esa-sceva/satcom-chunk-collection huggingface dataset:
# Understanding the Influence of AST-JS for Improving Malicious Webpage Detection
## 2 Related Works  
We summarize the previous works most relevant to ours and find the differences with ours. First, the study of malicious webpage detection has been ongoing for quite a long time, since Internet webpages became a source of information that users can access. Simple approaches to block malicious URLs or webpages, such as blacklisting, remain popular. Many websites and communities provide this service for free, such as PhishTank [12], jwSpamSpy [13], and DNS-BH [14]. Alternatively, some companies offer commercial products for this mechanism, such as McAfee's SiteAdvisor [15], Cisco IronPort Web Reputation [16], and Trend Micro Web Reputation [17].  
Moreover, machine-learning-based approaches for malicious webpage detection have been rapidly developing. Most of them have explored many features of webpages, such as lexicon-based features that include mostly static features, host-based features that use host information, or content-based ones such as HTML and JS. We can find some research that works on the same features with specific phishing URLs or websites that also consider the webpage's information. Some recent works on phishing detection, such as [18; 19], used light gradient boosting to capture the pattern of phishing URLs. The exploration of deep learning models is also an interesting way to create a more complex model that focuses on representing every detail of webpage text or URL characters [20; 21; 22; 23]. Additionally, some research with the latest cloud technology uses machine learning to detect specific conditions [24; 25] or analyze phishing URLs based on multi-domain study [26].  
Furthermore, the existence of JS code on malicious webpages has prompted some researchers to work on detecting malicious code injected into a webpage that probably can be a threat, such as XSS. Most of them use a machine learning approach, such as ensemble classifiers [27]; or deep learning models, e.g., convolutional neural networks [28], deep belief networks [29; 30], and graph neural networks [31]. Table 1 summarizes some related works that have been working close to malicious webpage detection.  
Meanwhile, regarding AST representation, the use of ASTs for JS feature representation has been adopted as the leading and best way to capture the semantic meaning of a program. We can find some studies that used AST for defect prediction of JS code [32; 33; 34], vulnerability detection [35], or code summarization [36]. AST representation is useful for determining how the programmers wrote the code to help us find defects or vulnerabilities. In addition, we can use the AST representation to summarize the semantics of the code and evaluate its maliciousness. We can use machine learning or deep learning for a detection model in which an AST is considered as a list of tokens [37] or graph representation [10; 38]. We hope to extend the scope of the research for AST representation for malicious websites [39] or webpages.
# Satellite Network Security Routing Technology Based on Deep Learning and Trust Management
## 4 Experimental Simulation and Analysis
### Analysis of Results
#### 4.2.2 Packet Loss Rate
Consequently, it maintains a lower packet loss rate, positioning its curve at the bottom of Figure 7.
## 2 Result and Discussion  
### Analysis of Citrus Flower Volatiles by HS-SPME-GC-MS  
In this study, a total number of 110 flower volatiles were detected by headspace solid phase microextraction and gas chromatography spectrometry (HS-SPME-GC-MS) from different citrus types at three blooming stages (Table 1). HS-SPME is a simple and efficient method with solventless extraction technique as compared to conventional extraction methods that involved complex sample preparation and large amounts of solvent [28; 29; 30]. Moreover, this technique is sensitive enough to identify plant odors from specific tissues [31], such as flower organs, pollen [32], and nectar [32]. The fiber with three components divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS) has been extensively used due to its high ability to extract a large number of volatiles compared to other fibers [21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33], and therefore was employed in the current study.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Citrus types** & **Common name** & **Cultivars** & **Code** \\ \hline
1 & _C. reticulata_ Blanco & Mandarin & Ponkan & PK \\
2 & _C. unshiu_ Marc. & Mandarin & Satsuma & ST \\
3 & _C. sinensis_. (L.) Osbeck & Sweet orange & Liubencheng & LBC \\
4 & _C. sinensis_. (L.) Osbeck & Sweet orange & Qingjia & QJ \\
5 & _C. limon_. (L.) Burm. & Lemon & Eureka & ERK \\
6 & _C. medica_. (L.) & Citron & Bergamot & BM \\
7 & _C. grandis_ (L.) Osbeck & Pomelo & Yuhuanyou & YHY \\
8 & _C. grandis_ (L.) Osbeck & Pomelo & Zaoxiangyou & ZXY \\
9 & _C. changshanensis_ Chen et. [PERSON] (_C. aurantium_\(\times\)_C. grandis_) & Citrus hybrid & Huyou & HY \\ \hline \hline \end{tabular}
\end{table}
Table 1: Citrus cultivars included for the study of flower volatiles.  
