esa-sceva/satcom-chunk-collection huggingface dataset:
# A Robust Machine Learning Model for Detecting XSS Attacks on IoT over 5G Networks
## 3 Proposed Methodology
### Feature Selection Methods
#### 3.3.1 Wrapper Feature Selection Method  
A technique for determining the ideal subset of features that enhances a machine learning model's performance is the wrapper feature selection method. Because the feature selection procedure is integrated within the machine learning algorithm itself, it is known as a \"wrapper\" method. By analyzing different feature combinations and choosing the one  
Figure 2: Feature selection methods.  
that yields the best results, the primary objective is to determine the subset of characteristics that maximizes the model's performance [36].  
Typically, the wrapper feature selection approach is an iterative search procedure that uses a learning algorithm or model with cross-validation to examine different subsets of features. The optimal subset \(S^{*}\) is determined by selecting the subset that maximizes the goal function [37].  
\[S^{*}=\text{argmax}(f(X_{S},y)) \tag{1}\]  
Here, \(X_{S}\) represents the matrix of input features with dimensions (\(n\times|S|\)), corresponding to the subset \(S\) of selected features. The objective function \(f\) is specific to the problem and can be defined based on the learning algorithm or performance metric used, which might include criteria such as accuracy, precision, recall, or other measures of model performance.  
Figures 3 and 4 present the feature selection processes across different datasets and models for detecting XSS attacks. Figure 3 showcases the adopted selected features using three ML models: Naive Bayes, Random Forest, and Decision Trees. Each horizontal bar represents a specific feature, with color-coded sections indicating which models selected it as relevant. Notably, features such as L4_DST_PORT, CLIENT_TCP_FLAGS, and DNS_QUERY_ID were commonly chosen by both Naive Bayes and Decision Trees, suggesting their broad applicability in accurately detecting XSS attacks. L7_PROT0 was uniquely selected by both Naive Bayes and Random Forest, while IN_BYTES was shared between Random Forest and Decision Trees, highlighting its significance. On the other hand, Figure 4 demonstrates the wrapper approach on the Edge-IIoTset dataset [38], utilizing Decision Tree, Naive Bayes, and Random Forest (RF). Key features like Contains Alert, Script Tag, and Contains Question Mark were consistently selected across all methods, emphasizing their critical role. Random Forest favored a broader range, including Contains Semicolon and Contains Greater Than, while Decision Tree uniquely emphasized features like Symbols Ratio and Numbers Ratio. Naive Bayes showed preferences for Letters Ratio. These analyses highlight how different algorithms prioritize diverse aspects of the data, leveraging complementary strengths to enhance detection accuracy and generalization across complex datasets.  
Figure 3: The Selected Features by Wrapper Feature Selection Across Three Algorithms—NF-ToN-IoT-v2 dataset.
## 2 Problem Description  
For many years, the power quality (PQ) field has been focused on waveform distortion problems below 2 kHz. Engineers started to deal with emission problems in the frequency range above 2 kHz relatively recently. The term \"supraharmonics\" was first introduced during the IEEE Power & Energy Society General Meeting in 2013. It refers to high-frequency harmonics, i.e., from the 40 th up to the 3000 th. The main causes of concern over the PQ degradation that occurs in the wide frequency range, or from 0-150 kHz, are the increasing number of non-intentional conducted emission sources, such as personal computers (PC), modern lighting devices, power converters, and battery chargers, as well as the popularity of smart grids with power line communication (PLC) systems and the integration of distributed generation (DG) units [28; 29; 30; 31].  
Light-emitting diode (LED) lamps and fluorescent light sources belong to the category of lighting equipment with electronic modules. Their advantages are low energy consumption, the possibility of flexible shaping of the light beam and smooth change of colour. However, this type of appliance can be a source of electromagnetic disturbances. The electronic power supply systems' chosen structural solutions determine the level of generated emissions. Most modern lighting devices are powered according to the supply structure which consists of a rectifier bridge with a smoothing capacitor and a controlled pulse width modulation (PWM) converter. For example, modern converters dedicated to supplying LED bulbs work in the frequency range from 20 kHz to 300 kHz, providing energy to the receiver in a pulsed manner. This solution is economically advantageous, and the power supply is more efficient. However, the disadvantage is the generation of electromagnetic disturbances, which may noticeably disturb the operation of other devices supplied from the same power network [32; 33; 34].  
