esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 BER and Data Rate Calculation of the Proposed Network
### The Proposed System Using an MIMO System  
This section proposes to use of a smart MIMO antenna system with two antennas at the SU-TX tower and two antennas at the SU-RX node, i.e., \(MIMO_{2\times 2}\). The MIMO system leads to a significant increase in spectral efficiency and data rates, high QoS, improved BER, a wide coverage area, and low maintenance and low operation costs [42]. Figure 7 shows a simple \(M\ \times\ N\) MIMO system with channel gain matrix \(H=\begin{bmatrix}h_{11}&h_{12}\\ h_{21}&h_{22}\end{bmatrix}\) that sends signals \(x_{s}=\{x_{s1},\ x_{s2},\ x_{s3},\cdots\}\) and receives noisy signals \(y_{s}=\{y_{s1},\ y_{s2},\ y_{s3},\ \cdots\}\). The BER estimation and the capacity calculations of smart MIMO systems through different channel types are suggested in different research works, i.e., [43; 44; 45].  
The BER through the white Gaussian channel and Rayleigh fading channel is calculated as \(P_{AWGN}=Q\big{(}\sqrt{SINR_{u}}\big{)}\) and \(P_{Ray}=Q\bigg{(}\sqrt{|H|^{2}SINR_{u}}\bigg{)}\), respectively.  
Also, the capacity derived below in Equation (24) assumes that there is no channel state information (CSI) at the SU-TX tower that represents the practical case. As the user can randomly move from one point to another point, the capacity can be expressed as  
\[C_{MIMO}=\log_{2}\det\Big{(}I_{M}+SINR_{u}\Big{(}HH^{H}\Big{)}\Big{)}. \tag{24}\]  
where \(I_{M}\) is the identity matrix with dimension 2\(\times\)2, \(H\) is the channel gain matrix, and \(H^{H}\) is the conjugate transpose of the channel matrix, i.e., Hermitian.
## 5 Conclusions
Future research should prioritize enhancing the efficiency of the HGA, aiming to reduce computational overhead while maintaining the quality of the solutions. This endeavor might entail the development of more streamlined generation strategies or the fusion of the HGA and MOOP methodologies to capitalize on their respective strengths. In addition, investigating parallel computing methods or hardware acceleration can help alleviate the computational burden. The practical application of our integrated methodology in addressing the bike-sharing problem underscores its potential for enhancing operational efficiency as demonstrated in real-world contexts such as the Mapo-gu bike-sharing systems. Nonetheless, overcoming computational hurdles within the HGA methodology and refining the entire methodological framework are imperative for its widespread adoption across diverse sectors. Conceptualization, H.L., K.C. and S.L. ; methodology, H.L. and S.L. ; software, H.L. and S.L. ; validation, H.L., K.C. and S.L. ; formal analysis, H.L., K.C. and S.L. ; writing--original draft preparation, H.L., K.C. and S.L. ; writing--review and editing, H.L., K.C. and S.L. ; supervision, S.L. ; project administration, H.L. ; and funding acquisition, H.L. and S.L. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean Government (NRF-2020S1A5A2A03047527). **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request. **Conflicts of Interest:** The authors declare no conflicts of interest.
## 3 Performance Analysis  
### Error Performance for Negligible Phase Noise  
#### 3.1.1 Exact ASEP Expression  
Assuming that the phase noise is negligible and \(I\) is known to the receiver, the conditional symbol error probability (SEP) for the \(M\)-PSK schemes along with the non-zero boresight pointing errors is determined by [PERSON] and [PERSON] in [PERSON] and [PERSON] (2010), Equation (8.22) of [PERSON] and [PERSON] in [PERSON] and [PERSON] (2010)):  
\[p_{I}(I)=\frac{1}{\pi}\int\limits_{0}^{\pi-\pi/M}\exp\left(-\frac{\gamma\sin^ {2}\frac{\pi}{M}}{\sin^{2}\varphi}\right)d\varphi, \tag{15}\]  
where \(\gamma=I^{2}/2\sigma_{n}^{2}\) stands for the instantaneous SNR at the receiver. Introducing the expected SNR at the receiver as \(\mu_{NZB}=E[I]_{NZB}^{2}/(2\sigma_{n}^{2})\), where \(E[\cdot]\) denotes expectation, we have \(\gamma=\mu_{NZB}I^{2}/E[I]_{NZB}^{2}\), where the subscript \(NZB\) indicates the presence of non-zero boresight pointing errors. The expected value of amplitude fading over non-zero boresight pointing errors is then obtained as follows [PERSON] and [PERSON] (2010); [PERSON] (2010):  