The percentage and retention indices of identified compounds are listed in Table 2, where the reported volatiles and their amounts are presented as peak area percentage for each cultivar and each stage. Compounds detected belonged to different chemical classes; aldehydes (9), monoterpne hydrocarbons (17), oxygenated monoterpenes (26), sesquiterpene hydrocarbons (32), oxygenated sesquiterpenes (6), ketones (2), esters (4), and miscellaneous (14) (Table S1). The number of volatiles differed according to the stage of flower blooming and cultivar. Of 110 volatiles detected, 68 were identified and reported in previous studies [32,34,35,36,37,38,39], while the other 42 have not been reported previously (Table S1). This might be explained by the more advanced analysis techniques or wide range of citrus cultivars or larger number of blooming stages used in this study.
# Landslide Displacement Prediction Method Based on GA-Elman Model
## 3 Application Research and Method Comparison
### Landslide Displacement Prediction of GA-Elman Model  
The weights and thresholds of Elman neural network are usually initialized by random numbers between intervals [\(-0.5\), \(0.5\)], and the initialization parameters have great influence on network training. Therefore, the purpose of this paper is to optimize the initial weight and threshold value of Elman neural network by GA[32,33]. The parameters that often need to be set in GA include population size, crossover rate, mutation rate, code length, etc. [34]. The setting of these parameters largely determines the operating performance of GA. According to the research result, the greater the crossover rate, the greater the chance of excellent individuals appearing, and the replacement of new and old individuals is quicker [35]. The empirical value is between [0.25, 1]. The variation rate is generally between [0, 1].  
In the network structure designed in this paper, the genetic algorithm runs for 50 generations, and the other control parameters are set as follows: population size: 40, crossover rate: 0.7, mutation rate: 0.01, individual binary code length: 10. According to the GA-Elman workflow introduced in 2.2, the first 97 days between 6 July 2020 and 30 October 2020 are selected as the training set, and the last 20 days of monitoring data are selected as the test set. Considering the rain factor and the non-rainfall factor respectively, the rolling forecast method is used to predict the displacement deformation trends for 1 day, 10 days and 20 days, respectively.  
Figure 5 is the displacement time series of GA-Elman model for displacement training set data and prediction data. The results show that the effective and reasonable control parameters of GA provide a scientific and reliable input vector for the Elman model, and the displacement change can be effectively predicted by the constructed GA-Elman model.  
After the GA-Elman model was used for training, a test set was selected to verify and analyze the trained model. Quantitative comparison was made between predicted values and test set. Figure 6 shows the prediction and comparison of GA-Elman model on test set.  
Figure 6 shows that when the GA-Elman model continuously predicts 1 day, 10 days and 20 days, the predicted value of landslide displacement is very close to the actual value. The results show that a reasonable and reliable input vector is set for the construction of the GA-Elman model and the GA-Elman machine learning model can be used to accurately predict the landslide displacement. Figure 7 is the prediction and comparison of measured displacement and predicted displacement by GA-Elman model. The model has a certain convergence speed and accuracy, and has a relatively good adaptability, which can effectively and accurately predict the displacement change in advance.  
Figure 4: Comprehensive analysis curve of rainfall and displacement.  
Figure 5: Prediction and comparison of GA-Elman model on training set.  
Figure 6: Prediction and comparison of GA-Elman model on test set.
# Patch-Wise-Based Diffusion Model with Uncertainty Guidance for Low-Light Image Enhancement
## 4 Experiment
### Qualitative Comparison with Different Methods  
Qualitative Comparison of Low-Light Image Enhancement Methods on LOLv1 and LOLv2 Datasets  
To evaluate the performance of different low-light image enhancement methods, we conducted a qualitative analysis on the LOLv1 and LOLv2 datasets, as shown in Figures 2 and 3.  
The methods compared included KinD++ [69], SCI [75], Zero-DCE++ [50], FourierDiff [76], GSAD [28], and our proposed method. The original low-light images are characterized by poor illumination, low contrast, and loss of detail. KinD++ [69] significantly improves brightness and contrast but introduces over-enhancement artifacts, particularly in bright regions, leading to a loss of naturalness. SCI [75] enhances structural details and reduces noise but results in a slightly darker output, with less balanced overall brightness and contrast. Zero-DCE++ [50] provides a noticeable improvement in brightness and contrast while maintaining a good level of detail, but it tends to introduce color distortion and over-enhancement in certain areas. FourierDiff [76] effectively enhances the image by improving both the brightness and the fine details, but it sometimes introduces minor artifacts and noise, particularly in dark regions. GSAD [28] produces well-balanced and natural-looking images with improved brightness and contrast, effectively reducing  
Figure 3: Qualitative comparison of LLIE methods on LOLv2 dataset. The methods compared include KinD++ [69], SCI [75], Zero-DCE++ [50], FourierDiff [76], GSAD [28], and our proposed method.  
