esa-sceva/satcom-chunk-collection huggingface dataset:
# Hardware-Based Architecture for DNN Wireless Communication Models
## 3 Background  
### Channel Estimator  
Channel estimators in wireless communication systems generate information about the channel. Channel information is derived from the pilot signals that pass from the transmitter to the receiver. From this channel information, the received signals are recovered. The Least Square algorithm is popularly used for channel estimators due to its simplicity [14].  
The LS channel estimation method seeks to estimate the channel \(\hat{H}\) in such a way that the following cost function is minimized:  
\[\begin{split} J(\hat{H})&=||Y-X\hat{H}||^{2}\\ &=(Y-X\hat{H})^{H}(Y-X\hat{H})\\ &=Y^{H}Y-Y^{H}X\hat{H}-\hat{H}^{H}X^{H}Y+\hat{H}^{H}X^{H}X\hat{H} \end{split} \tag{3}\]  
By setting the derivative of the function with respect to \(\hat{H}\) to zero, we have:  
\[\begin{split}\frac{\partial J(\hat{H})}{\partial\hat{H}}& =-2(X^{H}Y)+2(X^{H}X\hat{H})=0\\ &=>X^{H}X\hat{H}=X^{H}Y\end{split} \tag{4}\]  
From that, we can derive the formula of the LS algorithm as follows:  
\[\hat{H}_{LS}=\left(X^{H}X\right)^{-1}X^{H}Y \tag{5}\]
# Multi-Templates Based Robust Tracking for Robot Person-Following Tasks
## 3 Method
### Reliability Estimation  
The instance segmentation clusters responses into instance-aware GMM mixtures. When tracking in reliable circumstances, each object has its own cluster as illustrated in Figure 3d, and the size of the cluster remains stable. However, when a potential occlusion occurs, the instance clusters merge together given their small divergence values, resulting in a large size variation. The upper row of Figure 4 illustrates example scenes before and after potential occlusion. We define the standard deviation matrix of the \(i\)th instance as \(s_{i}\), and \(\sigma_{i}\) as the max eigenvalue of \(s_{i}\). We introduce a reliability parameter \(\tau\):  
\[\tau=\frac{max\{\sigma_{i}\}}{0.5*SIZE_{score}}, \tag{4}\]  
where \(SIZE_{score}\) is the size of the response map.  
Figure 4 shows the values of \(\tau\) over frames on Girl2 of the OTB dataset. When a potential occlusion occurs, \(\tau\) presents a peak. We set a threshold parameter \(\tau_{l}\). When \(\tau\geq\tau_{l}\), it indicates a potential occlusion (see the \(\tau\) values in frames 50, 70, 100, 120, etc.); the tracking results are unreliable. Conversely, if \(\tau\) satisfies \(\tau\leq\tau_{l}\) in \(N_{r}\) successive tracking frames, the result is considered reliable.
## 3 Risk Model
### Multi-Layer Perceptron (MLP)  
Multi-layer perceptron imitates the human brain to perform intelligent tasks [15]. It can represent complicated relationships between input and output and acquire knowledge about these relationships directly from the data. Suppose we are given a data set \(X=\{X_{1},X_{2},\ldots,X_{m}\}\) for the water supply in a certain area, and each \(X_{n}=[x_{1}^{n},x_{2}^{n},\ldots,x_{d}^{n}]^{T}\) (\(n=1,2,\ldots,m\)) belongs to one of two classes (the ground subsidence occurs or vice versa). The output of the MLP, as shown in Figure 5, is represented by  
\[g_{l}(X_{n})=f(\sum_{j=1}^{n_{h}}w_{lj}^{th}f(\sum_{i=1}^{d}w_{ji}^{hi}x_{i}^{n }+w_{j0})+w_{l0})\ (l=1,2) \tag{1}\]  
where \(w_{ji}^{hi}\) is a weight between the \(i\)th input node and the \(j\)th hidden node, \(w_{lj}^{oh}\) is a weight between the \(j\)th hidden node and the \(l\)th output node, \(n_{h}\) is the number of hidden nodes, and \(f(\cdot)\) is a actuation function.  
