esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Special Cases
### Special Cases for Outage Probability Analysis  
The obtained analytical results for the \(\kappa-\mu/\kappa-\mu\), \(\kappa-\mu/\)Rician, \(\kappa-\mu/\)Nakagami-\(\hat{m}\), \(\kappa-\mu/\)Rayleigh, Rician/\(\kappa-\mu\), Rician/Nakagami-\(\hat{m}\), Rician/Rayleigh, Nakagami-\(\hat{m}\)/Nakagami-\(\hat{m}\), Nakagami-\(\hat{m}/\)/\(\kappa-\mu\), Nakagami-\(\hat{m}\)/Rician, Nakagami-\(\hat{m}\)/Rayleigh, Rayleigh/Rayleigh, Rayleigh/\(\kappa-\mu\), Rayleigh/Rician, and Rayleigh/Nakagami-\(\hat{m}\) fading links given in Table 2 are already reported in [45].  
The remaining derived analytical results given in Table 2 for the \(\kappa-\mu\) shadowed/\(\kappa-\mu\), \(\kappa-\mu\) shadowed/Rician, \(\kappa-\mu\) shadowed/[PERSON]-\(\hat{m}\), \(\kappa-\mu\) shadowed/Rayleigh, \(\kappa-\mu/\)\(\kappa-\mu\) shadowed, \(\kappa-\mu/\)Rician-shadowed, Rician-shadowed/Rician-shadowed, Rician-shadowed/\(\kappa-\mu\) shadowed, Rician-shadowed/\(\kappa-\mu\), Rician-shadowed/Rician, Rician-shadowed/[PERSON]-\(\hat{m}\), Rician-shadowed/Rayleigh, Rician/\(\kappa-\mu\) shadowed, Rician/Rician-shadowed, Nakagami-\(\hat{m}/\)\(\kappa-\mu\) shadowed, Nakagami-\(\hat{m}/\)Rician-shadowed, Rayleigh/\(\kappa-\mu\) shadowed, and Rayleigh/Rician-shadowed fading links are new in the literature.
# Human Activity Recognition Based on Non-Contact Radar Data and Improved PCA Method
## 4 Experiments and Results
### Test and Evaluation
#### 4.3.1 Performance Comparison  
We used the Skimage Python package to convert image files into pixel information and stored the data in a python array. The shape of the image data array is [224,224,3], representing a PNG file with a width of 224 pixels and height of 224 pixels, with 3 representing the pixel values of red, green and blue (RGB). We then converted the image to a grayscale image, removed the color, and changed the array shape to [224,224,1]. This data array was saved in CSV format for machine learning processing. We then compared the proposed method with some commonly used machine learning methods, with the results shown in Table 4.  
According to the results shown in Table 4, our proposed method had the best performance in terms of accuracy, precision, recall, F1-score, and training time. In addition to our proposed method, KNN had better performance than the other machine learning algorithms when using original radar spectrograms, mainly due to its simple logic and insensitivity to abnormal values. SVM had a shorter training time than the other methods due to its advantages in processing small-scale datasets. However, the traditional SVM only gave the binary classification algorithm, so the results of SVM were not ideal when dealing with the problem of six classifications. The overall performance of Bi-LSTM was acceptable, but the training time was too long because of its complex network structure. Overall, the results of traditional machine learning methods in radar data classification were not satisfactory. Our proposed method based on improved PCA and an improved VGG16 model is more suitable for processing small-scale radar data, which is superior to other methods in terms of results and training time reduction.
# Trajectory Tracking Control of a Manipulator Based on an Adaptive Neuro-Fuzzy Inference System
## 3 Kinematic Analysis of the Manipulator  
### Kinematic Analysis  
Kinematics analysis mainly studies the relationship between the joint variables and the pose of the end-effector in space, including forward kinematics inverse kinematics.  
Forward kinematics determine the pose of the end-effector based on the given joint variables. In this paper, the D-H method is adopted to establish the link coordinate systems, as shown in Figure 4, where, \(\theta_{i}\) is the joint angle and \(O_{i}X_{i}Y_{i}Z_{i}\) is the coordinate system of each link. The homogeneous transformation matrix is defined [21]:  
\[T_{i}^{i-1}=\begin{bmatrix}R_{i}^{i-1}&P_{i}^{i-1}\\ 0&1\end{bmatrix} \tag{1}\]  
where, \(R_{i}^{i-1}\) and \(P_{i}^{i-1}\) represent the orientation and position of \(\{i-1\}\)th frame with respect to the \(\{i\}\)th frame.  
