esa-sceva/satcom-chunk-collection huggingface dataset:
# A Robust Machine Learning Model for Detecting XSS Attacks on IoT over 5G Networks
## 3 Proposed Methodology
### Classification Methods
It operates under the assumption that features are conditionally independent given the class label, a simplification that often leads to effective and rapid classification for high-dimensional data [45]. Naive Bayes is particularly popular in text classification and other applications where feature independence can be assumed. [PERSON] theorem is expressed in Equation (5) [46]. \[P(A|B)=\frac{P(B|A)\times P(A)}{P(B)} \tag{5}\]  
where \(P(A|B)\) is the posterior probability, \(P(B|A)\) is the likelihood, \(P(A)\) is the prior, and \(P(B)\) is the evidence. Naive Bayes models, while simple, are computationally efficient and perform well with large feature spaces, making them a strong choice for initial classifications in complex datasets. Algorithm 4 details the workflow for Naive Bayes classification. ```
Input: Training dataset \(D\) with features \(X\) and labels \(Y\) Output: Probability model for classification
1 Calculate prior probabilities \(P(Y=c)\) for each class \(c\)
2 for each feature \(X_{i}\) in \(X\)
3 for each class \(c\)
4 Calculate the likelihood \(P(X_{i}|Y=c)\) based on the distribution of \(X_{i}\) given \(c\)
5 Predict for new instance \(X_{\text{new}}\): for each class \(c\)
6 Calculate posterior probability \(P(Y=c|X_{\text{new}})=P(Y=c)\prod_{i}P(X_{i}|Y=c)\) returnClass with the highest posterior probability
```  
**Algorithm 4**Naive Bayes Classification
## 6 Experiment and Results
### Target-Tracking Experiments
Compared with the SSD, the FDA-SSD used in this study improved the operation speed by 16.5%, which is consistent with the conclusions in experiment 1. It can be concluded that the FDA-SSD can effectively save computing resources and improve real-time performance compared to the original method. Figure 15: (**a**) Target tracking results of experiment 3: Comparison of motion trajectories under different depth values, (**b**) trajectory comparison in x-axis, (**c**) trajectory comparison in y trajectory comparison in x axis, (**d**) trajectory comparison in z-axis. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Axis** & **X (m)** & **Y (m)** & **Z (m)** \\ \hline RMSE & 0.0392 & 0.0372 & 0.0472 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: RMSE of target tracking in experiment 3.
# FE-Study on the Effect of Gradient Concrete on Early Constraint and Crack Risk
## 2 Finite Element Model and Material Properties Used in Analysis (Materials and Methods)
### Material Properties
#### 2.2.2 Time Dependency of the Material Properties  
In young hardening concrete the material properties depend on the degree of hydration. The progress of hydration is calculated considering the temperature-dependent reaction rate using the concept of effective time as described in Section 2.1.4 (or also [13]). Due to the gradient mixture of the concrete the material properties vary over space even if the temperature is constant. Therefore in this simulation the value of a material property in every point of the structure at a determined time is dependent on the mixture of the concrete and its degree of hydration at this point. In particular, the following material properties are calculated depending on the degree of hydration:  
* Heat generation
* Thermal conductivity
* Young's modulus of the concrete  
Figure 3: Material distribution in the models; (**a**) homogeneous material property distribution of durable concrete; (**b**) zoned material property distribution; (**c**) gradient material property distribution; (**d**) homogeneous material property distribution of hydration energy optimized concrete.  
Figure 2: Zones of the used models and their geometry in the modeled quarter of the concrete block.  
* Poisson ratio
* Concrete compressive strength
* Tensile strength  
Other material parameters (for example thermal capacity, concrete density) are assumed to be constant during hydration, even if they might vary in reality.
# An Approach to Improve the Resolution of DLP 3D Printing by Parallel Mechanism
## 3 Experimental Device
### Mechanical Structure
#### 3.1.1 Kinematic Analysis of Tripteron Parallel Mechanism  
The motion diagram of the Tripteron parallel mechanism is shown in Figure 5. The mechanism consists of two platforms (static platform and moving platform) and three branch chains. The static platform is the fixed frame. The moving platform is connected to the fixed support by three branch chains, each of which is composed of a moving pair (P) and three rotating pairs (R). The P and R axes on a single branch chain are parallel to each other, and the P axes between branches are orthogonal and parallel to the X, Y and Z axis respectively. Under the joint action of the three branch chains, the pure movement of the moving platform in the X, Y and Z directions is realized. Point \(M\) is the center point of the moving platform. \(S_{x}\), \(S_{y}\) and \(S_{z}\) are the motion distances of the slider relative to the origin of global coordinates, respectively. \(l_{zi}\), \(l_{yi}\) and \(l_{zi}\) represent the length of two rods in the three branch chains, respectively.  
