esa-sceva/satcom-chunk-collection huggingface dataset:
# DTS-Net: Depth-to-Space Networks for Fast and Accurate Semantic Object Segmentation
## 4 Experiments
### Joint Semantic Segmentation and Depth Estimation  
We further trained the proposed method on the simultaneous task of semantic segmentation and depth estimation. NYUV2 provides dense depth data captured by a Kinect sensor that is suitable for direct use, and CITYSCAPES provides disparity data that are equivalent to depth (as the disparity has a linear relationship with the depth). Here, we trained DTS-Net on the disparity without conversion. We trained our main model, DTS-Net, on NYUV2 with an image size of \(640\times 480\) and on Cityscape with an image size of \(1024\times 512\) for the depth estimation and semantic segmentation tasks. For the depth estimation, we added a third branch, which was identical to the branch for the segmentation branch, that is, a \(1\times 1\times 1024\) convolutional layer followed by a DTS layer to construct the depth map. We retained the two main branches, that is, the semantic segmentation branch and the multi-label classification branch. We then trained the network for 1000 epochs under the same hardware configuration, as previously mentioned in Section 4.4. As the depth estimation and semantic segmentation have a common training procedure and the aim is a dense map, we believe that the two tasks help each other. This is confirmed in Section 5, where the accuracy and error values for the depth are reported; these values were obtained simultaneously with the performance of the semantic segmentation task.
# Joint Content Recommendation and Delivery in Mobile Wireless Networks with Outage Management
## 4 Heuristic Algorithms for Joint Content Recommendation and Delivery
### Radio Resource Allocation
#### 4.1.2 Minimum Rate Maximization  
Another useful heuristic is to maximize the minimum data rate among multiple users. This means that users with poorer channel conditions will have higher priorities in resource allocation and that the capacity allocated to users tends to be equal and fair. The optimization problem is a max-min problem formulated as:  
\[\begin{split}\max\min_{u=1\cdots U}&\sum_{k=1}^{K}c _{uk}\rho_{uk}\\ \text{subject to}& B*\sum_{u=1}^{U}\sum_{k=1}^{K}\frac{f(c_{uk})}{ \alpha_{uk}^{2}}*\rho_{uk}\leq P_{T}\\ &\sum_{u=1}^{U}\rho_{uk}\leq 1.\end{split} \tag{22}\]  
This problem can be effectively solved by a heuristic algorithm proposed as Algorithm 2.  
```
0:\(U\), \(K\), \(P_{T}\), \(\alpha_{uk}^{2}\)
0:\(c_{u}\), \(P_{u}\)
1:(1) Initialization:  Let \(A=\{1,2,\ldots,K\}\) denote unassigned subcarrier sets; \(P_{u}=0\) is the power allocated to user \(u\); \(c_{uk}=0\) represents the number of bits allocated on the subcarrier \(k\) allocated to the user \(u\); \(c_{u}=0\) indicates the total number of bits allocated to the user \(u\); \(A_{u}=\varnothing\) denotes the set of subcarriers assigned to the user \(u\);
2:(2) Carrier allocation:
3:while\(A\
eq\varnothing\)do
4:for\(u=1,2,\ldots,U\)do
5: find the subcarrier \(k\) with the smallest \(\Delta p_{uk}(0)\) in the set \(A\), and assign them to the corresponding user \(u\); meanwhile \(A_{u}=A_{u}\cup\{k\}\), \(A=A-\{k\}\)
6:if\(A=\varnothing\)then break;
7:(3) Bit and power allocation:
8:while\(\sum_{u=1}^{U}p_{u}\leq P_{T}\)do
9:for\(u=1,2,\ldots,U\)do
10: Traverse the subcarriers corresponding to the user \(u\); find the subcarrier \(k\) with the smallest \(\Delta p_{uk}(c_{uk})\), and allocate \(1\) bit of data to the subcarrier; meanwhile \(c_{uk}=c_{uk}+1\), \(P_{u}=P_{u}+\Delta p_{uk}(c)\), \(c_{u}=c_{u}+1\)
11:if\(\sum_{u=1}^{U}p_{u}>P_{T}\)then break;
12:return\(c_{u}\), \(P_{u}\)