Figure 4: Support vector machine (SVM) model.  
We train the neural network such that the output \(g_{l}(X_{n})\) approaches the target value  
\[\begin{array}{ll}t(X_{n})=[t_{1},t_{2},\ldots,t_{C}]^{T},\\ t_{l}(X_{n})=\left\{\begin{array}{ll}1&\mbox{if }X_{n}\in\omega_{l}\\ 0&\mbox{otherwise}\end{array}\right.\ (l=1,2,\ldots,C).\end{array} \tag{2}\]  
As in [16], the network parameters are chosen to minimize the following function  
\[\begin{array}{ll}J(W)&=\sum\limits_{n=1}^{N}\sum\limits_{l=1}^{C}\left[g_{l }(X_{n})-t_{l}(X_{n})\right]^{2}\\ &=\sum\limits_{l=1}^{C}\big{(}\sum\limits_{X_{n}\in\omega_{l}}\left[g_{l}(X_{n })-1\right]^{2}+\sum\limits_{X_{n}\
otin\omega_{l}}\left[g_{l}(X_{n})-0\right] ^{2}\big{)}\\ &=N\sum\limits_{l=1}^{C}\left\{\frac{N_{l}}{N}\frac{1}{N_{X_{n}}\sum\limits_{ X_{n}\in\omega_{l}}}\left[g_{l}(X_{n})-1\right]^{2}+\frac{N-N_{l}}{N}\frac{1}{N-N_{l}} \sum\limits_{X_{n}\
otin\omega_{l}}\left[g_{l}(X_{n})-0\right]^{2}\right\} \end{array} \tag{3}\]  
Here, \(W\) is the weight vector of the network, and \(N_{l}\) is the number of samples belong to \(\omega_{l}\). In the limit of infinite data, we can use [PERSON] formula to express (3) as  
\[\begin{array}{ll}\lim\limits_{N\rightarrow\infty}\frac{1}{N}J(W)\equiv \widetilde{J}(W)\\ \begin{array}{ll}=\sum\limits_{l=1}^{C}\left\{P(\omega_{l})\int\big{(}g_{l}( X_{n})-1\big{)}^{2}p(X_{n}\big{|}\omega_{l})dX_{n}+P(\omega_{i\
eq l})\int g_{l}( X_{n})^{2}p(X_{n}\big{|}\omega_{i\
eq l})dX_{n}\right\}\\ \begin{array}{ll}=\sum\limits_{l=1}^{C}\left\{\int g_{l}(X_{n})^{2}p(X_{n}) dX_{n}-2\int g_{l}(X_{n})p(X_{n},\omega_{l})dX_{n}+\int p(X_{n},\omega_{l})dX_{n} \right\}\\ \begin{array}{ll}=\sum\limits_{l=1}^{C}\left\{\int\left[g_{l}(X_{n})-P( \omega_{l}|X_{n})\right]^{2}p(X_{n})dX_{n}+\int P(\omega_{l}|X_{n})P(\omega_{i \
eq l}|X_{n})p(X_{n})\,dX_{n}\right\}\end{array}\end{array} \tag{4}\]  
Since the second term in Equation (4) is independent of \(W\), to minimize \(\widetilde{J}(W)\), we obtain  
\[g_{l}(X_{n})\approx P(\omega_{l}|X_{n}). \tag{5}\]  
Therefore, we can compute the probabilities of ground subsidence occurrences by using output values of MLP [11].
# BDCOA: Wavefront Aberration Compensation Using Improved Swarm Intelligence for FSO Communication
## 2 BDCOA Based Optimum Control Signal  
In modern communication systems using FSO, the WA in the receiver reduces the coupling efficiency of broadcasting a laser signal (i.e., optical signal). The SLAO system is one of the important modules used for avoiding the losses that occur due to the WA. In this research, the SLAO is utilized with BDCOA to choose the optimum control signal values for actuators of DM. According to the optimum control values from BDCOA, the phase compensation is obtained in DM by minimizing the phase distortion and enhancing the FSO communications.
