esa-sceva/satcom-chunk-collection huggingface dataset:
# Mode Coupling Properties of the Plasmonic Dimers Composed of Graphene Nanodisks
## 2 Models and Simulation Method  
The 2D schematic of the plasmonic dimer molecule composed of graphene nanodisks is illustrated in Figure 1, where \(R\) is the radius of the nanodisks; \(g\) is the gap between the two nanodisks; the chemical potential of each nanodisk is \(\mu_{\text{c2}}\) and \(\mu_{\text{c3}}\), respectively. The chemical potential of the infinite area is \(\mu_{\text{c1}}\). Perfectly matched layer (PML) is employed as the boundary condition in order to avoid the reflected wave at the boundaries. In this article, the plasmonic properties of the dimers are investigated by utilizing the commercially available software COMSOL Multi-physics, RF module, eigen frequency solver. The graphene monolayer is modeled as an ultrathin film which can be characterized by the effective index defined as \(n_{\text{eff}}=\beta/k_{0}\), where \(k_{0}=2\pi/\lambda\) is the wavenumber in free space [26]. The propagation constant of surface plasmon polaritons (SPPs) supported by monolayer graphene can be written as [27; 28]  
\[\text{table}\beta=k_{0}\sqrt{1-\left(\frac{2}{\eta_{0}\sigma_{\text{g}}} \right)^{2}} \tag{1}\]  
where \(\eta_{0}\) (377 \(\Omega\)) is the intrinsic impedance of the air, which can be expressed by \((\mu_{0}/\varepsilon_{0})^{1/2}\) where \(\mu_{0}\) and \(\varepsilon_{0}\) represent the permeability and permittivity of vacuum, respectively. The surface conductivity of graphene \(\sigma_{\text{g}}\), composed of the interband electron transitions \(\sigma_{\text{inter}}\) and the intraband electron-photon scattering \(\sigma_{\text{intra}}\), is given by the Kubo formula [17; 27; 28; 29],  
\[\sigma_{\text{g}}=\sigma_{\text{intra}}+\sigma_{\text{inter}} \tag{2}\]  
with  
\[\sigma_{\text{intra}}=\frac{-ie^{2}k_{B}T}{\pi\hbar^{2}(\omega-i/\tau)}\bigg{[} \frac{\mu_{\text{c}}}{k_{B}T}+2\ln\left(1+\exp(-\frac{\mu_{\text{c}}}{k_{B}T}) \right)\bigg{]} \tag{3}\]  
\[\sigma_{\text{inter}}=\frac{-ie^{2}}{2\hbar}\ln\left[\frac{2\left|\mu_{\text{c }}\right|-\hbar(\omega-i/\tau)}{2\left|\mu_{\text{c}}\right|+\hbar(\omega-i/ \tau)}\right] \tag{4}\]  
where \(\mu_{\text{c}}\) is the chemical potential of graphene; \(\omega\) is the angular frequency of the plasmon; \(\hbar\) is the reduced [PERSON]'s constant; \(e\) is the electron charge; \(k_{B}\) is the [PERSON]'s constant; \(T\) is the temperature;and \(\tau\) is the electron momentum relaxation time. Specifically, the chemical potential of graphene is tunable by either chemical doping or external electrical gating locally [27; 30]. Pioneering work showed that a chemical potential as high as 2 eV has been obtained experimentally [31]. Also, a relaxation time as high as 3 ps has been achieved experimentally [32]. Thus, in this paper, a relaxation time less than 1 ps and a chemical potential less than 1 eV are chosen, which are conservative enough to ensure the reliability of our calculation.
