esa-sceva/satcom-chunk-collection huggingface dataset:
# A Nested UNet Based on Multi-Scale Feature Extraction for Mixed Gaussian-Impulse Removal
###### Abstract  
Eliminating mixed noise from images is a challenging task because accurately describing the attenuation of noise distribution is difficult. However, most existing algorithms for mixed noise removal solely rely on the local information of the image and neglect the global information, resulting in suboptimal denoising performance when dealing with complex mixed noise. In this paper, we propose a nested UNet based on multi-scale feature extraction (MSNUNet) for mixed noise removal. In MSNUNet, we introduce a U-shaped subnetwork called MSU-Subnet for multi-scale feature extraction. These multi-scale features contain abundant local and global features, aiding the model in estimating noise more accurately and improving its robustness. Furthermore, we introduce a multi-scale feature fusion channel attention module (MSCAM) to effectively aggregate feature information from different scales while preserving intricate image texture details. Our experimental results demonstrate that MSNUNet achieves leading performance in terms of quality metrics and the visual appearance of images.  
multi-scale feature extraction; nested UNet; channel attention; mixed noise removal +
Footnote †: journal: Applied Sciences
# A Review of Plant Disease Detection Systems for Farming Applications
## 2 Literature Review: Precision Agriculture Research Developments
### Plant Disease/Pest/Weed Detection System Basic Principles
#### 2.1.1 Image Processing
the black pixels denote the background, the gray pixels denote the features to be extracted, and the white pixels correspond to watershed lines [61]. On the other hand, Grabcut is a very popular and innovative segmentation technique that takes into consideration the textural and boundary conditions of an image [62]. This segmentation method is based on the iterative graph-cut method in which a mathematical function is derived to implement the background as well as the foreground [63]. Each pixel in an image is then assessed to decide whether it falls in the background or the foreground [62; 63]. The Grabcut segmentation method is preferred in most applications because of minimal user interference in the operation of this technique; however, it is not without its drawbacks [62]. The Grabcut sequence cycles take a long time to implement because of the complexity of the thresholding equation [63]. The segmentation is also poor in scenarios where the background is complex and there is minimal distinction between the features of interest and the background [64]. Several distinct segmentation methods and algorithms exist in the literature. The suitability of a particular method is based on a particular application, and hence this study was not able to rule out certain segmentation methods or determine which ones outperform the others.
## 4 Algorithms to Determine the Diameter of the DG Tree and Minimum Delay for Data Aggregation
### Time Complexity and Proof of Correctness  
In this subsection, we discuss the time complexity and proof of correctness for the benchmarking algorithm to determine minimum aggregation delay of a data gathering tree. In the worst case, an intermediate node could have the rest of the nodes as its child nodes and it would take \(\Theta(|V|*\log|V|)\) time to sort the delays of the \(|V|\)-1 child nodes. To calculate the _Temp-Delay_ values and the delay at each of the nodes, we spend one time unit for each edge at its upstream node. Hence, the overall time complexity of the algorithm to compute the aggregation delay of the DG tree is \(\Theta(|V|*\log|V|)\).  
The proof of correctness of the algorithm to determine the minimum aggregation delay for a DG tree simply follows from line 7 of the pseudo code (see Algorithm 6): For every intermediate node u, the estimate of the delay in each iteration of the loop (lines 6-8) while aggregating data received from a child node v is the maximum of the two values: one more than the current estimate of the delay at node u and one more than the aggregation delay at node v. The increment by one for both the parameters of the maximum function is the minimum possible increment that can be assigned to transmit data on a link. An intermediate node has to merely wait for data if none of its child nodes are ready with the data. However, if all the child nodes are ready with the data, the intermediate node aggregates data at a rate of just spending one additional time unit per child node.
```
Input: Diameter, Nodes-At-Levels, Child-Nodes-List, Leader Node s Output: Delay[s] Auxiliary Variables: Temp-Delay; Sorted-Delay-Child-Nodes Initialization:\(\forall i\in V:Delay[i]=0\), Temp-Delay[i] = 0, Sorted-Delay-Child-Nodes[i] = \(\phi\) Begin Algorithm Aggregation-Delay-DG Tree
1 for(Node-level = Diameter to 0)do
2 for(Node u Nodes-At-Levels.get(Node-level) )do
3 for(Node v Child-Nodes-List[u])do
4 Insert (v, Delay[v]) at appropriate entry in Sorted-Delay-Child-Nodes[u] endforfor
6 for(tuple(v, Delay[v]) in Sorted-Delay-Child-Nodes[u])do
7 Temp-Delay[u] = Maximum(Temp-Delay[u] + 1, Delay[v] + 1) endfor
8\(Delay[u]\) = Temp-Delay[u] endfor
9 endfor
10 endfor
11 endfor
12 returnDelay[s]
