esa-sceva/satcom-chunk-collection huggingface dataset:
# Analytical Solutions of Water Inflow for Foundation Pit in Confined Water Stratum
## 2 Water Inflow Solutions of Circular and Strip Foundation Pits
### The Hydraulic Head at the Maximum Drawdown Position of the Aquifter \(H_{w}\)  
Under the condition of ignoring the thickness of the water-proof curtain, the reduction of hydraulic head is greater than the outer side of the curtain when the water flows through the inner side of the curtain. Based on the existing experimental and field monitoring data [19; 20; 21; 22], the reduction of the hydraulic head when the water flows through the inner side of the curtain accounts for about \(2/3\) of the total reduction in the unconfined aquifer. However, the direction and characteristics are the same whether the water flows around the water-proof curtain in the confined water stratum or the unconfined water stratum. The conclusion applied to the confined water case can be expressed as:  
\[h_{0}=H_{d}+\frac{2}{3}(H_{w}-H_{d}) \tag{13}\]  
Based on the principle of seepage continuity and combining Equations (7), (12), and (13), the hydraulic head at the maximum drawdown position of the aquifer \(H_{w}\) for the circular foundation pits can be expressed as:  
\[H_{w}=\frac{H_{d}\bigg{[}k_{r}(3M-2L)+\bigg{(}\frac{6\ell_{0}^{2}k_{r}\ln \frac{r_{0}}{R+r_{0}}}{\alpha(2H_{d}+r_{0}-M+L)}\bigg{)}\bigg{]}-9k_{r}MH}{ \frac{6\ell_{0}^{2}k_{r}\ln\frac{r_{0}}{R+r_{0}}}{\alpha(2H_{d}+r_{0}-M+L)}-2k _{r}(3M+L)} \tag{14}\]  
To substitute Equations (13) and (14) into Equation (12), the water inflow in the foundation pit can be obtained.
# Lem-Detector: An Efficient Detector for Photovoltaic Panel Defect Detection
###### Abstract  
Photovoltaic panel defect detection presents significant challenges due to the wide range of defect scales, diverse defect types, and severe background interference, often leading to a high rate of false positives and missed detections. To address these challenges, this paper proposes the LEM-Detector, an efficient end-to-end photovoltaic panel defect detector based on the transformer architecture. To address the low detection accuracy for Crack and Star crack defects and the imbalanced dataset, a novel data augmentation method, the Linear Feature Augmentation (LFA) module, specifically designed for linear features, is introduced. LFA effectively improves model training performance and robustness. Furthermore, the Efficient Feature Enhancement Module (EFEM) is presented to enhance the receptive field, suppress redundant information, and emphasize meaningful features. To handle defects of varying scales, complementary semantic information from different feature layers is leveraged for enhanced feature fusion. A Multi-Scale Multi-Feature Pyramid Network (MMFPN) is employed to selectively aggregate boundary and category information, thereby improving the accuracy of multi-scale target recognition. Experimental results on a large-scale photovoltaic panel dataset demonstrate that the LEM-Detector achieves a detection accuracy of 94.7% for multi-scale defects, outperforming several state-of-the-art methods. This approach effectively addresses the challenges of photovoltaic panel defect detection, paving the way for more reliable and accurate defect identification systems. This research will contribute to the automatic detection of surface defects in industrial production, ultimately enhancing production efficiency.  
photovoltaic panel; multi-scale defect detection; data augmentation; feature fusion; transformer
# A Short-Term Wind Speed Forecasting Model Based on EMD/CEEMD and ARIMA-SVM Algorithms
###### Abstract  
In order to ensure the driving safety of vehicles in windy environments, a wind monitoring and warning system is widely used, in which a wind speed prediction algorithm with better stability and sufficient accuracy is one of the key factors to ensure the smooth operation of the system. In this paper, a novel short-term wind speed forecasting model, combining complementary ensemble empirical mode decomposition (CEEMD), auto-regressive integrated moving average (ARIMA), and support vector machine (SVM) technology, is proposed. Firstly, EMD and CEEMD are used to decompose the measured wind speed sequence into a finite number of intrinsic mode functions (IMFs) and a decomposed residual. Each of the IMF subseries has better linear characteristics. The ARIMA algorithm is adopted to predict each of the subseries. Then, a new subseries is reconstructed using the sum of the predicted errors of all subseries. The high nonlinear features of the reconstructed error subseries are modeled using SVM, which is suitable to process nonlinear data. Finally, the superposition of all prediction results is performed to obtain the final predicted wind speed. To verify the stability and accuracy of the model, two typhoon datasets, measured from the south coast of China, are used to test the proposed methods. The results show that the proposed hybrid model has a better predictive ability than single models and other combined models. The root mean squared errors (RMSEs) of the hybrid model for the three wind speed datasets are 0.839, 0.529, and 0.377, respectively. The combination of CEEMD with ARIMA contributes most of the prediction performance to the hybrid model. It is feasible to apply the hybrid model to wind speed prediction.  