## 3 Results and Discussion
### Hierarchical Cluster Analysis (HCA) and Volatile Profiles Development
#### 3.3.5 Changes in the Concentration Profiles of Esters  
Esters are generally known to the fruity and floral flavor of foods derived from esterification of fatty acids with alcohols [34]. As shown in Figures 3 and 4, most of the esters were identified in the early-stage samples (S-0 to S-120). Typically, 2,2,4-trimethyl-1,3-pentanediodidisobutyrate (no odor) has the highest content (5.60 \(\mathrm{\SIUnitSymbolMicro g}\)/g) in S-0 followed by dibutyl phthalate (faint odor, 5.14 \(\mathrm{\SIUnitSymbolMicro g}\)/g), ethyl acetate (etherial, fruity and rum-like odor, 2.67 \(\mathrm{\SIUnitSymbolMicro g}\)/g), and octyl formate (fruity odor, 1.31 \(\mathrm{\SIUnitSymbolMicro g}\)/g). It is worth noting that octyl formate has recently been identified as volatile biomarker in Jintao (_A. chinensis_), providing a pleasant odor [35], and ethyl acetate has been recognized as a vital flavor component of sugarcane juice [36]. Methyl salicylate (minty odor) and acetate (banana-like odor) were the esters that developed only in S-240, with concentrations of 0.22 and 1.15 \(\mathrm{\SIUnitSymbolMicro g}\)/g, respectively. Methyl hexadecanoate and ethyl hexadecanoate with waxy, fruity, and creamy odor esters found in _Castanopsis fissas_ wood [37] were both identified in S-0 and S-90 and absent in other intervals, except the latter, which developed in a tiny amount in the final NCS product (0.29 \(\mathrm{\SIUnitSymbolMicro g}\)/g).
## Materials
### Characterization
#### 2.2.3 Swelling Behavior  
Samples with a dimension of 20 mm \(\times\) 30 mm \(\times\) 2 mm size with an initial weight (\(W_{i}\)) were swelled in toluene at room temperature until they reached an equilibrium state of swelling. The swelled samples were then taken out and the adhered solvent was wiped off from the surface using a filter paper, and the weights (\(W_{s}\)) were immediately recorded. From the values of \(W_{i}\) and \(W_{s}\), and the molecular weight of the solvent (\(M_{w}\)), the equilibrium swelling in percentage and the solvent uptake in mol percentage were calculated using Equations (2) and (3), respectively, [9; 19]. To understand the speed of swelling, the solvent uptake of the blends cured with CV and CV/bismaleimide were also measured at different time intervals from 0 to 2880 min.  
\[Equilibrium\ swelling\ (\%)=\frac{Ws-Wi}{Ws}\times 100 \tag{2}\]  
\[\text{Solvent\ uptake}\ (\text{mol}\%)=\frac{1}{Mw}\left(\frac{Ws-Wi}{Wi}\right) \times 100 \tag{3}\]  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Mix No.** & **Mix ID** & **NR** & **CR** & **BIIR** & **ZnO** & **MgO** & **St. Acid** & **Sulfur** & **CBS** & **MF** \\ \hline
1 & NR-CV & 100 & - & - & 5 & - & 2 & 2.5 & 0.5 & - \\
2 & NR-CVMF\({}_{3}\) & 100 & - & - & 5 & - & 2 & 2.5 & 0.5 & 3 \\
3 & CR-ZnO & - & 100 & - & 5 & 4 & 0.5 & - & - & - \\
4 & CR-ZnOMF\({}_{3}\) & - & 100 & - & 5 & 4 & 0.5 & - & - & 3 \\
5 & BIIR-ZnO & - & - & 100 & 5 & - & - & - & - & - \\
6 & BIIR-ZnOMF\({}_{3}\) & & - & 100 & 5 & - & - & - & - & 3 \\
7 & NR/CR-CV & 50 & 50 & - & 5 & 2 & 1.25 & 1.25 & 0.25 & - \\
8 & NR/CR-CVMF\({}_{3}\) & 50 & 50 & - & 5 & 2 & 1.25 & 1.25 & 0.25 & 3 \\
9 & NR/BIIR-CV & 50 & - & 50 & 5 & - & 1 & 1.25 & 0.25 & - \\
10 & NR/BIIR-CVMF\({}_{3}\) & 50 & - & 50 & 5 & - & 1 & 1.25 & 0.25 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Formulation of the mixes.
