esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Experiments and Discussions
### Algorithm Feasibility Analysis  
Assume that three UAVs perform tasks in a complete environment containing 10 task nodes with known locations to be monitored, which are labeled as numbers in Figure 3. Task nodes and virtual nodes are illustrated by red and green dots, respectively. The  
Figure 2: Flowchart of the OMACO algorithm.  
blue solid lines represent adjacency relationships within the graph. All the simulation parameters are listed in Table 1. The expected periods of the task nodes are shown in Table 2. All simulation examples in this paper are implemented on a computer with Matlab R2020a installed and the system configuration is Intel Core i7-9750H, 2.59 GHz, 16 GB RAM.  
Figure 4 shows the persistent monitoring flight path of the three UAVs obtained by the proposed method in this paper, where the x-axis represents the time, and the y-axis represents the node that the UAV arrived at the corresponding time step. The solid line represents the UAV flight path consisting of passing nodes.  
\begin{table}
\begin{tabular}{c c l} \hline
**Parameters** & **Value** & **Notes** \\ \hline \(v\) & 8 m/s & UAV speed \\ \(\delta\) & 40 m & interval for discretization \\ \(Z\) & 15 & ant quantity \\ \(c\) & 5 s & constant \\ \(N_{c}\) & 200 & maximum iteration \\ \(\alpha\) & 1.2 & pheromone importance factor \\ \(\beta\) & 4 & heuristic function importance factor \\ \(\rho\) & 0.3 & pheromone volatility factor \\ \(Q\) & 10 & pheromone quantity \\ \(K\) & 500 & monitoring Horizon \\ \(w\) & 0.6 & weight parameter \\ \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
Figure 3: Discretization of a completely connected graph.  
Figure 5 shows the expected period and the actual monitoring period of the task nodes. It can be seen that the actual monitoring period of all task nodes is less than the expected period, which indicates that the monitoring process of the UAV meets the monitoring frequency requirements of all nodes. Figure 5 also shows that each node has been visited multiple times in the monitoring horizon, obtaining multiple actual monitoring periods which are all lower than their expected periods, i.e., meeting the monitoring frequency requirements.  
More importantly, the actual monitoring period of each node is different, that is, the waiting time when each node is monitored has a good random distribution. The simulation shows that the method proposed in this paper can cover all monitoring nodes, meet the monitoring frequency requirements, and also improve the privacy protection of the monitoring path.
# Application of Augmented Reality in Waterway Traffic Management Using Sparse Spatiotemporal Data
## 1 Introduction
By integrating an edge server equipped with Jetson Orin into the intelligent beacon, AIS data and video surveillance data are fused. Furthermore, an improved single-stage object detection algorithm enhances the detection of small targets and complex backgrounds. A target-tracking algorithm ensures continuous vessel trajectory tracking, improving tracking stability in multi-ship encounter scenarios. Additionally, this paper introduces a matching method based on trajectory prediction and regional set partitioning, effectively mitigating AIS data latency and achieving temporal and spatial synchronization between AIS and video data. In recent years, augmented reality (AR) technology has gained increasing attention in spatial perception. By integrating virtual information with the real environment, AR technology enhances spatial awareness, improves navigation and positioning accuracy, and provides innovative solutions for visualizing complex spatial information. These advancements highlight AR's potential applications in waterway traffic situation awareness [5; 6; 7; 8]. Building upon this, we have developed a traffic situation awareness framework utilizing AR. By dynamically displaying vessel types, navigation speed, and traffic flow, the framework provides traffic statistics and periodic predictions, offering more comprehensive and intelligent service support for inland waterway vessels.
## 5 Performance Evaluation
### Numerical Results and Evaluation  
In this section, we explore the numerical data obtained to evaluate our model. For simplicity, \(\alpha_{r}\) is represented by \(\alpha\) on the figures, and \(\mu_{1,r}\), \(\mu_{2,r}\), and \(\mu_{3,r}\) are represented by \(\mu_{1}\), \(\mu_{2}\), and \(\mu_{3}\) respectively.  
Figure 3 shows that, for equal capacities of receiving, computing and transmitting stations, the efficiency of the system is at its optimal for small chunks of data sizes or for small arrival rate. It also shows that the system blocks increase with bigger arrival rate and bigger data chunk. When the data chunk is relatively big (\(\alpha_{r}=0.2\)), the system probability to block is exponential _versus_ an increase in arrival rate.  