```  
**Algorithm 2** Minimum rate maximization algorithm.  
Once the pre-allocation of radio resource is completed, the data rate of user \(u\) can be calculated as:  
\[c_{u}=\sum_{k=1}^{K}c_{uk}p_{uk}\qquad(\text{bit/symbol}). \tag{23}\]  
The bit rate assigned to the \(u\)-th user is approximately:  
\[R_{u}=c_{u}*B=\sum_{k=1}^{K}c_{uk}p_{uk}*B\qquad(\text{bit/s}). \tag{24}\]
## 3 Results  
### Annual and Seasonal Vegetation Trends (2000-2019)  
A pixel-based trend analysis was performed for the annual and seasonal NDVI trends for the last two decades to properly understand the vegetation dynamics to the recent climate variability and changes over the Horn of Africa. Figure 3a represents the annual mean MODIS NDVI trend and the slope increased at an average rate of 6.2\(\times\) 10\({}^{-4}\) per year during the period 2000-2019; 1, 205, 709 km\({}^{2}\) of the area (51.7%) represent an increasing trend (\(p<0.05\)). Similarly, the MODIS NDVI trends in the Horn of Africa increased at an average rate of 3.5 \(\times\) 10\({}^{-4}\), 2.34 \(\times\) 10\({}^{-4}\), 2.01 \(\times\) 10\({}^{-4}\), 7.7 \(\times\) 10\({}^{-4}\) per year from March to May (53.1% of the pixels), from June to August (56.2%), from September to November (56.9%), and from December to February (50.8%) seasons (Figure 3b-e), respectively. The vegetation change trends did not show considerable variations among the different seasons of the Horn of Africa.  
\begin{table}
\begin{tabular}{c c c} \hline
**No.** & **Resilience Status** & **Range** \\ \hline
1 & Resilient & \(\geq\) 1 eRd \\
2 & Slightly Non-Resilient & 0.9 \(\leq\) eRd \(<\) 1 \\
3 & Moderately Non-Resilient & 0.8 \(\leq\) eRd \(<\) 0.9 \\
4 & Non-Resilient & eRd \(<\) 0.8 \\ \hline \end{tabular}
\end{table}
Table 1: Ecosystem resilience to drought using dimensionless ecosystem Resilience Index (eRd).  
Even though the annual mean NDVI trend slightly showed an increasing trend, yet the trend differs in the area depending on the type of agroecological zones and land cover types. For instance, the maximum positive NDVI trends were common in western parts of Ethiopia and Kenya, while negative NDVI trends were exhibited in large parts of Somalia, Djibouti, and Eritrea (Figure 3). Figure 4 shows the integration of the mean annual NDVI trend with ESA land cover and agroecological zones. The forest and cropland land covers showed the largest increase in the annual mean NDVI trend (Figure 4a), while the smallest trend change was observed in grassland cover during the past two decades apart from the sparse vegetation and the barren land as most of the barren land covers are masked out in the analysis.  
Figure 3: NDVI trends during 2000–2019 in the Horn of Africa. (**a**) Annual mean and (**b**–**e**) seasonal mean trends.  
We also carried out zonal statistics of the annual mean NDVI trend for the agroecological zone (Figure 4b). The topic-cool humid agroecological zones comprised the highest increasing NDVI trend, whereas the topic-cool arid characterized the lowest increasing annual mean NDVI trend as illustrated in Figure 4b. The humidity and aridity factors played key roles in the spatial distribution and trend of the vegetation change in the region. All the seasons in the Horn of Africa showed similar patterns like the annual mean NDVI trend over different agroecological zones and land cover types, yet the highest increasing trend and variation was detected in the season from September to November, even for the sparsely vegetated land cover and arid zones.