###### Abstract  
The issue of the cut-off splicing of an additional fiber-reinforced polymer (FRP) bar in the near-surface mounted (NSM) technique for reinforced concrete (RC) beam strengthening exposed dominantly to bending is insufficiently investigated. A possible solution of this issue is a new proposed technique: a double-strap joint. It implies the widening of the groove at the cut-off location and the symmetrical installing of additional supplements of FRP reinforcement. In this research, beam strength has been determined for the following cases: additional NSM FRP reinforcement without a cut-off, with a cut-off, and without overlapping, and with different lengths of splice overlapping. A nonlinear analysis based on the finite element method (FEM) has been applied. The length of the cut-off splice of the additional FRP reinforcement with glass fibers (GFRP) was \(20\mathcal{O},40\mathcal{O},\) and \(60\mathcal{O}\). The validation of the numerical model and a comparison of the results were conducted by using the authors' experiments. It has been shown that, in the case of a cut-off of NSM GFRP bars, a significant loss in strengthening efficiency occurs, and that, with an increase in the overlapping length, this loss decreases. An overlapping length of \(60\mathcal{O}\) provides full strengthening. An efficiency assessment was carried out via the use of a parametric study, varying the FRP bar material type and its diameter for a constant splicing length.  
FEM analysis; double-strap joint; NSM FRP strengthening; FRP reinforcement cut-off; RC beams +
Footnote †: journal: Applied Sciences
# Traffic Conflict Prediction for Sharp Turns on Mountain Roads Based on Driver Behavior Patterns
## 2 Materials and Methods
### Prediction Model Design
#### 2.3.2 Model Parameter Settings  
The LSTM prediction model is implemented using the PyTorch 1.12.1 deep learning framework. The main hyperparameter settings are as follows:  
(1) LSTM layer:  
Number of hidden layer neurons: 128.  
Number of layers: 2.  
Enter the time step: 30.  
(2) Full connection layer:  
Number of neurons: 64.  
(3) Output layer:  
Number of neurons: 4 (corresponding to four conflict levels).  
(4) Batch size: 64.  
(5) Learning rate: 0.001.  
(6) Optimization algorithm: Adam.  
(7) Loss function: cross-entropy.  
(8) Early Stop method Patience: 10 epochs.  
To illustrate the parameter selection process, Figure 7 shows the learning rate vs. model performance validation accuracy graph.  
A small-batch gradient descent method is adopted for model training, parameters are iteratively updated on the training set, and performance is evaluated on the verification set. The training process is as follows:  
(1) Data loading: Using PyTorch's DataLoader, set batch_size = 64, shuffle = True.  
(2) Forward propagation: Input 30 s of sequence data to obtain predictive output.  
(3) Back propagation: Calculate the loss and update the model parameters.  
(4) Validation: Evaluate the model on the validation set after each epoch.  
To visualize the training process, record the training loss, training accuracy, validation loss, and validation accuracy for each epoch (as shown in Figure 8).  
As can be seen from the figure, the performance of the model on both the training set and the verification set improves with the increase in the number of epochs. Training losses and validation losses continue to decline, while accuracy continues to rise. Validation set performance is optimal at about Epoch 42, when the validation set accuracy is 85.6% and the loss is 0.38.  
Figure 7: Learning rate and verification accuracy.  
To prevent overfitting, the early stop method is applied. When the verification set performance has not improved for 10 consecutive epochs, the training process is automatically stopped. In the experiment, after the model reached the best performance at the 42 nd Epoch, it was trained for another 10 epochs without further improvement in performance, so the training stopped at the 52 nd Epoch. As shown in the Figure 9. It should stop before the dark blue dashed line.  
In this way, the training is stopped in time when the model reaches the best performance, and the overfitting problem is avoided.
