esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Computational Efficiency  
To assess potential increases in computational overhead due to additional preprocessing required by RPE and CBS compared to the baseline, we measured the average training time and memory usage over 100 epochs for each experimental configuration in the 1\(\times\) setting; results are summarized in Table 6.  
Using RPE increased training time by only 1.3% compared to FPE due to the additional step of verifying valid regions within each patch. In contrast, CBS introduced negligible computational overhead, as it merely involves selecting the class from which patches  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**(a) Original Image (Scale: 1\(\times\))**} & \multicolumn{3}{c}{**Patch Level**} & \multicolumn{3}{c}{**Parcel Level**} & \multicolumn{3}{c}{**Difference**} \\ \cline{2-10} \multicolumn{1}{c}{**Method**} & \multicolumn{2}{c}{**Accuracy**} & \multicolumn{1}{c}{**Macro F1**} & \multicolumn{1}{c}{**Kappa**} & \multicolumn{1}{c}{**Accuracy**} & \multicolumn{1}{c}{**Macro F1**} & \multicolumn{1}{c}{**Kappa**} & \multicolumn{1}{c}{**Accuracy**} & \multicolumn{1}{c}{**Macro F1**} & \multicolumn{1}{c}{**Kappa**} \\ \hline NCB + FFE (baseline) & 0.6809 & 0.5956 & 0.6290 & 0.6957 & 0.6168 & 0.6506 & +0.0148 & +0.0212 & +0.0216 \\ NCB + RPE & 0.7323 & 0.6482 & 0.6834 & 0.7531 & 0.6775 & 0.7130 & +0.0238 & +0.0293 & +0.0296 \\ CBS + FFE & 0.7310 & 0.6695 & 0.6852 & 0.7291 & 0.6798 & 0.6891 & \(-\)0.0019 & +0.0103 & +0.0039 \\ CBS + RPE (Ours) & 0.7724 & 0.6862 & 0.7280 & 0.7811 & 0.7043 & 0.7444 & +0.087 & +0.0181 & +0.0164 \\ \hline \multicolumn{1}{c}{**(b) Enlarged Image (Scale: 2\(\times\))**} & \multicolumn{3}{c}{**Patch Level**} & \multicolumn{3}{c}{**Parcel Level**} & \multicolumn{3}{c}{**Difference**} \\ \cline{2-10} \multicolumn{1}{c}{**Method**} & \multicolumn{2}{c}{**Accuracy**} & \multicolumn{1}{c}{**Macro F1**} & \multicolumn{1}{c}{**Kappa**} & \multicolumn{1}{c}{**Accuracy**} & \multicolumn{1}{c}{**Macro F1**} & \multicolumn{1}{c}{**Kappa**} & \multicolumn{1}{c}{**Accuracy**} & \multicolumn{1}{c}{**Macro F1**} & \multicolumn{1}{c}{**Kappa**} \\ \hline NCB + FFE (baseline) & 0.4794 & 0.4121 & 0.4139 & 0.4857 & 0.4394 & 0.4277 & +0.0063 & +0.0273 & +0.0138 \\ NCB + RPE & 0.6217 & 0.5235 & 0.5594 & 0.6638 & 0.5672 & 0.6129 & +0.0421 & +0.0437 & +0.0535 \\ CBS + FFE & 0.7296 & 0.6165 & 0.6765 & 0.7814 & 0.6077 & 0.7434 & +0.0518 & +0.0542 & +0.0669 \\ CBS + RPE (Ours) & 0.7656 & 0.6751 & 0.7198 & 0.8033 & 0.7166 & 0.7667 & +0.0377 & +0.0415 & +0.0489 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison between patch-level and parcel-level performance on the test set: accuracy, macro F1, and Kappa (difference = parcel level \(-\) patch level).  
are extracted, exhibiting computational efficiency similar to that of the baseline. Overall, the combined approach of RPE and CBS effectively enhanced classification performance at the cost of a marginal increase in computation.
