esa-sceva/satcom-chunk-collection huggingface dataset:
# Influence of Cracking on Oxygen Transport in UHPFRC Using Stainless Steel Sensors
## Materials and Methods
### Concrete Characterization
#### 2.2.2 Flexural--Tensile Strength of UHPFRCs  
Four-point bending tests were performed on prisms of size \(100\times 100\times 500\) mm\({}^{3}\), with the loading points at beam length thirds, and the deflection at mid-span being measured by two displacement transducers on the front and back sides. The tensile response was obtained from the load vs deflection curves while using the simplified inverse analysis method that was proposed in [21; 22]. This tensile constitutive model is defined by the parameters: elastic modulus (\(E\)), cracking strength (\(f_{t}\)), ultimate cracking strength (\(f_{th}\)), and its associated strain (\(\varepsilon_{tu}\)). After reaching the ultimate strength, the first macrocrack localization is assumed to occur, and the final softening behavior is defined by a lineal stress/crack opening relation until reaching the crack opening axis at (\(w_{0}\)).  
Table 2 shows the stress-strain constitutive response that was obtained on the two UHPFRCs. All of the tested concretes presented deflection-hardening in the four-point bending test. The tensile behavior, as obtained through the inverse analysis, showed a low strain-hardening behavior (type 2 UHPFRC according with AFGC). The UHPFRC-n mix obtained a slightly lower mechanical response when compared to the UHPFRC mix (see representation in Figure 1). A type 3 UHPFRC according with AFGC (the most demanding UHPFRC type) requires a tensile strain hardening response. In this work, it was decided to use the developed type 2 UHPFRC due to previous knowledge with this mix and its good enough structural response at a competitive cost for real applications [23]. Table 2 also displays the coefficient of variation (CV) of the different parameters, being calculated as the ratio of the standard deviation to the average value of the data.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Concrete Type**} & \multirow{2}{*}{**Spec.**} & \multicolumn{5}{c}{**Constitutive Tensile Law**} \\ \cline{3-6}  & & \(\mathbf{f_{t}\) (MPa)} & \(\mathbf{f_{tu}\) (MPa)} & \(\varepsilon_{tu}\) (\%\({}_{\infty}\)) & \(\mathbf{E}\) (MPa) & \(\mathbf{\omega_{o}\) (mm)} \\ \hline \multirow{2}{*}{UHPFRC} & avg. & 9.80 & 8.10 & 4.48 & 49,639 & 2.87 \\ \cline{2-6}  & \(\boldsymbol{CV}\) & 6.74\% & 17.72\% & 24.86\% & 4.77\% & 16.46\% \\ \hline \multirow{2}{*}{UHPFRC-n} & avg. & 9.02 & 7.65 & 2.98 & 47,517 & 2.49 \\ \cline{2-6}  & \(\boldsymbol{CV}\) & 11.28\% & 11.79\% & 52.63\% & 1.72\% & 14.69\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of the constitutive tensile law obtained for Ultra-High-Performance Fiber-Reinforced Concrete (UHPFRC) and UHPFRC-n.
## 2 Materials
### Test Data  
To verify the validity of the proposed approach and its bathymetric performance, we selected two waveform datasets where the water depth at the site is within the observation range of the Seahawk system (maximum depth \(<\) 35 m), and seafloor observations for some local areas are missing. Figure 1 shows the locations and seafloor height (WGS84 Ellipsoid) ranges of the two test sites. The first test data were acquired from the eastern coast of Donghae-Si, Gangwon-Do, South Korea. The eastern coast of the Korean Peninsula is ridge-shaped with a monotonous coastline and steep slope where the seafloor rises to land, and the tidal difference is small. The coast has a predominantly sandy substrate and is subject to coastal erosion influenced by waves [33]. The dataset consists of 286,720 waveforms for a 381,392 m\({}^{2}\) area with water depth ranging from 5 to 16 m in water depth (Table 2). The second test site is located offshore of the southeastern coast of Jeju Island, which is an uplifted terrain created by volcanic activity. It includes various volcanic coastal terrains, such as coastal cliffs and rock formations, on the southern coast [34]. The dataset was acquired from a rocky shore mainly comprising basalt with an uneven seafloor topography. It includes 143,360 waveforms for a 215,152 m\({}^{2}\) area ranging from 0 to 9.5 m in water depth (Table 2).  
