esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Evaluation of the Ability of Various Features to Identify Fusarium Head Blight  
#### 3.1.1 The Features of First-Order Derivatives  
Compared to the original spectrum, it is found that the correlation between the first-order derivative spectrum and the severity of FHB is significantly improved, and the correlation coefficient is up to 0.8 (Figure 5).  
Table 5 shows that six spectral features of first-order derivative for the front and side samples, namely, SDy, SDy/SDb, SDg/SDb, (SDr - SDy)/(SDr + SDy), (SDy - SDb)/(SDg + SDb), are significantly correlated (\(p\)-value \(<\) 0.001) with disease severity and the absolute values of correlation coefficients are greater than 0.7 (\(|\)R\(|\) \(>\) 0.7). Four spectral features for erect samples, namely, SDy, SDg/SDb, (SDr - SDy)/(SDr + SDy), and (SDg - SDb)/(SDg + SDb), are significantly correlated (\(p\)-value \(<\) 0.001) with severity and the absolute values of correlation coefficients are greater than 0.7 (\(|\)R\(|\) \(>\) 0.7).  
Figure 5: Correlation coefficient curves of different spectra and severity of disease. (**a**) The original spectrum. (**b**) First order derivative spectrum.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Features**} & \multicolumn{3}{c}{**Front**} & \multicolumn{3}{c}{**Side**} & \multicolumn{3}{c}{**Erect**} \\ \cline{2-10}  & **R** & **R2** & \(p\)**-Value** & **R** & **R2** & \(p\)**-Value** & **R** & **R2** & \(p\)**-Value** \\ \hline Db & \(-\)0.626 & 0.391 & 0.000 & \(-\)0.437 & 0.191 & 0.000 & \(-\)0.205 & 0.042 & 0.053 \\ Dy & 0.504 & 0.254 & 0.000 & 0.641 & 0.411 & 0.000 & 0.550 & 0.302 & 0.000 \\ Dr & \(-\)0.569 & 0.324 & 0.000 & \(-\)0.512 & 0.262 & 0.000 & \(-\)0.377 & 0.142 & 0.000 \\ \(\lambda\)b & \(-\)0.184 & 0.034 & 0.084 & 0.132 & 0.017 & 0.217 & \(-\)0.595 & 0.354 & 0.000 \\ \(\lambda\)y & 0.310 & 0.096 & 0.003 & 0.188 & 0.035 & 0.078 & 0.337 & 0.114 & 0.001 \\ \(\lambda\)r & \(-\)0.173 & 0.030 & 0.105 & \(-\)0.448 & 0.201 & 0.000 & \(-\)0.212 & 0.045 & 0.046 \\ SDb & \(-\)0.567 & 0.322 & 0.000 & \(-\)0.334 & 0.112 & 0.001 & \(-\)0.057 & 0.003 & 0.596 \\ SDy & 0.816 & 0.666 & 0.000 & 0.778 & 0.605 & 0.000 & 0.787 & 0.620 & 0.000 \\ SDr & \(-\)0.553 & 0.306 & 0.000 & \(-\)0.494 & 0.244 & 0.000 & \(-\)0.331 & 0.110 & 0.002 \\ SDg & \(-\)0.615 & 0.378 & 0.000 & \(-\)0.418 & 0.175 & 0.000 & \(-\)0.196 & 0.038 & 0.492 \\ SDr/SDb & 0.315 & 0.099 & 0.003 & 0.096 & 0.009 & 0.372 & 0.134 & 0.018 & 0.212 \\ SDr/SDy & 0.087 & 0.008 & 0.416 & 0.258 & 0.067 & 0.015 & 0.237 & 0.056 & 0.025 \\ SDr/SDg & 0.425 & 0.181 & 0.000 & 0.230 & 0.053 & 0.030 & 0.195 & 0.038 & 0.253 \\ SDy/SDb & 0.800 & 0.640 & 0.000 & 0.770 & 0.593 & 0.000 & 0.583 & 0.340 & 0.000 \\ SDg/SDb & \(-\)0.794 & 0.630 & 0.000 & \(-\)0.835 & 0.697 & 0.000 & \(-\)0.765 & 0.585 & 0.000 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Correlation between derivative features and disease severity.
