esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
SCTP supports multiple transmission modes and is suitable for multi-path parallel transmission [21], and the performance of SCTP is better than TCP in the 4G network [22]. However, TCP and SCTP have their own disadvantages. SCTP improves TCP's congestion control strategy. In parallel transport, SCTP adopts conservative congestion control, which cannot match the SCTP multi-path transmission. In the wireless network environment and the wired-cum-wireless network environment, because of SCTP's conservative congestion control mechanism and detection mechanism, TCP tends to be more stable. After the establishment of the coupling of SCTP, though the secondary path remains idle, only the primary path can be used for data transmission. [PERSON] et al. [23] proposes two practical parallel approaches of the subspace marginalization with interference suppression detector for large MIMO systems. [PERSON] et al. [24] proposes a flow splitting algorithm, which splits a data flow to multiple sub-flows by extending the open-flow protocol in SDN. A multiple-path routing algorithm is also proposed to implement the multi-path parallel transmission in the paper. [PERSON] et al. [25] present SCTPCL, a convergence layer protocol for the bundle protocol, which brings reliability and low-power to delay tolerant networks (DTN) implementations. In this work, we simulated TCP and SCTP with different packet error rate (PERs) in a heterogeneous network and compared the performance of TCP and SCTP with different network quality. Based on the analysis and simulation results, the multipath switching SCTP scheme (MS-SCTP) and the TCP-SCTP method were proposed to improve the network transmission quality. Our contributions in this work can be summarized as follows:  
* Based on the analysis and simulation results of the IEEE 802.11 wireless multi-hop network, the performance and problems of SCTP and TCP under different network environments are analyzed and compared. * A multipath switching scheme MS-SCTP is proposed to apply SCTP to data transmission in high speed networks. MS-SCTP judges the network transmission situation of each link based on packet loss rate, and then selects the main line in advance to ensure the link transmission quality. * TCP and SCTP are more suitable for wireless cable network transmission. Based on packet loss rate, the TCP-SCTP method predicts the current link transmission condition. When the link transmission quality becomes worse, the TCP-SCTP switch process improves the transmission quality. * Simulation and analysis of TCP-SCTP and MS-SCTP in network transmission show that TCP-SCTP and MS-SCTP improve throughput and stability, and improve network transmission quality. The remainder of the article is organized as follows. Section 2 shows the simulation results of TCP and SCTP in different PER scenarios, which are displayed with the performance of SCTP and TCP in the wireless-cum-wired network. Section 3 details the MS-SCTP and the TCP-SCTP methods. Section 4 determines the analysis and simulation. Section 5 concludes this article and presents the future work.
## 5 Conclusions and Future Work  
### Conclusions  
This paper proposes a dual-feature ResNet-Transformer (DFTRT) network model, which systematically optimizes the problem of modulation-style recognition for UWA  
Figure 8: Confusion matrix for different inputs at 18 km. (**a**) TFR; (**b**) TFR-HOC.  
Figure 7: Classification accuracy of different input features.  
communication signals. In the feature extraction module of the model, experimental simulations were conducted using three architectures--CNN, CNN-Transformer, and ResNet-Transformer. As shown in Figure 6, the ResNet-Transformer architecture demonstrates significant advantages in accuracy across different distances, achieving an average improvement of 2.92% while maintaining a baseline accuracy of 90%. This validates the effectiveness of the architectural enhancements. In the classification module, a dual-feature fusion framework combining time-frequency domain and HOS was constructed and compared with a single time-frequency domain input. Figure 7 shows that the dual-feature fusion framework significantly improves the network's accuracy under low-SNR conditions. Additionally, to verify the framework's enhancement of phase-modulated signal recognition accuracy, Figure 8 demonstrates that this architecture effectively corrects previously misclassified OFDM and 2 PSK signals. Through these two key improvements, the integrated DFRT network is formed, showcasing superior performance in UWA signal modulation recognition. Through the optimization of these two approaches, the model proposed in this paper not only improves classification accuracy but also effectively addresses the gradient vanishing issue prevalent in previous models. It demonstrates superior performance and has been successfully applied to the underwater acoustic communication field, offering a robust solution for modulation recognition in complex aquatic environments.