When the transmission capability of the system is smaller than its computing capability, then the efficiency of the system increases. Figure 4 shows that the blocking probability can reach 5.2% (when \(\alpha_{r}=0.2\)) in case the transmitting power is smaller than the computing processing power vis-a-vis 7% when the computing power of the receiving, processing and transmitting are all equal. It also shows that if the transmission capability is higher than the computing capability, then the system efficiency decreases considerably, as the computing capability will not be able to cope with the transmission power. The system blocking probability can reach 8.9%.  
Figure 5: Computing Worker CPU Usage when \(\mu_{1,r}=\mu_{2,r}=\mu_{3,r}\).  
A Computing Worker CPU usage is affected by 3 factors: (1) the system efficiency, which is determined from the rate of blocking situations; (2) the chunk size; and (3) the chunks' arrival rates. Figures 5 and 6 show the CPU usage in 2 scenarios: (1) CPU usage during blocking and non-blocking situations (\(p_{010}+p_{011}+p_{0b1}+p_{111}+p_{b10}+p_{110}+p_{b11}+p_{1b1}\)) _versus_ an increase in arrival rate and chunk size; and (2) CPU usage for non-blocking situations only (\(p_{010}+p_{011}+p_{111}+p_{110}\)). These figures show that CPU utilization is higher with higher arrivals' rates and larger chunks sizes. The CPU usage percentage can reach up to 18% when the receiving, computing and transmitting capabilities of the system are equal (Figure 5). This usage increases when the receiving capability is higher than the computing and the transmitting capabilities (35%), as the computing power will not be able to cope with the receiving rate. When the receiving capability is less than the computing and the transmitting capabilities of the system, then the usage is 25%.
# When Social Networks Meet D2D Communications: A Survey
## 3 How Social Ties Can Improve D2D Communications
### Spectrum Resource Allocation and Management
Finally, some works focus on estimation accuracy. In [38], qualitative and quantitative evaluations are performed to assess performance growing by allowing social-aware D2D communication. In [42],  
Figure 4: Resource allocation works grouped considering key requirements to design social-aware resource allocation and optimization for D2D communication. the energy efficiency optimization (EEO) problem in D2D communications is formulated, analyzing both the social conjunctions and physical interference among mobile users. The starting idea is to allow D2D pairs in sharing spectrum resources of a cellular subscriber to increase spectrum efficiency. An adaptive genetic algorithm is applied to solve this EEO problem to maximize the energy efficiency, while at the same time assuring high system throughput and spectrum efficiency considering QoS constraints. In [65], the authors analyzed a relay-aided D2D network focusing on wireless channels uncertainty in order to provide a stable, optimal, and unique solution. The proposed method is close to the centralized optimal solution, showing a low level of computational complexity. Actually, it seems that privacy is an open issue and no relevant works have been found in the literature. Similarly, social information protection is a hot topic also considering the growing interest of users in defending their personal data. In fact, in the last two years, legal order started a process to improve laws and fundamental rights all over the world. In Figure 4, all papers presented in this section are grouped and summarized. The dimension of circles is proportional to the number of found references.
