esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Problem Statement and Preliminary Analysis
### Multiple Clusterings Issue  
In this section, taking K-means algorithm [46] for example, we illustrate multiple clusterings issue through two observations as follows.  
**Observation 1**.: _The optimal number of clusters \(K\) might not be unique due to different methods._  
For example in Figure 2, when different methods are considered to determine optimal \(K\) for SFD-out data, we might have multiple optimal \(K\)s as 2, 3 or 4. Thus, we have three different clusterings as shown in Figure 3.  
**Observation 2**.: _Evaluation of best clustering \(\mathbf{\pi}^{(*)}\) depends on what measures are considered._  
Generally, there are three categories of measures to validate clustering results: (1) internal measures evaluate compactness, connectedness and separation among partitions within each cluster, such as silhouette coefficient [50] and Dunn index [51]; (2) external measures compute similarity between different clusterings (when alternative clustering is provided), such as Rand index [52], Fowlkes-Mallows index [53], Jaccard index [54] and variation of information [55]; and (3) stability measures evaluate the consistency of clustering by comparing clusterings based on full data set and removal of specific column [56; 57].  
Table 3 demonstrates clustering performance validation with optimal \(K=2,3,4\) using SFD-out data, where \(\uparrow\) denotes larger is better while \(\downarrow\) means smaller is better, and better clusterings are highlighted by stars. Specifically packages _clValid_[58], _fpc_[59] and _NbClust_[60] in R are employed for computing each measure. We observe that for Silhouette and DI, \(K=2\) clusterer performs best; for ARI, FM and Jaccard index, \(K=3\) clusterer outperforms others; for VI measure, \(K=4\) clusterer  
Figure 2: Determine optimal \(K\) with different methods for SFD-out data, which shows that optimal number of clusters depends on the clustering algorithms and metrics. (a) Elbow method [46]: \(K=4\); (b) Silhouette method [47]: \(K=2\); (c) Gap statistic method [48]: \(K=4\); (d) [PERSON] criterion [49]: \(K=3\).  
stands out; in addition, depending on which stability measure is considered, any one of the three clusterers could be the best.
## 2 Study Area and Data Sources  
### Study Area  
Strategically positioned in the core of the Beijing-Tianjin-Hebei urban agglomeration, Xiong'an New Area (38\({}^{\circ}\)43\({}^{\prime}\)27\({}^{\prime\prime}\)-39\({}^{\circ}\)10\({}^{\prime}\)20\({}^{\prime\prime}\) N, 115\({}^{\circ}\)38\({}^{\prime}\)03\({}^{\prime\prime}\)-116\({}^{\circ}\)19\({}^{\prime}\)56\({}^{\prime\prime}\) E) represents China's third generation of special economic zones following Shenzhen and Pudong. Established in 2017, this approximately 2000 km\({}^{2}\) jurisdiction encompassing the Xiong, Rongcheng, and Anxin counties (Figure 1) embodies the national strategy for decentralized development, particularly the relocation of Beijing's non-administrative functions. The region prioritizes ecological civilization principles, aspiring to establish an international model of hydro-urban integration and sustainable habitation [34].  
The land cover composition features a predominance of cultivated land, urban construction land, and aquatic systems, with fragmented distributions of woodland and grassland. Characterized by a warm temperate monsoon climate (mean annual temperature: 12.4 \({}^{\circ}\)C; precipitation: 495.1 mm) [38], the terrain exhibits gentle northwest-to-southeast topographic gradients (elevation range: 5-26 m) [39]. Predominant soil types include chernozems and cambisols, with extensive hydrological networks comprising the Nanjuma and Daqing rivers, Baigou Irrigation Channel, and, the crown jewel, Baiyangdian Lake--North China Plain's largest freshwater body occupying the southeastern sector [40].
## 2 Literature Review
[13] proposed some texture features, namely Haralick, Histogram of Oriented Gradient (HOG), and run-length matrix, which have been extracted from nuclei and lumen images individually. They used a total of 149 images with \(4140\times 3096\) pixels, and the dataset was randomly divided into 50% for training and 50% for testing. An ensemble machine learning classification system was proposed, and achieved an accuracy of 88.9% for Grade 3 vs. Grade 4, 92.4% for benign vs. Grade 4, and 97.85% for benign vs. Grade 3. These accuracies were averaged over 50 simulation runs and statistical significance. [PERSON] et al. [14] used morphological and texture features to classify the sub-region of \(100\times 100\) pixels and subjected each to image-processing techniques. They classified a tissue image into either stroma or prostatic carcinoma. In addition, the authors used lumen area to discriminate benign tissue from the other two classes. As a result, 79.3% of sub-regions were correctly classified. [PERSON] et al. [15] introduced an automated image analysis framework capable of efficiently segmenting microglial cells from histology images and analyzing their morphology. Their experiments show that the proposed framework is accurate and scalable for large datasets. They extracted three types of features for SVM classification, namely Mono-fractal, Multi-fractal, and Gabor features. [PERSON] et al. [16] used image processing and machine learning algorithms to analyze the smear images captured by the developed image-based cytometer. A low-cost, portable image-based cytometer was built for image acquisition from Giemsa stained blood smear. The authors selected 50 images manually for the training set, out of these, 25 images were parasites and 25 images were non-parasites. The selected images were then segmented separately to extract the features for Support Vector Machine (SVM) classification, and they used linear kernel classifier to train and test these features.