# Predicting Saudi Stock Market Index by Using Multivariate Time Series Based on Deep Learning
## 5 Experiment Results
### Fitting the Models with LSTM  
Similar fitting processes were used for both the univariate and multivariate LSTMs. This involved the following parameters:  
Neurons: the quantity of neurons in the network's dense output layer. The number of projected values that a network will generate is determined by the output layer. For instance, in a regression problem, the output layer possesses a single neuron, whereas, in a classification problem, the output layer contains the same number of neurons as classes.  
Epochs: the number of times that a model will iterate over an entire training dataset. An epoch is defined as one complete iteration over the training data. Although more epochs may increase performance, it also increases the risk of overfitting.  
Figure 6: The original and denoised Zain dataset.  
Time steps: the number of time steps or sequence length of the input.  
Batch size: the number of samples in a single batch.  
Units: the quantity of LSTM units in the layer(s) of the network.  
Dropout rate: the rate of dropout regularisation applied to the output of each LSTM unit, which helps prevent overfitting.  
Input size: the number of input variables in the input.  
Several other parameters, such as the optimiser, loss function, and learning rate, also control the training process and the convergence of a network. Therefore, these parameters must be carefully chosen and tuned according to the specific problem and data at hand.  
Table 5 provides a summary of the parameters that were used in the model.
## 2 Materials and Methods
### Example  
Let us consider examples for calculating the absolute and relative robustness of DStr based on images and data presented in Figure 3. The result of the image binarization with thresholds \(\text{tr}_{1}\) = 20, \(\text{tr}_{2}\) = 110, and \(\text{tr}_{3}\) = 200 (Figure 3a) are denoted by \(\text{BIm}_{1}\), \(\text{BIm}_{2}\), andBIm\({}_{3}\). All data used to calculate the absolute and relative robustness of DStr for BIm\({}_{1}\), BIm\({}_{2}\), and BIm\({}_{3}\) are presented in the table shown in Figure 3b.  
Consider the sequence of steps in calculating the absolute robustness. Angles of rotation \(\varphi_{\rm r}\) are responsible for the alignment of BIm\({}_{1}\), BIm\({}_{2}\), and BIm\({}_{3}\). Row r of the table is comprised of data for alignment \(\varphi_{\rm r}\) which is used for the calculation of absRB(DStr(\(\varphi_{\rm r}\))). The value of the row 1, column 2 cell is equal to DStr(\(\varphi=0^{\circ}\)) = 0.866 for BIm\({}_{1}\). Consequently, the row 1, column 3 cell is equal to DStr(\(\varphi=0^{\circ}\)) = 0.774 for BIm\({}_{2}\) and the row 1, column 4 cell is equal to DStr(\(\varphi=0^{\circ}\)) = 0.666 for BIm\({}_{3}\). The following steps take place:  
Step 1. Calculate the arithmetical mean of DStr(\(\varphi=0^{\circ}\)) of BIm\({}_{1}\), BIm\({}_{2}\), and BIm\({}_{3}\). The result of the calculation is shown in the row 1, column 5 cell.  
Step 2. Calculate the deviation of DStr(\(\varphi=0^{\circ}\)) from the arithmetical mean, which is denoted by \(\Delta\)DStr(\(\varphi=0^{\circ}\)). The result of the calculations is shown in the following cells: row 1, column 6; row 1, column 7; and row 1, column 8 for BIm1, BIm2, and BIm3, respectively.  
Step 3. Deviations of \(\Delta\)DStr(\(\varphi=0^{\circ}\)) for BIm\({}_{1}\), BIm\({}_{2}\), and BIm\({}_{3}\) are averaged and denoted by Av[\(\Delta\)DStr(\(\varphi=0^{\circ}\))]; row 1, column 9 cell = Av[\(\Delta\)DStr(\(\varphi=0^{\circ}\))] for BIm\({}_{1}\), BIm\({}_{2}\), and BIm\({}_{3}\).  