By multiplying the matrix from the base frame to the end frame, the pose of the end effector is obtained, that is, the forward kinematics equation:  
\[T_{4}^{0}=T_{1}^{0}T_{2}^{1}T_{3}^{2}=\begin{bmatrix}n_{x}&\alpha_{x}&a_{x}&p_ {x}\\ n_{y}&\alpha_{y}&\alpha_{y}&p_{y}\\ n_{z}&\alpha_{z}&\alpha_{z}&p_{z}\\ 0&0&0&z\end{bmatrix}\begin{bmatrix}c_{1}c_{234}&-c_{1}s_{234}&s_{1}&L_{4}c_{1} c_{234}+L_{3}c_{1}c_{23}+L_{2}c_{1}c_{2}\\ s_{1}c_{234}&-s_{1}c_{234}&-c_{1}&L_{4}s_{1}c_{234}+L_{3}s_{1}c_{23}+L_{2}s_{1} c_{2}\\ s_{234}&c_{234}&0&L_{4}s_{234}+L_{3}s_{23}+L_{2}s_{2}+L_{1}\\ 0&0&0&1\end{bmatrix} \tag{2}\]  
In Equation (2), \(c_{1}=cos\theta_{1},s_{1}=sin\theta_{1}\); \(c_{234}=\cos(\theta_{2}+\theta_{3}+\theta_{4}),s_{234}=\sin(\theta_{2}+\theta _{3}+\theta_{4})\).  
The main purpose of inverse kinematics is to solve the joint variables according to the given pose of the end-effector. In this paper, the algebraic method is utilized to solve the joint variables as:  
\[\begin{bmatrix}\theta_{1}\\ \theta_{3}\\ \theta_{2}\\ \theta_{4}\end{bmatrix}=\begin{bmatrix}\tan^{-1}\biggl{(}\sqrt{4{L_{2}}^{2}{ L_{3}}^{2}-\Bigl{(}M^{2}+N^{2}-{L_{2}}^{2}-{L_{3}}^{2}\Bigr{)}^{2}}/\Bigl{(}M^{2}+N^{2}-{L_{2}}^{2}-{L_{3}}^{2} \Bigr{)}\biggr{)}\\ \tan^{-1}\bigl{(}(L_{3}c_{3}M+L_{2}M-L_{3}s_{3}N)/(L_{3}c_{3}N+L_{2}N+L_{3}s_{ 3}M)\bigr{)}\end{bmatrix} \tag{3}\]  
In Equation (3), \(M=p_{z}-L_{1}-L_{4}n_{z}\), \(N=p_{x}c_{1}+p_{y}s_{1}-L_{4}n_{x}c_{1}-L_{4}n_{y}s_{1}\), \(P=-c_{1}s_{23}p_{x}-s_{1}s_{23}p_{y}+c_{23}p_{z}+L_{2}s_{3}+L_{1}c_{23}\), \(Q=c_{1}c_{23}p_{x}+s_{1}c_{23}p_{y}+p_{z}-L_{3}-L_{2}c_{3}-L_{1}s_{23}\).
## 2 Materials and Methods
### Fabrication of i-ELISA  
The polymer-coated PHBV fiber mats were integrated into a novel design of 96-well plate. Figure 2a shows a schematic representation of the prototype's segments. Each lid has 96 attached PMMA pillars (6 mm diameter \(\times\) 8.7 mm length) which were fabricated through a layer-by-layer assembly of PMMA pillars and two pressure sensor adhesive layers (PSA; \(\sim\)100 \(\upmu\)m thickness, model FLEXmount DFM-200-Clear) (FLEXcon Company, Spencer, MA, USA) for each individual PMMA pillar. One PSA layer was used to affix the pillar section to the lid and the other one was used to secure the round-shaped polymer-coated PHBV electrospun fiber mat (\(\sim\)500 \(\upmu\)m thick), as shown in Figure 2a. A computer numerical controlled (CNC) precision tool (model Vision 2525, Vision Engraving and Routing Systems, Phoenix, AZ, USA) and a vinyl cutting/plotter machine (Model PUMA II, GCC, Taipei, China) were used for cutting out the PMMA pillars and fabricating the PSA layers from large PMMA and PSA sheets, respectively.  