## 4 Traffic Filtering-Based Techniques
### Quick Removing (QR)
#### 4.1.3 Advantages and Disadvantages  
QR is the simplest approach to reduce redundant traffic in HSR networks. The QR approach is easy to implement. The QR approach can be applied for any traffic such as unicast, multicast and broadcast traffic, in any network topology.  
However, the main disadvantage of QR is that it does not prune traffic for unused rings. In the QR approach, network unicast frames are forwarded into all rings of HSR networks, even DANH rings that do not contain the destination of the frames and QuadBox rings that are not used to deliver the unicast traffic from the source to the destination.  
Figure 3: Removing duplicates in a single ring under the QR approach.
# Research on Multi-Scale Point Cloud Completion Method Based on Local Neighborhood Dynamic Fusion
## 4 Experiments
### Experimental
#### 4.3.5 Generalization Experiment  
To further evaluate the completion performance of LNDM-Net on incomplete point clouds with varying degrees of missing data, three sub-models were designed based on the ShapeNet dataset. These sub-models were trained by altering the number of points in both the input and generated point clouds, with the goal of assessing the model's performance under different conditions of point cloud incompleteness.  
As shown in the experimental results in Figure 9, the green color represents the input incomplete point cloud, while the orange-red color indicates the completed point cloud output by the model. The overlap of these two visually illustrates the completion effect. Taking the chair sample as an example, under three missing ratios of 25%, 50%, and 75% (indicating the proportion of points missing from the original point cloud), the model consistently demonstrates stable performance.  
Notably, when the missing ratios are 25% and 50%, the model's completion results remain relatively consistent, with CD values being close to each other. Even in the extreme case of a 75% missing ratio, the model still maintains a satisfactory completion effect. This suggests that the model performs well across different levels of point cloud incompleteness.  
Further analysis reveals that the CD values under the 25% and 50% missing ratios are similar, indicating that the model achieves comparable completion accuracy in both scenarios. Even with a 75% missing ratio, the CD value for the chair category remains relatively low at 2.373/2.68. These results underscore the excellent completion capability of LNDM-Net, as it can accurately restore the full shape of a chair, even when only the seat and legs of the incomplete chair are provided as inputs.
## 5 Results
### The Test Results of the Optuna-XGBoost Model  
Based on the above, the optimal feature subset of T, N, F, SE, TPI, and PR, selected through RFECV, serves as the set of input variables for the Optuna-XGBoost model, with the surrounding rock grade as the output variable. Subsequently, the optimal hyperparameter combination provided in Table 3 configures the classification model, and the optimized model is trained on the training set. Finally, the model's ability to perceive and recognize surrounding rock grades is tested using 120 datasets from the test set, and the results are presented in Figure 5. The metrics used to evaluate Optuna-XGBoost are also presented, as shown in Table 5.  
The analysis of the results in Figure 5 and Table 5 reveals that the Optuna-XGBoost model is highly aligned with the actual situation in its predictions of surrounding rock  
Figure 5: Recognition results of the Optuna–XGBoost Model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1** \\ \hline Optuna–XGBoost & 0.9833 & 0.9803 & 0.9813 & 0.9807 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The metrics for evaluating Optuna–XGBoost.  
grades, with only two incorrect predictions. The accuracy of the Optuna-XGBoost model reached 0.983 in the recognition of surrounding rock grades, which demonstrates that the model is entirely precise. Overall, the Optuna-XGBoost model exhibits high metrics in predicting TBM surrounding rock grades. However, for a more scientifically rigorous evaluation of this model, a comparison with other models is necessary.