```  
**End**Algorithm 6** Pseudo Code for an Algorithm to Determine the Minimum Aggregation Delay of a Data Gathering Tree.
# Prediction of Blast Crushing Lumpiness Based on CPO-BP Modeling
## 3 Construction and Validation of CPO-BP Prediction Models
### CPO-BP Model Parameterization  
Based on the algorithm flow shown in Figure 1, MATLAB R2024a programming is used to realize the code compilation and training of CPO-BP hybrid model with the following parameter settings:  
(1) Input and output layer parameterization  
In this study, based on the blasting engineering database constructed by [PERSON] and other scholars, the following seven types of parameters were selected as input layer variables of the network: the ratio of step height to the minimum line of resistance, the ratio of gunhole spacing to the minimum line of resistance, the ratio of the minimum line of resistance to the diameter of the hole, the ratio of the length of the blockage to the minimum line of resistance, the unit consumption of explosives, the elasticity modulus of the rock, and the average blockiness of the original rock [10]. The target variable for the output layer is average blast lumpiness, which is used to quantify the accuracy of the prediction model. Prior to training, all input variables were normalized using Min-Max scaling to ensure  
Figure 1: CPO-BP model process.  
consistent value ranges and prevent any single feature from dominating the model training process in assessing the effectiveness of blast crushing.  
(2) Hidden layer parameterization  
The number of hidden layers is set to 1 and the number of nodes in the hidden layers is  
\[h=2\times i+1 \tag{16}\]  
where \(i\) is the number of nodes in the input layer (\(i=4\)). Based on empirical testing and using the heuristic formula, the number of hidden layer nodes was set to 9. This value provided a good balance between model complexity and prediction performance. The design aims to balance the model complexity and computational efficiency.  
(3) Transfer function and training function  
Logsig function is used to enhance the nonlinear feature extraction capability acting on the input layer to the hidden layer:  
\[f(x)=\frac{1}{1+e^{-x}} \tag{17}\]  
The Purelin function is applied between the hidden layer and the output layer, and the predicted values can be obtained directly. The Levenberg-Marquardt algorithm for the selection of the training function shows its efficient and fast convergence properties which are extremely suitable for small and medium sized data sets.  
(4) BP network hyperparameters  
Maximum number of training sessions: 1000. Initial learning rate: 0.1. Target error: 0.00001. Momentum factor: 0.01. Minimum performance gradient: 1 \(\times\) 10\({}^{-6}\). Maximum number of failures: 6.  
(5) Parameters of the crown porcupine optimization algorithm (CPO)  
Number of search agents: 30. Maximum number of iterations: 30. Solution space boundaries: weights and thresholds take values in the range of [\(-\)3, 3]. Fitness function: mean square error (\(MSE\)). Dimension: dim = (number of nodes in input layer \(\times\) number of nodes in hidden layer) + (number of nodes in hidden layer) + (number of nodes in hidden layer \(\times\) number of nodes in output layer) + (number of nodes in output layer).
## 5 Self-Training  
One of the semi-supervised learning techniques is self-training, which is utilized to improve the performance of a model by having it iteratively learn from its own predictions. In this technique, a model starts with a small amount of labeled data and gradually increases its size by pseudo-labeling unlabeled data using its own predictions. Over these iterations, the model's performance and generalization capabilities can improve as it incorporates more data. Additionally, the size of the labeled training data will be multiplied by the end of the whole process of the iterations [96; 97]. Therefore, the self-training process typically involves the following steps:Initialization: A model is trained on limited labeled data.
2. Pseudo-labeling: The trained model is then used to predict the labels of unlabeled data. These predictions are treated as pseudo-labels.
3. Expanded dataset: The pseudo-labeled data are combined with the original labeled dataset to create a new larger training dataset.
4. Retrained model: The model is retrained on the expanded dataset. This process helps the model to learn from its own predictions.
