esa-sceva/satcom-chunk-collection huggingface dataset:
# Clustering Optimization of IPMSM for Electric Vehicles: Considering Inverter Control Strategy
## 2 Clustering Optimization Design Procedure
### Design Framework  
The overall optimization procedure is represented in Figure 1, which includes the following four steps.  
Step 1: Analyze the driving conditions of EVs and make a preliminary design for the motor. The K-means clustering algorithm is used to identify the RPs of the motor for evaluating the performance of the motor in the whole driving cycle of EVs.  
Step 2: Establish the semianalytical models and evaluate the steady performance of the motor on the motor scale. The optimal parameters of the motor are selected by sensitivity analysis.  
Step 3: Evaluate the dynamic performance of the motor on the control scale with respect to the nonlinear factors given by the motor characteristic parameters. The full-speed domain control strategy is considered to meet the requirements of low-speed starting and high-speed cruising for the proposed IPMSM driving motor.  
Step 4: Performance evaluation of the system scale based on the fitness function and obtaining the optimal design with MOPSO. When the result satisfies the iterative stop condition, the Pareto solution is output. Otherwise, the motor parameters are updated based on the MOPSO and then returned to Step 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline Vehicle mass & 1100 kg & Radius of wheels & 0.308 m \\ Maximum gradeability & 20\% & Maximum climbing speed & 20 km/h \\ Motor rated speed & 3000 rpm & Motor maximum speed & 8000 rpm \\ Acceleration time & \textless{}10 s for 0–50 km/h, \textless{}15 s for 50–100 km/h \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specification of the investigated EV.
## 6 Experimental Results and Analysis
The ES-MBD method combines background difference binarization with Sobel operator binarization, which can avoid the shortcomings of both. The precision rate reaches 97.3% and the recall rate reaches 96.5%. The results prove that the ES-MBD method has an obvious detection effect.
## 5 Real Whistle Classification and D-LSTM Implementation
### Whistle Classification  
The training whistle data for the proposed biomimetic communication scheme were obtained from Watkins marine mammal sound database. The whistles of white sided dolphin were chosen and the total number of collected whistles was 704 [35]. The feature vectors in Equation (1) were calculated from the 704 whistles and classified into groups using k-means algorithm by Equation (2). The optimal number (\(K_{\text{opt}}\)) of groups by Equation (2) was eight, and the maximum number (\(M_{\text{opt}}\)) of bits per one whistle was three.  
For increasing the BER and DoM performances, the eight groups were reclassified into the lager number of subgroups. An expanding factor of \(\alpha\) was set as two, and the groups were reclassified into 32 subgroups, i.e., \(K_{\text{tot}}=32\). Figure 8 represents some examples of classified 704 whistles by the eight groups and 32 subgroups.  
Figure 7: Window processing of D-LSTM.  
In Figure 8, the horizontal and vertical axes denote the time and the frequency, respectively. Figure 8a,b represents the examples among the classified eight groups by Equation (2), and Figure 8c,d shows the examples of the reclassified 32 subgroups from Figure 8a,b, respectively. Figure 8a,b displays the down chirp patterns and scoop shapes, respectively. The whistles of the subgroups in Figure 8c were separated by the frequency and the time duration which are one of dominant features of the whistles. The whistles of the subgroups in Figure 8d were reclassified by the chirp shape that is another feature of the whistles.  
The element averages, e.g., \(\mu_{1},\mu_{3},\mu_{7},\mu_{8}\), of the subgroups from \(V(1)\) to \(V(3)\) are shown in Figure 9a. In Figure 9a, the distance between \(G_{3}\) and \(G_{8}\) is the farthest.  
The proposed method generates \(H\) that presents the link information among subgroups. Multiple \(H\) values can be available as described in Section 3. In this example, the number of the subgroup was 32 and the size of one subgroup set was eight. For \(H\), all possible subgroup sets were listed in the order of the largest distance among subgroups, and the short link loop within a few subgroup sets were avoided, and all subgroups were evenly shown in the \(H\) matrix. Then, \(H\) was made with the size of \(32\times 8\). Since the size of \(H\) is large, we omit the example of \(H\), but a link example is displayed in Figure 9b. In Figure 9b, the blue line and the red line denote the link information for bits 000 and 001, respectively. For the transmission, as in Section 3, the input bits are mapped into a whistle subgroup indicated by \(H\) and one of whistles in the subgroup is randomly chosen and transmitted.  
In the next subsection, the structures of the proposed machine learning networks and the learning results are presented.
