esa-sceva/satcom-chunk-collection huggingface dataset:
# Signal Detection for GSTFIM Systems with Carrier Frequency Offset +
## 5 Complexity Analysis  
In this section, the complexity of the proposed detectors is analyzed in terms of the number of real-valued flops [12], where a real-valued multiplication and a real-valued addition are both considered as one real-valued flop. The complexities of the proposed SC-MMSE, BC-MMSE, SC-MP, and BC-MP detectors are given in (33), (34), (35), and (36), respectively.  
\[\begin{split} C_{SC-MMSE}&=\underbrace{10N_{r}N_{t}T }_{(8)}+\underbrace{(4N_{t}T2^{b_{3}}N-2N_{t}TN)}_{(10)}+\underbrace{(8T+2)N_{t }^{2}2^{b_{3}}+(8T-2)N_{t}^{2}}_{(10)}+\underbrace{GNN_{t}N_{r}(6+8T)}_{(11)} \\ &\quad+\underbrace{\big{\{}G(8N_{t}^{2}N_{r}+8N_{r}^{2}N_{t}-2N_{ t}N_{r})+4N_{r}^{3}+8N_{r}^{2}\big{\}}N}_{(21)}+\underbrace{\big{\{}9N_{r}+8N_{t}+1 \big{\}}N}_{(21)}+\underbrace{(8N_{r}-2)N_{t}TN}_{(20)}.\\ &\quad+\underbrace{(8N_{r}-2)N_{t}^{2}N}_{(22)}+\underbrace{(8N_{t }+8N_{r}+2)N_{t}^{2}N}_{(23)}+\underbrace{N_{t}N+N}_{(24)}+\underbrace{(8N_{t} ^{2}T+4N_{t}T+3)2^{b_{3}}N-N}_{(25)}\\ &\quad\quad\quad\quad\quad\quad\quad\quad\quad C_{BC-MMSE}=C_{ SC-MMSE}+\underbrace{2^{b_{3}}K(N_{b}-1)}_{(30)}\\ C_{SC-MP}&=\underbrace{10N_{r}N_{t}T}_{(8)}+ \underbrace{(4N_{t}T2^{b_{3}}-2N_{t}T)N}_{(33)}+\underbrace{\big{\{}(6N_{r}N_{ t}+8N_{r}N_{t}T)(G-1)-2N_{r}T\big{\}}NG}_{(33)}\\ &\quad+\underbrace{\big{\{}(8T+2)N_{t}^{2}2^{b_{3}}+(8  
In fact, the complexity of the proposed SC-MMSE detector is due mainly to (7), (9), (10), and (13)-(18). More specifically, the number of flops of \(\mathbf{E}(\mathbf{X}_{l})\) in (9) is given as follows. \(\beta_{i}P(\mathbf{X}_{l}=\beta_{i})\) needs \(2N_{l}T\) flops and this operation is executed \(2^{b_{3}}N\) times. \(\sum\limits_{\beta_{i}\in\mathbf{Y}}\beta_{i}P(\mathbf{X}_{l}=\beta_{i})\) needs \(2N_{l}T(2^{b_{3}}-1)\) flops and this operation is executed \(N\) times. Therefore, the number of flops required for (9) is \(4N_{l}T2^{b_{3}}N-2N_{l}TN\), while the number of flops for the other equations can be calculated in a similar way.
