esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Model Development
### Propagation Filtering
#### 2.3.1 Effects Due to Source Motion and Propagation Delay
The minimal kernel size \(b\) required for a decent sound quality cannot be stated in general, as it strongly depends on the application, _i.e._, the source signal, the propagation situation and, not least, the sampling frequency. However, in the example of Figure 6, already, \(b=10\) reaches a good sound quality, without audible artifacts. Nevertheless, to be on the safe side, a value of \(b=100\) was adopted. The careful choice of \(b\), however, provides the potential for optimization in terms of sound quality and computational cost. For sound speed \(c_{0}=340\) m/s, the Mach number \(M\equiv v/c_{0}\approx 0.12\). At times \(t=\pm\infty\), the received frequencies \(f^{\prime}\) due to the Doppler shift are given by [2,52]:  
\[f^{\prime}_{t=\pm\infty}=f\cdot\mathcal{D}_{t=\pm\infty}=\frac{f}{1\pm M} \tag{27}\]  
with \(f\) being the emitted frequency. In our example, according to Equation (27), the received frequency changes by a factor of 1.28 across the pass-by, which corresponds to a musical interval that is larger than a major third. The sum of the Doppler and the convective amplification amounts to [48]:  
\[G_{t=\pm\infty}=-40\cdot\log 10\left(1\pm M\right)\quad\text{[dB]} \tag{28}\]Equation (28) yields an amplification of 2.3 dB at \(t=-\infty\) and an attenuation by 2.0 dB at \(t=\infty\), resulting in a level difference of 4.3 dB across the pass-by. The numerical implementations of Equation (22) corresponded well with these theoretical values. Figure 5: Spectral attenuation due to different resampling strategies. Figure 6: Non-linear distortions (aliasing) due to different resampling strategies: linear interpolation (**a**) and Hamming sinc interpolations with different filter lengths, \(b=10\) for (**b**) and \(b=100\) for (**c**), respectively. The simulation was performed for a source emitting a 1 kHz pure tone that travels at constant speed \(v=150\) km/h and passes a static receiver at a distance of \(D=7.5\) m.
## 4 Metrics Used
Previously published experiments [2] have been carried out with the different scheduling algorithms, proving that both the reduction in frequency and the associated need for more resources help to reduce the energy consumed by the system. Thus, this research has been extended and the possibility of reducing the parameter \(\alpha\) is also investigated. Moreover, because different structures are compared and the cores can process only a single task, another coefficient, \(AP\)--the average power necessary to execute a single task--is introduced. Additional metrics that allow comparing and analyzing various architectures concern the amount of the utilized FPGA resources.
## 4 Experiments and Results
### Ablation Experiment  
To evaluate the impact of residual blocks on model performance, we conducted a series of ablation studies. Using 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\) LR data as input, we compared architectures containing 2, 4, and 8 residual blocks. A CNN without residual blocks was included for comparative analysis; Figure 15 shows the CNN framework.  
Table 2 summarizes model performance under different frameworks. The results indicate that residual blocks yield significant performance gains over CNN architecture. Notably, the 8-residual blocks framework achieves optimal performance metrics. We believe  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **RMSE** & **MAE** & **MAPE** & **Corr** & **PSNR** & **SSIM** \\ \hline Single-Time (0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\)) & 0.2897 & 0.1711 & 0.0625 & 0.9997 & 58.8929 & 0.9721 \\ Mean-Time (0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\)) & 0.2866 & 0.1774 & 0.0630 & 0.9995 & 58.7328 & 0.9789 \\ Single-Time (1\({}^{\circ}\)\(\times\) 1\({}^{\circ}\)) & 0.5168 & 0.3355 & 0.1221 & 0.9991 & 53.8646 & 0.9465 \\ Mean-Time (1\({}^{\circ}\)\(\times\) 1\({}^{\circ}\)) & 0.5286 & 0.3498 & 0.1242 & 0.9989 & 53.5304 & 0.9589 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Error estimations of the model under different data conditions.  
Figure 14: SR results for the average moment at a resolution of 1\({}^{\circ}\)\(\times\) 1\({}^{\circ}\): (a) LR temperature data; (b) SR temperature data; (c) true surface temperature data; (d) comparison between true data and LR data; (e) comparison between true data and SR data; (f) comparison between SR data and LR data.  
