esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive Rate-Compatible Non-Binary LDPC Coding Scheme for the B5G Mobile System
## 4 Design and Performance Simulation of the Adaptive RC-NB-LDPC Coding Scheme
### Design and Performance Simulation of the Adaptive RC-LDPC Coding Scheme
#### 1.2.2 Image quality evaluation  
We evaluate the performance of the transmission scheme by comparing the quality of the reconstructed image with the original image. The evaluation methods include subjective evaluation and objective evaluation.  
Subjective evaluation is a direct and important evaluation method for subjective judgment by observing and comparing the difference between the reconstructed image and the original image. However, in practice, individual differences in observers are likely to cause subjective bias. Moreover, subjective evaluation often requires many labor and material resources, and it is impossible to perform real-time detection.  
The objective evaluation method usually uses the error between the reconstructed image and the original image to measure the quality of the reconstructed image. Common evaluation methods include Mean Squared Error (MSE) and Peak Signal Noise Ratio (PSNR) [51]. The calculation formula for MSE is:  
\[\text{MSE}=\frac{\sum\limits_{i=1}^{M}\sum\limits_{j=1}^{N}\left(f(i,j)-f(i,j) ^{{}^{\prime}}\right)^{2}}{M\times N} \tag{16}\]  
where \(f(i,j)\) and \(f(i,j)^{{}^{\prime}}\) represent the pixel values of the original image and the reconstructed image at \((i,j)\), respectively, and \(M\times N\) is the size of the image. MSE reflects the quality of image restoration. It can be seen that the smaller the MSE value, the smaller the deviation between the reconstructed image and the original image, and vice versa. The formula for calculating PSNR is:  
\[\text{PSNR}=10\log_{10}\frac{255}{\text{MSE}} \tag{17}\]  
PSNR and MSE are essentially the same. The larger the PSNR value, the smaller the deviation between the restored image and the original image, and vice versa. PSNR is often viewed as a quality metric and MSE as a distortion metric.
## 6 Conclusions  
In order to solve the problem of detecting LTE-R cells with degraded communication performance in the Shuo Huang Railway, we designed a deep neural network structure that can achieve imbalanced sequence feature extraction based on the CNN network structure and the idea of shapelet transformation. Then, considering the imbalance between the numbers of degraded cell samples and normal cell samples, the optimization objective of the imbalanced sequence feature extraction neural network was designed based on the Fisher criterion. Afterward, in order to determine the appropriate weights of the feature extraction network, we developed a two-stage training method. In the first stage, we used SGD and contrastive loss to preliminarily train the model. In the second stage, we used the differential evolution algorithm to further optimize the weights of the distance calculation layer. Finally, based on the sequences of key performance indicators from the drive test data for each Shuo Huang Railway LTE-R cell, as well as the corresponding EARR data, we constructed a degradation detection dataset and conducted experiments on this dataset using the proposed approach and other comparison approaches. The experimental results show that the proposed feature extraction network could effectively extract the features of imbalanced sequences and that its classification performance was significantly better than that of the other comparison approaches. The proposed approach achieved an accuracy of 98% or above in detecting LTE-R cells with degraded communication performance, which means that it could be a powerful tool for the operation and maintenance of the Shuo Huang Railway LTE-R network and has high application value.  
Conceptualization, J.Q. and H.M.; methodology, J.Q.; software, J.Q.; investigation, J.Q. and H.M.; resources, J.Q.; data curation, J.Q.; writing--original draft preparation, J.Q. and H.M.; writing--review and editing, J.Q., H.M. and C.Q.; visualization, J.Q.; supervision, C.Q.; project administration, J.Q. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the Key Scientific and Technological Project of Henan Province (no. 222102210006) and the Science and Technology Development Project of China Railway Design Corporation (no. 2022A02538005).  
The data can be shared up on request and the data are not publicly available due to information security issues.  
[PERSON], [PERSON] and [PERSON] are employees of China Railway Design Corporation (China) Company. The authors declare no conflicts of interest regarding the publication of this research article.