## Materials and Methods
### Firefly Algorithm and Its Improvements
#### 2.1.3 Simulation Analysis
FA: firefly algorithm. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Function**} & \multirow{2}{*}{**Parameter**} & \multicolumn{2}{c}{**The Average Operation Time (s)**} \\  & & **FA** & **LFA** & **IFA** \\ \hline \(f_{1}\) & \(0.2729\) & \(0.2769\) & \(0.2608\) \\ \(f_{2}\) & \(0.2659\) & \(0.2631\) & \(0.240933\) \\ \(f_{3}\) & \(0.24796\) & \(0.2591667\) & \(0.2313333\) \\ \(f_{4}\) & \(0.258367\) & \(0.252167\) & \(0.247967\) \\ \(f_{5}\) &As shown in Figure 2, the IFA algorithm proposed in this paper has achieved good results in the search for the global optimal process in different test functions, and the convergence speed is the fastest. IFA has a stronger ability to jump out of local optimality than the other two algorithms. As shown in Table 3, the accuracy of the optimal output value of IFA is higher, which basically belongs to the same level as the FA algorithm, and the accuracy of LFA optimal value search is lower. From the perspective of variance values, the variance values of the IFA algorithm are much smaller than those of the FA algorithm and LFA, indicating that the IFA algorithm is very stable and has a very high robustness. Figure 2: Iteration curves for the improved firefly algorithm (IFA), Levy-flight Firefly Algorithm (LFA), firefly algorithm (FA): (**a**) Iterative curve of function \(f_{1}\); (**b**) Iterative curve of function \(f_{2}\); (**c**) Iterative curve of function \(f_{3}\); (**d**) Iterative curve of function \(f_{4}\); (**e**) Iterative curve of function \(f_{5}\); (**f**) Iterative curve of function \(f_{6}\); (**g**) Iterative curve of function \(f_{7}\); (**h**) Iterative curve of function \(f_{8}\). As shown in Table 4, the operation time of the IFA algorithm is the shortest. In summary, the IFA algorithm has achieved the best results in terms of convergence speed, calculation accuracy, robustness, and calculation speed, and is particularly prominent in terms of robustness and calculation speed.
# Virtualized MME Design for IoT Support in 5G Systems
## 5 Virtualized MME Design for M2M
### Baseline Schemes  
#### 5.2.1 Baseline Scheme  
This scheme is based on the system model presented in Section 3 and also shown in Figure 6. BS is composed of the aforementioned three tiers:  
* FE: Front End,
* SL: Service Logic and
* SDB: State Database tier.  
In this scheme, all resources are shared by all traffic classes, and all tiers have to convey the same target arrival rate.  
For the baseline scheme, we set the target arrival rate such that each tier is able to serve the mean arrival rate from MBB UEs, mM2M devices and IM2M devices. Hence, with this scheme, each tier \(j\) is not able to satisfy \(\overline{D}_{j}\) during the M2M traffic peaks. For them, we apply the intense smoothing approach mentioned above. Hence, the vMME will be overloaded for a period of time during and after traffic peaks.  
Let \(\lambda_{FE}\), \(\lambda_{SL}\) and \(\lambda_{SDB}\) respectively denote the target arrival rate of control messages in the FE tier, the SL tier and the SDB tier. In BS, we calculate them as:  
\[\lambda_{FE}=\lambda_{SL}=\lambda_{SDB}=\lambda_{MBB}^{t}+\lambda_{mM2M}^{t}+ \lambda_{IM2M}^{t}=\overline{\lambda}_{MBB}+\lambda_{mM2M}^{intsmooth}+ \lambda_{IM2M}^{intsmooth} \tag{9}\]  
Note that this design requires enough memory resources to store all packets while they are queued, as assumed in Section 3.  
Figure 6: Baseline scheme block diagram.
## 2 Theoretical Analysis
### Principle of Continuous-Exposure Method  
Figure 3 illustrates the schematic diagram of the continuous-exposure method.  
Consider a vibrating point P on the object surface with vibration amplitude \(A(x)\) and angular frequency \(\omega\). The instantaneous displacement at time \(t\) is expressed as  
\[z(x,t)=A(x)\cos(\omega t) \tag{30}\]  
Compared to the equilibrium position, the phase change of point P at time \(t\) is  
\[\varphi_{0}(x,t)=\frac{2\pi}{\lambda}\cos(\omega t)(\cos\theta_{1}+\cos\theta_ {2}) \tag{31}\]  
where \(\theta_{1}\) and \(\theta_{2}\) denote the angles of incidence and reflection of the illumination beam on the object surface, respectively.  
The object wavefield reaching the holographic plate from point P is  
\[\widetilde{O}(x,t)=\widetilde{O}_{0}(x)\exp[j\varphi_{0}(x,t)] \tag{32}\]  
Figure 3: Optical path for recording in continuous-exposure holographic interferometry.  
