esa-sceva/satcom-chunk-collection huggingface dataset:
# Integrating a LiDAR Sensor in a UAV Platform to Obtain a Georeferenced Point Cloud
## 2 Materials and Methods
### Data Collection and Processing Procedure Development
#### 2.2.4 Generation of Georeferenced Point Cloud
By interpolation in euler_bi, obtain the orientation value corresponding to each r_s (i) and store it in euler_i. Obtain R_i, rotation matrix corresponding to euler_i. Calculate: r_i(i) = R_i * r_b(i). By interpolation in p_eun, obtain p_eun_i, the position value corresponding to each r_s (i). Calculate: r_eun(i) = r_i(i) + p_eun_i End of For Convert r_eun to the UTM reference system to obtain r_utm. Generate the file from r_utm. ```  
**Algorithm 1**Generation of Georeferenced Point Cloud for Each Path  
Table 5 lists the equivalences between the calibration equation and the parameters from the georeferencing equation for developing Algorithm 1. Figure 12: Point cloud PC calibrated using static variables in the UAV reference system (x\({}^{\prime}\), y\({}^{\prime}\), z\({}^{\prime}\)). \begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithm Equation** & **Georeferencing Equation** \\ \hline r\_enu & \(p_{(t)}^{g}\) \\ p\_enu\_i & \(p_{(t)}^{g}\) \\ R\_i & \(R_{b}^{g}(t)\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Parameter equivalence for Algorithm 1. Figure 13 denotes the final point cloud results after adding the dynamic values from the georeferencing equation. Finally, the point cloud is georeferenced in the UAV reference system. * R_bs_0: Time vector. Its values are adapted to the parameters from the point cloud within the X, Y and Z parameters. * X_enu: Vector generated by concatenation. Here, concatenation is initialized, and a copy is generated. Figure 14 below illustrates the conversion of the vectors generated by concatenation to the UTM reference system. X_enu is converted to the geodetic system (latitude, longitude) from the ENU system. The height is based on the ellipsoid when converting X_enu from the geodetic system to UTM.
## 4 Results  
### Petrological Characteristics  
#### 4.1.1 Mineral Composition Characteristics  
During the sedimentation process, the mineral composition in different regions often varies due to influences such as sedimentary environment and diagenetic processes. These differences among minerals result in variations in the development of reservoir pore-fracture systems [31; 32; 33]. Therefore, clarifying the characteristics of the mineral composition is beneficial for gaining a deeper understanding of the factors influencing pore and fracture development in the study area. In this study, a total of 18 samples were selected for XRD experiments, and the results are shown in Table 1. The mineral composition of the Fengcheng Formation shale reservoir in the Mahu Depression is dominated by siliceous minerals and carbonate minerals (Figure 2a). The siliceous mineral content ranges from 34.9 to 90.0%, with an average of 62.2%, including quartz and feldspar. Among them, the quartz content is between 14.2 and 58.7%, and the average is 34.1%; the feldspar content is between 11.3 and 47.4%, and the average is 28.2%, mainly plagioclase. The content of carbonate minerals is between 5.6 and 42.1%, with an average of 25.6%, mainly dolomite. Its content ranges from 3.1 to 42.1%, with an average of 19.7%. The content of clay minerals is low, ranging from 0.4 to 34.2%, with an average of only 5.0%. In addition, there are other minerals, mainly pyrite, between 0.9 and 12.9%, with an average of 7.2%.