wind speed prediction; empirical model decomposition; autoregressive integrated moving average; support vector machine; hybrid model +
Footnote †: journal: Applied Sciences
# A Computational Platform for Automatic Signal Processing for Bender Element Sensors +
## 4 Validation Using the International Parallel Test Benchmarks
### Shear Modulus Extraction
#### 4.2.1 Time Domain Interpretation Techniques  
The input and output signals obtained in the bender element test are presented with blue and orange lines in Figure 13. The input signal is a single sine pulse with a frequency of 4 kHz. The output signal presents the interpretation challenges that typify bender element experiments. For instance, the first significant signal occurs around 0.65 ms, but is disregarded here because the numerical models suggest it is caused by compression waves, not by shear waves. However, in the absence of this information, it could be used as an arrival indicator, leading to very erroneous results. The input and output signals use different intensity scales to enable the clear visualisation of the latter.  
Three of the four classes of time domain interpretation techniques presented in Section 2.1 are used to obtain the arrival time from the plots presented in Figure 13. The method based on the second arrival is not employed as the second arrival signal is too weak.  
Figure 12: Experimental setup.  
Figure 13: Input (blue) and output (orange) signals, and characteristic points used for the time domain interpretation of the output signal.  
Interpretation Using Characteristic Points  
The three characteristic points are defined in Section 2.1.1 and identified on the output signal in Figure 13. The first three lines of Table 2 show the arrival times and the shear moduli corresponding to each of the characteristic points. The travel distance is defined between the tips of the transmitter and receiver, and the shear modulus is computed according to Expression (1).  
Interpretation Using Peak-to-Peak Distance  
The peak-to-peak distance is measured between the maximum amplitudes of the input and output signals, as seen in Figure 13. The travelling time obtained according to this technique and the corresponding shear modulus are listed in the fourth line of Table 2.  
Cross-Correlation  
The cross-correlation between input and output signals is computed according to Expression (4). The cross-correlation plot, shown in Figure 14, presents a clear peak at 1.037 ms, which corresponds to a shear modulus of 48.71 MPa.
# Enhancing Detection of Arabic Social Spam Using Data Augmentation and Machine Learning
## 1 Introduction
Therefore, this paper proposes applying Data Augmentation for the Arabic Spam Detection (DASD) domain which would lead to significant improvements in overall accuracy. The Embedding Replacement as a data augmentation substitution technique is used in the proposed framework. It replaces a word in the sentence according to its similarities using a particular pre-trained word embedding vectors. The main contributions of this paper are the following:  
* Handling different categories of Arabic spam content such as advertising, spreading malware, adult content, hate speech, and meaningless content. * Creation of a public and available Arabic spam dataset containing the original tweets and the corresponding annotations. * Applying replacement embedding data augmentation technique for Arabic text. * Evaluation of the overall framework by comparing its accuracy to the original (non-augmented) dataset, which leads to improvement of the overall Macro F1 up to \(\sim\)32% and recalls improvement of the spam class of up to \(\sim\)38%. The rest of this paper is organized as follows: Section 2 presents background and related work; Section 3 introduces the proposed framework; experimental results are in Section 4; discussion are in Section 5; conclusions and future work are finally presented in Section 6.
## 4 Modified Pitch Angle Controller for Power Regulation  
The profile of the power reference is generated based on the banks' switches, manually turned ON by the supervisor, or the available power from wind. Therefore, the power must be regulated to track the power reference. In the experimental wind turbine, used in this work, adjustment of the pitch angle of the blades is the only way to regulate the power required for each bank or a combination of banks, and is done by the pitch servo-motor as shown in Figure 1.  
