esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance of Millimeter Wave Dense Cellular Network Using Stretched Exponential Path Loss Model
## 2 System Model
### Antenna Model  
The BSs and UEs employ directional beamforming that estimates AoAs and steers the main lobe beam in the preferred direction to achieve maximum directivity gain. For an approximation of the beamforming pattern sectored antenna model is utilized and is defined by three parameters: the main lobe gain \(\left(G_{0j}\right)\), the main lobe beamwidth \(\left(\theta_{j}\right)\) and the side lobe gain \(\left(G_{sl_{j}}\right)\) for \(j\in\left\{BS,UE\right\}\)[22]. The beam direction of typical UE and tagged BS are perfectly aligned leading to a maximum gain of \(G_{0 BS}G_{0 UE}\). The beam direction of interferers and typical BS is independent and has uniform distribution from \(\left[0,2\pi\right]\). Therefore, the gain of the interfering beams is a discrete random variable, \(G_{Z}=a_{k}\) having a probability \(b_{k}\) for \(k=1,2,3,4\) is given below [38]:  
\[G_{Z}=\left\{\begin{array}{ll}G_{0 UE}G_{0 BS}&\text{with probability}&P_{G_{0 UE}}c_{0 BS}=(\frac{\phi_{UE}}{2\pi})(\frac{\phi_{BS}}{2\pi})\\ G_{0 UE}G_{sl_{BS}}&\text{with probability}&P_{G_{0 UE}}c_{i_{BS}}=(\frac{\phi_{UE}}{2 \pi})(1-\frac{\phi_{BS}}{2\pi})\\ G_{sl_{UE}}G_{0 BS}&\text{with probability}&P_{G_{sl_{UE}}c_{0 BS}}=(1-\frac{ \phi_{UE}}{2\pi})(\frac{\phi_{BS}}{2\pi})\\ G_{sl_{UE}}G_{sl_{BS}}&\text{with probability}&P_{G_{sl_{UE}}c_{i_{BS}}}=(1- \frac{\phi_{UE}}{2\pi})(1-\frac{\phi_{BS}}{2\pi})\end{array}\right. \tag{7}\]  
Figure 1 presents our system model of the downlink mmwave cellular network. Two interfering BSs are shown, BS\({}_{1}\) is in LOS to the typical BS and BS\({}_{2}\) is in NLOS to the typical UE, thus causing LOS and NLOS interference, respectively. The path loss on the LOS interfering link is \(D_{1}^{-a_{L}}\), where \(D_{1}\) is the path length between typical UE and interfering BS\({}_{1}\). The path loss on the NLOS interfering link is \(\exp(-\kappa D_{2}^{\zeta})\), where \(D_{2}\) is the path length between typical UE and interfering BS\({}_{2}\). The path loss on the desired link is \(r^{-a_{L}}\). HMTable 1 provides a list of various notations related to the system model used throughout this work.  
Figure 1: Downlink mmwave system model with exponential attenuation in the NLOS path.
# Micro-FL: A Fault-Tolerant Scalable Microservice-Based Platform for Federated Learning
## 5 Methodology
### Monitoring and Evaluating the Performance
#### 5.4.1 Federated Learning Performance Metrics  
Performance metrics associated with federated learning are gathered and assessed in the Aggregator microservice and are defined as follows:  
* Global Model Performance: is measured as the accuracy of training and testing. These metrics indicate the efficacy of the global model in the federated learning process. The aim is to obtain a high performance during training and testing. Given that the datasets address a classification issue, the goal is to achieve high training and testing accuracies. Notably, in this study, the aggregator evaluates these accuracies on the entire training and testing datasets, to which it has complete access.
* Experiment Execution Time: denotes the time it takes to complete each experiment. In assessing the fault tolerance of the proposed architecture, the objective is to maintain consistent execution times for each experiment, regardless of whether the operation conditions are healthy or faulty.
