esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Research on the Data Derivation Method Based on CTGAN
### Principle of the Self-Attention Mechanism  
In high-latitude data, there is often a certain correlation between different dimensions. When mining key features, the influence of other features on this correlation cannot be ignored, so the self-attention mechanism needs to be used.  
Self-attention allows each unit to capture the overall information, while different units can be calculated or processed in parallel, which can be understood as self-attention, find the relationship between each feature and consider whether one feature will have an impact on the other. The basic principle is shown in Figure 4.  
Figure 3: CTGAN structure diagram.  
The workflow for self-attention is shown in Figure 5.  
The thought steps are as follows:  
Step 1: Transform the input \(X\) through the linear transformation matrix \(W_{q}\), \(W_{k}\), and \(W_{v}\) into \(Q\), \(K\), and \(V\), where \(Q\) is the query vector, \(K\) is the key vector, and \(V\) is the value vector.  
Step 2: Calculate the similarity by the dot product operation of \(Q\) and \(K\).  
\[\alpha_{i,j}=q_{i}\cdot k_{j} \tag{1}\]  
Step 3: SoftMax normalization of the similarity obtained in Step 2.  
\[\hat{\alpha}_{i,j}=\exp(\alpha_{i,j})/\sum_{j}\exp(\alpha_{i,j}) \tag{2}\]  
Step 4: Calculate the comprehensive output \(B\) of each unit after self-attention.  
\[b_{i}=\sum_{i}\alpha_{i,j}v_{i} \tag{3}\]
# Dynamic Induction Heating Temperature Field Analysis of Spiral Bevel Gears
## 2 Modeling of Induction Heating Coils for Spiral Bevel Gears
### Mathematical Model
Gear steel is an isotropic material, and the heat transfer control equation of its three-dimensional transient temperature field is:  
\[c(T)\rho(T)\frac{\partial T}{\partial t}=\frac{\partial}{\partial x}\bigg{(} \lambda(T)\frac{\partial T}{\partial x}\bigg{)}+\frac{\partial}{\partial y} \bigg{(}\lambda(T)\frac{\partial T}{\partial y}\bigg{)}+\frac{\partial}{ \partial z}\bigg{(}\lambda(T)\frac{\partial T}{\partial z}\bigg{)}+Q \tag{4}\]  
By introducing the Laplace operator \(\
abla_{L}\), the above equation can be simplified to:  
\[c\rho_{m}\frac{\partial T}{\partial t}=\lambda\
abla_{L}^{2}T+Q \tag{5}\]  
where \(c\) is the specific heat capacity of gear steel materials, \(\rho_{m}\) is the density of gear steel materials, \(\lambda\) is the thermal conductivity, \(T\) is the gear temperature and \(Q\) is the joule thermal power density. The gear convection and radiation boundary condition equations are:  
\[\lambda\frac{\partial T}{\partial n}=-k(T-T_{m})-\varepsilon_{a}\sigma_{a}(T-T _{m}) \tag{6}\]  
where \(k\) is the heat transfer coefficient between gear surface and air, \(T_{in}\) is the ambient temperature, \(n\) is the external normal to gear surface, \(\varepsilon_{a}\) is the gear surface emissivity and \(\sigma_{a}\) is the Stefan-Bozeman constant.
## 5 Matching-Iteration-Based Joint User Access and Resource Allocation Algorithm
### Matching-Based User Access and Resource Allocation Algorithm
D2D user \(D_{j}\) or unpaired, in this case, the CU \(C_{k}\) will match with the new D2D user \(D_{i}\), i.e., \(\varphi(C_{k})=\{D_{j},D_{i}\}\) or \(\varphi(C_{k})=\{D_{i},-\}\). In addition, if a D2D user \(D_{i}\) proposes to a CU \(C_{k}\) who has paired with two D2D users, i.e., we assume \(\varphi(C_{k})=\{D_{j},D_{l}\}\) at a certain time and \(C_{k}\) prefers \(D_{j}\) compared with \(D_{l}\), if \(C_{k}\) prefers \(D_{i}\) to \(D_{j}\), the form D2D user \(D_{l}\) will be moved, then the CU will match with \(D_{i}\) as a new assignment, i.e., \(\varphi(C_{k})=\{D_{j},D_{i}\}\). The matching pair is also obtained when \(C_{k}\) prefers \(D_{i}\) to \(D_{l}\) but prefers \(D_{j}\) to \(D_{i}\). The last one, if \(C_{k}\) prefers \(D_{l}\) to \(D_{i}\), the matching pair \(\varphi(C_{k})=\{D_{j},D_{l}\}\) will keep unchanged. 4. Steps **1-3** are repeated until the round times exceeds the number of the D2D users and all the licensed subchannel are altogether reused by the D2D users. In the end, a stable matching \(\varphi\) will be output.