## 3 Results and Discussion
### Fatty Acid Composition  
The results of the lipid profile for the 28 fatty acids that could be quantified in at least 80% of the ham samples and the sums by functional groups (SFAs: total saturated fatty acids, MUFAs: total monounsaturated fatty acids, PUFAs: total polyunsaturated fatty acids, n3: total n3 PUFAs, n6: total n6 PUFAs) are shown in Table 1. It can be seen that both the values of the individual fatty acids and the sums show a wide range of variability. This is due to the fact that the hams come from animals reared in three different farms with different times in the fattening stage (_montanera_) and of different genetic purity (100% Iberian and 50% Iberian). As reported in other studies, the time in _montanera_ influences the lipid profile of the animals [33]. In addition, genetic purity also affects the fatty acid profile, with significantly higher percentages of monounsaturated fatty acids and significantly lower percentages of polyunsaturated fatty acids being observed in 100% Iberian pigs [55]. The values of the different fatty acids and summates are within the values previously described for the _Biceps femoris_ muscle of 100% Iberian and 50% Iberian animals raised in fattening systems similar to that of the present study [56; 57].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Fatty Acid** & **Mean** & **Min** & **Max** & **SD** & **CV** \\ \hline C12:0 & 0.09 & 0.07 & 0.20 & 0.02 & 20.88 \\ C13:0 & 0.00 & 0.00 & 0.04 & 0.01 & 247.33 \\ C14:0 & 1.34 & 1.03 & 2.06 & 0.15 & 11.36 \\ C14:1 n5 & 0.03 & 0.01 & 0.10 & 0.01 & 36.45 \\ C15:0 & 0.03 & 0.02 & 0.08 & 0.01 & 26.90 \\ C15:1 & 0.00 & 0.00 & 0.07 & 0.01 & 613.80 \\ C16:0 & 21.91 & 19.05 & 24.91 & 1.26 & 5.74 \\ C16:1 & 4.53 & 3.15 & 5.68 & 0.58 & 12.86 \\ C17:0 & 0.18 & 0.14 & 0.24 & 0.02 & 12.68 \\ C17:1 & 0.19 & 0.12 & 0.27 & 0.03 & 14.75 \\ C18:0 & 8.23 & 6.27 & 10.77 & 0.97 & 11.74 \\ C18:1n94 & 0.23 & 0.13 & 0.34 & 0.04 & 18.69 \\ C18:1 & 50.68 & 46.38 & 53.61 & 1.61 & 3.17 \\ C18:1 n7 & 4.66 & 3.43 & 5.48 & 0.48 & 10.27 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Fatty acids analyzed in Iberian hams, expressed as % by weight of total FAs.  
Figure 1: Average of the 60 raw spectra registered in the lean meat and in the fat part of the slice with the different equipment. Yellow bands correspond to the absorbance of C-H groups related to fatty acids. Blue bands correspond to the absorbance of O-H groups related to moisture.
# A Lightweight Entropy-Curvature-Based Attention Mechanism for Meningioma Segmentation in MRI Images
## 4 Experiments
### Experimental Setup
#### 4.1.2 Evaluation Metric  
The model's performance is evaluated based on the following two key metrics commonly used in the meningioma segmentation task: the Dice similarity coefficient (DSC) and 95% Hausdorff distance (HD95). The goal of meningioma segmentation is to predict the tumor region as accurately as possible, and DSC directly measures the degree of match between prediction and the ground truth. DSC still has good performance, even if the volume of the tumor is small. HD95 focuses on the maximum difference between the boundary of prediction and the ground truth, which can effectively measure whether the shape of the prediction is accurate or not. Compared with the original Hausdorff distance (HD), HD95 removes 5% of the extreme outliers, reducing the effect of instability caused by individual noise points.  
The formulas for their calculation are shown as follows:  
\[HD95(A,B)=\textit{percentile}_{95}\bigg{\{}\max_{a\in A}\min_{b\in B}d(a,b), \max_{b\in B}\min_{a\in A}d(b,a)\bigg{\}} \tag{5}\]  
where \(d(a,b)\) represents the distance between point \(a\) and point \(b\), and \(A\) and \(B\) are the sets of points on the prediction and ground truth, respectively. HD95 measures the 95 th percentile of the distances between the two segmentation boundaries, used to assess the boundary differences.  
\[HD95(A,B)=\textit{percentile}_{95}\bigg{\{}\max_{a\in A}\min_{b\in B}d(a,b), \max_{b\in B}\min_{a\in A}d(b,a)\bigg{\}} \tag{6}\]  
where \(d(a,b)\) represents the distance between point \(a\) and point \(b\), and \(A\) and \(B\) are the sets of points on the prediction and ground truth, respectively. HD95 measures the 95 th percentile of the distances between the two segmentation boundaries, used to assess the boundary differences.