Figure 3: Absolute and relative robustness of DStr of a grayscale image against its binarization. (**a**) Grayscale image of tidal sand ripples and its binarization. (**b**) Absolute robustness of DStr with respect to binarization thresholds 20, 110, and 200. (**c**) Relative robustness of DStr as interclass correlation coefficient.  
Step 4. Steps 1-3 are repeated for \(\varphi=10^{\circ}\), \(\varphi=20^{\circ}\),..., \(\varphi=180^{\circ}\). Column 9 comprises the results of calculating \(\text{Av}[\Delta\text{DStr}(\varphi_{t})]\).  
Step 5. Equation (1) was used to calculate the absolute robustness of DStr with respect to binarization thresholds \(\text{tr}_{1}=20\), \(\text{tr}_{2}=110\), and \(\text{tr}_{3}=200\); absRB[DStr(Bim\({}_{1}\), BIm\({}_{2}\), BIm\({}_{3}\))] = 0.938.  
Figure 3c shows the high level of correspondence (i.e., ICC values) and the consequently high level of relative robustness of DStr with respect to \(\text{tr}_{1}\), \(\text{tr}_{2}\), and \(\text{tr}_{3}\).
# Efficient and Safe Strategies for Intersection Management: A Review
## 2 Safety
### Intersection Collision Detection
#### 2.1.1 Sensor-Based Detection Technology  
As mentioned previously in the congestion, sensors are primarily used to collect road information and we choose it to detect the situation of the road. The advances in sensors technologies enable real-time collection of high-fidelity spatiotemporal data on transportation networks. For example, a set of speed sensors that are spatially distributed along the street and can communicate through an externally determined network. The speed sensor observes the average speed value of the vehicle within a certain time interval, and uses a threshold-based method to generate a local prediction. Each sensor exchanges its local predictions with its neighbors and aggregates the local predictions it receives using a weighted majority aggregation rule to generate a final prediction [8]. However, this method makes assumptions about the transportation infrastructure that are currently unsupported, and do not explicitly model the detection delay and false alarm rate. [PERSON] et al. propose a two-stage approach. In the first stage, each sensor generates a decision using the Bayesian fastest change detection framework. In the second phase, individual sensor decisions are aggregated by an optimal stopping method, which optimizes the trade-off between aggregation costs and misclassification costs [9]. The proposed two-stage approach achieves up to 65.2% and 87% improvement in the average detection delay and probability of false alarm respectively, compared to the state-of-the-art. [PERSON] [10] propose a Bayesian quickest change detection framework for accurate detection in near-real-time of freeway accidents based on speed sensors. To exploit the spatial  
Figure 3: HV and RV drive to the intersection simultaneously.  
Figure 2: HV starts at the intersection.  
correlation between sensors, and to improve the robustness of the proposed solution, four aggregation schemes are also proposed. We choose the second method to detect collision.
## 6 Conclusions and Future Work  
ML techniques are used to build a decision system based on past experiences to make data more informative and reliable. These algorithms can be used in various domains, such as science, engineering, and business. There are some issues noticed in these algorithms related to scalability and stability along with benefits. These issues are solved step by step by different researchers. The increasing growth of data needed an effective algorithm that extracts some patterns in data. The core contribution of the proposed research study is to develop a novel approach to minimize the radius of MEB for enhancing the classification accuracy of the conventional SVM. To improve the performance of SVM, a lower and upper bound of MEB is introduced. Instead of using the feature weighting technique, WR-SVM learns classifier and feature transformation simultaneously. Due to which the training time of the proposed model is decreased. The radius of MEB proposed by F-SVM was shifted to a higher value, due to which the size of MEB was too large. The proposed method first checks the linear separability and then applies the kernel to transform the data into feature space. The kernelized feature space is then converted to weighted kernelized feature space. Instead of getting the mean from kernelized features pace, the weighted mean was computed from weighted kernelized feature space. The experimental results showed that the radius of MEB is minimized. The experiments were conducted on nine different benchmark datasets and one synthetic dataset to show the significance of the proposed WR-SVM compared to the conventional SVM. The performance of WR-SVM is significantly better than traditional SVM and F-SVM. The possible future direction of the proposed WR-SVM is to extend radius-margin bound to other conventional learning models for enhancing classification accuracy and fast training.  
