esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Overview of Near-Infrared Reflectance Spectroscopy (NIRS)
### Constraints in the Application of NIRS  
One of the limitations of NIR is that the prediction accuracy relies on the quality of the laboratory reference results [42]. The initial difficulty of developing a robust and accurate model is the major constraint of the NIRS technique; once the calibration models are achieved, the NIRS offers a rapid, cost-effective alternative for screening a large population with the ease of use [43]. Another disadvantage of NIR spectroscopy is its low sensitivity for predicting mineral composition because there is no mineral absorption in the near-infrared region unless it is combined with other detection systems such as X-ray fluorescence spectroscopy and UV light [44]. Even though NIR spectroscopy predicts significant physical and biochemical traits in food products with reasonable speed, simplicity, and accuracy, its measurement focuses only on a relatively small portion of the sample to produce average composition values [45; 46; 47]. It could not, however, provide some basic information, such as the spatial distribution of the quality parameters, which is a disadvantage when the samples are heterogeneous [45]. However, using NIRS-hyperspectral imaging systems, spatial and spectral information could be acquired concurrently. NIR-hyperspectral imaging (NIR-HSI) captures hundreds of contiguous wavelength bands in the NIR region for each pixel. When near-infrared spectroscopy is combined with hyperspectral imaging (NIR-HSI), it provides exceptional capability, broadening its application and use across various industries.
## 3 System Model of HAP Drones and LEO Satellite Collaborative Networks
### Overall Delay Analysis
#### 3.3.4 Processing on LEO Satellites  
In addition HAP drones can also forward tasks to connected LEO satellites. After the task is forwarded to LEO, it can be processed on the server carried by LEO. The overall delay of the task processed on the LEO satellite can be formulated as follows:  
\[t_{j}^{LEO}=\sum_{n\in\mathcal{N}}\alpha_{j}^{n}(\frac{(y_{j}+z_{j}+w_{j})D_ {j}}{R_{j}^{n}}+\sum_{m\in\mathcal{M}}\zeta_{n,m}(\frac{w_{j}D_{j}}{R_{n}^{m} }+\frac{w_{j}D_{j}c_{j}}{f_{j}^{m}})), \tag{10}\]  
where \(\zeta_{n,m}\in\{0,1\}\) denotes whether the HAP drone \(n\) is connected to the LEO satellite \(m\). If the HAP drone \(n\) is connected to the LEO satellite \(m\), \(\zeta_{n,m}=1\); otherwise, \(\zeta_{n,m}=0\). \(f_{j}^{m}\) is the computing frequency assigned by the LEO satellite \(m\) to the terrestrial IoT device \(j\).  
Therefore, the total delay of task \(j\) can be formulated as follows:  
\[t_{j}=\max\{t_{j}^{l},t_{j}^{HAP,dirc},t_{j}^{HAP,fd},t_{j}^{LEO}\}. \tag{11}\]  
For all IoT devices, the total delay can be calculated as follows:  
\[t_{sum}=\sum_{j\in\mathcal{J}}t_{j}. \tag{12}\]
## 2 Theory Background
### The Fundamental of SVD Denoising Method
The averaging value of the anti-diagonals element \(\mathrm{x}_{i}\) of the matrix \(\mathbf{A_{m}}^{\prime}\) is calculated, which is regarded as the denoised signal for the original vibration signal [5], as shown in Equation (7):  
\[\mathrm{x}_{i}=\frac{1}{\beta-\alpha+1}\mathrm{\sum}_{j=\alpha}^{\beta} \mathbf{A}_{i-j+1,j} \tag{7}\]  
For traditional SVD denoising methods such as the median value of singular values, the mean value of singular values, and DSSV based SVD denoising, the mostergic singular components are reserved. However, for the vibration signal of the rolling elements bearing, the incipient impulse fault feature is often submerged by the noise and interference harmonics, and its energy may be very weak. As a result, the traditional energy based evaluation method of SVD denoising could be invalid for extracting the informative impulse fault feature of a rolling bearing. It is important to find a new method for evaluating the fault information of the singular components.