## 3 Research Findings
### Parameter Tuning for Metaheuristic Algorithms
By averaging the outputs across different factor levels, the plot highlights how the NSGA-II algorithm behaves under varying conditions. The results here demonstrate the model's robustness in addressing multi-objective optimization challenges, especially in terms of balancing exploration and exploitation across the search space. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Experiment** & **nPop (Population** & **Pc (Crossover** & **Pm (Mutation** \\
**Number** & **Size)** & **Probability)** & **Probability)** \\ \hline
2 & 1 & 2 & 2 \\
3 & 1 & 3 & 3 \\
4 & 2 & 1 & 2 \\
5 & 2 & 2 & 3 \\
6 & 2 & 3 & 1 \\
7 & 3 & 1 & 3 \\
8 & 3 & 2 & 1 \\
9 & 3 & 3 & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Orthogonal arrays L9(33) for NSGA-II algorithm. Figure 2: Average of averages plot for NSGA II algorithm [22]. Figure 3 presents the \"Average Signal-to-Noise (S/N) Ratio\" plot for the NSGA-II algorithm. The S/N ratio is a key performance metric used to evaluate the robustness of the algorithm's solutions by measuring the trade-off between mean performance and variability. This plot illustrates the stability and consistency of the NSGA-II algorithm's performance across different factor levels. A higher S/N ratio indicates a more stable and reliable solution, which is crucial for optimizing the multi-objective facility location model. The analysis of the S/N ratio highlights how changes in the experimental factors impact the performance and helps identify the optimal factor levels that lead to the best solution. Based on the plots, the optimal levels of the NSGA-II algorithm factors are obtained. Table 3 presents the optimal levels of factors used in the NSGA-II algorithm based on the Taguchi experiment. The table shows how different levels of key parameters (such as population size nPop, crossover probability pc, and mutation probability Pm) are set to achieve optimal performance. These optimal levels were derived through systematic experimentation and sensitivity analysis, offering a structured approach to fine-tuning the algorithm's performance. The factors listed in the table are critical for ensuring that the NSGA-II algorithm efficiently solves the multi-objective optimization problem for BTS site selection, balancing exploration and exploitation in the solution space.
## 5 Experimental Results and Discussion
### Comparison between Single-Frequency Hammering and Variable-Frequency Hammering
#### 5.3.1 Classification Using Single-Frequency Hammering MFCCs  
Tables 3 and 4 show the SVM classification results of the single-frequency hammering MFCCs in the two- and four-classification cases under the three kernel functions. In the case of two classes, the accuracy of each single-frequency hammering can exceed 90%. In the case of the four-category classification, the accuracy of the three single-hammer frequencies  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Features** & **ReliefF** \\ \hline The 3 rd dimension of 5 Hz & 0.0659 \\ The 1 st dimension of 5 Hz & 0.0574 \\ The 6 th dimension of 5 Hz & 0.0567 \\ The 12 th dimension of 5 Hz & 0.0530 \\ The 5 th dimension of 2 Hz & 0.0527 \\ The 10 th dimension of 5 Hz & 0.0519 \\ The 2 nd dimension of 5 Hz & 0.0478 \\ The 11 th dimension of 5 Hz & 0.0477 \\ The 4 th dimension of 5 Hz & 0.0476 \\ The 13 th dimension of 5 Hz & 0.0471 \\ The 7 th dimension of 5 Hz & 0.0463 \\ The 11 th dimension of 10 Hz & 0.0446 \\ \hline \hline \end{tabular}
\end{table}
Table 1: First 13 dimensional features in two-category classification.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Features** & **ReliefF** \\ \hline The 3 rd dimension of 5 Hz & 0.0813 \\ The 4 th dimension of 5 Hz & 0.0795 \\ The 5 th dimension of 2 Hz & 0.0772 \\ The 1 st dimension of 5 Hz & 0.0669 \\ The 12 th dimension of 5 Hz & 0.0645 \\ The 7 th dimension of 5 Hz & 0.0623 \\ The 6 th dimension of 5 Hz & 0  
decreased slightly compared with that of the two-level classification. As observed, the accuracy at 2 Hz is lower than that at 5 Hz and 10 Hz; this indicates that MFCCs are effective as an acoustic feature for classification, whereas the sensitivity of the hammering frequency varies for different forms of damage classification.