In a basic control strategy, the controlled power is compared with its reference and the error signal is sent to a PI controller to produce the optimum pitch angle  
\[\beta_{opt}=K_{P}(P_{ref}-P_{gen})+K_{I}\left[(P_{ref}-P_{gen})dt\right. \tag{6}\]  
where, \(\beta_{opt}\) is the optimum pitch angle, \(K_{P}\) and \(K_{I}\) are the proportional and integral gains of the controller, respectively.  
Using such a controller, the pitch angle is continuously changing, which can damage the pitch actuator, as it is in high activity. Moreover the pitch actuators have the limitation of influencing the power regulation performance of the wind turbine [20]. In this work, a control strategy is proposed to maintain the pitch angle at a constant during steady state operation in order to reduce the activity of the pitch actuator.  
\begin{table}
\begin{tabular}{c|c} \hline
**Error** & **Bank’s switch** \\ \hline + & ON \\ \hline – & OFF \\ \hline
0 & ON \\ \hline \end{tabular}
\end{table}
Table 2: Switch’s states based on the power error.  
The power management system is developed for step changes in the profile of the power reference based on banks' switching states and the available power. Therefore, the operation of the system is based on the power error compared to the error-tolerance \(\varepsilon\). If the power error is outside the error band \(\pm\)\(\varepsilon\), the operation is considered transient, and when it is inside \(\pm\)\(\varepsilon\), it is considered steady state. Following the behavior of the generated power, two cases of operation can be observed:  
1. A change between two steps, as shown in Figure 4, due to a change in the power demand from the supervisor or the available power from wind.
2. A change in the power while the reference is constant, as shown in Figure 4, due to disturbance, fluctuation and the undesirable behavior of the system.  
The modified pitch angle controller is based on the variation of the power error. If the power error is outside the error band \(\pm\)\(\varepsilon\), a PI controller is applied to have the turbine track the power reference provided by the supervisor. Meanwhile, the value of the optimum pitch angle is continuously saved in a memory, and when it is inside \(\pm\)\(\varepsilon\), the last value of the optimum pitch angle from the PI controller saved in memory, is held constant and used as a reference for the pitch actuator. The proposed pitch angle controller and its flow chart are shown in Figures 5 and 6, respectively.  
Figure 4: Power reference profile and generated power.  
Figure 5: Proposed PI fluctuation pitch angle control strategy.
## 6 Summary and Conclusions  
In this contribution, we explored the use of an array of GNSS antennas for attitude determination and relative positioning for direct geo-referencing. The MC-LAMBDA method exploits the known antenna geometry to improve the reliability of resolving rotating frame ambiguities and, hence, to improve the reliability of the rotating frame attitude determination. Furthermore, the reliable estimation of rotating frame ambiguities enables the strengthening of the estimation of the baseline between the rotating frame and a reference station. Our analysis includes epoch-by-epoch processing, as well as recursive filtering. We demonstrated the improved performance of the proposed method using data from two experiments with a prototype MSS representing a rotating frame. The use of constrained attitude determination and array-aided positioning increases the reliability (in terms of ambiguity resolution) and improves the achievable position accuracy. It enables instantaneous ambiguity resolution, which is immune to cycle slips, and, hence, enables instantaneous mapping. Furthermore, the reliability and accuracy can further be improved by employing more antennas on the rotating frame and at the reference station. With a sufficient number of low-cost GNSS receivers, the potential of instantaneous mobile mapping for low-cost applications will be explored in future studies.  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Rotation**} & \multicolumn{2}{c}{**Epoch-by-Epoch**} & \multicolumn{2}{c}{**Filtering**} \\ \cline{2-5}  & **Previous** & **Proposed** & **Previous** & **Proposed** \\  & **Method** & **Method** & **Method** & **Method** \\ \hline
1 & 6.1 & 3.1 & 3.5 & 1.5 \\
2 & 4.2 & 2.9 & 3.4 & 1.6 \\
3 & 5.5 & 3.2 & 2.9 & 1.5 \\
4 & 5.1 & 3.4 & 3.2 & 1.5 \\
5 & 5.3 & 4.7 & 3.4 & 2.6 \\ \hline \end{tabular}
\end{table}
Table 7: 3D position root mean square (RMSE) (mm) for kinematic data (20-m baseline).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Rotation** & **Epoch-by-Epoch** & **Filtering** \\ \hline
1 & 12.4 & 9.7 \\
2 & 12.3 & 10.4 \\
3 & 15.3 & 14.3 \\
4 & 21.0 & 20.0 \\
5 & 13.1 & 7.8 \\ \hline \end{tabular}
\end{table}
Table 8: 3D position RMSE (mm) for kinematic data (6-km baseline) processing with the proposed method.