Considering that the number of energy-saving lighting devices is exceptionally large on a global scale, the article presents the results of the study on the conducted emissions generated by randomly selected compact fluorescent lamps (CFLs) and LED lamps.  
Spectrum analysis characterizes the contribution of particular frequency components in points of magnitude or phase. There is a distinction between the approach to harmonics analysis up to 50 th harmonic (2.5 kHz for 50 Hz systems, 3.0 kHz for 60 Hz systems) and supraharmonics analysis (up to 150 kHz). Different test and measurement methods are used. In the case of harmonics, it is desirable to find the relation between supply voltage level and particular spectrum components. In supraharmonics analysis, it is more reasonable to examine the influence of the supply voltage on frequency bands rather than on particular spectrum components. While there is a lot of valuable literature presenting the emission of electric equipment, certain evaluations of the impact of specific supply condition patterns have not been collected so far. It might be significant when the digital twin of electrical equipment is considered.
## 2 Simplified Analytical-Based Method  
### Method  
In practice, to reproduce the in-plane deformability of orthotropic floors, engineers usually take account of the orthotropy by adopting shell elements with different in-plane (membrane) stiffnesses in the main directions and by neglecting the contribution of lightening elements. Consequently, the stiffness in the joists' direction, \(k_{eq,j}\), is obtained by imposing an equality between the transverse stiffness of shell elements and that of floor RC elements (i.e., the joists and the slab). In the orthogonal direction, the in-plane stiffness \(k_{eq,j}\) is usually taken to be equal to that of the floor RC slab, neglecting any contribution of joists.  
This procedure is basically derived from the schematization of the floor constitutive elements through single degrees of freedom (DOF) springs with elastic axial stiffness equal to that of each element. The in-plane axial stiffness of the equivalent 2D element is obtained by the sum of the elastic axial stiffness of the springs representing each floor element. With \(k_{s}\), \(k_{j}\), and \(k_{le}\) denoting the axial stiffness of the slab, the joints, and the lightening elements, respectively, the value of \(k_{eq,j}\) is determined as  
\[k_{eq,j}=k_{s}+k_{j}+k_{le}, \tag{1}\]  
where the springs' stiffnesses are evaluated by the product of the Young's modulus of the material and the cross-section area of the element. With \(E_{c}\) and \(E_{le}\) denoting the Young's moduli of the concrete and lightening elements, respectively, and \(A_{s}\), \(A_{j}\), and \(A_{le}\) being the transverse area of the slab, the joists, and the lightening elements, respectively, Equation (1) becomes  
\[k_{eq,j}=E_{c}\cdot(A_{s}+A_{j})+E_{le}\cdot A_{le} \tag{2}\]  
If the contribution of lightening elements is neglected, it becomes  
\[k_{eq,j}=E_{c}\cdot(A_{s}+A_{j}). \tag{3}\]  
Similarly, the value of \(k_{eq,t}\) is determined as  
\[k_{eq,t}=E_{c}\cdot A_{s}. \tag{4}\]  
If the thickness of the shell elements is set equal to that of the RC floor slab, the in-plane stiffness of the shell element in the direction orthogonal to joists is simply equal to \(k_{eq,t}\). However, if this were the case, the stiffness in the joist direction would also be equal to \(E_{c}\cdot A_{s}\). Hence, a stiffness modifier \(k_{mod}\) is commonly used in the applications chosen so that  
\[k_{mod}\cdot E_{c}\cdot A_{s}=k_{eq,j} \tag{5}\]  
Since the in-plane shear stiffness equivalence is not forced, the equivalent shell element may not be suitable to reproducing the actual in-plane stiffness of floors under seismic actions. However, the simplified method can certainly be useful in those cases in which floors are subjected to constant axial stresses along the edges. This situation may occur, for instance, in inclined roof floors under static loads.  
In the next subsection, the effect of floors' in-plane stiffness on the forces arising in the structural elements of a real case study with inclined floors, subject to gravitational loads, is investigated.
