esa-sceva/satcom-chunk-collection huggingface dataset:
# Secure PD-NOMA with Multi-User Cooperation and User Clustering in Both Uplink and Downlink PD-NOMA
## 1 Introduction
### Motivation and Contributions  
The majority of research has been focused on power allocation strategies, either fixed or dynamic, which are responsible for overall performance improvement in PD-NOMA. Besides this, some of the literature also addressed the cooperative relaying schemes that are used to aid the weak node users in PD-NOMA systems. Moreover, secure communication in the PD-NOMA system is currently one of the main research areas to be explored. Thus, this study focuses on the major issues that PD-NOMA confronts in both downlink and uplink scenarios and offers solutions to address them. These issues include security, user clustering, and cooperative relaying for performance improvement.  
The main contributions of this paper are:  
* For secure PD-NOMA communication, this paper presents the spreading-based technique with short codes that ensure the privacy of the PD-NOMA system from unregistered users or eavesdroppers.
* Through multi-user decode and forward (D&F) cooperative relaying schemes in PD-NOMA (Cop-PD-NOMA), strong users (near-users) have been used as relays to help increase the coverage area of the gNodeB and improve the BER performance at weak users (far-users).
* A low-complexity, three-step-based user clustering strategy is proposed to make sure that PD-NOMA clusters have low outage probabilities.  
The rest of the article is arranged as follows: In Section 2, the system model for multi-user downlink and uplink PD-NOMA is proposed. Section 3 describes PD-NOMA's proposed secure communication, multi-user cooperation, and user clustering strategy. The simulation results and conclusions are presented in Sections 4 and 5, respectively.
# TTH-Net: Two-Stage Transformer-CNN Hybrid Network for Leaf Vein Segmentation
## 3 The Proposed Method
### Second Stage: Fine-Grained Feature Recovery  
To enhance the TTH-Net of recognizing fine-grained features, we expand ViT in stage one by cascading a U-shaped CNN. Although transformers excel at modeling global context, fine-grained vein recognition is equally important for leaf vein segmentation tasks. The fine-grained feature restoration network consists of the encoder and decoder. The outputs \(\{z_{3}^{\prime},z_{6}^{\prime},z_{9}^{\prime},z_{12}^{\prime}\}\) form stage one is set as the inputs of stage two. Specifically, \(z_{3}^{\prime}\) is first sent to stage two and convolved by \(3\times 3\) convolution, Batch Normalization (BN), and ReLU function. Then, \(z_{6-2}^{\prime}\) is acquired from \(z_{3}^{\prime}\) and further stacked together with \(z_{6-1}^{\prime}\), which is downsampled by \(z_{6}^{\prime}\). After the \(3\times 3\) convolution, BN, and ReLU function, the new feature map \(z_{6-3}^{\prime}\) can be extracted more vein-related details. By repeating the above process, our proposed TTH-Net can generate more advanced representations. For the decoding part, bilinear interpolation functions are used to restore the vein semantics. To better combine the advantages of Transformer and CNN, we introduce the cross-stage semantic enhancement (CSE) module [33] to facilitate the cross-stage fusion of representations between long-distance features. For example, the output of the encoder \(z_{12-3}^{\prime}\) and the feature \(z_{12}^{\prime}\) are sent into the CSE module and generates \(z_{9-4}^{\prime}\). By concatenating \(z_{9-4}^{\prime}\) with \(z_{9-3}^{\prime}\), the new feature maps \(z_{9-5}^{\prime}\) are obtained after the \(3\times 3\) convolution, BN, and ReLU function. By stacking these operations, the target classes in the features are divided by adding a convolution at the end of the network. More details about the CSE module are introduced in the following subsection.
## 3 Theoretical Background and Method
### GSOP and Lowdin Orthogonalization Compensation Scheme  
The GSOP is based on defining a new vector that is orthogonal to the initial vector that makes the two vectors orthogonal, as shown in Figure 2a. Lowdin orthogonalization is a symmetric orthogonalization where both vectors are rotated by the same angle to make them orthogonal, as shown in Figure 2b; on the other hand, the ellipse-correction method finds the least-square ellipse that is best fitted to the constellation of the received signal and then reshapes the ellipse into a perfect circle, as shown in Figure 2c.  
