esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Aeroelastic Model and Analysis Method
### Aerodynamic Model  
[PERSON] [37] and [PERSON]'s [38] piston theory, a simple and practical method, has been applied to many supersonic aeroelastic problems [39]. In this study, [PERSON]'s second-order piston theory [40] considering thickness effects was selected as the aerodynamic model. The pressure coefficients can be expressed as  
\[C_{p}=\frac{2}{M^{2}}\bigg{[}\frac{M}{\beta}\Big{(}\frac{w}{a}\Big{)}+\frac{G }{2\beta}\Big{(}\frac{w}{a}\Big{)}^{2}\bigg{]} \tag{6}\]  
where  
\[\beta=\sqrt{M^{2}-1},G=\frac{M^{4}(\gamma+1)-4\beta^{2}}{2\beta^{3}}\]\(M\) is the Mach number, \(a\) is the local speed of sound, \(w\) represents the piston upwash, and \(\gamma\) is the ratio of specific heat. The total pressure differential \(\Delta p\) can be expressed as  
\[\Delta p(x,y,t)=-(\frac{1}{2}\rho V^{2})\bigg{(}\frac{4}{\beta}\bigg{)}\sum_{j=1 }^{n}\bigg{[}1+G\frac{\partial H(x,y)}{\partial x}\bigg{]}\bigg{[}\frac{ \partial\Phi_{j}(x,y)}{\partial x}q_{j}(t)+\frac{1}{V}\Phi_{j}(x,y)q_{j}(t) \bigg{]} \tag{7}\]  
where \(\Phi_{j}(x,y)\) is the \(j\)th modal shape, \(q_{j}(t)\) is the \(j\)th generalized modal coordinate, \(n\) is the number of modes, \(H(x,y)\) is the thickness function measured form the middle profile of the fin, \(\rho\) is the air density, and \(V\) is the velocity of the free stream. The \(i\)th generalized aerodynamic forces can be expressed as  
\[Q_{i}=\iint\limits_{S}\Delta p(x,y,t)\Phi_{i}(x,y)\mathrm{d}x\mathrm{d}y \tag{8}\]  
Substituting Equation (7) into Equation (8) yields  
\[Q_{i}=-(\frac{1}{2}\rho V^{2})\bigg{(}\frac{4}{\beta}\bigg{)}\sum_{j=1}^{n} \bigg{[}\big{(}E_{ij}+GC_{ij}\big{)}q_{j}+\frac{1}{V}\big{(}B_{ij}+GD_{ij} \big{)}q_{j}\bigg{]} \tag{9}\]  
where  
\[E_{ij}=\iint\limits_{S}\frac{\partial\Phi_{j}(x,y)}{\partial x} \Phi_{i}(x,y)\mathrm{d}x\mathrm{d}y\] \[B_{ij}=\iint\limits_{S}\[PERSON]_{j}(x,y)\[PERSON]_{i}(x,y)\mathrm{d}x \mathrm{d}y\] \[C_{ij}=\iint\limits_{S}\frac{\partial H(x,y)}{\partial x}\frac{ \partial\[PERSON]_{j}(x,y)}{\partial x}\[PERSON]_{i}(x,y)\mathrm{d}x\mathrm{d}y\] \[D_{ij}=\iint\limits_{S}\frac{\partial H(x,y)}{\partial x}\[PERSON]_{j }(x,y)\[PERSON]_{i}(x,y)\mathrm{d}x\mathrm{d}y\]  
Equation (9) can be written in matrix form:  
\[\mathbf{Q}=-(\frac{1}{2}\rho V^{2})\bigg{(}\frac{4}{\beta}\bigg{)}\bigg{[}( \mathbf{E}+GC)\mathbf{q}+\frac{1}{V}(\mathbf{B}+GD)\dot{\mathbf{q}}\bigg{]} \tag{10}\]  
Thus, the generalized aerodynamic forces in the time domain can be expressed as:  
\[\mathbf{Q}=\frac{1}{2}\rho V^{2}\mathbf{A}_{0}\mathbf{q}+\frac{1}{2}\rho V \mathbf{A}_{1}\dot{\mathbf{q}} \tag{11}\]  
where  
\[\mathbf{A}_{0}=-\bigg{(}\frac{4}{\beta}\bigg{)}(\mathbf{E}+GC)\]  
\[\mathbf{A}_{1}=-\bigg{(}\frac{4}{\beta}\bigg{)}(\mathbf{B}+GD)\]
# VAE-Based Real-Time Anomaly Detection Approach for Enhanced V2X Communication Security
## 1 Introduction
For example, in V2X communication, VAEs can identify anomalous patterns in vehicle dynamics, such as irregular speed variations, abnormal acceleration, or positional deviations, by learning a probabilistic representation of normal patterns. VAEs can also significantly improve security training through data augmentation [10]. By creating realistic synthetic data, these models can augment training datasets for security algorithms, enabling the development of more robust models capable of detecting a wide range of attack scenarios. In terms of authentication, generative models can produce diverse biometric data, such as voice and facial features [11]. They can also generate synthetic behavioral data, like driving patterns, to enhance the accuracy of behavioral biometrics-based authentication. Furthermore, adversarial training, which involves training Intrusion Detection Systems (IDSs) with adversarial examples generated by GANs, makes these systems more resilient to sophisticated attacks that attempt to evade detection [12]. Overall,integrating generative AI into V2X security frameworks promises a substantial improvement in safeguarding these critical communication networks. Our main contributions can be summarized as  