Figure 4: Overall mechanical structure model.  
Figure 5: The motion diagram of the triperon parallel mechanism.  
Since point \(M\) is the intersection of three mutually orthogonal branch chain planes, the displacement of point \(M\) in the X, Y and Z directions is synchronized with the sliding block displacement \(S\) of the three branch chains. Therefore, the position solution of the mechanism is obtained from the following expression of Equation (1):  
\[M=S \tag{1}\]  
where \(M\) is \(\left[M_{x},\ M_{y},\ M_{z}\right]^{T}\) and \(S\) is \(\left[S_{x},\ S_{y},\ S_{z}\right]^{T}\). Then, by differentiating Equation (1), the velocity relation between the moving platform and the slider can be obtained following the expression of Equation (2):  
\[\dot{S}=J\dot{M} \tag{2}\]  
where \(\dot{S}\) is \(\left[\dot{S}_{x},\ \dot{S}_{y},\ \dot{S}_{z}\right]^{T}\), \(\dot{M}\) is \(\left[\dot{M}_{x},\ \dot{M}_{y},\ \dot{M}_{z}\right]^{T}\) and \(J\) is a Jacobian 3 \(\times\) 3 identity matrix. It can be seen from the above analysis that the movements of the slider and the moving platform in the X, Y and Z directions are one-to-one corresponding.
# Microcontroller Unit-Based Wireless Sensor Network Nodes: A Review
## 3 MCU-Based Hardware Platforms for Wireless Sensor Nodes
### General IoT Applications
#### 3.4.6 MICAz Mote  
MICAz mote (MEMSIC) is one of the popular commercial wireless sensor motes. It was developed for applications that include indoor building monitoring and security, acoustic, video, vibration and other high-speed sensor data and large-scale sensor networks. Regarding the processor and memory specifications, the MPR2400 that is based on the Atmel ATmega128L low-power microcontroller was utilized. It comprises 128 kB program flash memory, 512 kB measurement flash memory and 4 kB of EEPROM for configurations. Communication between the motes is accomplished using the IEEE 802.15.4-compliant MICAz radio (MPR2400). It is capable of operating within the 2.40 GHz-2.48 GHz band at up to 250 kbps. The RF transceiver's output power ranges from \(-\)24 dBm to 0 dBm. In outdoor and indoor scenarios, communication ranges of up to 100 m and up to 30 m, respectively, are achievable.  
The mote can accommodate a number of different sensor and data acquisition boards through a standard 51-pin expansion connector. In addition, custom sensor boards are also offered. Concerning the power supply, the mote can be powered by two AA batteries. It requires 2.7 V-3.3 V power to operate. The RF transceiver draws approximately 17.4 mA of current when sending data at a power of 0 dBm, 1 mA in the sleep mode and 20 mA in the idle mode. The processor board draws about 8 mA of current in the active mode and below 15 mA in the sleep mode.
# Mutual Authentication Protocol for Role-Based Access Control Using Mobile RFID
## 1 Introduction  
The popularization of computers and the development of network technologies have made the Internet an integral transmission medium for modern information systems. Completeness, security, and confidentiality are the requirements when transmitting information over the Internet. Thorough security mechanisms ensure that only legal users log into remote servers to retrieve services and data. The effective prevention of malicious online attacks is an important issue in information security. With advances in radio frequency identification (RFID) technology, handheld devices with an RFID reader have become more prevalent. As such, users can utilize handheld RFID readers via wireless Internet to obtain the information or services stored within electronic tags. The largest problem of mobile RFID is privacy. Therefore, the protection of users' privacy must be considered when establishing a mobile RFID network.  
RFID systems are contactless identification systems that transmit data via radio waves so that large amounts of data can be read without requiring any contact. With modern technological advances, RFID has been applied in numerous industries. Mobile RFID and traditional RFID technologies differin that the system need not be stored in a fixed location, but can be embedded into a mobile reader, such as a personal digital assistant (PDA) or cell phone. Mobile RFID uses high frequency RFID as a close-range sensor and uses telecommunication services to transmit data. However, the largest issues with this technology are security and privacy for the reader [1].  