\[A_{q}=E[I]_{NZB}=\int\limits_{0}^{\infty}I\,f_{I}(I)\,dI=\frac{A_{0}\,g\,(c+ \Omega)}{1+q^{-2}}, \tag{16}\]  
which means that \(\mu_{NZB}=A_{q}^{2}/2\sigma_{n}^{2}\). By averaging Equation (15) with respect to \(I\), the ASEP might be expressed as:  
\[p_{I}=\int\limits_{0}^{\infty}p_{I}(I)f_{I}(I)\,dI=\frac{1}{\pi}\int\limits_{0 }^{\infty}\int\limits_{0}^{\pi-\pi/M}\exp\left(-\mu_{NZB}\,I^{2}\frac{\sin^{2} \frac{\pi}{M}}{A_{q}^{2}\sin^{2}\varphi}\right)f_{I}(I)\,d\varphi\,dI. \tag{17}\]  
Replacing in the next step the exponential term in Equation (17) with the corresponding Meijer-G function provided by [PERSON] and Marichev in [PERSON] and Marichev (2010) and applying the integration rule in (Equation (07.34.21.0013.01) of Wolfram Function site Adamchik and Marichev (2010)), we finally obtain:  
\[p_{I}=\frac{q^{2}A_{(\aleph\,\text{or}\,\Re)}}{2\pi^{2}}\sum\limits_{(\aleph\, \text{or}\,\Re)}2^{q+k-3}a_{k(\aleph\,\text{or}\,\Re)}B_{(\aleph\,\text{or}\, \Re)}^{-\frac{q+k}{2}}\int\limits_{0}^{\pi-\pi/M}G_{5,2}^{1.5}\left(\begin{array}{c }\frac{16\,\mu_{NZB}(1+q^{-2})^{2}\sin^{2}\frac{\pi}{M}}{(c+\Omega)^{2}B_{( \aleph\,\text{or}\,\Re)}^{2}}&\frac{2-q^{2}}{2},\frac{1-g}{2},\frac{2-q}{2}, \frac{1-g}{2},\frac{2-k}{2}\\ \frac{1}{(c+\Omega)^{2}B_{(\aleph\,\text{or}\,\Re)}^{2}}&0,\frac{-q^{2}}{2} \end{array}\right)d\varphi. \tag{18}\]
## 1 Introduction
In general, the intrusiveness brings to an increase in both network traffic and signal/data processing. This drawback is extremely undesirable since the nodes are mostly battery-powered and any additional energy consumption could compromise the network lifetime. On the basis of these considerations, in this paper we aim at evaluating the suitability of a passive method for the estimation of the physical layer performance in a wireless sensor network. Herein, by passive we mean that the nodes of the WSN are not involved in the monitoring process, and the measurements are performed by eternal nodes that carry out a series of observations on the wireless channels concerning the transmissions of the network nodes. These observations are then used to estimate the status of the wireless links in the network. The major contributions of this paper are the followings:  
* we designed a new process for the monitoring of the physical layer in WSNs making use of a completely passive methodology. From data sniffed by external nodes, we first estimate the position of the nodes in the WSN by applying the Weighted Least Squares (WLS) to the method of indirect observations. The resulting information on the nodes position is then used to estimate the status of the communication links among the network nodes;
* we performed a significant number of measurements on the field to evaluate the accuracy of the proposed approach in both indoor and outdoor environments. In the experiments, the proposed method achieved an accurate estimation of the channel links status so that we could acquire the status of the channel links with an average error lower than 1 dB, which is around 5 dB lower than the error introduced without the application of the proposed method. The structure of the paper is the following. In Section 2 we review past works related to performance monitoring in WSNs. In Section 3.1 we define the multilateration problem in estimating the position of the nodes in the network and describe the weighted least squares algorithm applied to the method of indirect observations. In Section 4 we present the proposed approach for the passive monitoring of the WSN physical layer and in Section 5 we provide the analysis of the effectiveness of the proposed approach through experiments in realistic scenarios. Final conclusions are drawn in Section 6.
# Attention Map-Guided Visual Explanations for Deep Neural Networks
## 3 Methods  
### Grad-CAM  
Grad-CAM uses gradient calculations instead of GAP. As shown in Figure 3, Grad-CAM is a method for combining feature maps using gradient weights without any modifications to the network structure. It allows any gradients to flow into the final convolutional layer to build an explanation map that highlights the regions essential in the image for predicting the class. We found through our experiments that using Grad-CAM as a base gave the best results, so we built on it for our subsequent research.  