* We propose a novel real-time anomaly detection framework that leverages a sliding window mechanism in combination with a VAE integrated with a CNN architecture, resulting in a lightweight and robust model. * The performance of the proposed model is rigorously evaluated using a custom Basic Safety Message dataset, where various anomalies were systematically injected to simulate real-world scenarios. The remainder of this paper is organized as follows. Section 2 presents a review of related work in the field of V2X communication security, with a focus on generative models for anomaly detection. Section 3 provides details of the proposed method, which involves integrating CNNs with VAEs to enhance anomaly detection in V2X systems. Section 4 outlines the experimental setup, discusses the results, and evaluates the effectiveness of our proposed model in detecting various types of anomalies. Finally, Section 5 concludes the paper by summarizing the key findings and suggesting potential directions for future research.
###### Abstract  
The dynagraph card plays a crucial role in evaluating oilfield pumping systems' performance. Nevertheless, classifying dynagraph cards can be quite difficult because certain operating conditions may exhibit similar patterns. Conventional classification approaches mainly involve labor-intensive manual analysis of these cards, leading to subjectivity, prolonged processing times, and vulnerability to human prejudices. In response to this challenge, our study introduces a novel approach that leverages transfer learning and the Swin Transformer model for classifying dynagraph cards across various operating conditions in rod pumping systems. Initially, the Swin Transformer model undergoes pre-training using the ImageNet-22k dataset. Subsequently, we fine-tune the model's weights using actual dynagraph card datasets, facilitating direct classification analysis with dynagraph cards as input variables. The adoption of transfer learning significantly reduces the training time while enhancing the accuracy of condition diagnosis. To assess the effectiveness of our proposed method, we conducted a comparative evaluation against conventional models like ResNet50, DenseNet21, LeNet, and ViT. The findings demonstrate that our approach outperforms other methods, achieving an accuracy of 96%, thereby improving classification accuracy by 3-4%. Therefore, our approach, based on transfer learning and the Swin Transformer model, provides a better solution for practical problems involving similar dynagraph cards. It meets the requirements of oil field operations, enhancing economic benefits and work efficiency.  
Keywords:swin transformer; dynagraph card; self-attention; transfer learning; convolutional neural network; rod pump +
Footnote †: journal: _aciences_
# The Car-Following Model and Its Applications in the V2X Environment: A Historical Review
## 3 Research on Car-Following Behavior in the V2X Environment
### Applications of the Car-Following Models
#### 3.3.2 Analyzing the Characteristics of Traffic Flow
However, for most vehicles in the system, they cannot obtain the information and gain knowledge on the adjustment of connected vehicles (if they are not close enough to be recognized by the drivers) without the V2X environment. Thus, for these vehicles, the adjustment of car-following behavior conducted by the connected vehicles according to the V2X information equals a disturbance, which would exert negative effects on traffic flow. To sum up, the discussion based on a comprehensive comparison of traffic flow in which the vehicles are controlled by various car-following models is vital for providing a reference for planning, constructing, and managing the transportation system with the V2X environment. According to this, works such as the simulation framework which can combine with various car-following models to simulate the traffic flow [141] are important for further research.
