 & & (16, 32) & 0 & (16, 32) & 0 \\
12 & convolution1d\_5 & 32, 5, 1 & (16, 32) & 5152 & (16, 32) & 5152 \\
13 & convolution1d\_6 & 16, 1, 1 & (16, 16) & 528 & (16, 16) & 528 \\
14 & convolution1d\_7 & 32, 5, 4 & (4, 32) & 2592 & (4, 32) & 2592 \\
15 & batch\_normalization\_3 & & (4, 32) & 128 & (4, 32) & 128 \\
16 & activation\_3 & & (4, 32) & 0 & (4, 32) & 0 \\
17 & max\_pooling1d\_2 & & (4, 32) & 0 & (4, 32) & 0 \\
18 & add\_2 & & (4, 32) & 0 & (4, 32) & 0 \\
19 & convolution1d\_8 & 32, 5, 1 & (4, 32) & 5152 & (4, 32) & 5152 \\
20 & convolution1d\_9 & 16, 1, 1 & (4, 16) & 528 & (4, 16) & 528 \\
21 & convolution1d\_10 & 32, 5, 4 & (1, 32) & 2592 & (1, 32) & 2592 \\
22 & batch\_normalization\_4 & & (1, 32) & 128 & (1, 32) & 128 \\
23 & activation\_3 & & (1, 32) & 0 & (1, 32) & 0 \\
24 & max\_pooling1d\_3 & & (1, 32) & 0 & (1, 32) & 0 \\
25 & add\_3 & & (1, 32) & 0 & (1, 32) & 0 \\
26 & flatten\_1 & & (32) & 0 & (32) & 0 \\
27 & dense\_1 & & (64) & 2112 & (64) & 2112 \\
28 & dense\_2 & & (48) & 3120 & (90) & 5850 \\ \hline \hline \end{tabular}  
* Total parameters: 30,752 (MIT-BIH) and 33,482 (ECG-ID). Trainable parameters: 30,496 (MIT-BIH) and 33,226 (ECG-ID). Non-trainable parameters: 256 (MIT-BIH) data 256 (ECG-ID).  
\end{table}
Table 2: Summary of the proposed RDSCNN algorithm.
## 11 Limitations and Future Lines of Research  
The study of injuries in female youth soccer players faces notable limitations due to methodological inconsistencies and variability in reporting systems. Factors such as differences in injury definitions, data collection methods, and exposure measurements make cross-study comparisons challenging. Additionally, most studies rely on short-term designs, limiting the understanding of the long-term implications of injuries or the effectiveness of preventive strategies over time. These gaps underline the need for standardized injury surveillance protocols that incorporate exposure-adjusted data, uniform injury definitions, and classifications by severity and recovery timelines.  
Future research should focus on longitudinal studies that examine the interplay between growth, maturation, and injury risk in youth female soccer players. Investigating the role of biomechanical, hormonal, and psychosocial changes during puberty could provide valuable insights into injury etiology. Moreover, exploring the impact of contextual factors such as playing position, match intensity, and playing surface on injury patterns would help tailor prevention strategies. Collaborative, multidisciplinary approaches that integrate coaches, physiotherapists, and medical staff are essential for developing holistic injury prevention models that can be implemented across diverse competition levels.
## 4 Results
### Results of Conditioning Factor Interpretability Analysis Based on the SHAP  
To clarify the influence of different conditioning factors on landslide evaluation modeling in DL models and enhance the model's explanatory capacity, we adopted the SHAP methodology. We calculated the SHAP value of the ResNet18 model, and the results are presented in Figure 12. The results revealed that precipitation had the greatest influence on model training, followed by profile curvature, NDVI, distance to faults, and land cover type (Figure 12a). As depicted in Figure 12b, the eigenvalues on the right side of the graph, represented in red to signify high precipitation levels, exhibited a positive correlation with the SHAP values, while the side indicates contributions to the likelihood of landslide events. This suggests that increased precipitation correlates with a heightened probability of landslides. Analyses of LCFs and the results of our spatial analysis show a positive correlation between landslide distribution in China and precipitation levels, corroborating the findings derived from Shapley value calculations.  