## 3 The Statistical Regression Adaptive Kalman Filtering Algorithm
### The Maximization Approach  
The maximization step includes the maximization of \(\Omega(\[PERSON]_{k},\[PERSON]_{k}^{(l)})\). In order to find the extreme point, the first-order derivative of \(\Omega(\[PERSON]_{k},\[PERSON]_{k}^{(l)})\) should be obtained and set as equal to zero.  
\[\frac{\partial\Omega(\[PERSON]_{k},\[PERSON]_{k}^{(l)})}{\partial\Phi_{k}}\big{|}_{ \Phi_{k}=\Phi_{k}^{(l+1)}}=0 \tag{29}\]  
Employing \(\Phi_{k}=\left\{\begin{array}{cc}\mathbf{P}_{k|k-1}&\mathbf{R}_{k}\end{array}\right\}\), Equation (29) can be computed as  
\[\left\{\begin{array}{c}\frac{\partial\Omega\big{(}\Phi_{k}\Phi_{k}^{(l)} \big{)}}{\partial\mathbf{r}_{k|k-1}}=-\frac{1}{2}\mathbf{P}_{k|k-1}^{-1}+\frac {1}{2}\mathbf{P}_{k|k-1}^{-1}\mathbf{J}_{k}\mathbf{P}_{k|k-1}^{-1}=0\\ \frac{\partial\Omega\big{(}\[PERSON]_{k}\[PERSON]_{k}^{(l)}\big{)}}{\partial\mathbf{R}_{ k}}=-\frac{1}{2}\mathbf{R}_{k}^{-1}+\frac{1}{2}\mathbf{R}_{k}^{-1}\mathbf{L}_{k} \mathbf{R}_{k}^{-1}=0\end{array}\right. \tag{30}\]  
Solving Equation (30), then  
\[\left\{\begin{array}{c}\mathbf{P}_{k|k-1}^{(l+1)}=\mathbf{J}_{k}\\ \mathbf{R}_{k}^{(l+1)}=\mathbf{L}_{k}\end{array}\right. \tag{31}\]  
After iteration \(N\), the estimations of \(\mathbf{P}_{k|k-1}\) and \(\mathbf{R}_{k}\) are derived as follows, respectively.:  
\[\left\{\begin{array}{c}\mathbf{P}_{k|k-1}=\mathbf{P}_{k|k-1}^{(N)}\\ \mathbf{R}_{k}=\mathbf{R}_{k}^{(N)}\end{array}\right. \tag{32}\]  
Similarly, the estimation of \(\mathbf{X}_{k|k}\) and \(\mathbf{P}_{k|k}\) are denoted as  
\[\left\{\begin{array}{c}\mathbf{X}_{k|k}=\mathbf{X}_{k|k}^{(N)}\\ \mathbf{P}_{k|k}=\mathbf{P}_{k|k}^{(N)}\end{array}\right. \tag{33}\]  
Figure 4 shows the flowchart of the proposed SRAKF. First, the nine-axis sensor processed data are calculated to obtain the measurements. Subsequently, \(\mathbf{X}_{k|k-1}\) and \(\mathbf{P}_{k|k-1}\) are computed by the process of time update according to Equations (13)-(15). \(\mathbf{X}_{k|k-1}\) and  
\(\mathbf{P}_{k|k-1}\) are calculated as the initial value of the expectation maximization method iteration. After iteration \(N\), the accurate \(\mathbf{R}_{k}\) and \(\mathbf{P}_{k|k-1}\) at time \(k\) are acquired. \(\mathbf{R}_{k}\) is renewed by Equation (27). \(\mathbf{P}_{k|k-1}\) is updated by Equation (28) because the state formula is nonlinear. Values of \(\mathbf{X}_{k|k}\) and \(\mathbf{P}_{k|k}\) with higher accuracy at time \(k\) have not been obtained.