## 3 Overview of Charging Stations
### Types of Chargers  
1. Type 1 AC Charger: This is the most basic EV charger, which is used by some entry-level electric vehicles. It may charge a vehicle slowly using an AC outlet or a home charging system. It has a charging power of up to 220 volts and a maximum current of 16 amps. It can handle up to 3 kW of single-phase input electricity. When using these types of chargers, the vehicle must convert AC electricity to DC, which is a time-consuming procedure.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  
Type 2 Charger: The Type 2 Charger can charge at a quicker rate and works with both AC and DC charging methods. These chargers are designed to work with three-phase power systems. In European charging stations, it's fairly frequent. With a 400 volt AC supply, it can handle input power ranging from 7.4 kW to 43 kW. These chargers are also commonly installed in EV owners' houses for faster charging periods because they are compatible with vehicles that use CCS connections.
3. CCS or Combined Charging System: With new-generation electric vehicles, a combination charging system plug, often known as a CCS type plug (or CCS Type 2), is becoming more widespread. These charging systems are capable of offering DC fast-charging for cars from commercial charging stations as well as standard charging from home charging stations. For DC rapid charging, the plug contains two additional contact points. Input power for most DC fast chargers is 50 kW, however, this type of socket can handle charge power of up to 350 kW.
4. CHAdeMo Charger: It was developed by Nissan, Tokyo Electric Power Company (TEPCO), Mitsubishi, Subaru, and Toyota and was first deployed in Japan [34]. This was one of the first fast-charging systems to be created, and it is now used in over 70 nations across the world. It can handle up to 50 kW of DC fast charging [35]. Newer automobiles, on the other hand, are converting to the CCS system since it is more versatile.
5. GB/T Charger Under the Bharat DC 001 standard, the Indian Government suggested the GB/T type charger for EVs. These chargers, which were erected by the government's Energy Efficiency Services Limited (EESL), are capable of DC fast-charging with a 10-15 kW output for low-power EVs. On the other hand, this sort of connector can handle capacities of up to 230 kW.
## 3 Electrocardiogram  
One of the most commonly used tools for clinical evaluation of the heart is an electrocardiogram (ECG) due to its low-cost, simplicity and risk-free operation [110,111]. It records the electrophysiological events in the heart, which is an indication of the electrical signal that is generated in the atria and ventricles through the process of depolarization and repolarization [111]. The P wave, the QRS complex and the T wave are the three main components of the ECG signal representing one cardiac cycle during a heartbeat [112]. Each waveform contains its interval and amplitude characterized by peaks and duration; this provides clinically useful information for cardiac arrhythmia detection [113,114]. The analysis of these waves is also critically useful for the detection of commonly known breathing disorders such as obstructive sleep apnea syndrome, as well as for studying the autonomic regulatory process of the cardiovascular system during sleep and/or hypertension [115].  
In a computer-based ECG analysis, the accuracy of detecting QRS complexes is a necessary requirement. This task, however, is a difficult one due to interference with other internal noises, such as muscular and motion artefacts, noises and baseline drift changes [116]. Differentiating other ECG component waves (i.e., P and T waves) from the QRS complex is another factor that increases the complexity of QRS detection [116]. Moreover, false detection of these component waves may cause incorrect results in a computer-based ECG analysis. Several QRS detection algorithms have been developed by researchers in the last decades and have been applied in different application domains,  
\begin{table}
\begin{tabular}{c c c c} \hline
**S/No** & **Author** & **Year** & **Objective** & **Methodology** \\ \hline
94 & [PERSON] [107] & 2022 & To predict impaired Heart Rate Recovery based on resting ECG waveform patterns & Deep learning with CNN \\ \hline
95 & [PERSON], [PERSON] [108] & 2022 & To predict the presence of both LV and RV disease in a large large and ethnically diverse population. & Fiducial and contextual details. Deep learning with 2-dimensional CNN \\ \hline
96 & [PERSON] [109] & 2022 & To develop a deep learning-enabled ECG interpretation model for automatically identify patients with Brugada syndrome (a rare variant of arrhythmia) at an early point in time & Fiducial and Non-fiducial. Multilayer deep learning model based on transfer learning. \\ \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 2: Percentage distribution of the studies per year.  
such as a heart rate variability (HRV) analysis, arrhythmia classification and heart rate estimation [117].