Furthermore, to investigate the interactions between dynamic and static factors in influencing landslide susceptibility, we examined the combined effects of precipitation, NDVI, and land cover with key static factors such as aspect, elevation, and distance to faults. The SHAP results indicate that while precipitation is the most dominant factor, its influence is significantly modulated by topographic and geological conditions. For example, areas with high precipitation and steep slopes exhibit a markedly higher probability of landslides, suggesting a coupling effect between dynamic and static variables. Similarly, regions with dense vegetation (high NDVI) tend to exhibit reduced landslide susceptibility, but this mitigating effect weakens in areas with fragile lithology or near fault zones. These findings align with the results presented in Figure 6, which illustrates the _MI_ values for both static and dynamic factors. The _MI_ analysis confirms that precipitation, NDVI, and land cover are among the most influential dynamic variables, while static factors such as surface soil taxonomy, lithology, and distance to faults also play a critical role in determining landslide susceptibility.  
Figure 11: Temporal of landslide susceptibility in typical regions of China, 2001–2022 (red solid line is linear regression, the blue circle is the predicted probability of landslide in the corresponding year).  
Figure 12: Ranking of feature importance based on SHAP method (**a**) and summary plot (**b**).
# Co(II) Complex of Quercetin-Spectral, Anti-/Pro-Oxidant and Cytotoxic Activity in HaCaT Cell Lines
## Materials and Methods
### Anti-/Pro-Oxidant Studies  
The DPPH assay was carried out according to the spectroscopic method described in [36]. The methanolic solution of quercetin or Co(II)-quercetin complex (0.1 mM) and 2 mL of methanolic solution of DPPH\({}^{\bullet}\) (2,2-diphenyl-1-picrylhydrazyl) radical (60 \(\upmu\)M), were mixed and incubated in the dark, at room temperature for 1 h. The final concentrations of tested substances were in the range 0.5-20 \(\upmu\)M. The absorbance of the samples was measured by the use of UV/VIS Agilent Cary 5000 spectrophotometer (Santa Clara, CA, USA) at a 516 nm wavelength against methanol as blank. The percentage (%I) of DPPH\({}^{\bullet}\) radical scavenging activities in the tested substances was calculated using the following formula:  
\[\%I=\left(\frac{A_{control}-A_{sample}}{A_{control}}\right)\times 100\% \tag{1}\]  
where %I is the percent of inhibition of DPPH\({}^{\bullet}\) radical; Acontrol is the absorbance of the control sample; and Asample is the absorbance of the tested substance. The concentrations of the tested compounds were plotted against the percentages of inhibition, and the IC\({}_{50}\) values were determined using a linear regression analysis.  
The FRAP assay shows the ferric-reducing antioxidant activity of the substance [37]. 0.3 M acetate buffer (pH 3.6), 10 mM of 2,4,6-tripyrid-s-triazine (TPTZ; in 40 mM HCl) and 20 mM of FeCl\({}_{3}\) (in water) were mixed in a volumetric ratio of 10:1:1. Next, 3 mL of the FRAP mixture and 0.4 mL of the tested substance (50 \(\upmu\)M) were mixed. After 7 min the absorbance was measured at 595 nm against blank. The blank was 3 mL of FRAP mixture and 0.4 mL of methanol. The antioxidant activity was expressed as a FRAP value (Fe\({}^{2+}\) equivalents) (\(\upmu\)M) using the calibration curve prepared for FeSO\({}_{4}\) (y = 3.296.9x \(-\) 0.0331; R\({}^{2}\) = 0.9997).  
The pro-oxidant activity was measured as the rate of oxidation of Trolox [38]. The following solutions were mixed in test tubes: 0.5 mL of Trolox (C = 100 \(\upmu\)M), 0.5 mL of H\({}_{2}\)O\({}_{2}\) (C = 50 \(\upmu\)M), 0.5 mL of horseradish peroxide (C = 0.01 \(\upmu\)M) in phosphate buffer (pH = 7), 0.05 mL of the tested substance (\(\upmu\)M), and 0.495 mL of distilled. The absorbance measurements against the phosphate buffer were made every 10 min for 50 min (at 272 nm). The control sample contained 0.05 mL of pure methanol instead of the tested substance.  
All anti-/pro-oxidant assays were taken in five repetitions for three independent experiments. The results were expressed as the mean of the values obtained for the replications. The averages, standard deviation calculations, and graphs were performed calculated with Microsoft Excel 2019.