5. Iteration: Steps 2 to 4 are repeated for a predefined number of iterations or when performance plateaus.  
We propose to use self-training in our method, as illustrated in Figures 1 and 2. Instead of relying on the predictions of a single model, self-training in our proposed method employs the majority voting of the six base models to determine the final label for each example. Initially, each of the six base models predicts labels for unlabeled data. Each base model's prediction is treated as a vote, and the label with the most votes becomes the pseudo-label. To ensure the reliability of pseudo-labels, we set a confidence threshold of 5. This means that only predictions that receive 5 agreements (i.e., exceed the threshold) among the models are considered confident enough to be included to the training set for the next iteration.  
At each iteration, not only are the base models retrained; the SVM meta-learner, which takes advantage of the new predictions from the base models, is also retrained. In our proposed method, the process of pseudo-labeling, majority voting, and retraining is iteratively repeated for a predefined number of iterations. The final model is the SVM meta-learner in the last iteration.
# Signal Source Localization of Multiple Robots Using an Event-Triggered Communication Scheme
## 1 Introduction
In order to reduce both computational resources and communication burden, several event-triggered communication schemes have been designed [33; 34; 35; 36] such that communication resources can be saved. However, there is no result available for the problem of signal source localization, which can combine the particle filter approach with the cooperative control approach with an event-triggered communication scheme. One challenge is how to design event-triggered communication rules based on the given cooperative control approach. The other challenge is how to derive stability conditions for the multi-robot systems with the proposed cooperative control approach using an event-triggered communication rule. Therefore, how to  
Figure 1: Search environment where the red point denotes the robot, and the red star is the signal source. The colors of the background represent the signal strength and are also labeled by the numbers. develop the decision-control approach for the problem of signal source localization in the face of the aforementioned challenges motivates the present study. The proposed decision-control approach has two advantages. One advantage is that the use of the event-triggered communication scheme can effectively decrease the communication times and lower the updating frequency of control input such that the communication and chip resources are saved. The other advantage is that the detection information from the multi-robot system can be well used to estimate the position of the signal source by the particle filter and cooperative controller. The remainder of this paper is arranged as follows. In Section 2, we will briefly give the preliminaries on the dynamics of mobile robots and communication topologies. In Section 3, we will use a particle filter to estimate the position of the signal source and propose a cooperative control approach with an event-triggered communication scheme to coordinate the mobile robots to locate the signal source. In Sections 4 and 5, we will show the effectiveness of the proposed decision-control approach with the event-triggered communication scheme by simulation and experimental results, respectively. Finally, we will conclude this paper in Section 6.
# Low Delay Inter-Packet Coding in Vehicular Networks
## 3 Packet Recovering Codes
### Nonbinary Convolutional Codes
#### 3.2.2 Encoding and Decoding for the RS-Convolutional Codes  
We do not obtain a generator matrix of the RS-convolutional code. Instead, we construct a codeword in the systematic form calculating a single parity-check symbol of each subblock recursively.  
A semi-infinite parity-check matrix of the RS-convolutional code is defined by Equations (2) and (12). The encoding procedure can be simplified by rewriting columns of \(H^{(m)}\) in Equation (12) in the reverse order. In particular, for \(R=3/4\) and \(n=4\), the reordered matrix has the form  
\[H^{(m)}=\begin{pmatrix}H_{0}\\ H_{1}\\ H_{2}\end{pmatrix}=\begin{pmatrix}1&1&1&1\\ \alpha^{3}&\alpha^{2}&\alpha&1\\ \alpha^{6}&\alpha^{4}&\alpha^{2}&1\end{pmatrix} \tag{17}\]  
Denote by  
\[\widehat{H}=\begin{pmatrix}1&1&1\\ \alpha^{3}&\alpha^{2}&\alpha\\ \alpha^{6}&\alpha^{4}&\alpha^{2}\end{pmatrix}\]  
the information part of \(H^{(m)}\). The codeword of the RS-convolutional code over GF(2\({}^{c}\)) can be written in the form \(\mathbf{u}_{1},v_{1},\mathbf{u}_{2},v_{2},...\) where \(\mathbf{u}_{i}=(u_{i1},u_{i2},u_{i3})\) are message blocks consisting of three information symbols from GF(2\({}^{c}\)) and \(v_{i}\) are the corresponding parity-check symbols. Denote by \(\mathbf{s}_{i}=(s_{i1},s_{i2},s_{i3})=\mathbf{u}_{i}\widehat{H}^{\text{T}}\) a partial syndrome computed for the information block. It is easy to see that the first check symbol \(v_{1}=s_{11}\), the next one depends on \(s_{12}\), etc. The following recurrent equations describe encoding for the RS-convolutional code.  