This study proposes a certification mechanism so that the back-end database can verify readers and authorize them in a mobile RFID architecture. The reader accesses the role-based access control (RBAC) server in a back-end database and receives permission for two-way identification of the database and electronic tag while restricting the number of tag reads and the readable information content.
# Semi-Supervised Classification via Hypergraph Convolutional Extreme Learning Machine
## 4 Results and Discussion
### Parameter Sensitivity Study
#### 4.3.2 Impact of \(\lambda\) and \(k\)  
In this experiment, we further explore the impact of the other two important hyper-parameters, i.e., \(\lambda\) and \(k\). Figure 6a-d shows the results by grid searching. Here, we set \(\lambda=\left\{10^{-7},10^{-6},\cdots,10^{2}\right\}\) and \(k=\left\{3,6,\cdots,30\right\}\), respectively. We can observe two tendencies from the results. First, to obtain a better performance, the regularization coefficient \(\lambda\) should be set to be less than 1. Theoretically, using a larger \(\lambda\) will punish HGCELM to be a compact model but also resulting in an increased risk of under-fitting the training data. Second, a larger neighbor size \(k\) will guarantee a better classification accuracy in general. However, there will exist noise samples in hyperedges if \(k\) becomes too large. We provide the suitable ranges of the two parameters as \(\lambda\in\left[10^{-3},10^{-1}\right]\) and \(k\in\left[5,10\right]\).  
Figure 5: Classification performance comparison under different number of hidden neurons on (**a**) Iris, (**b**) Wine, (**c**) WDBC, and (**d**) Segmentation datasets.
## 2 Materials and Methods
### Pre-Processing and Processing EO Data
#### 2.4.2 Machine Learning Classification Algorithms
The optimal parameters were determined using the grid search method considering its shorter run time for finding the best combination of \(C\) and gamma parameters compared with other methods. \(C\) is the penalty coefficient that determines the allowed level of error or misclassification. Therefore, a larger \(C\) always achieves a better result in training, but the generated model has a risk of overfitting, which decreases the classification's generalization. The smaller the \(C\), the higher the number of unclassified samples [57]. Hyperparameters tuning: The performance of the ML algorithm depends on hyperparameter adjustment. The grid search method was used in sequence to tune the hyperparameters of ML algorithms. In the grid search method, the domain of hyperparameters is discretized to a grid. Then, the performance of all possible combinations is assessed using statistical metrics (here, cross-validation (CV)). The set of hyperparameters that can maximize the average value in CV is selected as an optimal one for training the model. Table 4 presents the tuned values for the hyperparameters of the ML algorithms. For the other parameters of ML algorithms, the default settings were used. All the ML classification methods and hyperparameter optimization of the present work were implemented in the Classification Learner App in MATLAB 2023b.
## 2 Materials and Methods  
### Chemicals and Reagents  
The following chemicals and reagents were purchased from Sigma-Aldrich (St. Louis, MO, USA): Trolox (6-hydroxy-2578-tetramethylchroman-2-carboxylic acid), 2,2-diphenyl-1-picrylhydrazil (DPPH), 2,2'-azobis (2-methylamidinopropane)-dihydro (AAPH), 2,2'-azinobis-(3-ethylbenzothazoline-6-sulfonic acid)-diammonium salt (ABTS), fluorescein, Folin-Ciocalteu reagent, methanol and formic acid grade HPLC, quinic acid (PubChemChem CID: 6508) and all phenolic compound standards (protocatechuic acid (PubChemCID: 72), catechin (PubChemCID: 9064), chlorogenic acid (PubChemChem CID: 1794427), epicatechin (PubChem CID: 72276), p-coumaric acid (PubChemCID: 637542), sinapic acid (PubChemCID: 637775), ferulic acid (PubChemCID: 445858), rutin (PubChem CID: 5280805), quercetin (PubChemCID: 5280459) with a purity of \(\geq\)96%. The other solvents and reagents used in this study were of analytical grade. All solutions were prepared with ultra-pure water (18 M\(\Omega\cdot\)cm\({}^{-1}\)) obtained from a Milli-Q water purification system (Millipore, Bedford, UK).
# 2-D Seismic Response Analysis of a Slope in the Tyrrhenian Area (Italy)
## 3 Site Response Analysis  
### Seismic Input Motion and Soil Dynamic Properties  
This work takes into specific consideration the site effects' role on the predictable ground motions in the Caronia area. More precisely, when it comes to site effects, we generally refer to the influence of the first 40-50 m of near-surface stratigraphy on the seismic response of the soil. Great importance is also given to the source and the path effects [23].  