where \(\widetilde{O}_{0}\) represents the static object wavefield (without vibration). Assuming the reference beam is a plane wave,  
\[\widetilde{R}(x)=r_{0}(x)\exp[j\varphi_{r}(x)] \tag{33}\]  
The recorded intensity on the hologram becomes  
\[I(x,t)=r_{0}^{2}+\left|\widetilde{O}_{0}(x)\right|^{2}+\widetilde{O}\widetilde{ R}^{*}+\widetilde{O}^{*}\widetilde{R} \tag{34}\]  
For exposure durations significantly longer than the vibration period \(T\), the time-averaged intensity is  
\[\left\langle I(x,t)\right\rangle=\frac{1}{T}\int_{0}^{T}I(x,t)dt=r_{0}^{2}+ \left|\widetilde{O}_{0}\right|^{2}+\frac{1}{T}\widetilde{R}^{*}\int_{0}^{T} \widetilde{O}(x,t)dt+\frac{1}{T}\widetilde{R}\int_{0}^{T}\widetilde{O}^{*}(x,t )dt \tag{35}\]  
When the hologram is illuminated with the original reference beam \(\widetilde{R}\), the reconstructed wavefield contains three components. Focusing on the transmitted wavefield related to the original object wave \(\widetilde{O}\), we obtain  
\[\widetilde{U}_{t}(x)=\frac{\widetilde{R}\widetilde{R}^{*}}{T}\int_{0}^{T} \widetilde{O}(x,t)dt=\frac{r_{0}^{2}}{2\pi}\int_{0}^{2\pi}\widetilde{O}(x,t) d(\omega t)=\frac{r_{0}^{2}\widetilde{O}_{0}(x)}{2\pi}\int_{0}^{2\pi}\exp[ jkA(x)\cos(\omega t)(\cos\theta_{1}+\cos\theta_{2})]d(\omega t) \tag{36}\]  
The continuous-exposure method captures vibration patterns through prolonged exposure, offering full-field measurement capabilities. However, it only captures time-averaged amplitude distributions and requires extensive post-processing for hologram reconstruction, making it unsuitable for real-time vibration monitoring. This technique is particularly effective for studying micro-vibrations and it enables high-precision measurement of vibration amplitudes.  
From the three methods discussed (single-, double-, and continuous-exposure), the double-exposure approach demonstrates superior performance in calculating object deformations due to its enhanced fringe contrast and noise immunity.
## 2 Materials and Methods  
### Study Area and Sample Collection  
The Guangxi Zhuang Autonomous Region is located in the southern part of China (\(104^{\circ}26^{\prime}\)-\(112^{\circ}04^{\prime}\) E, \(20^{\circ}54^{\prime}\)-\(26^{\circ}24^{\prime}\) N) (Figure 1). It has high temperatures and an abundance of rain due to its subtropical monsoon climate. Carbonate rocks make up the majority of the study area, that is, about 42% of the total land area, and they are mostly found in Guangxi's middle and western regions [31].  
In total, 305 paired soil-rice samples were obtained from the karst region in the study area in cooperation with the Guangxi Bureau of Geological and Mineral Exploration (Figure 1), and the samples were pre-processed in compliance with the Specification of Land Quality Geochemical Assessment [32]. Each rice sample consisted of an equal amount of rice grains from 3 sub-sampling points. The collected rice grains were placed in a mesh bag, hung in a cool place, air-dried, and then sent to the laboratory after threshing. Alongside the rice grain collection, the corresponding rhizosphere soil was also collected. After uprooting the rice roots, the soil adhering to the roots was shaken off. The depth of the rhizosphere soil collection depended on the depth of the rice roots. After uniformly mixing the soil shaken off from all sub-sampling points, the Four-Quarter Method was used to select a 1000 to 2000 g sample, which was then placed in a clean cloth bag and taken back to the base for air drying and processing. To minimize anthropogenic pollution inputs, the sampling locations were purposefully selected far from towns, highways, and mining regions.  
Previous studies have shown that the enrichment of soil As in karst areas is closely related to the extensive development of Fe-Mn nodules in the soil [29; 33]; therefore, six typical karst areas enriched with rooted soil As and where Fe-Mn nodules had extensively developed were selected in the study area (Figure 2), and the screening of the samples of Fe-Mn nodules with different diameters was carried out (Figure 1).