# Crack Segmentation on Earthen Heritage Site Surfaces
## 2 Materials and Methods
### Training Configuration  
All models were trained for 100 epochs until the F1 score on the validation set stopped increasing. The data were supplied to the network with a batch size of 4. The optimization algorithm employed was Adam, which is efficient for gradient descent and requires less memory. The learning rate was set as 0.001. The Binary Cross-Entropy loss function (see Equation (1)) was implemented here, as the function works well for classification objectives, such as segmentation at the pixel-level [55]. Binary Cross-Entropy is defined as:  
\[L_{BCE}(y,\hat{y})=-(ylog(\hat{y})+(1-y)log(1-\hat{y})) \tag{1}\]  
where \(y\) is the ground truth and \(\hat{y}\) is the prediction. Additionally, \(y\) can have values of 0 (background) or 1 (crack), whereas \(\hat{y}\) can have values between 0 and 1.  
The evaluation of the network is based on the values of _Precision_, _Recall_, _IoU_, and _F1 score_. These metrics are given as:  
\[Precision=\frac{TP}{TP+FP} \tag{2}\]  
\[Recall=\frac{TP}{TP+FN} \tag{3}\]  
\[F1\ score=\frac{2\times Precision\times Recall}{Precision+Recall} \tag{4}\]  
\[IoU=\frac{GroundTruth\cap Prediction}{GroundTruth\cup Prediction} \tag{5}\]  
The \(TP\), \(FP\), \(TN\), and \(FN\) terms are defined as follows:  
_True Positive_ (\(TP\)): the pixel is a crack (Positive) and is identified as a crack (True); _False Positive_ (\(FP\)): the pixel is intact (Positive) and is identified as a crack (False); _True Negative_ (\(TN\)): the pixel is intact (Negative) and is identified as intact (True);  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Backbones** & **Parameters** \\ \hline \multirow{4}{*}{Unet} & vgg16 & 23,752,273 \\  & resnet152 & 67,295,194 \\  & densenet201 & 26,378,577 \\  & inceptionv3 & 29,933,105 \\ \hline \multirow{4}{*}{Linknet} & vgg16 & 20,325,137 \\  & resnet152 & 63,517,466 \\  & densenet201 & 22,549,393 \\  & inceptionv3 & 26,268,401 \\ \hline \multirow{4}{*}{FPN} & vgg16 & 17,572,545 \\  & resnet152 & 61,645,898 \\ \cline{1-1}  & densenet201 & 21,523,905 \\ \cline{1-1}  & inceptionv3 & 25,029,281 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Total number of parameters for each model.  
_False Negative_ (\(FN\)): the pixel is a crack (Negative) and is identified as intact (False).  
Precision and Recall are the two most common metrics. Additionally, they form the basis of the F1 score. Precision is the percentage of correct positive predictions versus the total number of positive predictions. Recall indicates the completeness of the positive predictions. Normally, poor precision is caused by high recall scores and vice versa. The F1 score combines the accuracy and recall metrics and is defined as the harmonic mean of precision and recall.  
The F1 score is a useful model performance metric when working on classification models with an imbalanced dataset. The intersection over Union (IoU) is a metric used to measure the accuracy of an annotation based on a task. The F1 score and IoU score were used as the main indicators to evaluate the model.
# Water Quality Monitoring: A Water Quality Dataset from an On-Site Study in Macao
## 2 System Design
### Hardware Components
#### 2.1.1 Ph Sensor  
The pH sensor measures the acidity or alkalinity of water on a scale from 0 to 14, where values below 7 indicate acidity, values above 7 indicate alkalinity, and 7 represents neutrality. Its output voltage is linearly correlated with the pH level and is interfaced with an Arduino via a connection. The typical measurement error of the pH sensor is \(\pm\)0.1 pH. The accuracy may be affected by calibration status, ambient temperature, and sensor aging.  
Figure 4 illustrates the relationship between pH voltage and pH concentration. The formula for calculating the pH is given as [15]  
\[\mathrm{pH}=-5.887\cdot V+21.677 \tag{1}\]  
where \(V\) is the voltage measured by the sensor.
# Mechanical Mechanism and Dynamic Characteristics of Barge-Whole Bridge Collision Behaviours
## 4 Factors Influencing Behaviors of Barge-_Whole Bridge_ Collision
### Influence of Barge-Impact Angle on Collision  
The impact angle is defined as the angle between the barge-driving direction and the direction perpendicular to the central axis of the bridge, expressed as \(\alpha\). The schematic diagram is displayed in Figure 14.  