## 3 Bayesian Estimation under the SEL, GEL, and QEL Functions
### Bayesian Estimation under the Informative Prior  
Assume that \(\beta\) follows the informative Gamma prior distribution with the hyperparameters \((a,\frac{1}{b})\), which is expressed as  
\[\pi_{1}(\beta)=\frac{b^{a}}{\Gamma(a)}\beta^{a-1}e^{-b\hat{\theta}},a>0,b>0, \beta>0. \tag{18}\]  
Therefore, the posterior distribution function of \(\beta\) is  
\[\pi_{1}(\beta|\underline{X})=\frac{(b+S)^{a}}{\Gamma(n+a)}\beta^{n+a-1}e^{-( b+S)\beta}. \tag{19}\]  
The Bayesian estimations of the parameter \(\beta\) under the above loss functions are, respectively, computed as  
\[\hat{\beta}_{SEL}=E(\beta|\underline{X})=\frac{n+a}{b+S}, \tag{20}\]  
\[\hat{\beta}_{GEL}=\left[E\big{(}\beta^{-p}|\underline{X})\right]^{-\frac{1}{ p}}=\frac{1}{b+S}\bigg{[}\frac{\Gamma(n+a-p)}{\Gamma(n+a)}\bigg{]}^{-\frac{1}{ p}}, \tag{21}\]  
\[\hat{\beta}_{QEL}=\bigg{[}\frac{E(\beta^{q}|\underline{X})}{E(\beta^{-q}| \underline{X})}\bigg{]}^{\frac{1}{2q}}=\frac{1}{b+S}\bigg{[}\frac{\Gamma(n+a +q)}{\Gamma(n+a-q)}\bigg{]}^{\frac{1}{2q}}. \tag{22}\]  
Furthermore, the Bayesian estimations of \(R(t)\) under the SEL, GEL, and QEL functions are calculated separately as  
\[\hat{R}_{SEL}(t)=E(R(t)|\underline{X})=\bigg{[}\frac{b+S}{b+S+\ln(1+t^{a})} \bigg{]}^{n+a}, \tag{23}\]  
\[\hat{R}_{GEL}(t)=\Big{[}E\Big{(}R(t)^{-p}|\underline{X}\Big{)}\Big{]}^{-\frac {1}{p}}=\bigg{[}\frac{b+S}{b+S-p\ln(1+t^{a})}\bigg{]}^{-\frac{n+a}{p}}, \tag{24}\]  
\[\hat{R}_{QEL}(t)=\Bigg{[}\frac{E\big{(}R(t)^{q}|\underline{X}\big{)}}{E\Big{(} R(t)^{-q}|\underline{X}\Big{)}}\bigg{]}^{\frac{1}{2q}}=\bigg{[}\frac{b+S-q\ln(1+t^{a})}{b+S+q \ln(1+t^{a})}\bigg{]}^{\frac{n+a}{2q}}. \tag{25}\]
# Survey on Q-Learning-Based Position-Aware Routing Protocols in Flying Ad Hoc Networks
### Contribution of This Study  
The key contributions of our study can be summarized as follows:  
* We discuss all the realistic mobility models for highly dynamic FANETs based on suitable applications.
* We extensively review recently published QL-based position-aware routing protocols and their advantages and limitations.
* Existing routing protocols are qualitatively compared in terms of their main concepts, key features, performance metrics, and implementation aspects.
* We summarize all the important performance enhancement criteria (in the Lessons Learned section) to design QL-based routing protocols using the position information.
* We identify open issues and research challenges in designing QL-based position-aware routing protocols and their potential research directions in highly dynamic FANETs.
## 4 Results  
### Performance of GPP Simulation  
Figure 3 depicts the variation in the loss function over the epochs. The model shows signs of stabilization after approximately 800 iterations. Table 2 summarizes the accuracy of the GPP simulations, with \(R^{2}\) values of 0.994 for the training period and 0.978 for the testing period. The corresponding \(RMSE\) values are 0.075 and 0.127, respectively. Additionally, a  
comparison of other metrics, such as the _MAE_ and _PBIAS_, between the two periods further underscores the strong generalization capability of the CNN model. Overall, the \(R^{2}\), _RMSE_, _MAE_, and _PBIAS_ for the entire period are 0.988, 0.093, 0.070, and 0.577%, respectively, demonstrating that the CNN model could simulate the GPP effectively.  