Conceptualization, A.R., N.I., R.A. and D.-H.K.; Methodology, A.R. and D.-H.K.; Software, A.R., N.I. and R.A.; Validation, N.I., R.A. and D.-H.K.; Formal Analysis, A.R. and N.I.; investigation, A.R. and R.A.; Data Curation, A.R. and R.A.; writing--original draft preparation, A.R. and N.I.; writing--review and editing, A.R., N.I. and R.A.; Visualization, A.R. and N.I.; Supervision, D.-H.K.; Project Administration, D.-H.K.; Funding Acquisition, D.-H.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the 2020 scientific promotion program funded by Jeju National University\". Any correspondence related to this paper should be addressed to [PERSON]. The authors declare no conflict of interest.
# VAE-Based Real-Time Anomaly Detection Approach for Enhanced V2X Communication Security
## 4 Experimental Setup and Results
### Anomaly Injection and Evaluation Metrics
This approach ensures that 98% of the normal instances are correctly classified as non-anomalous, thereby focusing on maximizing recall, which is crucial in anomaly detection tasks. High recall is significant as it minimizes the risk of missing anomalies, which could have considerable consequences in real-time applications.
## 2 Theory
### Boundary Conditions
#### 2.2.2 Physical Quantities in the Fluid  
The displacement components of the non-viscous fluid are expressed as  
\[u_{rf}=\frac{\partial\Phi_{fl}}{\partial r}=\frac{\partial}{\partial r}\phi_{fl }\cos n\theta e^{i(k_{z}z-\omega t)} \tag{35}\]  
\[u_{\theta f}=\frac{1}{r}\frac{\partial\Phi_{fl}}{\partial\theta}=-\frac{n}{r} \phi_{fl}\sin n\theta e^{i(k_{z}z-\omega t)} \tag{36}\]\[\hat{u}_{zf}=ik_{z}u_{zf}=ik_{z}\frac{\partial\Phi_{fl}}{\partial z}=-k_{z}^{2} \phi_{fl}\cos n\theta e^{i(k_{z}z-\omega t)} \tag{37}\]  
The stress tensor components of the inviscid fluid are  
\[\tau_{rrf}=-\lambda\frac{\omega^{2}}{c_{fl}^{2}}\phi_{fl}\cos n\theta\cdot e^{ i(k_{z}z-\omega t)} \tag{38}\]  
\[\tau_{rbf}=\tau_{rzf}=0 \tag{39}\]  
The wave equations in Equation (11), Equation (14), and the boundary conditions in Equations (15)-(19) constitute an eigenvalue problem. The traditional root-finding method is to select the appropriate general solution form of the wave equations, and to substitute the general solution into the appropriate boundary conditions to obtain a set of uniform linear geometric equations. The determinant of the coefficient matrix is set equal to zero, and the so-called dispersion equation is obtained. Finally, the root of the dispersion equation is found using numerical methods, and the dispersion relationship \(\omega(k_{z})\) of modes is obtained.  
The general solution of the wave equations in the cylindrical coordinate system contains various forms of Bessel functions, and it is often difficult to separate and determine the root of different modes. If factors such as fluid viscosity and solid loss need to be considered, the dispersion equation becomes more difficult to solve. The spectral method introduced in the next section can help effectively avoid these problems.
## 4 Case Study on the Multiple-Update-Infill Sampling Methods
### Case Study II: The Borehole Model  
A borehole model simulates water flow through a borehole drilled from the ground surface through two aquifers. This model is commonly used for testing various methods in computer experiments [29; 30; 31]. The input variables and their ranges are tabulated in Table 3. The output of this model is defined as  
\[f(x)=\frac{2\pi x_{3}(x_{4}-x_{6})}{\ln(x_{2}/x_{1})\bigg{(}1+\frac{2x_{7}x_{3 }}{\ln(x_{2}/x_{1})x_{1}^{2}x_{8}}+\frac{x_{3}}{x_{5}}\bigg{)}} \tag{32}\]  
where the output is water flow rate.  
