esa-sceva/satcom-chunk-collection huggingface dataset:
# Constrained Optimization of FPGA Design for Spaceborne InSAR Processing
## 4 System Analysis under Double Constraints
### Analysis of the Matrix Transposition Schemes  
This part focuses on the matrix transposition structure when the thread of interferogram generation was 4 and the thread of coarse registration was 1. The transpose of the matrix with different burst lengths had different efficiencies of AXI \(\eta\). The utilization of resources and efficiency of each scheme are shown in Table 5.  
The utilization of resources comes from the synthesis in Vivado, not HLS. Since matrix transposition structure could be designed separately, the resources \(R_{i}\) could be found after synthesis in Vivado. The structure efficiency could be tested on the FPGA board. Then, coarse registration processing time \(T_{c}\) could be calculated.  
Similarly, the \(\
abla G\) calculation results are shown in Figure 7. \(\
abla G\) of \(\Delta gh\) was 8.16, which meant that, compared with scheme \(g\), the time of scheme \(h\) deteriorated by 1.0%, but the resources were saved by 8.7%. However, since the values of \(\Delta hi\) and \(\Delta hj\) were much less than 3, the scheme h was more balanced among the four above schemes. There would be efficiency loss during the storage process, so \(T_{g}\) was above 167.77 ms. Then \(\Delta T/T_{i}\) would be reduced, and the value of \(\
abla G\) would be increased, again confirming that scheme \(h\)  
Figure 6: The theoretical analysis of coarse registration with different threads.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Scheme** & **Length** & \(\eta\) & \(R_{i}\) & \(R_{\textbf{BRAM\_36K}}\) & \(R_{i}\)/\(R_{\textbf{BRAM\_36K}}\) & \(T_{c}\) & \(T_{g}\) & \(T_{i}\) \\  & & & & **(\%)** & **(ms)** & **(ms)** & **(ms)** \\ \hline \(g\) & 1024 & 90\% & 260 & 1470 & 17.69 & 8.7 & 167.77 & 176.47 \\ \(h\) & 256 & 75\% & 132 & 1470 & 8.98 & 10.5 & 167.77 & 178.27 \\ \(i\) & 64 & 50\% & 68 & 1470 & 4.63 & 15.6 & 167.77 & 183.37 \\ \(j\) & 16 & 20\% & 36 & 1470 & 2.45 & 39.3 & 167.77 & 207.07 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Theoretical resource usage and calculation time of different matrix transposition schemes.  
was the balanced design. To sum up, when the thread of interferogram generation was 4 and the thread of coarse registration was 1, the scheme with a burst length of 256 was the balanced design.  
Similarly, when the thread of interferogram generation was 2, the system processing time would be prolonged and scheme h, with 256 burst length, would still be the balanced design. The scheme g with 1024 burst length would exert less impact on the whole system while consuming more resources.
# A Simplified Free Vortex Wake Model of Wind Turbines for Axial Steady Conditions
## 5 Results and Discussions
### Low Speed Shaft Torque  
The computational time of the proposed VSRW model can be greatly reduced and in the following section, we analyze its accuracy. The aerodynamic load of the LSST is predicted by the three different wake models. Figure 11 shows the aerodynamic estimates from the models compared with the values measured directly at the shaft [35]. The trends of the calculated results are consistent with the measurement results. Above 15 m/s, the estimated values begin to differ from each other and from the value measured at the shaft. The reason is that most of the blade is undergoing a stall flow. Although the 2D airfoil data are modified by the stall-delay model, the accurate prediction of the aerodynamic load in rotational and stall conditions is still challenging. This is also reflected in the studies by [PERSON] et al. [37] who used a prescribed vortex wake technique and by [PERSON] et al. [6] who used a free vortex wake technique. All the results from the VSRW model are slightly higher than those from the TVW and FVSW models. Except for 20 m/s, the results from the VSRW model could have errors no more than 10% relative to the experiment data. At 20 m/s, the error relative to the experiment data is about 16%. However, it is gratifying that the simplification of the far wake in the VSRW model did not significantly affect the accuracy of the aerodynamic load prediction.  