# Message Passing Detectors for UAV-Based Uplink Grant-Free NOMA Systems
## 3 Adaptive UAV-BS Detector Based on the HMP Rule
### Computation of the HMP for GAMP-BG-MC Algorithm
#### 3.1.1 Part I GAMP Part Messages  
The GAMP algorithm is used to obtain the likelihood messages \(n^{\text{BP}}_{b_{k,j}\to f_{\mathbb{R}_{k,j}}}\)\((b_{k,j})=\mathcal{CN}(b_{k,j};\hat{q}_{k,j},\
u_{q_{k,j}})\) of the variable \(b_{k,j}\) based on the received signal \(\mathbf{Y}\). The detailed scheme of the GAMP algorithm [21] is summarized in Algorithm 1. In Algorithm 1, the input message is \(\mathbf{Y},\mathbf{H}\) and the output message \(v_{q_{k,j}},\hat{q}_{k,j},\forall k,j\) is obtained after one iteration. At the beginning of Algorithm 1, the belief expectation and variance of the variable \(b_{k,j}\), denoted as \(\hat{b}_{k,j},v_{b_{k,j}}\), are first initialized. These values will be updated during the following iterations. In Algorithm 1, lines 1-2, the symbols \(\hat{p}_{n,j},v_{p_{n,j}}\) denote the expectation and variance of the message \(m^{\text{BP}}_{f_{Z_{n,j}}\to z_{n,j}}(b_{k,j})=\mathcal{CN}(b_{k,j};\hat{p}_{n,j},v_{p_{n,j}})\), respectively. In addition, \(t_{n,j}\) denotes an intermediate variable, which will be updated during the iteration. Finally, Algorithm 1 outputs the message \(n^{\text{BP}}_{b_{k,j}\to f_{\mathbb{R}_{k,j}}}(b_{k,j})=\mathcal{CN}(b_{k,j}; \hat{q}_{k,j},v_{q_{k,j}})\), as shown in lines 4-5. Based on the message \(n^{\text{BP}}_{b_{k,j}\to f_{\mathbb{R}_{k,j}}}(b_{k,j})=\mathcal{CN}(b_{k,j}; \hat{q}_{k,j},v_{q_{k,j}})\) is calculated by Algorithm 1. Then, we first initialize the beliefs \((v_{k,j})=\operatorname{Ga}(v_{k,j};\hat{e}_{k,j},\hat{q}_{k,j})\), whose parameters are updated in (32). The message \(m^{\text{BP}}_{f_{\mathbb{R}_{k,j}}\to s_{k,j}}(s_{k,j})\) can be calculated by the HMP rule (Equation (5) in [15]), resulting in  
\[m^{\text{BP}}_{f_{\mathbb{R}_{k,j}}\to s_{k,j}}(s_{k,j})=\overrightarrow{ \pi}_{k,j}\delta(1-s_{k,j})+(1-\overrightarrow{\pi}_{k,j})\delta(s_{k,j}), \tag{12}\] \begin{table}
\begin{tabular}{c c c} \hline
**Factor** & **Distribution** & **Function** \\ \hline \(f_{\mathbb{V}_{x,j}}\) & \(p(y_{n,j}|z_{n,j})\) & \(\mathcal{CN}(y_{n,j};z_{n,j},\lambda^{-1})\) \\ \(f_{\mathbb{Z}_{n,j}}\) & \(p(z_{n,j}|b_{j})\) & \(\delta\Big{(}z_{n,j}-\sum_{k=1}^{K}h_{n,k}b_{k,j}\Big{)}\) \\ \(f_{\mathbb{B}_{k,j}}\) & \(p(b_{j,k}\,|\,s_{k,j}\,\
u_{k,j})\) & Equation (6) \\ \(f_{\mathbb{B}_{k,j}}\) & \(p(b_{k,j})\) & \(\operatorname{Ga}(v_{k,j};\epsilon_{k,j},\eta_{k,j})\) \\ \(f_{\mathbb{S}_{k,1}}\) & \(p(s_{k,1}|p_{k}^{10})\) & \((p_{k}^{10})^{s_{k,1}}(1-p_{k}^{10})^{1-s_{k,1}}\) \\ \(f_{\mathbb{S}_{k,j}}\) & \(p(s_{k,j},s_{k,j}|-|\,p_{k}^{10},p_{k}^{10})\) & Equation (8) \\ \(f_{p_{n}^{01}}\) & \(p(p_{k}^{01})\) & \(\operatorname{Be}(p_{k}^{01};\epsilon_{k},f_{k})\) \\ \(f_{p_{k}^{10}}\) & \(p(p_{k}^{10})\) & \(\operatorname{Be}(p_{k}^{10};\epsilon_{k},d_{k})\) \\ \hline \end{tabular}
\end{table}