## 2 Dimensionless Parametric Approach for Voltec Analysis
### Dimensionless Speeds, Powers and Mechanical Losses in Voltec Full-Electric Operation  
The relationships exploited in [34; 35] to analyze the power-split operation were rearranged in [36] to model also the FEV functioning mode. These are exploited in this section to apply the unified parametric model to the Voltec in FEV operation for the first time. Nonetheless, to find the best balance between brevity and self-consistency of the paper, this section presents the only implementation of formulas that were introduced and explained in the previous works [33; 34; 35; 36], to which we refer the reader for more information. However, Appendix A outlines the major features of the model to facilitate the understanding of the content of this section. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Mode** & \(\tau_{\#i}\) & \(\tau_{\#o}\) & \(\tau_{\sigma ni}\) & \(\tau_{\tilde{n}\omega}\) \\ \hline Input-Split & 0.247 & 0 & 2.00 & \(-1.87\) \\ Compound-Split & 0.247 & 0.510 & 2.00 & 2.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Basic functional parameters of Voltec derived in [34]. As the engine is inactive in FEV driving (clutch C0 is engaged), speeds and power can be more conveniently normalized to the output ones. Since the shaft _in_ is motionless, the speed ratio between every electric machine and the shaft _out_ is univocally defined as:  
\[ \begin{split}\left.\frac{\omega_{i}}{\omega_{nat}}\right|_{\omega_{ \text{in}}=0}&=\frac{\tau_{\text{f}_{\text{g}_{\text{g}_{\text{g}_{ \text{g}}}}}}}{\tau_{\text{g}_{\text{g}}}-\tau_{\text{g}_{\text{g}_{\text{g}}}} }=7.58\\ \left.\frac{\omega_{\text{g}_{\text{g}}}}{\omega_{\text{int}}} \right|_{\omega_{\text{in}}=0}&=\frac{\tau_{\text{g}_{\text{g}_{ \text{g}_{\text{g}}}}}}{\tau_{\text{g}_{\text{g}}}-\tau_{\text{g}_{\text{g}}} }=8.12\end{split}  \tag{1}\]  
The functional parameters used in Equation (1) are those related to the input-split mode, since it is the only mode exploited by General Motors to perform the FEV operation. The dimensionless PSU power losses can be computed by the fast black-box method proposed in [35] adapted to the FEV analysis, as described in [36] and summarized in Appendix A. The considered efficiencies of the final drive (\(\eta_{fd}=0.953\)) and of the PGs in fixed-carrier functioning (\(\eta_{0}=0.96\)) are the same as those assumed in [35]. The total mechanical power losses are the sum of those occurring in the final drive, calculated as:  
\[\left.\overline{p}^{\prime}_{L}\right|_{OG}=-\frac{\left.\overline{P}_{Loss} \right|_{OG}}{P_{out}}\approx-\left|\left(1-\eta_{fd}\right)\,p^{\prime}_{out}\right| \tag{2}\]  
Figure 2: Results of Voltec analysis in power-split operation carried out in [34; 35]. **(a)** CVU speed ratios; **(b)** mechanical power losses in the PSU as a fraction of the input power; **(c)** MG I real power as a fraction of the input power; **(d)** MG O real power as a fraction of the input power.