The GSOP [10] enables a set of nonorthogonal samples to be transformed into a set of orthogonal samples. Given two nonorthogonal components of the received signal, denoted by \(r_{I}(t)\) and \(r_{Q}(t)\), the GSOP results in a new pair of orthonormal signals, denoted by \(I^{o}(t)\) and \(Q^{o}(t)\), as follows:  
\[\left[\begin{array}{c}I^{o}(t)\\ Q^{o}(t)\end{array}\right]=\left[\begin{array}{cc}\frac{1}{\sqrt{P_{Q}}}&0 \\ -\frac{1}{\sqrt{P_{Q}^{o}P_{I}}}&\frac{1}{\sqrt{P_{Q}^{o}}}\end{array}\right] \left[\begin{array}{c}r_{I}(t)\\ r_{Q}(t)\end{array}\right] \tag{5}\]  
where \(\rho=E\{r_{I}(t)\cdot r_{Q}(t)\}\) is the correlation coefficient, \(P_{I}=E\{r_{I}{}^{2}(t)\}\), \(P_{Q}=E\Big{\{}Q^{\prime 2}(t)\Big{\}}\), \(Q^{\prime}(t)=r_{Q}(t)-\frac{\rho r_{I}(t)}{P_{I}}\), and \(E\{\bullet\}\) denotes the ensemble average operator.  
On the other hand, the Lowdin orthogonalization is a symmetric orthogonalization method where both the vectors are rotated by the same angle to make them orthogonal. The transformation can be written as:  
\[\left[\begin{array}{c}I^{o}(t)\\ Q^{o}(t)\end{array}\right]=\frac{1}{2}\left[\begin{array}{cc}\frac{1}{\sqrt{1 +\rho}}+\frac{1}{\sqrt{1-\rho}}&\frac{1}{\sqrt{1+\rho}}-\frac{1}{\sqrt{1-\rho} }\\ \frac{1}{\sqrt{1+\rho}}-\frac{1}{\sqrt{1-\rho}}&\frac{1}{\sqrt{1+\rho}}+\frac{ 1}{\sqrt{1-\rho}}\end{array}\right]\left[\begin{array}{c}I^{\prime}(t)\\ Q^{\prime}(t)\end{array}\right] \tag{6}\]  
where \(\rho\) is the inner product given as \(\rho=\langle I^{\prime}|Q^{\prime}\rangle\), \(I^{\prime}(t)\), and \(Q^{\prime}(t)\) are the normalized vectors of \(r_{I}(t)\) and \(r_{Q}(t)\).
## 5 Computational Experiments and Numerical Results
### Numerical Results
#### 5.2.4 Evaluation of Server Cost Rate  
This experiment evaluates the effect of the increasing server cost rate. Various servers with different levels of capacities are deployed in a data center. It is impossible to replace all the servers when the data center upgrades [19]. Different levels of servers have diverse costs and capacities. Maintaining all the servers homogeneously in a data center all the time is difficult. The results of a cluster trace published by Google indicate that the servers are heterogeneous [36].  
Based on the situation, the servers with various capacities and costs were emulated. The base server generates a growing cost rate with the minimum capacity and corresponding cost. For example, the value of 1.2 implies that the cost and capacity are 1.2 times higher than the base server. The diversities of levels for servers are emulated in the experimental environments. They are lowest levels cost rate of servers 0.1 up to the highest levels cost rate of servers 1.3.  
The curves in Figure 11 exhibit trends similar to those of the previous cases' task penalty section. Increasing the cost rate causes a decrease in the objective value. However, the cost and objective values are not represented linearly in the regular descending order in the _SP_, _OI_, _UP_, and _CP_ strategies.  
The results are that changing the cost rates in different servers provides alternative decisions to switch on the cost-efficient servers. For high server cost rates, the curves of _SP_, _OI_, _UP_, and _CP_ are moderate compared with that of _FF_. Concerning active servers in the serving status, the _SP_, _OI_, _UP_, and _CP_ strategies have cost-effectiveness and achieve higher objective values than _FF_. The results also reveal that _SP_ is the best strategy with the highest feasible solutions and the minimum solution gap for most server cost configurations.  
Figure 11: Objective value versus different cost rate.