# Understanding Query Combination Behavior in Exploratory Searches
## 5 Automatic QCB Classification
### Features  
In addition to the features mentioned, we found some features in existing works which may correlate with our study for classification [46; 47; 48; 49; 50], for example, topic features, which have been provided to be a factor to differ from search behaviors [51]. In addition, we also utilize some common features in exploratory search studies [52], such as session length (the number of web search engine queries issued in a session) [53]. A summary of the features used for automatically classifying QCB sessions is shown in Table 3.  
\begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{2}{c}{**QCB Features**} \\ \hline \multicolumn{2}{c}{**Name**} & \multicolumn{1}{c}{**Description**} \\ \hline \multicolumn{2}{c}{**Query Features**} \\ \hline Number of queries & Number of queries in QCB session. \\ \hline Semantic similarity of queries & Average semantic similarity of queries in QCB session. \\ \hline Subtask belonging of queries & Subtask belonging of CPQ in QCB session. \\ \hline Distance between queries & Number of queries between the first CPQ and the last CPQ in QCB session. \\ \hline End of session & Whether the CBQ is the last query in QCB session. \\ \hline Time between queries & Average time between CPQ. \\ \hline Similarity queries & (1) Average Jaccard coeff between the term sets of CPQ; \\ \multicolumn{2}{c}{(2) Average Jaccard coeff between the term sets of CBQ and CRQ.} \\ \hline Edit distance between queries & Average norm edit distance between query strings. \\ \hline Inter-query time & (1) Average inter-query time per CPQ; \\ \multicolumn{2}{c}{(2) inter-query time of CBQ.} \\ \hline Query length & Query length in number of terms. \\ \hline Embedding distance * & Average cosine distance between embedding vectors of the CBQ and CPQ. \\ \hline \multicolumn{2}{c}{**Click Features**} \\ \hline \multirow{3}{*}{Number of clicks} & (1) Average number of clicks per CPQ; \\  & (2) Number of clicks after CBQ; \\ \cline{1-1}  & (3) Number of clicks in QCB session; \\ \cline{1-1}  & (4) Number of CPQ with no click; (5) Number of CBQ with no click. \\ \hline Number of repeating clicks & Same click after CBQ and any CPQ. \\ \hline Content Features & \\ \hline Learn from contents & Number of combined queries which can be directly found on clicked on web pages or on SERP. \\ \hline Similarity of SERP & (1) Average SERP Similarity returned by CPQ; \\  & (2) Average SERP Similarity between CBQ and each CPQ. \\ \hline \multirow{3}{*}{Content semantic similarity} & (1) Average content semantic similarity of web pages per clicks after CPQ; \\  & (2) Average content semantic similarity of web pages between CPQ; \\  & (3) Average content semantic similarity of web pages between CPQ and CBQ. \\ \hline Other Features & \\ \hline Total dwell time & Total dwell time in QCB session. \\ \hline Topic * & Binary variable for every visited URL topic. \\ \hline Topic Entropy * & Topic distribution entropy. \\ \hline \multicolumn{2}{c}{**} \\ \end{tabular}
\end{table}
Table 3: Features used for automatic QCB classification.
# Vacuum Ultraviolet Single-Photon Positionization of Amino Acids
## 1 Introduction
Alternatively, a VUV light source with a short wavelength can be used. VUV photon energy tunable from 8.0 to 12.5 eV generated from synchrotron radiation was employed in the SP ionization of antibiotics and extracellular neutrals that were laser desorbed from bacterial biofilms [44]. The VUV light source from synchrotron radiation is a semi-continuous wave source, which is unsuitable for use in pulsed laser desorption and time-of-flight mass spectrometry (TOF-MS). A VUV source with a short wavelength (118 nm, 10.5 eV) can be obtained using the ninth harmonic generation by focusing the third harmonic (355 nm) of Nd:YAG into a xenon cell [50; 51]. This source is used extensively because of its suitable photon energy and short pulse duration (~5 ns), which is appropriate for TOF-MS. Compared with other nonlinear optical generation schemes, which have complicated experimental configurations, ninth harmonic generation from a Nd:YAG laser provides a convenient, low-cost, and compact VUV source for SP ionization in a mass spectrometer. Several SP ionization methods using the ninth harmonic of a Nd:YAG laser coupled with TOF-MS have been demonstrated [52; 53; 54; 55; 56]. In this study, the ion signals of 20 common amino acids obtained using UV-MALDI and VUV-SP-positionization were compared. The detection sensitivity of VUV-SP-positionization is generally 100 times higher than that of conventional UV-MALDI. In particular, the ion signal from VUV-SP-positionization is considerably larger than that from UV-MALDI for analytes with low proton affinity, such as glycine [57]. Quantitative measurement was performed using glycine/histidine and tryptophan/phenylalanine mixtures. The measurement indicated that the dynamic range of VUV-SP-positionization is larger than that of UV-MALDI by one order of magnitude. This technique provides a powerful ionization platform for further applications.
