esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Analysis of Influence Factors
### Influence of Anchor Cable Parameters
#### 5.3.2 Influence of Tilt Angle  
It can be seen from Figure 26 that, when the tilt angle of the anchor cable increases from 10\({}^{\circ}\) to 30\({}^{\circ}\), both the horizontal displacement of the pile and the ground settlement increase obviously. When the tilt angle is 25\({}^{\circ}\) and 30\({}^{\circ}\), the horizontal displacement of the pile top exceeds the deformation control scope, and the supporting effect are poor. When the tilt angle is 10\({}^{\circ}\)-20\({}^{\circ}\), the deformation of pile and ground settlement are well restrained, and the maximum horizontal displacement of the pile and ground settlement have little difference at each tilt angle. In order to give full play to the role of anchor cable, it is suggested that the tilt angle of the anchor cable should be between 10\({}^{\circ}\) and 20\({}^{\circ}\).  
Figure 26: Influence curves of different tilt angle: (**a**) Horizontal displacement of piles, (**b**) Ground settlement.  
Figure 25: Influence curves of different prestress: (**a**) Horizontal displacement of piles,(**b**) Ground settlement.  
Comparing the ground settlement and the horizontal displacement of the pile under various parameters in Figures 21-26, some deformation laws can be found:  
1. With the increase of distance, both the ground settlement and the horizontal displacement of the pile increase first and start to decrease after reaching the maximum value at a certain position;
2. The maximum values of both the ground settlement and the displacement of the pile occur at the position of 6-8 m of the ground or the pile;
3. The maximum horizontal displacement of the pile is 15-30 mm, while the maximum settlement of the ground is 7.5-15 mm. The length of significant deformation occurring in the pile is approximately 19 m and the extent of significant ground settlement is approximately 40 m wide.  
In the field monitoring section, the maximum horizontal displacement of the pile is twice the maximum settlement of the ground, while the length of the pile deformation is half the width of the settlement occurring in the ground. According to this law, the surface settlement and pile deformation of similar foundation excavations can be predicted in advance to ensure construction safety.
# Study on Gearbox Fault Warning Based on the Improved M-IALO-GRU Model
## 2 Wind Turbine Parameter Description and Feature Selection
### Selection of Feature Parameters
#### 2.2.2 Pearson Correlation Coefficient  
However, during the MISO model-training process, the more input parameters, the longer the training time, which may lead to the overfitting phenomenon of the model. Therefore, in order to simplify the dimension, it becomes very important to extract the parameters that affect the oil temperature change in the gearbox. Therefore, in Refs. [27; 28; 29], the Pearson correlation coefficient method is used to reduce the input dimension and select the operating parameters with strong correlation. The correlation coefficient is expressed as follows:  
\[\mathrm{r}=\frac{\sum_{i=1}^{n}(x_{i}-\overline{x}_{i})(y_{i}-\overline{y}_{i })}{\sqrt{\sum_{i=1}^{n}(x_{i}-\overline{x}_{i})^{2}}\sqrt{\sum_{i=1}^{n}(y_{i }-\overline{y}_{i})^{2}}} \tag{3}\]  
where r is the Pearson correlation coefficient, \(x_{i}\) and \(y_{i}\) are the mean of the sample, and n is the number of sample values.  
Characteristic selection of preprocessed wind turbine SCADA operating data used Pearson's correlation coefficient method (results are shown in Table 1). Parameters with an absolute value of the correlation coefficient greater than 0.7 are selected as the input of the model. The selected variables include 17 parameters, such as ambient wind speed, ambient temperature, gearbox bearing temperature, active power, generator speed, reactive power, impeller speed, and generator bearing temperature.
