esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Interference and Network Optimization Difficulties
#### 3.9.3 Optimization Challenges in 5G Networks
Cross-Link Interference occurs when a transmission is made to a neighboring cell in different directions simultaneously, leading to performance failures. Inter-Beam interference occurs when using MIMO technology, with the spatial division of the multiple beams causing interference from adjacent beams either by the same cell or by neighboring cells. Finally, Multi-User interference occurs when multiple users try to transmit information simultaneously, leading to problems during user management in uncoordinated cells.
## 3 Methodology
### The Multi-Head Attention Mechanism  
The Multi-Head Attention mechanism in the Transformer parallelizes the self-attention process by mapping the input queries, keys, and values into multiple subspaces. Self-attention is computed for each subspace, and the outputs are combined and linearly transformed to produce the final result. This approach allows the model to capture diverse relationships within the input sequence, enhancing its representational capacity.  
As shown in Figure 3, the multi-head attention (MA) mechanism starts by applying scaled dot-product attention to the inputs \(V\), \(Q\), and \(K\) through linear transformations, computing each head separately. Although there are multiple heads with distinct parameters WWW for the linear transformations of \(Q\), \(K\), and \(V\), it is collectively referred to as multi-head attention. Finally, the outputs of all heads are concatenated and linearly transformed to produce the final output of the MA mechanism.  
The MA mechanism involves executing multiple self-attention operations on the initial input sequences \(V\), \(Q\), and \(K\). Afterward, it concatenates the outcomes from each self-attention set and applies a single linear transformation to derive the final output. Specifically, its calculation formula is:  
\[\left\{\begin{aligned} & MultiHead(Q,K,V)=Cincat(head_{1},\dots,head_{h })W^{o}\\ & where\ head_{i}=Attention\Big{(}QW_{1}^{Q},KW_{i}^{Q},VW_{i}^{V} \Big{)}\end{aligned}\right. \tag{8}\]  
The multi-head attention mechanism enables the model to focus on information from various subspaces at different positions, making it more effective than single self-attention.
# The Objective Space and the Formulation of Design Requirement in Natural Laminar Flow Optimization
## 2 CFD Solver and Its Validation
### NLF Optimization of RAE2822 Airfoil in Transonic Flow under Fixed Lift  
The AIAA Aerodynamic Design Optimization Discussion Group (ADODG) has defined benchmark optimization problem of airfoil RAE2822 in viscous (Reynolds number is 6.5 million), transonic flow (freestream Mach number is 0.734), subject to lift (0.824), pitching moment (not smaller than \(-\)0.092), and area (decrement is not allowed) constraints [36]. The problem has been studied by many scholars, which has significance with respect to engineering applications [37; 38; 39; 40].  
This paper intends to introduce NLF concerns (i.e., skin friction drag) to the original problem. For better illustration of the effect of laminar design, the fixed value of lift force is therefore modified form 0.824 to 0.5 to prevent the angle of attack being too high which goes against the favorable direction of laminar design. In recognition of the above, the authors formulate an optimization problem as follows:  
\[\begin{split}\text{minimize}\ w_{1}\cdot C_{p}+w_{2}\cdot C_{f} \\ \text{w.r.t.}y,\alpha\\ \text{s.t.}C_{l}=0.500\\ A\geq A_{\text{baseline}}\end{split} \tag{2}\]  
where \(C_{l}\), \(C_{p}\), \(C_{f}\) are the lift, pressure drag and skin friction drag applied on the airfoil samples; \(\alpha\) is the angle of attack; \(y\) is the vertical coordinates of airfoil; \(A\) is the area occupied by the airfoil samples while \(A_{\text{baseline}}\) is the area occupied by RAE2822. The farfield flow comes at Mach number 0.734. The lift coefficient is fixed (hence the angle of attack is determined by dichotomy method in this paper).  
\(w_{1}\) and \(w_{2}\) are the weights assigned to problem. This paper will set up two optimizations with different design requirement. In the basic case, the weights will be assigned as 0.5 and 0.5 for  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & **Transition Position Detection\%** & **Transition Position Improvement \%** \\ \hline Experiment & 60.1 and 63.5 & 3.4 \\ Computational Fluid Dynamics & 60.14 and 63.74 & 3.6 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of laminar length: wind-tunnel experiment vs. CFD simulation.  