\begin{table}
\begin{tabular}{l l l} \hline  & **Parameter** & **Specifications** \\ \hline \multirow{6}{*}{Sensor} & Laser wavelength & 532 nm (green) \\  & 1064 nm (NIR) \\  & 7 mrad (green) \\  & Laser beam divergence & 10.5 mrad (NIR) \\  & Laser pulse width & 1.7 ns \\  & Scan method & Rotating prisms \\  & Field of view & 20\({}^{\circ}\) \\  & Pulse repetition rate & 10 kHz \\ \hline \multirow{3}{*}{Operation} & Operating flight altitude & 400 m \\  & Swath width & Flying altitude at 70\% \\ \cline{1-1}  & Aircraft speed & 140 kts \\ \cline{1-1}  & Minimum range (1.5 Secchi depth) & 10 m \\ \hline \end{tabular}
\end{table}
Table 1: Specifications of Seahawk system.  
To objectively evaluate the experimental results, the echo-sounding data acquired at a similar time with the test data were utilized as the ground truth. Multibeam echo-sounder (MBES) data are generally used as ground truth for bathymetry; however, a ship-mounted MBES system usually measures water depths greater than 4 m. Accordingly, single-beam echo-sounder (SBES) data were used in test site 2, which has shallow water. The MBES data for test site 1, acquired in November 2021, were surveyed approximately four months before the Seahawk data were obtained. The SBES data for test site 2 were measured in June 2022, representing a three-month gap from the date the Seahawk data were acquired.
# A Robust Machine Learning Model for Detecting XSS Attacks on IoT over 5G Networks
## 5 Conclusions  
The security of fifth-generation (5G) networks remains a primary concern in the telecommunications sector. The involvement of multiple stakeholders, each with varying security standards and solutions, introduces significant challenges for 5G security. Discrepancies in security management among these stakeholders can lead to vulnerabilities and potential security breaches. Among these vulnerabilities, Cross-Site Scripting (XSS) attacks pose a considerable threat to IoT devices. Therefore, developing robust security solutions is essential for the secure deployment of various 5G verticals, such as Industry 4.0 and IoT. Ensuring integrity, availability, and confidentiality remains critical due to the interconnected nature of 5G networks and fully integrated IoT systems.  
This paper presents a novel machine learning-based approach for identifying and detecting cross-site scripting (XSS) attacks in IoT systems operating over 5G networks. The  
Figure 8: Comparing the detection accuracy of XSS Injection attacks with previous studies ([PERSON] et al., 2022; [PERSON] et al., 2022; [PERSON] et al., 2022; [PERSON] et al., 2022).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Author**} & \multirow{2}{*}{**Dataset Used**} & \multicolumn{2}{c}{**Feature Selection/Extraction**} & \multirow{2}{*}{**Highest Accuracy Achieved**} \\  & & **Method** & \\ \hline ([PERSON] et al., 2022) ([PERSON] et al., 2022) & NF-ToN-IoT-V2 & - & 95.70\% using DLGNN \\ \hline ([PERSON] et al., 2022) ([PERSON] et al., 2022) & AWID & Recursive feature elimination, constant removal & 99\% using Decision Trees, Random Forest, SVM \\ \hline ([PERSON] et al., 2019) ([PERSON] et al., 2019) ([PERSON] et al., 2019) & AWID-CLS-R & Custom feature selection method & 99.87\% using a deep network intrusion detection model \\ \hline ([PERSON] et al., 2022) ([PERSON] et al., 2022) & NF-ToN-IoT-V2 & Feature Importance model & 98\% using RF \\ \hline ([PERSON] et al., 2022) ([PERSON] et al., 2022) & NF-ToN-IoT-V2 & - & 96.83\% using Extra Tree \\ \hline \multirow{2}{*}{**Proposed Method**} & \multirow{2}{*}{**NF-ToN-IoT-V2**, **Edge-IIoTest**} & \multirow{2}{*}{**Wrapper and filter**} & \multirow{2}{*}{**99.89\% using Random Forest with Wrapper**} \\  & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparing the detection accuracy of XSS Injection attacks with previous studies.  
proposed approach was rigorously evaluated, and the results demonstrate its effectiveness. Using machine learning classifiers, specifically the Random Forest classifier, the proposed method was able to detect XSS attacks with an astounding 99.89% classification accuracy, accuracy of 99.88, precision of 0.996, recall of 0.998, and F1-score of 1.00.