## 3 Results
### Statistical Analysis  
The results of the statistical analysis are presented in Figure 1. The samples were clearly grouped into three distinct clusters based on their measured values. Principal Component Analysis (PCA) showed that PC2 and PC3 accounted for 23.29% and 12.65% of the total variance, respectively. The primary factors driving this separation were differences in the volatile compound profiles--specifically, the absence of 2-methylfuran, 3-methylbutanal, and 2-methylbutanal in conventional coffee, and the absence of 2-ethyl-5-methylpyrazine and 3-ethyl-2-hydroxy-2-cyclopenten-1-one in elephant coffee. Additional key distinguishing markers included significantly higher levels of pyridine in both elephant and civet coffee, elevated 2-ethyl-5-methylpyrazine in civet coffee, and higher concentrations of 1-acetoxy-propan-2-one in both civet and conventional coffee. Conventional coffee also contained significantly greater amounts of (+)-limonene, furfural, and 5-methylfurfural. Other compounds, such as pyrazine, 3-ethyl-2,5-dimethylpyrazine, furfuryl acetate, furfuryl alcohol, and methylpyrazine, also contributed to sample differentiation--elephant coffee appeared to be particularly rich in these. Additionally, phenolic acid content played a role: civet coffee was richer in chlorogenic acid, while conventional coffee had higher levels of caffeic and cinnamic acids. For more detailed data, see Supplementary Figures S2-S5.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Compound** & **RI (Measured) 1** & **RI (Literature) 2** & **Content (\% RPA)** & & \\  & & &
\begin{tabular}{l} **Conventional** \\ **Coffee** \\ \end{tabular} & **Civet Coffee** & **Elephant** \\ \hline
1-acetoxy-propan-2-one & 872 & 876 & 4.86 \(\pm\) 0.35 & 5.12 \(\pm\) 1.16 & 3.57 \(\pm\) 0.38 \\
2-methylbutanoic acid & 878 & 886.2 & 0.32 \(\pm\) 0.05 & 0.36 \(\pm\) 0.12 & 0.20 \(\pm\) 0.03 \\ furfuryl formate, & 909 & 902 & 1.63 \(\pm\) 0.39 & 1.52 \(\pm\) 0.45 & 1.09 \(\pm\) 0.17 \\
5-methylfurfural & 968 & 969 & 21.21 \(\pm\) 2.37 & 14.43 \(\pm\) 2.23 & 15.77 \(\pm\) 2.25 \\ furfuryl acetate & 996 & 998 & 14.46 \(\pm\) 0.00 & 20.96 \(\pm\) 0.56 & 18.00 \(\pm\) 0.03 \\
2-ethyl-5-methylpyrazine & 1003 & 1004 & 2.69 \(\pm\) 0.39  & n.d. \\ \(N\)-acetyl-4\(H\)-pyridine & 1024 & 1038 & 1.28 \(\pm\) 0.13 & 0.86 \(\pm\) 0.13 & 1.13 \(\pm\) 0.25 \\ (+)-limonene & 1033 & 1035 & 4.53 \(\pm\) 0.63  & 0.41 \(\pm\) 0.06  & 0.82 \(\pm\) 0.21  \\
2,3-dimethyl-2-cyclopenten-1-one & 1044 & 1052 & 0.95 \(\pm\) 0.16 & 0.81 \(\pm\) 0.14 & 0.67 \(\pm\) 0.21 \\
1-(1\(H\)-pyrrol-2-yl)-ethananone & 1067 & 1063 & 0.37 \(\pm\) 0.02 & 0.44 \(\pm\) 0.08 & 0.46 \(\pm\) 0.06 \\
3-ethyl-2,5-dimethylpyrazine & 1081 & 1083 & 1.94 \(\pm\) 0.21 & 2.16 \(\pm\) 0.38 & 3.73 \(\pm\) 0.63 \\
3-ethyl-2-hydroxy-2-cyclopenten-1-one & 1098 & 1082 & 0.48 \(\pm\) 0.03  & 0.73 \(\pm\) 0.02  & n.d. \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._
## 2 LDPC Codes  
The channel coding technique involves extending the original bit information by introducing parity bits and recovering the original information at the receiving end based on the correlation between the bit information. LDPC codes are linear block codes, the continuous binary sequence of symbols output from the source is called a message bit and the redundant binary sequence output after encoding according to certain rules is called a code word. In \((n,k)\) linear packet codes, the message bits are often divided into multiple groups of message packets of length \(k\) and sequentially encoded as code words of length \(n\). A message code of length \(k\) can represent a total of \(2^{k}\) types of messages. The message is mapped one-to-one with the codeword. Its code rate \(R=\frac{k}{n}\) denotes the average number of message bits carried by a single code bit. Benefiting from the low-density feature of their check matrix, LDPC codes with iterative decoding based on belief propagation can obtain good error correction.
## 2 Related Literature Review
### Deep Learning Models  
Deep learning architectures, such as long short-term memory (LSTM), bidirectional long short-term memory (BiLSTM), and Bidirectional Gated Recurrent Unit (BiGRU) networks, have demonstrated efficacy in the field of word sense disambiguation (WSD). These models adeptly capture sequential information and address challenges such as the loss of word order, rendering them especially beneficial in low-resourced languages, where conventional methods fall short due to insufficient data.
