esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this paper, the effects of nanoparticle materials on prebreakdown and breakdown characteristics in nanofluids under positive lightning impulse voltage were studied, and the conclusions are drawn as follows:  
(1) Prebreakdown and breakdown characteristics of nanofluids are considerably affected by three types of nanoparticle materials. In Fe\({}_{3}\)O\({}_{4}\) and TiO\({}_{2}\) nanofluids, positive streamers with more thicker and denser branches present greatly lowered propagation velocity, resulting in much higher  
\begin{table}
\begin{tabular}{c c c} \hline
**Oil Samples** & **Total Trapped Charges (nC)** & **Trap Level (eV)** \\ \hline Pure oil & 7.86 & 0.340 \\ Fe\({}_{3}\)O\({}_{4}\) NF & 13.01 & 0.345 \\ TiO\({}_{2}\) NF & 12.50 & 0.346 \\ Al\({}_{2}\)O\({}_{3}\) NF & 9.01 & 0.346 \\ \hline \end{tabular}
\end{table}
Table 1: Trap characteristics of pure oil and nanofluids.  
Figure 8: Thermally stimulated currents of pure oil and nanofluids.  
breakdown voltage than that in pure oil. Whereas in Al\({}_{2}\)O\({}_{3}\) nanofluid, streamers are similar with that in pure oil, just exhibiting a slight increase of only 7.4% in breakdown voltage.  
(2) Densities of shallow traps in Fe\({}_{3}\)O\({}_{4}\) and TiO\({}_{2}\) nanofluids are significantly increased by the addition of nanoparticles. The rather higher density of shallow traps in nanofluids provides more opportunity to capture the fast electrons and convert into negative charges, inhibiting the propagation of positive streamers and improving the breakdown strength. But for Al\({}_{2}\)O\({}_{3}\) nanofluid, the density of shallow traps is only slightly increased, making a less obvious improvement of breakdown strength.  
The authors would like to thank the National Natural Science Foundation of China for supporting this research under Contract No. 51337003, 51472084, 51477052 and 51607012.  
All authors contributed to this work. [PERSON], [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON] analyzed the data; [PERSON], [PERSON] and [PERSON] contributed reagents, materials and analysis tools; [PERSON] wrote the paper.  
The authors declare no conflict of interest.
# Analytical Model for the Structural Behavior of Pipelines During Lowering-In
## 3 Validation of Segmental Pipeline Models
### Comparison of the Analytical and FE Analysis Results  
The analysis results for the vertical deflections, bending moments and shear forces of Model 1 are shown in Figure 8. Compared to the results of FE model, the deflections show good agreement, where the bending moments and shear forces are only slightly different near the TDP and SSP.  
Figure 9a,b show the structural behavior of Model 2 compared to the FE model results. It should be noted that the analytical model provides better accuracy in the vertical plane than the lateral plane. For the lateral plane, the deformation of the pipeline is almost the same as in the FE analysis. However, the maximum and minimum bending moments and shear forces are somewhat different form the results of the FE analysis. Further, a difference appeared near the TDP and SSP.  
The segmental pipeline model simulates the interaction of the pipeline and the support layer (e.g. soil or skid) by applying vertical springs and shear layers. In the lateral plane, the TDP and SSP of the segmental pipeline model were simplified to pin-supported conditions and the pipeline  
Figure 8: Comparison of Model 1 to the FE analysis results during lowering-in: (**a**) deflection; (**b**) bending moment; (**c**) shear force.  
Figure 9: Comparison of Model 2 to FE analysis results during lowering-in: (**a**) vertical plane (x-y plane); (**b**) lateral plane (x-z plane).  
supported by the skid and contacted with the soil was assumed to be a rigid beam. In addition, the effects of the soil and skid on the pipeline behavior in the lateral plane were assumed to be negligible. In the segmental pipeline model, the axial force of the pipeline was assumed to be constant and the deformation corresponding to the axial force was ignored. In contrast, for the FE analysis the interaction between the pipeline and the support layer was rigorously implemented by considering the material properties in addition to the normal and tangential behaviors of the contact surface. In particular, the TDP and SSP of the FE model were affected by the interaction between the trench and the pipeline in the lateral plane. Therefore, it seems that simple modeling of the interactions between the pipeline-trench and simplified boundary conditions of the segmental pipeline model lead to some difference, compared to the results of the FE analysis.  