# Theoretical Optimization of Trapped-Bubble-Based Acoustic Metamaterial Performance
## 3 Results and Discussion
### Numerical Validation
However, the trend becomes more moderate with increasing \(p\) until it becomes independent of \(q\) for large values of \(p\). Finally, consider the case of varying reflection coefficient, \(r_{0}\). It is shown in Figure 10. Here, we fix \(q=1\) (lowest possible value) and examine the behavior of normalized frequency of the system with \(p\). In Figure 10a,c the \"positive\" branch of the solution is presented without (Figure 10a) and with dissipation (Figure 10c). The trend is overall repeatable with normalized frequency reaching saturation for large \(p\). The dramatic difference is that for small dissipation the rate of reaching saturation decreases with increasing \(r_{0}\) whereas it increases with increasing \(r_{0}\) when dissipation is taken into account. For \"negative\" branch of the solution (Figure 10b,d) the trend is almost the same: abrupt decrease of normalized frequency with increasing \(p\). However, the starting point of abrupt decay moves to lower values of \(p\) with increasing \(r_{0}\), opposite to the trend observed in Figure 8d. Figure 8: Normalized frequency as a function of \(p\) (distance between bubble centers) for metascreen (a) w/2 interfaces and w/o reflector, “positive” branch, \(kh\ll 1\); (b) w/2 interfaces and w/o reflector, “negative” branch, \(kh\ll 1\); (c) w/2 interfaces and w/reflector (\(r_{0}=0.5\)), “positive” branch, \(kh\ll 1\); (d) w/2 interfaces and w/reflector (\(r_{0}=0.5\)), “negative” branch, \(kh\ll 1\).
# Correction of Young's Modulus Calculation in the Flexural Mode of Resonant Column Test
## 2 Analysis Method of Flexural Test  
### Calibration of Flexural Test  
\(E\) was theoretically calculated from the flexural test following the formula by [PERSON] et al. [6]. During the flexural vibration, the effect of the inertia of driving systems on the resonant frequency cannot be directly assessed because of the complex EDD structure of the resonant column apparatus (Figure 2). Prior to using the calculation formula, the inertia effect of the EDD must first be calibrated to accurately compute for the \(E\) of the specimen.  
The configuration of the calibration test is shown in Figure 3. An aluminum calibration rod (a dummy specimen) is locked on the base and EDD through the top cap. Calibration for the EDD and calculation of \(E\) are conducted as follows:  
1. The flexural resonant frequency f1 is measured for the condition with an aluminum calibration rod through the flexural resonant test.
2. A calibration block with known mass is added to the EDD, and the flexural resonant frequency f2 is measured after the plate is added.
3. The inertial effect of the EDD is assessed using f1 and f2.
4. The \(E\) of the calibration rod is calculated according to f1 while accounting for the inertial effect of the EDD in Step 3.  
Figure 1: Resonant column torsional and flexural test mode.  
Figure 3 shows that the rotational inertia of the EDD during the flexural vibration has greater influence on the flexural vibration frequency because its radius is larger than that of other accessories. This condition must be considered in amending the calculation formula.
## 2 Laboratory Tests and Results
### Vickers Microhardness Test  
Hardness is the measure of the strength to resist localized plastic deformation induced by the mechanical impression due to a given applied load [19] and is obtained by measuring the permanent depth of the impression. The Vickers hardness test method is used for small objects, as well as polished thin sections. Due to the small indentation in a Vickers test, it can test small areas, the surface of a part, or individual microstructures [20]. It can also determine the depth of hardening by dissecting it and creating a series of indentations to find the change in hardness [21]. This method can evaluate almost any type of material, such as ceramics, metals, and rocks. The Vickers microdurometer uses devices developed by the authors of [22], i.e., a pyramid shape is created by a diamond indenter tool (opening of 136\({}^{\circ}\), as shown in Figure 5). By measuring the diagonals of the footprint (d in Figure 5, performed optically as described by the authors of [23]), its surface can be obtained and, consequently, the hardness can be computed as the ratio between the applied load and this area. When the applied load is less than 1 kg, it is common to refer to this test as microhardness. The Vickers test is easy to perform, as the required calculations are independent of the indenter size [24] and the indenter can be used for all types of materials, regardless of hardness. For these reasons, Vickers microhardness tests were chosen for the analysis. The tests were performed according to the authors of [25]. Unlike the standard, the dwell time was slightly longer, close to 40-45 s.  