Figure 11: Measured and calculated results of LSST of NREL Phase VI wind turbine for wind speeds ranging from 7 m/s to 25 m/s.  
Figure 10: Convergence history of the RMS of the error in the wake geometry.
## 3 Applications  
The applications for WSNs involve tracking, monitoring and controlling. WSNs are mainly utilized for habitat monitoring, object tracking, nuclear reactor control, fire detection, and traffic monitoring. Area monitoring is a common application of WSNs, in which the WSN is deployed over a region where some incident is to be monitored. For example, a large quantity of sensor nodes could be deployed over a battlefield to detect enemy intrusions instead of using landmines. When the sensors detect the event being monitored (heat, pressure, sound, light, electro-magnetic field, vibration, _etc._), the event needs to be reported to one of the base stations, which can than take some appropriate action (e.g., send a message on the internet or to a satellite). Wireless sensor networks are used extensively within the water/wastewater industries. Facilities not wired for power or data transmission can be monitored using industrial wireless I/O devices and sensor nodes powered by solar panels or battery packs. Wireless sensor networks can use a range of sensors to detect the presence of vehicles for vehicles detection. Wireless sensor networks are also used to control the temperature and humidity levels inside commercial greenhouses. When the temperature and humidity drops below specific levels, the greenhouse manager can be notified via e-mail or a cell phone text message, or host systems can trigger misting systems, open vents, turn on fans, or control a wide variety of system responses. Because some wireless sensor networks are easy to install, they are also easy to move when the needs of the application change.
###### Abstract  
This paper presents a low-noise chopper operational amplifier using a lateral PNP input stage with bipolar junction transistor (BJT) current mirror base current cancellation. The BJT has a lower noise characteristic than the metal-oxide-semiconductor (MOS) transistor, where low-noise characteristics can be achieved by implanting the BJT to the input stage of the amplifier; however, the base current of the BJT input stage causes low input impedance of the amplifier. The BJT current mirror base current cancellation technique is implemented to enhance the input impedance of the BJT input stage by canceling the base current. BJT current mirror base current cancellation is implemented with a simple scheme using NPN transistors with deep n-well in a generic complementary metal-oxide-semiconductor (CMOS) process. For further noise reduction with the BJT input stage, a chopper amplifier scheme is adopted to reduce low-frequency components such as 1/f noise terms in the low-frequency range. The prototype chip is fabricated in a 0.18-\(\upmu\)m CMOS process. The active area of the prototype amplifier is 0.213 mm\({}^{2}\). The measured input-referred noise is 5.43 nV/vHz. The measured input base current of the amplifier with base current cancellation is 67.971 nA. The total amplifier current consumption is 278.3 \(\upmu\)A, with a power supply of 3.3 V.  
low-noise operational amplifier; chopper amplifier; lateral PNP; BJT current mirror base current cancellation
## 3 Time-Varying Carrier Frequency Offset Estimation
\right., \tag{17}\]  
where \(\langle\cdot\rangle\) is an operation that obtains the remainder. Next, the offspring are evaluated. Lastly, the selection operation is executed. For a minimization optimization problem, the individuals with lower fitness values in the parents and offspring survive to the next generation. Then, a new population is obtained. The selection operation is as follows:  
\[\epsilon_{i}^{g+1}= \begin{cases}\xi_{i}^{g}&\text{if }f\left(\xi_{i}^{g}\right) \leq f\left(\epsilon_{i}^{g}\right),\\ \epsilon_{i}^{g}&\text{otherwise}\end{cases}  \tag{18}\]  
where \(f(\cdot)\) represents the fitness value of the corresponding vector, (i.e., the function value of the cost function); \(\xi_{i}\) represents the offspring individuals; and \(\epsilon_{i}\) represents the parent individuals. (5) Judge termination conditions  