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Authors (year)** & **Methods** & **Data** & **Results** \\ \hline [PERSON] et al. (2022) ([PERSON] et al., 2022) ([PERSON] et al., 2022) & Sparsity-based method, during iterative reconstruction algorithms & Real data and simulations & 0.9682 (SSIM) \\ \hline [PERSON] et al. (2004) ([PERSON] et al., 2018) & Post-reconstruction filtration method & Real data & \begin{tabular}{c} Only visual results, no \\ quantitative data \\ \end{tabular} \\ \hline [PERSON] et al. (2001) ([PERSON] et al., 2001) ([PERSON] et al., 2001) & Wavelet analysis pre-reconstruction method & Callibration cells & \
\begin{tabular}{c} Only visual results, no \\ quantitative data \\ \end{tabular} \\ \hline [PERSON] et al. (2014) ([PERSON] et al., 2014) & Pre-reconstruction method of calculating the ratio of adjacent detector elements in the projection data, termed the line ratio & Sheep–Logan phantom and human organs models & \(7.13\times 10^{-7}\) (MSE) for Sheep-Logan phantom \\ \hline [PERSON] et al. (2011) ([PERSON] et al., 2011) & Comparison five ring artifact removal methods: Modified Wavelet (MWPN) ([PERSON] et al., 2018), Wavelet-Fourier (WF) method ([PERSON] et al., 2011), Ring corrections using homogeneity test (RCHT) ([PERSON] et al., 2018), Ring correction in polar coordinate (RCP) ([PERSON] et al., 2011), and Strength based ring correction (SBRC) method ([PERSON] et al., 2018) &
\begin{tabular}{c} quantitative data for head \\ Phantom (MSSIM): \\ MWPN—0.97 \\ WF=0.81 \\ RCHT—0.82 \\ RCP—0.77 \\ SRRC—0.98 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A table showing papers on methods for reducing ring artifacts, along with the type of data on which the method was tested and example results.
# Deep Visual Waterline Detection for Inland Marine Unmanned Surface Vehicles
## 4 Experiments and Evaluation
### Experimental Settings  
The proposed waterline detection approach (Algorithm 1) has been deployed in the visual perception subsystem of our own USV customized to patrol within inland water, which is equipped with an on-board computer, a compass, GPS unit and IMU unit, as shown in Figure 5. To assist navigation, the visual perception subsystem mounts a general digital camera with auto-focus and exposure mode, which is connected to the on-board computer through the USB-3.0 bus and capable of capturing and handling the video of resolution 1080 \(\times\) 1440 pixels at 10 frames per second. All experimental data (optical images) came from varied waterfront scenarios under different weather and illumination conditions, such as sunset with weak illumination, sunny weather with strong illumination, and foggy weather, when our USV was traveling in the East Lake, one of the largest urban lakes in China.
## 3 Collective-Rotation Noise Analysis
### Multi-Photon Analysis
\begin{split}\varepsilon_{2k+2}=&\,(\frac{1}{2}\, \rho_{k}^{\text{odd}}+\rho_{k+1}^{\text{odd}}...+\rho_{2k+1}^{\text{odd}}) \varepsilon_{0}+(\frac{1}{2}\,\rho_{k+1}^{\text{odd}}+...\rho_{2k+1}^{\text{ odd}})\overline{\varepsilon_{0}}\\ =&\,\frac{1}{2}\,\rho_{k}^{\text{odd}}\varepsilon_{ 0}+\rho_{k+1}^{\text{odd}}\varepsilon_{0}+\frac{1}{2}\,\rho_{k+1}^{\text{odd }}\,\overline{\varepsilon_{0}}+\rho_{k+2}^{\text{odd}}+...+\rho_{2k+1}^{\text {odd}}.\end{split}  \tag{15}\]  
From Equation (9), it is clear that  
\[\rho_{k}^{\text{odd}}=\frac{\overline{\varepsilon_{0}}}{\varepsilon_{0}}\, \rho_{k+1}^{\text{odd}} \tag{16}\]  
Therefore,\[ \begin{split}&\frac{1}{2}\,\rho_{k}^{\text{odd}}\,\mathcal{E}_{0}+ \rho_{k+1}^{\text{odd}}\,\mathcal{E}_{0}+\frac{1}{2}\,\rho_{k+1}^{\text{odd}} \,\overline{\mathcal{E}_{0}}\\ &=\frac{1}{2}\,\frac{\overline{\mathcal{E}_{0}}}{\mathcal{E}_{0} }\,\rho_{k+1}^{\text{odd}}\,\mathcal{E}_{0}+\rho_{k+1}^{\text{odd}}\, \mathcal{E}_{0}+\frac{1}{2}\,\rho_{k+1}^{\text{odd}}\,\overline{\mathcal{E}_{ 0}}\\ &=\frac{1}{2}\,\rho_{k+1}^{\text{odd}}\,\overline{\mathcal{E}_{0} }+\rho_{k+1}^{\text{odd}}\,\mathcal{E}_{0}+\frac{1}{2}\,\rho_{k+1}^{\text{odd }}\,\overline{\mathcal{E}_{0}}\\ &=\rho_{k+1}^{\text{odd}}\,.\end{split}  \tag{17}\]  
So \(\mathcal{E}_{2k+2}\) will be written as  