# TRUST-ME: Trust-Based Resource Allocation and Server Selection in Multi-Access Edge Computing
###### Abstract  
Multi-access edge computing (MEC) has attracted the interest of the research and industrial community to support Internet of things (IoT) applications by enabling efficient data processing and minimizing latency. This paper presents significant contributions toward optimizing the resource allocation and enhancing the decision-making process in edge computing environments. Specifically, the TRUST-ME model is introduced, which consists of multiple edge servers and IoT devices, i.e., users, with varied computing tasks offloaded to the MEC servers. A utility function was designed to quantify the benefits in terms of latency and cost for the IoT device while utilizing the MEC servers' computing capacities. The core innovation of our work is a novel trust model that was designed to evaluate the IoT devices' confidence in MEC servers. This model integrates both direct and indirect trust and reflects the trustworthiness of the servers based on the direct interactions and social feedback from other devices using the same servers. This dual trust approach helps with accurately gauging the reliability of MEC services and ensuring more informed decision making. A reinforcement learning framework based on the optimistic Q-learning with an upper confidence bounds action selection algorithm enables the IoT devices to autonomously select a MEC server to process their computing tasks. Also, a multilateral bargaining model is proposed for fair resource allocation of the MEC servers' computing resources to the users while accounting for their computing demands. Numerical simulations demonstrated the operational effectiveness, convergence, and scalability of the TRUST-ME model, which was validated through real-world scenarios and comprehensive comparative evaluations against existing approaches.  
multi-access edge computing; Internet of things; reinforcement learning; game theory +
Footnote †: journal:  
00 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# Simulation of Fidelity in Entanglement-Based Networks with Repeater Chains
###### Abstract  
We implement a set of simulation experiments in NetSquid specifically designed to estimate the end-to-end fidelity across a path of quantum repeaters or quantum switches. The switch model includes several generalizations that are not currently available in other tools and are useful for gaining insight into practical and realistic quantum network engineering problems: an arbitrary number of memory registers at the switches, simplicity in including entanglement distillation mechanisms, arbitrary switching topologies, and routing protocols. An illustrative case study is presented, namely a comparison in terms of performance between a repeater chain where repeaters can only swap sequentially and a single switch equipped with multiple memory registers that is able to handle multiple swapping requests.  
quantum networks; entanglement distribution; resource allocation; simulation +
Footnote †: journal: applied sciences
# A Review of Image Inpainting Methods Based on Deep Learning
## 2 Datasets and Evaluation Metrics
### Evaluation Metrics
Depending on the model algorithm, one can prioritize different metrics. If the primary loss during model training is reconstruction loss, one should focus on SSIM and PSNR; if it is the adversarial loss in generative models, subjective evaluations should be emphasized. Moreover, objective evaluation metrics often use the original image as a reference, posing significant challenges for diverse image inpainting.
## 3 Key Technologies Achieved by the System
### Duplicated Data Filtering and Missing Data On-line Filling
#### 3.3.1 Multiple Regression Analysis Estimation Based on Spatial Correlation  
The strength of the spatial correlation between nodes is decided by the value of the Pearson's correlation coefficient. The larger the absolute value of the coefficient, the stronger the correlation [15]. It is generally believed that a coefficient of 0.8-1.0 indicates a high correlation. In this study, nodes with a coefficient of larger than 0.8 are chosen to be included in estimation. Regression coefficients \(\beta 0\),..., and \(\beta n\) are solved using the least square method and a regression function \(\dot{y}i=\beta 0+\beta 1Y1+\beta 2Y2+......+\beta nYn\) is constructed, where Y1,..., and Yn are acquisition values of related nodes in a same sampling cycle and \(\dot{y}i\) is the estimated value based on spatial correlation.