There are two main types of termination conditions: when the error accuracy of the best solution reaches a pre-set value and when the maximum iteration number meets a pre-set value according to the computing complexity. If the termination conditions are met, the individual corresponding to the best quality is output as the optimal solution in this loop. Otherwise, the execution is repeated from step 4 until the termination conditions are met. Algorithm 1 presents the pseudocode of the proposed DE-aided TVCFO estimation. **Step 1 Initialization**  
Set population size \(Np\), dimension \(N\), maximum generation number \(g_{max}\), and generation counter \(g=0\). Then initialize the original population \(\mathbf{P}^{0}=\left\{\epsilon_{1}^{0},\,\epsilon_{2}^{0},\,\dots,\,\epsilon_{N}^ {0}\right\}\) with \(\epsilon_{i}=(\epsilon_{i,1},\,\epsilon_{i,2},\,\dots,\,\epsilon_{i,N})\) uniformly distributed in the limits, and evaluate the original population. **Step 2 Evolution Iteration**  
WHILE ( \(g\leq g_{max}\) ) DO  
**Step 2.1 Preparation**  
Sort current population \(\mathbf{P}^{g}\) according to the fitness values;  
**Step 2.2 Mutation Operation**  
Determine the value of mutation factor \(f_{0}\);  
Perform the mutation operation according to (15);  
**Step 2.3 Crossover Operation**  
Determine the crossover rate \(Cr\) in the type of exponential crossover;  
Execute the crossover operation based on (17) to obtain the offspring;  
**Step 2.4 Selection Operation**  
Substitute the offspring into (12) and get the fitness value;  
Carry out the selection operation according to (18);  
**Step 2.5 Update the Generation Counter**  
Increase the generation counter by \(1\), that is \(g=g+1\);  
END WHILE  
**Step 3 Output the Solution**  
Solution with the smallest fitness value is output as the best TVCFO estimation. **Algorithm 1** Pseudocode of the proposed scheme
# Temperature State Awareness-Based Energy-Saving Routing Protocol for Wireless Body Area Network
## 4 TSAER: Protocol Description
### TSAER: Operational Steps  
In order to balance the energy consumption of the WBAN and control the hot node formation, this paper proposes a temperature state awareness-based energy-saving routing protocol. The protocol is mainly divided into three processes: initialization, routing, and data transmission. Figure 3 is the flow chart of the protocol.  
Figure 3: Flow chart of the TSAER.
# An Optimal Fuzzy PID Controller Design Based on Conventional PID Control and Nonlinear Factors
## 4 Conclusions  
Based on the equivalence between fuzzy PID controllers and conventional PID controllers, this paper proposed an optimal fuzzy PID controller design with each fuzzy variable's MFs tuned by a nonlinear factor. Firstly, for a controlled plant, one can make a preliminary conventional PID controller design by some traditional techniques. Then the conventional PID controller design can be transformed into an equivalent FLC with four parameters defining the operating ranges of the I/Os. Subsequently, for each fuzzy I/O variable, one nonlinear factor is used for tuning the nonlinearity of MFs. Thus, there will be a total of eight parameters needed to be optimized by GA for an optimal fuzzy PID controller. To enhance the GA efficiency, each parameter must be given a crossover point. Finally, we set the aforementioned equivalent FLC to be one individual in the first generation of GA. This can yield quick and global convergence. In fact, we also found that in some simulations it made no significant difference if we did not take the equivalent FLC to act as one individual in the initial population of GA. This implies that the GA programming in this paper is really efficient. In conclusion, the proposed design approach makes an optimal fuzzy PID controller design more straightforward, and the controller structure more concise. This is quite advantageous for designers because they will have a clearer concept about the operating ranges of fuzzy I/O variables. As a result, we can now simply and appropriately increase the searching space for the optimal fuzzy PID controller design.  
Figure 22: The controller outputs.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model Name** & **Number of Tuning Parameters** \\ \hline [PERSON]’s Fuzzy PD [19] & 69 \\ [PERSON]’s Fuzzy GA database [20] & 21 \\ [PERSON]’s FLC-PID with KF [21] & 20 \\  & (FLC-PD:10 \& FLC-PI:10) \\ The proposed optimal FPID & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Number of tuning parameters for different controllers.  
**Author Contributions:** [PERSON] and [PERSON] developed the methodology and drafted the manuscript. Moreover, N.S. implemented the Matlab/Simulink simulations, and [PERSON] verified the results. The authors approved the final manuscript.  