Seven different inputs were used, consisting of six synthetic seismograms related to the Messina and Reggio Calabria 1908 earthquake ([PERSON] et al. [24]; [PERSON] et al. [25]; [PERSON] et al. [26]; DISS Aspromonte Est; DISS Gioia Tauro; DISS Messina Strait) and one related to the 1693 Catania scenario earthquake [27; 28; 29; 30]. The seismograms were scaled to the (Peak Ground Acceleration) PGA = 0.176 g provided by the Italian Code NTC 2018 for the site.  
Soil dynamic properties were also taken into account in the numerical analyses through the stiffness attenuation G/G\({}_{\text{o}}\)-\(\gamma\) and damping ratio evolution with strain D-\(\gamma\) curves [31] related to similar materials of the same area [32; 33].  
The dependency of the shear modulus and damping ratio on shear strain level was obtained by interpolating the experimental resonant column tests (RCT) data by [PERSON] et al.'s [34] equation to the experimental data points. For the Caronia area, the following equations were used:  
\[\frac{\text{G}(\gamma)}{\text{G}_{\text{o}}}=\frac{1}{1+\alpha\gamma(\%)^{ \beta}} \tag{1}\]  
to describe the shear modulus decay with shear strain level, in which G(\(\gamma\)) = strain dependent shear modulus, \(\gamma\) = shear strain and \(\alpha\), \(\beta\) = soil constants, with  
\[\text{D}(\gamma)(\%)=\eta\cdot\exp\left[-\lambda\cdot\frac{\text{G}(\gamma)} {\text{G}_{\text{o}}}\right] \tag{2}\]  
to describe the inverse variation of damping ratio with respect to the normalized shear modulus, in which D(\(\gamma\)) = strain dependent damping ratio, \(\gamma\) = shear strain and \(\eta\), \(\lambda\) = soil constants.  
The values of \(\alpha\), \(\beta\) and of \(\eta\), \(\lambda\) are reported in Table 4.
# Multi-Color Channels Based Group Sparse Model for Image Restoration
## 3 Proposed Multi-Color Channels Based GSR Model  
The image restoration algorithm based on the GSR model has achieved good results in applications. However, most image restoration algorithms based on the GSR model transform the image into YCbCr space when processing color images, and only process the Y channel. Finally, the processed Y channel image is combined with other unprocessed channels to obtain the final restoration image. Although the Y channel of the image is formed by the integration of the three channels of a RGB color image in a certain proportion, it cannot preserve the maximum fidelity of the original R, G and B channels. This paper proposes a new GSR model based on multi-color channels, which processes three color channels of a RGB color image at the same time.
## 2 System and Channel Models
### Lognormal Distribution Model for Weak Turbulence  
According to [13], with respect to \(I\), the probability density function (PDF) of a lognormal distribution can be expressed by  
\[p(I)=\dfrac{1}{I\sqrt{2\pi}\sigma_{x}^{2}}\exp\left(-\dfrac{\left(\ln I+ \frac{\sigma_{x}^{2}}{2}\right)^{2}}{2\sigma_{x}^{2}}\right), \tag{3}\]  
Figure 1: System model for optical wave propagation in atmospheric turbulence.  
where \(\sigma_{x}^{2}\) is the log irradiance variance, which can be written as  
\[\sigma_{x}^{2}=1.23C_{n}^{2}k^{\frac{2}{6}}L^{\frac{11}{6}}, \tag{4}\]  
where \(C_{n}^{2},k,\) and \(L\) are the structure constants of the atmospheric turbulence, wave number, and communication distance, respectively.