# A Novel 3D Topography Stitching Algorithm Based on Reflectance and Multimap
## 2 Materials and Methods
### Description of the Evaluation Criteria for Stitching Algorithms
#### 2.3.4 Stitching Error Estimator (Quantitative Criterion)  
The coefficient of determination \(R^{2}\) is an indicator of linearity, and the \(p\)-value indicates if linearity is obtained by chance or not. \(R^{2}\) would drop and \(p\)-value would rise if stitching performed badly or failed. As \(p\)-value is dependent on the number of elementary maps to stitch and is lower if the stitching quality is good, a derived indicator was created, the stitching error estimator \(SEE\) (Equation (3)).  
\[SEE=\frac{log_{10}(p)}{N} \tag{3}\]  
where \(p\) is the \(p\)-value and \(N\) is the number of elementary maps to stitch. Dividing by \(N\) limits the dependence on the number of elementary maps in the calculation of the \(p\)-value of the linear regression. SEE is then lower when the stitching quality is higher and would increase if stitching has failed or has generated defects on the stitched surface. For correct result interpretation and comparison of 3D topography stitching algorithms, the mean repositioning error and the stitching error estimator SEE should be coupled. As the mean repositioning error increases (i.e., the slope of the cumulated repositioning error), the \(p\)-value (and by extension, the stitching error estimator SEE) should be higher for a given algorithm compared to other algorithms to bring relevance to the result. Note that computing the mean repositioning error and the stitching error estimator SEE is not applicable for geometric stitching algorithms, as only the theoretical displacements (linked to the stitching grid) are applied to elementary maps, and not a finer registration comparing overlapping regions of elementary topographic maps.  
Figure 7: Cumulative repositioning errors with two different stitching algorithms: one has failed (**a**) and the other has succeeded in stitching (**b**) the elementary maps.  
These five criteria and indicator (visual inspection, run time, memory usage, mean repositioning error and stitching error estimator SEE) can then be compared for each tested stitching algorithm to choose the best stitching algorithm to apply to a given test case.
# Investigating the Influential Factors of Ride-Hailing Usage Frequency in the Post-Pandemic Era
## 3 Data
### Survey Design and Data Source  
In order to deeply understand the change mechanism of factors that affect the travel frequency of ride-hailing users in the post-pandemic era, this study examines and compares the impact of multi-dimensional factors on the usage frequency of ride-hailing users before and after the pandemic.  
The questionnaire in this study mainly includes the following parts: the usage frequency of ride-hailing services and attitude perception factors before and after the pandemic, personal attributes, and travel attributes. The attitude perception factors mainly consider the degree of agreement on the 15 different factors that affect ride-hailing usage, such as weather conditions, travel time, and so on. Personal attributes mainly include gender, age, education level, occupation, monthly family income, and private car ownership. Travel attributes mainly include original travel modes, travel purposes, and travel duration.  
In order to ensure the quality of the survey, relatively simple and easy-to-understand questions and options were designed to ensure that the respondents could quickly understand the questionnaire's contents during the survey. Considering the risk of infection and the necessity of maintaining a safe social distance during the pandemic, this survey used an anonymous online method. The IP addresses were restricted in Nanjing, and each IP address could only fill out the questionnaire once. To encourage participants to join the survey, we set cash rewards for them after they completed the questionnaire.  
To verify the rationality and validity of the questionnaire design, a pilot survey was conducted from 20 October to 23 October 2022. The survey was distributed through an  
Figure 1: The location of surveyed city.  
online platform, and a total of 152 questionnaires were collected, of which 137 were valid, with an effective recovery rate of 90.13%. The demographic information of respondents indicated that the survey encompassed diverse age, gender, and occupational groups, with the majority having some experience with ride-hailing travel, ensuring the diversity and representativeness of the sample. In the questionnaires, some participants made constructive comments, such as to increase the questions on attitude perception toward the in-car environment and the risk of contact with others.  
The formal survey was conducted from 1 November to 10 November 2022. At this time, Nanjing's education, economy, and daily life activities had fully resumed, and the social environment was relatively stable, which provided good conditions for data collection. A total of 650 questionnaires were distributed, and 609 valid samples were collected after removing the samples that lacked personal information such as age and income, those that had missing information, and those that had been answered in less than 3 minutes; the recovery rate was 93.69%.
# ETAS Space-Time Modeling of Chile Triggered Seismicity Using Covariates: Some Preliminary Results
## 4 Choosing the Covariates in the ETAS Model Applied to Chile Seismicity  
First analysis conducted on Chile catalogs, showed that probably the use of covariates could have improved the performance of the model. Possible choices have also been encouraged by the availability of GPS measurements, which were supposed to be useful in the explanation of triggered seismicity. To explain the intensity of the triggered seismicity, we estimated several models with different sets of covariates, in addition to the magnitude.  