## 4 Discussion  
### More Discussion on Pattern Classification Models  
The implementation and experimental evaluation of our proposed method have been described in Section 3. The deep learning-based pattern classification model was designed to consist of a ResNet-50-based extractor and a multi-layer fully-connected classifier. The rationale for determining the extractor and the hyper-parameters of the classifier was attaining high accuracy and less computational time. To analyze the accuracies of different model structures, we collected a total of 10,180 samples that were captured from four batches of wafers to form Dataset-II of which contained: (1) 2008 background patterns, (2) 408 alignment mark patterns, (3) 6184 normal patterns, and (4) 1580 abnormal patterns. Similarly, the random subsampling validation was repeated multiple times to assess each of the classification models. Table 7 lists the size of the feature vector obtained by a specified extractor and the total number of parameters of a model with a specified composition of hidden layers. In this table, the tuple (1000) denotes that the classifier contains one hidden layer with 1000 neurons, and the tuples (1000, 100) and (1000, 100, 10) respectively indicate two and three hidden layers with their neurons. It is evident that the ResNet-50 generated the most compact feature vector (having the minimum size) and thus structured the smallest classification model. Table 8 lists the validation accuracy of Dataset-II for the aforementioned compositions. The results showed that VGG-16, VGG-19, and ResNet-50 achieved comparatively high accuracy. For every extractor, the best accuracy was obtained when the classifier comprised two hidden layers. To sum up, the architecture consisting of a ResNet-50 extractor and a two-hidden-layer classifier was selected for our proposed classification model. Subsequently, a grid search approach was used to find the number of neurons in each of the hidden layers. During this experiment, we evaluated several combinations for two-hidden-layer composition \((n_{1},n_{2})\), where \(n_{1}\) and \(n_{2}\) were respectively selected from \(n_{1}\in\) {1000, 500, 200} and \(n_{2}\in\) {200, 100, 50}. The results were tabulated in Table 9, in which the accuracies were close to each other. However, the two-layer composition has unlimited possible combinations, and cannot be completely assessed; thus, the composition (1000, 100) that reached a high accuracy is an acceptable hyper-parameter setting in the model. In this subsection, the experimental results in terms of accuracy  
Figure 18: Results of our proposed inspection method for three SAT images: (**a**) _img01_; (**b**) _img02_; (**c**) _img03_.
## 3 Results  
### Machined Surface Topography under Different Clearances between the Horn Tip and the Workpiece  
Figure 4 shows the SEM images of the surfaces machined with different clearances between the horn and the workpiece under an abrasive concentration of 10% before and after polishing for 5, 10, 20, and 30 min. For both cases, the original surfaces (at 0 min) demonstrate the typical surface topography of metal-based AM parts which have numerous partially melted powders, larger agglomeration and balling structures, and step discontinuities due to the layer interface (Figure 4a,b). All these contribute to a high initial Ra value of about 9.48 \(\upmu\)m--measured before polishing. After 5 min of machining, we can notice that most of the partially melted powders were removed from their original sites, as shown in area A for both cases. The larger structures were also smoothed when the clearance was 1 mm, as shown in area B of Figure 4a, while almost no change happened for 2 mm clearance (Figure 4b). With the increase in the machining time, almost all of the partially melted powders were removed for 1 mm clearance. Step discontinuities at the layer interface were also reduced. The roughness Ra was measured, which decreased to 2.93 \(\upmu\)m after 30 min of machining, and an obvious smoothing of the AM surface was obtained as shown in Figure 4a. However, it was found that the partially melted powders were predominately removed in the first 5 min for 2 mm clearance, and only slight material removal occurred in the remaining 25 min. The larger structures and step discontinuities that remained on the surface after 30 min of machining were almost the same as the original ones. It is also noted that the removal process for the tightly attached, partially melted powder needs more time and leaves shallow pits on the machined surface, as shown in area C for both cases. The results indicate that  
Figure 3: Snapshot of the initial construction of the simulation model.  
a clearance of 1 mm is appropriate for effective polishing. In addition, the clearance is sufficient to prevent the direct hammering of abrasive particles and extends the working life of the horn.  
Figure 4: SEM images of the surfaces machined with different clearances between the horn and the workpiece under 10% abrasive concentration with the increase in polishing time: (**a**) 1 mm; (**b**) 2 mm.
