esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Architecture
### Complexity Analysis  
This section presents a complexity analysis of the proposed algorithm, React-UCB. For this purpose, the big-\(O\) notation is used to describe the worst-case asymptotic execution time of the algorithm, \(T(n)\), as the number of arms (\(n\) in the context of complexity analysis) grows to infinity. The goal of this analysis is to assess the scalability of the algorithm as the problem size increases. The number of arms was considered the growing variable as it is the one with highest impact in terms of execution time.  
As depicted in Algorithm 1, in each time step \(t\), the algorithm is mostly composed of a set of sequential and conditional constant time operations (e.g., Line **5**), which have a joint order of complexity \(O(1)\). In addition to these constant time operations, Algorithm 1 and the equations evaluated by it also include vector-wise operations, such as element-wise products, summations, and \(max\) operations (e.g., Line **10**), which exhibit a linear order of complexity, \(O(n)\), as well as their resulting sequences.  
```
1:Input:\(\mathcal{K}=\{1,2,...,25\}\), \(\gamma=0.99,w=100,C=0.01\)
2:Input:\(\alpha_{p}=0.05,\alpha_{\Delta}=0.8,\epsilon=0.1,\zeta=0.1,\zeta=0.9\)
3:\(\tau\gets 1\)
4:for\(t=1,2,\ldots\)do
5:if\(t<|\mathcal{K}|\)or\(t-\tau<|\mathcal{K}|\)then
6:\(I_{t}\gets t-\tau+1\)
7:else
8:if\(|\mu_{2,t-1}-\xi|/\sigma_{1,t-1}>\alpha_{\Delta}\)then
9:\(I_{t}\gets I_{t-1}\)
10:else if\(u_{t}<1-\epsilon\) with \(u_{t}\sim U(0,1)\)then
11:\(I_{t}\leftarrow\arg\max_{i}\{U_{t}(k)\}_{\forall k\in\mathcal{K}}\) [Equation (24) with \(\gamma,C\)]
12:else
13:\(I_{t}\gets f_{t}\) with \(f_{t}\sim F_{Y_{t}}(\mathcal{K})\) [Equation (29)]
14:endif
15:endif
16: Pull arm \(I_{t}\) and collect reward \(X_{t}(I_{t})\in[0,1]\)
17:if\(R_{t}=1\) [Equation (18) with \(w,\alpha_{p},\alpha_{\Delta},\zeta\)] then
18:\(\tau\gets t\)
19:endif
20:endfor
```  
**Algorithm 1** Reactive-UCB (with default parametrisation)  
The computation of the probabilistic mass function described by Equation (29), used to account for correlated arms, requires a quadratic execution time, \(O(n^{2})\) due to the marginalization in Equation (28). As a result, the algorithm's asymptotic global execution time, per time step \(t\), is, at most, \(T(n)=O(n^{2})\). Nevertheless, the quadratic component applies only to a small set of mathematical operations, and thus has a limited impact to the global execution time unless the number of arms grows to unpractical numbers. Section 4.4 presents a set of empirical results that corroborate this prediction and the underlying theoretical analysis.
# Traffic Signal Optimization for Multiple Intersections Based on Reinforcement Learning
## 3 Methods
### State  
The state is defined as Equation (1). \(Q_{t}\) represents the queue of each lane at time \(t\). At an intersection with \(k\) incoming lanes, \(q_{t}^{i}\) represents the number of vehicles stopped in the incoming lane \(i\) at time \(t\). \(P_{t}\) indicates which phase is currently on. When there are \(n\) phases, if the \(j\)th phase is active, \(p_{j}=1\), otherwise \(p_{j}=0\). \(d_{t}\) means the elapsed time of the currently turned-on phase.  
\[S_{t} =[Q_{t},P_{t},d_{t}]\] \[Q_{t} =[q_{t}^{1},q_{t}^{2},\ldots,q_{t}^{k}] \tag{1}\] \[P_{t} =[p_{1},p_{2},\ldots,p_{n}]\]  
Figure 2 shows an example of traffic control at a single intersection. There were 10 incoming lanes into the intersection, and phase 4 was in progress for 10 s. Stopped vehicles are marked in red. Therefore, in the order of the lanes, \(Q_{t}=[0,\ 1,\ 0,\ 0,\ 3,\ 1,\ 2,\ 0,\ 0,\ 0]\) and with phase 4 on, \(P_{t}=[0,\ 0,\ 0,\ 1]\) and accordingly gave a green signal to the 3 rd, 4 th, 8 th, and 9 th lanes. Finally, the phase lasted for 10 s, \(d_{t}=[10]\).