## 4 Numerical Model
### Influence of Grouting Reinforcement Layer Thickness
#### 4.4.4 Pipeline Deformation  
Figure 17 shows the deformation of the pipeline; the abscissa was the distance from the center line of the middle partition wall and the dotted line was the position of the central axis of the left tunnel. With the increasing distance from the center line of the middle partition wall, the pipeline deformation first increased and then decreased. The pipeline deformation reached the maximum near the central axis of the left tunnel. When the distance from the center line of the middle partition wall was greater than 25 m, the pipeline deformation was small and the deformation was basically stable. The thickness of the reinforcement layer had an obvious influence on the deformation of the pipeline. With the increase of the thickness of the reinforcement layer, the deformation of the pipeline was smaller and tends to stabilize faster.  
Figure 18 shows the relationship between the maximum displacement of the pipeline and the thickness of the grouting reinforcement layer. When the reinforcement layer thickness was h = 0 m, 0.5 m, 1.0 m, 1.5 m, 2.0 m, and 2.5 m, the maximum displacement of the pipeline was 37.7 mm, 32.1 mm, 18.3 mm, 13.0 mm, 11.2 mm, and 10.6 mm, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Grouting Reinforcement Layer Thickness** & **h = 0** & **h = 0.5 m** & **h = 1.0 m** & **h = 1.5 m** & **h = 2.0 m** & **h = 2.5 m** \\ \hline Shear plastic zone area (m\({}^{2}\)) & 384.78 & 354.35 & 182.15 & 126.17 & 137.64 & 142.73 \\ \(\omega_{1}\) & 1 & 0.92 & 0.47 & 0.33 & 0.36 & 0.37 \\ Tensile plastic zone area (m\({}^{2}\)) & 286.16 & 252.30 & 61.73 & 22.41 & 24.37 & 17.98 \\ \(\omega_{2}\) & 1 & 0.88 & 0.22 & 0.08 & 0.09 & 0.06 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Shear and tensile plastic zone area of surrounding rock under different grouting reinforcement layer thickness.  
Figure 17: Pipeline deformation.  
Compared with the results of the previous reinforcement layer thickness condition, the amplitude of the maximum displacement of the pipeline was 5.6 mm, 13.8 mm, 5.3 mm, 1.8 mm, and 0.6 mm. With the increasing grouting reinforcement layer thickness, the maximum displacement of the pipeline gradually decreased and the variation amplitude first increased and then decreased. When h = 1.0 m and h = 1.5m, the maximum displacement of the pipeline was small (less than 20 mm) and the variation amplitude was large, indicating that the effect of grouting reinforcement was good.  
In conclusion, when the thickness of the grouting reinforcement layer was h = 1.5 m, the reinforcement effect of tunnel surrounding rock was better and the efficiency was higher.
## 3 Applications to a Waste Separation System
### Parameter Estimation by Importance Sampling
#### 3.3.2 Identifying the Estimable Parameters  
In case of the Hungarian waste separation system, the model of which is described in Section 3.3.1, the aim is to estimate the separation efficiencies (\(r_{i}^{j}\)) of the operation units. As seen from the formalization in Section 3.3.1, the mass flows of the components of the system can be calculated independently for each component from the relevant separation efficiencies. Therefore, the parameters \(r_{i}^{c}\) (\(i=1,\ldots 7\)) have an effect only on the mass flows of the \(c\) component and not on the others. As expert-based information about the output is available only for four outflows out of the seven and only for the component in which that flow is rich, separation efficiencies belonging to the rest of the components are not estimable based on the provided data. The reduced vector \(\mathbf{y}\) can be defined as follows:  
\[\mathbf{y}=[\frac{m_{12}^{PET}}{u^{PET}},\frac{m_{14}^{LDPE}}{u^{LDPE}},\frac{ m_{8}^{Iron}}{u^{Iron}},\frac{m_{9}^{Aluminium}}{u^{Aluminium}}]^{T} \tag{17}\]  
where \(u^{c}\) represents the input mass of component \(c\) and \(m_{i}^{c}\) refers to the mass of component \(c\) in stream \(i\). Here, the output streams in which component \(c\) needs to be collected are considered, corresponding to Figure 8.  