**Funding:** This research was funded by the Ministry of Science and Technology, Taiwan, under Grant no. MOST 106-2221-E-218-001-MY2. And the APC was funded by the Ministry of Science and Technology.  
**Acknowledgments:** This work is supported by the Ministry of Science and Technology, Taiwan, under Grant no. MOST 106-2221-E-218-001-MY2. Furthermore, [PERSON], one of the authors, is the lecturer of Jakarta State Polytechnic and studies in PhD program of Southern Taiwan University of Science and Technology (STUST) with scholarship supported by DGHE (Directorate General of Higher Education (Indonesia)) and STUST.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods
### Multivariate Data Analysis  
#### 2.5.1 Support Vector Machine  
Support vector machine (SVM) is a supervised pattern recognition method [18]. Due to the effectiveness of handling linear and nonlinear data efficiently, SVM has been widely used in spectral data analysis. SVM maps the original data into higher dimensional spaces and constructs a hyperplane or a set of hyperplanes to maximize the distance between the nearest samples of different classes. The selection of an appropriate kernel function is essential in SVM and affects the performance of SVM. Radial basis function (RBF) is a widely used kernel function with the ability to deal with nonlinear data efficiently. To conduct SVM using RBF kernel, penalty coefficient \(C\) and the kernel parameter \(g\) should be determined to obtain optimal performances, and a grid-search procedure is generally used to determine \(C\) and \(g\). In this study, a grid-search procedure was applied to optimize \(C\) and \(g\), and the searching range of \(C\) and \(g\) were from \(2^{-8}\) to \(2^{8}\).
# Estimating Position from Millisecond Samples of GPS Signals (the \"FastFix\" Algorithm)
## 3 The FastFix Positioning Algorithm
### Least-Squares Estimators  
The algorithm uses two least-squares estimators: one based on Doppler measurements, the other based on the relative fractional codephase. The least-squares estimator for Doppler shift, \(L_{\
u}\), is  
\[L_{\
u}(t,\mathbf{r}_{0})=\sum_{i}(\Delta f^{i}_{m}-\Delta f^{i}(t,\mathbf{r}_ {0}))^{2}, \tag{7}\]  
where \(\Delta f^{i}_{m}\) is the measured Doppler-shift for the \(i\)th satellite, and \(\Delta f^{i}(t,\mathbf{r}_{0})\) is the predicted Doppler-shift for the \(i\)th satellite from Equation (6). The least-squares measure for relative fractional codephase, \(L_{\delta\phi}\), is  
\[L_{\delta\phi}(t,\mathbf{r}_{0})=\sum_{i,j}(\delta^{ij}_{\phi m}-\delta^{ij}_{ \phi}(t,\mathbf{r}_{0}))^{2}, \tag{8}\]  
where \(\delta^{ij}_{\phi m}\) is the measured relative fractional codephase between the \(i\)th and \(j\)th satellite, and \(\delta^{ij}_{\phi}(t,\mathbf{r}_{0})\) is the predicted relative fractional codephase given by Equation (5).
# Thermal-Mechanical Behaviour of Road-Embedded Wireless Charging Pads for EVs
## 3 Material Characterization
### Simulant Pavement Material  
Embedding the pad inside asphalt is difficult, due to the pressure and heat required to compact and consolidate the material. It is also difficult to compact a sufficiently thick asphalt slab to provide realistic conditions around the pad (the thickness of the material below the pad would be too low) without the additional reading layers underneath (i.e., subbase and subgrade). Taking these factors into consideration, the asphalt was replaced with an aggregate-epoxy composite. ADR 230 epoxy resin from Adhesive Technologies was mixed with a DG7 aggregate mixture, which had a maximum aggregate size of 6.7 mm. Replacing the bitumen binder with epoxy allowed the 'pavement material' to be poured around the pad without the use of external pressure. This was analogous to the replacement of asphalt with sand in [PERSON]'s thermal experiments [6].  
