esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodologies
### Random Forest  
Random Forest (RF) is an extension of the Bagging algorithm; it is a typical ensemble method composed of multiple decision trees [66]. A decision tree is a tree-like structure  
Figure 8: SMOTE schematic diagram.  
where each internal node represents a test on an attribute, each branch represents an outcome of the test, and each leaf node represents a class label. In Random Forest, each decision tree is created through random sampling, where approximately two-thirds of the training samples are used to train the decision tree, while the remaining samples are used to quantify the accuracy of the prediction. Finally, by collecting the results from each decision tree, a simple averaging strategy is employed to predict the properties of new samples, according to the following formula:  
\[\hat{f}_{rf}^{K}(x)=\frac{1}{K}\sum_{k=1}^{K}f_{i}(x) \tag{3}\]  
where \(\hat{y}\) represents the ensemble regression model, \(f_{i}(x)\) denotes the individual decision tree regression models, and \(K\) is the number of regression trees (i.e., \(N\) estimators).  
In Random Forest (RF), each Decision Tree (DT) follows the same distribution, and the classification error is influenced by both the individual classification ability of the trees and their correlations. To increase the diversity between trees and reduce their correlations, sampling methods are typically employed to generate different subsets for training. However, since each tree uses only a portion of the training data, this may lead to suboptimal training performance. To address this issue, overlapping sampling techniques, such as bootstrap aggregation, are utilized. In this process, random sampling with replacement is performed on a dataset containing m samples to generate a training set of m samples. Approximately 36.79% of the samples are not selected; referred to as Out-of-Bag data, they do not contribute to the model training and can therefore be used for generalization validation. Random Forest effectively reduces the risk of overfitting through this method and enhances classification accuracy by aggregating multiple weak classifiers. This results in higher accuracy, smaller generalization error, and stronger overfitting resistance, outperforming other machine learning algorithms such as neural networks, regression trees, and Support Vector Machines [62]. Currently, Random Forest has been widely applied in various research fields of mineral resource exploration [33; 67; 68; 69].
## 3 Research on Image Acquisition System of Lining Cracks
### Shooting System Hardware Analysis  
#### 3.1.1 Shooting System Selection Subsubsection  
When the camera is shooting, there is a difference in the amount of light received by the subject. Consequently, the gray levels of each pixel on the image will be different because of the nonuniformity of charge accumulation on the sensors. When the width of the crack is less than one pixel on the image, it can still be distinguished by the different gray values.  
To determine the camera resolution required by the shooting system, cracks were created on a concrete piece randomly, and the width of each crack was precisely measured with a crack meter. Subsequently, the camera with a resolution of 1.5 mm/pixel was used for shooting. The relationship between the minimum resolution and the recognition width was analyzed by analyzing the captured images. As illustrated in Figure 4, the cracks with a width of 0.1 mm can be distinguished with 1.5 mm/pixel [20].  
Analysis of the test piece shows that the camera resolution can be 15 times the minimum crack width identification accuracy when identifying concrete cracks. Considering the usual influencing factors such as insufficient light and dust in the tunnel, the conversion coefficient between the camera resolution and the minimum recognition accuracy of this system is selected as ten times. Consequently, the resolution of 1.0 mm/pixel is used to identify pixels with cracks of 0.10 mm wide.
# Seismic Response and Mitigation Analysis of a Subway Station in the Site with Weak Interlayers
## 2 Seismic Response of the Subway Station in the Site with Weak Interlayers  
### Engineering Profile  
The structural model used in this study is a double-story double-span rectangular frame structure for the station, with cross-sectional dimensions and reinforcement details shown in Figure 1. The station's dimensions are as follows: a width of 17 m and a height of 12 m. The thickness of the top slab, middle slab, bottom slab, and side wall of the station are 0.75 m, 0.45 m, 0.8 m, and 0.8 m, respectively. The cross-sectional size of the central column is 0.7 m \(\times\) 0.7 m, with a longitudinal spacing of 7 m. The site consists of 11 layers of soil, with a vertical distance of 40 m from the soil surface to the bedrock. The site has a width of 119 m and the structural depth is 7 m. Table 1 provides the relevant parameters for the soil in the normal layered site, including soil density (\(\rho\)), shear wave velocity (\(Vs\)), and Poisson's ratio (\(\
u\)).  