## 3 Results and Discussion
### Real-Time Approach
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **NVS** & **u-blox** \\ \hline Estimated-reference coordinates & & \\ East [m] & 0.107 & \(-\)0.002 \\ North [m] & 0.112 & 0.006 \\ h [m] & 0.192 & 0.081 \\ Planimetric residual at 95\% [m] & 0.186 & 0.009 \\ Altimetric residual at 95\% [m] & 0.283 & 0.113 \\ Percentage of FIX epochs & 13.6\% & 95.3\% \\ \hline \hline \end{tabular}
\end{table} 
Table 16: Planimetric errors considering the LEIAR10 antenna and VRS correction—only GPS constellation. Figure 4: Float positions obtained by the NVS receiver (RTKNAVI window). \begin{table}
\begin{tabular}{c c c} \hline \hline  & **NVS** & **u-blox** \\ \hline Estimated-reference coordinates & & \\ East [m] & \(-\)0.231 & \(-\)0.001 \\ North [m] & \(-\)0.123 & \(-\)0.008 \\ h [m] & 0.294 & 0.234 \\ Planimetric residual at 95\% [m] & 0.259 & 0.009 \\ Altimetric residual at 95\% [m] & 0.352 & 0.237 \\ Percentage of FIX epochs & 7.6\% & 97.2\% \\ \hline \hline \end{tabular}
\end{table} 
Table 17: Planimetric errors obtained with the Garmin antenna and VRS correction considering only the GPS constellation. is very bad. As shown in Figure 5, the point cloud is very large so the solutions are more than five meters from the correct position. Also the epochs with fixed phase ambiguity (in green in the previous figure) are very far from each other. This means that we are in the presence of several false fixes of the ambiguity phase, which are very dangerous conditions for positioning. At the same time, the u-blox receiver provides better results both in terms of fixing percentage and in the quality of the positioning.
# Global Time-Varying Path Planning Method Based on Tunable Bezier Curves
## 6 Simulation  
### Feasibility Analysis of Obstacle Avoidance  
The experiment was run on a CPU of Inter i5-6500 with 4G of RAM. According to Section 3, the GTVP method can ensure that the manipulator avoids obstacles in a single-obstacle environment. It can be seen from Section 4 that, when the manipulator moves in a multi-obstacle environment, it still keeps a certain distance from the obstacles. The movement process of the manipulator is shown in Figure 8. It can be seen from Figure 8 that complex obstacles are characterized by the basic obstacle model and the corresponding obstacle feature points are generated in the process of motion planning. Whereafter, a continuous path with global dynamic change is generated according to the dynamic feature points, so that the manipulator can repeat the time-varying following movement along the time-varying path, so as to achieve obstacle avoidance.  
During the movement of the manipulator from 90 s to 100 s, the closest distance between the key node of the manipulator and all obstacles can be analyzed to obtain the change curve shown in Figure 9. The curve is the closest distance between each key node on the manipulator and the obstacle, where \(d_{8}\) is the closest distance between the end point of the manipulator and all obstacles and the remaining \(d_{i}\) corresponds to the closest distance between the start point of the \(i\)th link and all obstacles.  
As can be seen from the figure, the maximum distance between the manipulator and the obstacle is 15 mm, which is not less than the set minimum distance. Because, when the closest distance between the manipulator and the obstacle is less than the specified value, the real-time path can be quickly adjusted by adjusting the curve feature point of the symbolic path in this method, so that the manipulator can retreat to a safe area. Through the above simulation, it can be proved that the path  
Figure 8: Diagram of the motion process of the manipulator in the environment of multiple obstacles. (a) \(t=90\) s. (b) \(t=95\) s. (c) \(t=100\) s.  
method meets the following conditions: \"global\", \"time-varying\" and \"obstacle avoidance in multi-obstacle environment\".
## 4 Results
### Regression Performance  
For the regression task, we used MAE, MSE, and RMSE as evaluation metrics. Figure 5 shows a bar chart comparing the regression performance across the models, while Figure 6 illustrates a direct comparison between the predicted and ground truth values for representative samples. The corresponding numerical results are presented in Table 3.  
Figure 5: Comparative evaluation of regression metrics across models.  
Figure 6: Prediction vs. ground truth comparison across models in the regression task.  
The results indicate that BiCA-LI achieves superior regression performance, reducing MAE and MSE by significant margins compared to both LSTM-only and Informer-only baselines. This demonstrates the benefit of integrating both short- and long-term dependencies through the dual encoder and fusion mechanism.