## 4 Materials and Methods
### New Model after Throwing the Same Permutation Assumption
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**DPFSP Model**} & & \multicolumn{3}{c}{**DFSP Model**} \\  & **Interval** & **Boolean** & **No\_Overlap** & **Other** & **Interval** & **No\_Overlap** & **Other** \\  & **Variables** & **Variables** & **Constraints** & **Constraints** & **Variables** & **Constraints** & **Constraints** \\ \hline Ta001\_2-Ta010\_2 (2/20/5) & 200 & 760 & 2 & 4740 & 200 & 10 & 180 \\ Ta011\_2-Ta020\_2 (2/20/10) & 400 & 760 & 2 & 8740 & 400 & 20 & 380 \\ Ta021\_2-Ta030\_2 (2/20/20) & 800 & 760 & 2 & 16,740 & 800 & 40 & 780 \\ Ta031\_2-Ta040\_2 (2/50/5) & 500 & 4900 & 2 & 29,850 & 500 & 10 & 450 \\ Ta041\_2-Ta050\_2 (2/50/10) & 1000 & 4900 & 2 & 54,850 & 1000 & 20 & 950 \\ Ta051\_2-Ta060\_2 (2/50/20) & 2000 & 4900 & 2 & 104,850 & 2000 & 40 & 1950 \\ Ta061\_2-Ta070\_2 (2/100/5) & 1000 & 19,800 & 2 & 119,700 & 2000 & 10 & 900 \\  & & & & & & & \\ \hline Ta001\_7-Ta010\_7 (7/20/5) & 700 & 2660 & 7 & 16,540 & 700 & 35 & 580 \\ Ta011\_7-Ta020\_7 (7/20/10) & 1400 & 2660 & 7 & 30,540 & 1400 & 70 & 1280 \\ Ta021\_7-Ta030\_7 (7/20/20) & 2800 & 2660 & 7 & 58,540 & 2800 & 140 & 2680 \\ Ta031\_7-Ta040\_7 (7/50/5) & 1750 & 17,150 & 7 & 104,350 & 1750 & 35 & 1450 \\ Ta041\_7-Ta050\_7 (7/50/10) & 3500 & 17,150 & 7 & 191,850 & 3500 & 70 & 3200 \\ Ta051\_7-Ta060\_7 (7/50/20) & 7000 & 17,150 & 7 & 366,850 & 7000 & 140 & 6700 \\ Ta061\_7-Ta070\_7 (7/100/5) & 3500 & 69,300 & 7 & 418,700 & 3500 & 35 & 2900 \\ \(\cdots\) & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Number of decision variables and constraints for DPFSP model and DFSP model for selected problem instances of the large dataset (rows for factories \(\in\{3,4,5,6\}\) and jobs \(\in\{200,500\}\) are not shown). Figure 4: Optimal schedule (makespan = 436) according to the DFSP model for the same problem instance as in Figure 3.  
Figure 3: Optimal schedule (makespan = 440) according to the DPFSP model for the fourth problem instance with two factories, six jobs, and five machines (per factory) of the small dataset (problem instance: L_2\(6\)5_4).
# Performance Evaluation of Heading-and-Winning Machines in the Conditions of Potash Mines
## 1 Introduction  
Companies from Russia and the Commonwealth of Independent States (hereinafter CIS) engaged in the extraction of potash and magnesium ores widely use room-and-pillar mining with strip pillars to mine productive formations, which involves sets of mining and transporting machines referred to as mechanized combine complexes. Each mechanized complex includes the mining unit, which is a heading-and-winning machine; a storage capacity, which is a feeder-breaker; and the delivery vehicle, which is a self-propelled car [1, 2, 3, 4].  
At Russia's potash mines, the most widely used mining units are the Ural heading-and-winning machines (Figure 1) manufactured by Kopeysk Machine-Building Plant JSC (Chelyabinsk Region). Continuous work on improving the design of Ural combines, carried out by the manufacturer, has ensured high reliability indicators (MTBF (mean time between failures), maintainability) and adaptability of the combines to the conditions of potash enterprises in Russia and CIS countries. Comparative tests with similar heading-and-winning machines manufactured in Western Europe and North America have shown higher average annual productivity of Ural combines in fracturing of sticky or strong potash ores of the Verkhnekamskoye deposit [5]. Ural-20R units are used in rooms withheight H = 3.1 m and a cross-sectional area of 15.75 m\({}^{2}\). The rated power of each of the actuator engines is 160 kW in relative motion; the rotary actuator has a power of 75 kW.  
A comparative assessment of the performance of each heading-and-winning machine in the course of operating in the conditions of potash mines is necessary in order to determine their field of application, develop technical solutions for the modernisation of existing models, control the stability of technological processes in the stope and evaluate the performance of the services on mining enterprises.