## 3 Methods
### Network Inference Structure  
Figure 3 compares the inference process used by MCDCNet with those of previous methods. Traditional semantic segmentation networks generate a feature map that matches the dimensions of the GT. Therefore, the output of traditional semantic segmentation networks can be directly compared to the GT, as shown in (a). However, this direct approach does not fully utilize the features extracted by the network.  
Then MaskFormer [10] proposes a simple approach to adapt the mask classification method for semantic segmentation. As shown in (b), the network outputs two predictions: class and mask. MaskFormer first multiplies the class and mask to obtain a feature map of the same size as GT and then compares it to GT to perform semantic segmentation. This method, compared to traditional per-pixel classification, refines the previous global com  
Figure 2: The proposed architecture of MCDCNet. The bottom shows the Spatial Extraction Module (SEM) and inference structure proposed in this paper.  
parison into category-wise and mask-wise comparisons, allowing the network's extracted features to be more fully utilized.  
Inspired by MaskFormer, we devised a simple and effective method to apply the IAM-based decoder for semantic segmentation. First, the IAM-based decoder outputs three predictions, class, scores, and mask, with sizes \(\mathbb{R}^{N\times c_{ls}}\), \(\mathbb{R}^{N\times 1}\), and \(\mathbb{R}^{N\times H\times W}\), respectively. \(N\) represents the number of instances and \(cls\) represents the number of categories in the dataset. Prediction class, scores, and mask represent the class scores for each predicted instance, the confidence of each predicted instance, and the predicted mask values for each instance, respectively. To maximize the use of these three predictions, we first perform matrix multiplication on class and scores, combining the confidence and class scores into a single matrix, and then normalizing the resulting scores using the square root. Afterward, we multiply the resulting scores with the mask prediction to obtain the category score of the corresponding mask for each instance. This inference process effectively leverages the strengths of the IAM-based decoder, integrating the multiple features output by the IAM-based decoder. As a result, the predictions encompass all the valuable information learned by the network, significantly enhancing the performance of MCDCNet for semantic segmentation.
## 4 Implementation and Results
### Results
To measure the graphic quality through optimization, the influence and quality of rendering were tested. It should be verified that real-time VR rendering content can be provided by importing CAD files from VR Parser and then exporting them to convert the extracted FBX files into 3D formats. The test was conducted as shown in Figure 19, while focusing on the frame rendering speed and the resolution of the 3D content. Based on the 3D format-converted 2D files, 3.5.99 frames per second was used to measure the number of frames per second of VR image rendered through CAD converter, and the number of frames per second was checked after moving to the left, right, and top of VR images. Further, the resolution of the rendered VR image was confirmed. Rendering to CAD files in a VR system can result in high transmission load, slow rendering speed, and poor 3D control due to the generation of large amounts of polygons, so it should be verified that it is efficient for VR systems. In existing studies [47] aiming to simplify CAD files, it was measured at 75 frames when optimization was performed at 50%. Converting CAD data into FBX, in most cases, decomposing a single object into a sub-object, damaging the object structure, which was feared to degrade performance, but the frame measurement results of the model provided as VR Parser are shown in Table 6. Full view from parent of 1, the overall foreground was 86 frames per second, Full foreground from left end 2, the preliminary foreground was 88 frames per second, and Full foreground from right end of 3, the overall foreground was 83 frames, showing an average result of 85.6 frames. It also provides a high resolution of 2K for resolution.