# Active Sonar Target Classification Method Based on Fisher's Dictionary Learning
## 4 Method Performance Verification Based on Measured Data
### Comparative Analysis of the Classification Results  
In order to compare the classification performance of FDDL with those of SVM, SRC, D-KSVD, and LC-KSVD, we conducted experiments with signal-to-mix ratios of \(-5\) dB, \(-3\) dB, \(0\) dB, \(3\) dB, and \(5\) dB. The results are shown in Figure 9 and Table 1.  
Figure 9 illustrates a comparison trend line graph of the recognition rate of different methods in different reverberation environments. Table 1 compares the specific classification recognition rates of the different methods in different reverberation environments. SVM has a low recognition rate for similar targets in a reverberant environment, as shown in Figure 9. This is because it is mainly suitable for two-class recognition; however, there are difficulties in multi-classification problems. Thus, we conclude that SVM is not suitable for underwater multi-target classification and recognition based on a reverberation environment.  
Compared with the traditional classification method, SRC exhibits a relatively high overall recognition rate owing to the accurate extraction of essential information regarding the signal and the adaptive learning of the redundant dictionary. Therefore, this method is more suitable for multi-target classification and the recognition of similar features in a more complex reverberation environment. In the sparse classification algorithm, SRC directly classifies the training signal with reverberation as a redundant dictionary, and considerable  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**SRR** & **SVM** & **SRC** & **D-KSVD** & **LC-KSVD** & **FDDL** \\ /**dB** & **/\%** & **/\%** & **/\%** & **/\%** & **/\%** \\ \hline \(-5\) & 21.5 & 59.5 & 75.5 & 87 & 94 \\ \(-3\) & 23 & 70.5 & 78 & 90 & 95.5 \\ \(0\) & 25.5 & 73.5 & 80.5 & 92.5 & 96 \\ \(3\) & 26 & 74.5 & 83.5 & 93.5 & 96.5 \\ \(5\) & 33 & 76 & 88.5 & 94.5 & 97 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison table of the methods under different reverberations.  
Figure 9: Contrast line chart with different reverberation methods.  
noise information is added to the dictionary, which leads to a poor classification recognition rate. The Fisher dictionary learning classification algorithm has a stronger recognition ability owing to the strong restriction of the Fisher criterion on the sparse coefficient; thus, the classification recognition rate under each signal-to-noise ratio is higher, and it has a better classification effect. The above experiments verify that FDDL has a high classification accuracy rate under various signal-mix ratios and still exhibits a suitable classification effect in underwater conditions with low signal-mix ratios; moreover, it exhibits suitable classification performance and anti-reverberation performance.
## 3 Results
### Machine Learning Model for Regression Results  
The performance of the models analyzed by AutoML is evaluated in terms of the R\({}^{2}\) for correlation and mean absolute error (MAE) for model errors versus experimental data. The results obtained for each model for melt pool depth and width prediction are listed in Tables 7 and 8 with respect to the depth and width, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Sample**} & \multirow{2}{*}{**Scanning Speed (mm/s)**} & \multirow{2}{*}{**Laser Power (W)**} & \multicolumn{2}{c}{**Depth (\(\mu\)m)**} & \multicolumn{2}{c}{**Width (\(\mu\)m)**} \\ \cline{3-8}  & & & **Avg.** & **SD** & **Avg.** & **SD** \\ \hline
3.4 & 1400 & 310 & 23.0 & 9.5 & 96.3 & 13.6 \\ \hline
3.5 & 1550 & 310 & 9.7 & 7.6 & 57.7 & 49.4 \\ \hline
3.6 & 950 & 344 & 1
# Research on Passive Ranging Technology of Moving Ship Based on Vertical Hydrophone Array
## 1 Introduction
In the middle of the 20 th century, this technology was introduced into the underwater acoustic field and was mainly used for underwater detection, target recognition and underwater localization. [PERSON] et al. [16] fused the data from bow and side arrays to improve the detection performance with a low signal-to-noise ratio (SNR). [PERSON] et al. [17] applied the fusing data from several image domains to realize underwater localization. They also combined deep neural networks with adaptive Monte Carlo localization technology. Reference [18] discussed the target localization problem in underwater sensor networks and designed a deterministic weight for consensus fusion in order to mitigate the malicious measurements. The experiment results after four sensor fusions validated that the fusion of measurements was significant due to smaller localization errors. [PERSON] et al. [19] adopted the Modified Gain Bearings only Extended Kalman filter (MGBEKF) algorithm to judge whether there were one or two targets. This method made use of various sensors such as the structure-mounted array and towed array to perform the state vector fusion. In this paper, we propose a range localization method of a moving target based on a vertical hydrophone line array. A simulation and sea experiment of a 16-element vertical hydrophone array in the deep sea were conducted. Both results indicate that this algorithm can estimate the range and achieve more accurate results. This paper is organized as follows. In Section 2, we introduce the basic theory of this ranging technology. A simulation is performed to evaluate the performance of the proposed algorithm. Section 4 discusses the sea trial results. Section 5 gives a summary.
