esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Thus, there is need for an IRS to distinguish malicious activity (cyber-criminal) from non-malicious activity (a person mistakenly obtaining access) and select an appropriate response option accordingly. Furthermore, the response options should be selected according to the attacker's activity. For example, when an attacker has already logged out, terminating the session of the attacker will not be appropriate and will have no effect. Similarly, the attackers try to launch denial of service (DOS) or distributed denial of service (DDoS) attacks to make the resources unavailable to authorized users. A response option that increases service availability and performance is imperative. In spoof attacks, internal attacks, distributed attacks, and password-based attacks, attackers attempts to get access to personal data that is stored on a personal computer. Once the attacker gets access to the confidential information, the attacker can easily modify the stored data; in such cases, the response should be to enforce data integrity and confidentiality. In probing, phishing, and eavesdropping, attackers mostly attempt to collect credentials from the worldwide network and information about susceptibility as well. Thus, these types of attacks require a response option that improves data confidentiality and service availability. This study aims to categorize network attacks and review the general attacks that may occur in computer systems. In addition, this study proposes response options according to intrusion and attack statistics. The rest of the paper is organized as follows. A description of different attacks detected by IDSs and a comparison of attacks according to affected parameters are presented in Section 2. Section 3 explains the common solution to intrusions. Section 4 describes the comparison of attacksaccording to the affected parameters and various threats. Section 5 illustrates the responses to possible attacks. Sections 6 and 7 present the current challenges faced by IRSs and future directions. Finally, the conclusions of the study are presented in Section 8. Table 1 briefly outlines a list of acronyms used in this research paper.
# Segmented Composite Optical Parametric Amplification
###### Abstract  
We propose a novel optical parametric amplification scheme that combines quasi-phase-matching with a composite pulse approach that involves crystal segments of specific lengths. The presented scheme highly increases the robustness of the frequency conversion against variations of the nonlinear coupling and of the pump, idler, or signal wavelengths, and has therefore the potential to enhance high amplification and broadband operation. Simulation examples applied to LiNbO\({}_{3}\) are given.  
nonlinear optics; optical parametric amplification (OPA); composite pulses; broadband conversion +
Footnote †: journal: Applied Sciences
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & & & \(\varepsilon_{\infty}\) & \(\varepsilon_{\rm s}\) & \(\tau\) (s) & \(\alpha\) & \(\sigma_{s}\) (S/m) & **RMSRE** \\ \hline \multirow{4}{*}{\(\tilde{x}\) Debye} & & \(x_{\rm true}\) & 2.3 & 73.3 & \(8.72\times 10^{-12}\) & 0.1 & 1.99 & - \\ \cline{2-8}  & \multirow{2}{*}{\(\tilde{x}\) Debye} & LMA & 2.3 & 73.3 & \(8.72\times 10^{-12}\) & 0.1 & 1.99 & \(6.5\times 10^{-13}\) \\  & & VPA & 2.3 & 73.3 & \(8.72\times 10^{-12}\) & 0.1 & 1.99 & \(7.83\times 10^{-12}\) \\  & & LMA & 12.9 & 71.8 & \(1.07\times 10^{-11}\) & 0 & 2.03 & 1.64 \\  & & VPA & 12.9 & 71.8 & \(1.07\times 10^{-11}\) & 0 & 2.03 & 1.64 \\  & & VPA & \(2.68\times 10^{-11}\) & \(2.86\times 10^{-12}\) & \(4.74\times 10^{-24}\) & \(1.89\times 10^{-13}\) & \(9.23\times 10^{-14}\) & \(3.06\times 10^{-12}\) \\  & & VPA & \(7.27\times 10^{-10}\) & \(7.06\times 10^{-11}\) & \(1.31\times 10^{-22}\) & \(5.12\times 10^{-12}\) & \(2.32\times 10^{-12}\) & \(8.57\times 10^{-11}\) \\  & & LMA & \(0.000135\) & \(2.31\times 10^{-5}\) & \(4.05\times 10^{-17}\) & 0 & \(3.85\times 10^{-7}\) & \(1.67\times 10^{-5}\) \\  & & VPA & \(4.31\times 10^{-5}\) & \(7.35\times 10^{-6}\) & \(1.29\times 10^{-17}\) & 0 & \(1.23\times 10^{-7}\) & \(5.31\times 10^{-6}\) \\ \hline \end{tabular}