Figure 15: The CNN framework.  
that residual blocks help preserve detailed information in low-level structures, such as high- or low-temperature points and edges of temperature variations caused by topographical changes. By combining these details with high-level semantic information through skip connections, the model's performance is enhanced. Residual blocks also improve the training efficiency of the model. As shown in Figure 16, under identical training data, the CNN required approximately 125 epochs to achieve convergence, while ResNet-Subpixel converged significantly faster. This acceleration can be attributed to the skip connections offering the model a favorable training state, smoothing the optimization process, and preventing the redundant relearning of features, thereby improving parameter efficiency.
# Generative Adversarial Networks in Retinal Image Classification
## 2 Background  
In a basic GAN architecture, two networks coexist, the generator model and the discriminator model. The term \"adversarial\" in GANs reflects their simultaneous training and competitive nature, resembling a zero-sum game like chess. The generator's primary objective is to produce realistic images that can deceive the discriminator. In a simple GAN architecture for image synthesis, random noise is typically fed as input to the generator, which generates a corresponding image as output. On the other hand, the discriminator functions as a binary image classifier, responsible for determining the authenticity of an image by classifying it as real or fake.  
To summarize, the basic GAN architecture involves the generator generating fake images, the discriminator classifying both real and fake images, and their performances being assessed separately.  
Unlike most deep learning models that optimize towards minimizing a cost function (e.g., image classification), GANs operate differently. The generator and discriminator each have their own cost functions with opposing objectives. The generator aims to deceive the discriminator by generating fake images that resemble real ones, while the discriminator aims to accurately classify real and fake images.  
During training, both the generator and discriminator improve their capabilities over time. The generator becomes more adept at producing images that closely resemble the training data, while the discriminator becomes more skilled at distinguishing between real and fake images.  
Training GANs involve finding an equilibrium in the game, where the generator generates data that closely approximates the training data, and the discriminator can no longer differentiate between fake and real images.  
A well-performing GAN model should exhibit high-quality images, such as non-blurry images resembling the training data, and diversity, meaning it should generate a wide range of images that capture the distribution of the training dataset.  
Several noteworthy GAN variants have emerged, setting the stage for future advancements in the field. One prominent example is the DCGAN, which was the first GAN to integrate convolutional neural networks (CNNs) into its architecture. DCGAN has become one of the most widely adopted GAN models. Hence, in this paper, we employ the DCGAN framework for image generation.
# A New Resource Allocation Protocol for the Backhaul of Underwater Cellular Wireless Networks
## 3 Description of UBSI Resource Allocation Protocol  
In this section, a new resource allocation protocol for a UCWN called UBS Initiating (UBSI) resource allocation protocol is described, including its queue model, the way to determine the backhaul capacity of a UBS, and the corresponding protocol messages and procedures. While it is common to [16] and the UBSI resource allocation protocol that the backhaul capacity of a UBS is determined to be proportional to its uplink traffic, the followings are remarkable differences between them.  
* The UBSI resource allocation protocol is basically designed to efficiently manage the non-guaranteed UBSC capacity which implies that the capacity of the UBSC is insufficient to deal with the uplink traffic induced by UBSs.
* A UBS triggers to adjust its backhaul capacity for itself according to its queue status which does not affect the actions of other UBSs. To do this, a UBS requests additional backhaul capacity in case of occurring packet drops due to the shortage of current backhaul capacity. This can improve the performances of a UCWN in terms of energy efficiency, message overhead, throughput, and latency. A UBS also returns a portion of its backhaul capacity to the UBSC when it detects that its resource is under-utilized in order to manage the limited backhaul capacity of the UBSC.
* A ratio-based backhaul capacity is employed in order to be interoperable with any multiple access protocols (e.g., TDMA, CDMA, FDMA, or OFDMA) which have different resources, respectively.