## 2 Background and Literature Review
Additionally, studies dealing with the resource allocation problems of a large amount of resources and devices have been conducted using deep neural networks to learn the environment and make decisions regarding the allocation problems according to network conditions, such as service latency and requirements [31]. Previous studies related to effective object learning methods for tasks that require human expertise remain useful to overcome the limitations of preprocessing learning, which requires large amounts of computation and numerous samples. Reinforcement learning has evolved in recent years in terms of its applicability to complex sequential decision-making tasks which are generally modeled using the Markov decision process (MDP). Reinforcement learning is a methodology for deciding the next action after inferring the reward based on the achievement of the goal, which is in turn based on the agent's current state and the interactions between the systems exhibiting the current state [32]. The typical learning method among traditional reinforcement learning algorithms is the Q-learning method which infers the maximized reward value by calculating the Q-function, which is a behavioral value function as shown in Equation (1), at each time period [33]. \[Q_{t+1}(s_{t},a_{t})=(1-\alpha)Q_{t}(s_{t},a_{t})+\alpha(r_{t+1}+\gamma maxQ_{ t}(s_{t+1},a)) \tag{1}\]  
In (1), \(s_{t}\) is the state at time \(t\), \(a_{t}\) is the behavior of time \(t\), \(r_{t+1}\) is the reward of time \(t+1\), and \(\alpha\) is the learning rate in the range of (0,1). The closer the \(\alpha\) is to 1, the more likely it is to induce learning to place a greater emphasis on the current situation and to adjust the share of rewards for future predicted behavior through a discount rate, \(\gamma\)[34].
## Case Study
### Application of Fast Optimization Approach  
#### 6.2.1 Quasi-Affine Transformation  
The quasi-affine transformation is utilized with an M-matrix to implement individual mutations within a population. Specifically, when there are 50 individuals with 18 features in the population, the entire M-matrix has a dimension of \(R^{n\times d}\), where n is the population size and d is the dimension of individuals. In practical applications, since the number of n is much larger than d, M is constructed by the method of random ordering after concatenating multiple D-dimensional lower triangular matrices and takes the form of Equation (16). Through this approach, the mutated individuals are more likely to explore the area around the global solution, which enhances cooperation among individuals and accelerates the convergence speed.  
\[\begin{bmatrix}1\\ 1&1\\ 1&1&1\\ \cdots\\ 1&1&\ldots\\ 1&1\\ 1&1&1\\ 1&1&\\ \cdots&1\\ 1&1&\ldots\\ 1&1&\ldots\\ 1&1&\ldots\\ \end{bmatrix}\ \sim\ \begin{bmatrix}1\\ 1&1&1\\ 1&1&\ldots\\ 1&1&\ldots\\ 1&1&\ldots\\ 1&1&\ldots\\ 1&1&\ldots\\ 1&1&\ldots\\ \end{bmatrix}=M \tag{16}\]
# Log Volume Measurement and Counting Based on Improved Cascade Mask R-CNN and Deep SORT
## 3 Methods
### Cascade Mask R-CNN  
Cascade Mask R-CNN [34] is an enhanced version of Mask R-CNN that aims to tackle the problems of false positives and inaccurate bounding boxes in object detection. Traditional object detection networks, including Faster R-CNN and Mask R-CNN, generally employ a fixed Intersection over Union (IoU) threshold to differentiate between positive and negative samples, commonly set at 0.5. Although this threshold successfully boosts the number of positive samples during training, the quality of these samples is frequently inadequate, leading to a high incidence of false positives and imprecise bounding boxes generated by the detector. Increasing the IoU threshold directly (for instance, to 0.7) may yield more accurate positive samples; however, it would substantially decrease their quantity, risking overfitting and leaving insufficient training data to enhance performance. Therefore, achieving a balance between the quality and quantity of positive samples poses a significant challenge for object detection models.  
To address the challenges of false positives and imprecise bounding boxes, Cascade Mask R-CNN implements a cascade structure, training a series of detectors with progressively higher IoU thresholds (such as 0.5, 0.6, and 0.7). Each detector enhances the bounding boxes produced by the previous stage using a higher IoU threshold, thereby progressively improving the quality of positive samples and the accuracy of the bounding boxes. The first stage utilizes a lower IoU threshold to generate initial bounding boxes and class predictions, which are subsequently forwarded to the next detector. Following several stages of optimization, the model produces more accurate detection results. The network structure of Cascade Mask R-CNN is shown in Figure 3. The network structure consists of four main components, the ResNet feature extraction network, the parallel CBAM attention module, the FPN multi-scale feature fusion module, and three cascaded detection heads using different IOU thresholds, where C and B represent the classification branch and the bounding box branch, respectively.