## 4 State-Feedback Control  
### Feedback Law  
In this section, we ignore the effect of sampling and we explain how to design the controller in continuous-time. In view of (3), if the pair \((A,B)\) is controllable, one can design \(K\), such that the closed-loop eigenvalues are located at any desirable locations using the eigenstructure assignment. Alternatively, the linear quadratic regulator (LQR) approach can be used to compute the gain matrix \(K\) in an optimal sense according to the desired state/input response, based on the solution of the algebraic Riccati equation (ARE), to find a symmetric positive definite matrix \(P\in\mathbb{R}^{11}\times\mathbb{R}^{11}\) that satisfies  
\[A^{T}P+PA+Q-PBR^{-1}B^{T}P\,=\,0, \tag{6}\]  
where the matrix \(Q\in\mathbb{R}^{11}\times\mathbb{R}^{11}\) is symmetric and positive semi-definite and \(R\in\mathbb{R}^{2}\times\mathbb{R}^{2}\) is symmetric and positive semi-definite. The matrices \(Q,R\) are constructed based on the desired state/input, such that the following objective function is minimized by the controller \(J=\int_{0}^{\infty}(x^{T}Qx+u^{T}Ru)dt\). Then, by solving the algebraic Riccati equation, the optimal state-feedback law \(K\) that minimizes the cost function \(J\) is given by \(K=R^{-1}B^{T}P\). Note that the solution of the ARE (6) is always guaranteed if \((A,B)\) is controllable. However, due to the large sizes of matrices \(A,B\), and since the state vector \(x(t)\) involves dummy states \(x_{4},x_{8}\) and augmented states \(x_{10},x_{11}\), the requirement that \((A,B)\) be controllable can be conservative. Hence, to relax such a requirement, we assume that the pair \((A,B)\) is only sterilizable, which implies that the uncontrollable modes of \(A-BK\) are naturally stable. To that end, we need to check which of the closed-loop eigenvalues are controllable and design the controller accordingly. For this purpose, we use the Popov-Belevitch-Hautus (PBH) test or PBH for stabilizability, which states that the pair \(A,B\) is stabilizable if and only if the following condition holds  
\[\text{rank}[\lambda I_{n}-A\quad B]=n\qquad\forall\lambda\in\mathbb{C}^{+}, \tag{7}\]  
Figure 2: Networked control system of the HWDPS system.  
where \(n\) is the dimension of the state vector \(x\), and \(I_{n}\) is an identity matrix of dimension \(n\), i.e., the rank conditions hold for all unstable eigenvalues \(\lambda\) of the closed-loop system \(A-BK\).
# Dichotomy Graph Sketch: Summarizing Graph Streams with High Accuracy Based on Deep Learning +
## 5 Performance Evaluation
### Performance Metrics
#### 5.2.3 Normalized Discounted Cumulative Gain (NDCG)  
_Normalized discounted cumulative gain_[44] is a measure of ranking quality. Given a ranking list of heavy nodes reported by a graph sketch, _discounted cumulative gain (DCG@k)_ measures the usefulness (also called gain) of a node based on its position in the ranking list, and it is formally defined as follows:  
\[DCG@k=\sum_{i=1}^{k}\frac{rel_{i}}{log_{2}(i+1)} \tag{6}\]  
where \(rel_{i}\) is the relevance value of the result at position \(i\), and \(rel_{i}\in\{0,1\}\). If the \(i\)th node in the ranking list is indeed a heavy node, then \(rel_{i}\) will be 1; otherwise, it will be 0. Using the definition above, \(NDCG@k\) is formulated as follows:  
\[NDCG@k=\frac{DCG@k}{IDCG@k} \tag{7}\]  
where \(IDCG@k\) represents the \(DCG@k\) of an _ideal ranking list_. The ideal ranking list can be obtained by sorting the nodes in the ranking list in descending order with respect to their relative scores. It is obvious that \(NDCG\in[0,1]\), and the higher NDCG indicates a stronger ability to find top-k heavy nodes.
# Vibration Energy Harvesting from Raindrops Impacts: Experimental Tests and Interpretative Models
## 2 Experimental Set-Up  
The response of the harvester excited by the impact of raindrops was experimentally measured. The unimorph piezoelectric harvester (Mide PPA-1001), shown in Figure 1, was constrained in a cantilever configuration using a steel clamp. The geometrical and material parameters of the harvester are summarized in Appendix A. A square spoon (27.2 mm inside, with 3 mm high walls) was attached at the free end of the cantilever using cyanoacrylate glue. The presence of the spoon allows for a small pool of water in which raindrops can fall: this modifies the impact dynamics as the drop hits a liquid surface instead of a solid one. The clamped end of the harvester -- where the electrical contacts are present -- is placed inside a plastic box, which provides protection against water splashes.  