The compressive modulus of the composite between 21 \({}^{\circ}\)C and 100 \({}^{\circ}\)C was measured on cast cylindrical specimens with a diameter of 40 mm and a length of 75 mm, using a procedure based on ASTM D695 [33]. An extensometer with a gauge length of 25 mm was used to measure strain. Specimens were tested at a slow crosshead speed of 0.13 mm/min. The results of the compressive tests are presented in Figure 5. The stress-strain curves show that the composite can be well approximated as a linearly elastic material over the entire temperature range. The glass transition temperature of the epoxy, however, is around room temperature. This is evidenced by the modulus of the composite dropping greatly from 10,085 MPa at 21 \({}^{\circ}\)C to 454 MPa at 40 \({}^{\circ}\)C.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**E\({}_{\mathrm{x}}\)** & **E\({}_{\mathrm{y}}\)** = **E\({}_{\mathrm{z}}\)** & **G\({}_{\mathrm{xy}}\)** = **G\({}_{\mathrm{xz}}\)** & **G\({}_{\mathrm{yz}}\)** & **\(\rho_{\mathrm{xy}}\)** = **\(\rho_{\mathrm{xz}}\)** \\ \hline
9100 MPa & 155 MPa & 42.4 MPa & 41.2 MPa & 0.40 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Transversely isotropic properties of litz wire (X-direction is along the wire).  
The thermal expansion of the composite was measured using the DMA Q800 in film tension mode. Based on the results, shown in Figure 6, an average CTE of 9.35 \(\times\) 10\({}^{-5}\)/\({}^{\circ}\)C was obtained through a linear regression of the four curves. A thermal conductivity of 1.11 W/m\(\cdot\)K was measured at 30 \({}^{\circ}\)C using the transient hot wire method and the Mathis TC 30.  
Figure 5: (**a**) Stress–strain curve and (**b**) compressive modulus of the aggregate–epoxy composite at different temperatures.  
Figure 6: Thermal expansion vs. temperature of the aggregate–epoxy sample.
# Review of Anomaly Detection Algorithms for Data Streams
## 6 Algorithm Complexity and Scalability Analysis
### Offline Learning Based Data Stream Anomaly Detection Algorithm
* Scalability: GANomaly algorithms are relatively less scalable in data streaming scenarios. The training and inference processes of generating adversarial networks are typically time consuming, and as the data stream increases, the computational and storage overheads significantly increase. * AEAD algorithm:
* Time complexity: Multiple iterations are involved, and each iteration requires passing data through the encoder and decoder. The time complexity depends on the structure and number of parameters of the encoder and decoder and is usually high. * Space complexity: The Auto-Encoder algorithm needs to store the parameters of the encoder and decoder, and its space complexity depends on the network structure and the number of parameters, which is usually high. * Scalability: The Auto-Encoder algorithm has good scalability in data streaming scenarios. It can detect anomalies only for new arrivals and does not need to traverse all data points. However, the computation and storage overhead in the training phase may be affected by the model complexity and update frequency. In summary, combining the information from Table 9, the complexity and scalability of data stream anomaly detection algorithms based on offline learning can be summarized as follows:  
1. The algorithm based on Subspace Partitioning utilizes subspace data structures and has a unique advantage in feature extraction, thus exhibiting the best scalability among the options. 2. The algorithm based on the Similarity of Data Distribution has a complexity that is correlated with the statistical characteristics of the data, resulting in relatively good scalability. 3. The algorithm based on the Classification Principle typically requires significant computational resources and time for training the classification model, leading to relatively poor scalability. 4. The algorithm based on Deep Learning usually involves training and storage issues related to deep neural networks, resulting in the poorest scalability among the types.
# A Performance Analysis of VANETs Propagation Models and Routing Protocols
## 4 Performance Metric and Simulation Setup
### Performance Evaluation Metrics
#### 4.1.1 Average Throughput  
Throughput is the rate of successful packet delivery over a communication channel. The computation is done by dividing the total bits transmitted over a link per given unit of time or the number of received packets by the last received packet minus the first transmitted packet. In Equation (5), the number 8 indicates the packets received converted to bits. It is usually measured in bps, Kbps, or Mbps. High throughput states better network performance.  
\[Th_{avg}=(P_{r}*8)/(T_{l})-(T_{f}) \tag{5}\]