## 4 Derived Products and Data Processing
### Dem
#### 4.1.2 Hillshade and Stroke Profile  
Second, a hillshade was created using the shading tool (Applications/Shaded Relief). In this case, the program allows you to enter the slope in the X and Y directions. The initial values (15 and 12 microns) are automatically calculated from the average slope within the image. However, the smaller these are, the greater the shading effect. Therefore, it was decided to use a value of 5 microns in both directions. The position of the light source was left by default. The result was an image in grey levels (see Figure 5c) with associated data from the original DEM, which allowed us to measure the grooves of the engraving. The tool used was the Line Profile Plot (Functions/Plot/Profile). Figure 8 shows 9 outlines of the lines, on the mane, ears, forehead, snout, jaw, and neck. The hillshade layer is frequently used in cartography to give an impression of depth and to facilitate the interpretation of the relief and is often combined with other layers of information to improve its visualization. The image in Figure 8 shows the result of applying the hillshade layer overlay with a transparency of 35% on the high pass layer. In addition, in Figure 5d, it is superimposed applying the same transparency on the orthophotomosaic.  
The engraved stroke is multiple and superficial, but well visible, and it seems, as in other pieces by [PERSON], aimed at precisely tracing the external animal's figure contour. In the areas where the line is wider, between 8 and 10 passes are counted, and their total width does not exceed 1 mm. The delimitation of the painting and the contrast by eliminating the original patina could be the visual effects sought [17]. Regarding the depth of the lines, we can indicate that on average they present values between 30-40 microns, there are more superficial lines that present values between 10-20 microns and deeper with values between 50-60 (see Figure 8).
## 6 Conclusions  
This paper proposed an RSM method for predicting the connection load of GRS segmental walls constructed with cohesionless backfill and on competent foundations. This method replaced the active earth pressure coefficient used in the classical earth pressure method with an RSM of the lateral earth pressure coefficient. The RSM was established using multiple parameters that affect the distribution of the total earth load between wall toe and reinforcement layers. The unknown coefficients of the RSM were determined using the regression analysis of 705 connection load values from 65 numerical GRS segmental wall models. The measured connection loads from two field walls and three centrifuge test walls were utilized to verify the proposed method. The following conclusions can be drawn from this study:  
1. In GRS segmental walls with a typical toe restraint, the sum of the total connection load and toe resistance is close to the Coulomb active earth load acting on the facing column.
2. The ratio of the earth loads carried by the toe increases with the friction angle of the facing block-leveling pad interface but shows little change for interface friction angles greater than 25\({}^{\circ}\).
3. Under the same toe restraint conditions, the toe load ratio increases with the facing batter, reinforcement spacing, and backfill friction angle, and decreases with wall height and reinforcement stiffness. In most cases in this paper, the toe load ratio was not higher than 50%.
4. No matter what toe restraint conditions, the predictions of the proposed RSM method are in better agreement with the measured connection loads than those of the stiffness method and the earth pressure method.
5. For GRS walls with proper toe restraint conditions, the reinforcement connection loads calculated using the proposed method show a bulge distribution shape, which is consistent with observations in GRS walls under working stress conditions.
6. For GRS walls with the loss of toe resistance, the proposed method yields a positive, approximately linear relationship between the connection loads and wall depth, which is close to the results of the earth pressure method.
7. The proposed method cannot consider the effects of the cohesion of backfill soil and the down-drag force induced by differential settlement of foundations on the connection loads and needs further optimization.  
Writing--original draft preparation, W.Z. and J.C.; funding acquisition, W.Z. and J.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant Nos. 42007264 and 41772289) and the Science and Technology Program of the Shaanxi Province, China (2023-JC-QN-0630).  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
# Data-Driven Pavement Performance: Machine Learning-Based Predictive Models
## 8 Conclusions  
Since sampling of the training set is further optimized based on the gradient size in the case of LightGBM, it leads to the generation of fewer sampling points when the gradient is calculated. Therefore, LightGBM provides faster training speed with higher accuracy of prediction. LightGBM, CatBoost and gradient boosting are all ensemble models. These models use multiple weak learners (often decision trees) to create a strong learner, improving accuracy and generalization. By combining the results of many trees, these models are able to capture complex patterns in the data that other models do not possess. The findings of this research are as follows:  
1. CatBoost exhibits the reduced MSE by 188% when compared to linear regression
2. The least difference in computed MSE values exists between decision tree and random forest at 38%.
3. Gradient boosting, LightGBM and CatBoost exhibit almost similar performance in terms of computed MSE values.
4. KNN and linear regression exhibit almost similar performance in terms of computed MSE values.
5. Gradient boosting outperforms KNN by 199% in terms of computed MSE.
6. The highest R\({}^{2}\) value is exhibited by LightGBM and CatBoost which is 5% higher than computed R\({}^{2}\) for linear regression.