In order to analyze the seismic response patterns of subway station structures situated within weak interlayer sites, three factors such as the thickness, location, and strength of the weak interlayer are discussed. The simplified diagram of the conditions is presented in Figure 2. By modifying the shear wave velocity of soil layer 4 (adjusted from 275 m/s to 80 m/s), a weak interlayer site is introduced at the midsection of the subway station structure. The thickness of the weak interlayer is varied (1, 1.5, 2, and 2.5 m) to study its impact on the seismic response of the subway station structure. Five different conditions  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Layer** & **Thickness (m)** & \(\rho\) & \(Vs\) & \(\
u\) \\
**No.** & & **(kg/m\({}^{3}\))** & **(m/s)** & \\ \hline
1 & 4 & 2000 & 170 & 0.3 \\
2 & 4 & 2000 & 205 & 0.3 \\
3 & 4 & 2000 & 240 & 0.3 \\
4 & 2 & 2000 & 275 & 0.3 \\
5 & 2 & 2000 & 275 & 0.3 \\
6 & 4 & 2000 & 310 & 0.3 \\
7 & 4 & 2000 & 345 & 0.3 \\
8 & 4 & 2000 & 380 & 0.3 \\
9 & 4 & 2000 & 415 & 0.3 \\
10 & 4 & 2000 & 450 & 0.3 \\
11 & 4 & 2000 & 485 & 0.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Site soil parameters.  
Figure 1: Size and reinforcement of the subway station. (**a**) Size of the subway station. (**b**) Reinforcement diagram of the subway station.  
are set up to discuss the effects of the interlayer's position relative to the subway station structure. To analyze the influence of the interlayer's strength on the seismic response of the subway station structure, assuming that the interlayer passes through the center of the structure, this paper designs five conditions with varying shear wave velocities for the weak interlayer (80, 90, 100, 110, and 120 m/s). The parameters for each condition are summarized in Table 2.
# Securing IPv6 Neighbor Discovery Address Resolution with Voucher-Based Addressing
## 5 Discussion
### Performance Comparisons  
The performance of VBA in real-world scenarios is dependent on each individual node tasked with generating or verifying addresses, similar to existing solutions like SEND and Trust-ND. Each node employing these techniques bears the full expense of every required check on its neighbors' validities. Since the procedure for any VBA's verification is equivalent to that of its generation, its computational cost is equally incurred at both ends of each NDAR transaction, warranting a brief comparison of the results in Section 4 to other address generation and verification methodologies.  
The address generation mechanism used by VBA intentionally increases its computational requirements through the use of a work factor value, shown in the x-axis for Figures 10-13, inclusive. The use of a variable KDF type (CPU-bound or memory-bound) makes it difficult to predict the exact time required to produce each VBA, but the cost-to-work-factor relationships should always express the same linearity on nodes with varying resources. Nonetheless, the data gathered by our experiments in generating VBAs with all three default algorithms demonstrates a high granularity in the selection of a suitable generation time, with reasonable and accommodatable limits well below several seconds. This forms a clear contrast with CGA, whose selected _Sec_ values produce wildly varying address generation times. For example, the experiments by [PERSON] and [PERSON] in [34] demonstrate that a _Sec_ value of one might require anywhere from 0.2 to 0.6 s to generate a valid CGA, while incrementing the _Sec_ value to two might require more than an hour.  
Neighbor verification performance comparisons between VBA, SEND, and other methodologies are more ambiguous and difficult to determine because each cost depends on aggregate network composition, where any two nodes can have disparate resources. SEND requires an evaluation of a digital signature with every received NDP message from neighbors without utilizing any optimizing state information to increase processing speeds. Likewise, proposals such as Trust-ND must spend time evaluating social scores and decomposing neighbor addresses before completing verifications. VBA finds a significant advantage over other security mechanisms because its stateless verification cost is only incurred once per instance of a unique, slow-changing voucher in an ideal setting. It is a simple algorithm with a \"yes\" or \"no\" response to its inputs. This means actual, cumulative verification performance with VBA is always guaranteed to surpass SEND or SEND-like mechanisms in the long term.
# Comprehensive Analysis of Maritime Cybersecurity Landscape Based on the NIST CSF v2.0
## 5 NIST CSF-Based Analysis of Maritime Cybersecurity
### Protect
#### 5.3.2 Awareness and Training  
As defined by the NIST CSF v2.0, the awareness and training category stipulates that an organization's personnel and partners should be provided with cybersecurity education to enhance awareness of their cybersecurity-related duties and responsibilities while ensuring alignment with the relevant policies, procedures, and agreements. In the realm of research, this category has primarily centered on addressing the vulnerabilities associated with the human factor in the cybersecurity equation.  