Figure 8 shows the contour plots for the response surfaces of the borehole model. Some of the input variables (\(x_{2},\ x_{3}\), and \(x_{5}\)) seem to be non-influential variables for the output. As a result, some of the contour plots are flat (for example, \(x_{2}\) versus \(x_{3}\)). The contour plot between \(x_{1}\) and \(x_{8}\) exhibits the non-linear response surface, while the remaining contour plots show the linear response surface. Therefore, the borehole model seems as though the complex response surface is made in the eight-dimensional space.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Input Values** & **Baseline** & **Lower Bound** & **Upper Bound** \\ \hline \(x_{1}\) & Radius of borehole & 0.1 & 0.05 & 0.15 \\ \(x_{2}\) & Radius of influence & 25,050 & 100 & 50,000 \\ \(x_{3}\) & Transmissivity of upper aquifer & 89,335 & 63,070 & 115,600 \\ \(x_{4}\) & Potentiometric head of upper aquifer & 1050 & 990 & 1110 \\ \(x_{5}\) & Transmissivity of lower aquifer & 89.55 & 63.1 & 116 \\ \(x_{6}\) & Potentiometric head of lower aquifer & 760 & 700 & 820 \\ \(x_{7}\) & Length of borehole & 1400 & 1120 & 1680 \\ \(x_{8}\) & Hydraulic conductivity of borehole & 8250 & 9855 & 12,045 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The nomenclature of the borehole model [30].  
Figure 8: The contour plots for the response surfaces of the borehole model (water flow rate).
# A Review of Research on Dropwise Condensation Heat Transfer
## 2 The Mechanism of Dropwise Condensation  
### Dropwise Condensation and Its Forming Decision  
Different surfaces have different condensation forms because of their characters. Filmwise condensation forms a continuous liquid film on the surface, but dropwise condensation forms small droplets [4].  
In general, the condensation forms (dropwise or filmwise) on the surface are determined by the surface's wettability. According to the surface wettability, the surface can be classified into either a hydrophilic surface, hydrophobic surface, or superhydrophobic surface. The droplet morphology on the surface with different wettability is shown in Figure 1a, when the contact angle is \(\theta<90^{\circ}\), the surface is considered a hydrophilic surface, and for filmwise condensation, when the contact angle is \(\theta>90^{\circ}\), the surface is considered a hydrophobic surface and forms dropwise condensation [8]. When the contact angle is \(\theta>150^{\circ}\) and the rolling angle is less than \(10^{\circ}\), the surface is considered a superhydrophobic surface. The rolling angle above is the critical value of the angle \(\alpha\) formed between the inclined surface and the horizontal surface when the droplet begins rolling off. When the inclined angle is less than the rolling angle, the droplet will depart due to gravity. As shown in Figure 1b, the advancing and backward contact angles get bigger and smaller. Where the \(\theta_{a}\) is the advancing contact angle, \(\theta_{r}\) is the receding contact angle, and the \(\Delta\theta=\theta_{a}-\theta_{r}\) is the contact angle hysteresis.  
However, some scholars found that some surfaces can still promote complete dropwise condensation when the contact Angle is less than 90\({}^{\circ}\)[9]. [PERSON] [10] proposed that the form of condensation can be classified according to the difference between the surface and liquid free energy \(\Delta\gamma\), where \(\Delta\gamma=\gamma_{l}-\gamma_{s}\). When \(\Delta\gamma\leq 0\) MJ/m\({}^{2}\), it forms filmwise condensation, when \(0\leq\Delta\gamma\leq 33.3\) MJ/m\({}^{2}\), the coexistence state of filmwise condensation and dropwise condensation, when \(\Delta\gamma\geq 33.3\) MJ/m\({}^{2}\), it forms absolute dropwise condensation. Compared with the conventional contact angle method, this method is not restricted to contact angle measurement, which is its main advantage.