## 3 Results
### The Influence of Relative Azimuth Angle on the Prediction Results  
The acquired multispectral reflectance is influenced by the relative azimuth angle. To investigate the impact of spectral reflectance obtained at different relative azimuth angles on prediction results, multispectral data were collected at 0\({}^{\circ}\), 45\({}^{\circ}\), 90\({}^{\circ}\), 135\({}^{\circ}\), 180\({}^{\circ}\), 225\({}^{\circ}\), 270\({}^{\circ}\), and 315\({}^{\circ}\). Using these data, prediction models were constructed using PLS and SVM. The models were then compared and analyzed.
###### Abstract  
To mitigate the control overhead of Software-Defined Mobile Ad Hoc Networks (SD-MANETs), this paper proposes a novel approach, termed Software-Defined Greedy Perimeter Stateless Routing (SD-GPSR), which integrates geographical location information. SD-GPSR optimizes routing functions by decentralizing them within the data plane of SD-MANET, utilizing the geographic location information of nodes to enhance routing efficiency. The controller is primarily responsible for providing location services and facilitating partial centralized decision-making. Within the data plane, nodes employ an enhanced distance and angle-based greedy forwarding algorithm, denoted as GPSR_DA, to efficiently forward data. Additionally, to address the issue of routing voids in the data plane, we employ the \(A^{*}\) algorithm to compute an optimal routing path that circumvents such voids. Finally, we conducted a comparative analysis with several state-of-the-art approaches. The evaluation experiments demonstrate that SD-GPSR significantly reduces the control overhead of the network. Simultaneously, there is a notable improvement in both end-to-end latency and packet loss rate across the network.
# Spatial-Temporal Self-Focusing of Partially Coherent Pulsed Beams in Dispersive Medium
###### Abstract  
Partially coherent pulsed beams have many applications in pulse shaping, fiber optics, ghost imaging, etc. In this paper, a novel class of partially coherent pulsed (PCP) sources with circular spatial coherence distribution and sinc temporal coherence distribution is introduced. The analytic formula for the spatial-temporal intensity of pulsed beams generated by this kind of source in dispersive media is derived. The evolution behavior of spatial-temporal intensity of the pulsed beams in water and air is investigated, respectively. It is found that the pulsed beams exhibit spatial-temporal self-focusing behavior upon propagation. Furthermore, a physical interpretation of the spatial-temporal self-focusing phenomenon is given. This is a phenomenon of optical nonlinearity, which may have potential application in laser micromachining and laser filamentation.  
Keywords:coherence and statistical optics; propagation; partially coherent; pulses +
Footnote †: journal: Applied Sciences
## 3 Results  
In Figure 2, there is a captured image that illustrates the 3D coordinates of each tracker placed on aligners, on the upper and lower arches.  
The mean displacement values of the upper and lower arches for each of the tested aligners are presented in Table 2. The mean values were obtained considering the values of the coordinates of each axis from the reference stage and final loading stage, for each tooth and each arch.  
In Figure 3, it can be observed that the highest mean values for each axis were recorded by the samples in group SP. For axis X, the highest mean value was 0.264 \(\pm\) 0.068 mm; for axis Y, it was 0.840 \(\pm\) 0.106 mm; and for axis Z, it was 0.239 \(\pm\) 0.068 mm.  
When analyzing the differences between the study groups for axis X, we recorded statistically significant differences between groups CA vs. SP (\(p\) = 0.00); TG vs. SP (\(p\) = 0.00) and CA vs. TG (\(p\) = 0.031). For axis Y, significant differences were observed between groups CA vs. SP (\(p\) = 0.00); TG vs. SP (\(p\) = 0.00). For axis Z, statistical significance was recorded between groups CA vs. SP (\(p\) = 0.00); CA vs. TG (\(p\) = 0.00) and TG vs. SP (\(p\) = 0.00).  