## 4 Practical Engineering Application  
The schematic diagram of the adaptive multi-stationary stochastic resonance structure is shown in Figure 8. The \(a,b,c\) parameters of the multi-stable system are updated by analyzing the output signal of the system. The specific workflow is shown in Figure 9. Firstly, the population of the ant colony algorithm is initialized, and then the initial input is input into the multi-stationary stochastic system. The ant colony algorithm is used to determine whether the SNR signal-to-noise ratio is out of range. The pheromone is evaluated to obtain the optimal \(a,b,c\) values, even for the optimal solution of the system.  
Figure 8: Schematic diagram of an adaptive multi-stationary stochastic resonance structure.  
Figure 7: \(R\) curve with parameter \(c\) and \(U\) changes with \(R\).  
The test platform is shown in Figure 10a as the \"Sailfish\" AUV. Through the analysis of the AUV dynamics model, it can be seen that the AUV actuator collision, winding, and other faults directly affect the current parameters, so this paper uses the current data after the AUV collision. A collision failure occurs in the AUV experiment, and the spindle bending phenomenon occurs, as shown in Figure 10b.  
From the experimental data, we can see that the data has unreasonable singular values. It is more appropriate to eliminate the singular value by using the standard deviation method and take four standard deviations. The data for 3500 rpm under normal no load (a) and singular value map (b), and 3500 rpm data under fault no load (c) and singular value map (d) are shown in Figures 11 and 12.  
Figure 10: “Sailfish” autonomous underwater vehicle (AUV) and actuator spindle bending failure.  
Figure 9: Ant Colony-based adaptive multi-stationary stochastic resonance flow chart.  
From the experimental data, it can be seen that the current increases obviously, and the number of singular values increases obviously after collision, but it is difficult to judge whether the current increases slightly due to fault under normal navigation conditions. The experimental data are used to verify the bi-stable and tri-stable stochastic resonance fault diagnosis. The noise intensity is 0.05, and the noise function is \(A*\cos(2*pi*f0*t)\), where \(A=0.01\) and \(f0=0.008\). The fourth-order Runge-Kutta algorithm is used to solve Equation (26).  
\[\left\{\begin{array}{l}x(n+1)=x(n)+\frac{1}{b}(k_{1}+2k_{2}+2k_{3}+k_{4})+h^ {\frac{8}{7}}\xi(n)\\ k_{1}=h\left(-ax(n)+\frac{1+a}{b}x^  
We can see that the bi-stable stochastic resonance part of the signal is submerged in the noise, and the weak fault of collision is not well diagnosed, but the tri-stable system can detect the fault better. Therefore, the effect of the tri-stable system in the collision fault diagnosis of the AUV propeller is better than that of the bi-stable system.
# Credibility Based Imbalance Boosting Method for Software Defect Proneness Prediction
## 4 Experimental Design
### Experimental Settings
#### 4.5.2 Parameter Settings  
We set the maximum number of iterations \(T\) to 20 (see Line 10 in Algorithm 1), which is the same as that of three boosting related baselines, i.e., AdaC2, and AdaBoost. Because SMOTE is one step of our CIB, in this paper we just use WEKA data mining toolkit [96] to implement SMOTE, which has two key parameters: the percentage of synthetic minority class samples to generate \(P\) and the number of nearest neighbors \(k\). We let \(k=5\) and \(P=(floor(n_{neg}/n_{pos})-1)*100\), where \(n_{neg}\) and \(n_{pos}\) denote the number of majority class samples and the number of minority class samples for a given training dataset. The reason for setting \(k=5\) is twofold: (1) it is the default value in WEKA, and (2) it was used by [PERSON] et al. [40] when they proposed the SMOTE algorithm. The reason for setting \(P=(floor(n_{neg}/n_{pos})-1)*100\) not a fixed value is that the value of \(P\) can be automatically adjusted according to the defect ratio of different defect datasets. Parameter \(\beta\) in Equation (2) is set in order to make the value of \(cf\) equal to 0.9 when the \(r=\frac{2}{K-2}\), so as to make the credit factors of synthetic samples having two more real minority class samples be above 0.9.  
With respect to the baselines, the settings follow the description of original studies if any, otherwise the default settings in the toolkit. The cost factor of AdaC2 is set to the imbalance ratio. The AdaBoost is implemented by WEKA in this paper and the parameter, i.e., the maximum number of iterations (i.e., \(T\)), is also set to 20. The reason for setting \(T=20\) is that too small value may be unable take advantage of ensemble learning and too big value may cause over-fitting problem and be time-consuming. For baseline SMOTE, we also use the WEKA data mining toolkit [96] to implement it with the same setting as that in CIB. The number of removed majority class samples by RUS is equal to the difference between the number of majority class samples and that of minority class samples.