## 4 Stretchable MSP Antenna-Based Strain Sensor
### PNN for Classification of the Resonance Frequency  
In the final step, the reliability of the optimal MSP antenna was analyzed in accordance with the reliable range of \(\theta\). The antenna must operate within the range even though it is under deformation. The range was estimated by the non-deformed MSP antenna. From the previous step, the dual bands antenna under no displacement took the following range (\(\theta\)):  
\[\begin{array}{ll}2.3454\;\mathrm{GHz}\leq\theta_{f,2.5\mathrm{GHz}}\leq \;2.6539\;\mathrm{GHz}\\ 4.9130\;\mathrm{GHz}\leq\theta_{f,5.0\mathrm{GHz}}\leq \;5.1070\;\mathrm{GHz}\end{array}, \tag{24}\]  
For reliable classification, the limit state function was exploited. According to the concept of the limit state function [53], the capacity or resistance of the antenna must be appointed to 2.5 GHz. However, if the absolute value of \(g\) is greater than 0.3085 (difference between 2.3454 GHz and 2.6539 GHz) and 0.194 (difference between 4.9130 GHz and 5.1070 GHz) at 2.5 GHz and 5 GHz, respectively, the designed strain MSP antenna will stay in class \(B\), implying that the system must be ignored owing to the unreliable \(\mathrm{R_{f}}\). The Monte Carlo simulation (MCS) [53; 54] was employed to estimate the probability of failure (\(P_{f}\)). On the basis of the \(P_{f}\), the results of MCS with 10,000 random variables and PNN with 121 random variables were compared to assure results of the reliability estimation. As explained in Table 4, each \(P_{f}\) difference between PNN and MCS was 6.46% and 6.91% at 2.5 GHz and 5 GHz, respectively. In comparison with the original variables, the truncated coordinates obtained by IFT had a \(P_{f}\) of 0.291 and 0.325 by drawing 4.67% and 4.06% of \(P_{f}\) increase, respectively. Furthermore, the \(P_{f}\) acquired by IFT was close to that obtained by MCS (9.48% and 9.41%, respectively), which is less than 10%, so it can be a reliable threshold, eliminating computation-intensive tasks, for the classification process.
# Detection of Cavities from Dental Panoramic X-ray Images Using Nested U-Net Models
## 2 Methodology
### U-Net3+ Model for Cavity Segmentation  
Image segmentation is one of the major topics in image processing and computer vision techniques. Image segmentation divides an image into several segments with similar features. Medical imaging, object detection, and recognition tasks are the fundamental applications of image segmentation. Medical image segmentation is a basic requirement in medical applications for diagnosing and identifying diseases [35]. Here, we describe the adaptation of U-Net 3+ ([23]) for the detection of dental cavities. U-Net 3+ is a DL-based segmentation method validated on two organs: the liver and the spleen [23]. To the best of our knowledge, we are the first to apply it in the dental field.  
In this study, the contraction path follows the typical CNN architecture. The contraction path consists of the repeated application of two \(3\times 3\) convolutions (Conv2D), each followed by a Gaussian error linear unit (GeLU) activation function with stride 2 for downsampling. It doubles the number of feature channels at each downsampling step. GeLU is a high-performance neural network activation function that aims to combine the properties of dropout and ReLU.  
Conversely, the transposed convolution operation is performed in the expansion path. In each step of the expansion path, there is an up-sampling of the feature map followed by a \(2\times 2\) convolution of up-scaled layers that has half the number of feature channels. Additionally, it concatenates with the correspondingly cropped feature map from the contracting path decoder blocks and two \(3\times 3\) convolutions, each followed by the ReLU activation function. In the final layer, the output is a \(1\times 1\) convolution layer with one output, predicted ground truth, which maps each 64-component feature vector to the desired number of classes.  
U-Net 3+ combines the full-scale features by redesigning skip connections and using full-scale deep supervision. There are five encoder blocks and four decoder blocks in the architecture of this project. The numbers of filters are 32, 64, 128, 256, and 512. The addition of Gaussian noise (GN) to the input variables is the most common type of noise used. GN is a regularization layer for NN that is only active during training time and helps to mitigate over-fitting. It is also an important part of the feature-learning process. The GN function was applied to the U-Net 3+ model, then the dice loss function and Adam optimization algorithm were used to train the model. Dice loss is used in medical image segmentation to address the data imbalance problem. The flowchart of the entire project is illustrated and summarized in Figure 1.
# Research on Gangue Detection Algorithm Based on Cross-Scale Feature Fusion and Dynamic Pruning
## 4 Experimental Validation and Analysis  
### Parameter Settings and Experimentaldata  
In this study, the experimental setup included an Intel(r) Core(tm) i9-10900X CPU @ 3.70 GHz, NVIDIA GeForce RTX 3090 GPU, and the Ubuntu 20.04.3 operating system. The deep learning framework used was PyTorch 1.11.3, programming language was Python 3.8, and CUDA version was 11.6. The experimental parameters included a batch size of 80, image size of 640, training epochs of 200, and a learning rate of 0.01. To ensure the comparability of training results, all algorithm comparisons were tested under the same set of parameters mentioned above.  