# Channel Code-Book (CCB): Semantic Image-Adaptive Transmission in Satellite-Ground Scenario
## 2 System Model
### The Training Process of the End-to-End Transmission Model
1: Initialize the denoiser model architecture;
2: Generate encoder1's convolution kernel \(\mathbf{W}_{conv}\) and bias term \(\mathbf{b}_{conv}\) from \(f_{conv}(\mathbf{x}^{\prime})=\mathbf{W}_{conv}*\mathbf{x}^{\prime}+\mathbf{b}_ {conv}\);
3: Generate decoder1's convolution kernel \(\mathbf{W}_{deconv}\) and bias term \(\mathbf{b}_{deconv}\) from \(f_{deconv}(\mathbf{a})=\mathbf{W}_{deconv}*\mathbf{a}+\mathbf{b}_{deconv}\);
4: Get specific \(\theta_{dn}\) from Section 3;
5: Transform \(\theta_{dn}\) into \(\mathbf{W}\) and \(\mathbf{b}\). ```  
**Algorithm 4** Training Algorithm for Channel DenoisingNet  
```
0: Initial transmission point \(\mathbf{x}_{0}\), total transmission number \(M\), pre-stored guiding vector \(\mathbf{x}_{n}^{\prime}\) (\(n=1,2\ldots N\)). 0: Denoiser parameter \(\theta\). 1: Initialize denoiser parameter \(\theta=0\);
2:for transmission number = 1 : \(M\)do
3:if transmission number == 1 then
4: Transmit \(\mathbf{x}_{0}\);
5: Receive the transmitted data \(\mathbf{x}_{n}^{\prime}\);
6:for Pre-stored guiding vector \(\mathbf{x}_{n}^{\prime}=\mathbf{x}_{1}^{\prime}\) : \(\mathbf{x}_{n}^{\prime}\)do
7: Compute the difference and set parameter: \(\mathbf{x}_{\text{min}}^{\prime}=\text{argmin}(d|\mathbf{x}_{i}^{\prime}- \mathbf{x}_{0}^{\prime}|)\);
8:endfor
9: Converted into denoising parameters: \(\theta\leftrightarrow\mathbf{x}_{\text{min}}^{\prime}\);
10: Denoiser apply denoising parameters \(f_{\text{d}ht}=\theta\). 11:endif
12:endfor
```  
**Algorithm 4** Denoiser Model Parameter Selection Mechanism
## 4 Simulation Environment and Performance Metrics  
### Simulation Environment Configuration  
Table 3 shows the simulation configurations. The network setup assumes 100-300 nodes for the physical infrastructure. These nodes are then divided into the access, transport, and core network nodes. For the node computing and link capacity, a range of 20 to 50 are assumed. For every NSR, a total of 15 nodes, computing and link capacity within 5 to 25, transmission delay of 0.05 to 1, and NSR lifetime of 10 to 50 are assigned. In addition, the number of NSRs that are simultaneously served is limited to 20 NSRs.  
Since the proposed scheme implements Deep RL, the properties of the deep neural network are defined as follows: a sequential model where each layer uses a linear transformation, rectifier linear unit (ReLU) as the activation function, Adam as the optimizer, and mean squared error (MSE) for loss calculation.
## 2 Problem Statement
### Rational Basis for Archaeological Marks
#### 2.1.3 Soil and Damp Marks  
The occurrence of archaeological remains can change soil types and contents (e.g., nutrients, _etc._) compared with their surroundings resulting in the well-known phenomenon of soil marks, which appear as a change of color or texture in the absence of vegetation cover. Soil marks could be detected using SAR images by exploiting backscattering anomalies induced by the variations in grain size, soil density and texture.  
Damp marks appear on bare ground because buried archaeological remains can alter drainage capability and, in turn, water distribution. The detectability of damp marks is linked to soil type, climate and meteorological conditions, and therefore to acquisition time as well as to the spatial distribution and nature of the remains. Damp marks in SAR images are indicated by backscattering anomalies induced by moisture variation that determines the amplitude of the dielectric constant of soil. Increasing moisture may lead to an increase in radar backscattering due to the increase in the soil's dielectric constant, while small ponds of water formed by heavy rain can increase specular reflectance locally (_i.e._, scattering on a nearly smooth surface) on the surface and therefore result in a dark area in the SAR image. Consequently, these marks may be more noticeable after heavy rain because the difference in the soil moisture between the underlying archaeological remains, and surrounding surfaces may be highlighted.
# Causal Inference-Based Self-Supervised Cross-Domain Fundus Image Segmentation
## 4 Experiments
### Discussion on Causal Inference-Based Pseudo-Label Fusion Module
#### 4.3.2 Effectiveness of Confidence-Based Dynamic Fusion  
During the pseudo-label generation process, we also introduced a confidence map generation mechanism. To validate the effectiveness of this strategy, we compared it with a simple averaging fusion method, which combines the dual-path prediction results using equal weights (i.e., multiplying each segmentation output by \(\frac{1}{2}\)) to obtain the fused pseudo-labels; the relevant results are presented in Table 4. The results show that the confidence-based dynamic fusion strategy is capable of adaptively adjusting the weight of each pixel based on its prediction certainty during the fusion process, thereby generating more refined and accurate pseudo-labels that provide a more reliable supervisory signal for model training.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Method** & \(\mathbf{DI_{cup}}\) & \(\mathbf{DI_{disc}}\) & \(\delta\) \\ \hline \multirow{3}{*}{Drishti-GS} & w/o pseudo-labels & 0.822 & 0.941 & 0.097 \\  & Single-path pseudo-labels & 0.847 & 0.952 & 0.085 \\  & Causal Inference-based & **0.876** & **0.971** & **0.081** \\  & w/o pseudo-labels & 0.757 & 0.873 & 0.103 \\ \multirow{3}{*}{RIM-ONE-r3} & Single-path pseudo-labels & 0.798 & 0.896 & 0.093 \\  & Causal Inference-based & **0.818** & **0.922** & **0.083** \\  & w/o pseudo-labels & 0.831 & 0.941 & 0.056 \\ \multirow{3}{*}{REFUGE Val} & Single-path pseudo-labels & 0.840 & 0.952 & 0.052 \\  & Causal Inference-based & **0.885** & **0.958** & **0.049** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance of different pseudo-label generation strategies. The best results are highlighted in bold.