## 3 Workflow Description
### Output Generated
To readily have access to the \(\Delta\hat{p}_{in-out}\) parameter is essential for the designer, as it facilitates the quick identification of the radiator model that ensures low pressure losses. The FlowRate_Standard_Deviation file contains the value \(Q_{std}\), which represents the dimensionless standard deviation of the flow rate over the ducts  
\[Q_{std}=\sqrt{\frac{\sum_{k=1}^{N}\biggl{(}Q_{k}-\frac{Q_{in}}{N}\biggr{)}^{2 }}{N}}\frac{1}{Q_{in}}, \tag{11}\]  
where \(Q_{k}\) is the volumetric flow rate in the \(k\)-th channel, \(Q_{in}\) is the volumetric flow rate at the inlet, and \(N\) is the number of radiant channels. The mean value for the computation of the standard deviation in Equation (11) has been set to \(Q_{in}/N\), i.e., an \"ideal case\" where the flow is evenly distributed among all the channels. FlowRate_Standard_Deviation therefore indicates, quantitatively and synthetically, how much the distribution of the volumetric flow rate over the different channels deviates from the \"ideal\" case. Finally, T-WorkFlow populates the FlowRateVisualization folder with a series of graphical outputs in.png format, generated at runtime. These images provide the designer with a simple visual representation of the actual flow distribution over the channels; see Figure 11. These plots are created by using the well-known Python library matplotlib[17] and accessing at runtime the volumetric flow rate values of each control surface. In particular, Figure 11 shows the dimensionless volumetric flow rate \(\hat{Q}_{k}=Q_{k}/Q_{in}\) as a percentage relative to the total inlet flow rate to the radiator. It is worth noting that these operations, while they might appear trivial, allow for the full automation of the main post-processing tasks necessary for the designer to identify the best configuration among those tested. T-WorkFlow was developed to automatically manage all interactions with OpenFOAM, dramatically simplifying the set-up of the case and specific numerical configurations. This enables even non-expert users of this open-source CFD tool to efficiently produce accurate results and make informed decisions within the tight deadlines typical of this type of car design. Figure 11: Example of a plot generated at run-time by the software containing the volumetric flow rate distribution as percentage values in the different channels.
# Speckle Noise Removal in OCT Images via Wavelet Transform and DnCNN
## 2 Materials and Methods
### Wavelet Transform
#### 2.1.1 Discrete Wavelet Transform  
Employing DWT, the image undergoes hierarchical decomposition to isolate and extract specific sub-bands for targeted processing. Subsequently, denoising operations are applied to these sub-bands to enhance signal quality. The processed sub-bands are then reconstructed through the inverse DWT, with final image synthesis achieved via merging in the spatial domain according to Equation (2).  
\[DWT\left\{\begin{array}{l}LL^{k}=\sum\limits_{n}\sum\limits_{m}h(n)h(m)LL^{k- 1}(2x-n  
DWT decomposes images into four orthogonal frequency sub-bands, enabling multi-resolution analysis.  
Low-Low (LL) captures the low-frequency approximation of the original image, preserving coarse-grained spatial information and overall structural features. It essentially contains the averaged version of the image with reduced resolution. Low-High (LH) encodes high-frequency information along the horizontal axis. It captures vertical edge features and texture variations occurring in the horizontal direction, corresponding to abrupt intensity changes between adjacent columns of pixels. High-Low (HL) contains high-frequency information along the vertical axis. It represents horizontal edge features and texture variations in the vertical direction, reflecting intensity discontinuities between adjacent rows of pixels. High-High (HH) captures high-frequency information along both spatial dimensions. It primarily represents diagonal edge features and texture details that manifest as intensity variations along oblique directions, corresponding to fine-grained structural components in the image.  
These sub-bands facilitate targeted image feature processing by separating signal components into distinct frequency channels. Visualization of wavelet transform applications tailored to specific image characteristics is presented in Figure 2.  
Through analysis of wavelet transform coefficients derived from OCT images, we observe significant horizontal texture features in the imagery, with noise primarily concentrated in the high-frequency components. This distribution underscores the structural characteristics of OCT data and highlights the necessity for targeted denoising strategies in high-frequency sub-bands to preserve diagnostic image quality.