## 4 Simulation Model and Results
### Dynamic Behaviour  
For verifying the dynamic performance of the controller, some parameters are changed during the running condition. The simulation is tested when the current reference is changed from 4 A to 6 A and vice-versa. The effect of these transitions on the load current are shown in Figure 9a,b, respectively. From Figure 9a,b, it can be observed that when the reference current is changed at 5 s, the load current is changed to track the reference current. The output current is tracking the reference current accurately whether its amplitude is increased, decreased, or kept constant. Thus, the smooth closed loop control and fast dynamic response of the proposed control scheme is verified.  
Figure 8: Simulation results of HMLI (enabled TC): (**a**) common mode voltage, and (**b**) leakage current.  
Figure 7: Simulation results of HMLI (disabled TC): (**a**) common mode voltage, and (**b**) leakage current.
# 100 Picosecond/Sub-10\({}^{-17}\) Level GPS Differential Precise Time and Frequency Transfer
## 2 Differential Precise Time and Frequency Transfer
### Time Difference Estimation of DPT  
In order to obtain the time difference solution between two stations, we introduce the SD integer ambiguities into SD carrier-phase measurements, achieving accurate time transfer. For a short baseline of several kilometers, the inter-station receiver clock offset is shown in Equation (11) after ignoring the SD ephemeris error residuals, tropospheric delay residuals, and ionospheric delay residuals.  
\[t_{rf(m,n)}=\frac{1}{c}\Big{(}L_{f(m,n)}^{j}-\rho_{f(m,n)}^{j}-\lambda_{f} \cdot N_{f(m,n)}^{j}\Big{)} \tag{11}\]  
For the IF combination, the receiver clock offset is shown in Equation (12).  
\[t_{rIF(m,n)}=\frac{1}{c}\Big{(}L_{IF(m,n)}^{j}-\rho_{IF(m,n)}^{j}-\lambda_{IF} \cdot N_{IF(m,n)}^{j}-T_{IF(m,n)}^{j}\Big{)} \tag{12}\]  
where \(t_{rf(m,n)}\) represents the \(f\) frequency DPT time transfer result and \(t_{rIF(m,n)}\) represents the IF combination DPT time transfer result.
# Laser R-Test for Angular Positioning Calibration and Compensation of the Five-Axis Machine Tools
## 5 Experiment and Results
### Angular Positioning Measurement and Compensation  
This section is for the angular positioning error of the rotary axis calibration. The compensation amount of the rotary axis error is provided by the measurement results. In this experiment, the C-axis is rotated from 0 degrees to 360 degrees, the measurement pitch is 30 degrees, and there is a total of 13 times. The A-axis is rotated from \(-90\) degrees to 30 degrees, the measurement pitch is 10 degrees, and it is finished directly from forward to reverse measurement.  
Figure 18 shows the error of the C-axis before compensation according to ISO230-2 regulation. The bidirectional positioning accuracy is 12.76 arcsec and the inverse error is 1.32 arcsec. The error of the C-axis after compensation is shown in Figure 19. There is a bidirectional positioning accuracy of 4.49 arcsec and an inverse error of 1.68 arcsec.  
Figure 19: The C-axis after compensation.  