Figure 3: Comparison of laminar transition position and pressure distribution between CFD (Computational Fluid Dynamics) simulation and EXP (experiment) result.  
and \(w_{2}\); in the case with modified design requirement formulation, the weights will be calculated via analysis in the following section with consideration of objective space information.
## 4 Experimental Results and Discussion
### Visual Quality Comparison Before and After Texture Mapping
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **Category** & **Original** & **Optimized** & **Improvement Rate** \\ \hline
1 & File Size (MB) & 796 & 216 & 72.86\% reduction \\
2 & Loading Speed (s) & 4.74 & 1.74 & 63.29\% reduction \\  & FPS (Average & & & 13.71\% \\
4 & Rendering & 48.86 & 55.56 & improvement \\  & Performance) & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 18: Comparison of Original vs. Optimized OpenGL 2.21.0 Rendering. high-performance 3D visualization. In addition to improvements in rendering performance, an objective texture quality evaluation was conducted to compare WIT against industry-standard mapping services, Bing Maps and V-World. To ensure the reliability of the results, the evaluation was performed across ten different geographic locations, including Suseodong (Seoul), Jungang-dong (Cheonan), and Myeongji-dong (Busan). These locations were selected to represent diverse urban and suburban environments, ensuring that the findings are not biased by a single dataset. The evaluation focused on two key metrics Table 19:  
* Edge Sharpness (Laplacian Variance), which measures the clarity and definition of edges within the texture. * Contrast (Pixel Intensity Standard Deviation, STD), which indicates the level of detail preservation by analyzing brightness variations across pixels. The results indicate that WIT significantly outperforms Bing Maps and V-World in texture clarity and contrast. The 3008.24 Laplacian Variance score demonstrates that WIT produces sharper edges, preserving fine details in buildings and terrain structures. Additionally, WIT achieves the highest contrast value (78.87), effectively maintaining texture details without excessive smoothing, resulting in a more visually coherent and high-fidelity rendering. These enhancements make WIT particularly advantageous for large-scale GIS applications requiring high-resolution 3D rendering, detailed feature distinction, and enhanced terrain visualization.
# FE Analysis of Motorcycle Helmet Performance under Severe Accidents
## 3 Finite Element Model  
A commercial motorcycle helmet from Thailand was used to analyze and investigate the structural performance under various impact severities compared with the test from TIS 369-2557, which was performed in this study. Accurate results from finite element analysis depend on the proper element types of the structure and material model. In this study, all components of the helmet's structure were created based on a three-dimensional model using SolidWorks (version 2021 from Dassault Systemes, Waltham, MA, USA) and imported for meshing in Hypermesh (version 2019 from Altair, Troy, MI, USA). The models were then assigned different material models in LS-DYNA (R111 from ANSYS, Inc., Washington County, PA, USA) before starting other pre-processing and analysis procedures. LS-DYNA is powerful software for performing dynamics analysis with a nonlinear explicit problem using the central difference method [37]. Furthermore, LS-DYNA also provides a variety of material models for simulation, which is suitable for a helmet structure that consists of different materials.
## 2 Related Works  
Visual tracking is a challenging research issue [22] in computer vision. The existing trackers are generally partitioned into two categories [23]. The first category is to achieve the purpose of tracking by modeling the target and combining the target detection method, which is called generative [24, 25, 26, 27, 28]. [PERSON] et al. [2] have proposed a visual tracking decomposition algorithm that employs a diverse observation-motion model to explain a comparatively apparent shape change caused by lighting variation and fast motion. The algorithm has been further developed to search for proper trackers by Markov Chain Monte Carlo sampling [3]. Another category of tracking methods, which is called discriminative, is to treat the issue as a classification task of foreground (target) and background, by training classifiers in combination with detection and tracking to track target. Discriminative methods are usually similar to tracking-by-detection [22]. Tracking-by-detection trackers [1, 5, 29, 30] are more popular in current research, because of their the high performance and efficiency. [PERSON] et al. [1] propose a Structured Output Support Vector Machine (SVM) and Gaussian kernels that locate a target directly. [PERSON] et al. [5] make use of structural constraints to direct the sampling of a boosting method. [PERSON] et al. [29] propose compressive tracking that learns a Naive Bayesian Model using a compressive sensing theory for dimensionality reduction of Haar-like features to accelerate calculation. [PERSON] et al. [31] merge multiple features into tracking to improve robustness. Additionally, the Sparsity-based Collaborative Model (SCM) [4] integrates both discriminative and generative classifier to generate a more robust tracker.  
