esa-sceva/satcom-chunk-collection huggingface dataset:
# A Study on Aerodynamic and Structural Design of Fairing Using Glass Fabric Composite Structure
## 3 Aerodynamic Design and Analysis
### Evaluation of Aerodynamic Performance  
The drag coefficients are obtained as Equation (10) below after measuring drag values through a wind tunnel test [13].  
\[C_{D}=\frac{F}{\frac{1}{2}\rho V^{2}A} \tag{10}\]  
Figure 4: 3D modelling result for CFD analysis.  
Figure 5: The result of CFD analysis.  
Figure 6: The aerodynamic design result of the final 3D fairing.  
In this work, since there was no wind tunnel test, [PERSON]'s numbers were found to evaluate drag coefficients of the commercial vehicle equipped with the fairing, and the drag coefficients by the Reynold's number and shape were investigated.  
\[\text{Re}=\frac{\rho VD}{\mu} \tag{11}\]  
The above equation is the equation of [PERSON]'s number. The \(\rho\) is air density. The \(V\) is velocity. The \(D\) is the value of full length, width, and height, which is a characteristic length of commercial vehicles. The \(\mu\) is viscosity coefficients of air, respectively. The value of \(\rho\) is 1.184 kg/m\({}^{3}\), \(V\) is 80 km/h, which is the average speed when commercial vehicles are driving on the highway. The \(\mu\) is 1.85 \(\times\) 10\({}^{-5}\) N\(\cdot\)s/m\({}^{2}\). It could be verified that the characteristic of drag coefficients for commercial vehicles in this study is between a flat plate and a circle. Therefore, the drag coefficient of commercial vehicles is expected to be about 0.5-0.7.  
To do precise performance evaluation for the fairing designed, Fluent, a commercial program, was used to perform flow analysis. Figure 7 shows definition of inlet and outlet for analysis. The scope of analysis was set as the size that the flow separation occurred on commercial vehicles that could be recovered enough, and the commercial vehicle and fairing area was composed with hexa grids by considering boundary layers and the remaining area was made up of tetra grids [14].  
The principal boundary conditions for the flow analysis on the fairing are as shown in Table 4 below. The flow analysis was simulated the same way as a wind tunnel test, the basic air model within Fluent was used for the fluid model, and for the turbulence model, the shear stress transport (SST) transition model, which is based on -\(\omega\) model with high reliability for the transition region from laminar flow to turbulence, was applied [15]. The target drag reduction value of the aerodynamic design is 20%, and by examining the result of the numerical analysis using commercial codes as shown in Table 5, it was verified that the drag coefficients of the commercial vehicle's basic shape with no fairing, the 2D aerodynamic designed fairing, and the 3D aerodynamic designed fairing were 0.523, 0.423, and 0.417, respectively. It could be verified that the drag was reduced by 20.1% when installing the fairing with 3D aerodynamic design, compared to the basic commercial vehicle's shape. Figure 8 shows the pressure distribution of CFD analysis.  
Figure 7: The definition of inlet and outlet for analysis.
## 4 Discussion
### Future Directions of Safety 4.0 and AI Development
#### 4.3.3 Integration of AI with Emerging Technologies  
Combining AI with emerging technologies like Edge Computing, 5G connectivity, and Blockchain can further enhance safety management by improving data processing speeds, ensuring secure data transmission, and maintaining tamper-proof records.  
Addressing AI-Related Risks and Ethical Issues  
AI development must consider potential risks such as algorithmic bias and over-reliance on automated systems. Establishing ethical guidelines and regulatory frameworks will help mitigate these risks and promote responsible AI deployment.  
Enhancing Human-AI Collaboration  
Promoting effective collaboration between humans and AI systems can leverage the strengths of both, enhancing overall system performance and safety outcomes through user-friendly interfaces and intuitive interaction methods.  
Global Standardization and Best Practices  
Developing international standards and best practices for AI integration in safety management will promote consistency and reliability across industries, ensuring that safety measures are universally effective and trustworthy.  
Fostering Interdisciplinary Research and Education  
Encouraging interdisciplinary research that brings together experts in AI, safety management, ethics, and human factors will drive comprehensive solutions. Educational programs that equip professionals with both technical and domain-specific knowledge are essential for the effective implementation of AI in safety management.