\end{table} 
Table 1: Statistical diagnostics on fitting results. Set A of the initial points and 100 mg/dL glucose concentrations were considered. \begin{table}
\begin{tabular}{c c c c c c c c} \hline  & & \(\varepsilon_{\infty}\) & \(\varepsilon_{\rm s}\) & \(\tau\) (s) & \(\alpha\) & \(\sigma_{s}\) (S/m) & **RMSRE** \\ \hline \multirow{4}{*}{\(\tilde{x}\) Debye} & & \(x_{\rm true}\) & 2.31 & 73.3 & \(8.76\times 10^{-12}\) & 0.1 & 1.97 & - \\ \cline{2-8}  & \multirow{2}{*}{\(\tilde{x}\) Debye} & LMA & 2.31 & 73.3 & \(8.76\times 10^{-12}\) & 0.1 & 1.97 & \(3.\times 10^{-24}\) \\  & & VPA & 2.31 & 73.3 & \(8.76\times 10^{-12}\) & 0.1 & 1.97 & \(4.12\times 10^{-22}\) \\  & & LMA & 12.9 & 71.8 & \(1.08\times 10^{-11}\) & 0 & 2.01 & 1.07 \\  & & VPA & 12.9 & 71.8 & \(1.08\times 10^{-11}\) & 0 & 2.01 & 1.07 \\  & & VPA & \(2.45\times 10^{-11}\) & \(2.58\times 10^{-12}\) & \(4.36\times 10^{-24}\) & \(1.73\times 10^{-13}\) & \(8.39\times 10^{-14}\) & \(3.97\times 10^{-23}\) \\  & & VPA & \(2.88\times 10^{-10}\) & \(2.83\times 10^{-11}\) & \(5.2\times 10^{-23}\) & \(2.04\times 10^{-12}\) & \(9.31\times 10^{-13}\) & \(1.26\times 10^{-20}\) \\  & & LMA & \(0.000136\) & \(2.33\times 10^{-5}\) & \(4.09\times 10^{-17}\) & 0 & \(3.9\times 10^{-7}\) & \(1.56\times 10^{-9}\) \\  & & VPA & \(4.28\times 10^{-5}\) & \(7.34\times 10^{-6}\) & \(1.29\times 10^{-17}\) & 0 & \(1.23\times 10^{-7}\) & \(8.53\times 10^{-10}\) \\ \hline \end{tabular}
\end{table} 
Table 2: Statistical diagnostics on fitting results. Set A of the initial points and 250 mg/dL glucose concentrations were considered. Figure 6: Real part of relative permittivity and conductivity of synthetic data (250 mg/dL glucose concentration) and [PERSON]’s and Cole–Cole’s fitted curves. LMA (**a**) and VPA (**b**) with Set C of initial points are used.
# Research on the Accumulated Plastic Strain of Expansive Soil under Subway Loading
## 1 Introduction
In summary, the research on the strain development patterns of expansive soil under dynamic loading is still relatively limited, and a comprehensive and scientifically grounded theory has yet to be established. Furthermore, due to variations in soil properties across different regions, research outcomes also exhibit disparities. Therefore, this study employs indoor dynamic triaxial tests to investigate the impact of various factors on the strain behavior of expansive soil in the Hefei region under subway loading. The dynamic loading in the experiments is designed to closely emulate the authentic characteristics of subway loading to the greatest extent possible.
## 5 Conclusions  
The color matching ability levels of FT, OM-C, and OM-UC were significantly different. FT showed significantly better color matching in comparison to OM-UC specimens for shades B1, B2, and A3. Shade C3 showed comparable color matching ability for FT and OM-UC. Regarding the color stability in cola, FT samples showed significantly lower \(\Delta\)E(better color stability) compared to OM samples for all four shades (B1, B2, A3, C3). The \(\Delta\)E values of OM and FT samples in coffee were comparable for shades B2, A3, and C3, showing comparable color stability.  
The color matching ability of the single-shade resin composite (OM) was influenced by the tooth color. The conventional resin composite showed better color matching ability than the single-shade composite (OM). The color stability of the single-shade resin composite (OM) was dependent on the staining medium. The OM composite showed more compromised color stability than the conventional resin composite in cola and comparable color stability to the conventional resin composite in coffee.  