\[\mathcal{E}_{2k+2}=\rho_{(k+1)odd}+\rho_{(k+2)odd}+...+\rho_{(2k+1)odd}=\sum_ {i=k+1}^{i=2k+1}\rho_{i}=\mathcal{E}_{2k+1} \tag{18}\]  
We have proved that the error rate of using \(2k+1\) photons is the same as that of using \(2k+2\) photons under the same value of \(k\). The physical meaning can be found in the possible scenarios above. Essentially, the \((2k+2)-th\) photon either makes no contribution to the final outcome, or contributes to a tie situation. Therefore, it has no overall impact on the mean error rate. This only applies to the pair of photon numbers that share the same value of \(k\). As \(k\) increases, the mean error rate will drop, which is demonstrated in Section 3.4. In this section, the error rate of multi-photon transmission in three-stage protocol is analyzed under the collective-rotation noise model. The relationship between the error rate and the deflection angle is established. Numerical results will be shown in the following section.
## 3 Results and Discussion
### Adsorption Studies
#### 3.3.3 Effects of Initial Concentration  
As illustrated in Figure S2a,b, the adsorption capacity derived using Equation (2) is plotted as a function of the initial target molecule concentration in the range of 5 to 50 mg L\({}^{-1}\). The results show a linear relationship between the two for the MIP. In the case of NIP, there is an increase in adsorption with an increase in target molecule concentration. However, a decrease in adsorption efficiency was observed for NAP and IBU as their concentrations reached 40 mg L\({}^{-1}\). This decrease was not observed for the MIP because it has the designed imprints of the template molecules. Similar trends were observed  
Figure 7: pH effects on the adsorption of ARVs and NSAIDs.  
in other multi-template studies for MIP adsorption [12]. High adsorption capacities of EMI and TENO, the two ARV drugs, were observed in the NIP with capacities of 49.05 and 22.85 mg\(\cdot\)g\({}^{-1}\), respectively. This might be due to the flexibility of the non-imprinted polymer to fit the size of these compounds with no steric hindrance. Also, as seen in the SEM and BET results above, the NIP has non-distinct cavities with a larger total pore volume than the MIP; hence, these two compounds with larger molecular weight have been adsorbed more in this material (Figure 5).
## 3 Apparent Power Vectors Proposed  
As described in the previous sections, from the symmetric components of voltages, any unbalanced linear three-phase system is divided into three linear three-phase systems. \(S_{v+}\), \(S_{v-}\), and \(S_{v0}\) are the apparent powers in each of these three systems, and their RMS values are calculated from (19), (22), and (23). Each of these expressions can be expressed in vector form three apparent power vectors: positive-sequence voltage apparent power vector \(\overrightarrow{S_{v+}}\), negative-sequence voltage apparent power vector \(\overrightarrow{S_{v-}}\), and zero-sequence voltage apparent power vector \(\overrightarrow{S_{v0}}\). Each of these vectors has six components that are orthogonal to each other and are determined from the following expressions:  
\[\overrightarrow{S_{v+}}=\sqrt{3}\bigg{(}P_{a+}^{a}\overrightarrow{up_{u+}}+P _{b+}^{b}\overrightarrow{up_{b+}}+P_{c+}^{c}\overrightarrow{up_{v+}}+Q_{a+} ^{a}\overrightarrow{up_{a+}}+Q_{b+}^{b}\overrightarrow{up_{a+}}+Q_{c+}^{c} \overrightarrow{up_{a+}}\bigg{)} \tag{28}\]  
\[\overrightarrow{S_{v-}}=\sqrt{3}\bigg{(}P_{a-}^{a}\overrightarrow{up_{u+}}+P _{b-}^{b}\overrightarrow{up_{b-}}+P_{c-}^{c}\overrightarrow{up_{v-}}+Q_{a+} ^{a}\overrightarrow{up_{a-}}+Q_{b-}^{b}\overrightarrow{up_{a+}}+Q_{c-}^{c} \overrightarrow{up_{v-}}\bigg{)} \tag{29}\]  
\[\overrightarrow{S_{v0}}=\sqrt{3}\bigg{(}P_{a0}^{a}\overrightarrow{up_{u0}}+P _{b0}^{b}\overrightarrow{up_{u0}}+P_{c0}^{c}\overrightarrow{up_{v0}}+Q_{a0} ^{a}\overrightarrow{up_{u0}}+Q_{b0}^{b}\overrightarrow{up_{u0}}+Q_{c0}^{c} \overrightarrow{up_{u0}}\bigg{)} \tag{30}\]where  
* \(\overrightarrow{u_{pz+}}\) and \(\overrightarrow{u_{qz+}}\) are the unit vectors that are associated with the six components of the apparent power vector due to the positive-sequence voltage. These unit vectors are perpendicular to each other.