# Real-Time Traffic Sign Detection and Recognition Method Based on Simplified Gabor Wavelets and CNNs
## 3 Overview of Our Method  
The processing flow of our traffic sign detection and recognition system is illustrated in Figure 2. The red line with the arrow indicates the offline training flow of the subclass classified network, and the blue line with the arrow indicates the whole process of online detection and classification. In detail, during the feature extraction, we filtered the road scene grayscale images by four orientations, and two scale simplified Gabor filters, where its parameters were optimized by experiments. The simplified Gabor filters were used to enhance the edges and smooth the non-edge areas of the images. Then, the eight output feature maps were synthesized into one map by maximizing the value at each pixel. In the synthesized Gabor feature map, the edges of the traffic signs were strengthened and the areas inside the traffic signs were smoothed. The feature extraction process is shown in the green area of this figure. During the detection stage, we first used the maximally stable extremal regions (MSERs) algorithm to obtain the regions of interest (ROI) and filtered out the areas where there was little possibility for existence of traffic signs, according to our defined filter rules. Then, the proposed regions were classified by SVM to get the super-class of the traffic signs which each area belonged to. The detection process is shown in the yellow area of the figure. Finally, we classified the subclasses of the traffic signs using CNN, which is shown in the blue area of the figure. The input of the CNN was eight simplified Gabor feature maps, which were convoluted to traffic signs by the SGW. What needs to be stressed is that in the real time processing stage, the input of the CNNs was the matrix that was cut from the eight simplified Gabor feature maps in the detection stage. The matrix corresponded with the area that was detected as a traffic sign at the same stage. In the red line flow part of Figure 2, we trained the CNN with eight simplified Gabor feature maps, convoluted from the traffic signs of GTSRB.
# Verifiable (2, \(n\)) Image Secret Sharing Scheme Using Sudoku Matrix
## 3 Research Methods
### The Procedure of Hiding Secret Image and Authentication Codes to Cover Images
Later, the digits \(\beta_{1}^{1}\) and \(\beta_{2}^{1}\) are hybridized with the authentication codes for embedding into pixels \(B_{1}^{3}\), \(B_{1}^{4}\), \(B_{2}^{3}\), and \(B_{2}^{4}\) based on Equations (4) and (5). Here, the values \(\beta_{1}^{1\prime}\) and \(\beta_{2}^{1\prime}\) are generated according to Equations (6) and (7), respectively. \[z_{2}=f_{3}x_{i}+\beta_{2}^{1\prime}\mod 9 \tag{5}\]  
\[\beta_{1}^{1\prime}= \begin{cases}\beta_{1}^{1}&\text{if }f_{1}=0\\ \beta_{1}^{1}+4&\text{if }f_{1}=1\end{cases}  \tag{6}\]  
\[\beta_{2}^{1\prime}= \begin{cases}\beta_{2}^{1}&\text{if }f_{4}=0\\ \beta_{2}^{1}+4&\text{if }f_{4}=1\end{cases}  \tag{7}\]  
As for embedding the hybrid authentication codes, the pixel pairs (\(B_{1}^{3}\), \(B_{1}^{4}\)) and (\(B_{2}^{3}\), \(B_{2}^{4}\)) are used to embed the digits \(z_{1}\) and \(z_{2}\), respectively, using the Sudoku matrix as denoted by (\(B_{1}^{3\prime}\), \(B_{1}^{4\prime}\)) and (\(B_{2}^{3\prime}\), \(B_{2}^{4\prime}\)). For example, we assume that the two secret pixels \(p_{1}\) and \(p_{2}\) are 127 and 126. The average value is 126. The generated authentication codes \(f_{1}\), \(f_{2}\), \(f_{3}\), and \(f_{4}\) are 0, 0, 7, and 0, respectively. Since the values of \(f_{1}\) and \(f_{4}\) are all 0 s, the values of \(\beta_{1}^{1\prime}\) and \(\beta_{2}^{1\prime}\) will be 1 and 1, respectively. The two equations are composed as \(z_{1}=x_{i}+7\) mod 9 and \(z_{2}=0x_{i}+1\) mod 9. If \(x_{i}\) equals 1, \(z_{1}\) and \(z_{2}\) are 8 and 1, respectively. After hiding the authentication codes, the stego pixels (\(B_{1}^{3\prime}\), \(B_{1}^{4\prime}\)) and (\(B_{2}^{3\prime}\), \(B_{2}^{4\prime}\)) are (100, 119) and (99, 120), respectively.
## 2 Foundational Concepts  
This section lays the groundwork for our review by introducing the fundamental concepts underpinning the integration of IoT, LLMs, and FL. We will briefly define each core technology, including IoT systems and their characteristics in advanced networks, the capabilities and challenges of LLMs, the principles of FL such as FedAvg, and key PETs like differential privacy and Homomorphic Encryption. Understanding these foundational elements is crucial for appreciating the synergistic approach and addressing the complexities discussed in later sections.