# Power Grid Load Forecasting Using a CNN-LSTM Network Based on a Multi-Modal Attention Mechanism
## 3 Methodology
### LSTM Module  
Long Short-Term Memory (LSTM) networks consist of three main gating mechanisms: the forget gate, the input gate, and the output gate [30]. The forget gate determines how much of the previous memory information and the new input information at the current time step should be retained, using the sigmoid function. When the output of this gate is 1, all information is retained, while a value of 0 causes all information to be discarded. This gate design effectively addresses the common vanishing gradient problem in Recurrent Neural Networks (RNNs), enabling the model to better capture long-term dependencies. Through these gating mechanisms, LSTM can implement selective memory between different time steps, retaining important historical information while discarding irrelevant data, thus enhancing the model's ability to handle sequential data. However, LSTM may still lose some key information when processing very long input sequences. To address this issue, CNN is used for preprocessing the input data, filtering out redundant and irrelevant parts and thereby effectively improving the accuracy of load forecasting. Figure 2 shows the basic structure of the LSTM network. The output of LSTM is denoted as \(F\), and the output \(h_{t}\) at time step \(t\) is calculated as follows:  
\[i_{t}=\sigma(W_{i}x_{t}+G_{i}h_{t-1}+b_{i}), \tag{3}\]  
\[f_{t}=\sigma(W_{f}x_{t}+G_{f}h_{t-1}+b_{f}), \tag{4}\]\[\alpha_{t}=\sigma(W_{o}x_{t}+G_{o}h_{t-1}+b_{o}), \tag{5}\]  
\[\tilde{c}_{t}=\tanh(W_{c}x_{t}+G_{c}h_{t-1}+b_{c}), \tag{6}\]  
\[c_{t}=f_{t}\odot c_{t-1}+i_{t}\odot\tilde{c}_{t}, \tag{7}\]  
\[h_{t}=o_{t}\odot\tanh(c_{t}), \tag{8}\]  
\[F_{t}=Kh_{t}, \tag{9}\]  
where \(\sigma\) represents the Sigmoid activation function, tanh represents the hyperbolic tangent activation function, \(f_{t}\) represents the forget gate, \(i_{t}\) represents the input gate, \(o_{t}\) represents the output gate, \(\odot\) represents the element-wise multiplication operation, \(W_{*},G_{*},b_{*},K\) represents the parameters that the network needs to learn, and \(*\in\{i,f,o,c\}\) and \(\tilde{c}_{t}\) represent the candidate states generated by non-linear functions, which are stored in the long-term memory.
# Effect of Grain Size and Incidence Angle on Erosive Wear of Polyurea Coating
## 2 Materials and Methods  
### Materials  
Erosive wear tests were conducted on a two-component, solvent-free polyurea coating obtained by mixing resin and hardener at a volume ratio of 100:100 (weight ratio of 100:112). The 3 mm thick coating was applied to a 3 mm thick S235 steel sheet via air spraying. Selected parameters of the coating are presented in Table 1. The samples for erosive wear tests were in the form of plates with dimensions of 30 mm \(\times\) 10 mm \(\times\) 6 mm and were cut from a coated sheet via water jet cutting to avoid heating the coating (Figure 1).  
In this study, brown fused aluminum oxide was used as erosive grains. Its basic properties are shown in Table 2. Aluminum oxide with five different grain sizes, defined according to the FEPA standard as F70, F90, F120, F150 and F220, was selected for this study (Table 3). In addition, microscopic images of the grains were taken to show their shape (Figure 2).  
\begin{table}
\begin{tabular}{c c c c} \hline
**Shore Hardness** & **Density [g/cm\({}^{3}\)]** & \
\begin{tabular}{c} **Tensile Strength** \\ **[MPa]** \\ \end{tabular} &
\begin{tabular}{c} **Elongation at** \\ **Break [\%]** \\ \end{tabular} \\ \hline
95 ShA & 1.10 & 21.0 & 425 \\ \hline \end{tabular}
\end{table}
Table 1: Selected properties of the polyurea coating.