* \(\overrightarrow{u_{pz-}}\) and \(\overrightarrow{u_{qz-}}\) are the unit vectors that are associated with the six components of the apparent power vector due to the negative-sequence voltage. These unit vectors are perpendicular to each other.
* \(\overrightarrow{u_{pz0}}\) and \(\overrightarrow{u_{qz0}}\) are the unit vectors that are associated with the six components of the apparent power vector due to the zero-sequence voltage. These unit vectors are perpendicular to each other.  
The total apparent power according to (3) is calculated from the vector moduli \(\overrightarrow{S_{p+}}\), \(\overrightarrow{S_{p-}}\), and \(\overrightarrow{S_{p0}}\).  
Moreover, active power \(P\) and reactive power \(Q\), used in the classical theories, are determined by the arithmetic sum of all the components of their nature, their expressions being the following:  
\[P=\sum P_{z+}^{z}+\sum P_{z-}^{z}+\sum P_{z0}^{z}\quad z=\{a,b,c\} \tag{31}\]  
\[Q=\sum Q_{z+}^{z}+\sum Q_{z-}^{z}+\sum Q_{z0}^{z}\quad z=\{a,b,c\} \tag{32}\]
## 2 Preliminaries  
For the purpose of conducting our current research we consider the SAT problem encoded in Conjunctive Normal Form (CNF). CNF formulas (\(\mathcal{F}\)) consist of a conjunction \(\bigwedge\) of _clauses_, and each clause is a disjunct \(\bigvee\) of literals where each literal is a propositional variable or its negations. The task is to find boolean values (true, or false) assignment for all the literals that can evaluate the formula (\(\mathcal{F}\)) to true. Applying systematic search techniques to this task is computationally costly as they require exponential run time because they cover every possible assignment of literals. Therefore, SLS is the method of choice when handling the large SAT problems [35], as they consider only a subset of the search space (known as candidate solution), which is evaluated in polynomial time. A candidate solution to an SAT encoded problem in CNF formula (\(\mathcal{F}\)) is an assignment of all literals in CNF such that the objective cost function \(\
eq 0\) (or incomplete solution). A solution to an SAT problem is reached when the objective function of the given CNF (\(\mathcal{F}\)) is \(=0\).  
Given a CNF (\(\mathcal{F}\)) formula, a clause \((cl)\in(\mathcal{F})\) can be either _satisfied_ or _unsatisfied_. A clause is satisfied if \(\exists\) a true literal \(\in cl\), otherwise the clause \(cl\) is unsatisfied. The objective function of the CNF formula (\(\mathcal{F}\)) is \(=0\) (where all the clauses are satisfied), iff there is an assignment of all literals \(\in(\mathcal{F})\), that is, all the clauses are satisfied. We denote the size of a clause \(sCl\) as the number of all literals \(\in cl\). Furthermore, if a same signed literal \(l\) occur in \(cl_{i}\) and clause \(cl_{j}\) where \(i\
eq j\) then the clauses \(cl_{i}\) and \(cl_{j}\) are neighbors. \(sNcl\) denotes the number of all neighboring clauses of the clause \(cl\).
## 4 Case Study
### Three-Player Game  
To further show the performance of the proposed trading strategy, a cooperative case for two WPPs and a DRA is considered.  