Figure 1: Procedure of the adaptive context-aware and structural correlation filter. For a new frame, sampling optimal context patches for holistic and local parts. Then computing the response and Peak to Sidelobe Ratio for every filter. Finally, combining confidence maps of each parts with adaptive weight to obtain the final confidence map.
# Generalized Replay Spoofing Countermeasure Based on Combining Local Subclassification Models
## 3 Related Works
### Deep Learning Approaches  
Due to the boost achieved by deep neural networks (DNNs) in the machine learning field, particularly in classification tasks, antispoofing approaches based on a deep learning paradigm have been proposed. For this purpose, several DL models have been exploited. More specifically, the system outlined in [46] utilizes a dilated residual network (DRN) deep learning model [47] including a ResNet [47] model with an attention filtering mechanism to discard irrelevant audio segments such as background noise. The ResNet [47] deep learning model was also utilized in the system described in [48]. Here, two low-level cepstral features, MFCCs [43] and CQCCs [4], were fed into the network instead of the raw data. A similar model was deployed in the system described in [49]. However, rather than conveying MFCCs [43] as input, this model uses high-frequency Cepstral coefficients (HFCCs). Similarly, the authors in [50] employed ResNet along with SENet [51], Mean-Std ResNet [51], and Dilated ResNet [52] to analyze CQCCs and spectrogram features. The fusion in these models is performed using the greedy fusion scheme presented in [53]. As a result, the fusion of these deep learning models was found in [50] to yield a system that outperformed reported state-of-the-art approaches when using the Asvspoof 2019 Replay Benchmark.  
Recurrent neural networks (RNNs) [9] have also been exploited to design countermeasures. As such, the research in [54; 55] employed long short-term memory (LSTM) [56]. The research in [57; 58] exploited RNN [9] along with a convolutional neural network (CNN) [59]. In these works, CNN functioned as a feature extractor, while RNN performed long dependency processing. Similarly, the study in [60] exploited a combination of CNN and RNN. Specifically, the study combined three i-vector [61] systems, namely, the light convolutional neural network (LCNN) [62] system and the CNN + RNN one. LCNN was also employed along with a small Bayesian neural network [63] in [63; 64]. In [65], the softmax function was replaced with the softpus function to estimate the deep learning model prediction uncertainty. Alternatively, a light convolutional gated recurrent neural network (LC-GRNN) was used in [66], and the authors in [67] adopted a variety of LCNNs based on context gate CNN (CGCNN), which used gated linear unit (GLU) activations as a context-gate for each filter. The adopted feature for this system was Log-CQT.  
Recently, variational autoencoders (VAEs) [68] have also been adopted to devise new countermeasure, such as those used in [69]. Additionally, a combination of two visual geometry group (VGG) [70] models were employed in [71].
# Multi-Granularity User Anomalous Behavior Detection
## 4 Methodology
### Data Fusion and Preprocessing
#### 4.2.1 Behavior Data Fusion  
UEBA conducts threat detection through various user behaviors. The user behavior analyzed in ITD typically includes the following types:  
* Biometric behaviors: user biological behaviors that can be observed and recorded by instruments, such as mouse movement, keyboard input, etc.;
* Cyber activity behavior: cyber activity behaviors of users, such as login events, network packet, or network traffic;
* Psychosocial behaviors: user's psychological and emotional state, such as the power spectrum analysis of electroencephalogram (EEG) data, which can be used to identify insiders using brain wave features;
* Physical behaviors: such as door access, traffic sensor data, etc.;
* Other behaviors: other behavior features or combined behaviors from the above features.  