## 3 Metrics for Evaluating Prediction Performance
### Improved Evaluation Metrics  
Among the aforementioned evaluation metrics, each time step's prediction error is assigned equal weight (i.e., 1). However, in practical engineering scenarios, the accuracy of peak responses often holds greater significance [24]. For instance, the maximum ground motion acceleration in a seismic event correlates with seismic intensity, and the maximum inter-story displacement angle serves as a crucial parameter in structural design. To enhance the emphasis on peak data values within the evaluation process while mitigating the impact of smaller data values, several weighted evaluation metrics are proposed. The weight of the response data corresponding to the \(i_{\mathrm{th}}\) time step is shown in Equation (28):  
\[w^{(i)}\ =\ |y_{i}|/\max(|\mathbf{y}|) \tag{28}\]  
Upon introducing weights, the MAE, MSE, RMSE, and \(\mathrm{R}^{2}\) are, respectively, represented as the weighted absolute error (WMAE), weighted mean squared error (WMSE), weighted root mean squared error (WRMSE), and weighted determination coefficient (\(\mathrm{R}^{2}_{\mathrm{W}}\)). The calculation of the weighted metrics can be performed according to Equations (29)-(32):  
\[\mathrm{WMAE}(\hat{\mathbf{y}},\ \mathbf{y})\ =\ \frac{\sum_{i\ =\ 1}^{N}w^{(i)}|\hat{y}_{i}-y_{i}|}{ \sum_{i\ =\ 1}^{N}w^{(i)}} \tag{29}\]  
\[\mathrm{WMSE}(\hat{\mathbf{y}},\ \mathbf{y})\ =\ \frac{\sum_{i\ =\ 1}^{N}w^{(i)}(\hat{y}_{i}-y_{i} )^{2}}{\sum_{i\ =\ 1}^{N}w^{(i)}} \tag{30}\]  
\[\mathrm{WRMSE}(\hat{\mathbf{y}},\ \mathbf{y})\ =\ \sqrt{\mathrm{WMSE}(\hat{\mathbf{y}},\ \mathbf{y})} \tag{31}\]  
\[\mathrm{R}^{2}_{\mathrm{W}}\ (\hat{\mathbf{y}},\ \mathbf{y})\ =\ 1-\frac{\sum_{i\ =\ 1}^{N}w^{(i)}(\hat{y}_{i}-y_{i})^{2}}{\sum_{i\ =\ 1}^{N}w^{(i)}(\overline{y}-y_{i})^{2}} \tag{32}\]  
As per relevant tests [24], it has been demonstrated that the sensitivity of the weighted coefficient of determination to predicted response phase deviation is markedly diminished. Consequently, this enhances the overall scientific evaluation of structural response prediction.
# High-Accuracy Entanglement Detection via a Convolutional Neural Network with Noise Resistance
## 2 Methods
### CNN Training  
We use Werner states as follows to implement the network training and entanglement detection:  
\[\rho=p|\psi\rangle\langle\psi|+\frac{1-p}{4}\mathbf{I}, \tag{9}\]  
here \(|\psi\rangle=\frac{\sqrt{2}}{2}(|10\rangle-|01\rangle)\), \(p\in(0,1)\), and \(\mathbf{I}\) represents the \(2\times 2\) identity matrix.  
We implement entanglement detection with a CNN constructed by the Deep Learning Toolbox of MATLAB. The networks' hidden layers include two convolution layers activated by the leaky rectified linear unit (ReLU) function, two max-pooling layers, and one fully connected layer, and the softmax function is used to activate the output layer. The specific architecture and hyperparameters used in our neural network training are detailed in Tables 1 and 2. This information is crucial for understanding and replicating our experimental results.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Complete Measurement** & **Incomplete Measurement** \\ \hline
**Image Input Layer** & \(16\times 1\times 1\) & \(4\times 1\times 1\) \\ Input channels & 1 & 1 \\
**2D-Convolution Layer** & & \\ Input channels & 1 & 1 \\ Kernel & \(2\times 1\) & \(1\times 1\) \\ Stride & 1, No Padding & 1, NO Padding \\ Output channels & 8 & 8 \\ Activation function & leakyReLU (\(\alpha=0.01\)) & leakyReLU (\(\alpha=0.01\)) \\
**Max-Pooling Layer** & & \\ Input channels & 8 & 8 \\ Kernel & \(2\times 1\) & \(1\times 1\) \\ Stride & 2, No Padding & 1, NO Padding \\ Output channels & 8 & 8 \\ Kernel & \(2\times 1\) & \(2\times 1\) \\ Stride & 1, No Padding & 1, NO Padding \\ Output channels & 16 & 16 \\
**Classification Output Layer** & & \\ Activation function & Softmax & Softmax \\ Loss function & Crossentropy & Crossentropy \\ \hline \hline \end{tabular}
\end{table}
Table 1: The structure of our model.  