The dataset was acquired from a video recorded by a conveyor belt at a coal factory. Non-identifying images were removed after image processing. The dataset consisted of 5000 images, with two identification targets: coal and coal gangue, respectively. The images were annotated using Labelme, which generates a JSON-format file. Subsequently, the JSON file was converted into a textual label. The datasets are partitioned into training, validation, and test sets in a ratio of 7:2:1, consisting of 3500 training samples, 1000 validation samples, and 500 test samples. The training process of the coal gangue detection network is depicted in Figure 6.  
Figure 5: Improved network model of the algorithm for the detection of coal gangue.
## 2 Primer Knowledge  
### WiFi Receiver Processing  
The data processing process on the WiFi receiver is shown in Figure 2. Packet detection detects the arrival of the signals. Timing synchronization and frequency correction correct the detected signals from the time domain and the frequency domain, respectively. Demodulation converts the received analog signal to a digital signal. Channel estimation evaluates the impact of the transmission channel on packets. Finally, the data are recovered from the signals. All these operations work together to ensure that the transmitted data can be decoded correctly. Among them, packet detection is the first and fundamental operation.
## 1 Introduction
### Durability  
The durability of concrete may be defined as the ability of the concrete to bear potential chemical attacks, crushing, weathering, and abrasion based on the structure permeability for gas and water, absorptivity, initial absorption capacity, water tightness, diffusion, and others [23]. Meanwhile, durable concrete maintains its desired engineering properties during operation. For instance, the durability index for aggregate is a value which represents the relative resistance of the material to produce detrimental clay when subjected to the suggested mechanical degradation techniques [24]. The design service life of a structure should be prepared based on the requirements of the owners and operators. The service life of an important and large bridge may be 50 years to 100 years [25]. Cutting-edge design for durability deals with the inherent uncertainties in material and environmental features [26; 27]. Non-destructive tests (NDT) assess the quality of concrete in terms of durability. Several common defects, such as the corrosion of steel in concrete, are related to durability. Additionally, recent research shows some non-destructive electrochemical tools are useful and commonly used for monitoring these types of defects, especially in corrosion investigations. Electrical resistivity is an NDT tool used to measure the durability of concrete along with its quality. The electrical resistance of an object is a measure of its opposition to the flow of electric current. This feature of concrete shows its durability based on void connectivity [28; 29]. Currently, this research aims at uncovering the \(D_{i}\) estimation and the possibilities of condition monitoring, along with subsequent data-gathering based on image processing tools. Therefore, this research has prepared a maintenance plan for a case study based on SHM results and the extracted \(D_{i}\) according to the semantic approach. Finding critical elements in the structure and estimating their lifetime is the outcome of this maintenance plan. Finding critical elements in the structure will mostly involve the filtering of the enormous dataset in terms of quality. The rate of growth for progressive defects and cracks is a piece of evidence to assist in finding critical concrete elements in terms of their quality comparison. Numerous superfluous and costly data will be eliminated from the input dataset with this approach. This means that durable elements will not need to be checked by frequent inspections within short timeframes. Meanwhile, critical elements with expanding damage are important for inspectors and maintenance managers. Finally, the main contribution of this research work is presenting a relative index based on damage detection by monitoring changes and representing optimized maintenance plans by considering the durability of concrete elements as well as other aspects of quality and critical zones.