## 3 Theoretical Analysis and Simulation of the Frequency Synthesis Chain
### Simulation Results
#### 3.2.2 Stability Analysis  
The impact of the loop filtering parameters of PLL1 on the transient characteristics and system stability is evident from the analysis in Section 3. Bode plots illustrating the frequency domain with the application of an external modulation signal are presented in Figure 7.  
Figure 5: Simulation results showing the phase step response. (**a**) Parameter \(\tau_{1}\) was fixed, while parameter \(k_{p1}\) was varied. (**b**) Parameter \(k_{p1}\) was fixed, while parameter \(\tau_{1}\) was varied.  
Figure 6: Simulation results showing the phase step response. Parameter \(\tau_{2}\) was fixed, and parameter \(k_{p2}\) was varied.  
Figure 7a indicates that when the \(\tau_{1}\) parameter is fixed, increasing the loop filter gain \(k_{p1}\) leads to an increase in the gain margin, phase margin, and stability. Conversely, Figure 7b demonstrates that when the \(k_{p1}\) parameter is fixed, an increase in \(\tau_{1}\) results in a decrease in the gain margin, phase margin, and stability. System stability is maintained when the modulation frequency is below 150 Hz (equivalent to 150 \(\times\) 2\(\pi\) rad/s), as the phase deviation between the stepping signal and the reference source remains close to zero. However, when the modulation frequency exceeds 150 Hz, the phase gain starts to decrease. As the modulation frequency increases, the phase gain decays rapidly, leading to reduced system stability. Based on the simulation results, the following guidelines for setting parameters \(k_{p1}\) and \(\tau_{1}\) are recommended:  
(1) Moderately increasing \(k_{p1}\) optimizes the settling time.  
(2) Parameter \(\tau_{1}\) should be adjusted in combination with parameter \(k_{p1}\). An appropriate reduction in parameter \(\tau_{1}\) enhances the settling time and effectively suppresses overshoot.
## 3 Simulation Analysis of Path Planning Algorithm Based on Improved Genetic Algorithm
### Field Experiment
Therefore, the ROS is used for sensor data collection and analysis. The ROS toolbox in MATLAB (2020 version) is used to extract and process the data packet that was recorded using the Rosbag tool. The ROS toolbox can provide an interface to connect MATLAB and Simulink (2020 version) with ROS. The toolbox includes MATLAB functions and Simulink blocks, which can visualize and analyze ROS data by recording, importing, and replaying the Rosbag files. Figure 7 shows the experimental site where the robot tracks the planned path. After the experiment is completed, the data is extracted by the ROS toolbox and a planned robot tracking trajectory map is drawn. The trajectory and tracking trajectory of the path planning are shown in Figure 8, with the X axis representing longitude and the Y axis representing latitude. In the figure, the solid line is the planned path trajectory and desired path, and the point line is the trajectory tracked by the robot. From Figure 8, it can be seen that the robot can complete trajectory tracking along the planned path line. Using the planned path as a reference, the root mean square error of the robot's lateral position is 0.369 m, the average lateral error is 0.228 m, and the maximum lateral error is 0.566 m, as shown in Table 3. After on-site inspection of the trajectory points with significant lateral errors, it was found that the locations with large errors have some bumpy terrains, which indicates that the terrain on the ground has a certain impact on the walking trajectory of robots. In summary, the experimental results demonstrate the effectiveness and feasibility of the MCGA.