The positive examples considered include entangled states and separable states, which are characterized by five indices: accuracy (Acc), the precision of entangled states (Pre-Ent), the precision of separable states (Pre-Sep), the recall of entangled states (Rec-Ent), and the recall of separable states (Rec-Sep).
###### Abstract  
Measurements of the curvatures and warpages of a printed circuit board (PCB) during a thermal solder reflow process using strain gauges are proposed in this study. In the experiments, a shadow moire is used for measuring the out-of-plane deformations (or warpage) of a bi-material plate and a PCB with dual in-line memory module (DIMM) sockets during solder reflow heating, while the finite element method (FEM) is used to analyze the thermally-induced deformation of the PCB specimen for ensuring the validity of the measurement. Conventional strain gauges are employed to measure the strains (albeit as in-plane strain data) in both specimens during the solder reflow process. The results indicate that the strain gauge-measured strain data from the top and bottom surfaces of both specimens during the solder reflow can be converted into curvature data with specific equations, and even into global out-of-plane deformations or warpages with a proposed simple beam model. Such results are also consistent with those from the shadow moire and FEM. Therefore, it has been proved that the strain gauge measurement associated with the simple beam model can provide a method for the real-time monitoring of PCB deformations or warpages with different temperatures during the solder reflow process.  
Keywords:strain gauge; shadow moire; warpage; printed circuit board; solder reflow; finite element method +
Footnote †: journal: Applied Physics Letters B
# Image Deblurring Based on Convex Non-Convex Sparse Regularization and Plug-and-Play Algorithm
## 5 Numerical Experiment  
In this section, we experimentally assess the deblurring effect of the proposed PnP-FBS-CNC algorithm and compare it with other state-of-the-art methods in terms of both visual effect and numerical metrics. The numerical metrics employed are the CPU time and the peak signal-to-noise ratio (PSNR) value. We compare the performance of all the deblurring algorithms on CBSD68 dataset [47], which contains 68 color images and is widely used for many image tasks. We also utilize the eight real-world camera shake kernels introduced by [PERSON] et al. [17] as blur kernels. The blur kernels are shown in Figure 1. Additionally, we incorporate Gaussian noise with three noise levels \(\
u\in\{0.01,0.03,0.05\}\). For all noise levels, we specify the hyperparametersettings of the PnP-FBS-CNC algorithm. We set uniform parameters with the step size \(\alpha=0.4\), regularization parameter \(\lambda=0.1\), and non-convexity parameter \(b=0.25\). As stated in [7], DRUNet utilizes a single model to address various noise levels and exhibits superior performance compared to other denoisers. Therefore, we select DRUNet as the denoiser for PnP-FBS-CNC. When evaluating our algorithm, we adopt a comparative approach rather than relying solely on subjective judgments to assess its deblurring effect. By comparing it with other methods, we draw conclusions based on the observed differences. Specifically, three state-of-the-art image deblurring methods are considered: (1) IRCNN [36], which employs the PnP-HQS algorithm, with IRCNN serving as the denoiser. (2) DPIR [7], which also employs the PnP-HQS algorithm but incorporates DRUNet as its denoiser. (3) GS-PnP [13], which is a PnP algorithm with the gradient step (GS) denoiser. The GS-PnP method distinguishes itself from the previous two methods by inherently incorporating a denoiser in the gradient step. Further, it is worth noting that the hyperparameter settings of the above three methods still adhere to their respective default settings. We first analyze the numerical results to evaluate the performance of our proposed algorithm. The average CPU time and PSNR values for deblurring using various methods are presented in Table 1, effectively demonstrating the deblurring effects of the CBSD68 dataset with various blur kernels and noise levels. According to Table 1, it can be observed that the CPU time of PnP-FBS-CNC can be comparable with the other three algorithms. Moreover, PnP-FBS-CNC exhibits the highest average PSNR value irrespective of the chosen noise level and blur kernel. Simultaneously, we observe that when the noise level is 0.01, all methods yield significantly higher average PSNR values compared to those obtained at the other two noise levels. This indicates a positive correlation between decreasing noise levels and increasing average PSNR values.