# Enhancing Propaganda Detection in Arabic News Context Through Multi-Task Learning
## 5 Results and Discussion
### Ablation Experiments on the Proposed MTL Models  
Several ablation experiments were conducted to prove the main components' effectiveness in MTL and the auxiliary task. The experiments involved cumulatively addi components to obtain the proposed solution. The experiments started by testing the propaganda detection task alone, as in our previous work [22], which will be discussed in Section 5.3. Then, we trained the base MTL model with each auxiliary task separately--sentiment detection (MTL-Sent) and emotion detection (MTL-Emo)--followed by training the model with both auxiliary tasks combined.  
As Table 8 summarizes, the MTL models, comprising several configurations, demonstrated consistent performance within a narrow range, with accuracy scores between 77% and 79% and Macro-F1 scores between 0.763 and 0.778. Among these, the MTL model employing LS (MTL-LS) achieved the highest accuracy of 79% and a Macro-F1 score of 0.775, whereas MTL-UAW yielded 77% accuracy and a Macro-F1 score of 0.763. Regarding the incorporation of sentiment and emotion tasks into propaganda detection models, the MTL-Sent-LS (PSMTL-LS) and MTL-Emo-UAW (PEMTL-UAW) models achieved 79% accuracy. Their Macro-F1 scores were 0.778 for the MTL-Sent-LS and 0.775 for MTL-Emo-UAW. In contrast, MTL-Sent-UAW and MTL-Emo-LS exhibited slightly lower performances, with accuracy scores of 78% and Macro-F1 scores of 0.770 and 0.773, respectively. Overall, these results suggest that the inclusion of auxiliary tasks, such as sentiment and emotion (e.g., MTL-Sent-LS and MTL-Emo-UAW), can significantly enhance the main task's performance.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Actual/Predicted** & **Propaganda (True)** & **Nonpropaganda (False)** \\ \hline Propaganda (True) & True Positive (TP) & False Negative (FN) \\ Nonpropaganda (False) & False Positive (FP) & True Negative (TN) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Confusion matrix.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model Categories** & **Model Name** & **Accuracy** & **Macro F1** & **Micro F1** \\ \hline \multirow{4}{*}{MTL Models} & MTL-LS & 79\% & 0.775 & 0.790 \\ \cline{2-4}  & MTL- UAW & 77\% & 0.763 & 0.777 \\ \cline{2-4}  & MTL-Sent-LS & 79\% & 0.778 & 0.792 \\ \cline{2-4}  & MTL-Sent- UAW & 78\% & 0.770 & 0.784 \\ \cline{2-4}  & MTL-Emo- LS & 78\% & 0.773 & 0.789 \\ \cline{2-4}  & MTL-Emo- UAW & 79\% & 0.775 & 0.791 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Ablation experiments on the proposed MTL folds.