## 2 Background
### Robot Position Control  
#### 2.2.1 Kinematic Model for the Robot  
A differential wheeled robot is a mobile vehicle that moves using two independently driven wheels located on either side of its body. The tangential velocities of these wheels denoted as (\(v_{L}\)) and (\(v_{R}\)), left and right, respectively, are perpendicular to the axis of the wheels. The wheels are assumed to roll without sliding, a restriction known as non-holonomic [27]. The robot changes direction by the differential rotation of the wheels, eliminating the need for an additional steering mechanism. The robot's kinematic model in Cartesian coordinates is described as follows [23; 28]:  
\[\left\{\begin{array}{c}\hat{x}_{c}=v\cos(\theta)\\ \dot{y}_{c}=v\sin(\theta)\\ \dot{\theta}=\omega,\end{array}\right. \tag{1}\]  
Figure 1: CoppeliaSim simulator environment.  
where \((x_{c},y_{c})\) is the robot's position, and \(\theta\) is the heading direction angle, perpendicular to the turning radius (\(R\)). The linear and angular velocities of the robot are obtained from \(V=(v_{L}+v_{R})/2\), and \(\omega=(v_{L}-v_{R})/L\), respectively, with being \(L\) the distance between the wheels. The angular velocity is defined concerning the Instantaneous Center of Curvature (ICC). The robot has a maximum linear velocity \(V_{\max}\) and angular \(\omega_{max}\), and a minimum turning radius \(R_{\min}\). It can only move forward or backward in the heading direction; this is known as non-holonomic constraints [24].
# A Design Method of Code Correlation Reference Waveform in GNSS Based on Least-Squares Fitting
## 3 Results
### Boc(1,1)  
The same procedures as for BPSK signal are repeated for the BOC(1,1) signal. The settings for BOC(1,1) signals are listed in Table 2. However, the type of CCRW in limited bandwidth situations is different from BPSK signals. The code correlation reference waveform for the infinite bandwidth case presents at each code transition, and its value is determined by the sign of the next code at transitions, like the W2 waveform. However, the code correlation reference waveforms for the finite bandwidth cases present at the center of each code, just like BGP waveforms. Its value is determined by the sign of the current code. The waveforms are demonstrated in Figure 4.  
The code correlation reference waveform diagrams for the infinite bandwidth case are shown in Figures 15 and 16. The structures are quite similar to the BPSK signal with infinite bandwidth, except that an additional waveform appears at the position of \(-0.5\) chips with twice the amplitudes.  
Figure 14: Fitted S-curves for the BPSK(1) signal (4.092-MHz bandwidth).  
Figure 13: Code correlation reference waveform diagram for the BPSK(1) signal non-coherent discriminator (4.092-MHz bandwidth).  
The results for the 6.138-MHz bandwidth case are shown in Figures 18-20. The results of the BGP waveforms are added in the simulation, since the generated waveform has a similar structure to it. The distribution of waveforms was increased to \(-0.4\)-\(0.4\) chips for the coherent case and \(-0.6\)-\(0.4\) chips for the non-coherent case. Because we had to increase \(\mu\) and the size of the linear region due to the limited bandwidth, the resulting S-curves have greater peak values and a sharper slope in the linear region. Unlike W2 and the double-delta correlator, the fitted S-curves do not have a false lock point in the position of 0.5 chips. However, the offset in the ideal S-curve leads to larger fluctuations in the S-curve outside the linear region than W2 CCRW, which would increase the multipath error envelope.  
The same simulation of Figure 2 was performed for the proposed non-coherent discriminator of the BOC(1,1) signal in Figure 21. This shows the tracking error result returned to the correct positions, because the false lock point is avoided in the S-curve design.  
Figure 17: Fitted S-curves for the BOC(1,1) signal (infinite bandwidth).  
Figure 18: Code correlation reference waveform diagram for the BOC(1,1) signal coherent discriminator (6.138-MHz bandwidth).  
Figure 21: Simulation results of the false lock problem when the LS non-coherent discriminator is applied in a noisy environment (input \(C/N_{0}\) = 35 dBHz, injecting a short period of strong interference at a time interval of 1 s, double-sided bandwidth = 6.138 MHz).  
Figure 19: Code correlation reference waveform diagram for the BOC(1,1) signal non-coherent discriminator (6.138-MHz bandwidth).  
Figure 20: Fitted S-curves for the BOC(1,1) signal (6.138-MHz bandwidth).