Researchers from Carnegie Mellon University have constructed public datasets called CERT (computer emergency response team) for ITD and UEBA. The CERT dataset is an assembly of synthetic insider threat data compiled by CERT in collaboration with various partners ([[https://kilthub.cmu.edu/articles/dataset/Insider_Threat_Test_Dataset/12841247](https://kilthub.cmu.edu/articles/dataset/Insider_Threat_Test_Dataset/12841247)]([https://kilthub.cmu.edu/articles/dataset/Insider_Threat_Test_Dataset/12841247](https://kilthub.cmu.edu/articles/dataset/Insider_Threat_Test_Dataset/12841247)), accessed on 6 November 2024). It is crafted through diverse scenarios incorporating traitorous actions and masquerading activities. This comprehensive dataset encompasses records of login activities, web browsing histories (including HTTP traffic), email communications, file access logs, device utilization metrics, LDAP (Lightweight Directory Access Protocol) data, as well as psychometric profiles of the users involved.  
This study is based on CERT R4.2 (version 4.2 of the CERT dataset), released by Carnegie Mellon University [23]. The user behavior types of CERT R4.2 includes logs of login activities, HTTP or browsing histories, email exchanges, file access records, device utilization logs, LDAP data, and psychometric information [14].  
The CERT R4.2 dataset includes two parts: user identity information and user behavior data. User identity information includes static information that is not frequently updated, such as user ID and entry time. User behavior data include user behavior extracted from various user activity logs. The five types, examples, and descriptions of user behavior data are listed in Table 2. Five different types of user behavior data come from different logs and are described with different attributes. Therefore, all activity logs need to be fused to store all user behavior information in a unified data format and order for easy use in subsequent modules.
# Study on Classification Method of Soil Liquefaction Potential Based on Decision Tree
## 7 Conclusions  
(1) Based on the analysis of the shear wave velocity data of existing liquefied sites, it was found that the shear wave velocity did not distinguish the liquefaction of the sites well. The liquefaction potential grade criteria based on the liquefaction probability could effectively improve the applicability of the liquefaction evaluation method based on the shear wave velocity.  
(2) After analyzing the database using the importance ranking method, it was found that the ground motion intensity was the main parameter affecting the liquefaction of saturated soil. Based on this conclusion, a liquefaction probability calculation equation of saturated soil was proposed to enhance the influence of the earthquake intensity. A critical value equation of the shear wave velocity liquefaction for different preset probabilities was proposed, and the reliability of the formula was verified.  
(3) Based on the CHAID and CRT methods, an evaluation table was proposed to classify the sites into four grades of liquefaction potential: low, medium-low, medium-high, and high. The method is simple in form, and the classification was correct for more than 75% of the samples. This showed that the decision tree method could play a role in the establishment of a shear wave velocity liquefaction evaluation method. However, in the vicinity of critical liquefaction with a probability of 50%, the evaluation effect was not outstanding, and further studies need to be conducted in this range.  
This paper explored the application of decision trees for shear wave velocity liquefaction potential assessment. In future studies, with the support of sufficient data, it is necessary to further refine the classification criteria based on the soil type to achieve better assessment results. In addition, the method in this paper is limited to the liquefaction potential of saturated soil, and further study is needed on how to extend it to all sites.  
Conceptualization, Y.Y. and Y.W.; methodology, Y.Y.; software, Y.Y. and Y.W.; validation, Y.Y. and Y.W.; formal analysis, Y.Y. and Y.W.; investigation, Y.Y. and Y.W.; resources, Y.Y. and Y.W.; data curation, Y.Y. and Y.W.; writing--original draft preparation, Y.Y. and Y.W.; writing--review and editing, Y.Y. and Y.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Northeast Forestry University.  
Not applicable.  
No new data were created.  
The authors thank the editor and anonymous reviewers for providing helpful suggestions for improving the quality of this manuscript.  
The authors declare no conflict of interest.
## 1 Introduction
Considering major wildfire events in Chile, according to the Centro de Estudios Publicos (CEP), 2017 wildfires affected over 518,000 hectares across multiple regions [15]. The 2023 wildfires began in late January and impacted more than 430,000 hectares in central and southern Chile, destroying over 800 homes and with 26 fatalities [16]. More recently 2024, devastating fires consumed approximately 43,000 hectares, mainly affecting the regions of Via del Mar and Quilpue, resulting in at least 131 deaths [17]. Despite the potential benefits, this study has several limitations. The precision of DRL-based wildfire prediction models depends on the availability and quality of historical wildfire data, which may be incomplete or inconsistent in certain regions [18]. Furthermore, computational constraints may pose challenges for real-time implementation in emergency response systems [19]. Future work should explore methods to improve data quality, improve model interpretability, and assess the feasibility of deploying DRL-based models in real-world operational settings. The remainder of this paper is structured as follows: Section 2 provides an overview of wildfires, their causes, and the role of artificial intelligence in prediction. Section 3 describes the dataset and key variables used in the study. Section 4 presents the implementation details of the three AI-based approaches. Section 5 discusses the experimental results and performance comparison, followed by the conclusions, limitations, and future research directions in Section 6.