From a seasonal perspective, the model performs best in simulating the summer GPP, followed by autumn and then spring, with winter showing the lowest accuracy. Notably, the model tends to overestimate the GPP during winter and spring, while underestimating it in summer and autumn. This pattern suggests that the CNN model struggles in capturing extreme GPP values, leading to the overestimation of lower values and the underestimation of higher values.  
The coefficient of determination \(R^{2}\) between the simulated and observed GPP in the testing period, as depicted in Figure 4, was used to assess the spatial performance of the CNN model. The spatial distribution of the \(R^{2}\) shows a gradual decrease from east to west, with higher values concentrated in the Qilian Mountains and the basins of the Yellow, Yangtze, Lancang, and Yarlung Zangbo Rivers. In the Qilian Mountains and the Yellow, Yangtze, and Lancang River Basins, the \(R^{2}\) values generally range from 0.7 to 0.9, with some low-altitude areas exceeding 0.9, indicating a strong correlation between the simulated and observed GPP. In contrast, the \(R^{2}\) values in the Yarlung Zangbo River Basin range from 0.5 to 0.7, reflecting slightly lower accuracy in this region. In other areas, the \(R^{2}\) values range between 0 and 0.3, likely due to lower GPP levels, which introduce greater uncertainty into the simulation. Despite these regional variations, the overall performance of the CNN model remains robust, underscoring its potential as a valuable tool for the prediction of GPP on the Tibetan Plateau under future climate scenarios.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Evaluation Criterion** & **Training** & **Testing** & **All** & **Spring** & **Summer** & **Autumn** & **Winter** \\ \hline \(R^{2}\) & 0.994 & 0.968 & 0.978 & 0.799 & 0.863 & 0.762 & 0.778 \\ _RMSE_ & 0.075 & 0.127 & 0.093 & 0.041 & 0.064 & 0.071 & 0.052 \\ _MAE_ & 0.061 & 0.091 & 0.070 & 0.036 & 0.046 & 0.051 & 0.049 \\ _PBIAS_ & 2.077\% & \(-\)1.786\% & 0.577\% & 5.342\% & \(-\)0.663\% & \(-\)4.537\% & 9.182\% \\ \hline \hline \end{tabular} Note: the unit of the _RMSE_ and _MAE_ is g C-m\({}^{-2}\)-d\({}^{-1}\).  
\end{table}
Table 2: Assessment of historical simulation accuracy for seasonal and annual GPP.  
Figure 3: Summarized results of loss for CNN model on training and testing datasets.
## 6 Numerical Simulation Results
### Parametric Analysis of the Effect of Plastic Strains on Loading/Unloading Moduli  
In the standard uniaxial compression test on rock samples, the linear portion of the stress-strain curve represents the elastic modulus of the rock. As loading is increased beyond the elastic limit, micro-cracks propagation and coalescence lead to the formation of larger cracks and shear bands which appear in the form of plastic deformations within the rock. Plastic deformations cause the reduction of rock mass deformation modulus and rock mass damage. The computed plastic strains that occurred during loading/unloading cycles were analyzed to see how the occurred plasticity affects the PLT results. Variations of plastic strain, as a function of change in loading plate diameter, for the three simulated plate diameter scenarios are illustrated in Figure 16. Moreover, plastic strain variations were evaluated at varying depths beneath the plate. As can be seen from this figure, by an increase in plate diameter, the amount of plastic strain at a given measurement point is increased leading to the reduction of the rock mass stiffness and consequently deformation modulus. Referring to Figure 16, the plastic strain increases in a linear fashion as a function of loading plate diameter. This increase is more pronounced at shallower depths beneath the loading plate. At greater depths, less plastic deformations occur and the applied load level (loading cycle) does not influence the rock deformation and plastic failure as such. This can be seen from Figure 16b,c.  
Figure 14: Induced stress state (Pa) in the direction of loading for two plate diameters in an in-situ plate load test, (**a**) loading plate diameter of 80 cm (**b**) loading plate diameter of 100 cm.  
Figure 15: The effect of plate load diameter on deformation modulus variation as a function of displacement measurement depth.