## 6 Conclusions  
In this paper, a smart UAV BS positioning mechanism was proposed by taking altitude regulations as well as NFZs into account along with some hard constraints, including maximum transmit power and directivity of the UAV BS antenna, to provide sustainable wireless coverage and services to the ground users under more realistic conditions. As such, firstly, two different optimization models were developed for the minimization of the energy consumption and maximization of the number of users covered. Then, these two distinctive models are combined with a weighting mechanism, and a multiobjective optimization problem formulation was developed. With the developed weighting mechanism, wireless networks operators become capable of positioning the UAV BSs according to their requirements by relatively ranking the energy consumption and the number of users covered. We proposed a \(Q\)-learning-based approach for UAV-assisted communication systems, and the OpenAI Gym tool was used to build the RL environment. The objective is to find the optimal position of the UAV and minimize the energy consumption while maximizing the number of users covered. The results demonstrate that the proposed solution outperforms the baseline \(k\)-means method in terms of covered users, while achieving the desired minimization of the energy consumption.  
Since a single UAV BS has a certain amount of radio resources and is capable of accommodating a limited number of users, in future we plan to investigate the UAV  
Figure 4: Monte Carlo test results. Single UAV position optimisation comparison for different set of weights.  
assisted coverage extension problem in multi-UAV scenario, wherein more than one UAV BS are deployed to serve to the ground users to further maximize the number of connected users. That would be a more challenging problem due to the fact that the interference between the UAV BSs will be another aspect to consider, as such interference will have an impact on the communication performance and so the positions of the UAV BSs. Future work would also consider to investigate the impacts of the speed of the UAV BS in the overall performance. Moreover, as in a realistic case, having a dynamic speed for the UAV BS, in which case the speed should also be optimized, will be also considered.  
Conceptualization, M.O. and M.Z.A.; Investigation, G.C.V.; Methodology, I.A., M.O. and G.C.V.; Software, I.A. and G.C.V.; Validation, I.A. and G.C.V.; Visualization, I.A. and G.C.V.; Writing--original draft, I.A., M.O., G.C.V. and M.Z.A.; Writing--review & editing, M.O. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Convolutional-Neural-Network-Based Hexagonal Quantum Error Correction Decoder
## 3 Designing a Machine-Learning-Based Decoder for the Heavy Hexagonal Code
### Training  
The objective of training the dataset through the CNN model is to obtain a syndrome with an error rate below the threshold, which can better predict and correct the model to complete the error correction. This is a significant challenge for the neural network model when the error rate of the syndrome is high. Therefore, in model training, we trained the neural network with the configuration corresponding to an error rate close to the threshold, which can enhance the accuracy of the model.  
In our work, we first chose the RestNet7 network architecture to perform small code-distance training on the collected dataset and generated a prediction model on this dataset with a low error rate.  
\[C=\sum_{i=1}^{N}Y_{i}\times\left[\log(G*(X_{i}))+(1-Y_{i})\right]\times\log(1- G*(X_{i})), \tag{13}\]  
where \(N\) represents the total quantity of data, \(GX_{i}\) represents the true class label, and \(Y_{i}\) represents the predicted probability returned by the neural network. To further verify the error correction performance of the heavy hexagonal code, we increased the code distance. Correspondingly, the dataset also increased, so we adjusted the network architecture and used RestNet14 to train the increased code distance. Figure 4 shows a schematic diagram of the iterative training of the two network architectures of RestNet7 and RestNet14. It can be seen from the figure that the training accuracy of the RestNet14 network architecture is better than that of RestNet7 and can achieve an ideal accuracy of more than 96%. Table 1 provides the training dataset, iteration steps, and accuracy of the MWPM algorithm and CNN decoder provided by us.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Trainable Data** & **Steps** & **Accuracy** \\ \hline MWPM & \(1.46\times 10^{5}\) & \(4.7\times 10^{4}\) & 74.699\% \\ RestNet7 & \(1.35\times 10^{3}\) & \(3.3\times 10^{3}\) & 84.593\% \\ RestNet14 & \(2.78\times 10^{3}\) & \(3.5\times 10^{3}\) & 84.967\% \\ \hline \end{tabular}
\end{table}
Table 1: The accuracy of the prediction model.