# Regular Wave Effects on the Hydrodynamic Performance of Fine-Mesh Nettings in Sampling Nets
## 3 Results
### Hydrodynamic Coefficients of Fine-Mesh Nettings in Waves  
Figure 7 presents the drag coefficients of the fine-mesh nettings as a function of the Reynolds number and \(KC\) number under different wave conditions. The Reynolds number ranged from 16.66 to 55.73, while the \(KC\) number varied between 208.04 and 1230.92. For Net 1, the maximum drag coefficient of 2.52 was observed at a wave period of 2.3 s and a wave height of 0.1 m (\(Re=23.71\); \(KC=647.85\)), while the minimum drag coefficient of 0.3 occurred at a wave period of 1.3 s and a wave height of 0.19 m (\(Re=31.66\); \(KC=489\)). For Net 2, the maximum drag coefficient of 1.74 was recorded at a wave period of 2.3 s and a wave height of 0.1 m (\(Re=29.33\); \(KC=523.68\)), and the minimum drag coefficient of 0.23 was measured at a wave period of 1.3 s and a wave height of 0.19 m (\(Re=39.17\); \(KC=395.28\)). When the wave period was held constant, the drag coefficients for both  
Figure 5: Comparison of wave elevations from numerical simulations, experimental measurements, and theoretical values.  
Figure 8 illustrates the inertial coefficients of the fine-mesh nettings as a function of the Reynolds and _KC_ numbers in different wave conditions. The maximum inertial coefficient for Net 1 was 0.45 at a wave period of 2.3 s and a wave height of 0.1 m, while the minimum was 0.06 at a wave period of 1.3 s and a wave height of 0.19 m. For Net 2, the maximum inertial coefficient of 0.58 occurred at a wave period of 2.3 s and a wave height of 0.1 m, while the minimum value of 0.1 was observed at a wave period of 1.3 s and a wave height of 0.19 m. Similar to the drag coefficients, when the wave period was held constant, the inertial coefficients for both nettings generally decreased with increasing Reynolds and _KC_ numbers. Furthermore, the inertial coefficient of Net 2 was higher than that of Net 1 under the same wave conditions.
## 2 System Establishment and Modeling
### Modeling
#### 2.2.4 Heat Exchanger Model  
The heat transfer from the combustor outlet hot fluid to the cathode inlet cold fluid is performed by the heat exchanger. It is modeled according to the \(\xi\)-NTU method obtained through the following:  
\[\xi=\frac{C_{\textit{hside}}(T_{\textit{hside},in}-T_{\textit{hside},out})}{C _{\textit{min}}(T_{\textit{hside},in}-T_{\textit{cside},in})}=\frac{C_{\textit{ cside}}(T_{\textit{cside},out}-T_{\textit{cside},in})}{C_{\textit{min}}(T_{ \textit{hside},in}-T_{\textit{cside},in})} \tag{52}\]  
where subscripts \"_hside_\" and \"_cside_\" mean hot fluid and cold fluid flow through the heat exchanger. \(C\) is the heat capacity rate and \(C_{\textit{min}}\) the smallest between \(C_{\textit{cside}}\) and \(C_{\textit{hside}}\). The \(\xi\) is the effectiveness of the heat exchanger evaluating how well it facilitates for heat transfer. The pressure loss through the heat exchanger is 4%.  
\[P_{\textit{HX},out}=P_{\textit{HX},in}*(1-0.04) \tag{53}\]  
It is important to acknowledge that, given the unique configuration employed in this study, the heat exchangers utilized are not of a conventional nature. This is due to the requirement of facilitating heat transfer, as depicted in Figure 1, where the Air HX must handle extremely high temperatures exceeding 1700 K emanating from the combustor outlet. As commercially available heat exchangers capable of meeting these specifications are not currently accessible, the proposed solution involves the utilization of specialized heat exchangers constructed from refractory materials. The following options can be considered:  
Ceramic heat exchangers: These heat exchangers are manufactured using high-temperature ceramic materials such as silicon carbide (SiC) or alumina (Al2O3). They possess the ability to withstand temperatures up to 1700-1800 \({}^{\circ}\)C (1973-2073 K) and find applications in areas such as high-temperature gas cooling, chemical processing, and power generation [40].  