Morphology Analysis by Scanning Electron Microscopy (SEM) and Water-in-Air Contact (WCA) Angle Analysis  
Surface morphology of the coated and uncoated PHBV fibers was analyzed with scanning electron microscopy equipped with a field emission gun (JSM7600F, JEOL, Santa Barbara, CA, USA) following the previously reported experimental method [24]. Frontal and cross-section images of the coated and uncoated samples were analyzed in order to study the effect of coating on the morphology of the PHBV fibers.  
Water-in-air contact angle (WCA) was measured for the droplets of distilled water deposited on the coated and uncoated surfaces of PHBV fiber mats. The average contact angle for each sample was calculated from the measurements performed on five separate droplets of distilled water on three samples from each composition [24]. Contact angles were measured 1 min after deposition of the droplets at the center and on the four corners of the samples. Standard deviations, in these experiments, were negligible as a minor spreading (\(\pm\)0.3\({}^{\circ}\)) was observed for almost all of the measurements (\(n=15\)). Contact angle measurement was also performed on the 96-well plate made from polystyrene (PS) for a clear comparison between wettability of the commercially available ELISA well plates and the customized platforms.
## d Comparison of Different Protocols with Node Mobility
### Experiment  
#### 4.3.1 Experiment Setup  
For the experiments, we used the _usens_ mode developed in our lab, running ContikiOS 2.6. It was equipped with Texas Instrument's CC2630 which integrates the ultra-low-power ARM CorTex M3 and CC2420 radio chip (that conforms to a 250 kbps 2.4 GHz IEEE 802.15.4 wireless transceiver). Micro controller unit has 20 KB of RAM and 128 KB of flash memory. The current draw, excluding the radio, was 1.45 mA in active mode and 1.2 \(\mu\)A in sleep mode. The CC2630 consumed 5.9 mA  
Figure 11: Comparison of different protocols with varying K (\(N=4\)).  
Figure 12: Packet delivery ratio according to the percentage of mobile nodes (\(K=6\)).  
in receiving or listening mode, and 9.1 mA when transmitting at level +5 dBm [22]. One gateway (GW) and twenty-five sensor nodes were artificially deployed along a corridor in the building of the computer engineering department in the University of Ulsan, as shown in Figure 13, with an equal distance of about 7 m between parent and child. The GW is located at the middle. In the experiments, 1 to 3 selected nodes were moved at an average speed of 1 m per second along the traces, pausing at each stop point for 5 s. The other key experimental parameters and values are shown in Table 6.
# Classification of Imbalanced Data Represented as Binary Features
## 2 Materials and Methods
### Oversampling Methods
Another improvement of SMOTE, namely safe-level SMOTE [8], highlighted the drawbacks of SMOTE, in that SMOTE naively ignores nearby majority instances in synthesizing the minority samples along a joining line of a minority sample and its selected nearest neighbors. This newly generated synthetic sample by SMOTE causes classifiers to create larger and less specific regions, which results in overgeneralization. Therefore, safe-level SMOTE will determine whether a sample in a minority class is safe to use in generating synthetic samples or not. In other words, safe-level SMOTE is designed to generate synthetic samples around the selected positive samples that are considered as safe. Relocating-safe-level SMOTE (RSLS) is as an improvement on safe-level SMOTE [9]. It highlights the fact that safe-level SMOTE ignores the possibility that some synthetic samples are generated closer to negative samples than to positive samples. It contradicts the procedure of generating synthetic samples in safe-level SMOTE, as it tries to avoid negative samples when generating synthetic samples. Therefore, RSLS introduced an additional algorithm to relocate the generated synthetic samples if they are generated near to negative samples. Another variation of SMOTE-based oversampling techniques is the density-based minority oversampling technique, or DBSMOTE. It is an integration of DBSCAN and SMOTE [10]. DBSCAN, or density-based spatial clustering of application with noise, aims at discovering clusters with an arbitrary shape [26]. DBSMOTE aims at generating synthetic samples of an arbitrarily shaped cluster found using DBSCAN. Inspired by borderline-SMOTE in maintaining the detection rate of majority class, DBSMOTE focuses the work on the overlapping region. However, this technique also highlights the drawbacks of borderline SMOTE, which fails in maintaining the detection rate of positive samples while improving the detection rate of negative samples. To resolve this drawback, DBSMOTE developed a different approach towards precise oversampling, both in the overlapping and the safe region, by synthesizing a minority instance along the shortest path retrieved in a directly density-reachable graph from each sample to the pseudo-centroid cluster. While the methods mentioned above focus on where to generate synthetic samples, Adaptive Neighbor SMOTE (ANS) focuses on the number of neighbors needed to ideally synthesize a new sample [11]. In other words, ANS concentrates on deciding the appropriate value for the parameter \(k\) in \(k\)-nearest neighbors of each positive sample that is needed in synthesizing a new sample. The parameter \(k\) is selected according to the density level of each positive sample's region. By utilizing the value of \(k\), the area of the generated synthetic samples will be more spread out inside the dense area and not sparsely distributed, as in SMOTE.