## 3 Results
### Rate Equation Modeling and Effective Active Volume
Figure 4a shows the \(k\) value as a function of the \(C\) coefficient. As shown in the figure, \(k\) increases with increasing \(C\), implying that, as \(C\) decreases, a smaller volume of QWs is effectively used as a light-emitting region. For all values of \(C\), the \(k\) of Sample B is higher than that of Sample A, indicating a larger effective active volume of the latter, which resulted in a lower efficiency droop, as shown in Figure 2. As mentioned before, we obtained the same IQE curves for the different values of \(C\) and \(k\) parameters in Figure 4a. Figure 4: **(a)** Ratio of the effective active volume to the physical volume of MQWs (\(k\)) as a function of the \(C\) coefficient for Sample A and Sample B. **(b)** Ratio of the effective volume of Sample B to that of Sample A as a function of the \(C\) coefficient. Figure 3: Measured LED efficiency (solid dots) and theoretical fit (red lines) to obtain the internal quantum efficiency (IQE) as a function of the injection current. **(a)** LED on sapphire (Sample A) and **(b)** LED on silicon (Sample B). Figure 4b shows plots of the ratio of \(k\) of Sample B to the \(k\) of Sample A as a function of \(C\). More specifically, it shows the relative ratio of the effective active volume between the two LED samples. With \(k\) values vary significantly with the used \(C\) coefficient, the ratio of the effective active volume varies in the range from 1.43 to 1.48. That is, the effective active volume of Sample B is \(\sim\)1.45 times larger than that of Sample A irrespective of the choice of \(C\). One may perform the IQE fitting with a fixed \(k\) value using the \(A\) and \(C\) coefficients as the fitting parameters. When the effective active volume was assumed to be the same as the physical volume (\(k=1\)), the coefficient \(C\) was obtained to be \(1.703\times 10^{-30}\) cm\({}^{-3}\) for Sample A and \(1.415\times 10^{-30}\) cm\({}^{-3}\) for Sample B. Then, the ratio of \(C\) between two samples is 1.204. This ratio was almost the same for other choices of \(k\) values. Since the QWs grown on sapphire and silicon substrates are identical, having the same indium composition and thickness, such a large difference in the coefficient \(C\) between LED on sapphire and silicon would be unreasonable. Therefore, we believe that it would be more reasonable approach in the IQE fitting to assume that the coefficient \(C\) is the same, whereas \(k\) is different for the two LED structures.
# Design and Performance Analysis of Navigation Signal Based on OFDM
## 5 Performance Evaluation of OFDM Navigation Signals
### Anti Interference Ability
#### 5.5.2 Narrowband Interference  
DSSS technology can bring a spread spectrum gain to the navigation signal, and the spread spectrum gain can reflect the anti-interference ability of the navigation signal. Figure 13 shows the equivalent carrier-to-noise ratio of the narrowband interference to the navigation signal. The receiver bandwidth is 36 MHz and the CN0 is 45 dB. The narrowband interference bandwidth is 1 MHz, and the narrowband interference is located at the center of the main lobe of the navigation signal. Because OFDM(128) and OFDM(8) have the same spreading gain, their equivalent carrier-to-noise ratios are the same. Under the influence of the same narrowband noise, we find that the carrier-to-noise ratio of the OFDM-modulated navigation signal is about 5 dB higher than that of BOC(10,5) and BPSK-R(10). Therefore, OFDM modulation has a strong ability to resist narrowband interference.