# Enabling Horizontal Collaboration in Logistics Through Secure Multi-Party Computation
## 4 Toward an MPC-Based Solution for the CFDP
### Implementation and Evaluation
#### 4.4.3 Quality of the Solution  
In order to validate the effectiveness of our approach, we re-used a problem instance with a comparable regime of parameters as the one used in the \"plaintext\" heuristic approach of [3], compute a solution and its associated objective value (cf. Equation (1)), and compare it to the objective value of the plaintext approach. Given that the MPC approach securely implements a heuristic that is quite similar to the plaintext approach, we do not expect any significant difference between the results of the two, and therefore only focus on a specific regime of parameters for validation purposes.  
In particular, in a similar spirit as Figure 1, we consider a setting with a total of 500 orders and compute the mean objective value for varying number of trucks (ranging from 1 to 5) and maximum order volume (10, 20, or 32 units). We obtain a mean value of 0.1162, very close to the 0.1126 of [3], and almost _halving_ the empty runs compared to the non-collaborative approach, thereby proving that we can achieve the same improvement as [PERSON] et al. [3] without requiring plaintext access.
# Customized Convolutional Neural Networks Technology for Machined Product Inspection
###### Abstract  
Metal workpieces are an indispensable and important part of the manufacturing industry. Surface flaws not only affect the appearance, but also affect the efficiency of the workpiece and reduce the safety of the product. Therefore, the appearance of the product needs to be inspected to determine if there are surface defects, such as scratches, dirt, chipped objects, etc., after production is completed. The traditional manual comparison inspection method is not only time-consuming and labor-intensive, but human error is also unavoidable when inspecting thousands or tens of thousands of products. Therefore, Automated Optical Inspection (AOI) is often used today. The traditional AOI algorithm does not fully meet the subtle detection requirements and needs to import a Convolutional Neural Network (CNN), but the common deep residual networks are too large, such as ResNet-101, ResNet-152, DarkNet-19, and DarkNet-53. Therefore, this research proposes an improved customized convolutional neural network. We used a self-built convolutional neural network model to detect the defects on the metal's surface. Grad-CAM was used to display the result of the last layer of convolution as the basis for judging whether it was OK or NG. The self-designed CNN network architecture could be customized and adjusted without using a large network model. The customized network model designed in this study was compared with LeNet, VGG-19, ResNet-34, DarkNet-19, and DarkNet-53 after training five times each. The experimental results show that the self-built customized deep learning model avoiding the use of pooling and fully connected layers can effectively improve the recognition rate of defective samples and unqualified samples, and reduce the training cost. Our custom-designed models have great advantages over other models. The results of this paper contribute to the development of new diagnostic technologies for smart manufacturing.  
metal workpieces; custom-designed models; smart manufacturing Article
# Remote Sensing Road Extraction by Road Segmentation Network
## 2 Materials and Methods
### The Proposed Method  
The proposed method is introduced in this section. First, the network architecture is illustrated. Then, a self-attention mechanism is exploited to enhance the inter-layer context features. Finally, a refinement network is designed to refine the road map.
## 3 Controller Design
### PSO-Fuzzy PID Controller  
PSO was selected for its simplicity, fast convergence, and suitability for real-time implementation in industrial environments, efficiently tuning controller parameters without high computational overhead. While Fuzzy PID enables real-time adjustment of PID control parameters, its reliance on fixed fuzzy rules limits its ability to enhance optimal control. To enhance system stability and robustness, this paper employs PSO to adaptively optimize fuzzy control settings, resulting in a fuzzy PID coordination control approach.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Error Rate** & \multicolumn{6}{c}{**Error (t)**} \\ \hline \(\Delta\)**et(t)** & **NB** & **NM** & **NS** & **ZE** & **PS** & **PM** & **PB** \\ \hline
**NB** & NB & NB & NM & NM & NS & NS & ZE \\ \hline
**NM** & NB & NM & NM & NS & NS & ZE & PS \\ \hline
**NS** & NB & NM & NS & NS & ZE & PS & PS \\ \hline
**ZE** & NM & NS & NS & ZE & PS & PS & PM \\ \hline
**PS** & NS & NS & ZE & PS & PS & PM & PM \\ \hline
**PM** & NS & ZE & PS & PS & PM & PM & PB \\ \hline
**PB** & ZE & PS & PS & PM & PM & PB & PB \\ \hline \hline \end{tabular}
\end{table}
Table 2: Fuzzy controller design linguistic rules.  
Figure 15: The surface viewer of the fuzzy logic.  