The response of the harvester consists in the generated voltage, measured by means of an acquisition module for sound and vibration (National Instruments NI9234) and the software SignalExpress. The trigger was set to start the measurements when the absolute value of the generated voltage exceeded 0.05 V. The voltage was measured for 3 s from the trigger, with a sampling frequency of 2 kHz.  
The piezoelectric device was tested in both indoor and outdoor conditions. Indoor tests exploited an intravenous drip set to generate water drops of consistent size; the drop hit the harvester after a 1 m fall. These tests provide repeatable water impacts thanks to the controlled testing environment. Outdoor tests were performed by exposing the harvester to actual raindrops and offering the necessary benchmark for the indoor tests with artificial raindrops. In general, the voltage generated by the impact of a water drop on the harvester qualitatively resembles a damped sinusoidal signal. For this reason, the following three main parameters were selected in order to characterize the time-domain response of the device. The first relevant parameter is the peak-to-peak voltage, i.e., the difference between the maximum and the minimum values of the voltage signal caused by one drop impact. The second parameter is the viscous damping ratio, calculated using the well-known logarithmic decrement method [22] (which rigorously applies to second-order linear systems). The damping ratio is computed from positive or negative peaks \(i\) and \(i+n\) as:  
\[\zeta_{i}=\frac{\delta_{i}}{\sqrt{(2\pi)^{2}+\delta_{i}^{2}}}, \tag{1}\]  
where  
\[\delta_{i}=\frac{1}{n}\ln\frac{V_{i}}{V_{i+n}}. \tag{2}\]  
\(V_{i}\) is the amplitude of the \(i\)-\(th\) peak and \(n\) is the number of periods considered in the calculation (in the following \(n=10\)). The damping ratio is computed for every peak in the time record, and the mean value of the whole signal is also obtained. The third parameter is the dominant frequency, computed by averaging multiple period measurements obtained from the identified positive and negative peaks.
# Feature Mining: A Novel Training Strategy for Convolutional Neural Network
## 3 Materials and Methods  
### Observation  
In order to explore the semantic difference among the local features, we conducted an experiment on the visualization of the CNN. During the network training, we multiplied the feature of the last layer of the network by different fixed binary masks, so that only part of the feature in the last layer of the network could participate in the network training. We used class activation maps (CAMs) [20] to visualize the trained model. Specifically, ResNet-50 [23] trained on CUB-200-2011 [25] was chosen as the baseline model. All the models were trained from scratch for 200 epochs with a batch size of 32, and the learning rate was decayed by a factor of 0.1 at Epochs 100 and 150. The choice of epochs and batch size followed the common practice. Training with a few epochs cannot guarantee network convergence, while training with too many epochs may lead to the overfitting of the network. Network convergence can be guaranteed without overfitting when training with 200 epochs. Theoretically, the larger the batch size, the better the network performance is. However, a large batch size will result in a large memory overhead. When the batch size was set to 32, we could obtain the best network performance under existing computing resources.  
As shown in Figure 1, the second line shows the visualization results of the global features in the last layer, while the following lines show the visualization results of different local features. We can see that the complete features generally focused on the global information of the target, while the local features focused more on the relevant local information according to their locations. Obviously, the image information concerned in the local features is different from that of the global features, and the semantic information between different local features is different. However, the network only obtains the output depending on the global feature information due to the feedforward mechanism of the CNN, which may cause the loss of information contained in the local features. How can we make full use of the local features in the network training? We see how to address this issue next.