# Research on 3D Geological Modeling Method Based on Deep Neural Networks for Drilling Data
## 5 Experiments and Results  
### Experiment Parameters  
#### 5.1.1 Neural Network Structure  
Geological entities are primarily shaped by their spatial coordinates, while their characteristics, such as geological age, geological genesis, and geotechnical type, collectively determine their types. Although constructing neural networks for training and prediction using multi-label classification tasks can accelerate model training, using the 3D vector representations of geological age, geological genesis, and geotechnical type features as sample labels may limit the accuracy of neural network representation due to their linear independence. To overcome this limitation, this paper employs three separate submodules to represent geological age, geological genesis, and geotechnical type features individually. These submodules are designed to capture the unique characteristics of each attribute. Subsequently, the outputs of these submodules are integrated into a unified output to prevent information loss due to data dimensionality reduction and mitigate the issue of the model underfitting high-dimensional data. The network structure, depicted in Figure 1, involves dividing the input data into three separate samples based on distinct attribute labels. These samples are then input into three independent deep neural networks. DNN-1 predicts stratigraphic epochs, DNN-2 focuses on geological genesis prediction, and DNN-3 is responsible for geotechnical category prediction. Each of these three independent networks has specific conditions, including the number of hidden layers and hyperparameters, which were fine-tuned based on their respective training and testing results.
# Priority Queues with Fractional Service for Tiered Delay QoS
## 5 Simulation  
In Section 3, we used diffusion approximations to reach analytical results describing the probability of overflow associated with each class. OPNET simulations were used to validate these results. We note that extensive studies have been done [10; 17] on the diffusion approximation with various arrival time and service time distributions. For simplicity, our simulation assumed relative small buffer sizes and exponential distributions for both packet sizes and inter-arrival times. Note that our analytical results are applicable to any buffer sizes with any stationary inter-arrival time and service time distributions. In particular, we consider a five-buffer system with five traffic classes. For \(m=1\), we set \(\lambda_{i}=\lambda\), \(0\leqslant i\leqslant 4\), and vary \(\lambda\) to study the loading effect on each buffer. The assumption that every buffer, except Buffer-0, always has traffic to be promoted, does not hold in the simulations but is expected to be sufficient under heavy traffic loads. Figure 9 compares our analytical results with the simulation results: they track each other very closely in terms of the probabilities of overflow. These probabilities of overflow do not exhibit any particular trend across different classes. The approximation appears to be slightly less accurate on higher-priority classes; this may be because the effective load of the aggregate lower buffers \(L_{i}\) increases with \(i\) and the fact that our analytical result is based on heavy traffic assumptions. Therefore, for small is, our result yields a conservative approximation to the actual probabilities of overflow; the actual probabilities of overflow are expected to be slightly lower than our approximations.
# Data-Driven Voltage Control in Isolated AC Microgrids Subject to Sensor Saturation
## 4 Simulation Results
### DAILC Under Sensor Saturation and Topology Variations  
In this case, we implemented the DAILC method on the test AC microgrid that was subject to both sensor saturation and topological changes. The specific topology changes are shown in Figure 6. The adaptability of microgrids to topological changes prominently demonstrates their plug-and-play characteristics. This feature not only enhances the flexibility and economic efficiency of the grid but also facilitates the broader integration of renewable energy sources and the implementation of grid automation. In the face of sudden increases in load or instability in energy output, the plug-and-play microgrid can quickly adjust its energy configuration, effectively ensuring the stability of the microgrid operation. As illustrated in Figure 7, the saturation threshold for the sensors was set to 315 V, and the DG4 was removed at 1 s. Observations from Figure 7 reveal that during the transient process of the microgrid, the peak output voltage was constrained by the saturation limit of 315 V. After applying the DAILC method, despite the topological changes, the output voltage quickly returned to the preset value of 311 V, and the final error converged to zero. These results demonstrate the effectiveness and practicality of the DAILC method in responding to topological changes.  
To address the issue of communication network reliability, the results from this case serve to validate the robustness of the DAILC algorithm. Although the formal convergence proof in Section 3.3 assumes a strongly connected and time-invariant topology for mathematical tractability, the simulation results demonstrate that the algorithm possesses strong practical resilience. When a communication link fails, the topology is altered. As long as the remaining network remains connected (i.e., each node can still reach the virtual leader directly or indirectly), the local neighborhood error signals can continue to drive the system towards consensus, thereby ensuring voltage stability. Regarding communication delays, they have not been explicitly modeled in this paper. While the iterative learning nature of DAILC, which corrects errors in each cycle, may offer some inherent robustness to small, constant delays, a rigorous analysis of the impact of significant or time-varying delays remains a key topic for our future research.  
Figure 5: Under varying load conditions with sensor saturation considered. (**a**) Output voltage amplitude. (**b**) Output voltage error.  
Figure 6: Change in communication link.