# Semi-Global Stereo Matching Algorithm Based on Multi-Scale Information Fusion
## 4 Conclusions  
In this paper, an SGM algorithm based on multi-scale information fusion (SGM-MSIF) is proposed by combining multi-path cost aggregation and cross-scale cost aggregation (CSCA) framework. Compared with classic SGM and CSCA + GF algorithms in terms of matching accuracy, the average error matching rates of the proposed algorithm are reduced by 1.96% and 0.92%, respectively. For the matching efficiency, the average runtime of the proposed algorithm is reduced by 58.7% compared with CSCA + GF algorithm. The overall performance of the proposed SGM-MSIF algorithm outperforms the classic SGM and CSCA algorithms.  
In SGM-MSIF algorithm, a novel cost aggregation method by combining multi-path cost aggregation is proposed based on one-direction dynamic programming and cross-scale cost aggregation. The matching accuracy of the algorithm in low-texture regions is significantly improved. We also present an approach of internal left-right consistency check. The right cost volumes can be converted from the left cost volumes, which can effectively reduce the complexity of SGM-MSIF and improve the stereo matching efficiency. Experimental results prove that the proposed SGM-MSIF algorithm can obviously improve the matching accuracy of low-texture regions and raise the processing efficiency of cross-scale stereo matching algorithms. It can provide a theoretical reference for 3D reconstruction of low-texture scene. In future, combining this algorithm with machine learning [33] is a possible way to further improve the matching accuracy and efficiency of stereo matching algorithms.  
Conceptualization, C.D.; Funding acquisition, B.S.; Methodology, C.D. and B.S.; Project administration, B.S.; Software, C.D., D.L. and H.Z.; Supervision, B.S.; Writing--original draft, C.D.; Writing--review & editing, C.D., J.L. and B.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (Grant No. U20A20201), the National Key R&D Program of China (Grant No. 2019 YFB1312102), the Key R&D Program of Hebei Province (Grant No. 20311803D).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors thank the editor and anonymous reviewers for providing helpful suggestions for improving the quality of this manuscript.  
The authors declare no conflict of interest.
# First Request First Service Entanglement Routing Scheme for Quantum Networks
## 3 Modeling for Entanglement Distribution Network
### Modeling of Edges  
Assume \(e_{\alpha\beta}^{ij}\) is the edge (fiber or free-space link) from the \(\alpha\)-th port of node \(v_{i}\) to the \(\beta\)-th port of node \(v_{j}\), where \(1\leq i,j\leq k,1\leq\alpha\leq m_{i}\) and \(1\leq\beta\leq m_{j}\). \(e_{\alpha\beta}^{ij}\) is a five-variable vector defined as  
\[e_{\alpha\beta}^{ij}=\big{(}i,\alpha,j,\beta,c_{\alpha\beta}^{ij}\big{)}, \tag{13}\]  
where \(c_{\alpha\beta}^{ij}\) is the link loss and \(e_{\alpha\beta}^{ij}[\gamma]\) represents the \(\gamma\)-th element in \(e_{\alpha\beta}^{ij}\), \(\gamma\in\{1,2,3,4,5\}\). If there is no link between the \(\alpha\)-th port of node \(v_{i}\) and the \(\beta\)-th port of node \(v_{j}\), \(c_{\alpha\beta}^{ij}=\infty\).  
Thus, the link loss matrix from node \(v_{i}\) to node \(v_{j}\) can be defined as  
\[C_{ij}=\begin{bmatrix}c_{11}^{ij}&\cdots&c_{1m_{j}}^{ij}\\ \vdots&\ddots&\vdots\\ c_{m_{i}1}^{ij}&\cdots&c_{m_{i}m_{j}}^{ij}\end{bmatrix}. \tag{14}\]
## 1 Introduction  
Tea (_Camellia sinensis_) plants are important woody economic crops with shoots and leaves that can be used to make the popular beverage [1]. Tea is usually grown in temperate and subtropical regions, such that insect attack is a normal annual phenomenon. In response to herbivore invasion, plants can release chemical compounds, known as herbivore-induced plant volatiles (HIPVs) [2, 3]. HIPVs from economic crops, including tea plants, have been researched. Studies have indicated that (_Z_)-3-hexenol, nerolidol, (_E_)-\(\beta\)-ocimene, (_E_)-\(\beta\)-caryophyllene, (_E_,_E_)-\(\alpha\)-farnesene and other terpenids can be released when herbivores attack tea trees [4, 5]. Furthermore, \(\alpha\)-farnesene is a common volatile released by tea plants when suffering attack [6]. \(\alpha\)-Farnesene has been reported to have an important effect on insect resistance, such as defense against of _Lobesia botrana_ through locating spawning herbivores [7]. Compared with other economic plants, \(\alpha\)-farnesene in tea plants has received little study.  