The scholarly emphasis on education demonstrates its central role in advancing cybersecurity culture. As posited by [PERSON], an elevated cybersecurity culture is principally achievable through enhanced education [76]. Reinforcing this argument, [PERSON] et al. [20] elucidated the correlation between education and maritime safety, proposing a solution for developing maritime digital competencies based on the NIST CSF. [PERSON] et al. [16] painted a picture of the current state of the maritime sector, highlighting its significant lack of comprehensive cybersecurity knowledge. They argued that there is an immediate need for extensive training spanning the entire industry, from ports and supply chains to interfaces. Echoing this sentiment, [PERSON] et al. [77] offered an innovative approach for promoting cybersecurity. They provided in-depth yet easily understandable information to shipping experts, facilitating a better understanding and approach to safety among average users. Taking a human-centered approach, [PERSON] et al. [78] focused on the responsibilities of navigation officers in ensuring the cybersecurity of the Electronic Chart Display and Information System (ECDIS). Their research unveiled the likelihood of human error during tasks and underscored the importance of considering behavioral and cultural nuances in the maritime realm. [PERSON] et al.'s [79] research accentuated the importance of embedding cybersecurity knowledge in maritime educational curricula in response to the mounting concern of cyberattacks in the maritime sector. They proposed a \"lantern\" framework to aid course designers in developing comprehensive cybersecurity courses and setting minimum standards for maritime education and training. In their response to maritime cyber threats, [PERSON] et al. [68] introduced MaCySte, an open-source project that emphasizes realistic training, to raise awareness and build resilience among maritime operators. Meanwhile, [PERSON] et al. [80] utilized the NICE framework to craft the profile of a Security Operation Centre (SOC) operator in the maritime domain. They proposed a practical training program specifically tailored for maritime SOC operators to enhance cybersecurity awareness and training in the maritime sector.
## 3 Results and Discussion
### Texture Profile Analysis  
In Table 7, we present the results of the texture profile analysis of dried sweet potatoes subjected to different impregnating solutions and drying methods. We identified significant differences in hardness between the impregnation solution and drying method (\(p<0.05\)). The hardness of fresh vegetables was higher than that of the dried sweet potatoes. Hardness, which is determined as the first peak force on the TPA curve, is defined as the force required to compress a substance between molar teeth [29]. These results indicate tissue softening after drying. Higher hardness values of fresh fruits or vegetables than those of dried fruits were also observed for cape gooseberry [53], pumpkin, and green bell pepper [54]. Overall, the application of VI lowered the hardness depending on the drying method.  
The dried samples (untreated and pretreated) presented higher cohesiveness and springiness values than did the fresh samples. The differences in values were not high, which means that fresh and dried sweet potatoes have similar strengths of internal bonding. Our results are in agreement with those reported by [PERSON] et al. [53] and [PERSON] et al. [55].  
The drying and VI of sweet potatoes significantly reduced the gumminess, once again due to the intense decrease in hardness, as observed earlier (\(p<0.05\)). VI lowers the gumminess; therefore, for dried sweet potatoes, lower energy is required to disintegrate and chew the products to a ready state for swallowing. Similarly, lower gumminess in cape gooseberry subjected to freezing/thawing prior to drying was observed by [PERSON] et al. [53].  
Figure 1: Control sample: fresh sweet potato (F), dried sweet potato by freeze drying (FD), vacuum drying (VD), sweet potato after VI with 2% NaCl solution (N), onion juice (O), kale juice (K), onion and kale juice (OK).  
The softest samples and lower values of cohesiveness, springiness, and gumminess were obtained as a result of the FD process. This result is similar to that reported by [PERSON] and [PERSON] [54] in the case of pumpkin processed with FD.
# Improving Threat Detection in Wazuh Using Machine Learning Techniques
## 3 Materials and Methods
### Training Supervised Machine Learning Models
#### 3.6.1 K-Nearest Neighbors (KNN) Classifier  
The K-Nearest Neighbors (KNN) [19] model is a supervised learning technique employed for classification and regression tasks. It forecasts the label of a data point by referencing the labels of its nearest neighbors in the feature space, utilizing the idea that analogous data points aggregate together.  
**Implementation Steps:**  
* Defined a parameter grid (param_grid_knn) with n_neighbors ranging from 1 to 100 and used both Euclidean and Manhattan distance metrics;
* Employed GridSearchCV for hyperparameter tuning using 10-fold cross-validation;
* Trained the model on the training set (X_train, y_train);
* Evaluated the best model on the test set (X_test, y_test) to provide an unbiased estimate of its performance;
* Used the best model to make predictions on the test set, obtaining the predicted labels (y_predict_knn).