## 3 Materials and Methods
### Quality Control Data (QCD)
#### 3.4.4 Enrichment Factor (EF)  
The Enrichment Factor (EF) is a widely used tool for assessing the degree of metal accumulation in river sediments [49]. It is calculated using the following equation:  
\[\text{EF}=(\text{C}_{\text{x}}/\text{C}_{\text{ref}})_{\text{sediment}}/( \text{C}_{\text{x}}/\text{C}_{\text{ref}})_{\text{Background}}\]  
where Cx represents the concentration of the element of interest, and Cref is the concentration of a reference (or conservative) element, both in the sediment sample and in the background or crustal material. Reference elements are those whose concentrations are primarily influenced by natural (lithogenic) sources and are minimally affected by anthropogenic activities. They are typically characterized by low variability in natural abundance. Elements such as Ti, Ce, Sc, Mn, Zr, and Hf have been employed as reference elements [50]; however, Al and Fe are the most commonly used due to their consistent geochemical behaviour [51; 52]. Because the average composition of the Earth's crust may not accurately reflect local conditions, crustal values are often replaced with site-specific background values [48].  
In this study, Sc was selected as the reference (or conservative) element for geochemical normalisation, based on the following considerations: (1) it is commonly associated with fine-grained sediment particles; (2) its geochemical behaviour is similar to that of many trace elements; and (3) its natural concentration tends to be relatively uniform [48]. EF values were interpreted according to the classification proposed by [PERSON] et al. [49], where: EF < 1 = no enrichment; EF = 1-3: minor enrichment; EF = 3-5: moderate enrichment;EF = 5-10: moderately severe enrichment; EF = 10-25: severe enrichment; EF = 25-50: very severe enrichment; EF > 50: extremely severe enrichment.
## 5 Optimal Dispatch Model for DN
### Constraints of the Optimal Dispatch Model
#### 5.2.1 Power Flow Constraint of DN  
Based on the Distflow model, the power flow constraints of DN are established as follows [31].  
\[\left\{\begin{array}{l}P_{i,t}^{\text{N}}=P_{i,t}^{\text{SG}}+P_{i,t}^{\text{ PV}}+P_{i,t}^{\text{WT}}-P_{i,t}^{\text{load}}-P_{i,t}^{\text{D-E}}-P_{i,t}^{\text{D -H}}\\ \mathcal{Q}_{i,t}^{\text{N}}=\mathcal{Q}_{i,t}^{\text{SG}}+\mathcal{Q}_{i,t}^ {\text{PV}}+\mathcal{Q}_{i,t}^{\text{WT}}-\mathcal{Q}_{i,t}^{\text{load}}\\ \end{array}\right.,\forall i\in N^{\text{D}} \tag{37}\]  
\[\left\{\begin{array}{l}\sum\limits_{i\in L^{\text{D}}}\left(P_{i,t}^{\text{ L}}-R_{i}I_{i,t}^{2}\right)+P_{i,t}^{\text{N}}=\sum\limits_{i\in L^{\text{D}}}P_{ij,t} ^{\text{L}}\\ \sum\limits_{i\in L^{\text{D}}}\left(Q_{i,t}^{\text{L}}-X_{i,t}I_{i,t}^{2} \right)+Q_{i,t}^{\text{N}}=\sum\limits_{i\in L^{\text{D}}}Q_{i,t}^{\text{L}} \end{array}\right.,\forall i\in N^{\text{D}} \tag{38}\]  
\[\mathcal{U}_{i,t}^{2}-U_{j,t}^{2}-I_{ij,t}^{2}(R_{ij}^{2}+X_{ij}^{2})-2(P_{ij,t }^{\text{L}}R_{ij}+Q_{ij,t}^{\text{L}}X_{ij})=0,\;\forall i\in N^{\text{D}}, \;\forall ij\in L^{\text{D}} \tag{39}\]  
\[\mathcal{U}_{i,t}^{2}=\frac{\left(P_{ij,t}^{\text{L}}\right)^{2}+\left(Q_{ij,t }^{\text{L}}\right)^{2}}{I_{ij,t}^{2}},\;\forall i\in N^{\text{D}},\;\forall ij \in L^{\text{D}} \tag{40}\]  