In addition to the above-mentioned, only the parameters of those units affect the outputs, which are before the output location in the row of units, as seen in Figure 9.  
Figure 9: Effects of the parameters on the outputs. The four outputs about which expert-based information was obtained are referred to \(y_{i}\) (\(i=1,\ldots,4\)). [PERSON], yellow, red, and blue colors mark which units have an effect on the different outputs. \(u\) denotes to the input, and \(r_{i}\) (\(i=1,\ldots,7\)) represents the parameters of the \(i\)th unit.  
Taking into account the above-mentioned issues, the parameters that can be estimated are summarized in Table 1. The table does not contain the pre-shedder unit as it has only one output; thus, its separation efficiencies are surely 100% for all components.
## 5 Discussion
### Relevance for Science and Practice
#### 5.2.4 Significance of Data Sovereignty  
In the context of the implications described, the commercial value of data and its relevance in optimizing operations and maintenance was described (see Table 2). It was shown that in the context of German rail transport, the legal legitimization of the use of data often lies with the manufacturers (see Sections 3.2 and 5.1). Due to currently valid contracts, it is difficult for DB to change this fact without creating a positive situation for all parties. This phenomenon of data sovereignty can also be observed in agriculture, where, for example, farmers are restricted in the use of the data of the agricultural machines they operate. The machines they have purchased collect a lot of data. However, this data is not accessible to them, as its use is in the custody of the manufacturers of the machines. Within their business models, the data becomes a central component, as it has immense economic value. With reference to the presented problem of DB and its ICE assets, the same problem arises for farmers [61]. Data sovereignty thus seems to be a central challenge not only for rail transport. For this reason, it is recommended for operators to include the legal use of the data collected by the acquired and operated asset as a central element in future contractual negotiations.
# A Comprehensive Evaluation Model for Optimizing the Sensor Array of Electronic Nose
###### Abstract  
As the acquisition unit of gas information, the sensor array directly determines the overall performance of the electronic nose system (E-nose). This paper proposed a new method for optimizing the sensor array. Firstly, four evaluation indicators (sensitivity, selectivity, correlation, and repeatability) were selected to evaluate the sensor array. Subsequently, different evaluation indicators were assigned different weight values according to their contributions to the overall performance of the E-nose. Finally, a comprehensive evaluation model was established based on the EWM-TOPSIS algorithm to optimize the sensor array. In order to verify the effectiveness of the as-proposed model, it was applied to the optimization of the E-nose sensor array composed of 10 gas sensors, and the influence of the sensor array optimization on the gas recognition ability of the E-nose was investigated. The experimental results showed that the optimized sensor array can identify the CO-CH\({}_{4}\) gas mixtures with an accuracy of 96.5%, which a significant improvement compared with the accuracy of 78.3% before the sensor array optimization.  