# Edge and Cloud Computing in Smart Cities
## 3 Enabling Technologies
### Comparative Analysis of Enabling Technologies  
The integration of enabling technologies within edge-cloud computing frameworks enhances efficiency, scalability, and resilience by optimizing latency, computational intelligence, security, and resource allocation. Their effectiveness depends on improving system performance while minimizing energy consumption, response time, and computational overhead. This section compares their contributions and trade-offs. High-speed communication protocols, such as 5G, 6G, and Wi-Fi 6, reduce latency and enhance data transmission rates, improving real-time interactions. While 5G supports ultra-reliable low-latency communication (URLLC), its infrastructure costs remain high. Future 6G networks promise lower latency but introduce higher power consumption and signal stability challenges over long distances. AI-driven resource management improves task scheduling and workload balancing through RL and FL. RL dynamically adjusts resource allocation, while FL decentralizesmodel training to enhance privacy. However, FL introduces synchronization delays and additional communication costs, requiring optimized coordination. Security mechanisms, particularly blockchain-based authentication, mitigate unauthorized access risks in decentralized edge-cloud environments. Traditional mechanisms impose high computational costs, making them less viable for real-time applications. Lightweight alternatives, incorporating ECC and zero-trust models, enhance security while minimizing overhead. Virtualization technologies, including containerization, facilitate dynamic resource allocation and multi-tenancy. Containers provide faster deployment and lower overhead than VMs, making them well suited for edge workloads. However, security concerns, particularly kernel vulnerabilities, necessitate robust isolation mechanisms. Energy efficiency is a major challenge in edge-cloud computing, requiring a balance between performance and power consumption. Dynamic Voltage Scaling (DVS) and adaptive workload migration help optimize energy use by reallocating tasks to nodes with higher efficiency. While these techniques improve sustainability, they require accurate predictive models to prevent performance degradation. Each enabling technology plays a distinct role in optimizing edge-cloud infrastructures. High-speed networks improve latency-sensitive applications, AI-driven optimization enhances resource management, blockchain strengthens security, and virtualization improves computational efficiency. The selection of an optimal technology combination depends on application-specific requirements, including latency constraints, security considerations, and computational trade-offs. A summarized comparative analysis is presented in Table 3, highlighting each technology's contributions, limitations, and trade-offs.
## 3 Materials and Methods
### Implementation Process  
The implementation process of the project comprises several key stages, as illustrated in Figure 2, beginning with the input images that undergo an enhancement step to improve their quality and prepare them for further processing. The enhanced images are then passed into the Adaptive Ensemble Attention-GAN, which integrates an adaptive ensemble attention mechanism for effective feature extraction and refinement. Within this module, a training phase for layers is conducted to optimize the performance of the network. Following this, testing images are fed into the trained network, which generates segmentation results by leveraging the refined features and the adaptive attention mechanism. The segmentation results are visually analyzed and further evaluated against ground truth data to assess the accuracy and effectiveness of the proposed system. This streamlined pipeline is designed to enhance segmentation performance, making it particularly suitable for applications such as autonomous driving and complex scene understanding Training Setup Details  
The training was conducted using the following configuration:  
* Learning Rate: Initially set to 0.001 with a step decay schedule to reduce it by a factor of 0.1 every 100 epochs.
* Optimizer: Adam optimizer was used for both the generator and discriminator due to its efficiency in converging GANs.
* Weight Initialization: Xavier (Glorot) initialization was used for convolutional layers to ensure stable gradients.
* Regularization: Dropout (rate = 0.5) was applied in intermediate layers to prevent overfitting. L2 regularization (\(\lambda\) = 0.0001) was also used.
* Data Augmentation: Applied random horizontal flipping, brightness variation, random cropping, and slight rotations to improve robustness and generalization.
## 3 Methodology to Develop and Execute a Redispatch Model in Real-Time Operation
### Mathematical Methodology to Implement the Modeling of a Redispatch
#### 3.2.7. Mathematical Formulation Defining Maximum Dispatch Power for System Security
The economic effects on marginal costs are unfavorably noted in this scenario, since the secondary axis of Figure 13 shows the marginal costs obtained in the redispatch, ranging from 148 to 193 USD/MWh. Finally, Figure 14 shows a summary of the dispatch of plants from a base scenario versus all the results of the redispatch model of the six extreme scenarios in real time that show an economic horizon with an hourly resolution. The results of each extreme scenario indicate the trajectory of marginal costs and total generation dispatch to supply demand due to variations in gas, wind, and hydro generation. The total economic dispatch of generation is different from the redispatch, because Figure 14 shows the total of the plants destined to supply the demand while the redispatch indicates the increases and decreases of generation that were not foreseen in the Business as Usual (BAU) model or in the dispatch used with the economic merit list. However, in the primary axis of Figure 14, as the real-time extreme scenario increases, the dispatches of high-cost plants with higher emissions of pollutant gases increase. In the case of RTES3, RTES5, and RTES6, diesel thermal generation is predominant and marginal costs exceed USD150/MWh, as shown in the secondary axis of Figure 8:2 In contrast, for the RTES1, RTES2, and RTES4 scenarios, dispatches of gas-fired thermal generation predominate, becoming a redispatch with lower emissions.