## 1 Introduction
Existing research rarely deeply integrates the special background of French-speaking African countries with advanced algorithmic models to build a competitiveness evaluation system that can overcome the limitations of traditional methods. For example, how to effectively integrate multi-source heterogeneous data, how to design an indicator system suitable for the characteristics of French-speaking African countries, and how to use algorithms such as machine learning and network analysis to capture the nonlinear, dynamic, and structural characteristics in the evolution of these countries' competitiveness are all issues that urgently need to be explored in depth. In view of this, this study specifically aims to (1) construct a data-driven competitiveness evaluation framework for 26 French-speaking African countries by integrating TOPSIS and adaptive LASSO, addressing the limitations of traditional methods in handling sparse data and multicollinearity; (2) quantify their competitiveness levels using 30 indicators across three dimensions (core, basic, and auxiliary competitiveness); and (3) identify key drivers of competitiveness and propose targeted sustainable development strategies, thereby providing actionable insights for China-Africa cooperation under the Belt and Road Initiative. The specific framework of this study is as follows: 1. Construct an evaluation system containing 30 indicators, covering the three dimensions of core competitiveness, basic competitiveness, and auxiliary competitiveness; 2. Use the TOPSIS method to quantitatively evaluate the competitiveness of 26 African French-speaking countries and reveal the spatial distribution pattern of national competitiveness; 3. With the help of the adaptive LASSO algorithm, screen key influencing factors, identify the mechanism of action of various indicators on competitiveness, and find key indicators that affect competitiveness; 4. Combine the spatial pattern with the results of factor identification to analyze the formation mechanism of competitiveness differences in different regions; 5. Based on key and sub-key factors, propose a coordinated intervention strategy for sustainable development and provide decision-making reference for China-Africa cooperation under the background of the \"Belt and Road Initiative\".
## 4 \"Multistage Anchorage System + CFST\" Combined Support Technology
### Numerical Simulations
#### 4.2.3 Simulation Results  
In order to highlight the effect of the new support scheme, a comparative test was carried out to simulate the deformation of a roadway without support.  
When a roadway is excavated without support, the simulation calculation cannot converge or balance, and the surrounding rock deformation will continue. Figures 18 and 19 show the results of the 5000 step calculation. It can be seen that the deformation of a roadway surrounding rocks is large, with the deformation on the two sides exceeding 70 cm and the deformation on the top and bottom exceeding 60 cm. A wide range of surrounding rock is affected by the roadway excavation and the distribution of the plastic zone is roughly an ellipse, with a long axis radius of 15 m and a short axis radius of 12 m. The direction of the long axis is nearly parallel to the \(Z\) axis, which is closely related to the great horizontal ground stress. The surrounding rocks in the plastic zone all experienced a shear yield state, and the surrounding rocks in the depth range of 2 m from the roadway surface experienced a tensile shear state, which is very unfavorable for the stability of the roadway.  
Figure 17: Numerical model of the initial support.  
Figure 18: The surrounding rock mass’s displacement without the support scheme: (**a**) Horizontal displacement., (**b**) Vertical displacement.  
Figure 20 depicts the simulation results of the surrounding rock mass's horizontal and vertical displacement under the new support scheme.  
It can be seen that the new support method can effectively control the roadway's convergence deformation. Especially in the vertical direction, the vault and floor's maximum deformation is only about 2 and 1.8 cm, respectively, and the scope of the deformation influence is rather small. Meanwhile, a 4 cm cumulative deformation appears on both sides, which is also an acceptable deformation in a deep roadway.  
Figure 21 displays the surrounding rock's plastic zone distribution. Around the roadway, the plastic zone has a uniform distribution, and the maximum influence distance is approximately 2 m on the two sides and 3 m on the vault and floor. Moreover, tension shear failure only took place on the roadway surface and nearly became stable. All of the numerical simulation results suggest the effectiveness of the new support scheme's control effect.  
Figure 19: The surrounding rock’s plastic zone distribution without the support scheme.  
Figure 20: The surrounding rock mass’s displacement under the new support scheme: (**a**) Horizontal displacement, (**b**) Vertical displacement.
## 2 Related Work
### Batch-Normalized 3D-CNN and Mish Activation  
In deep learning, the normalization layer is a very important part of the neural network that contributes to stabilizing the network during training, accelerates convergence, and improves the generalization ability of the model. Among them, normalization operations on cube data can adjust the voxel value to a fixed range, commonly 0 to 1, as shown in Figure 2, the cube data scatter plot.  