Through the optimization process, PSO determines the best parameters for the Fuzzy PID controller, ensuring an optimal control signal to the plant.
# Evaluation of Radio Access Protocols for V2X in 6G Scenario-Based Models
## 4 Analysis of Performance Results
### Theoretical Mathematical Model Results
#### 4.1.2 L and DR for LTE-V2X and NR-V2X Protocols
This is because the NR-V2X protocol uses advanced signal processing techniques, such as low-density parity-check (LDPC) coding and spatial diversity, to improve reliability and the data rate. These techniques also help to reduce latency by minimizing the need for retransmissions. In NR-V2X, a more flexible and adaptable subcarrier structure is introduced, allowing for more efficient and dynamic resource allocation to support a wide variety of use cases, including V2X communications. In order to expand the variety of data, different configurations were considered, varying parameters such as MCS options, the payload size, the SCS, and the BW. Therefore, it was possible to establish a criterion to identify the most efficient protocol.
## 3 Results and Discussion
### Extended Sugarcane Wine Washing  
In the next experiment, the soil mostly contained the following dioxin compounds: 1,2,3,4,6,7,8-HpCDF; 1,2,3,4,6,7,8-HpCDD; OCDF; and OCDD (Figure 3). We also conducted a control washing experiment with DI water to clarify the removal efficiency of the wine solvent (Figure 4). The initial total TEQ value of the contaminated soil was 8902.33 ng-TEQ/kg, which was mainly occupied by OCDD (3853.12 ng-TEQ/kg) and 1,2,3,4,6,7,8-HpCDF (2519.65 ng-TEQ/kg). The removal efficiency of sugarcane wine washing was clearly positive, finishing at a total of 78.7% removal, which was almost three times higher than the removal efficiency of the control washing experiment with DI water (28.08%) (H value \(>\chi_{\text{U}}^{2}\)). The washed soil of the sugarcane wine washing experiment was also mostly occupied by OCDD (571.36 ng-TEQ/kg) and 1,2,3,4,6,7,8-HpCDF (805.44 ng-TEQ/kg). The removal efficiencies of the sugarcane wine washing experiment for each congener involved were 1,2,3,4,6,7,8-HpCDF (72.0%), 1,2,3,4,6,7,8-HpCDD (75.5%), OCDF (75.1%), and OCDD (85.2%). The removal efficiencies of the control washing experiment were 1,2,3,4,6,7,8-HpCDF (35.67%), 1,2,3,4,6,7,8-HpCDD (46.15%), OCDF (33.94%), and OCDD (15.55%). It seems that different from other studies [6; 26; 27], our washing experiment with sugarcane wine as a solvent seemed to result in almost no distinct selective removal of dioxin congeners as the removal percentages of different congeners were not much different, which again indicates the non-selectivity washing potential of sugarcane wine. Although the highest and lowest removal percentages were 85.2% (OCDD) and 72.0% (1,2,3,4,6,7,8-HpCDF), it should be noted that we conducted our washing experiment on contaminated field soil, of which the characteristics may greatly vary and considerably affect the removal efficiency [3; 6]. Therefore, our results of sugarcane washing seemed to display more nonselective removal.  
Figure 3: Changes in dioxin toxic equivalent quantities (ng-TEQ/kg dry wt.) and final removal percentages in a soil six-cycle washing experiment using sugarcane wine as the major solvent (HpCDD: Hepta-chlorinated dibenzo-p-dioxins).