However, for the sectional force in the pipeline, the absolute value of the differences was very small compared to the overall values. Moreover, during lowering-in, the vertical displacement of the pipeline should be carefully controlled and investigated for structural safety. This means that the vertical plane behavior of the segmental pipeline model is more important and the model performance is dependent on the accuracy of the vertical plane results.
## 2 Materials and Methods
### Classification of Referable Diabetic Retinopathy
#### 2.1.1 Dataset  
We used the Kaggle 2015 dataset with 35,126 images (17,563 eyes) for training and 53,576 images (26,788 eyes) for testing. The datasets were labeled on a categorical scale from 0 to 4 representing the severity of DR in order of no-DR, mild, moderate, severe, or proliferative DR according to ICDRDSS [30]. The number of images for each grade amounts to 65,343 for no-DR, 6205 for mild, 13,153 for moderate, 2087 for severe, and 1914 for proliferative DR. After screening ungradable images, the entire dataset was randomly split into development set (80%) and test set (20%). Then, the development set was randomly split into 90% and 10% for training and validation. Referring to non-referable DR and referable DR as \(y=0\) and \(y=1\), respectively, the training/validation sets contained\(N_{0}=60,652\) non-referable and \(N_{1}=2845\) referable DR. To address the aforementioned noisy label issue prevalent in practice, we corrupted the training set by randomly flipping \(pN_{1}\) referable DR to non-referable and vice versa with probabilities \(p\in\{0,0.2,0.4,0.6,0.8\}\).
# Assessing the Role of Program Suspend Operation in 3D NAND Flash Based Solid State Drives
## 5 SSD Simulations to Understand the Role of Program Suspend
### SSD Bandwidth and Latency  
First, we extracted the achieved SSD's bandwidth measured in Input/Output operations per second (IOPS). This is a required measurement unit since we are dealing with a random workload. The results of Figure 10a show that the introduction of the program suspend operation increased the sustainable bandwidth irrelevant from the amount of write operations submitted to the drive (i.e., independent from the write/read ratio of the workload).  
The higher the number of allowed suspends is, the higher the achieved bandwidth since we are allowing more points in the program operation where the operation can be paused to serve preempting read operations. The biggest advantage in using the suspensions was perceived at high QD values (equal to 64 in our study) and with a  
Figure 10: (**a**) The SSD bandwidth sustained. (**b**) The average total latency of the drive. Both metrics are given as a function of the workload and QD.  
workload mostly dominated by the read operations. The achieved bandwidth increase was 113 kIOPS. Such behavior is ascribed to the fact that, in read-intensive workloads, there is a larger pool of preemptible operations and, therefore, enabling program suspension straightforwardly to allocate more servicing time.  
The program suspension also provides, as expected from the theoretical standpoint, benefits in terms of the latency experienced by the drive. In Figure 9(b), we report the SSD average latency calculated among all the read and write transactions involving the drive. Similarly, as observed earlier, the larger the amount of the allowable suspensions is, the more the experienced latency decreases as well as the performance benefit. For the QD = 64 case, in which the workload is read intensive, there is a latency decrease of 97 us when up to 10 suspensions are allowed.
# Prediction of Diabetic Nephropathy from the Relationship between Fatigue, Sleep and Quality of Life
## 4 Experiment Results
### Feature Selection Analysis  
Here, principal component analysis (PCA) [44; 45], the genetic algorithm (GA) [33; 34], and the C4.5 decision tree [46; 47] are used as the filter, wrapper, and embedded methods, respectively. In related literature, these three feature selection methods have been widely used as representatives of the three types of feature selection methods [48; 49; 50].  