Since HSI contains a large amount of spatial and spectral information, we add a BN layer in each 3D convolutional layer to improve model stability and speed up training convergence. There are many normalization methods, such as maximum and minimum value normalization, mean normalization, and standardization. This paper adopts a standardized approach; the results can be expressed as Equation (4), where \(\mu\) represents the mean of the sample data and \(\sigma\) is the standard deviation of the sample data. After  
Figure 1: A comparison graph of standard convolution and depth-wise separable convolution. (**a**) The structure of standard convolution; (**b**) the structure of depth-wise separable convolution.  
Figure 2: Data scatter plot of original data and normalized data. The red circles represent random numbers and blue triangles are corresponding normalized data. (**a**) Original randomly generated data; (**b**) normalized data.  
standardization, the optimal parameters can be obtained more easily, thus achieving the effect of accelerating convergence.  
\[x_{new}=\frac{x-\mu}{\sigma} \tag{4}\]  
The activation function is an important component of the neural network model, which is designed to help the network learn complex patterns in the data and introduce nonlinearity into the neural network architecture. A suitable activation function can speed up the back-propagation and enhance network convergence. We used a non-monotonic function, Mish, as opposed to the conventional ones: \(ReLU(x)=max(0,x)\). The equation for Mish and its differential coefficient is  
\[Mish(x)=x\times tanh(softplus(x))=x\times tanh(ln1+e^{x}) \tag{5}\]  
\[f^{\prime}(x)=\frac{e^{x}\omega}{\delta^{2}} \tag{6}\]  
where \(x\) is the input of the activation, \(\omega=4(x+1)+4e^{x}+e^{3x}+e^{x}(4x+6)\), and \(\delta=2e^{x}+e^{2x}+2\).  
As depicted in Figure 3, the comparison between Mish and ReLU reveals that Mish is unbounded above and bounded below, with a range approximately from \(-0.31\) to infinity, with similar characteristics to ReLU, while the ReLU function is a piecewise linear operator that disregards all negative inputs. Conversely, Mish preserves non-positive inputs as a non-monotonic function, contributing to the stabilization of the gradient flow within the network. If the input is non-positive, neurons using the ReLU activation function cannot be updated like Mish, even though inputs might contain valued information. In addition, the Mish function is a smooth function with enhanced generalization capability to elevate the outcome quality, accelerating the counter-propagation and convergence processes.
## 2 Material and Methods  
### Selected Materials  
Within the framework of a collaboration with the University of Mons in Belgium, two chalks from the Mons Basin were selected for our study. The Oboury Chalk samples were collected in a quarry located in Harmignies, and the Ciply Chalk samples in the La Malogne underground quarry in the same area. Table 1 summarizes the main characteristics of both rocks.  
Obourg Chalk is a white fine-grained microporous chalk mainly composed of coccolith fragments with punctic contact. It has a homogeneous composition (100% calcite) with a very small grain size (around 1 \(\upmu\)m) and pore size (around 0.3 \(\upmu\)m), and a large porosity (about 43%). The microstructural features in Oboury Chalk have been thoroughly described in our previous work [9]. Ciply Chalk has a more heterogeneous composition with mainly calcite but also fluorapatite and other minerals, which gives the rock a brownish color. In Figure 1, typical microstructure images obtained with SEM and CT scan techniques are shown for this chalk. Taking an X-ray CT scan slice of a 16 \(\times\) 18 cm block of Ciply Chalk revealed that the inner structure of the rock is heterogeneous, with the presence of dense material embedded in the rock matrix (Figure 1a). The SEM micrographs in Figure 1b,c show the presence of high-density nodules, with a composition (obtained by DRX analysis) made of oxygen, calcium, carbon, phosphorus, and fluor, a chemical composition close to that of fluorapatite. This has been observed also by [PERSON] and [PERSON] [10], who described such nodules as a brain-like cortex having precipitated on the grains due to the action of cyanobacteria.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Oboury Chalk** & **Ciply Chalk** \\ \hline mineralogical composition & calcite & calcite, fluorapatite \\ \hline grain density (kg/m\({}^{3}\)) & 2720 & 2730 \\ \hline bulk density (kg/m\({}^{3}\)) & 1550 & 1680 \\ \hline average porosity & 0.43 & 0.39 \\ \hline average permeability (m\({}^{2}\)) & \(6.4\cdot 10^{-16}\) & \(4.0\cdot 10^{-14}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Composition and petrophysical properties of the selected rocks.  