\[v_{1}=s_{1,1},v_{2}=v_{1}+s_{1,2}+s_{2,1},\]  
\[v_{i}=v_{i-2}+v_{i-1}+s_{i-2,3}+s_{i-1,2}+s_{i,1},i=3,4,... \tag{18}\]  
Notice that, if the field extension parameter \(c>n\), then all elements of parity-check matrix in Equation (12) have the Hamming weight 1 and all multiplications in Equation (18) are implemented as simple cyclic shifts.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(\mathbf{m}\) & \(\mathbf{R}\) & **Series Expansion Coefficients \(\mathbf{f_{4},...,f_{11}}\)** \\ \hline
2 & 3/4 & 1, 32, 342, 2282, 8756, 9657, \(-\)102562, \(-\)773838 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Coefficients of series expansion of \(f(D)\) for the RS convolutional code of rate \(R=3/4\).  
For the RS-convolutional codes SWML-decoding is implemented similarly to that for the binary WA codes. The difference is that the parity-check matrix is more dense (does not contain zeros on non-trivial positions) and arithmetic operations are performed over the field GF(\(2^{c}\)).
# Marine Diesel Engine Fault Detection Based on Xilinx ZYNQ SoC
## 2 Methods
### Neural Network Related
#### 2.4.3 Introduction to CNN Optimization Algorithms  
(1) Adam (Adaptive Moment Estimation) combines the advantages of momentum and adaptive learning rate techniques. It calculates the first-order moment estimate (the mean of the gradients) and the second-order moment estimate (the mean of the squares of the gradients), using these estimates to adjust the learning rate for each weight. Here is the formula for it:  
\[m_{t}=\beta_{1}m_{t-1}+(1-\beta_{1})g_{t} \tag{8}\]  
\[v_{t}=\beta_{2}v_{t-1}+(1-\beta_{2})g_{t}^{2} \tag{9}\]  
\[\hat{m}_{t}=\frac{m_{t}}{1-\beta_{1}^{t}} \tag{10}\]  
\[\hat{v}_{t}=\frac{v_{t}}{1-\beta_{2}^{t}} \tag{11}\]  
\[\theta_{t+1}=\theta_{t}-\frac{\alpha}{\sqrt{\hat{v}_{t}}+\epsilon}\hat{m}_{t} \tag{12}\]  
\(\theta_{t}\) is the parameters at step \(t\), \(g_{t}\) is the gradient at step \(t\), \(\alpha\) is the learning rate, \(\beta_{1}\) and \(\beta_{2}\) are decay factors, \(m_{t}\) and \(v_{t}\) are the first and second moment estimates of the gradients, and \(\epsilon\) is a small number used for numerical stability.  
Compared to other optimization algorithms, Adam is relatively insensitive to the choice of hyperparameters and is well-suited for dealing with sparse gradients.  
(2) SGDM (Stochastic Gradient Descent with Momentum) is a variant of stochastic gradient descent that introduces the concept of momentum. This allows updates to have inertia in their direction, which helps speed up convergence and reduce oscillation.  
Here is the formula for it:  
\[v_{t}=\beta v_{t-1}+\alpha g_{t} \tag{13}\]  
\[\theta_{t+1}=\theta_{t}-v_{t} \tag{14}\]  
\(\theta_{t}\) represents the parameters at step \(t\), \(g_{t}\) represents the gradient at step \(t\), \(\alpha\) is the learning rate, \(\beta\) is the momentum coefficient, and \(v_{t}\) denotes the momentum of the gradient.  
(3) RMSprop is an adaptive learning rate method designed to adjust the learning rate for each weight. It calculates the exponentially weighted moving average of the squared gradients and uses this to normalize the magnitude of the gradients.  
Here is the formula for it:  
\[v_{t}=\rho v_{t-1}+(1-\rho)g_{t}^{2} \tag{15}\]  
\[\theta_{t+1}=\theta_{t}-\frac{\alpha}{\sqrt{\hat{v}_{t}}+\epsilon}g_{t} \tag{16}\]  
\(\theta_{t}\) represents the parameters at step \(t\), \(g_{t}\) represents the gradient at step \(t\), \(\alpha\) is the learning rate, \(\rho\) is the decay coefficient, \(v_{t}\) denotes the exponentially weighted moving average of the squared gradients, and \(\epsilon\) is a very small number used for numerical stability.  
RMSprop automatically adapts the learning rate and maintains a smaller learning rate for parameters with large gradient variations, making it easier to use compared to other optimization algorithms.