## 4 Results and Discussion
### Incremental Training for Glaucoma Classifier  
#### 4.3.1 Dataset and Experimental Setting for Incremental Training  
In reality, due to different equipment settings in various fields, the classification accuracy will decrease. This phenomenon can use incremental training to allow the classifier to quickly learn the differences between old and new images and quickly transfer glaucoma features to the new classification model.  
Table 7 shows the number of image data sets used in this incremental training experiment. These images are provided by the Glaucoma Department of Chang Gung Memorial  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **ResNet-18** & **DenseNet-20** &
\begin{tabular}{c} **The Proposed** \\ **Transfer Learning** \\ \end{tabular} \\ \hline
**Precision** & 79.76\% & 77.42\% & **85.19\%** \\
**Sensitivity** & 89.33\% & 96.00\% & 92.00\% \\
**Specificity** & 46.88\% & 34.38\% & **62.50\%** \\
**Accuracy** & 76.63\% & 77.57\% & **83.17\%** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison with other methods.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Trained by Real** & **Train by Generated** & **Trained by Transfer** \\  & **OCT** & **OCT** & **Learning** \\ \hline
**Precision** & 86.25\% & 78.82\% & 85.19\% \\
**Sensitivity** & 92.00\% & 89.33\% & 92.00\% \\
**Specificity** & 65.62\% & 43.75\% & 62.50\% \\
**Accuracy** & **84.11\%** & 75.70\% & **83.17\%** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Training results of the classification model.  
Hospital, and the images are all fundus images. In this experiment, all fundus images were also labeled by glaucoma physicians for glaucoma.  
Furthermore, the experiment aims to demonstrate the effectiveness of our proposed model by using a new dataset. We feed additional fundus images into the generator model to generate new OCT images and use the 433 generated images at a 3:1 ratio for training and testing, as shown in Table 8.  
In this experiment, the relevant parameters for incremental training are: the number of the epoch is set to 50, the batch size is set to 8, the optimizer uses Adam, and the learning rate is set to \(10^{-5}\).
# Review of CMOS Integrated Circuit Technologies for High-Speed Photo-Detection
## 2 Silicon Photonics for High-Speed Data Communications
### Overview of Optical Link
Among various optical communication applications, the short-reach applications are the most popular and market size is growing rapidly owing to increasing demand in data centers and active optical cables (AOCs). Especially, the demand for the AOCs would increase significantly, as their usage is not restricted to data centers, but massively extended to everyday life. However, since silicon photonics is based on a silicon waveguide, only the telecom wavelengths are available in silicon photonics, which is not directly compatible with the short-reach applications, most of which rely on low-cost VCSEL-based solutions. Nevertheless, the prospect of silicon photonics is promising for the future inter-/intra-chip interconnections and the next-generation data centers covering a communication distance of up to 2 km as highlighted in Figure 5[32; 33]. Figure 4: Trends of copper-based electrical links from recent 10-year ISSCC papers.
## 5 Discussion
### Limitations  
Several limitations constrain the generalisability and practical application of this study's findings. The dataset spanned from 1 August 2017 to 21 January 2019, limiting the models' applicability to more recent market conditions or other cryptocurrencies. Additionally, the sentiment scores and tweet volume data were preprocessed and provided as-is, raising concerns about their reliability and the lack of transparency in the sentiment analysis methodology.  
The robustness of the models was evaluated during the training, validation, and testing phases. Some models performed well on training and validation, but less convincingly on test data (e.g., the Tuned Hybrid LSTM-Volume model). The study focused on a limited number of features, such as sentiment scores and tweet volume, without incorporating additional indicators such as external economic data. These factors could provide a more comprehensive view of market dynamics. Computational constraints also posed challenges, particularly in training the Bidirectional LSTM model, which required significant resources for processing and optimisation.  
Furthermore, the models were trained on historical data, with no attempt made to predict future prices due to dataset limitations. This restricts the study's applicability to  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **MSE** & **RMSE** & **MAE** & **MAPE** \\ \hline Univariate LSTM & 0.00011 & 0.01072 & 0.01013 & 13.34481 \\ Tuned Multi-LSTM-Sentiment & 9.29013 & 0.00304 & 0.00196 & 2.20874 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Test performance metrics for univariate and multivariate models.  
Figure 19: Univariate LSTM: actual vs. predicted prices.  
forward-looking financial strategies. A comparison of performance metrics for all models is presented in Table 7.