## 3 Derivation of Water Quality Criteria and Relevant Fish Assay
### Comparison of Acute and Chronic Toxicity for Fish and Priority Substances in WQS
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**US EPA [53]**} & \multicolumn{2}{c}{**Canada [54]**} & \multicolumn{2}{c}{**ECHA [55]**} \\ \cline{2-7} \cline{5-7} \multicolumn{1}{c}{**Substance**} & \multicolumn{2}{c}{**Freshwater**} & \multicolumn{2}{c}{**Freshwater**} & \multicolumn{2}{c}{**Emission Medium**} \\ \cline{2-7} \cline{5-7} \multicolumn{1}{c}{**Short-Term**} & \multicolumn{1}{c}{**Long-Term**} & \multicolumn{1}{c}{**Short-Term**} & \multicolumn{1}{c}{**Long-Term**} & \multicolumn{1}{c}{**Inland Surface**} & \multicolumn{1}{c}{**Surface Water \({}^{*}\)**} \\ \hline
4,4-DDT & 1.1 & 0.001 & — & — & 0.01 & 0.01 \\ \hline Acrolein & 3 & 3 & — & — & — & — \\ \hline Aldrin & 3 & — & — & 0.004 & 0.01 & 0.005 \\ \hline alpha- & 0.22 & 0.056 & 0.06 & 0.003 & 0.01 & 0.004 \\ \hline Arsenic & 340 & 150 & — & 5 & — & — \\ \hline Atrazine & — & — & — & 1.8 & — & — \\ \hline beta- & 0.22 & 0.056 & 0.06 & 0.003 & 0.01 & 0.004 \\ \hline Boron & — & — & 29,000 & 1500 & — & — \\ \hline Cadmium & 1.8 & — & — & — & 0.9 & 1.5 \\ \hline Carbaryl & 2.1 & 2.1 & 3.3 & 0.2 & — & — \\ \hline Chlordane & 2.4 & 0.0043 & — & 0.006 & — & — \\ \hline Chloride & 860,000 & 230,000 & 640,000 & 120,000 & — & — \\ \hline Chlorine & 19 & 11 & — & 0.5 & — & — \\ \hline Chlorpyrifos & 0.083 & 0.041 & — & 0.002 & 0.1 & 0.1 \\ \hline Chromium (III) & 570 & 74 & — & 8.9 & — & — \\ \hline Chromium (VI) & 16 & 11 & — & 1 & — & — \\ \hline Copper & — & — & — & — & — & — \\ \hline Cyanide & 22 & 5.2 & 5 & — & — & — \\ \hline Demeton & — & 0.1 & — & — & — & — \\ \hline Diazinon & 0.17 & 0.17 & — & — & — & — \\ \hline Dieldrin & 0.24 & 0.056 & — & 0.004 & 0.01 & 0.005 \\ \hline Endrin & 0.086 & 0.036 & — & 0.0023 & 0.01 & 0.005 \\ \hline gamma-BHC & & & & & & \\ (Lindane) & 0.95 & — & — & — & — \\ \hline Guthion & — & 0.01 & — & — & — & — \\ \hline Heptachlor & 0.52 & 0.0038 & — & — & 0.0003 & 0 \\ \hline Epoxide & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The freshwater quality standards of maximum allowable concentrations (\(\mathrm{\SIUnitSymbolMicro g}/\mathrm{L}\)) for priority substances in US EPA, Canada, and European Chemical Agency (ECHA).
## 5 Conclusions  
Aiming at the complex situations such as pests that are widespread in agricultural and forestry applications, this paper uses a variety of IoT devices to collect images of 132 pests to construct a fine-grained data set suitable for practical operations. Based on this, this paper proposes an effective probability fusion network for the fine-grained identification of insect pests. After the data enhancement preprocessing, this paper uses the CSPNet network to design a truncation backbone structure as a fine-grained feature extractor. The model feature vector is input into the soft-VLAD structure for feature aggregation and high-dimensional representation. Finally, the confidence results of the sub-module are integrated into the gated probability fusion layer, and the maximum parameters are used to iteratively estimate the hyperparameters of the fusion layer to achieve end-to-end model training and fast convergence. The experimental analysis shows that the PFNT model in this paper has higher recognition performance, with accuracy as high as 93.18%, and its efficiency and robust performance are better than other comparative deep network methods. The average leaflet recognition time is 61 ms, which can meet the fine-grained identification of pests at the terminals of the Internet of Things in agricultural and forestry  
Figure 10: Comparing recognition results of 120 th under each model.  
Figure 9: Comparing recognition results of 49 th under each model.  
practice and can play a positive role in scientific, reasonable, and effective early warning and control of plant pests for farmers.  
In future work, we will attempt to put more pest identification networks for certain crops into practice. At the same time, we would like to apply more advanced algorithms to further optimize the parameters and improve the reliability and safety of the model. Moreover, it will be one of our key tasks to apply the model to more scenes.  
K.M.; M.-J.N. and J.K.: Conceptualization, Methodology, Writing--original draft. M.-J.N.: Formal analysis. C.-C.-Y; M.-J.N.: Investigation, Software. S.L.; M.-J.N.: Data curation, Writing--review and editing. J.L.: Supervision. All authors have read and agreed to the published version of the manuscript.  
This research was financially supported by the National Natural Science Foundation of China (No. 62006008), National Key Research and Development Program of China (No.2020 YFC16068 01), Beijing Natural Science Foundation (No.4202014), Humanities and Social Sciences of Ministry of Education of China (No. 19 YJC790028, No.20 YJCZH229), and 2021 graduate research ability improvement program of Beijing Technology and Business University.  
Not applicable.