The response to stress conditions, including insect resistance, of \(\alpha\)-farnesene has been reported in several studies [8; 9; 10]. For instance, the functions of \(\alpha\)-farnesene, linalool, and other inducible compounds are the same as (\(Z\))-3-hexenol, which can be used by parasitoids to forage and locate hosts [11]. This indicates that many types of volatile can be considered as plant-to-plant and plant-to-herbivore communication signals [10; 12]. A recent study reported that (\(E\),\(E\))-\(\alpha\)-farnesene emitted by insect-induced soybean plays a defense role against nematodes [13]. Therefore, investigating the \(\alpha\)-farnesene biosynthesis is of significance. \(\alpha\)-Farnesene is classified as a sesquiterpene with coding genes that are part of or closely related to the plant terpene synthase gene (TPS) family [14; 15]. The formation of monoterpene and sesquiterpene have been investigated in many plant species, including apple (_Malus domestica_) [16], _Arabidopsis_ [17; 18], soybean [13], and tomato [19]. (\(E\),\(E\))-\(\alpha\)-Farnesene synthase has been found in apple fruit and expressed from the peel tissues [16]. However, the biosynthesis of \(\alpha\)-farnesene in tea leaves has remained unknown. This study aimed to investigate the formation of \(\alpha\)-farnesene in tea leaves exposed to herbivore attack and its potential signaling function.
# A Simulation Study on Pressure Control in Oil Well Drilling Using Gain-Scheduled PID Controllers
## 3 Design of PID Parameters
Specifically, in drilling systems, excessive time delay can hinder the controller's ability to react to disturbances, resulting in oscillations or even a loss of pressure control stability. The maximum allowable time delay for the considered system model was determined by evaluating the stability margin of the system. Using the transfer function approximation in Equation (20), the Nyquist criterion was applied to analyze stability under varying time delays. It was found that the system remains stable for delays up to \(\tau_{max}=0.25\) s. Beyond this threshold, the phase lag introduced by the delay leads to a violation of the stability margin, causing the system to become unstable.
## 4 Reed-Solomon Codes Effectiveness against Single-Frequency EMD
### Impact of the EMD Amplitude  
The amplitude of the induced voltage has a noticeable effect on the CTP and CFN ratios at the mentioned peak frequency, i.e., \(f_{\text{UCD}}\) in Equation (2). This effect is clearly observable in Figure 7 in which the increasing trends of CFN ratios for symbol size of 4 at \(f_{\text{UCD}}\) (i.e., 250 MHz, 300 MHz, 350 MHz, and harmonics) have been shown.  
The underlying reasons for this are the disturbance severity and the dependency demonstrated by Equation (2). In other terms, more intense disturbances are capable of pushing more data points (i.e., voltages) below or above the specified threshold, which can lead to repetitive patterns in code words' elements.  
Figure 7: Increasing trend of CFN ratio at specific fractions of the disturbance frequency harmonics.
# Random Forest-Based Stability Prediction Modeling of Closed Wall for Goaf
## 2 Experimental Data Description
### Dataset Splitting  
The data used in this study are based on the systematic collection of 167 groups of dimensions from different mined-out areas within the mine, along with the mechanical data of the closed wall. Among these, 94 groups acted on the closed wall with no damage, 52 groups with slight damage, and 21 groups with severe damage. The distribution pie chart is shown in Figure 3. Each group includes the shock wave speed, and the compressive strength and shear strength of the closed wall, along with the corresponding damage level. Following established practices in data segmentation, 80% of the data were randomly selected for the training set to develop the machine learning model (which consists of 75 sets of undamaged data, 42 sets with slight damage, and 17 sets with severe damage), while the remaining 20% were reserved for the test set to evaluate the accuracy of the trained model (comprising 19 undamaged, 10 slightly damaged, and 4 severely damaged cases). The training set (134 cases) was used for the model construction and hyperparameter optimization. Actual hyperparameter optimization was performed via ten-fold cross-validation within the training set, without using any data from the test set. The test set (33 cases) was solely used for the final performance evaluation.  