The impact angle as a single factor was analyzed on the dynamic behaviors of the barge-_whole bridge_ collision, and the included angle \(\alpha\) was set as \(0^{\circ}\), \(5^{\circ}\), \(10^{\circ}\), \(20^{\circ}\), and \(30^{\circ}\) in five cases. Additionally, the barge still impacts the bridge at 2 m/s. The time-history curves of impact force at each impact angle are illustrated in Figure 15.  
Figure 14: Schematic diagram of barge-impact angle \(\alpha\).  
Figure 13: Barge–_whole bridge_ stress nephograms at different impact velocities.  
As Figure 15 displays, for zero-angle impact, the case of which impact angle is \(0^{\circ}\), the initial contact area between the barge and the pier is the largest at the moment of barge impacting the _whole bridge_, and the bow structures participating in the collision are linearly loaded, almost entering the buckling unstable phase simultaneously. Therefore, the impact force curve significantly changes in the first two phases during the zero-angle impact. However, the existence of the impact angle reduces the initial contact area between the barge and the pier, and the internal trusses of the barge bow successively participate in the collision, resulting in a peak value of impact force substantially lower than that of the zero-angle impact, as shown in Figure 16. Furthermore, the barge is more difficult to disengage from the bridge, increasing the impact force duration, which can be clearly described in Figure 17. In addition, the loading phase, buckling unstable phase, and plastic deformation phase of different components of barge bow are staggered, incurring no obvious buckling unstable phase in the impact force curve. Hence, the curve of impact force remains stable between the loading phase and the unloading phase.  
Figure 16: Variation of peak values of impact force with respect to impact angles.  
Figure 15: Comparison of impact forces at different impact angles.  
As the impact angle increases, the location of barge-bow buckling damage gradually moves from both sides to the center, and the stress of the concrete pier is concentrated on one edge side, which is at the risk of damage. As displayed in Figure 18, in the case of zero-angle impact, the damage of the pier is located in both edges. The impact angle leads to intensively increased stress shifted to one edge side of the pier, and when the impact angle reaches 20\({}^{\circ}\), the damage of the bridge pier occurs.
# Vehicle Speed Estimation Based on 3D ConvNets and Non-Local Blocks
## 2 Related Work
### Video Action Recognition  
The primary objective of Video Action Recognition is to identify human behavior from video footage. Naturally, it is sometimes referred to as Human Action Recognition. Some methods to average vehicle speed estimation entail video analysis, thus we borrow ideas from video action recognition models to apply to the vehicle speed estimation problem.  
In particular, 3D ConvNets is very useful technique for capturing spatial-temporal features. In contrast to 2D ConvNets, 3D ConvNets, characterized by 3D convolution and 3D pooling in 3D ConvNets, can effectively capture spatial-temporal information. Specifically, 3D convolution is carried out by convolving a 3D kernel with a cuboid formed by several contiguous frames stacked together [24]. By this means, the motion and timing information in continuous frames can be extracted. [PERSON] et al. [25] propose a C3D model that includes eight convolutions, five max-poolings, and two fully connected layers. This C3D model yields better results than 2D ConvNets in video action recognition. Inspired by the C3D model, 3D ConvNets have been extensively studied. [PERSON] et al. [26] propose Spatiotemporal Residual Networks which integrates two stream ConvNets [27] and 3D ResNet together. In [28], the R3D network is constructed based on ResNet [12]. Recognizing that temporal and spatial dimensions are not symmetric, [PERSON] et al. [29] propose a P3D model, and [PERSON] et al. [30] propose a R(2+1)D model. Both models reduce 3D convolutions into 2D convolution operations in space domains and 1D convolution operations in time domains. This technique increases the non-linearity of the network and improves the performance compared to the original 3D ConvNets.  
Even though 3D ConvNets can capture features and motion information encoded in image sequences, it is difficult to train the models due to the great number of parameters. In an effort to solve this problem [14], inflates 3D ConvNets (I3D). More specifically, it initializes the parameters of a model by inflating the 2D pre-trained weight from ImageNet to 3D. Subsequently, the pre-trained 2D \(k\times k\) kernel weights are repeated along the temporal dimension \(t\) times and rescaled by \(1/t\).