The Grad-CAM technique computes the gradient of the class score \(y^{c}\) with respect to the feature map of the last convolution layer:  
\[\frac{\partial y^{c}}{\partial A_{ij}^{k}} \tag{1}\]  
It uses global-average-pooling gradients to get weights \(W_{k}^{c}\).  
\[W_{k}^{c}=\frac{1}{Z}\sum_{i}\sum_{j}\frac{\partial y^{c}}{\partial A_{ij}^{k}} \tag{2}\]  
Grad-CAM generalizes visual explanations using a weighted combination of feature maps with ReLU.  
Figure 3: Grad-CAM overview. The input image is processed by the CNN model, and a raw score for the specific class is obtained. The gradients were set to 0, and the “bull mastiff” class was set to 1. Then, it back-propagates the gradient to the rectified convolutional feature maps, which were combined to produce the coarse red heat map that depicts where the model looking.  
\[L^{c}_{\text{Grad-CAM}}=\text{ReLU}\left(\sum_{k}W^{c}_{k}A^{k}\right) \tag{3}\]  
In Equation (4), weight \(a^{c}_{k}\) represents a partial linearization of the deep network downstream from \(A\), \(Z\) is the total number of feature map cells, \(y^{c}\) is an activation class score for class \(c\), and \(A^{k}_{ij}\) represents activation of the cell at spatial location \(i\).  
\[\alpha^{c}_{k}=\frac{1}{Z}\sum_{i}\sum_{j}\frac{\partial y^{c}}{\partial A^{k} _{ij}} \tag{4}\]  
Grad-CAM assigns priority values to each neuron for a specific choice using the gradient information flowing into the last convolutional layer of the CNN model.
## 3 Reliability Verification of Finite Element Model
### Contact Force Verification  
The interaction force between the rolling element and the inner raceway of the outer ring was obtained based on the calculation formula for rolling bearings and compared with the contact force obtained from simulations to validate the model's effectiveness [19].  
After that, the rolling bearing static load under radial load is calculated according to Equation (13).  
\[Q_{\varphi}=Q_{\max}=[1-\frac{1}{2\epsilon}(1-cos\psi_{j})]^{10/9} \tag{13}\]  
Figure 4: Comparison of theoretical value and simulation value: (**a**) holding angular velocity; (**b**) the rolling contact force between the rolling body and the outer circle.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Theoretical Values** & **Simulation Values** \\ \hline Angular velocity & 56.1 rad/s & 54.6 rad/s \\ Inaccuracies & 2.6\% & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison results of angular velocity verification.  
where \(\psi\) is the angle between the roller and the \(Y\)-axis in the negative direction and \(\psi=0\) is the \(Y\)-axis in the negative direction; \(\varepsilon\) is the load distribution coefficient, which is taken as 0.3.  
Cylindrical roller bearings with normal radial clearance can be obtained from the following equation:  
\[Q_{\text{max}}=\frac{5F}{Z\cos\alpha} \tag{14}\]  
Figure 4b demonstrates the comparison between the simulation results and the theoretical calculation results of the contact force under radial load. The comparison reveals that the rolling body contact force obtained from the simulation is basically consistent with the theoretical value, which verifies the reasonableness of the constructed finite element model.
# Design of Nanosecond Pulse Laser Diode Array Driver Circuit for LiDAR
## 2 Circuit Principle
### LD Power Supply  
The operating voltage of a pulsed laser diode is typically between 15 V and 50 V when it is turned on. Figure 6 is a circuit schematic of a boost power supply, which is used to provide a higher driving voltage for the semiconductor LD. A boost conversion control chip is used in conjunction with an external N-MOSFET to form a boost circuit. In Figure 6, the field-effect transistor \(Q\), Schottky diode \(D\), power inductor \(L\), and energy storage capacitor \(C_{ES}\) form the basic boost circuit topology. Pin FB is used to set the output voltage, which is divided by resistors \(R_{1}\) and \(R_{2}\) to obtain \(V_{LD}\). The equation for the output voltage is as follows:  
\[V_{LD}=\frac{R_{1}+R_{2}}{R_{2}}\cdot V_{FB} \tag{7}\]  
Figure 4: RC pulse narrowing circuit. (**a**) Circuit schematic diagram. (**b**) Signal timing diagram.  
Figure 5: Gate delay pulse narrowing circuit. (**a**) Circuit schematic diagram. (**b**) Signal timing diagram.  