## 2 Materials and Methods
### Generation of Activated FX in Medium  
Cells with stable expression of rFVIIwt or rFVII-354V-464 Hfs were seeded onto 15 cm dishes (Thermo Scientific, Rockford, IL, USA) and treated with \(\pm\) 10 mM 4-PBA. Conditioned medium was harvested and concentrated approximately 30 times through the Amicon Ultra Centrifugal Filter devices (cut-off 10 kDa, Millipore, Carrigtwohill, Country Cork, Ireland). Generation of activated factor X (FXaG) was performed in FVII-deficient plasma (HemosIL(r) Instrumentation Laboratory, Bedford, MA, USA) added with the concentrated medium from transfected cells. The assay was performed in an HBS buffer (Hepes, 20 mM; NaCl, 150 mM; PEG-8000, 0.1%; pH 7.4) as previously described [29]. Briefly, after incubation for 3 min at 37 \({}^{\circ}\)C, FXaG was triggered by 50 pM Tissue Factor (Innovin, Dade Behring, Marburg, Germany) and evaluated by adding a specific FXa fluorogenic substrate (Spectrafluor FXa, American Diagnostic, Stamford, CT, USA). FXaG was also evaluated by inhibition of tissue factor pathway inhibitor (TFPI) by anti-TFPI RNA aptamer (5\({}^{\prime}\)-GGAAUAUAdCUUGGdCUdCGUUAGGUGdGCUAUAUAidT-3\({}^{\prime}\)), as previously described [30; 31].
# Rao and Wald Tests for Moving Target Detection in Forward Scatter Radar
## 3 Design of Adaptive Detectors without Secondary Data  
When the secondary data are not available, we formulate the detection problem as the following binary hypothesis test:  
\[\left\{\begin{array}{l}H_{0}:\widetilde{\mathbf{z}}=\widetilde{\mathbf{\alpha}}\,\bm {r}_{d}+\widetilde{\mathbf{n}}\\ H_{1}:\widetilde{\mathbf{z}}=\widetilde{\mathbf{\alpha}}\,\widetilde{\mathbf{r}}_{d}+ \widetilde{\mathbf{\beta}}\,\widetilde{\mathbf{r}}_{t}+\widetilde{\mathbf{n}}\end{array}\right. \tag{44}\]where \(\overset{\sim}{\boldsymbol{r}}_{t}\in\mathbb{C}_{N\times 1}\) and \(\overset{\sim}{\boldsymbol{r}}_{d}\in\mathbb{C}_{N\times 1}\) denote \(N\) -dimensional complex vectors containing samples collected from the target signal and direct signal, \(\overset{\sim}{\boldsymbol{\theta}}\) and \(\overset{\sim}{\boldsymbol{\pi}}\) are unknown complex amplitudes of the target signal and direct signal, \(\overset{\sim}{\boldsymbol{z}}\in\mathbb{C}_{N\times 1}\) denotes the primary data, \(\overset{\sim}{\boldsymbol{\pi}}\) denotes the noise which satisfies \(\overset{\sim}{\boldsymbol{n}}\sim\mathcal{CN}\left(\boldsymbol{0},\overset{ \sim}{\boldsymbol{\sigma}}^{2}\boldsymbol{I}_{N}\right)\), and \(\overset{\sim}{\sigma}^{2}\) denotes the noise variance.  
The PDF of the primary data is  
\[f\left(\overset{\sim}{\boldsymbol{z}}|H_{i}\right)=\frac{1}{\pi^{N}\overset{ \sim}{\sigma}^{2N}}\exp\left[-\frac{1}{\overset{\sim}{\sigma}^{2}}\left(\left\| \overset{\sim}{\boldsymbol{z}}-\overset{\sim}{\boldsymbol{\pi}}_{d}-i \overset{\sim}{\beta}\overset{\sim}{\boldsymbol{r}}_{t}\right\|^{2}\right)\right] \tag{45}\]  
where \(i=0\), \(1\).
# Applications of Satellite Radar Imagery for Hazard Monitoring: Insights from Australia
## 5 Natural Resource Extraction
### Coal Seam Gas Extraction  
Other applications of SAR to monitor natural resource extraction in Australia include Coal Seam Gas (CSG) operations in the Surat Basin, southern Queensland. CSG extraction involves the removal of significant volumes of groundwater to depressurise the reservoir and allow gas to be released from the pore spaces in the host formation. Therefore, subsidence is a highlighted concern, and the use of InSAR has been recommended to aid in subsidence monitoring and development of a surface deformation prediction [57].  