## 3 Results and Discussion  
### Initial Microstructure and Creep Property of K465 Alloy  
The grain size of as-cast K465 alloy is about 1.2 mm. After conventional heat treatment, the microstructure was observed by a SEM. From Figure 1a, it can be seen that the cuboidal \(\gamma^{\prime}\) particles in the dendritic core (DC) are homogeneously embedded in the \(\gamma\) matrix, the size of which ranges from 300 to 600 nm, which is evaluated by the linear intersection approach. However, the precipitation of \(\gamma^{\prime}\) in the interendritic region (ID) is quite different from that in the DC, which is likely caused by chemical segregation during dendritic solidification, as can be seen in Figure 1b. Two types of \(\gamma^{\prime}\) particles formed in the ID, namely, large ones with an irregular shape and small ones with a size similar to those in the DC.  
Apart from the precipitation of \(\gamma^{\prime}\), there is a large number of carbides which form in the ID after heat treatment. The BSE SEM image shown in Figure 1c reveals that these carbides have different contrast and can be classified into two types. Based on the Energy Dispersive X-ray spectroscopy (EDS) analysis, one can be identified as an MC-type carbide, which is rich in Ti, Nb, and W. The other is M\({}_{6}\)C, which is rich in Mo, W, and Cr. Both of these carbides contribute to the high temperature strength of K465 alloy during creep. Due to the variation in chemical composition, these carbides exhibit a different contrast in the BSE micrograph. To further confirm the type of carbides, the chemical composition of these carbides was analyzed by EPMA, the result of which is shown in Figure 1d. This observation indicates a good agreement with the carbides analysis reported in previous studies [26; 27].  
Figure 1: SEM micrographs showing the initial microstructure of K465 alloy after heat treatment: (**a**) \(\gamma^{\prime}\) precipitates in the dendrite core (DC); (**b**) \(\gamma^{\prime}\) precipitates in interendritic region; (**c**) back scattering electron (BSE) SEM image showing the different types of carbides and (**d**) EPMA analysis on the composition of carbides.  
Figure 2a shows one typical creep curve of K465 alloy tested to fracture. The creep strain vs. strain rate curve of the same specimen is shown in Figure 2b. It can be seen that creep strain rate initially declines to a minimum value with the increase of creep strain and then gradually rises until a rupture occurs. On the basis of this curve, creep tests were interrupted with the creep strain reaching 0.03, 0.05, and 0.07 respectively, which roughly corresponds to the 40%, 60%, and 80% of total creep rupture time, and are indicated by the dashed lines in Figure 2a. These samples subsequently received RHT and were creep-tested again to evaluate the effectiveness of RHT.
# Secure and Transparent Space Exploration Data Management Using a Hybrid Blockchain Model
## 4 Implementation and Experimental Analysis
### Experiment Scenarios
#### 4.2.2 Private Blockchain Access Control and Security  
The objective is to ensure that sensitive data on the private blockchain is accessible only to authorized users and that unauthorized access attempts are blocked by the smart contract-based access control. We used a set of private data entries. We defined access control rules such that one user had permission to view the data, whereas another user did not. On the Hyperledger Fabric network, we attempted to retrieve D_priv using both User A's credentials and User B's credentials. This invocation goes through the Fabric chaincode that checks permissions.  
When User A requested the data, the smart contract verified the credentials and returned the data successfully, logging the access event. In contrast, when User B attempted to fetch the same data, the smart contract immediately rejected the request. The contract threw an \"access denied\" response and did not disclose any information about the data.  
This behavior was consistent across different types of data we tested and different unauthorized accounts; any entity not on the approved list was uniformly blocked. The experiment confirms that the private blockchain's access control mechanism is effective: it strictly enforces confidentiality by preventing unauthorized reads. Additionally, we observed that even the authorized accesses were recorded on the private ledger's audit log, which could be reviewed by administrators to ensure proper data usage. No data from the private chain ever leaked to the public domain or to unauthorized parties during our tests, validating the security of the private data storage. This controlled access, combined with the integrity checks from the previous experiment, means that participants can trust that sensitive data remains both intact and confidential.