The marble specimens, 50 \(\times\) 30 \(\times\) 15 mm in size, had a perfectly polished upper surface. The dimensions were chosen in order to maximize the available surface, compatible with the constraints of the instrument. Four different samples were prepared, taking care to select material with different mineralogy. Considering the grey veins embedded in the calcite matrix (white color of the background of the samples), the patterns were selected by choosing marble pieces with different presences of impurities (grey veins), as shown in Figure 6. For each sample, 80 microhardness measurements were performed by applying a load of 200 g (1.96 N). On average, the distance between two successive footprints was 100 \(\upmu\)m. Regarding the outcome analysis, the microhardness values for each sample were sorted and, subsequently, the values in terms of cumulative frequency were displayed, giving a characteristic curve. Figure 7 shows the microhardness characteristic curves obtained.  
Figure 5: Indentation scheme [23]. F is the applied force, while d is the diagonal of the left footprint.
## 5 Discussion and Conclusions
### Theoretical Implications  
This study has four contributions to the existing literature. First of all, this study conducts research on the innovative adoption of collaborative robots at the enterprise level and successfully analyzes the determinants of collaborative robots' innovation adoption in SMEs in China. Most of the previous research focuses on the adoption at the individual level. This study enriches the research on adopting collaborative robot innovation at the enterprise level.  
Secondly, this research adopts the empirical analysis method. Through the empirical research on the innovation of collaborative robots in China's SMEs, this paper objectively reflects the factors of enterprises' adoption of collaborative robots innovation. It fills the gap in the empirical research on the determinants of collaborative robot adoption at the enterprise level.  
Thirdly, this study introduces the observability and trialability elements that are not widely used in previous studies, empirically analyze the significant positive effects on the innovation adoption of collaborative robots, and broadens the research on innovation elements in innovation adoption.  
Finally, this research extends the most prominent combination model of TOE and DOI at the organizational level to the innovative adoption of collaborative robots in SMEs. In previous studies, the combined model of TOE and DOI has been widely used to explain innovation adoption. This study creatively uses the combined model of TOE and DOI to explore the innovative adoption of collaborative robots in SMEs. Specifically, this study extends the combined model of TOE and DOI to the research of collaborative robots, exploring the determinants of SMEs' adoption of collaborative robot innovation from the perspective of technology, organization, and environment. In general, this research provides a theoretical perspective, which can well explain the response of SMEs to the adoption of collaborative robots' innovation.
## 7 Long-Range Underground Monitoring
### Conclusions  
LPWAN technologies have great potential in underground mining for Long-Range Underground Mining use cases where robust coverage in wide or dynamic underground environments is needed. They are characterized by a greater range and lower use of network infrastructure but come with lower data throughput.  
LPWAN technologies, which can be operated as private networks, are particularly suitable for underground applications. Network infrastructure must be modified, disman  
\begin{table}
\begin{tabular}{l|c c c c c} \hline \hline LoRa & 3 & 3 & 3 (nodes), 2 (gateways) & 2-3 [1] & 2-3 & 1 (pps-kbps) \\ \hline Weightless & 2 & 3 & 3 (nodes), 2 (gateways) & Not known & 2 & 1 (up to 100 \\ \hline Wi-SUN & 2 & 3 & 3 (nodes), 2 (gateways) & Not known & 3 & 1 (300 kbps) \\ \hline DASH7 & 3 & 3 & 3 (nodes), 2-3 [1] & 2-3 [1] & 2 & 1 (9.6-166 kb/s) \\ \hline IQRF & 2 & 3 & 3 (nodes), 2 (gateways) & 2 [113] & 3 & 1 (19.8 kb/s) \\ \hline MIOTY & 3 & 3 & 3 (nodes), 2-3 [1] & 2-3 & 1 (407 bit/s) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summarized Comparison and Evaluation of LoRa, Weightless, Wi-SUN, DASH7, IQRF and Mioty.  
tled, or rebuilt in dynamic mining processes, and with privately operated networks this can also be done by the mining operator as opposed to a third party-the network operator.  
Ref. [10] gives a good overview of the LPWAN technologies in use today and compares them with respect to different criteria. Private LPWANs suitable for underground use are LoRa, Weightless, Wi-SUN, DASH7, IQRF and MIOTY. LoRa is the LPWAN that has been best studied for underground deployment. LPWAN technologies with integrated mesh functionality-Wi-SUN and IQRF-have been sparsely studied for underground use but offer great potential in terms of scalability in dynamic mining environments due to the mesh functionality. MIOTY offers a potentially better protection from interference in harsh mining environments than the other LPWAN technologies due to the telegram splitting multiple access method. Nonetheless, further research must be conducted to solidify the validity of these technologies in the use cases and environments described.