# On the Energy Efficiency of On-Off Keying Transmitters with Two Distinct Types of Batteries
## 2 Power of the Battery and Transmitter  
Figure 1 shows a diagram of a battery-powered transmitter under study. As shown there, the transmitter's energy is supplied by a DC/DC convertor connected with a battery. Assume the convertor's efficiency is \(\eta\), we have  
\[\eta=\frac{P_{\text{tx}}(t)}{P_{\text{out}}(t)} \tag{1}\]  
where \(P_{\text{tx}}(t)\) denotes the transmitter's instantaneous power consumption, and \(P_{\text{out}}(t)\) is the battery's instantaneous discharge power. We will establish power consumption models for \(P_{\text{tx}}(t)\), \(P_{\text{out}}(t)\) and \(P_{\text{bat}}(t)\) denoting the battery's instantaneous power consumption in the following subsections.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbol Definitions** & **Physical Meaning** \\ \hline \(V_{\text{b}}\) & battery output voltage \\ \(l_{\text{b}}\) & discharge current of battery \\ \(\omega\) & BUF-related parameter \\ \(P_{\text{out}}(t)\) & instantaneous discharge power of battery \\ \(P_{\text{mean}}\) & mean discharge power of battery \\ \(P_{\text{bat}}(t)\) & instantaneous power consumption of battery \\ \(\gamma=\frac{\omega}{P_{\text{b}}}\) & battery characteristic parameter connecting \(P_{\text{bat}}(t)\) with \(P_{\text{out}}(t)\) \\ \(P_{\text{d}}\) & digital circuit power consumption \\ \(P_{\text{a}}\) & RF circuit power consumption \\ \(d\) & transmitter-receiver distance \\ \(\eta\) & DC/DC convertor efficiency \\ \(k\) & path-loss decay exponent \\ \(N_{0}\) & single-sided power spectral density of AWGN \\ \(M_{I}\) & link margin \\ \(G_{0}\) & channel path loss at 1 meter \\ \(E_{\text{bat,I}}\) & BECPB for IDC-based BUF \\ \(E_{\text{bat,M}}\) & BECPB for MDC-based BUF \\ \hline \hline \end{tabular}
\end{table}
Table 1: Symbol definitions and physical meanings.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Acronyms** & **Full Name** \\ \hline WSNs & Wireless Sensor Networks \\ OOK & On-Off Keying \\ BECPB & battery energy consumption per message bit \\ BUF & battery utilization factor \\ IDC & instantaneous discharge current \\ MDC & mean discharge current \\ SER & symbol error rate \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of acronyms used in the paper.
## 3 Result
### Analysis of EDEM-Fluent Coupled Simulation Results for the Screening Process
#### 3.2.3 Particle Velocity Analysis  
During the simulation analysis of raisin and foreign object screening, an optimal inlet air velocity of 11 m/s was selected for investigation. Upon entering the screening zone, raisins and foreign objects interact with the airflow, colliding with other particles, the inner walls of the screening zone, and baffles. This results in temporal velocity fluctuations characterized by a polygonal trajectory pattern over time. Ultimately, raisins and foreign objects separate into three distinct material collection channels within the screening zone. Velocity analysis of these particles provides direct insights into the screening performance of the module. The velocity variation curves obtained from simulations were imported into Origin software 2024 to generate comparative velocity profiles for raisins and various foreign objects, as illustrated in Figure 25.  
During the sieving process, raisins and foreign objects are separated under the combined action of airflow. Analyzing the motion of these materials within the sieving module is crucial for enhancing the efficiency and performance of the sieving system. As shown in Figure 25 (velocity variation curve of raisin particles), the velocity of raisin particles in the sieving zone reaches its peak at 0.22 s of simulation time. Subsequently, the velocity decreases due to collisions with the baffle, deflection chutes, and other materials during the falling process. Upon reaching the lower end of the inclined deflection chute in the visual inspection zone, the velocity begins to increase again, and after passing through the precision removal zone, the particles fall into the receiving bin. According to the velocity analysis, the peak velocities of stems, branches, stones, and plastic fragments occur at 0.17 s, 0.18 s, 0.36 s, and 0.38 s of simulation time, respectively. The velocity trends for all foreign objects follow a pattern of initial increase, followed by gradual decrease until they enter the receiving bin via the sieving channel. Comparative analysis reveals that stems, branches, stones, and plastic fragments exhibit greater velocity fluctuations over time compared with raisins.