# A Method for Underwater Acoustic Target Recognition Based on the Delay-Doppler Joint Feature
## 5 Conclusions  
This study presents a method for extracting features from underwater objects by analyzing their velocity characteristics in the Delay-Doppler domain. The Delay-Doppler features are used to represent the velocity characteristics of signals. A novel underwater acoustic target identification model is developed by integrating the Time-Frequency properties of signals with the target being detected based on ship-radiated noise. The target recognition model in this paper can significantly enhance the precision of underwater target recognition by using Delay-Doppler joint feature. Unlike conventional neural network recognition models, the suggested target recognition model in this research, which utilizes Delay-Doppler domain information, has a simpler structure. Additionally, the model requires fewer iterations for training, resulting in reduced training time. Moreover, the model exhibits significantly enhanced recognition accuracy.  
This study presents a comprehensive collection of models for recognizing underwater acoustic targets. The method that is presented in this study extracts characteristics from underwater acoustic data in the Delay-Doppler domain. The approach emphasizes two key elements: the identification of signal characteristics and the identification of targets. To accomplish precise identification of underwater sound targets, a target recognition model with great accuracy is created based on this property. This offers a novel approach to doing research on identifying targets using underwater acoustics.  
Conceptualization, Z.W. and Z.L.; methodology, Z.W.; validation, Z.W., Z.L. and D.H.; formal analysis, Z.W.; investigation, Z.W. and D.H.; writing-original draft preparation, Z.W.; writing-review and editing, Z.W., Z.L. and D.H.; supervision, L.D., Z.L., L.W., F.Y. and Q.L.; and project administration, Z.W. and Z.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China (2023 YFE0201900).  
**Data Availability Statement:** No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
**Acknowledgments:** The authors would like to thank the teachers and students from the Underwater Acoustic Laboratory of Shandong University of Science and Technology for their support in this paper. We would like to thank the team of [PERSON] from Universidad de Vigo for the ShipsEar database. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 7 Secrecy Capacity Analysis
Secrecy capacity of CA with heterogeneous SNR The secrecy capacity of CA with heterogeneous SNR is  
\[=W\log_{2}(e)[\mathcal{I}_{1}+\mathcal{I}_{2}-\mathcal{I}_{3}], \tag{16}\]1 \[=W\log_{2}(e)[\mathcal{I}_{1}+\mathcal{I}_{2}-\mathcal{I}_{3}], \tag{16}\]2 \[=W\log_{2}(e)[\mathcal{I}_{1}+\mathcal{I}_{2}-\mathcal{I}_{3}], \tag{16}\]3  
For \(N\) CCs, \(W=B(1-\alpha N)\). Introducing (10)-(11) in (16)-(19) yields a sum of integrals, each of which can be resolved as discussed in the proof of Theorem 1. By comparing (20), (21) and (22) to (13), (14) and (15), respectively, it can be noted that the secrecy capacity is equivalent to the ergodic capacity minus a term that quantifies the amount of information that can be transmitted through the channel, but not in a confidential manner due to the presence of an eavesdropper (i.e., the secrecy capacity is lower than the ergodic capacity, as expected).
## 3 Materials and Methods
### Total RNA Extraction, Purification, cDNA Synthesis and Real-Time qPCR Assay  
The RNA extraction, cDNA synthesis and Real-time qPCR methods were followed the previously reported methods [21; 40]. Approximately 30 berries were randomly selected and seeds were then removed, and the remaining parts were grounded into powder under continuous liquid nitrogen. Total RNA was isolated from grape berries without seeds using SpectrumTM Plant Total RNA Kit (Sigma-Aldrich, Shanghai, China) according to the protocol. The genome DNA was eliminated by the RNase-free DNase. The quality of the purified RNA was tested by demonstrating the existence of intact ribosomal bands on an agarose gel and A260/A280 by UV spectrophotometer. Then, the corresponding cDNA was synthesized using the qualified RNA as template by Reverse Transcription System Kit (Promega, Madison, WI, USA) according to the instruction.  