Conceptualization, E.M.A., S.A.-S., F.A., S.A.-s., and F.V. methodology, A.B., T.A., and K.A.-s., formal analysis, A.A., B., F.A., S.A.-S., S.A.-s. and F.V. investigation, A.B., F.A., and F.V.; data curation, E.M.A., K.A.-s., A.B., F.A., and F.V.; writing--original draft preparation, K.M.A., K.A.-s., A.B., and F.V.; writing--review and editing., E.M.A., S.A.-s., A.B., K.M.A., F.K.A., T.A., K.A.-s., A.B., and F.V.; supervision, F.V., K.M.A., F.K.A., and A.B. All authors have read and agreed to the published version of the manuscript.  
The authors are grateful to the Deanship of Scientific Research, King Saud University, for funding through the Vice Deanship of Scientific Research Chairs, Research Chair for Biological Research in Dental Health.  
Not applicable. No human tissue or patients were involved.  
Data Availability Statement. Data from this study are available on request form the corresponding author.  
Authors declare no conflict of interest.
## 2 Development of the Algorithm
### Dataset Structure
This approach was also utilized to create input data for the regression algorithm, which determines the degree of development of stage P.  
Thus, a hybrid classifier integrates several models, including a global classifier, two binary classifiers for processing S' and V' subclasses, and a regressor for processing the intermediate stage P. The general scheme of its structure is shown in Appendix A.1. Common information data were allocated for the S' subclass and the regression of class P, effectively identifying small and large predictors. Only large predictors were allocated for the V' subclass, which proved to be the most effective approach. The hybrid classifier's training process involved several stages. First, only the global classifier was trained. Then, the binary classifiers and the regressor were trained, and the global classifier processed the input data. To train the global classifier, classes S' and V' included examples from both their samples and from classes SI and VI, respectively. At the next stage, the binary classifiers S' and V' separated the classes S and SI, as well as V and VI. The algorithm for determining the severity of intermediate AMD included a regressor operating in tandem with the Label Propagation (LPA) algorithm [65]. The training was conducted in three iterations using three different versions of the labeled dataset (\(1/6,1/4\), and \(1/3\)). Accuracy was evaluated using a predetermined test dataset, which accounted for 10% of the total labeled data volume. Thus, a method was developed to create a hybrid classifier to address the issues related to the flat classification of AMD stages, particularly in situations involving class imbalance and the presence of overlapping visual predictors for different classes, such as those observed in classes P and S. For example, the results obtained from the four-layer encoder we used in flat classification were unsatisfactory. The flowchart depicting the developed method is shown in Figure 4, where the main steps of the process are presented in plain text. At the same time, specific examples related to our classification of AMD stages are highlighted in italics. Figure 4: A flowchart illustrating a method for creating a hybrid classifier to address flat classification problems.
# Comparing Monitoring Networks to Assess Urban Heat Islands in Smart Cities
## 2 Materials and Methods
### Methods  
The methodology used first evaluates the characteristics of the three networks mentioned above, concerning the determining considerations of the 'Guide to Instruments and Methods of Observation' (WMO) for systems measuring urban climate data. This guide [60] provides guidance and specific recommendations based on national and international norms and the specific bibliography. This first part is structured following the order, terminology, and constituent parts of the monitoring systems proposed by the said guide: choice of components and system programming (Section 3), followed by the placement and installation of the measuring devices (Section 4). Each section first details the most relevant specific considerations of the WMO with respect to each question, later contextualizing them for each network. This work examines, on the one hand, the extent to which the monitoring systems are aligned with the recommended requisites of the WMO; and on the other, allows the characteristics of each network that define their final performance to be known and therefore the recommended scope in the use of said data for measuring UHI.  
A comparative analysis is then carried out of the hourly thermal records from the meteorological stations of the three networks over the same period, on the understanding that the said records correspond directly to the accumulated urban heat (Section 5). The analysis of relative humidity records was excluded from this study, not a commonly used variable in the scientific literature [45; 61; 62]. Furthermore, it does not significantly impact climate comfort within the context of this case study. Before carrying out the data analysis, a quality control was performed to locate those values that could possibly be considered anomalous due to isolated errors in the measuring systems and also to evaluate the consistency and uniformity of the data in the entire set of each of the networks. The analysis was carried out considering two different scenarios: first, by checking the coherence of the data between the networks through the definition of the control points, i.e., through the nearby stations belonging to the different networks (an average distance of 160 m and a maximum of 300 m), and the analysis was later contextualized within a typical period of seven days; second, by differentiating between the control zones (UCZ corresponding to the local scale, i.e., HUZ) with similar topographical altitudes and vegetation that have a sufficient density of AWSs in all three networks; in this case, a typical sub-period of seven days within the general period was used. This work allows the quality of the registers to be known, and the variability of the data obtained to be evaluated according to whether they come from one network or another.  