\begin{table}
\begin{tabular}{c c} \hline
**Camera Sensor** & **CMOS** \\ \hline Camera Resolution & 4096 \(\times\) 3000 pixels \\ \hline Frame Rate & 25 fps @ full resolution \\ \cline{2-3}  & 43 fps @ 2496 \(\times\) 2096 pixels (5M mode) \\ \hline Illumination & Light Projector \\ \cline{2-3}  & Tracking Spots \\ \hline \multirow{4}{*}{Measuring Area [mm]} & Frame 150: 35 170 120 1180 \\ \cline{2-3}  & Frame 300: 110 1701 260 1400 1550 \\ \cline{2-3}  & Frame 600: 750 11500 \\ \cline{2-3}  & Frame 1200: 1500 13000 \\ \cline{2-3}  & Frame 1600: 5000 \\ \hline Control Device & ARAMIS Controller \\ \hline \multirow{4}{*}{Sensor Size [mm]} & Frame 150: approx. 260 \(\times\) 330 \(\times\) 300 \\ \cline{2-3}  & Frame 300: approx. 420 \(\times\) 330 \(\times\) 300 \\ \cline{2-3}  & Frame 600: approx. 730 \(\times\) 230 \(\times\) 130 \\ \cline{2-3}  & Frame 1200: approx. 1300 \(\times\) 230 \(\times\) 130 \\ \cline{2-3}  & Frame 1600: approx. 1700 \(\times\) 230 \(\times\) 130 \\ \hline Strain Measuring Range & 0.005\% up to \textgreater{}2000\% \\ \hline Strain Measuring Resolution & up to 0.005\% \\ \hline \end{tabular}
\end{table}
Table 2: DIC hardware parameters.  
In Figure 4, it can be observed that the highest mean values for each axis was recorded by the samples in group SP. For axis X, the highest mean value was 0.252 \(\pm\) 0.077 mm; for axis Y, it was 0.371 \(\pm\) 0.047 mm; and for axis Z, it was 0.134 \(\pm\) 0.097 mm.  
Figure 3: Mean values and standard deviations of rigid displacement for upper arch. * Statistically significant differences between groups are indicated by black line.  
Figure 2: Captured image of the 3D coordinates of each tracker placed on aligners.  
For the mandibular arch, the SP and TG groups showed significantly higher Z-axis values compared to the CA group.
# A Self-Scrutinized Backoff Mechanism for IEEE 802.11 ax in 5G Unlicensed Networks
## 6 Conclusions  
In future technologies, the IEEE 802.11 ax HEW hopes to become part of the 5G-U networks, as they promise four times higher network efficiency even in highly dense network deployments. However, currently WLAN itself faces huge challenge of efficient channel access due to its distributed contention-based nature. Currently, CSMA/CA is based on a BEB mechanism, which blindly increases and decreases the contention window for collided and successful transmissions, respectively. In this paper, we highlight some of the use-case scenarios for HEW deployments in 5G-U networks. Furthermore, to handle the performance degradation challenge caused by increasing density of the WLANs in those use cases, a channel observation-based scaled backoff (COSB) mechanism based on practical channel collision probability is proposed. COSB overcomes the limitation of BEB to achieve high efficiency and robustness in highly dense networks. A practical channel collision probability observed by the contending Wi-Fi equipment (WE) is presented to adaptively scale-up and scale-down the size of W during the backoff mechanism for collided and successfully transmitted data frames, respectively. The proposed COSB enhances the performance of CSMA/CA in dense networks. Furthermore, a recursive discrete-time Markov chain model (R-DTMC) is also presented to analyze the performance of COSB. The validated model helps to determine the maximum achievable throughput in highly dense networks. The analytical model helps to investigate the interesting importance of the practical channel observation-based collision probability. The analytical and simulation results show that the proposed protocol compared to the state-of-the-art BEB protocol offers a performance boost in terms of throughput and delay when the number of contending WEs increases. Furthermore, this protocol is designed with very few modifications to the existing BEB mechanism, which makes the COSB protocol a good candidate for upcoming high-efficiency WLANs (HEW) that are the IEEE 802.11 ax standard.  
Future research considerations include the implementation of the COSB mechanism for long term evolution (LTE)-based 5G-U networks, known as licensed assisted access (LAA). The addictiveness and practical collision probability measurement in COSB motivate us to integrate the algorithm in LAA.  
This work was supported in part by a 2018 Yeungnam University Research Grant and in part by a National Research Foundation of Korea (NRF) grant funded by the Korea government (No. 2018R1A2B6002399).  
**Author Contributions:** [PERSON] conceived and designed the algorithm. [PERSON] and [PERSON] designed and performed the experiments. [PERSON] and [PERSON] wrote the paper. [PERSON], [PERSON] and [PERSON] formulated the mathematical model. [PERSON] and [PERSON] supervised and finalized the manuscript for submission.  
**Conflicts of Interest:** The authors declare no conflict of interest.