\[\left(P_{ij,t}^{\text{L}}\right)^{2}+\left(Q_{ij,t}^{\text{L}}\right)^{2}\leq \left(S_{ij}\right)^{2},\;\forall ij\in L^{\text{D}} \tag{41}\]  
\[\left(U_{\min}\right)^{2}\leq U_{i,t}^{2}\leq\left(U_{\max}\right)^{2},\; \forall i\in N^{\text{D}} \tag{42}\]  
where \(L^{\text{D}}\) is the set of lines in DN; \(P_{i,t}^{\text{N}}\) and \(Q_{i,t}^{\text{N}}\) are the active and reactive power at node \(i\) at period \(t\), respectively; \(Q_{i,t}^{\text{SG}}\) is the reactive power purchased by node \(i\) from the superior grid at period \(t\); \(P_{i,t}^{\text{PV}}\) and \(Q_{i,t}^{\text{PV}}\) are the active and reactive outputs of the PV at node \(i\) at period \(t\), respectively; \(P_{i,t}^{\text{WT}}\) and \(Q_{i,t}^{\text{WT}}\) are the active and reactive outputs of the WT at node \(i\) at period \(t\), respectively; \(P_{i,t}^{\text{load}}\) and \(Q_{i,t}^{\text{load}}\) are the active and reactive power of the load at node \(i\) at period \(t\), respectively; \(P_{i,t}^{\text{L}}\) and \(Q_{i,t}^{\text{L}}\) are the active and reactive power transmitted by line \(ij\) at period \(t\), respectively; \(R_{ij}\) and \(X_{ij}\) are the resistance and reactance of line \(ij\), respectively; \(U_{i,t}^{2}\) is the squared value of the voltage at node \(i\) at period \(t\); \(I_{ij,t}^{2}\) is the squared value of the current of line \(ij\) at period \(t\); \(S_{ij}\) is the capacity of line \(ij\); \(U_{\min}\) and \(U_{\max}\) are the lower and upper limits of voltage, respectively.
## 5 Analysis of Simulation Model and Experimental Results  
### Validation of Compressor Performance Simulation Model  
Using the experimental setup and oil separation system, the OCR within the system was kept below 1%. The testing conditions included an evaporation temperature of 0 \({}^{\circ}\)C, a condensation temperature of 60 \({}^{\circ}\)C, and a speed range of 2400 to 4800 rpm. As shown in Figures 4 and 5, the OCR is less than 0.5%. Comparing the experimental OCR with the simulation model results indicates that the compressor speed has a minor impact on compressor efficiency. Therefore, from both the experimental and simulation results, when the OCR is minimal, it can be approximated to the volumetric efficiency and isentropic efficiency when the OCR is 0, which is consistent with the simulation model results without the influence of lubricating oil, which are derived from [PERSON]'s [13] study.  
Figure 4: Calculated data vs. test data for volumetric efficiency based on OCR \(\approx\) 0.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Parameter** & **Uncertainty** \\ \hline \multirow{4}{*}{Primary measurements} & Temperature/\({}^{\circ}\)C & \(\pm\)0.1 \({}^{\circ}\)C \\  & Pressure/MPa & \(\pm\)0.2\% \\  & Refrigerant Mass Flow & \(\pm\)0.1\% \\  & Rate/(kg/h) & \(\pm\)0.1\% \\  & Electric Heating Power/kW & \(\pm\)0.15\% \\  & Enthalpy Difference on & \(\pm\)0.218 \\  & Refrigerant Side /(kJ/kg) & \(\pm\)3.75\% \\  & Electric Heating Power (Q\({}_{1}\)) /kW & \(\pm\)3.75\% \\  & Cooling Capacity (Q\({}_{0}\)) /kW & \(\pm\)2.01\% \\  & Oil Circulation Rate (OCR) & \(\pm\)0.883\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Uncertainty analysis.