Finally, there is a comparative discussion of the results obtained in the previous sections from the three networks, analyzing the differences, as well as the scope and performance of each one for monitoring and identifying UHI in smart cities (Section 6).
# An Improved Arc Fault Location Method of DC Distribution System Based on EMD-SVD Decomposition
## 1 Introduction
The empirical modal decomposition method is used to decompose the fault current traveling wave on the line and obtain a series of intrinsic modal functions (IMFs) to extract the highest frequency component. A Hankel matrix is constructed for the highest frequency component signal, and singular value decomposition (SVD) is performed. Then, calibrate the optimum point of the detail component, which corresponds to the arrival moment of the faulty traveling wave head. A bipolar \(\pm 10\) kV distribution system is constructed based on the PSCAD simulation environment, and the accuracy of the proposed scheme is verified in MATLAB. The paper is structured as follows: the traveling wave ranging principle and traveling wave propagation characteristics are introduced in the second part. The third part introduces empirical modal decomposition and singular value decomposition and gives the wave head calibration and ranging scheme. A case study of the system is presented in Section 4. Finally, conclusions are drawn in Section 5.
# Efficient Sizing and Layout Optimization of Truss Benchmark Structures Using ISRES Algorithm
## 4 Benchmark Problems
### 18-Bar Truss Structure  
Figure 8 shows an 18-bar planar truss with five vertical concentrated loads which are directed downwards and applied to nodes 1, 2, 4, 6, and 8. The Young's modulus of elasticity is 10,000 ksi, and the material density is 0.1 lb./in\({}^{3}\). It is necessary to ensure that the maximum stress for all the members does not exceed \(\pm\)25 ksi in both compression and tension. Additionally, the buckling strength of each member is calculated as \(\frac{KEA_{1}}{L_{1}^{2}}\) for sizing optimization, and the bars are divided into four groups. All detailed information regarding this structure is given in Table 2. The ISRES algorithm is deployed once again to tackle the challenging sizing- and layout-optimization problems for this 18-bar truss structure. With appropriate parameter settings, the algorithm endeavors to minimize the truss weight while satisfying all the specified constraints.  
As the ISRES algorithm generates multiple optimal solutions due to its stochastic nature, the best-performing solutions are selected based on the objective function value, which aims to minimize the truss weight. The selected solutions are then subject to further examination and comparison.  
Figure 8: Schematic of the 18-bar structure.  
Figure 7: Stress constraint values with allowable limits for 15-bar structure.  
To assess the effectiveness of the ISRES algorithm, the obtained optimal truss designs are compared with solutions obtained from other well-established optimization methods. This comparative analysis includes various metaheuristic algorithms, gradient-based methods, and other state-of-the-art techniques. The comparison takes into account both the quality of the solutions and the computational efficiency of each method.  
Additionally, the study conducts a thorough structural analysis on the optimized truss configurations to verify their compliance with the imposed constraints. Finite element analysis is performed to evaluate the stress distributions, member sizes, and overall stability of the optimized truss designs.  
By comparing the results and verifying the solutions, this study provides valuable insights into the performance of the ISRES algorithm in handling complex truss optimization problems with multiple constraints. The findings contribute to a better understanding of the algorithm's applicability and effectiveness in the field of structural engineering.  
The convergence graph and constraints graph for allowable stress and buckling limit, depicted in Figures 9 and 10, offer a nuanced depiction of the ISRES algorithm's prowess in addressing the intricate optimization challenges of the 18-bar planar truss structure.
## 3 Results and Discussion  
### Dewatering Rate  
Figure 4 shows the dewatering rate of _Eucalyptus urophyllla_\(\times\)_E. grandis_ specimens after 1 h SuCO\({}_{2}\) dewatering. Moreover, the initial and final MCs of the specimens are also presented in the figure. The dewatering rate was compared with the drying rate of 5.5% per hour in CKD at 50 \({}^{\circ}\)C temperature and 84% relative humidity (RH) in previous study [35]. The dewatering rate using SuCO\({}_{2}\) is 5.2 to 11.7 times that of CKD. Fast removal of water in wood is important for timbers, which can prevent attacks of insects, shorten drying time and reducing storage cost of materials. Thus, SuCO\({}_{2}\) dewatering is beneficial for wood industry in these cases. For 10 MPa pressure, temperatures of 35, 40 and 50 \({}^{\circ}\)C dewatering, free water in wood was dewatered by 28.5, 32.6 and 34.8% per hour, respectively, and dewatering rate increased slowly with temperature rising. Similar results were also observed in dewatering at 30 MPa, 35 and 40 \({}^{\circ}\)C; however, in case of 55 \({}^{\circ}\)C, dewatering rate increased significantly. For the same temperature, dewatering rates increased more at higher pressure conditions. The average dewatering rate at 30 MPa is about 1.6 times to that at 10 MPa. All these findings suggest that dewatering using SuCO\({}_{2}\) is much faster than CKD; pressure significantly affects dewatering rate, and temperature has minor effect on dewatering rate at lower pressure. This finding is in agreement with previous report [28]. However, the effect of temperature on dewatering rate became obvious at higher pressure of 30 MPa in this study.  