Figure 20 shows the error of the A-axis before compensation. The bidirectional positioning accuracy is 15.12 arcsec, and the inverse error is 0.19 arcsec. Figure 21 shows the error of the A-axis after compensation. The bidirectional positioning accuracy is 2.03 arcsec and the inverse error is 0.48 arcsec.  
The error reduced significantly through the results. The performances thus allow us to move forward and compare our system with other systems to see the feasibility of this study.
# Dynamic Response Analysis of Mega-Sub Isolated Structures under Multiaxial Earthquakes
## 3 Analysis of the Influence of Horizontal Parameters of the Mega-Sub Isolated Structure
### Analysis of the Influence of the Horizontal Frequency of the Seismic Isolation Layer
#### 3.3.2 Influence of Frequency Ratio on Sub-Structure Response  
As shown in Figure 6, the peak acceleration response curve of the isolated subframe is similar to that of the main frame, which is in the shape of a quadratic parabola, and the best isolation influence is achieved when the frequency ratio \(f\) is greater than 0.6. The displacement of the seismic isolation layer decreases as the frequency ratio increases. When the frequency ratio \(f\) is greater than 0.6, the decrease gradually decreases and tends to level off.  
According to the analysis in this section, the response laws of the main frame and the subframe are similar when the frequency ratio changes. When the frequency ratio \(f\) is kept at about 0.6, the main frame's displacement and acceleration peak and the isolation subframe's acceleration peak are small, which has a good damping effect; The displacement of the isolation layer is also small. Therefore, it is recommended to set the frequency ratio at about 0.6 in the actual design.
## 3 General Concept and Passive Detection
### Practical Issues in Passive Detection
#### 3.2.1 Estimation of Transmission Cost on a Specific Route
According to [39], for the sake of successfully transmitting a packet from node \(m\) to its next-hop node \(m+1\), the average number of retries \(K(i,m)\) can be predicted by the following. \[K(i,m)=\frac{1}{1-\mathcal{P}_{out}(i,m)} \tag{8}\]  
Previous research [36] suggests that \(\mathcal{P}_{out}(i,m)\) can be expressed as a function in \(P(i,m)\).
## 3 The Proposed SET Hardened LDO Regulator
### Proposed SET Radiation-Hardened (RH) Structure  
The key to our hardening design is to reduce the possibility of \(MN7\) being turned off or to stabilize \(MN7\) to provide sufficient current to the differential pair transistors under single particle impaction. As Figure 4 shows, to achieve this goal, we split the required tail current into multiple parts, which are provided by different transistors (\(MN7\_1\)-\(MN7\_n\)), to ensure that they are independent of each other. When one of the transistors is impacted by a single particle and no longer provides sufficient current, other transistors still work normally.  
\[I_{tail}=\sum_{i=1}^{n}I_{tail\_i} \tag{8}\]  
In analog integrated circuit design, the gate width of the transistor is usually a multiple of 2 to match the layout. When we split the current \(I_{tail}\), the number of current splits \(n\) must be a power of 2. The number of split parts is limited by the size of the transistors in the unhardened circuit design.  
To make the split \(MN7\) parts independent of each other, all the splits are provided by independent bias circuits. When splitting \(MN7\), it is necessary to split \(MP1\), \(MP4\), \(MN5\), and \(MN6\), respectively, into the same number of parts as \(MN7\). To further attenuate the effect of the SET on the current, resistive decoupling is adopted in this design. The splits of the same node are connected by a resistor in the middle. The current is supplied  
Figure 4: Current \(I_{tail\_n}\) is provided by splitting transistor \(MN7\_n\).  
in the multi-path, and transistors are symmetrically split. The overall hardened circuit symmetrical multi-path splitting (SMPS) when transistors are split into two parts is shown in Figure 5.  