Graphite heat exchangers: Graphite, a form of carbon, exhibits exceptional thermal conductivity and resistance to high temperatures. Heat exchangers made from graphite are employed in situations where temperatures can surpass 1700 K [41].  
By considering the use of these specialized heat exchangers, this study aims to address the specific requirements of the heat transfer process in the given configuration. These alternatives offer the necessary properties to withstand the extreme temperatures involved, ensuring the efficient operation of the system.
# Ultrasonic Nondestructive Testing Image Enhancement Model Based on Super-Resolution Imaging
## 2 Theory and Method
### Laser Ultrasound Super-Resolution Network  
Some of the existing super-resolution models consider learning the degenerated features from HR to LR and some consider learning the basic features of LR. However, there is no model that learns both the degraded features of the image and the basic features of the image. We feel that the degraded features of the image are equally important as the basic features. Therefore, in order to make the imaging effect of laser ultrasound images better after super-resolution, the model used in this paper can learn these two features of laser ultrasound images at the same time. The model has two main structures. The first part is multi-layer up and down projection layers. To learn the degradation features from HR to LR, the second part is a multi-layer residual layer to learn the basic features of the image. The structure of laser ultrasound super-resolution network is shown in Figure 4.  
Figure 4: Laser ultrasound super-resolution network.  
Figure 3: Residual block.  
First, our model uses a 3 \(\times\) 3 convolutional layer to extract the required feature depth and then allows the model to learn the degraded features of the image through multiple layers of up and down projection layers. It then fully learns the basic features of the image through multiple layers of residual blocks. Finally, the super-resolution imaging of the laser ultrasound image is completed through the sub-pixel up-sampling layer.  
As mentioned above, residual networks have achieved great success in various tasks. We combine it with up and down projection layers and demonstrate a powerful super-resolution imaging capability of laser-ultrasound images. Figure 5 is a flowchart of the super-resolution method in this paper, and the detailed process is as follows:  
1. The laser ultrasound image data set is acquired through the material under test by laser ultrasound equipment.
2. The data set is divided into two parts according to a certain ratio. The first part is used to train the super-resolution model, which is called the training set, and the other part is called the test set to test the performance of the model.
3. Ultrasound super-resolution model design.
4. Use the training set to train the ultrasound super-resolution model until a satisfactory result is obtained.
5. Input the test set into the trained super-resolution model to realize super-resolution imaging of laser ultrasound.
6. Output super-resolution results.  
In addition, we use a residual scale factor of 0.1, and the final result is compressed to 0-255, which is beneficial to imaging output.
## 2 LSTM for Ship Motion Prediction
### LSTM Theory
Normalization can reduce the impact of the data magnitude differences, accelerate convergence speed, and improve computational efficiency. All data in this study are normalized first, and then LSTM training is conducted. After the prediction, the data  
Figure 1: LSTM structure. are restored by anti-normalization processing. The normalization method is expressed as follows:  
\[x^{*}=\frac{x-x_{\text{min}}}{x_{\text{max}}-x_{\text{min}}} \tag{7}\]  
where \(x^{*}\) is the normalized value of \(x\); \(x_{\text{max}}\) and \(x_{\text{min}}\) represent the maximum and minimum values of \(x\), respectively. The LSTM neural network can realize a multi-feature input, as shown in Figure 2. When training, it is necessary to establish the mapping relationship between the input duration data \(x_{ii}\) and the output duration data \(y_{t}\); where \(i\) represents the number of input feature types. First, the input duration data group is slided to form the input vector group \(X\). The window length \(j\) is the number of input steps. The output vector group \(Y\) is the output data \(y_{i+n}\) corresponding to each window, where \(n\) is the number of advance prediction steps and is used to control the advance prediction duration. The advance prediction duration is \(n\) times the sample data sampling interval. The wave elevation input data are used to predict the ship motion. When \(n=1\), the prediction model predicts the ship motion attitude at the current time. When \(n>1\), the prediction model predicts the ship motion attitude corresponding to the \(n\)-th data moment in the future. The maximum absolute error (MAE) and the root mean square error (RMSE) are used to evaluate the forecast results. The MAE represents the error of the data peak point, while the RMSE represents the overall prediction error.