The relative expression level of genes in LOX-HPL pathway was analyzed by Real-time qPCR using SYBR green method on an ABI 7300 Real-Time System (Applied Biosystems, Foster City, CA, USA) [21]. The difference between the cycle threshold (\(C_{\mathrm{t}}\)) of the target gene and the geometric mean of Ct values of reference genes (\(\Delta C_{\mathrm{t}}=C_{\mathrm{t}\,\mathrm{Target}}-C_{\mathrm{t}\,\mathrm{ RefGene}}\)) was used to obtain the normalized expression of target genes, which corresponds to \(2^{-\Delta C1}\)[41; 42]. Ef-\(\alpha\) (GenBank accession: EC959059), Actin (GenBank accession: EC969944), and Ubiquitin (GenBank accession: EC929411) were taken as internal controls according previous research [21; 40]. Primers for Real-time PCR were designed using PerlPrimer v1.1.19 (available at: [[http://perlprimer.sourceforge.net/](http://perlprimer.sourceforge.net/)]([http://perlprimer.sourceforge.net/](http://perlprimer.sourceforge.net/))) [21; 43]. The dissociation curves of the designed genes were displayed in Figure S2. Two independent extraction procedures were performed for each sample and three technical replications of Real-time qPCR analysis were undertaken.
# Microorganism Detection in Activated Sludge Microscopic Images Using Improved YOLO
## 2 Materials and Methods
### Channel Attention Mechanism  
The attention mechanism is a frequently employed technique within the fields of machine learning and deep learning, inspired by the human visual and perceptual systems. This mechanism directs models to concentrate selectively on portions of the input data instead of either entirely disregarding or uniformly treating all inputs. Through the assignment of diverse weights to different input portions according to their significance, attention mechanisms facilitate more efficient information processing by models. In our  
Figure 5: The architecture of YOLOv5.  
research, the utilization of the attention mechanism plays a pivotal role in enhancing the microorganism detection capabilities of the YOLO model.  
The Squeeze-and-Excitation (SE) module represents a type of channel attention mechanism designed to amplify valuable features and suppress irrelevant ones. The SE module consists of three essential parts: squeeze, excitation, and reweight, as shown in Figure 6. The initial squeeze part involves compressing global information onto the channel feature map. This compression is achieved through a pooling operation. The result is a global feature description vector that encapsulates essential information about the entire feature map. Then, the excitation part is executed. In this part, a fully connected layer utilizes the global feature description vector to calculate the weights associated with each channel. This process resembles a learning phase during which the model discerns the significance of each channel. The output of the excitation part is a vector that indicates the influence of every channel. In the final reweight part, the learned channel weights are employed to adjust the channel feature maps. By adapting the channel feature responses in accordance with their respective channel weights, the model directs more attention to the valuable features, thereby enhancing the utility of the information. These adjusted channel feature maps contain more valuable details, resulting in improving the performance and generalization of the model.  
The SE module functions through a series of operations: Initially, the feature X undergoes a transformation, compressing from \(H\times W\times C\) into a \(1\times 1\times C\) tensor by global average pooling. Subsequently, it proceeds through a series of steps, including passing through a fully connected layer, undergoing a non-linear transformation, and the application of activation functions. This sequence normalizes the tensor into a series of real numbers that span the range from 0 to 1. These real values represent the significance of each channel, with a value of 1 denoting high importance and 0 representing unimportance. In the end, the feature map undergoes a multiplication operation to generate the final output, denoted as X'.
## 1 Introduction
Considering the cooperative behavior among attackers, attackers can launch active attacks in addition to common passive attacks. In this paper, we use game theory to analyze the cooperation and competition between multiple attackers so as to design a comprehensive defense strategy against multiple threats;
* A virtual coordinate system transformation method is proposed as a means to protect the location privacy of source nodes. The real location information of the source node is effectively hidden to reduce the success probability of potential attackers;
* A new relay node selection strategy is proposed. The number of hops between the source node and the target node is increased to confuse the attacker's inference about the source node location, thus reducing the possibility that the attacker can obtain the location of the source node by monitoring network traffic and strengthening the network's defense against passive attacks. Furthermore, a secure data transmission method based on fountain codes is proposed to resist the active attack of attackers. By introducing redundant information, the reliability of data transmission is improved, the feedback and control overhead is reduced, the transmission efficiency is improved, and it can be adapted to different network environments and application requirements;
* A source location privacy protection scheme based on game theory is proposed. The interaction process between the attacker and the source node is described by evolutionary game equilibrium analysis, the balance point under different strategies is evaluated in time, and the defense strategy of the source node is dynamically adjusted to deal with multiple threats in time to ensure the security of source location privacy. The rest of this paper is organized as follows: in Section 2, the related work of source location privacy protection is analyzed. In Section 3, the network model, underwater acoustic communication model and attack model used in this research are described. In Section 4, the design of the SLP-MACGT model is introduced in detail. In Section 5, the experimental simulation and performance analysis of SLP-MACGT are carried out. In Section 6, conclusions are drawn, and future research directions are discussed.