In particular, the suitable covariates in both the areas are chosen based on a forward selection strategy: starting from the model with the first covariate _magnitude_, further covariates are added one by one, and the gain in terms of the Akaike Information Criterion (AIC) is computed. Since the best gain in the AIC values is obtained by adding the variable depth \(z\), it is the second variable introduced in the model. Then, a third variable, chosen from the remaining candidate covariates, is added, and the gain computed. The best result is obtained with the variable Mt5D, which is the third covariate introduced in the model for aftershocks. As a final step, a fourth covariate, Mt1D, gives some further improvement in the AIC value. No further covariates lead to appreciable improvements in terms of AIC.  
The ranking and the order of inclusion of the covariates, in the triggered seismicity component, is the same in both the areas, even with different estimated parameters. Then, the selected covariates are: magnitude, z, Mt5D, Mt1D.
# An e-Learning Toolbox Based on Rule-Based Fuzzy Approaches
## 2 Materials and Methods
### Causal Relevance Approaches: CR-FIR  
The idea behind causal relevance (CR) can be addressed through the following question: how much does each input variable influence the prediction of the output? If it is possible to quantify the importance of each input variable with respect to the output, it becomes easier to obtain good predictions, closest to the best that can be obtained for a specific set of training data.  
Equation (2) describes the \(R_{dis}\) formula used to compute the influence of each input variable, \(i\), that conforms a FIR mask, on the prediction of the output.  
\[R_{disi}=\left\{\begin{array}{l}E_{pi}-E_{pm};E_{pi}-E_{pm}>0\\ \min(E_{pi})*\Delta Inf;E_{pi}-E_{pm}\leq 0\end{array}\right. \tag{2}\]  
\(E_{pi}\) stands for the Mean Square Error in percentage (MSE) obtained when the mask that contains only the output and the \(i\)th input is used to predict the validation data set. \(E_{pm}\) is the MSE obtained when the optimal mask, identified by FIR, is used to predict the validation data set. \(\Delta Inf\) is a factor that helps to preserve the consistency of the model identified by FIR by given a low value to \(R_{dis\,i}\) when the prediction error obtained by the sub-optimal mask without the \(i\)th input is lower than the prediction error obtained by the optimal mask.  
Equation (3) presents the MSE formulae used in this study. \(y_{var}\) stands for the variance, \(y(t)\) is the real output value and \(\hat{y}(t)\) is the predicted output value.  
\[MSE=\frac{E\big{[}(y(t)-\hat{y}(t))^{2}\big{]}}{y_{\text{var}}}.100\% \tag{3}\]  
Once obtained, \(R_{dis}\) allows to compute a weighted Euclidean distance measure taking into account all the relevant input variables, to find better quality neighbors. Remember that the prediction process of FIR methodology is based on the k-nearest neighbor technique. For a deeper and detailed description of CR-FIR the reader is referred to [35].
## 3 Model Analysis
### Mixed-Type Attacks  
The intrusion attack contains a chain of associated programs, and multiple packets form a complete attack process, which may be mixed with some normal packets in nonlinear time. For example, two warezmaster attacks [1] (Table 1) separated by one normal packet (Figure 2). This mixing packet could fool some detection algorithms. In the experiment, the correlation of non-consecutive attacks is captured by FSM, describing the attack process with a chain of state transition.  
Figure 1: Procedures for the intrusion detection experiment. A dataset is divided to training and testing sets. The training set is used to adjust transition probabilities of FSMs via AIS according to known attack types. The testing set is used to calculate classification accuracy of trained FSMs by pretending that the attack types were unknown.  