# Cascaded Nonlinear-Optical Loop Mirror-Based All-Optical PAM Regenerator
## 2 Regeneration Characteristics of a Single NOLM-Based Regenerator
### Normalized Power Transfer Function of Single NOLM
Furthermore, we normalize the nonlinear power transferring relation by using the normalized input power \(x=\left|E_{in}\right|^{2}/P_{0}\), \(\overline{x}=\overline{\left|E_{in}\right|^{2}}/P_{0}\) and the normalized output power \(y=p_{out}=\left|E_{out}\right|^{2}/(G_{0}P_{0})\):  
\[y=y(x)=x\left[1+\eta\sin^{2}(\kappa_{0}x-2\kappa_{0}\overline{x})\right] \tag{6}\]  
where \(\kappa_{0}=\frac{1}{2}\gamma P_{0}L_{eff}(2\rho-1)\) and \(P_{0}\) is the step power of the proposed NOLM regenerator. Clearly, \(\overline{x}\) only shifts the normalized PTF (NPTF) curve through the input average power, which can be changed by choosing the duty cycle of input PAM signals. Therefore, we can simplify the NPTF when \(2\kappa_{0}\overline{x}=2\pi m(m=1,2,3\ldots)\):  
\[y=y(x)=x\left[1+\eta\sin^{2}(\kappa_{0}x)\right] \tag{7}\]  
Considering the periodicity of \(\sin^{2}(\kappa_{0}x)\), we take \(\kappa_{0}=\pi\), i.e., \(P_{0}=2\pi/\left[\gamma L_{eff}(2\rho-1)\right]\). Figure 2a,b show the calculated NPTF curves with \(\rho=0.97\) (\(P_{0}=0.564W\)) and \(\rho=0.92\) (\(P_{0}=0.632W\)). The other parameters used here were as follows: \(L=2\) km, \(\alpha=0.7\) dB/km, and \(\gamma=11\)/W/km. It can be seen that the NPTF curves oscillate around the line of \(y=x\), as predicted from Equation (7). The oscillating strength is proportional to the normalized input power \(x\) and the parameter \(\eta\). We obtain the oscillation period from Equation (7), i.e., \(\Delta x=\pi/\kappa_{0}=1\). Therefore, the step power of the multi-level NOLM regenerator is:  
\[\Delta P_{in}=\Delta x\cdot P_{0}=\frac{2\pi}{\gamma L_{eff}(2\rho-1)} \tag{8}\]  
To achieve the best regenerative performance, the power spacing of input PAM signals should be the same as the \(\Delta P_{in}\). Figure 2: Normalized PTF curves for (**a**) \(\rho=0.97\) and (**b**) \(\rho=0.92\).
## 2 Wake Effect and Lifetime Estimation in DFIG Wind Farm
### Katic Wake Model  
In wind farms, the wind turbine causes a wind speed deficit to its downstream area due to the wake effect. Many wake models have been developed to estimate the downstream wind speed deficit [17; 18]. The Katic wake model [29], which extends the previous work of [PERSON] [30], is one of the most widely used wake models. In this paper, the Katic wake model is adopted to estimate the wind speed for the downstream wind turbines.  
In Figure 3a, the Katic wake model, also known as a 'top hat' profile, represents the energy content of a downstream wind turbine with a constant wind speed. The wind speed deficit \(1-v_{2}/u\) of the downstream wind turbine is given by [29]:  
\[1-\frac{v_{2}}{u}=\left(1-\sqrt{1-C_{t}(\beta,\lambda)}\right)\left(\frac{D}{ D+2 kX}\right)^{2}\frac{A_{overlap}}{A_{R}} \tag{3}\]  
where \(X\) is the distance between the two wind turbines, \(u\) is the ambient wind speed, \(v_{2}\) is the wind speed of the downstream wind turbine, \(D\) is the blade diameter, \(A_{R}\) is the blade sweep area, \(A_{overlap}\) is the overlap area between the blade sweep area and the wake area, \(C_{t}\) is the thrust coefficient, which depends on the pitch angle and the tip speed ratio, and the decay constant \(k\) is 0.075 for onshore  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Rated Power & 5 MW \\ Rotor Diameter & 126 m \\ Cut-in, Rated, Cut-out Wind Speed & 3 m/s, 11.4 m/s, 25 m/s \\ Min. and Max. Rotor Speed & 6.9 rpm, 12.1 rpm \\ Gearbox Ratio & 97:1 \\ Number of Pole-pairs & 3 \\ Synchronous Frequency & 50 Hz \\ Electrical Generator Efficiency & 94.4\% \\ \hline \end{tabular}
\end{table}
Table 1: Parameters of NREL 5 MW wind turbine [28].  
Figure 2: NREL DFIG 5 MW wind turbine with: (**a**) power coefficient in respect to pitch angle and tip speed ratio; (**b**) active power, pitch angel, tip speed ratio, and rotor speed in respect to the wind speed with Maximum Power Point Tracking (MPPT) control.  
wind farms and 0.04-0.05 for offshore wind farms, as recommended by the Wind Atlas Analysis and Application Program-WAsP help facility [31]. The thrust coefficient for the NREL 5 MW wind turbine in terms of the pitch angle and the tip speed ratio is shown in Figure 3b [28]. It can be observed that the thrust coefficient can be reduced by changing the pitch angle and tip speed ratio compared with the MPPT method, where the wind turbine operates at the pitch angle of 0\({}^{\circ}\) and the tip speed ratio of 7.55. According to (3), the reduction of the thrust coefficient of an upstream wind turbine leads to the increase of the wind speed at the position of downstream wind turbine.