electronic nose; sensor array; performance optimization; evaluation model 2023
# A Circularly Polarized Non-Resonant Slotted Waveguide Antenna Array for Wide-Angle Scanning
## 4 Verification and Comparison
### Comparison and Discussion  
Table 5 compares the performance of the proposed antenna with those of several circularly polarized waveguide slot antennas reported in the literature. Our work achieved a beam scanning range of \(-45^{\circ}\)\(\sim\)\(+45^{\circ}\) in the azimuth plane and a scanning range of \(+11^{\circ}\)\(\sim\)\(+13^{\circ}\) in the elevation plane. Refs. [19; 20] pertain to frequency scan arrays, while [22] utilized a wideband CP dipole to expand the axial ratio (AR) band. Ref. [28] employed a similar structure antenna element and proposed a fixed beam array; however, the authors did not focus on the compact array research domain. Notably, our work achieved a low side-lobe level (SLL). Comparatively, our antenna realizes a larger beam scanning range, while maintaining an average or superior electrical performance in other aspects.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Ref.** & **Feed Type** & **Polarizer Type** & **Number of Elements** & **Excitation** & **Frequency** & **El. Plane** & **Gain (dBi)** & **Axial Ratio** & **Scan Type** & **Scan Angle (\({}^{\circ}\))** \\  & & & **Elements** & **Wave Type** & **Band (\(\circ\))** & **SLL (dBi)** & **Gain (dBi)** & **Axial Ratio** & **Scan Type** & **Scan Angle (\({}^{\circ}\))** \\ \hline
[17] & Waveguide & Slots & \(1\times 15\) & Traveling & 1.33 & \(-18\) (Sim.) & 21 (Sim.) & \(\gtrsim\)2 (Sim.) & - & - \\
[19] & SIW & Slots & \(64\times 12\) & Traveling & 8.03 & \(-14\) & 28 & \(\sim\)- & Freq & \(+36\)\(\leftrightarrow\)49 (El.) \\
[20] & RGW & Slots & \(1\times 7\) & Traveling & 21.43 & - & 12.5 & 21 & Freq & \(-15\)\(\leftrightarrow\)15 (El.) \\
[21] & Waveguide & Patches & \(1\times 10\) & Standing & 2.85 & \(-13\) & 15.94 & 3.2 & - & - \\
[22] & Waveguide & Dipoles & \(12\times 12\) & Standing & 7.33 & \(-12\) & 2.801 & 14 (Sim.) & - & - \\
[28] & Waveguide & Waveguide & \(2\times 8\) & Standing & 1.63 & \(-13\) (Sim.) & 17 (Sim.) & - & Phase & \(-18\)\(\leftrightarrow\)18 (Az.) \\ This work & Waveguide & Waveguide & \(5\times 8\) \& \(4\times 9\) & Traveling & 5.89 & \(<\)\(-20\) & 24.3 & 5.0 & Freq and Phase & \(-43\)\(\leftrightarrow\)45 (Az.) \\ \hline \hline \end{tabular} SIW = substrate integrated waveguide: RCW = rdged grip waveguide: Sun. = simulation result: El. = brown scan in elevation plane: Az. = brown scan in azimuth plane.  
\end{table}
Table 5: Performance of the waveguide slot planar array.
# EL-MTSA: Stock Prediction Model Based on Ensemble Learning and Multimodal Time Series Analysis
###### Abstract  
Predicting stock prices is a popular area of study within the realms of data mining and machine learning. Precise forecasting can assist investors in mitigating the risks associated with their investments. Given the unpredictable nature of the stock market, influenced by policy changes, stock data often display high levels of fluctuation and randomness, aligning closely with the prevailing market sentiment. Moreover, diverse datasets related to stocks are rich in historical data that can be leveraged to forecast future trends. However, traditional forecasting models struggle to harness this information effectively, which restricts their predictive capabilities and accuracy. To improve the existing issues, this research introduces a novel stock prediction model based on a deep-learning neural network, named after EL-MTSA, which leverages the multifaceted characteristics of stock data along with ensemble learning optimization. In addition, a new evaluation index via market-wide sentiment analysis is designed to enhance the forecasting performance of the stock prediction model by adeptly identifying the latent relationship between the target stock index and dynamic market sentiment factors. Subsequently, many demonstration experiments were conducted on three practical stock datasets, the CSI 300, SSE 50, and CSI A50 indices, respectively. Experiential results show that the proposed EL-MTSA model has achieved a superior predictive performance, surpassing various comparison models. In addition, the EL-MTSA can analyze the impact of market sentiment and media reports on the stock market, which is more consistent with the real trading situation in the stock market, and indicates good predictive robustness and credibility.  