As the heterogeneous texture of the Ciply Chalk probably affects its mechanical properties, the studied samples were systematically cored away from the nodule-rich areas using the CT scan density maps as a guide in the coring process. Note that despite the similar porosity (about 39% compared to 43%), the Ciply Chalk has a permeability two orders of magnitude higher than Obourg Chalk (Table 1).
## 1 Introduction
The average speed of vehicles crossing the bridge was 0.47 m/s, and the acquisition frequency was set to 600 Hz and re-acquired at 300 Hz, 200 Hz, and 100 Hz, respectively, so as to obtain the influence of different acquisition frequencies. The outcomes of relocating loads that were detected using various acquisition frequencies exhibited variations, and the precision of identification was assessed based on the strain error observed during midspan reconstruction. The findings indicate that the proposed method effectively detects the dynamic load across various frequency ranges of data acquisition. While it is true that increasing the frequency of acquisition can lead to a slight improvement in reconstruction strain, it also results in a significant increase in recorded data and a longer analysis time, which is not considered desirable. At present, the acquisition frequency of GPS at home and abroad can reach 100 Hz, but it is usually only 10-20 Hz in practical structural engineering, and it is easily affected by environmental factors, such as noise [14]. For the contact LVDT [15] (linear variable differential transducer) or the tension line, the maximum acquisition frequency can reach 100 Hz. In practical engineering, the frequency used in the monitoring system is mainly 1-2 Hz, and the acquisition should also meet the requirements of real-time alarm, data analysis, and application. In summary, in view of the small- and medium-span girder bridges all over the highway traffic network, there is no reasonable collection frequency assessment method, and so the study will be based on the probabilistic integral method of structural reliability and the signal analysis method, and put forward a simpler, applicable, and reasonable collection frequency setting method, in order to provide relevant bases for the optimization of the health monitoring and analysis methods of small- and medium-span girder bridges.
## 4 Comparative Analysis
### Rule Generation and Execution Efficiency  
The number of association rules generated by each algorithm was evaluated across varying support and confidence thresholds. Regardless of the parameters, FP-Growth consistently produced more rules than Apriori. This is attributed to its compact FP-tree structure and ability to avoid redundant candidate generation.  
Figure 1 illustrates the trend in average rule counts under increasing support levels (0.10, 0.15, and 0.20). FP-Growth demonstrated superior scalability and responsiveness to decreasing thresholds, whereas Apriori's rule output dropped significantly due to computational limitations.
# Multiple-Diffraction Subtractive Double Monochromator with High Resolution and Low Stray Light
## 4 Results
### Experiments  
Based on our simulation results, we constructed a physical prototype of an MSDM on an optical bench. The optical components used in this experimental prototype had parameters identical to those defined in the simulation. This allowed us to experimentally validate the design, including its spectral resolution and optical power distribution. The overall dimensions of the constructed MSDM system are 176 mm \(\times\) 168 mm \(\times\) 87 mm. The light source used was an Agilent 81640A tunable laser module. It operates at wavelengths ranging from 1510 nm to 1640 nm. The maximum output power is 4 dBm. The wavelength accuracy (absolute) is \(\pm\)0.015 nm, and the wavelength repeatability (typical) is \(\pm\)0.5 pm.  
Figure 9 shows the experimental results, where Figure 9a,b show the optical power distributions at S\({}_{2}\) and S\({}_{3}\), respectively. At S\({}_{2}\) and S\({}_{3}\), the spectral resolutions were similar, which was 18.8 pm. However, at S\({}_{3}\), as shown in Figure 9b, the OSNR was 69.17 dB, which was approximately twice that at S\({}_{2}\), as shown in Figure 9a. These results help verify that the MSDM is a powerful system for increasing spectral resolution and suppressing stray light for spectroscopic analysis.  
Figure 8: Simulation results of stray light using LightTools: (**a**) irradiance distribution at S\({}_{2}\); (**b**) irradiance distribution at S\({}_{3}\).  
Figure 9: Experimental results: (**a**) optical power distribution at S\({}_{2}\); (**b**) optical power distribution at S\({}_{3}\).