# SDN-Based Congestion Control and Bandwidth Allocation Scheme in 5G Networks
## 3 System Design  
In this paper, we propose a congestion control algorithm called BRF based on SDN and utilizing the in-network control mode. The algorithm employs routing feedback to allocate bandwidth for each flow on the router and provide feedback to the data sender. The OpenFlow controller is utilized for gathering network adapter information [13], identifying the bottleneck bandwidth of the network, and deploying OpenSwitch switches at the bottleneck. We deploy OpenSwitch switches in bridge mode, which preserves the existing network topology. The switch runs the Linux kernel with the BRF congestion control algorithm. BRF utilizes two additional bytes in the ACK option field to record feedback bandwidth information for each flow at the bottleneck. This information stores the bandwidth allocated by the bottleneck router and is forwarded after recalculating the checksum. Upon receiving this ACK, the data sender reads and calculates the pacing rate to regulate the data sending rate based on feedback information.  
BRF employs a centralized control mode to uniformly allocate flow bandwidth at the bottleneck, thereby ensuring congestion-free network operation while fully utilizing link bandwidth. Furthermore, we propose a hierarchical bandwidth allocation scheme based on an in-network control mode to equitably distribute bandwidth among each flow and categorize all flows according to their maximum data transmission speed. The hierarchy of each flow is dynamically adjusted to prioritize meeting the bandwidth requirements of low-bandwidth flows. We calculate the average bandwidth of a stream based on the quintuple of network connections (source IP, destination IP, source port, destination port, application type), take this bandwidth as the initial bandwidth value of this type of stream, and then allocate an appropriate bandwidth according to the measurement results so that it has a faster convergence speed. By uniformly distributing bandwidth along bottleneck routes, fairness among different streams is ensured. Simultaneously, by leveraging SDN to acquire network card bandwidth information, the unified allocation of bandwidth at the bottleneck route can effectively mitigate queuing delays when the available bandwidth is fully utilized. The BRF protocol can be directly deployed on the bottleneck route or at its entrance/exit using bridge mode. Figure 1 illustrates the deployment locations of the BRF protocol.
# WIG-Net: Wavelet-Based Defocus Deblurring with IFA and GCN
## 4 Experimental Section
### Ablation Study  
To analyse the impact of each module in our model on the deblurring effect, an ablation study was performed (Table 2). All models in the ablation study were trained under the same conditions. To evaluate the effectiveness of each module, the baseline model and its four variants were compared. For the baseline model, conventional convolutional layers  
Figure 7: The comparative restoration results of the contrastive experimental algorithms on the custom-made dataset’s test set [1, 4, 12, 23, 24].  
and residual blocks were used instead of the key modules designed specifically for our network. In this work, comparison tests were performed on the DPDD dataset [1].  
As shown in Table 2, the PSNR was 23.67 dB without introducing WtT, IFA, and GCN, which indicates that the baseline model alone has a limited effect on the defocus deblurring of images. With the introduction of WtT, IFA, and GCN, the sharpness of the deblurred images was improved to different degrees. The introduction of WtT, IFA, and GCN improved the average PSNR by 1.28 dB, 1.45 dB, and 1.37 dB, respectively, and the average SSIM by 0.06, enabling the network to obtain high-quality reconstructed images.  
Compared to the baseline model (the first and second rows in Table 2), the introduction of WtT significantly enhanced the network's deblurring capabilities. This confirms that incorporating wavelet transform into the code-and-decode structure increases the perceptual field of the network, and the contextual information of the image can effectively be used to produce clearer results at the edges, and the reversibility of wavelet transform avoids the loss of image information. Meanwhile, the image is sparse in the wavelet domain, and the downsampling of the wavelet transform enhances the sparsity of the image features and improves the learning ability of the network. Furthermore, introducing IFA into the baseline model can also improve the deblurring performance, as evident from the first and third rows in Table 2. This validates the advantage of IFA in adaptively handling spatial variations. The GCN module [18] also demonstrates good performance in deblurring, as shown in the first and fourth rows of Table 2. This substantiates the effectiveness and feasibility of the method that restores lost information by associating features across channels through graph convolution. Nevertheless, the introduction of the GCN module increases the network's parameter count, leading to longer training times. Ensuring reasonable training times can impose limitations on the depth of our network.