## 1 Introduction
The next generation of hydrographic information, S-100, will improve the accessibility, compatibility, and usability of hydrographic and marine data by providing a new universal standard data framework for next-generation navigation technologies [5]. Among these, the S-101 standard provides a new technical structure for the efficient integration and visualization of various marine data. However, S-101 currently lacks specific visualization measures to provide navigators with maritime traffic behaviors, such as maritime traffic density, traffic flow, and representative routes. Consequently, it is necessary to utilize large amounts of maritime traffic data to analyze ship movements and traffic patterns, thus providing more intuitive and integrated information to navigators. Particularly, it is necessary to monitor and verify maritime traffic using current hydrographic information. Based on the International Hydrographic Organization (IHO)'s development of next-generation hydrographic information [6], this study identifies and visualizes traffic behavior information, such as maritime traffic density, direction, and flow, to assist ship operators in effectively understanding maritime traffic trends in a specific sea area and inform on the application of next-generation hydrographic information systems. Consequently, we analyzed and preprocessed Automatic Identification System (AIS) data collected in a specific sea area to calculate vessel density distribution within a grid. Additionally, we used machine learning techniques, such as unsupervised learning, to identify traffic direction and flow and extract representative routes. Unlike existing visualization methods, we propose an integrated visualization processing method that combines maritime traffic data with time-series dynamic ship navigation data, enabling simultaneous display of both sets of information. The remainder of this paper is organized as follows: Section 2 reviews related studies; Section 3 describes traffic behavior identification and visualization processing of the identification results; Section 4 presents results from applying the proposed method to real-world data; and Section 5 summarizes the study conclusions.
## 3 Image Preprocessing
### Mean Filtering  
In this paper, the smoothing and denoising method of mean filtering is adopted. The mean filtering is a typical linear filtering algorithm, which can help eliminate the sharp noise of images and realize image smoothing and blurring.  
The mean filtering algorithm replaces the pixel points in the image by selecting an appropriate template operator and replaces the gray value of the pixel with a weighted average of the gray pixelvalues in its neighborhood. After smoothing and denoising by means of the mean filter algorithm, image \(f(i,j)\) is transformed into image \(g(x,y)\), and the equation is:  
\[g(x,y)=\frac{1}{M}\sum_{i,j\in S}f(x,y) \tag{4}\]  
In the formula, \(M\) is the total number of pixels, including the current pixel in the template. Generally, the template operators are \(m\times m\). If the template operator is \(3\times 3\), the total number of template pixels \(M\) is nine. The central pixel value is calculated using the following formula:  
\[S=\frac{1}{9}\sum_{i=1}^{9}a_{i} \tag{5}\]  
As shown in Figure 8, after the mean filtering, the value of the center pixel becomes:  
\[S=\frac{3+4+1+5+2+3+4+2+3}{9}=3 \tag{6}\]
# ICRSSD: Identification and Classification for Railway Structured Sensitive Data
###### Abstract
Semi-canonical data refers to textual content, such as Chinese or English character strings, that exceed a specified length threshold and do not conform to any specific data format. Examples include equipment descriptions, railway section descriptions, and similar free-text fields. In contrast, canonical data refers to data with well-defined structural formats, such as ID numbers, dates, and cell phone numbers. In the classification stage, we introduce a clustering-based dynamic classification, considering the relative sensitivity of attributes across different scenarios to further enhance the accuracy of classification. To further improve sensitive data identification and classification accuracy, we have also built a rule base for the identification and classification of sensitive data in the railway domain, which helps adjust the sensitivity levels of various attributes and manages the expiry of sensitive attribute labels. Our main contributions are as follows:  
* In the identification stage, we propose to further subdivide structured data into canonical data and semi-canonical data and identify each type of data separately. This improves the identification accuracy of structured data containing text classes. * In the classification stage, we propose a clustering-based dynamic classification method that classifies attributes based on the relative degree of their sensitivity across different scenarios. This enhances classification accuracy and flexibility by adapting to the relative distribution of attribute sensitivities. * We develop a rule base specifically for data in the railway domain. The rule base assists in the identification of semi-canonical data and provides classification mechanisms to enhance the accuracy of both identification and classification. At the same time, the rule base can be integrated with real-world business processes to enable expiry management of sensitive attribute labels. * We develop an automated system for identifying and classifying sensitive data in the railway domain. To evaluate its effectiveness, we conducted an empirical study on a real-world railway dataset, assessing the impact of our proposed fine-grained division, dynamic classification strategy, and rule base on the identification and classification of structured sensitive data. The results validate the performance and practicality of the proposed system.