# Wind Turbine Power Curve Modelling with Logistic Functions Based on Quantile Regression
## 4 Case Study
### Discussions  
At present, the proposed WTPC modelling method still has some limitations and needs to be improved. (1) Currently, the wind farm operators have not provided us with the detailed installation location of each wind turbine. Therefore, it is difficult to avoid the impact of turbine wakes on WTPC modelling. If the training set contains a large amount of measured data under wake effect, the established power curve will be \"lower\" than the real power curve (without turbine wakes). (2) The fitting accuracy of QRLF is sensitive to the initial settings of PSO. On the one hand, as mentioned in Section 2.4.3, we can enhance the reliability of the fitting results via repeating the optimization algorithm, i.e., PSO, multiple times. On the other hand, for the same type of wind turbine, we can use the model parameters of a trained wind turbine as the initial model parameters of a wind turbine to be trained to decrease the probability of falling into the local optimum.  
In future works, we plan to use a full year of SCADA for model training, and then study the seasonal effects on power curve modelling. In addition, we will optimize the QRLF based WTPC model according to the specific application scenarios, such as probabilistic wind power forecasting and blade icing detection.
# An Efficient Time-Variant Reliability Analysis Method with Mixed Uncertainties
## 5 Numerical Examples
### A Short Column of the Reinforced Concrete of a Structure  
The example discussed in this section is modified according to the literature [43]. The width and height of the cross-section of the reinforced concrete short column of a structure are denoted as B and h, respectively. Moreover, the parameter values of the width and height of the cross-section are interval values, whose parameter values are [270 mm, 330 mm] and [315 mm, 380 mm], respectively.  
The strength grade of the concrete is C30 and the area of the steel bar in the column is 1811.28 mm\({}^{2}\), which is a grade II steel bar. Moreover, \(R(t)\), \(G\) and \(Q\) denote the random resistance process of the reinforced concrete short column, the permanent load effect and the variable load effect, respectively. Table 3 presents the specific parameters and distribution.  
\(R(t)\) is mathematically defined as:  
\[R(t)=bhf_{c}(t)+A_{s}f_{y}(t)=bhf_{c}\varphi_{c}(t)+A_{s}f_{y}\varphi_{y}(t) \tag{38}\]  
where \(f_{c}\) and \(f_{y}\) denote the compressive strength of the concrete and the yield strength of reinforcement, respectively.  
The resistance variation coefficients \(\varphi_{c}(t)\) and \(\varphi_{y}(t)\) are:  
\[\left\{\begin{array}{l}\varphi_{c}(t)=1-8.0\times 10^{-7}t^{3}\\ \varphi_{y}(t)=1-2.2\times 10^{-6}t^{3}\end{array}\right. \tag{39}\]  
The limit state function is mathematically expressed as:  
\[g=R(t)-G-Q \tag{40}\]  
From Table 4, it is proved once again that the results of MCS method are convergent. With the increase of the number of samples, the calculation time increases linearly. Figure 5 illustrates that as the design reference period exceeds, the reliability of the structure or product gradually decreases. As can be seen from Table 5, the errors of the current method and MCS method are as follows: 1.02%, 1.33%, 1.36%, 1.67%, 1.99%, 2.02%, 2.06%, 1.80%, 2.15% and 1.89%. The maximum error is 2.02 and the minimum error is 1.02. The error can meet the needs of engineering, moreover the accuracy of the present method is proved again. From viewpoint of computational efficiency, the total number of calls to limit state functions in this method is only 171, while it is 9,997,832 times for the MCS method. The MCS method runs in 673.8 s, and the current method runs in 0.12 s. Therefore, it is proved that the computational efficiency of the presented approach is much higher than that  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Mean \(\mu\)** & **Standard Deviation \(\sigma\)** & **Type of Distribution** \\ \hline \(G\)(kN) & 530 & 37.1 & normal \\ \(Q_{T}\)(kN) & 700 & 203 & Extreme I type \\ \(f_{c}\)(N/mm\({}^{2}\)) & 26.1 & 4.437 & normal \\ \(f_{y}\)(N/mm\({}^{2}\)) & 384 & 28.591 & normal \\ \hline \hline \end{tabular}
\end{table}
Table 3: Variable distribution of reinforced concrete short columns of a structure.  