Figure 2: Qualitative comparison of LLIE methods on LOLv2 dataset. The methods compared include KinD++ [69], SCI [75], Zero-DCE++ [50], FourierDiff [76], GSAD [28], and our proposed method.  
noise and preserving the structural details, making it one of the better-performing methods in terms of visual quality. Our proposed method outperforms the others by significantly improving brightness and contrast while maintaining natural appearance and fine details, making it the superior choice for low-light image enhancement tasks, particularly in applications where visual quality is crucial.  
Furthermore, we conducted a qualitative analysis on various low-light image datasets, such as WW, NPE, MEF, LIME, and DICM, to evaluate several enhancement algorithms, as shown in Figure 4. The results show that all methods can improve image quality to varying degrees, enhancing brightness and contrast. However, each method also exhibits certain limitations: the SCI method [75] tends to overexpose sky areas, Zero-DCE++ [50] introduces slight color bias, KinD++ [69] generates noise in highlight regions, and GSAD [28] may blur edges. In comparison, FourierDiff [76] focuses on overall balance, while our method stands out in maintaining detail clarity, color accuracy, and overall visual appeal, especially on the more challenging LIME and DICM datasets.
## 2 Optimization Model for Reference Trajectory in ARP
### Optimization Model for Generating the Reference Trajectory in ARP
\begin{array}{l}\dot{x}(t)=v(t)\cos(\theta(t))\\ \dot{y}(t)=v(t)\sin(\theta(t))\\ \dot{\theta}(t)=\frac{v(t)\tan(\phi(t))}{l}\\ \dot{v}(t)=a(t)\\ \dot{\phi}(t)=w(t)\end{array} \right. \tag{5}\]  
where \((x,y)\) represents the center of the rear axle; \(v\) and \(a\) denote the velocity and acceleration of the vehicle, respectively; \(\theta\) is the heading angle; \(\phi\) is the steering wheel angle; \(w\) is the steering wheel angular velocity; \(l\) is the length between the front and rear wheels, and \(t\) signifies time. \(\dot{\theta}(t)=\frac{v(t)\tan(\phi(t))}{l}\) establishes the dynamic relationship between the steering angle \(\phi(t)\) and the change in the heading angle \(\theta(t)\). The steering angle \(\phi(t)\) directly controls the rate of change of the vehicle's heading angle \(\theta(t)\). To ensure that the trajectory generated during the vehicle's steering process complies with the mechanical limitations of the vehicle's steering mechanism, and at the same time meets the steering system's requirement for continuous changes in the steering wheel angle, the following constraints are added:  
\[\left\{ \begin{array}{l}\phi(t)\in[\phi_{\min},\phi_{\max}]\\ |k(t)|\leq k_{\max}\end{array} \right. \tag{6}\]  
where \(k=\frac{w(t)}{l\cos^{2}\phi}\) represents the derivative of the instantaneous curvature.
# Cryptographic Protocol with Keyless Sensors Authentication for WBAN in Healthcare Applications
## 2 Related Work
The protocol proposed by the authors has protection features against intermediate node compromise, sensor node spoofing, and base station compromise attacks, in addition to the security features proposed by [PERSON] et al. [25]. The protocol is executed between 3 main entities: Sensor Node (N), Intermediate Node (IN), and a Hub Node (HN). The protocol contemplates three primary phases to achieve authentication. The security assessment is performed with the help of the AVISPA [17] tool. The results thrown by the tool showed that the proposed protocol is secure in the different ways that can be reviewed. In 2020 the authors of [26] presented a lightweight ECC-based end-to-end authentication protocol for WBAN to overcome the vulnerabilities in the protocol of [PERSON] et al. [27]. The proposal considers three entities: the User (U), the Network Manager (NM), and the Application Server (AS). The protocol contemplates the communication between the entities along three main phases to guarantee authentication. The authors present a security analysis using the AVISPA [17] tool. The results showed that the protocol is secure in the different scenarios in which it was simulated. In the same way, an informal analysis is presented, showing that the proposal is secure against different attacks, unlike the resistance presented by the protocol of [PERSON] et al.