## 4 Experiments
\begin{table}
\begin{tabular}{c c c c c} \hline MovieLens 100K & Model & RMSE & MAE & \(p\)-Value \\ \hline  & LFM-SPE & 0.795 & 0.661 & \\  & GHRS & 0.887 & 0.685 & \\  & GLocal-K & 0.889 & 0.690 & \\  & MG-GAT & 0.890 & 0.692 & \\  & T-ULVD & 0.892 & 0.701 & \\  & Proposed model & 0.757 & 0.590 & 0.000 \\ \hline MovieLens 1M & Model & RMSE & MAE & \(p\)-value \\ \hline  & LFM-SPE & 0.736 & 0.638 & \\  & GLocal-K & 0.823 & 0.640 & \\  & SparseFC & 0.824 & 0.643 & \\  & CF-NADE & 0.829 & 0.645 & \\  & T-ULVD & 0.848 & 0.669 & \\  & Proposed model & 0.722 & 0.565 & 0.0174 \\ \hline Douban & Model & RMSE & MAE & \(p\)-value \\ \hline  & JK-DMC & 0.718 & 0.517 & \\  & GLocal-K & 0.721 & 0.521 & \\  & MG-GAT & 0.737 & 0.541 & \\  & SparseFC & 0.745 & 0.551 & \\  & Proposed model & 0.712 & 0.511 & 0.000 \\ \hline \end{tabular}
\end{table} 
Table 2: The comparison of different algorithms. In summary, TKGRS distinguished itself through its incorporation of a time decay factor and the use of GCNs, enabling the capture of complex non-linear relationships between users and items. Although computationally more intensive than some alternatives, its performance metrics justify its resource requirements, especially when juxtaposed with fully connected graph-based models such as MG-GAT. It remains a compelling option for systems where a balance between accuracy and computational efficiency is paramount. Nonetheless, the computational demands may escalate for exceedingly large graphs, a challenge that is not unique to TKGRS but is intrinsic to graph-based models.
## 2 Related Work
A distinct solution, the Deep Context-Aware Sentiment Analysis Model (DC ASAM), integrates the Deep Bidirectional Long Short-Term Memory Network (DBiLSTM) and the Densely Connected Graph Convolutional Network (DGCN) to overcome the limitations of traditional sentiment analysis methods [49]. Another innovative proposal combines local semantic features extracted by the Local Semantic Feature Extractor (LSFE) with the global features of BERT/RoBERTa models, resulting in the PConvBERT and PConvRoBERTa models. This method incorporates adversarial training to improve robustness and Focal Loss to mitigate the effects of imbalanced datasets [50]. Going beyond traditional machine learning and pre-training requirements, some studies introduced mathematical optimization models and decision-making frameworks. One such model employs game theory to calculate performance scores by combining context scores, ratings, and sentiment scores, achieving Nash equilibrium through non-cooperative game theory [51]. Another study developed a smarter decision support system using the Sentiment Analysis-based Multi-person Multi-criteria Decision-Making (SA-MpMcDM) methodology, which combines natural language comments and numerical ratings with the DOC-ABSADeepL multi-task deep learning model for aspect detection and opinion separation [52]. Finally, innovative approaches such as the population game model have been proposed to bypass large dataset pre-training requirements, relying instead on lexicon-based context and sentiment scores to achieve high performance across multiple domains [53,54]. Additionally, the Ontology-Based Sentiment Analysis Process (OSAPS) represents domain knowledge through a hierarchical structure, incorporating entities, object properties, and their relationships to create a machine-readable ontology model [55]. Large Language Models (LLMs) have revolutionized sentiment analysis by offering a more nuanced and contextually aware approach compared to traditional methods. Their ability to understand complex linguistic patterns and subtle emotional cues allows for more accurate sentiment classification, especially in cases with sarcasm, irony, or implicit sentiment [56,57]. LLMs can be fine-tuned on specific datasets to improve performance in particular domains, or utilized in a zero-shot learning fashion, leveraging their pre-trained knowledge to perform sentiment analysis without explicit training data [58,59]. This adaptability makes LLMs highly versatile for various sentiment analysis tasks, from basic polarity detection to more complex aspect-based sentiment analysis and emotion recognition [60]. However, challenges remain in addressing biases present in training data and ensuring the robustness of LLMs in handling noisy or ambiguous text [61].