# A New Maximum Likelihood Estimator Formulated in Pole-Residue Modal Model
## 2 Structure of the Combined LSCF-MLE-PMM
### The MLE-PMM (2 rd Step)
#### 2.2.5 Logarithmic MLE-PMM  
Another variant of the proposed MLE-PMM can be derived by minimizing the following \"log-like\" cost function  
\[l(\Theta)_{\text{logML-PMM}}=\sum_{k=1}^{N_{\text{o}}N_{\text{i}}}\sum_{f=1}^{ N_{f}}\left|E_{k}^{\text{log}}(\Theta,\omega_{f})\right|^{2} \tag{29}\]  
where \(E_{k}^{\text{log}}(\Theta,\omega_{f})\in\mathbb{C}^{1\times N_{\text{o}}N_{ \text{i}}N_{f}}\) is the logarithmic equation error given by  
\[E_{k}^{\text{log}}(\Theta,\omega_{f})=\frac{\text{log}(\hat{H}_{k}(\Theta, \omega_{f}))-\text{log}(H_{k}(\omega_{f}))}{\sigma_{H_{k}(\omega_{f})}^{\text{ log}}} \tag{30}\]  
with \(\sigma_{H_{k}(\omega_{f})}^{\text{log}}\) denoting the standard deviation of the \(k\)th element of logarithmic FRF matrix. The relationship between this standard deviation and the measured one is given by  
\[\sigma_{H_{k}(\omega_{f})}^{\text{log}}=\frac{\sigma_{H_{k}(\omega_{f})}}{ \left|H_{k}(\omega_{f})\right|},\quad\,k=1,\,2,\ldots,\,N_{\text{o}}N_{\text{ i}}. \tag{31}\]  
which is a measure of the noise-to-signal (power) ratio of a complex logarithmic function [27]. The strategy used to minimize the cost function (6) can be employed to Equation (29) and the partial derivatives of the logarithmic error Equation (30) with respect to the invariants of the pole-residue modal model are computed with the expressions presented in Appendix D.
# Resource Optimization for 3D Video SoftCast with Joint Texture/Depth Power Allocation
## 5 Simulation Results
### Target Distortion Optimization Performance  
#### 5.3.1 Power Distortion Optimization Performance  
Assume the sender delivers about 30 GOPs to users whose demanded quality is 35 dB, given the bandwidth constraint \(n=8192\) chunks per GOP. Figure 4 depicts the power allocation for the consecutive GOPs and the total bandwidth usage for _Kendo_ and _PoznanStreet_ sequence reference viewpoints. It is apparent that the power allocation for depth map video is lower than for texture due to depth map'specause of depth map distinct characteristics (i.e., the 3D scene's geometric information which comes in the form of homogeneous regions separated by sharp edges). The same is true for bandwidth usage. In some GOPs, depth maps may occupy less than 0.005% of the available bandwidth, as in the _PoznanStreet_ sequence. It is evident that the power allocation curves for both texture and depth map frames are relatively smooth, and the total bandwidth usage is kept at a comparatively low level. This complex resource allocation assists in maintaining the 3D video quality at the predefined PSNR value. Thus, the content is delivered with a favorable viewing experience at the user end, and other users can also benefit from the saved resources.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & \multicolumn{2}{c}{(c) \(\text{SNR}_{\text{clk}}\) = 10 dB} \\ \hline
**3D Video** & \multirow{2}{*}{**SoftCast**} & \multicolumn{2}{c}{**Distortion**} & \multicolumn{2}{c}{**Proposed Resource**} \\
**Sequences** & & **Resource Algorithm** & **Control Scheme** \\ \hline Balloons & 100\% & 9.3262\% & 4.5898\% \\ Kendo & 100\% & 12.0117\% & 7.2875\% \\ Newspaper & 100\% & 10.4980\% & 4.3212\% \\ Dancer & 100\% & 40.3809\% & 22.4365\% \\ gtFly & 100\% & 39.2578\% & 25.0805\% \\ PoznanHall2 & 100\% & 19.5801\% & 11.7187\% \\ PoznanStreet & 100\% & 17.2852\% & 10.3271\% \\ Shark & 100\% & 49.7558\% & 19.2138\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._