# Area-Time Efficient Hardware Architecture for CRYSTALS-Kyber
### Proposed Kyber Key Generation Architecture
Noise vectors \(\mathbf{s}\) and \(\mathbf{e}\) are generated from a binomial sampler. In addition, \(\hat{\mathbf{A}}\) is chosen from a uniform distribution. The architecture of the binomial sampler and rejection sampler is introduced in Figure 3c. To accelerate the NTT operation, we deploy the proposed DDF-based NTT architecture in the key generation architecture to obtain the values of \(\hat{\mathbf{s}}\) = NTT(\(\mathbf{s}\)) and \(\hat{\mathbf{e}}\) = NTT(\(\mathbf{e}\)). Specifically, coefficients of each noise vector \(\mathbf{s}\) and \(\mathbf{e}\) are divided into two groups to be processed in parallel in order to reduce processing time. The values of NTT(\(\mathbf{s}\)) and NTT(\(\mathbf{e}\)) are then stored in BRAM. In addition, the value of \(\mathbf{t}\) is calculated by adding the point-wise multiplication result \(\hat{\mathbf{A}}\circ\mathbf{s}\) to \(\hat{\mathbf{e}}\). Finally, a key pair \((pk,sk)\) is generated using two encoders \(pk=\text{Encode}(\hat{\mathbf{t}}\bmod^{+}\!q)||\rho\) and \(sk=\text{Encode}(\hat{\mathbf{s}}\bmod^{+}\!q)\). The public key \(pk\) and secret key \(sk\) then participate in encryption and decryption processes, respectively.
## 2 Gaussian Process Data Modelling and Maximum Likelihood-Based Data Fusion Method
### Maximum Likelihood Data Fusion  
After Gaussian process modelling, the mean surface and the associated uncertainty at any position can be estimated for each measurement. For a particular multi-sensor CMM measurement which considers measurement datasets from two different sensors, the mean surfaces and the associated measurement uncertainties can be denoted as \(m_{1},m_{2}\) and \(u_{1},u_{2}\), respectively. As the measurement noise is governed by Gaussian distribution, i.e.  
Equation (15) can be rewritten as  
\[\hat{m}=\frac{w_{1}m_{1}+w_{2}m_{2}}{w_{1}+w_{2}} \tag{17}\]  
Using the uncertainty propagation principle [21], the uncertainty for \(\hat{m}\) can be determined as  
\[\hat{u}=\sqrt{\left(\frac{w_{1}}{w_{1}+w_{2}}u_{1}\right)^{2}+\left(\frac{w_{2} }{w_{1}+w_{2}}u_{2}\right)^{2}}=\sqrt{\left(\frac{\frac{1}{u_{1}}}{\frac{1}{u_ {1}^{2}}+\frac{1}{u_{2}^{2}}}\right)^{2}}+\left(\frac{\frac{1}{u_{2}}}{\frac{1} {u_{1}^{2}}+\frac{1}{u_{2}^{2}}}\right)^{2}}=\frac{1}{\sqrt{\left(\frac{1}{u_ {1}^{2}}+\frac{1}{u_{2}^{2}}\right)}} \tag{18}\]  
Using the weight denotation yields the uncertainty value \(\hat{u}\)  
\[\hat{u}=\frac{1}{\sqrt{w_{1}+w_{2}}} \tag{19}\]  
Equation (19) shows that the fused uncertainty has a smaller value than that from each of the original measurement data.
## 4 Advanced Modeling Analysis  
All measured isotherms were fitted using three analytical models. Indeed, the adjusting findings show that the parameters of the monolayer model were not acceptable to supply a reasonable investigation of the CO\({}_{2}\) adsorption process. By way of example, the capacity values of CO\({}_{2}\) adsorption at certain temperature are not compatible with the experimental findings. This model is therefore inappropriate to investigate the CO\({}_{2}\) adsorption phenomenon. Concerning the double-layer model, we can notice that the temperature impact on the fitted parameters is not comprehensible and the model is also rejected. For the multilayer model, their parameters are very reasonable and the effect of the temperature on them is evident, in addition to the correlation coefficients R\({}^{2}\), which are very close to unity (from 0.997 to 0.999) (Table 1). Consequently, the multilayer model is chosen to analyze the CO\({}_{2}\) adsorption process on the ZSM-5 zeolite. The parameter values of the chosen model are listed in Table 2. These values are determined by a numerical simulation utilizing the Levenberg-Marquardt algorithm and a multivariable non-linear regression. Figure 4 depicts the adsorption isotherms fitted by the multilayer model. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Adsorbents**} & \multirow{2}{*}{**Temperature (K)**} & \multicolumn{4}{c}{**Coefficient of Determination R\({}^{2}\)**} \\ \cline{3-5}  & & **M1** & **M2** & **M3** \\ \hline \multirow{3}{*}{**Na-ZSM-5**} & 0 \({}^{\circ}\)C & 0.896 & 0.969 & **0.997** \\  & 30 \({}^{\circ}\)C & 0.861 & 0.970 & **0.999** \\  & 60 \({}^{\circ}\)C & 0.921 & 0.985 & **0.999** \\ \hline \multirow{3}{*}{**Mg-ZSM-5**} & 0 \({}^{\circ}\)C & 0.713 & 0.951 & **0.999** \\  & 30 \({}^{\circ}\)C & 0.689 & 0.902 & **0.998** \\  & 60 \({}^{\circ}\)C & 0.741 & 0.981 & **0.998** \\ \hline \multirow{3}{*}{**La-ZSM-5**} & 0 \({}^{\circ}\)C & 0.762 & 0.920 & **0.999** \\  & 30 \({}^{\circ}\)C & 0.802 & 0.911 & **0.997** \\  & 60 \({}^{\circ}\)C & 0.831 & 0.899 & **0.997** \\ \hline \multirow{3}{*}{**Zn-ZSM-5**} & 0 \({}^{\circ}\)C & 0.812 & 0.931 & **0.999** \\  & 30 \({}^{\circ}\)C & 0.791 & 0.962 & **0.999** \\ \cline{1-1}  & 60 \({}^{\circ}\)C & 0.851 & 0.973 & **0.999** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Values of adjustment coefficient R\({}^{2}\) for the three models. Figure 3: Schematic illustration of the distribution of the molecules adsorbed on a solid according to the multilayer model with saturation. Figure 4: Experimental adsorption isotherms of CO\({}_{2}\) on (**a**) Na-ZSM-5, (**b**) Mg-ZSM-5, (**c**) La-ZSM-5 and (**d**) Zn-ZSM-5 at three temperature and fitting by multilayer statistical physics model (M3).
# Three-Line Microstrip Array for Whole-Body MRI System at 7 T
## 3 Results
In terms of the SAR, the proposed array of t-MTLs also exhibits lower values and smaller peak values. It should also be noted that the peak values are separated and far from each other, whereas those of the patch antenna are close to the same position. The highest values of the SAR are located near the elbows and upper legs. This is because the microstrips are close to these areas. Figure 10 shows the \(\mid\)B\({}_{1}\)\({}^{+}\)\(\mid\)-field and SAR at different positions in the body for the proposed array. Figure 10 also includes a histogram of the distribution of SAR per m\({}^{3}\) of the human model. Table 2 shows some of the SAR values for specific tissues that present a greater safety concern. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & & & **Mean/CV \(\mid\)B1+\(\mid\)** & & **Mean/Max** \\ \hline \multirow{2}{*}{RF transmitter} & Whole body [\(\mu\)T] & \begin{tabular}{c} Sagittal \\ slice/y-z plane \\ [\(\mu\)T] \\ \end{tabular} & \begin{tabular}{c} Axial \\ slice/Legs [\(\mu\)T] \\ \end{tabular} & \begin{tabular}{c} Axial \\ slice/Torso [\(\mu\)T] \\ \end{tabular} & \
\begin{tabular}{c} Axial \\ slice/Head [\(\mu\)T] \\ \end{tabular} &
\begin{tabular}{c} SAR whole \\ body [W/kg] \\ \end{tabular} \\ \multirow{2}{*}{Patch antenna} & 0.066/0.60 & 0.073/0.17 & 0.063/0.11 & 0.067/0.08 & 0.143/0.13 & 0.019/1.283 \\  & 0.036/1.12 & 0.036/0.16 & 0.041/0.15 & 0.038/0.27 & 0.063/0.12 & 0.009/0.355 \\  & 0.078/0.55 & 0.073/0.14 & 0.071/0.07 & 0.069/0.09 & 0.103/0.08 & 0.013/0.120 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Mean and standard deviation of \(\mid\)B\({}_{1}\)\({}^{+}\)\(\mid\) field. Figure 9: The computed projected maximum SAR maps in sagittal and axial views, for (**a**) the path antenna, (**b**) birdcage coil, and (**c**) the array of triple-line microstrips. In this work we presented only the performance of the t-MTL in terms of \(\mid\)B\({}_{1}\)\({}^{+}\)\(\mid\)-field. However, some limitations can occur depending upon the interaction of the microstrips with ground plates and the gradient coils, which can add parasite currents. We also limited our study to a generic, average-size, 3D human phantom. The development cost of the proposed t-MTL array would match, or be lower than, that of a birdcage coil, since the amount of the acryl used to support the coil is less than the birdcage coil. The t-MTL has a planar shape that is easy to manufacture, compared to the cylindrical shape of the birdcage coil. The implementation of the microstrip array presented in this work has its resonance frequency generated from the geometry and does not require the use of lumped elements, such as capacitors, as in the case of the birdcage coil.