## 4 Optimal Control Modeling for the UAV-UGV Formation-Maintenance Controller
### Optimal Formation-Maintenance Controller Design  
According to the state-space equations of the system, the controller design problem for UAV-UGV formations is a non-zero set-point output-regulation problem with constant or slow-varying perturbations. Two steps can be taken to solve this problem: the first step is to design an optimal output regulation controller that can solve constant or slow varying perturbations. The second step is to design an optimal controller to solve the non-zero set-point problem so that the UAV-UGV formation can be stably maintained at the desired formation position.
## 3 Results and Analysis
### Raw Data Processing of Radial Stresses  
The radial stress on the extruded face of the lug plate can be obtained using the following the method described in the whole text. Figures 11-15 show the diffraction intensity curves at different azimuth angles for each detection point. According to the slope and intercept of the fitted straight line with the diffraction angle and azimuth angle sine squared, and then using the modulus of elasticity and Poisson's ratio material parameters of the TC4 lug plate sample, the radial residual stress values distributed along the specified paths on the extruded surface of the lug plate after cold expansion were obtained, and the main data of the radial stress calculated using the X-ray diffraction method are listed in Table 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Detection Point** & **2\(\theta_{0}\) (Deg)** & \(\frac{\partial 2\theta_{0}}{\partial\sin^{2}\psi}\) & **Stress Constant** & **Radial Stress (MPa)** \\ \hline I & 141.772 & 0.119 & \(-246.003\) & \(-29.274\) \\ II & 141.607 & \(-0.585\) & \(-247.149\) & 144.582 \\ III & 142.131 & \(-0.992\) & \(-243.515\) & 241.567 \\ IV & 142.339 & \(-1.454\) & \(-242.076\) & 351.979 \\ V & 142.160 & \(-0.984\) & \(-243.314\) & 239.421 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Main data of radial stress calculated using the X-ray diffraction method.  
Figure 11: Raw data of radial stress at detection point I. (**a**) Diffraction intensity profiles at different azimuth angles. (**b**) Straight lines fitting the diffraction angle to the azimuthal angle.  
Figure 12: Raw data of radial stress at detection point II. (**a**) Diffraction intensity profiles at different azimuth angles. (**b**) Straight lines fitting the diffraction angle to the azimuthal angle.  
Figure 14: Raw data of radial stress at detection point IV. (**a**) Diffraction intensity profiles at different azimuth angles. (**b**) Straight lines fitting the diffraction angle to the azimuthal angle.  
Figure 13: Raw data of radial stress at detection point III. (**a**) Diffraction intensity profiles at different azimuth angles. (**b**) Straight lines fitting the diffraction angle to the azimuthal angle.
# Application of Artificial Intelligence to Forecast Drought Index for the Mekong Delta
## 2 Materials and Methods
### Methodology
#### 2.3.6 Model Evaluation Method  
The evaluation of the accuracy of models forecasting the Standardized Precipitation Evaptranspiration Index (SPEI) in the Mekong Delta is based on the following indicators: Mean Absolute Error (MAE), the Mean Square Error (MSE), Root Mean Square Errors (RMSEs), the coefficient of determination (R\({}^{2}\)), and the Mean Absolute Percentage Error (MAPE) [46]. The criteria for evaluating (calibrating) the models are presented in Equations (21)-(25):  
\[MAE=\frac{1}{N}{\sum_{i=1}^{m}}|P_{i}-M_{i}| \tag{21}\]  
\[MSE=\frac{1}{n}{\sum_{i=1}^{m}}(P_{i}-M_{i})^{2} \tag{22}\]  
\[RMSE=\sqrt{\frac{1}{N}{\sum_{i=1}^{N}}(P_{i}-M_{i})^{2}} \tag{23}\]  
\[R^{2}=1-\left(\frac{ESS}{TSS}\right) \tag{24}\]  
\[\text{MAPE}=\frac{1}{N}{\sum_{i=1}^{N}}\bigg{|}\frac{M_{i-}P_{i}}{M_{i}}\bigg{|} \times 100 \tag{25}\]
## 5 Research Directions
### New DT-Driven Services  
By combining DT and IoT search services, we can interconnect different CPS systems and provide more services. Specifically, the DT bridges CPS in disparate domains, such as transportation, energy, and manufacturing, to obtain a ubiquitous connection. The DT enables more data sharing to create abundant content that improves the IoT search results. For example, the DT bridges electric cars that primarily consume energy with the smart grid that primarily generates and stores electricity. With DT, electric cars can obtain seamless and efficient energy supply via the information from digitized charging piles, solar planes, and so on. IoT search services such as location-based search and content-based search can also help us find IoT devices and data to build DTs [48; 106]. This is particularly important in the rapidly changing CPS environment.  