With the coordination, the payoff of the WPP and DRA can be significantly improved. Through the cooperative game theory methods, the profits of WPPs and DRAs are allocated and shown in Table 3. As presented in Table 3, the extra profits of WPPs and DRAs are higher than the values in the cases of section A, which verifies again that cooperation can result in more profits. Note that in the cases of Section 4.2, the extra profit is allocated equally. For the three-player game, more profit is allocated to the WPP1 whether through the Nucleolus-based or Shapley-value method.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Player**} & \multirow{2}{*}{
\begin{tabular}{c} **Uncoordinated** \\ **Profits (USD)** \\ \end{tabular} } & \multicolumn{2}{c}{**Nucleolus-Based Method**} & \multicolumn{2}{c}{**Shapley-Value Method**} \\ \cline{3-6}  & & \multirow{2}{*}{**Profit (USD)**} & \multirow{2}{*}{**Surplus** **Profit (USD)**} & \multirow{2}{*}{**Profit (USD)**} & \multirow{2}{*}{**Profit (USD)**} & **Surplus** **Profit (USD)** \\ \hline WPP 1 & 55,534.5 & 58,579 & 3044.6 & 58,375.0 & 2840.5 \\ \hline WPP2 & 38,683.0 & 41,078 & 2394.7 & 41,199.0 & 2516.0 \\ \hline DRA & 25,782.0 & 28,253 & 2471.2 & 28,336.0 & 2554.0 \\ \hline Total & 119,999.5 & 127,910.0 & 7910.5 & 127,910.0 & 7910.5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of results of allocated profits based on the game theory method.  
Figure 7: Comparison of individual and integrated bidding strategies for WPP and DRA, (**a**) results of Case S2, (**b**) results of Case S4, (**c**) results of Case S6.  
To further explore the capabilities of the proposed trading scheme, simulations over 1 week (1-7 May 2020) have been implemented and the daily profits are shown in Figure 8. The green and cyan bars represent the market profit for the cooperation case and sum of profits for independent cases, respectively, whereas the blue and orange bars denote the expected profit of coordinated power plants and uncoordinated power plants obtained from the regulation market. Although the daily profit fluctuates, it can also conclude that the market profit of the coordinated case is higher than the sum of profits in individual cases. The average regulation-related profit is also remarkable, accounting for 39.73% of the total profit of the cooperation case. More specifically, the average regulation-related revenue in the coordinated case increased by 31.0% with respect to that in the uncoordinated case.
## 3 Materials and Methods
### Vehicle Behavior Prediction
#### 3.3.2 Dynamic Scene Perception and Interactive Learning Mechanism  
After encoding the spatial relationships using DMR-GCN, a dynamic scene sensing and interactive learning mechanism is introduced. This mechanism dynamically adjusts the model's learning process by sensing real-time changes in the traffic scene and interaction patterns of vehicles. Through interactive learning, the model can adjust parameters and weights in real time during the prediction process, thereby improving predictions' real-time performance and accuracy based on the current traffic situation and vehicle interaction patterns.
# Influence of MOSFET Parasitic Capacitance on the Operation of Interleaved ZVS Boost Converters
## 3 Soft Switching Conditions  
Equation (13) allows for plotting the voltage ratio \(G\) as a function of normalized switching frequency \(f_{\text{s}}/f_{0}\) for set values of \(\overline{R}\) and \(N\). An example is shown in Figure 4, for different values of \(\overline{R}\) and \(N=2\) (two-phase converter). Switching frequency of the converter should not exceed the resonant frequency to ensure proper operation of the converter. Conditions for soft switching can also be found by analyzing (13). The value under the square root sign must be equal to or greater than zero. Therefore the following soft switching condition can be found:  
\[1-\frac{1}{\left(G-1\right)^{2}}\geq 0, \tag{14}\]  
which yields:  
\[G\geq 2. \tag{15}\]  
Conditions for soft switching are highlighted in Figure 4.  
Figure 4: Plot of the voltage ratio \(G\) as a function of normalized switching frequency for \(N=2\) and different values of \(\overline{R}\).  
current of the inductors (see Equation (A9) in Appendix A). This affects power loss in the converter and possibly could lead to magnetic core saturation of inductors \(L_{1}\) and \(L_{2}\). Therefore inductance \(L\) and capacitance \(C\) should be chosen not only to obtain a desired value of resonant frequency \(f_{0}\), but also by taking load resistance \(R\) and current values into consideration.