# Hybrid Clustering Algorithm Based on Improved Density Peak Clustering
## 2 Related Works
### Hybrid Clustering Algorithm  
A hybrid clustering algorithm is a methodology that combines multiple clustering algorithms to improve the quality of clustering outcomes. The following are notable works in the domain of hybrid clustering algorithms: [PERSON] and [PERSON] (2021) [17] introduced a hybrid clustering algorithm that integrates K-means with particle swarm optimization techniques. This approach harnesses the combined strengths of K-means and particle swarm optimization to achieve enhanced clustering performance. [PERSON] et al. (2017) [18] presented a hybrid clustering algorithm that amalgamates deep learning technology for feature extraction with K-Means for clustering. This fusion of methodologies serves to elevate clustering performance. [PERSON] (2019) [19] introduced an innovative approach to tackle the clustering challenge posed by mixed-type data. The method initially employs density peak clustering to identify potential data clusters. Subsequently, for discrete variables within mixed-type data, it utilizes partition-based techniques to enhance cluster partitioning. This combination of density peak clustering and partition-based methods aims to improve clustering performance. [PERSON] et al. (2022) [20] introduced an enhanced Grey Wolf Optimizer (GWO) algorithm combined with the K-harmonic means (KHM) clustering algorithm into a hybrid clustering algorithm. The enhanced GWO algorithm initiates the clustering centers, followed by the KHM clustering algorithm to fine-tune the outcomes. This hybrid algorithm aims to enhance clustering performance, particularly when dealing with intricate datasets.  
Hybrid clustering algorithms combine clustering techniques with hybrid models, effectively addressing challenges such as noise, uncertainty, and uneven data distribution in the clustering process. Recent research in this area has predominantly focused on proposing novel hybrid clustering algorithms and applying them to data analysis across diverse fields.
## 4 Analysis of Vertical Bearing Capacity of Large-Diameter Multi-Disc Soil Anchor  
### Analysis of Load-Displacement Curve of Large-Diameter Multi-Disc Soil Anchor  
The failure process of the large-diameter multi-disc soil anchor and equal-diameter soil anchor under tensile load is simulated. The relationship was studied between the displacement and load of the anchor head of the large-diameter multi-disc soil anchor and the equal-diameter soil anchor in this process, and the similarities and differences between the two were analyzed. The vertical surface load was applied to the end head of the large-diameter multi-disc soil anchor and the equal-diameter soil anchor models, separately, and the graded loading was adopted. The initial load was 15 kN, with an increase of 1 kN per stage. Combined with Origin 2017 64 Bit mapping software, load-displacement curves of the two soil anchor models were obtained, as shown in Figures 5 and 6.  
Figure 5: Load–displacement curve of the large-diameter multi-disc soil anchor.  
By comparing the load-displacement curve of large-diameter multi-disc soil anchors with that of equal-diameter soil anchors, it can be seen that the vertical displacement of both anchors increases continuously with the increase in the drawing load, and the vertical displacement of the anchor heads of the equal-diameter soil anchors increases faster. When the applied load reached 60 kN, the equal-diameter soil anchor reached the ultimate pulling capacity, but the large-diameter multi-disc soil anchor was far from reaching it. In addition, under the same load, the head displacement of large-diameter multi-disc soil anchors is much smaller than that of the same-diameter soil anchors.  
According to the provisions on soil anchor failure in the Geotechnical bolt (cable) technical code [24], when the displacement increment of the anchor head caused by the load applied at the later stage reaches or exceeds twice that of the displacement increment caused by the load applied at the previous stage, the soil anchor will fail. According to Figure 5, it can be seen that the ultimate pulling capacity of the large-diameter multi-disc soil anchor is 135 kN, and the displacement of the anchor head is 1.2597 mm. According to Figure 6, it can be seen that the ultimate pulling capacity of the equal-diameter soil anchor is 60 kN, and the displacement of the anchor head is 0.8634 mm. Under the load of 60 kN, the displacement of the head of the large-diameter multi-disc soil anchor is 0.4167 mm. Compared with the same-diameter soil anchors, the ultimate pulling capacity of large-diameter multi-disc soil anchors is increased by 125%, and the displacement of the anchor head is reduced by 51.74%.  
It can be concluded that the large-diameter multi-disc soil anchor can not only significantly improve the ultimate pulling capacity of the soil anchor, but also effectively reduce the displacement of the anchor head, which has a good supporting effect.