## 5 Results and Discussion
### Machine Learning Model Results  
Figure 3 presents the model's predictive performance of each ML method in terms of their respective confusion matrix. As shown in Figure 3, the XGBoost model shows better balance performance results than the other ML methods. For the XGBoost model, 2476 (85.20%) crashes were correctly classified as short durations, whereas only 430 (4.08%) were not classified as short-duration incidents, of which 153 (5.26%) were misclassified as medium, 109 (3.75%) crashes as intermediate, and 168 (5.78%) as long. For the medium duration, the model accurately classified 1944 (66.48%) crashes as medium duration, whereas 980 (33.51%) were misclassified, with an overall accuracy of 85.08%. Similarly, for the intermediate duration, around 74.52% of crashes were correctly classified. Considering the long durations, 2082 (71.69%) crashes were correctly classified, with an overall accuracy of 87.13%.  
The confusion matrixes of CatBoost and LightGBM can be interpreted in the same manner. For a short duration, the LightGBM achieved the highest accuracy of 93.17% and recall of 91.30%, followed by XGBoost and CatBoost, which indicates that LightGBM is the best model for predicting short durations. XGBoost outperforms other models in predicting medium, intermediate, and long durations, with accuracies of 85.08%, 87.15%, and 87.13%, respectively. Table 3 presents the performance metrics by individual ICT levels for the three developed models. Figures 4-7 is a graphical representation of the model comparisons based on the selected performance metrics.  
Figure 3: Confusion matrixes for ML models: (**a**) XGBoost; (**b**) CatBoost; (**c**) LightGBM.  
Figure 4: Comparison of classification ML models for short durations.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Model** & **Classes** & **TP** & **FP** & **FN** & **TN** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline \multirow{4}{*}{XGBoost} & Short & 2476 & 1300 & 430 & 6331 & 0.8354 & 0.6557 & 0.8520 & 0.7407 \\ \cline{2-9}  & Medium & 1944 & 581 & 980  
The average performance evaluation metrics, such as average accuracy, macro-average precision, macro-average recall, and macro-average F1-score of all the developed models, are summarized in Table 4. LightGBM, with an average accuracy of (88.49%) outperformed the other models. XGboost, on the other hand, has the best predictive capabilities in terms of its high macro-average recall, macro-average precision, and macro-average F1 score. XGBoost outperforms CatBoost and LightGBM in the collective model's predictive performance.
## 6 Application of the Optimization Techniques: Routing Protocols
### Multipath Routing Protocols  
In these protocols, a source knows multiple routes to a destination. The routes can be simultaneously used or one of them can be active while the others are maintained for future needs.
## 5 Experimental Results and Analysis
### Spoofing
As shown in Figure 10, the accuracy of the code phase discriminator is improved during the effective overlapping of the counterfeit signal and the authentic signal. Since the signal and noise are both amplified at the same time when generating the counterfeit  
Figure 8: Code phase tracking error \(\delta_{\rm cp}\) of the code phase discriminator of PRN22. Figure 7: Outputs of the prompt correlators of PRN22 I and Q branches. signal, after the two correlation peaks move away from each other, the accuracy of the code phase discriminator is restored to the level at which the spoofing was not executed.
# Investigating Master-Slave Architecture for Underwater Wireless Sensor Network
## 4 System Model
### Influence of Voice Signal Reflection/Refraction in an Underwater Environment  
The channel geometry and its properties of reflection and refraction impact the impulse response of an acoustic channel. These characteristics determine the total count of the major paths and their relative strengths as well as delays. After discarding the echoes which undergo multiple reflections from almost infinite signal echoes, only a few significant paths are left. The total multi-path spread is governed by the longest path delay, which is for the tuning of tens of milliseconds. These types of values are reported in the shallow water experiments [42]. The total multi-path spread is far greater than the dispersion of the individual paths. Therefore, it can be ignored for the systems whose channel cutoff is greater than most of the frequencies. For systems currently in use, this is typically the case. We ignore the reflection phenomena for both ERPMSA-UWSN and WDFAD-DBR, in our simulations, for the sake of fair analysis.