For the collected dataset containing 182 features, using different feature selection methods could result in different numbers of features being selected. To evaluate the representativeness of the selected features from different feature selection methods, the predication accuracy of the support vector machine (SVM) [51] was examined. That is, the selected features that allow the SVM to output the highest rate of prediction accuracy in distinguishing patients with and without neuropathy can be regarded as the most representative features.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & **Fatigue Score** & **ESS Score** & **PSQI Score** & **SF-12 Score** & **HbA1C** & **CCI** \\ \cline{2-7}  & \(\mathbf{r}\) (\(\mathbf{p}\)) & \(\mathbf{r}\) (\(\mathbf{p}\)) & \(\mathbf{r}\) (\(\mathbf{p}\)) & \(\mathbf{r}\) (\(\mathbf{p}\)) & \(\mathbf{r}\) (\(\mathbf{p}\)) \\ \hline Fatigue score & 1.000 & & & & & \\ ESS score & 0.192 (0.001 **) & 1.000 & & & & \\ PSQI score & 0.395 (0.000 **) & 0.060 (0.292) & 1.000 & & & \\ SF-12 score & \(-\)0.605 (0.000 **) & \(-\)0.299 (0.000 **) & \(-\)0.342 (0.000 ***) & 1.000 & & \\ HbA1c & 0.029 0.612 & \(-\)0.012 (0.840) & 0.012 (0.839) & 0.026 (0.647) & 1.000 & \\ CCI & 0.221 (0.000 ***) & 0.021 (0.714) & 0.067 (0.245) & \(-\)0.200 (0.000 ***) & 0.068 (0.238) & 1.000 \\ \hline \end{tabular} \({}^{*}\)\(p<0.05\), **\(p<0.001\), ***\(p<0.0001\).  
\end{table}
Table 2: Correlation between fatigue, ESS, PSQI, SF-12, HbA1C, and comorbidities (\(n=307\)).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & \multicolumn{3}{c}{**Univariate**} & \multicolumn{3}{c}{**Multivariate \#**} \\ \cline{2-7}  & **OR** & **95\% CI** & \(\mathbf{p}\) & **aOR** & **95\% CI** & \(\mathbf{p}\) \\ \hline Fatigue score & 1.033 & (1.004–1.062) & 0.023 \({}^{*}\) & 0.979 & (0.917–1.045) & 0.524 \\ ESS score & 1.004 & (0.955–1.056) & 0.866 & 1.010 & (0.916–1.112) & 0.847 \\ PSQI score & 1.006 & (0.945–1.072) & 0.842 & 0.898 & (0.782–1.031) & 0.126 \\ SF-12 score & 0.983 & (0.966–1.000) & 0.056 & 0.995 & (0.952–1.041) & 0.837 \\ HbA1c & 1.200 & (0.970–1.484) & 0.094 & 1.776 & (1.186–2.660) & 0.005 \({}^{**}\) \\ CCI & 7.779 & (4.749–12.742) & 0.000 *** & 9.777 & (5.435–17.591) & 0.000 *** \\ \hline \end{tabular} \({}^{*}\)Multivariate analysis has adjusted for fatigue, ESS, PSQI, SF-12, HbA1c, and CCI, \({}^{*}\)\(p<0.05\), **\(p<0.001\), ***\(p<0.001\).  
\({}^{***}\)\(p<0.0001\).  
\end{table}
Table 3: Logistic regression analysis of nephropathy risk factors with fatigue, ESS, PSQI, SF-12, HbA1c, and CCI included as continuous variables.
# A Relay Optimization Method for NOMA-Based Power Line Communication Systems
## 5 Conclusions  
In this study, a multi-user PLC system integrating NOMA, OFDM, and relay technologies, along with deep learning models and the improved simulated annealing algorithm, was investigated to optimize relay positions and power allocation. Detailed system models were established, and LSTM models were used to predict future CSI, while the improved simulated annealing algorithm was applied to optimize power distribution. Extensive simulations demonstrated the algorithm's effectiveness, with a significant reduction in total power consumption observed, as signal source power dropped from 100 W to 44.65 W, and user power distribution optimized between 25 W and 37 W. Despite some prediction errors, the LSTM model effectively predicted CSI, with MAE at 0.0835, MSE at 0.0110, and RMSE at 0.1050, indicating reliable data support for system optimization. The improved simulated annealing algorithm also displayed strong adaptability, optimizing power allocation and relay selection under constraints such as system capacity, bit error rate, and SINR, with final SINR values exceeding the 10 dB threshold, ensuring system performance. Future research could explore alternative optimization algorithms, along with adjustments to deep learning parameters and larger datasets, to further improve prediction accuracy and system performance in dynamic channel conditions, thereby enhancing the reliability of PLC systems in real-world applications.  