In the state transition diagram designed for attack incidents (Figure 2), suppose that the state transition probability can be represented by a function containing three parameters, i.e., \(\text{P}(\text{S}_{i}|\text{S}_{j})=\text{f}(\text{X},\text{Y},\text{Z})\), where X, Y, and Z denotes service, flag, and source_bytes, respectively. When the system begins to receive packet, it is at the initial state S0 with normal packets or not conforming to the first state transition function of attack. Given as an example, when the 76,632 nd packet is received, the parameters of the packet are service = ftp, flag = SF, and src bytes = 72. Then, the state transfers to M1. When two cases exist, normal packets are initially received continuously; then, they are kept under state M1 over a certain time t (we use number of packets in this study), wherein time t can be set by users with experience. The attacker might stop further attacks of network activities for certain reasons, or the network packet is lost. Hence, in order to limit the size of memory consumption, the FSM resource will be released, if no additional intrusive packets are received, and the system returns to state S0 for further detection. In the illustration, we manually set that the receiving packet has parameters of service = ftp data, flag = SF, and src bytes > 8334. Then, the state transfers to A1. After being calculated by the output function, the warezmaster attack can be determined.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline
**Packet** & **Duration** & **Protocol Type** & **Service** & **Flag** & **Src Bytes** & **Dst Bytes** & **Land** & **Wrong Fragment** & **Urugent** & **Hot** & **Label** \\ \hline
76,627 & 0 & tcp & http & SF & 210 & 542 & 0 & 0 & 0 & 0 & normal \\
76,628 & 1 & tcp & Smpt & SF & 965 & 328 & 0 & 0 & 0 & 0 & normal \\
76,629 &
# A Hybrid Deep Learning Model Based on FFT-STL Decomposition for Ocean Wave Height Prediction
## 1 Introduction
Most existing methods, both traditional and data-driven, rely heavily on external variables such as meteorological and oceanographic data. When these data are incomplete, as is often the case at some measurement-constrained stations, the accuracy of wave height predictions is severely affected. This study addresses the limitations of existing wave height prediction methods by effectively capturing the seasonal characteristics of wave height data. The model can independently learn the underlying patterns of wave height data without the need for external variables, thus reducing the risk of forecast bias due to missing information. Its ability to capture seasonal characteristics improves forecast performance, especially when ocean observations are sparse or limited. The hybrid model integrates seasonal-trend decomposition using Loess (STL) with component-specific prediction submodels. The proposed method leverages STL decomposition to partition the wave height time series into seasonal, trend, and remainder components, enabling targeted predictions for each. The Seasonal Autoregressive Integrated Moving Average (SARIMAX) model is used for the prediction of the seasonal component, a bidirectional long short-term memory (BiLSTM) model for trend prediction, and a one-dimensional convolutional neural network (1D-CNN) model to capture the remainder.
# A Lightweight Graph Neural Network Algorithm for Action Recognition Based on Self-Distillation
## 4 Experiments and Discussion
### Accuracy  
**When the backbone capacity is proper, the self-distillation guarantees better performance compared with pure supervision**. The evaluated accuracy on datasets NTU xview and NTU xsub are presented in Table 1 and Table 2, respectively. They demonstrate that the accuracies of the whole backbone ST-GCN 20 or ST-GCN 12 by self-distillation are better than the supervised results, while the ST-GCN 8 by self-distillation underperforms the supervised ST-GCN 8. One posssible explanation for the poor performance of ST-GCN 8 is that the self-distillation of a lower capacity backbone misleads the optimization to converge to a sub-optimal point [19]. When the capacities (layers) of backbones are diminished, the accuracy slightly decreases in ST-GCN 12 and extremely decreases in ST-GCN 8. However, the performance of ST-GCN 12 is still competitive compared with ST-GCN 20. Figure 6 gives an explicit impression between the accuracy from self-distillation and accuracy from supervision for each block (the previous tables only show the performance of the overall output). Notice that the supervised models are trained by exactly the same parameters as in the original ST-GCN 20 [1], except for the parallel computing strategy. This could explain the accuracies shown in our results released by the original supervised ST-GCN 20 (88.3 on NTU xsub, 81.5 on NTU xview).  
**When the capacity of the backbone is small, the accuracy from shallower blocks is better.** As shown in Figure 6, at ST-GCN 20 or ST-GCN 12, the contributions from deeper blocks 2 or 3 are explicitly higher than for block 1, while in ST-GCN 8, the contributions from block 1 are higher than the other deeper blocks. One reason for this is that when the capacity of the model is large, it is more difficult to distill the knowledge and then back propagate it by gradient flow across blocks considering the possible gradient vanishing, while when the capacity is small these difficulties are alleviated.  
**The backbones trained with mov + pos data are usually the best.** Tables 1 and 2 and Figure 6 all illustrate this. The _pos_ data contains the 3D positions of each skeleton joint to record the spatial movements of each frame, while the _mov_ data emphasizes the temporal movements amplitude for each skeleton joint at each frame. The _mov_ data helps to fulfill some missing information (e.g., the differences between running and walking) and therefore leads to the best result generated by _mov + pos_.