Dewatering rate relates to concentrations of dissolved CO\({}_{2}\) and wood permeability [38]. Higher pressure accelerates dissolving of CO\({}_{2}\), increasing the concentrations of CO\({}_{2}\) of the free water present in wood cell cavities [39]. During the decompression process, more CO\({}_{2}\) gas bubbles are generated in the free water of wood cell cavities, which expel water quickly from wood. Additionally, wood permeability affects CO\({}_{2}\) penetrating into wood and removal of water from wood. Wood permeability was improved by higher pressure of SuCO\({}_{2}\)[40], which benefits from penetration of CO\({}_{2}\) into wood and water removal from wood, thus resulting in higher dewatering rate at higher pressure of SuCO\({}_{2}\) dewatering.
# IAQ Prediction in Apartments Using Machine Learning Techniques and Sensor Data
## 3 Methods
### Prediction Models-MLT Models and Naive Approach
#### 3.2.2 Random Forest Regression (RFR)  
Random forest includes multiple decision trees. It provides a robust and stable estimate that handles complex relationships and leverages predictive performance [38]. RFR applies bootstrap sampling to create multiple subsets of the original dataset by randomly sampling with replacement [39]. The subsets serve as the training data for an individual decision tree, introducing diversity to minimize overfitting [40]. In this work, RFR was composed of 100 decision trees. This number was chosen arbitrarily to account for the description  
Figure 2: Prediction model structure.  
of a complex relationship between independent and dependent variables. Each decision tree independently predicts five outputs based on the provided 5 \(\times\) 29 input features. Subsequently, the algorithm aggregates the results from all individual trees and calculates their meaning as the final result [41].
# State of the Art of Tactile Micro Coordinate Metrology
## 5 Challenges for Further Development
### Probe/Surface Interaction  
The smaller the workpiece features, the probe sphere diameter and the targeted measurement uncertainty are, the more important becomes the interaction of the probe with the surface. Not only effects of surface indentation due to measurement forces [2], but also stylus wear and deposition of wear debris on the probe sphere become relevant [42]. These effects can be significantly reduced by selection of appropriate materials for the probe sphere, _e.g._, diamond coated spheres [43].  
For very weak probing forces, effects of attractive forces between stylus tip and surface get relevant and may cause sticking effects [2; 11]. These problems may be overcome by novel concepts such as vibrating probes [11; 14; 20].
## 3 Results and Reports
### Classification Metrics  
In order to validate a given model, it is required to estimate its performance using several metrics. In ML applications, learning from testing data is an essential step in approving the results obtained in the validation process. Overcoming unsatisfactory performance requires special attention to the detection of possible weaknesses related to overfitting and underfitting [27]. Throughout the rest of this paper, and in order to evaluate and then validate the performance of our approach, we would use several metrics as well as a confusion matrix that summarizes the rate of correct and incorrect predictions for our binary classification problem. The metrics were TP, FN, FP, TN and represent the number of true positives, false negatives, false positives and true negatives, respectively.  
These four variables were defined as follows based on the confusion matrix:  
* True positives (TP), where Class 1 is correctly predicted;
* True negatives (TN), where Class 1 is incorrectly predicted;
* False positives (FP), where Class 2 is incorrectly predicted;
* False negatives (FN), where Class 2 is correctly predicted.  
These metrics would therefore allow us to calculate some common used standard performance measures such as accuracy, precision, F1-score and recall. The meaning of each measure was explained as follows:  
* Accuracy--the overall performance of our model;
* Precision--the accuracy of the positive predictions;
* Recall--the coverage of the positive sample;
* F1-score--the harmonic average of the precision and recall.  
Tables 4 and 5 show the detailed calculations of confusion matrix and standard performance as well as measures respectively for scores cited below.