# Image Completion with Hybrid Interpolation in Tensor Representation
## 2 Preliminary
### RBF Interpolation  
The RBF interpolation [59] is a commonly used mesh-free method for approximating unstructured and high-dimensional data with high-order interpolants. Given the set of \(I\) distinct data points \((x_{i},y_{i})\)for \(i=1,\ldots,I\), \(\forall i:\mathbf{x}_{i}\in\mathbb{R}^{P},\ y_{i}\in\mathbb{R}\), the aim is to find the approximating function \(y(\mathbf{x})\), which is referred to as the interpolant, satisfying the condition: \(\forall i:y(x_{i})=y_{i}\). For the RBFs \(\psi:\ [0,\infty)\rightarrow\mathbb{R}\), the interpolant takes the form  
\[y(\mathbf{x})=\sum_{j=1}^{I}w_{j}\psi\left(||\mathbf{x}-\mathbf{x}_{j}||\right)\,, \tag{6}\]  
where \(\{w_{i}\}\) are real-value weighting coefficients. For \(I\) data points, we have  
\[y_{i}=\sum_{j=1}^{I}w_{j}\psi\left(||\mathbf{x}_{i}-\mathbf{x}_{j}||\right)\,,\quad \text{where}\quad i=1,\ldots,I \tag{7}\]  
The weighting coefficients \(\{w_{i}\}\) can be computed from the system of linear equations:  
\[\mathbf{y}=\mathbf{\
abla}\mathbf{w}, \tag{8}\]  
where \(\mathbf{y}=[y_{1},\ldots,y_{I}]^{T}\in\mathbb{R}^{I}\), \(\mathbf{\
abla}=[\psi\left(||\mathbf{x}_{i}-\mathbf{x}_{j}||\right)]\in\mathbb{R}^{I\times J}\), and \(\mathbf{w}=[w_{1},\ldots,w_{I}]^{T}\in\mathbb{R}^{I}\). If \(I\geq J\) and \(\mathbf{\
abla}\) is a full-rank matrix, system (8) can be solved with any linear least-square solver.
# Abstract
## 4 Use Case Scenario
### Architecture  
The abstraction layer is designed and implemented as a middleware, which sits between the front-end dashboard application and the HE, as shown in Figure 5. The primary role of the abstraction layer is to hide the bad endpoints of the HE API from the dashboard API. The secondary purpose of this layer is to translate requests and responses so that the dashboard API can be compliant with the RESTful standards. This allows the developed applications to gain robustness for deployment while increasing their compatibility for future developments. In addition, this middleware provides the foundations for additional features such as persistence layers, data aggregation and processing, and authentication methods.  
The abstraction layer was designed following a microservice architecture [57] using the Micronaut framework [58]. The microservice approach structures an application as a collection of smaller and consistent services. Under this type of architecture, microservices are separated autonomous components of an application, each accountable for a specific functionality and able to communicate with each other to form a coherent entity. The advantages of microservices oriented development are that it provides better maintainability in complex and large systems by enabling the deployment of many independent services, each of which may have a granular and autonomous life-cycle. An additional benefit is that microservices can scale out independently. Instead of having a single monolithic application that must be scaled as a unit, it can alternatively scale specific microservices to the consumer application need and to an extent to the demand on the network. In our network visualization application, each microservice is deployed using Docker container images.
# Enhancing Telexistence Control Through Assistive Manipulation and Haptic Feedback
## 6 Results and Analysis  
### IVBO Results and Analysis  
We employed a simple contrast analysis to analyze the data collected from the Alpha IVBO questionnaire. This statistical test was used to examine the mean scores for each component of the IVBO questionnaire across the four experimental conditions. The contrast analysis allowed us to determine whether significant linear trends existed in the scores between the conditions. We conducted pairwise comparisons using simple contrasts to investigate the specific differences between conditions further. These pairwise comparisons helped us identify which conditions were significantly different from each other based on their estimated marginal means. When selecting a statistical method to analyze data, researchers often decide between employing Analysis of Variance (ANOVA) or conducting contrast analysis. While ANOVA serves as a powerful tool for evaluating differences among group means, contrast analysis provides several specific advantages that can enhance the rigor and precision of research findings. However, contrast analysis has several benefits over ANOVA, including greater flexibility in specifying hypotheses and a reduced risk of type I error. In addition, contrast analysis can provide more precise information on specific group differences, making it a helpful tool for exploratory data analysis [37, 38]. Given these advantages, we used contrast analysis in our study to investigate the differences between the experimental conditions and identify the specific conditions that significantly affected the IVBO scores.  
(1) Acceptance  
The acceptance component of the IVBO questionnaire evaluates the extent to which participants accept the virtual body as part of themselves, or in other words, their sense of ownership of the virtual/robotic hand or body. It measures the degree to which participants feel the virtual body is their own. Table 3 presents the descriptive statistics for the acceptance component of the IVBO. The variable \"Mode\" has four categories (NN, NA, HN, and HA), comprising 12 participants. The acceptance level was found to be above 0.51 in all modes, except in NA, where it was 0.494. Therefore, assistance was observed to decrease the level of acceptance. However, adding haptic feedback could compensate for this decrease, as shown in Figure 11. The results of the simple contrast did not reveal any significant differences between the different modes for the acceptance component, as demonstrated in Table 4.  
Figure 11: The responses for the acceptance component per condition.