Figure 3 uses horizontal stacked bar charts and a pie chart to represent the distribution and proportion of data for each category within the dataset, training set, and test set. In the overall dataset, samples with no damage are predominant, followed by slight damage, with the fewest samples exhibiting severe damage. The proportions in the training and test sets are generally consistent with those in the dataset, ensuring that the model does not bias toward a particular category during training, thereby improving its generalization ability across different damage levels.
## 3 Results and Discussion
### Bulk Density and Surface Morphology of the POPs  
By the design of the PDVB-based functionalized POPs with varied functional comonomer(s), template agent, and solvents, a wide range of bulk density of the prepared POPs (0.02-0.39 g/cm\({}^{3}\)) could be obtained as seen from Table 1. In the ethanol/deionized water (9:1) mixture solvents, styrene, VBC, GMA, and DVB and their own polymer networks exhibited bad thermodynamic compatibility with the solvents system, which caused the early-phase separation of the prepared POPs and low bulk density (0.02-0.08 g/cm\({}^{3}\)). The SEM images of representative POPs were shown in Figure 8. From Figure 8, we can see that the P(St-co-DVB) and the P(DVB) particles consist of aggregates of nano-microspheres and their low bulk density was caused by fluffy stacking of the microspheres. When HEMA and HPMA functional comonomers were incorporated into the PDVB networks, the bulk density of the P(HEMA-co-DVB) and P(HPMA-co-DVB) particles increased dramatically, from 0.02 to above 0.20 g/cm\({}^{3}\), due to their good thermodynamic compatibility with the same solvents system, and the SEM image of S3 proved that the dramatic increase of bulk density was due to the compact stacking of the microspheres. Furthermore, when the HEMA functional comonomer was incorporated into the P(St-co-DVB) or P(VBC-co-DVB) networks, it could also help improve the thermodynamic compatibility with the selected solvents system; the prepared P(HEMA-co-St-co-DVB) or P(HEMA-co-VBC-co-DVB) particles obtained relatively high bulk density (0.16-0.28 g/cm\({}^{3}\)) by dispersion polymerization. As seen from the SEM image of Sample 14, the highly porous structure and compact stacking of the aggregates was observed due to their good thermodynamic compatibility with the chosen solvents. Samples 11 and 12 were prepared in an inhomogeneous system due to the bad solubility of VBC in the used solvents system, and higher bulk density (0.33-0.39 g/cm\({}^{3}\)) was obtained with a bad porous structure (Figure 8e,f) in suspension polymerization.  
Figure 7: IR spectra of the prepared POPs with different functional comonomers.
## 3 Fault Classification
### Support Vector Machine: SVM  
SVMs are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis. Categories in SVMs are separated by a clear gap which is as wide as possible. In addition, SVMs are a linear model but can be used to solve complex and nonlinear problems by converting data to another space that has more dimensions. This is called the kernel function. Popular kernel functions include the Gaussian kernel and polynomial kernel. Both kernel  
Figure 6: Training process of the back-propagation neural network (BPNN).  
functions were applied in this study. SVMs are divided into four models, which are used to classify different types of faults. The same data were input for all four SVM models, and the output showed different types of faults. Model 1 separates the types of fault at phase A (fault or no fault), Model 2 separates the types of fault at phase B (fault or no fault), Model 3 separates the types of faults at phase C (fault or no fault), and Model 4 separates the types of fault at zero sequence (ground fault or no ground fault).  
The training process starts with random parameters of the SVM, and the parameters are adjusted to 1000 rounds. The parameters of the SVM are adjusted to find the maximum accuracy. If the results show more than 99% accuracy, the SVM parameters are recorded. Then, the kernel function changes to select the best parameter. The training time is approximately 60 s. The process is shown in Figure 7. The best kernel is polynomial kernel at kernel option level 19.