For example, in the smart transportation system, suppose we are using smart cameras to build digital clones of vehicles. After a few seconds, the vehicle drives out of the range of the smart camera. We can use the location-based IoT search service to find other cameras around the vehicle to ensure that DT can continue to work. As another example, if we want to know the moving trajectory of vehicle, we can use a content-based IoT search service to find all the information that contains the vehicle license plate. Finally, some new services can be provided by DT on different CPS. For example, in the smart manufacturing system, operators can check DT through algorithms to quickly perform quality inspections on a large number of products. In the smart home, users can integrate smart cameras and VR devices to provide holographic remote social interaction. In the smart grid, DT can be used to monitor the status of infrastructures in real time. In smart health care, DT can be used to monitor and record the vital signs of patients so that doctors can make better treatment plans. Likewise, in the smart transportation system, DT can be used to dynamically plan routes and provide additional driving assistance functions such as blind spot monitoring, brake warning, etc.
## 2 Results
### Multivariate Statistical Analysis for MCG with 5-10-15-Growth Years  
#### 2.3.1 PCA  
As before, a PCA was used to intuitively refine group difference among \(\text{MCG}_{\text{5-10-15-years}}\). As shown in Figure 3A, the \(\text{MCG}_{\text{5-10-15-years}}\) were obviously separated into three blocks, indicating that the volatile component profiles of MCG change with growth years. R2X(cum) and Q2(cum) were 0.846 and 0.779, respectively, indicating good adaptability and prediction ability of the established PCA model (Figure 3A).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Groups for** & **Q-Marker’ Name** & **VIP Value** & \(p\) & \(\text{MCG}_{\text{5-15-years}}\) & \(\text{GCG}_{\text{5-15-years}}\) \\ \hline \multirow{4}{*}{\begin{tabular}{c} GCG\({}_{\text{5-15-years}}\) vs. \\ \end{tabular} } & Hexanal & 5.48586 & \textgreater{}0.000 & 1529.33 \(\pm\) 999.73 & 4116.45 \(\pm\) 1860.92 \\  & \(\beta\)-Gurjunene & 1.43177 & 0.006 & 464.68 \(\pm\) 217.47 & 290.00 \(\pm\) 111.51 \\ \cline{1-1}  & 2-Oxo-pentanoic acid & 1.42528 & \textgreater{}0.000 & 44.75 \(\pm\) 48.39 & 225.75 \(\pm\) 167.48 \\ \cline{1-1}  & \
\begin{tabular}{c} Heptanal \\ Octanal \\ \end{tabular} & 1.28533 & \textgreater{}0.000 & 97.54 \(\pm\) 47.60 & 241.62 \(\pm\) 131.77 \\ \cline{1-1}  &
\begin{tabular}{c} Octanal \\ \end{tabular} & 1.11063 & \textgreater{}0.000 & 62.17 \(\pm\) 32.43 & 170.21 \(\pm\) 94.00 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Detailed information of the five potential chemo-markers between \(\text{MCG}_{\text{5-15-years}}\) and \(\text{GCG}_{\text{5-15-years}}\).  
Figure 2: Multivariate statistical analysis base on volatile components from \(\text{MCG}_{\text{5-15-years}}\) and \(\text{GCG}_{\text{5-15-years}}\). (**A**) Principal component analysis (PCA) score plots; (**B**) Orthogonal partial least squares-discriminant analysis (OPLS-DA) score plots; (**C**) S-plots; (**D**) The contents of the five cultivation-dependent markers to distinguish \(\text{MCG}_{\text{5-15-years}}\) and \(\text{GCG}_{\text{5-15-years}}\). (\(p\)-value ** \(<\) 0.01; *** \(<\) 0.001).
## 4 Proposed System
### Feature Extraction and Classification Techniques
#### 4.3.1 Basic Concepts of Convolutional Neural Network (CNN)  
Usually, the traditional CNN is a feed-forward neural network identical to ordinary neural networks like multilayer perception. CNN architecture includes several convolutional layers, pooling layers, regularizations layers and some functions such as activation and loss functions. The description with the mathematical formula of each layer of uses CNN is described below [10; 58; 59].