## 4 Results and Discussions
### Experimental Results and Comparison
#### 4.3.2 Results Comparison on the Double Diode Model  
The experimental results of the second case are summarized in Table 5. Similar to the comparison results on the single diode model, SOS performs better than ANS, BLPSO, CSO, CTLA, and LWOA in various RMSE indicators on the double diode model. SOS is surpassed by GOTLBO, CARO, and IJAYA, but it outperforms GGHS, PS, and SA. It is worth noting that the standard deviation value of SOS is the smallest among all compared methods, which indicates that SOS is highly robust. The extracted parameters are tabulated in Table 6. The reconstructed characteristic curves provided in Figure 5 clearly demonstrate that the calculated current and power achieved by SOS match up well with the measured values. The curve fitting results presented in Table 7 manifest once again that SOS can yield the smallest SIAE value (0.0182), followed by ANS, LWOA, BLPSO, CTLA, and CSO, which demonstrates the high accuracy of the parameters extracted by SOS for the double diode model. The convergence graph illustrated in Figure 6 reveals that SOS exhibits noticeably faster convergence rate than BLPSO, CSO, CTLA, and LWOA, but not ANS, which is slightly faster than SOS during the intermediate stage. However, ANS is surpassed by SOS in other stages. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Method** & \(\mathbf{I_{\text{ph}}}\) **(A)** & \(\mathbf{I_{\text{sd1}}}\) **(\(\mathbf{\mu}\)A)** & \(\mathbf{R_{\text{s}}}\) **(\(\mathbf{\Omega}\))** & \(\mathbf{R_{\text{sh}}}\) **(\(\mathbf{\Omega}\))** & \(\mathbf{n_{1}}\) & \(\mathbf{I_{\text{sd2}}}\) **(\(\mathbf{\mu}\)A)** & \(\mathbf{n_{2}}\) & **RMSE** \\ \hline GGHS & 0.76079 & 0.97310 & 0.03690 & 56.8368 & 1.92126 & 0.16791 & 1.42814 & 9.8635 \(\times\)\(10^{-4}\) \\ GOTLBO & 0.760752 & 0.800195 & 0.036783 & 56.075304 & 1.999973 & 0.220462 & 1.448974 & 9.83177 \(\times\)\(10^{-4}\) \\ CARO & 0.76075 & 0.29315 & 0.03641 & 54.3967 & 1.47338 & 0.09098 & 1.77321 & 9.8260 \(\times\)\(10^{-4}\) \\ IJAYA & 0.7601 & 0.0050445 & 0.0376 & 77.8519 & 1.2186 & 0.75094 & 1.6247 & 9.8293 \(\times\)\(10^{-4}\) \\ PS & 0.7602 & 0.9889 & 0.0320 & 81.3008 & 1.6000 & 0.0001 & 1.1920 & 1.5180 \(\times\)\(10^{-2}\) \\ SA & 0.7623 & 0.4767 & 0.0345 & 43.1034 & 1.5172 & 0.0100 & 2.0000 & 1.9000 \(\times\)\(10^{-2}\) \\ ANS & 0.7609 & 0.1785 & 0.0369 & 51.5905 & 1.8181 & 0.2466 & 1.4581 & 1.0042 \(\times\)\(10^{-3}\) \\ BLPSO & 0.7607 & 0.5481 & 0.0338 & 78.6922 & 1.5442 & 0.0542 & 1.5765 & 1.5704 \(\times\)\(10^{-3}\) \\ CSO & 0.7628 & 0.7954 & 0.0409 & 15.7733 & 1.6936 & 0.6780 & 1.8138 & 1.7013 \(\times\)\(10^{-3}\) \\ CTLA & 0.7570 & 0.8542 & 0.0313 & 89.6464 & 1.7879 & 0.3812 & 1.5230 & 1.3216 \(\times\)\(10^{-3}\) \\ LWOA & 0.7597 & 0.2342 & 0.0355 & 86.8763 & 1.4679 & 0.3709 & 1.6989 & 1.3120 \(\times\)\(10^{-3}\) \\ SOS & 0.7606 & 0.5408 & 0.0365 & 55.5537 & 1.9346 & 0.2418 & 1.4579 & 9.8518 \(\times\)\(10^{-4}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Extracted parameters for the double diode model.