Conceptualization, L.Z.; methodology, H.R. and Y.Y; software, L.Z.; validation, H.R. and L.Z.; resources, D.L.; data curation, D.L. and L.Z.; writing--original draft preparation, P.L.; writing--review and editing, Y.Y. and P.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Natural Science Foundation of Beijing Municipality, grant number 4204093.  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflicts of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Threat Type** & **Impact** & **Potential Risk** & **Method Vulnerability Analysis** \\ \hline \multirow{3}{*}{Data tampering} & Malicious nodes modify & & LSTM relies on historical CSI, \\  & CSI feedback or power & BER rise & and the prediction error increases \\  & allocation instructions & & after it is tampered with \\ \hline \multirow{3}{*}{Eavesdropping attack} & Eavesdropping SIC decoding process between NOMA users & User privacy leakage, capacity decline & Disclosure of power distribution factor leads to the risk of \\  & Injection of high-frequency pulse noise destroys OFDM symbols & BER rise & Power distribution failure under interference \\ \hline \multirow{3}{*}{Replay attack} & Repeating old CSI data disrupts optimization & Optimization cycle delay & The time dependence of LSTM leads to cumulative \\  & & & prediction bias \\ \hline \hline \end{tabular}
\end{table}
Table 7: Core security threats in PLC-IoT systems.
# YOLO-SWD--An Improved Ship Recognition Algorithm for Feature Occlusion Scenarios
## 1 Introduction
For instance, models optimized for cloud occlusion often underperform in near-shore occlusion scenarios. Due to the diverse morphological features of occluded targets, effectively extracting residual features and accurately identifying their categories remains a challenge. Therefore, building on existing research, this paper explores efficient detection and recognition methods for occluded ship targets. By improving and optimizing general object detection algorithms, we aim to enhance model robustness in complex scenarios and improve accuracy for occluded ship targets. The main contributions of this paper are as follows:  
* A new ship detection and recognition dataset is constructed. This dataset integrates existing international ship satellite imagery and newly annotated data, covering six categories and scenarios such as cloud interference, dense ships, and port docks, providing a data foundation for ship-related research. * A novel robust and general-purpose ship detection and recognition model is designed to address the challenge of large differences in occluded ship features across scenarios. The model is validated in cloud-interfered, dense ship, and port dock scenarios, demonstrating stronger robustness and higher accuracy. * For feature occlusion and mutual interference, we first design a new StripPooling-based convolutional block in the backbone network to enhance perception of target boundaries and shapes during convolution. Dynamic deformable convolution and large-receptive-field attention mechanisms are introduced to strengthen the perception of local details and global context. Finally, wavelet convolution is employed to enhance local detail feature perception, improving feature extraction capabilities in complex scenarios. The remainder of this paper is organized as follows: Section 2 describes the sources and construction process of the new dataset. Section 3 details the selection of the new model and the principles of each module. Section 4 elaborates on the experimental process and results. Section 5 discusses the experiments and Section 6 summarizes the limitations of this study and future research directions.
# Neural Network SNR Prediction for Improved Spectral Efficiency in Land Mobile Satellite Networks
## 3 A Novel Channel State Prediction and MODCOD Selection Strategy
### SNR Prediction Algorithms
The initial evaluation of the proposed algorithms (OI, LR, SVM, NN2) was performed using the mean square error (MSE) between the predictions \(\hat{y}\) and labels \(y\) (with n samples):  
Figure 1: The architecture of the implemented neural networks. \[MSE=\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}. \tag{7}\]  
The training of the algorithms was performed on the first 75% of the signal and the evaluation was performed on the last 25% of the generated SNR signal. The inputs to the algorithm were estimated SNR values, while the labels were the true SNR values, as generated by the simulation. On each set (both training and test), input-output pairs were created by sliding a window of 10 samples for creating inputs and taking the next consecutive sample as the label that should be predicted.