In Figure 6, the capacitor \(C_{ES}\) also serves as an energy storage capacitor. The high-power pulse of the pulsed LD is achieved by utilizing the instantaneous discharge of the capacitor to provide a large current. Here, we conduct some theoretical calculations. Assuming the output voltage of the boost circuit is 20 V, and a 30 A, 5 ns pulse current is required to be provided to the semiconductor LD, the required charge quantity is as follows:  
\[\Delta Q=I\cdot\Delta t=30\text{A}\cdot 5\text{ns}=1.5\times 10^{-7}\text{C} \tag{8}\]  
The output voltage variation caused by instantaneous discharge is:  
\[\Delta U=\frac{\Delta Q}{C_{ES}}=\frac{1.5\times 10^{-7}\text{C}}{188\text{\mu F }}\approx 0.08\text{V} \tag{9}\]  
The output voltage variation caused by instantaneous discharge is 0.08 V, which amounts to 0.4% of the 20 V output voltage. The impact on power supply fluctuation can be disregarded. A sufficiently large energy storage capacitor \(C_{ES}\) is crucial for ensuring a high-power pulse output from the pulsed LD.
## 3 Proposed Method
### Multilevel Shared Decoder  
In the multilevel shared decoder, the multi-scale depth and RGB features are separately inputted from the skip connections of the depth and RGB encoders. The multilevel shared decoder can sufficiently blend the RGB and depth features with five cascading upsample blocks, i.e., UP\({}_{0}\), UP\({}_{1}\), UP\({}_{2}\), UP\({}_{3}\) and UP\({}_{4}\), as shown by Figure 3. The \(n\)th upsample block (UP\({}_{n}\)) gathers the \(n\)th level features \(r_{n}\) and \(d_{n}\), which come from the RGB and depth branches, to generate the \(n\)th decoding depth feature termed \(D_{n}\) from \(D_{n-1}\). Thus, the multilevel shared decoder iteratively generates multilevel decoding features, i.e., \(D_{0}\), \(D_{1}\), \(D_{2}\), \(D_{3}\) and \(D_{4}\), and sends them in parallel to the adaptive bin estimator.  
In this study, two types of upsampling blocks are designed to cover different application scenarios, which are named SimpleUp for high efficiency and UpCSPN-k for high-accuracy applications.  
Figure 2: Architecture of the proposed efficient dual encoders with MobileNet_s.
## 4 Numerical Optimization Experiments  
In this section, to testify the superiority of MSWOA, the proposed algorithm was compared with six well-designed algorithms base on nineteen benchmark functions. These algorithms include WOA [26], MFO [11], hybrid particle swarm optimization with spiral-shaped mechanism (HPSO-SSM) [48], GWO [10], PSO-GWO [49], and Levy flight trajectory-based whale optimization algorithm (LWOA) [40]. The detailed information about benchmark functions, experimental settings, and simulation results are illustrated in the remaining part of this section.
## 3 High Pressure Water Jet Experiments and Numerical Simulation  
### High Pressure Water Jet Experimental Procedure  
The basic water jet diffusion theory under high pressure was first put forward by [PERSON] et al. [PERSON] et al. (2007) as shown in Figure 4. It can be seen there are mainly three stages along the water jet direction: initial stage (stage 1), basic stage (stage 2) and dissipated stage (stage 3). The water jet velocity inside the potential core of stage 1 is always equal to the nozzle exit velocity, so the energy in stage 1 is huge and thus stage 1 is typically used for cutting or stripping. In stage 2, the velocity of the water jet near the centerline declines linearly and thus the energy is lower than stage 1. Stage 2 is mainly used for coating removal, optimization of coating adherence and creating rough surface for a dental implant [PERSON] et al. (2007). However, for stage 3, the water jet is dissipated too much, so stage 3 is not feasible as the energy of the water jet in this stage cannot be used efficiency. For our study, Stage 2 is the objective one and the proper stand-off distance should be adopted.  
In order to investigate the influence of processing parameters on the coating removal effect on the metal surface, several high pressure water jet experiments were carried out in this section. All the experiments were carried out using the robot system shown in Figure 1. For this study, the influence of water pressure, travelling speed, and stand-off distance on the coating removal rate were experimentally studied. Each investigated parameter was assigned four levels. For each case, the same travelling path was applied to provide consistent conditions for three times and a 100 \(\times\) 300 mm area was selectively removed.  