## 5 Qualitative Validation--Results and Analysis
### Analysis
#### 5.2.3 Intermittent Open Circuit  
The IOC shows an abrupt power interruption with electric connection disrupting intermittently every 5 s. The data from the actuator controller show the fluctuating behaviour of the electric motor with intermittent power cuts and the subsequent power spikes occurring every time the connection switches between \"Open\" and \"Closed\" state. It helps understand the impact on the EMA and the controller as they experience functional challenges whilst aligning the braking procedure.  
Under the IOC mode (contrary to OC), the aircraft manages to achieve a full stop but with a longer time (20% more) and distance (25% more) than it would under the normal operating conditions. The actuator becomes susceptible to damage or performance degradation with the increasing temperature values due to the power fluctuation and spikes and an improper modulation of the brake. As this information is validated qualitatively, an instant appreciation of the EMA and EBS health can be made and the maintenance regime can be adjusted accordingly.
# Acoustic Emission Analysis of Fracture and Size Effect in Cementitious Mortars
## 2 Experimental Program and AE Analysis Methods
### AE Analysis Methods
#### 2.4.2 \(b\)-Value Analysis  
AE peak amplitude is related to the magnitude of fracture [32]. The formation of micro-cracks will initiate a high number of low-amplitude AE events, while macro-cracks will generate a low number of high-amplitude events. Similarly, in seismology, the frequency of earthquake events decreases as the magnitude of the shocks increases [33,34]. The magnitude is directly proportional to the logarithm of the rupture area. This makes the magnitude and its level of re-occurrence a vital tool to assess the physical damage level [35,36]. In seismology, the frequency-magnitude relation is quantified by the Gutenberg-Richter empirical formula:  
\[log_{10}N=a-bM_{L} \tag{1}\]  
in which \(M_{L}\) is the Richter magnitude of the event, \(N\) is the number of events within a certain defined magnitude range, \(a\) and \(b\) are empirical constants [18,32,34,35,37].  
Hence, in AE analysis the damage stages can also be quantified with the slope of the AE amplitude distribution [18,20]. This slope is the same as the \(b\)-value in the modified version of the Gutenberg-Richter empirical equation:  
\[log_{10}N=a-b^{\prime}A_{dB} \tag{2}\]  
in which \(N\) is the number of events within a certain defined magnitude range, \(a\) and \(b^{\prime}(b^{\prime}=20\times b)\) are empirical constants, and \(A_{dB}\) is the peak amplitude of the AE events in decibels (dB) [18,32,35].  
In the early stage of material life, the dominance of micro-cracks will give rise to a higher \(b\)-value. As macro-cracks develop, more high-amplitude events start to occur, resulting in a lower \(b\)-value. [PERSON] and [PERSON] [38] investigated micro-crack formation in rock materials and found that at earlier loading stages, \(b\)-value remains high. However, this value lowers when approaching the peak load, finally dropping to a minimum value at the stress drop or at the highest event rate. In addition, the \(b\)-value was found to be linearly related to the fractal dimension of a crack length, meaning that a lower \(b\)-value indicates a more localized fracture (less distributed damage) that results in a greater crack length. [PERSON] et al. [18] performed \(b\)-value analysis on AE data gathered from simply supported reinforced concrete beams subjected to four-point bending. They observed that the \(b\)-value correlates to the fracture in the concrete and the degree of damage localization. [PERSON] et al. [39], [PERSON] [40] and [PERSON] et al. [18] related the fracture stages with a specific \(b\)-value: a \(b\)-value of 1.5 or higher is typically related to distributed micro-cracking, and a \(b\)-value of 1 or lower indicates macro-cracking.