# Shunt Damping Vibration Control Technology: A Review
## 6 Adaptive Shunt
The equivalent capacitance is  
\[C=-\left(\frac{C_{0}}{1+i\omega R_{0}C_{0}}\right)\frac{R_{2}}{R_{1}} \tag{34}\]  
This equation can be approximated as \(C=C^{\prime}(1-\mathrm{itan}\delta)\), the real part can be adjusted by \(R_{1}\) and \(R_{2}\), \(C^{\prime}=C_{0}(R_{2}/R_{1})\). The imaginary part can be adjusted by \(R_{0}\), \(\tan\delta=\omega R_{0}C_{0}\). The negative capacitor was controlled by \(R_{0}\) and \(R_{1}\). The vibration isolation of the shunt shown in Figure 14 was effective over a narrow frequency. [PERSON] et al. [126] proposed a broadband adaptive negative capacitance shunt circuit shown in Figure 15. The shunt circuit was built up based on NIC. Two resistors R\({}_{0}\) and R\({}_{1}\) constructed as a pair of a light-emitted diode and a photo-resistor were used to adjust the value of the negative capacitance. An iterative control algorithm was applied, and a range of 0.5-3 KHz vibration reduction was achieved. In general, a resonant shunt is effective to reduce a target mode vibration, but it is very sensitive to the variation of the natural frequency and the inherent capacitance of the piezoelectric transducer. To use a resonant shunt and make it more adaptive, [PERSON] et al. [127] proposed an adaptive resonant piezoelectric vibration absorber enhanced by a negative capacitance. The resonant shunt circuit autonomously adapts its inductance value by comparing the phase difference of the vibration velocity and the current flowing through the shunt circuit. The negative capacitance was added to the resonant shunted piezoelectric absorber to improve the vibration control performance. The results showed that the vibration of the shell can be reduced by 5 dB despite the frequency varying from 191 Hz to 193 Hz. The mechanical system becomes complicated with the development of technology. Generally, it may contain many structures, electronics, and a control system. The stiffness and electrical features of all these components are hard to obtain, which inevitably increase the difficulties of designing and optimization a shunt circuit. Then an adaptive shunt provides an effective way to overcome these uncertainties and thus to improve the vibration control performance of a mechatronic system. Figure 14: Controlled negative capacitance.
# Design and Performance Analysis of Hardware Realization of 3 GPP Physical Layer for 5G Cell Search
## Abbreviations  
The following abbreviations are used in this manuscript:  
3 GPP 3 rd Generation Partnership Project AXI  ARM Extensible Interface BPSK  Binary Phase Shift Keying BS  Base Station CI  Cell Identity CS  Cell Search DMRS  Demodulation Reference Signal DPFL  Double Precision Floating Point FFT  Fast Fourier Transform FPGA  Field Programmable Gate Array GSCN  Global Synchronization Channel  HPFL  Half Precision Floating Point IA  Initial Access LTE  Long Term Evolution MAC  Medium Access Control MSI  Minimum System Information OFDM  Orthogonal Frequency Division Multiplexing PBCH  Physical Broadcast Control Channel PCI  Physical Cell ID PHY  Physical Layer PRACH  Physical Random Access Channel PRBS  Pseudo-random Binary Sequence PSS  Primary Synchronization Signal QPSK  Quadrature Phase Shift Keying RB Resource Block RFNoC Radio Frequency Network on Chip SCS  Sub-carrier Spacing SNR  Signal to Noise Ratio
## 4 Simulation and Experiment  
### Simulation  
To verify the correctness of the proposed SWPIT system, the forward and backward communication are studied through the PLECS software. The overall schematic diagram is depicted in Figure 13. The simulation step of the solver is set to 20 ns to ensure the accuracy of simulation results. The modulators and demodulators are implemented by C-scribe block and analog circuit, respectively. The parameters related to the simulation are listed in Table 4.  
For the forward communication, the transmitted bits (\"1010...\") with the data rate of 25 kbps are loaded on the primary side and mixed with the power modulator. After about three switching periods,  
Figure 12: Diagram of the half-duplex communication. (**a**) Flowchart of “Tx” and “Rx” on primary and secondary side. (**b**) Operation sequence of SWPIT system.  
Figure 13: Overall diagram of simulation circuit.  
the transmitted bits are recovered by the demodulator. The related waveforms of secondary current \(i_{\rm s}\), transmitted bits and received data are shown in Figure 14a.  
Compared with the forward communication, the transmitted bits of backward communication are the same but the data rate is different, as shown in Figure 14b. Due to the constant current characteristic in the SS compensated circuit, the fluctuation of the primary current \(i_{\rm p}\) is smaller than the secondary current \(i_{\rm s}\).