From the previous simulation results, it can be seen that single particle impaction on either \(MN6\) or \(MP1\) will cause the VCO to stop oscillation for a different duration. When the single particle impaction occurs on \(MP1\), it takes a certain path to transmit the \(MN7\); meanwhile, SET has its transfer characteristics that will be attenuated by physical distance, thus causing a relatively shorter stop of oscillation. However, the impaction on \(MN6\) directly affects the bias of \(MN7\), causing a longer stop of oscillation. Since the \(MN6\) is more sensitive to SET, we will use \(MN6\) as an example to explain the hardening design in the following discussion.
# Seismic Velocity Anomalies Detection Based on a Modified U-Net Framework
## 2 Materials and Methods
### Modified U-Net Neuron Network  
U-Net is an efficient deep learning framework for image segmentation task. As Figure 4 shows, it can be divided into the front and latter part. The front is the downsampling part for feature extraction, which is composed of convolution layer and pooling layer. The latter one is the upsampling part for image reconstruction, which is composed of an up-convolution layer and a convolution layer. It should be noted that the net concatenates the front part and the latter part in each parallel layer, so that more information of feature extraction can be reserved.  
For the classical U-Net neuron network, suppose that \(Z_{i}\) is the data obtained after upsampling at the \(i\)-th layer, \(Y_{i}\) is the data before upsampling, \(X_{i}\) is the data mapped from the left side of the network at the \(i\)-th layer and \(C\) represents the upsampling process. Then its mathematical formula can be expressed as  
\[Z_{i}=X_{i}+CY_{i}. \tag{1}\]  
Our task is to invert seismic waveforms to generate velocity field and anomalies locations. A modified U-Net is proposed with a different structure from the classical one. Initially, the convolution operation starts from 1 pixel outside the edge of the seismic image, which guarantees that the generated velocity field does not change the size while acquiring the features of the original data. In addition, since it is found in the classical U-Net experiments that the generated velocity fields are contaminated by the contour of the seismic waveform, and the reason is that the classical U-Net transmits some parts of the data to the output directly, we modify the classical U-Net structure and omit the transmitted part. The structure of the modified U-Net is shown in Figure 4, and the dashed line indicates the transmitting process in traditional U-Net. The corresponding formulation is as follows:  
\[\left\{\begin{array}{ll}Z_{i}=CY_{i},&if\quad i=1;\\ Z_{i}=X_{i}+CY_{i},&if\quad i>1.\end{array}\right. \tag{2}\]  
In addition, the upsampling process is divided into two branches, which are used to predict the velocity field and the anomaly mask, respectively. Under this setting, which is shown in Figure 5, the velocity and the anomaly mask can be generated from the corresponding seismic waveform simultaneously.  
Figure 3: Verification procedure.  
Velocity field image generation is a regression problem, and the MSE type of loss function is often used. On the other hand, anomaly image generation is a classification problem, where the BCE type of loss function should be employed. Classification and regression problems cannot use the same loss function as the criterion. Therefore, we define an ensemble loss function of the entire U-Net as a weighted summation of MSE and BCE loss with a fine-tunable parameter \(\alpha\). The accuracy requirement of the model is that the ensemble loss function is less than a threshold, so that the accuracy of two branches can be guaranteed simultaneously. The ensemble loss function is defined as  
\[Loss=\alpha\text{BCE}+(1-\alpha)\text{MSE} \tag{3}\]
# OFDM Network Optimization Using a QPSK Based on a Wind-Driven Genetic Algorithm
## 5 Results  
### Noiseless Channel Simulation Outputs  
The simulation charts below are for a noiseless OFDM network without any fading characteristics or AWGN.  
Figures 4-9 show the output of running the two different QPSKs in a noiseless channel. In the noiseless channel, the transmitted signal is what is expected at the receiver stage after demodulation. From the figures above, the GAWDO QPSK encounters no bit errors  
whereas the customary QPSK has bit errors regardless of how many data points have been transmitted.  
Figure 4: GAWDO QPSK output for 64 data points in a noiseless channel.  
Figure 5: Customary QPSK output for 64 data points in a noiseless channel.  
Figure 6: GAWDO QPSK output for 256 data points in a noiseless channel.  