7. Decision tree, random forest and SVR exhibit almost identical R\({}^{2}\) values.
8. SVR outperforms KNN by 5.2%, marking the highest performance difference between any two algorithms.  
Conceptualization, M.F. and N.B.; methodology, N.B.; software, N.B.; validation, M.F. and N.B; formal analysis, M.F.; investigation, M.F.; resources, N.B.; data curation, N.B.; writing--original draft preparation, M.F.; writing--review and editing, M.F.; visualization, N.B.; supervision, M.F.; project administration, M.F. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
# A Novel Mobile Communications Authentication Scheme with Roaming Service and User Anonymity
## 5 Security Analysis
### Balanced Calculation of Session Key  
The load of session key computation is balanced between the senders and the receivers in the authentication phase. Table 4 shows the balanced calculation of the session key.
## 2 Materials and Methods
### Experimental Stand  
The idea of the research stand work is to change the movement of the operator's hand-- \(\mid\)A\({}^{\prime}\)B\({}^{\prime}\)\(\mid\), during rectilinear movement, to the movement of the laser point (effector) displayed on the screen \(\mid\)AB\(\mid\) by a constant size ratio--\(K\) value (Figure 3).  
The operator, moving the joystick (5) from point A\({}^{\prime}\) to B\({}^{\prime}\), rigidly connected to the lever (2) along the guide (3), and forces the lever (1) to rotate in relation to point C. At the end of the lever (1) there is a laser pointer whose direction of the outgoing laser point is consistent with the longitudinal direction of the lever (1). The laser point falls on the screen (4) creating point A. The projection of the screen surface (4) onto the xy plane is parallel to the guide (3). The kinematics developed in this way allows for a proportional increase in the velocity of the laser point (effector) on the screen \(v_{2}\) relative to the velocity of the operator's hand \(v_{1}\) according to the equation:  
\[v_{2}=Kv_{1}, \tag{1}\]  
where \(K\) = a/b--scale ratio value.  
Point D in the developed mechanism moves with a complex movement resulting from the translational movement of the lever (2) and a rotating lever (1) relative to point C.  
Assuming linear velocities \(v_{1}\)/, \(v_{2}\)/ depends on the angular velocity of the lever (1) \(\omega\) and distances \(x_{1}\) and \(x_{2}\) (Figure 3) according to Equations (2) and (3):  
\[{v_{1}}^{\prime}=\omega x_{1}, \tag{2}\]  
\[{v_{2}}^{\prime}=\omega x_{2}, \tag{3}\]  
Figure 3: The research stands kinematic scheme, described in the main text.  
where \(\omega\)--angular velocity of the lever (1), \(x_{1}\)--the distance between points C and D, and \(x_{2}\)--the distance between points C and A.  
Depending on the velocities \(v_{1}\prime\), \(v_{2}\prime\) on \(v_{1}\), \(v_{2}\) and the distances \(x_{1}\) and \(x_{2}\) on the dimensions \(b\) and \(a\) (Figure 3) according to Equations (4)-(7):  
\[{v_{1}}^{\prime}=v_{1}sin\beta, \tag{4}\]  
\[{v_{2}}^{\prime}=v_{2}sin\beta, \tag{5}\]  
\[x_{1}=\frac{b}{sin\beta}, \tag{6}\]  
\[x_{2}=\frac{a}{sin\beta}, \tag{7}\]  
and after substituting them to (2) and (3) and transforming, the equation for velocity \(v_{2}\) in function \(v_{1}\) was obtained:  
\[v_{2}=v_{1}\frac{a}{b}. \tag{8}\]  
The ratio of the distance between point C and the screen surface (\(a\)) and the lever mounting point D (\(b\)) is the value of the size ratio \(K\).  
Based on the developed kinematic structure (Figure 3), a research stand was designed, and its model and idea of the operation of it are shown in Figure 4.  
The constructed research stand, based on the adopted assumptions, is shown in Figure 5.  
Figure 4: The research stand 3D model.  
Figure 5: Realization of exemplary research.  
The basic measuring element of the research stand, which allowed to determine the value of the operator's hand movement, was the absolute magnetic sensor LA10 manufactured by Kuebler with a measuring accuracy of 0.03 mm over the entire working range [44].
# A Novel Geo-Localization Method for UAV and Satellite Images Using Cross-View Consistent Attention
## 4 Experiments
### Visualization and Interpretation  
In the transformer encoder, each attention block can output an attention map representing attention distribution learning from scene information. The output attention map of the last attention block is obtained and visualized in the UAV and satellite encoder networks. As shown in Figure 7, the networks trained after color transfer can better extract the attention of the mutual landmarks in both UAV and satellite images. For example, there are two green buildings, and the model with a color transfer module can correctly recognize the green building with a dome. In contrast, the attention information of the model without a color transfer module is disturbed by the other green building. The attention maps illustrate that the networks extract the global features with cross-view consistency. As a result, the proposed method can better conduct the image retrieval task.