## 2 Materials and Methods
### Data Processing  
The raw EEG from the amplifier data was preprocessed offline using EEGLAB (version 14.1.1) [29] and ERPLAB (version 6.1.4) [30] running on MATLAB 2021b (The MathWorks, Inc, Natick, MA, USA.). The raw data were first bandpass-filtered at (0.1 Hz-10 Hz) using a 2 nd order IIR Butterworth. A high-pass cutoff of 0.1 Hz was used to remove the DC drift which corrupts these low-frequency signals, and a low-pass cutoff of 10 Hz removed colored noise components from the signal. This provided a noise-free clean signal to use for signal detection.
# Study of Data Transfer in a Heterogeneous LoRa-Satellite Network for the Internet of Remote Things
## 5 Experimental Results
As we expected, there was no significant difference in the two scenarios, as packaging only affected the total size of each container, which had a small influence on the network delay. However, measuring the Iridium latency on its own was not our primary aim. The most important characteristic in our experimental setup was the full processing time of each message from the moment it arrived from the LoRa network (Figure 7a). Here, the effect of the packaging algorithm can be seen much more clearly. Without packaging applied, new messages had to wait for the previous ones to be processed by the Iridium network. A container packaging algorithm enabled a steady network operation already at the rate of 1 LoRa message in 4 s.  
The second key characteristic was the maximal queue size formed of incoming LoRa messages (Figure 7b). It was quite obvious that when the satellite network was not able to process all incoming messages in time, the queue size grew infinitely unless messages were discarded. This directly correlates with the previous result related to the average message processing time. The queue size went down to 0 as soon as each incoming message was immediately processed by the Iridium modem. Figure 5: SBD message transmission through the Iridium network: (**a**) raw delay data; (**b**) success rate as a function of maximal application delay. A packaging algorithm can be evaluated by the container utilization rate. In our work, we studied the case of a fixed container size, which on average accumulated three incoming LoRa messages. As shown in Figure 8, packaging was extremely important for high-load network scenarios, with the container utilization rate reaching above 90%. When the rate of incoming messages fell below 0.1 messages/second, all containers accumulated no more than a single LoRa frame; hence, the utilization rate stabilized at roughly 33%, which matches simulation results (Table 2, row 2). Figure 6: Distribution of delivery time through for the frequency of 1 message per second. Figure 7: Satellite network performance as a function of inter-arrival time of LoRa messages: (**a**) average message processing time (s); (**b**) maximal queue size.
## 2 Literature Review
### Time Scale in Wind Speed Prediction  
One of the important subjects in wind speed prediction is the time scale requirement of forecast horizons; since the different application of wind speed prediction requires different types of time scale, the classification of the time scale of forecast horizons in wind speed prediction methods is an ambiguous subject [3]. Turbine control often necessitatesa response time of seconds or fractions of seconds, whereas grid integration production planning and market response require longer time horizons. The time scale of prediction also differs according to the energy markets; the real-time energy market requires the response in minutes, whereas the day-ahead energy market requires the prediction up to 24 h ahead as it requires information for energy trading for the next day [5,6]; also, there may be a requirement of different time scale forecast horizons in between these two time scales. For example, economic load dispatching and load increment/decrement decisions require a time scale of 30 min to 6 h ahead [3]. In this study, we focus on short-term wind speed prediction (a few hours ahead prediction, not exceeding 12 h).
## 4 Challenges and Future Research Areas
### Antenna Diversity  
Antenna diversity is another research field that requires further study. The application of spatial diversity at ground stations is not sufficient to counterbalance the potential shadowing events when UAVs maneuvers. The placement of multiple antennas and their orientation onboard a UAV is one solution to this problem. However, it is challenging to optimally place these antennas in order to minimize the shadowing effects, as well as the air-frame and motor noise during the flight. This is quite cumbersome and probably vehicle-specific. Moreover, customized antennas should be selected and studied in terms of their mechanical feasibility onboard UAVs. These should be compact, lightweight, low-profile, and able to preserve specific aerodynamic standards (e.g., fin-shark-type and patch antennas are more appropriate for mounting on wingtips). The number of mounted antennas necessary to optimize the antenna diversity depends on the operating frequency (e.g., millimeter waves are more appealing for compact antennas), the UAV volume, and the mission environment. Multiple antennas may be favorable, as they enhance coverage and provide sufficient diversity gain; however, there is a significant need for greater computation, space, and power supply.