of the MCS method. The results of an example show that the proposed method is an effective approach.
## 3 High-Dimensional Mapping Entropy
### High-Dimensional Mapping Theory  
The essence of high-dimensional mapping entropy theory can be attributed to the principle of high-dimensional space mapping and the idea of kernel function classification. The literature [11] proposes a Gaussian kernel-based clustering method, which adopts  
Figure 1: Schematic of the research route.  
the Mercer kernel mapping idea to map samples to Hilbert space on the basis of retaining essential features and achieves kernel space sample separability by enhancing the variability of data features in the high-dimensional space. Moreover, the kernel function high-dimensional space mapping theory is also widely used in the field of support vector machine (SVM) pattern recognition. The mapping theory process is detailed in Figure 2.  
Conducting a mapping of the sample data points into a high-dimensional space, aimed at identifying the optimal hyperplane for dividing the distinct data points. The hyperplane that implements the decision boundary for transforming a non-linear problem into a linear problem can be expressed as a linear equation [25]:  
\[\omega^{T}+\phi(X)+b=0 \tag{8}\]  
where \(\phi\): \(X\rightarrow\mathrm{H}\) is the mapping function, \(X\) is the input space, and \(\mathrm{H}\) is the feature space (Hilbert space).  
Traditional SE evaluates the distance between samples in a reconstructed phase space. HDME employs kernel mapping to project data samples into higher-dimensional feature spaces, enhancing the distinction between noise signals and characteristic signal data points. This approach aims to identify the optimal hyperplane for separating distinct data points, thereby converting the non-linear problem into a linear one. While preserving the original attributes of the sample, HDME generates a new dataset within this feature space that minimizes noise and outlier interference. Subsequently, it measures the similarities of different features within this newly defined feature space.  
The critical points in high-dimensional mapping entropy are the choice of the high-dimensional mapping kernel function, the measurement of the distances in space, and the determination of the similarity tolerance. These aspects are essential for precisely calculating high-dimensional mapping entropy.
## 3 Empirical Study  
### Case Mine Overview  
A certain iron mine in Liaoning Province, China was selected as an example for conducting applied research. The mining area is located in the Liaodong region, with a flat terrain and an inclination angle of 62\({}^{\circ}\) to 87\({}^{\circ}\) for the ore body, with a thickness of 6.0 to 43.2 meters. The ore body occurs at an elevation between +200 m and \(-\)300 m. The main minable ore bodies are Fe10, Fe11-1, and Fe11-2. The main useful mineral is magnetite with a TFe content of 25-35%, which belongs to magnetite lean ore.  
The mining of this deposit adopted open-pit multi-step mining operation and a highway transportation method in the early stage. After nearly a decade of open-pit mining, the service life of open-pit mining was only 2.38 years due to the combined constraints of the comprehensive cost of deposit mining and surface terrain conditions. In order to ensure the production efficiency of the enterprise and achieve the full exploitation and utilization of the mining resources, according to the provisions of the \"Code for Geological Exploration of Iron, Manganese, and Chromium Mines\" (DZ/T0200-2002), the retained reserves of the mine were evaluated. Based on the resource estimation results, as of the evaluation date, the three main minable ore bodies had a total reserve of 17,178.39 kt (122 b + 333), of which the ore bodies below +70 m were mined underground, and the usable reserve of ore was 13,243.82 kt. The mining and beneficiation production capacity of the mine was 1300 kt/a. The main production technical and economic indicators of iron mine in Liaoning Province are shown in Table 1[33].