Figure 5 shows the experimental condition. The travelling platform for the wall-climbed robot is one large magnetic Q235 steel wall with thickness of 15 mm. The surface of the magnetic steel wall is covered with a paint of coat, which is hard to remove manually. The surface quality after high pressure water jet is measured by a small instrument called a coating thickness gauge as shown in Figure 6. The gauge can be used to measure the thickness of the residual coating on the metal wall quickly, accurately, and conveniently. For each case, at least 50 positions were measured and the average value was used to describe the thickness of the residual coating. The coating removed rate can be obtained by the following equation.  
\[w=\left(1-\frac{h}{\textit{initial}}\right)\times 00\% \tag{1}\]  
where \(h\) is the residual coating thickness and \(\textit{initial}\) is the initial coating thickness. The larger the value of \(w\), the better the coating removal effect.  
Figure 4: Schematic diagram of high pressure water jet diffusion.
# A Survey of Defensive Measures for Digital
## 3 The Scope: Examples of Digital Persecution in the Global South
### Iran  
Iran's government controls the internet through the Telecommunication Company of Iran, a state-owned monopoly. All ISPs are linked to this company and they must adhere to its standards. This means censorship can take place at the ISP level. Aside from blocking millions of websites, the government also suppresses communication by throttling connection speeds to 128 Kbps [14]. The regime has also imprisoned neitzens for expressing objectionable views online. During the \"Green Revolution\" of 2009, the regime blocked access to Facebook, YouTube, and other sites. Secret police infiltrated social media sites to surveille and intimidate political dissidents [22].  
One of the stranger responses to the Green Revolution is the government's effort to create a \"halal internet\". Rather than relying on a complicated filtering system, like China's Golden Shield Project, the Iranian government is pursuing a national intranet that will not connect to foreign sites. Iranians would only be able to connect to sites that are based in Iran. This would empower the regime to further isolate Iranian neitzens from the rest of the web [24].
## 4 Results
### Performance of MoAS-CNN  
The training and test process in each epoch are recorded. The training losses and test losses are shown in Figure 7. It can be seen that the losses of all datasets decrease significantly and stabilize after 80 epochs. Accordingly, as shown in Figure 7, the recognition accuracies achieved are 95.73% for 16 \(\times\) 16, 98.37% for 20 \(\times\) 20 and 98.65% for 32 \(\times\) 32. This can be attributed to the fact that more information is captured through higher density and larger area, and thus the extracted features are more separable.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **MoAS-CNN** & \multicolumn{2}{c}{**Bilinear CNN**} & **Traditional** \\ \hline
**Layer** & **Stream1** & **Stream2** & **Stream1** & **Stream2** & **CNN** \\ \hline Input & \(32\times 32\times 1\) & & \(32\times 32\times 1\) & \(32\times 32\times 1\) \\ Conv1 & \(3\times 3\times 8\) & \(5\times 5\times 16\) & \(3\times 3\times 8\) & \(5\times 5\times 16\) & \(3\times 3\times 8\) \\  & \(3\times 3\times 16\) & & \(3\times 3\times 16\) & \(3\times 3\times 16\) & \(3\times 3\times 16\) \\  & Global Pooling & & Global Pooling & \\ CA module & FC: \(1\times 1\times 16\) & FC: \(1\times 1\times 16\) & / \\  & FC: \(1\times 1\times 16\) & FC: \(1\times 1\times 16\) & \\ Conv2 & \(3\times 3\times 32\) & \(5\times 5\times 32\) & \(3\times 3\times 32\) & \(5\times 5\times 32\) & \(3\times 3\times 32\) \\ Conv3 & \(3\times 3\times 64\) & \(5\times 5\times 64\) & \(3\times 3\times 64\) & \(5\times 5\times 64\) & \(5\times 5\times 64\) \\ second- & \(14\times 14\times 1\) & & \(64\times 64\times 1\) & / \\ order(output) & \(1\times 1\times 120\) & \(1\times 1\times 120\) & \(1\times 1\times 120\) & \(1\times 1\times 120\) \\ FC2 & \(1\times 1\times 26\) & & \(1\times 1\times 126\) & \(1\times 1\times 26\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Detailed parameters of CNNs.  
Figure 6: The dataset of 26 letter-shape samples (**A**–**Z**).  
We also record the gradients for all three datasets simultaneously, as shown in Figure 8a-c. The gradient values become larger in the backpropagation, and there is no gradient disappearance. Among them, the gradient value of Conv1 is the largest in the 32 \(\times\) 32 dataset, making the loss converge quickly. There is no overfitting or underfitting, which indicates that our model and datasets are reasonable.  
Figure 9 shows the test confusion matrix of the 32 \(\times\) 32. Obviously, shapes with simple contours are more distinguishable, e.g., I. Conversely, complex contours are more prone to confusion, especially with similar categories, e.g., Q and G (mean Bhattacharyya correlation coefficient of 0.89).