stock sequential prediction; market sentiment indicator; ensemble learning optimization; multidimensional data mining; deep-learning neural network +
Footnote †: journal: Applied sciences
# Distribution of Volatile Compounds in Different Fruit Structures in Four Tomato Cultivars
## 2 Results and Discussion
### Volatile Compounds in Tomato Fruits
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Compounds** & **Odor Description** &
\begin{tabular}{c} **Odor Threshold in** \\ **Water (mg L\({}^{-1}\))** \\ \end{tabular} & **RI** \\ \hline  & & **Aldehydes** & & \\
1 & 2-Methylpropanal & pungent, malt, green & 0.0009–0.001 & 567 \\
2 & Butanal & Pungent, green & 0.009 & 590 \\
3 & 3-Methylbutanal & Malt & 0.00015–0.0002 & 638 \\
4 & 2-Methylbutanal & Cocoa, almond, malt & 0.003 & 646 \\
5 & 2-Methyl-2-butrenal & Green, fruit & 0.5 & 719 \\
6 & cis-3-Hexenal & Leafy, green & 0.00025 & 771 \\
7 & Hexanal & Grass, tallow, fat & 0.0045–0.005 & 774 \\
8 & trans-2-Hexenal & Green, leafy & 0.017 & 828 \\
9 & Heptanal & Fat, citrus, rancid & 0.003 & 875 \\
10 & trans, trans-2, 4-Hexadienal & Green & 0.06 & 887 \\
11 & Benzaldehyde & Almond, burnt sugar & 0.35 & 945 \\
12 & Octanal & Fat, soap, lemon, green & 0.0007 & 969 \\
13 & Nonanal & Fat, citrus, green & 0.001 & 1059 \\  & & **Hydrocarbons** & & \\
1 & Cymene & Solvent, gasoline, citrus & 0.15 & 994 \\
2 & Limonene & Lemon, orange & 0.01 & 998 \\
3 & Terpinolene & Smokey, woody & 0.2 & 1048 \\
4 & Undecane & Alkane & 10 & 1051 \\
5 & Dodecane & Alkane & 10 & 1137 \\
6 & Tridecane & Alkane & & 1222 \\  & **Alcohols** & & \\
1 & 2-Methylpropanol & Alcoholic, grassy, sweet & 12.5 & 612 \\
2 & 3-Methylbutanol & Whiskey, malt, burnt & 0.25–0.3 & 707 \\
3 & 2-Methylbutanol & Malt, wine, onion & 0.25–0.3 & 711 \\
4 & 4-Methylpentanol & Pungent & 0.82–4.1 & 809 \\
5 & 3-Methylpentanol & Pungent & 0.83–4.1 & 817 \\  & **Ketones** & & \\
1 & Acetone & Pungent, irritating, floral & 40 & 533 \\
2 & 1-Penten-3-one & Fruity, floral, green & 0.0015 & 665 \\
3 & 6-Methyl-5-hepten-2-one & Fruity, floral & 0.05 & 950 \\
4 & Geranyl acetone & Sweet, floral, estery & 0.06 & 1367 \\  & **Oxygen-containing heterocyclic compounds** & & \\
1 & 2-Methylfuran & Chocolate & 0.2 & 602 \\
2 & 2-Ethyl furan & Rum, coffee and chocolate & - & 676 \\  & **Esters** & & \\
1 & Butyl acetate & Pear & 0.066 & 744 \\
2 & 2-Methylbutyl acetate & Fruit & 0.005–0.011 & 847 \\
3 & Methyl salicate & Peppermit & 0.04 & 1156 \\  & **Nitrogen compounds** & & \\
1 & 1-Nitro-pentane & Pleasant, fruity & 22 & 916 \\  & **Sulfur- and nitrogen-containing heterocyclic compounds** & & \\
1 & 2-Isobutylthiazole & Tomato leafy, green & 0.0035 & 1002 \\
2 & 1-Nitro-2-phenylethanolamine & Flower, spice & 0.002 & 1250 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Identification of volatile compounds identified in tomato fruits, along with their odor descriptions, odor thresholds in water, and RI values. \begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Compounds**} & \multicolumn{12}{c}{**Classification (log L\({}^{-1}\))**} \\ \cline{2-13}  & \multicolumn{1}{c}{**Task-Late**} & \multicolumn{1}{c}{**Types**} & \multicol