Baseline subsidence monitoring efforts in the Surat Basin using ALOS PALSAR imagery between 2006 and 2011 have revealed conflicting results. [58] did not identify any large-scale pattern of ground displacement with a magnitude greater than 8 mm/yr, whereas [59] identified displacements coincident with CSG mining at rates of up to \(-28\) mm/yr. Consequently, this region is now the site of a network of 40 radar corner reflectors to validate relative ground displacements recorded by InSAR (from Sentinel-1 data on both ascending and descending passes) with ground-based absolute displacement measurements made with GNSS [60]. This will aid in further monitoring of this gas field.
# Network Diffusion Algorithms and Simulators in IoT and Space IoT: A Systematic Review
## 4 Analysis
### Analysis of Research Question 4: Network Architecture
#### 4.3.5 Layered and Multi-Layered Networks  
Layered networks represent different communication levels (e.g., physical and application layers), while multi-layered networks capture interactions between different layers, allowing for more complex modeling of IoT systems.  
Single Layer  
Diffusion occurs within a single type of connection (e.g., device-to-device communication), which simplifies modeling but may not fully represent real-world IoT networks.  
Multi-Layer Networks  
Consider interactions between multiple types of connections (e.g., physical and virtual communications). These networks offer more realistic simulations but require more complex algorithms and higher computational resources.
# Routing in Mobile Wireless Sensor Networks: A Leader-Based Approach+
## 1 Introduction
On the other hand, a more complex routing protocol could require additional stability conditions for a sensor node to communicate with the sink [10]. This paper, which extends a preliminary work of ours [11], presents a routing approach for Mobile Wireless Sensor Networks. The approach is inspired on leader-election techniques for dynamic systems that we have explored previously [9; 12]. It uses a measure of the link quality to maintain a reconfigurable spanning tree in the network graph in order to minimize the transmission cost from the mobile sensor nodes to the mobile sink while providing a high message delivery rate. We present two alternative protocols which implement this approach. The first one (LBR1) uses a reactive, asynchronous forwarding mechanism to propagate leader election messages, while the second protocol (LBR2) is based on a periodic communication pattern. We also present an evaluation of the protocols, basically on the basis of the message delivery rate, regarding their scalability and their resilience to mobility. Approaches based on the use of a reconfigurable spanning tree have been proposed before [13]. However, as far as we know, our proposal is the first one for MWSN that uses leader election as a basis for the reconfiguration. The rest of the paper is organised as follows. In Section 2, we review some works related to ours. Section 3 describes our approach and the two routing protocols that implement it. Section 4 presents the evaluation environment and the performance results of our protocols in comparison with two other related ones. Finally, Section 5 is devoted to conclude the work and present future research directions.
# Privacy-Preserving E-Voting System Supporting Score Voting Using Blockchain
## 8 Conclusions  
This paper proposes a privacy-preserving ledger-based e-voting system that supports score-based ranked voting to ensure that the system maintains maximum ballot secrecy, fairness, and self-tallying. The proposed scheme allows voters to cast their votes encrypted to the Blockchain while allowing the voters to prove that the selected score lies in the predefined range to ensure fairness. This verification is done in a zero-knowledge environment to preserve privacy. A set of experiments were set up to evaluate the performance of the proposed system, and an analysis of the results revealed that the current scheme is practical for both small and mid-sized elections where low latency is desired. Additionally, no matter the size of the workload, the Ethereum network is able to maintain a 0% error rate when executing the system's transactions, proving that, if deployed on the Ethereum network, our system has extremely high reliability. The proposed scheme is able to resolve the issue of maintaining the privacy of the voter and the election results while keeping the Blockchain transparent and verifiable through the use of zero-knowledge proofs.  
Conceptualization, A.A., G.S. and M.B.; formal analysis; funding acquisition, A.A. and M.B.; investigation, M.B.; methodology; A.A., G.S., W.R. and M.A. ([PERSON]) project administration, G.S. and; resources, G.S. and M.A. ([PERSON]); software, M.B.; supervision, G.S., M.B. and G.S.; validation, W.R. and M.A. ([PERSON]); writing--review and editing, A.A., G.S., W.R., M.A. ([PERSON]), M.A. ([PERSON]) and M.B. All authors have read and agreed to the published version of the manuscript.  
The authors extend their appreciation to the Deanship of Scientific Research at University of Tabuk for funding this work through Research No. 0270-1443-S.  
The authors declare that there is no conflict of interest.