Figure 8: GAWDO QPSK output for 1024 data points in a noiseless channel.  
Figure 7: Customary QPSK output for 256 data points in a noiseless channel.  
Figure 9: Customary QPSK output for 1024 data points in a noiseless channel.  
This reflects weakness within the system itself and its data transmission processes since no noise or external interferences existed in the network. The bit errors increase with an increase in the number of transmitted data points which means that the conventional QPSK is not very reliable for multi-user systems which need a high data throughput.
## 1 Introduction  
Nowadays, sensors and sensor networks have become widespread in all areas of human activity. The use of sensors is extensive due to their many advantages including small size, different measured values, easy software development, low energy consumption, mobility, and lifetime. Additionally, many tasks such as data collecting, analysis, identification, and measuring can be resolved with the use sensors [1, 2], and sensors are a part of worldwide concepts and technologies such as the Internet of Things, Internet of Everything, and smart dust [3, 4].  
Sensor networks must be able to maximize coverage and energy efficiency. Due to the use of high-tech materials and technologies in the production of sensors, the cost of sensors and their maintenance is increasing. Thus, minimizing the cost of the network must be added to the conditions that the sensor network must meet. This condition can be achieved by reducing the number of sensors used, which can reduce the area covered. The intersection of the sensor coverage areas should also be taken into account as this also affects the number of sensors in the coverage area and the total cost of the network. Thus, the problem is to find the optimal ratio of the radius of coverage and the value of the intersection of coverage areas at which the maximum coverage of the territory is achievedat the minimum cost of the network. An important property of any information system is survivability. Thus, the assessment of network survivability and finding vulnerable network components is an important step in the design and usage of sensor networks.  
The aim of this work is to develop a method of building sensor networks of minimum cost and maximum coverage with the ability to assess survivability and find vulnerable components in the network.
## 3 Model Construction
### Problem Description
The observation time window of the satellite for the target mission \(W^{Target}_{satj}=\left\{w^{Target}_{1 satj},w^{Target}_{2 satj},\ldots,w^{ Target}_{satj}\right\}\), \(\forall w^{Targetj}_{satj}\in W^{Targeti}_{satj},w^{Targetj}_{satj}=<st^{i, \delta}_{k},et^{i,\delta}_{k}>\), where \(st^{i,\delta}_{k}\) and \(et^{i,\delta}_{k}\) denote the start and end times of the observation time window \(g\) of satellite \(j\) for the target \(i\), respectively. It is worth noting that the target mission visibility time window is different from the observation time window in that the target mission visibility time window is the time interval during which the target mission is visible to the satellite resource, while the observation time window is the time interval during which the satellite resource is allocated to the target mission given in the mission planning scheme.
# Color Visual Secret Sharing for QR Code with Perfect Module Reconstruction
## 3 The Proposed VSS-QR
### VSS-QR Decoder
#### 3.2.2 Color to Grayscale Conversion  
The conversion from color to grayscale can be accomplished by a standard QR code decoder. However, the QR code standard does not specify how this conversion should be done. Different implementations are allowed. One possible solution is to convert the RGB space to HSI (Hue, Saturation and Intensity) color space, and then retain only the intensity component. Here we use \"weighted average method\" that assigns different weights to different color channels [31]. Using weighted average, the perceptually more important green channel are assigned with the largest weight. The weight vector is **w**=[0.299, 0.587, 0.114]. So, the grayscale image can be calculated as:  
\[\hat{\textbf{T}}[i,\ j] = \textbf{w}^{T}\cdot\textbf{T}[i,j] \tag{15}\] \[= 0.299\times T_{r}[i,\ j]+0.587\times T_{g}[i,\ j]+0.114\times T _{b}[i,\ j],\]  
for all \(1\leq i,\ j\leq LD\), where \(\textbf{T}[i,\ j]=\left[T_{r}[i,\ j],T_{g}[i,\ j],T_{b}[i,\ j]\right]^{T}\).