esa-sceva/satcom-chunk-collection huggingface dataset:
# Hypergraph Position Attention Convolution Networks for 3D Point Cloud Segmentation
## 2 Related Work
### Deep Learning-Based Semantic Segmentation of 3D Point Clouds  
The voxel-based methods [28] provide a way to convert unstructured geometric data into regular 3D meshes that can be directly applied by standard CNNs. The methods effectively motivate the regularization of unorganized point clouds. However, the inhomogeneity of the point cloud leads to redundant 3D meshes, which require a lot of computation and consume significant amounts of memory. Multiview-based methods [29; 30] map 3D objects to 2D images from different perspectives and then process them using 2D convolution followed by fusion of these features to predict the results. Compared to voxel-based methods, the methods reduce the memory cost, but they are vulnerable to virtual viewpoints, leading to the loss of geometric feature information in the point cloud. Point cloud-based methods such as the well-known PointNet [4] and PointNet++ [5] employ direct processing of raw point cloud data to maximize the preservation of spatial features of the point clouds and achieve impressive results in testing. However, this kind of methods ignore the geometric relationships between points, resulting in a lack of localized features. The graph convolution-based methods [8; 9; 31] consider each point in the point clouds as a vertex of the graph and generate directed edges with neighboring points, which learn the point and edge features in the spatial or spectral domain to capture the local geometric structure features of point clouds. However, the higher-order relationships between objects are not taken into account, and it is difficult to capture the complex structures present in 3D data.
## 2 Materials and Methods
### The Biotoxicity Assays  
#### 2.6.1 The Bioluminescent Bacteria Light Emission Inhibition Assay  
Lyophilized aliquots of luminescent bacteria _V. fischeri_ cultures were prepared at our laboratory starting from fresh cultures originating from a batch supplied by the Pasteur Institute (Paris, France). The 96-well \"Black Cliniplate\" microplates were supplied by Thermo Scientific (Vantaa, Finland) and the luminometer was the Victor Light 1420 model from PerkinElmer, USA.  
According to [39], lyophilized aliquots of _V. fischeri_ were reconstituted with 1 mL of distilled water and re-suspended in 10-30 mL of nutrient broth. To treated and untreated samples was added NaCl to reach the concentration of 3% \(w/v\). 200 \(\upmu\)L of the bacteria suspension and 100 \(\upmu\)L of each sample were dispensed into the microplate wells. The controls consisted of 200 \(\upmu\)L of bacteria plus 100 \(\upmu\)L of a 3% NaCl solution in tap water. The emitted light was recorded at fixed intervals in the range 0-48 h. For each sample, 5-12 replicates were prepared and the light emission intensity, reported as relative luminescence units (RLU), was expressed as mean \(\pm\) SD. Moreover, the bioluminescence inhibition percentage (\(I\%\)) was used to express the toxicity of the samples and calculated according to:  
\[I\%=\frac{L\ blank-L\ sample}{L\ blank}*100 \tag{1}\]  
where \(L\) is the light emitted intensity of the _sample_ or of the control _(blank)_.
# GPS-Based Indoor/Outdoor Detection Scheme Using Machine Learning Techniques
## 6 Conclusions  
In this paper, we proposed an indoor and outdoor environment detection scheme that classifies indoor and outdoor environments at high accuracy under a simple implementation. We also proposed a data preprocessing technique. Using the proposed data preprocessing process and suitable machine learning techniques, the proposed I/O detection scheme obtains quick and accurate detection results in various time and environments. We comprehensively tested I/O detection through a prototype implementation and evaluated the system based on different data samples collected in the Kookmin University area. The prototype systems obtained an accuracy of 97%. We particularly conducted a case study where we made use of I/O detection results to infer the GPS availability and accuracy under various indoor/outdoor environments.  
All authors contributed to this paper: [PERSON] and [PERSON] proposed the idea and implementation methodology, reviewed and edited paper; [PERSON] performed all experiments and wrote the paper, verified the experiment process and results; [PERSON] collected data and created the first version of data augmentation software; [PERSON] proposed and optimized machine learning techniques, performed parts of experiments; and [PERSON] supervised the work and provided funding support. All authors have read and agreed to the published version of the manuscript.  
This research was financially supported by the Ministry of Trade, Industry and Energy (MOTIE) and Korea Institute for Advancement of Technology (KIAT) through the International Cooperative R&D program.  
The authors declare no conflict of interest.
## 4 Case Study  
An experimental CEPR-CPS is an ADN case, and it is designed from the actual system provided by China Electric Power Research Institute. The topology and node numbering of PN and CN in CEPR-CPS are shown in Figure 4. Node numbers in the PN are from 1 to 80, and node numbers in the CN are from 81 to 109. Nodes 1-4, 23, 60, 75-80 (abstracted from the contact switch) are distribution nodes. Nodes 17, 18, 24, 25, 43, 44, 56 are substation nodes. Nodes 66-74 are power generation nodes (66, 68, and 70: battery energy storage nodes, 67 and 73: photovoltaic power generation nodes, 69 and 74: wind power generation nodes, 71: a hydraulic turbine node, 72: a micro-gas turbine node). Nodes 63-65 are external nodes. The rest in the PN are load nodes. A node 81 is the control node, nodes 82-87 are information relay nodes, and nodes 88-109 are sensor/actuator nodes. There are 109 nodes in the CEPR-CPS. There are 80 nodes and 107 edges in the PN, and there are 29 nodes and 28 edges in the CN. The directed independence edges between the PN and CN in the CEPR-CPS are shown in Table 1. A directed independence edge is from a node to its successor node. Nodes of PN supply power to nodes of CN. Accordingly, nodes of CN collect the information of nodes in the PN and thus control the actions of them. For example, the node 43 of PN supplies power to nodes 104, 106, and 107 of CN (as shown by the dotted orange line in Figure 4) and the node 99 of CN collects the information of the node 16 in the PN (as shown by the dotted blue line in Figure 4).  
Figure 4: Topology and node numbering of PN and CN in the CEPR-CPS.  
The CEPR-CPS is used to verify the effectiveness of the PCP against CCF generated from the PCMD compared with the one from the multi-agent method.
## 4 Numerical Simulations  
### Generating Training Data  
The training data are created by solving the forward problem using the finite element method in a circular domain of unit radius. The forward problem was solved using a mesh with 2304 triangular elements shown in Figure 2. We used a mesh with 576 triangular elements to avoid the inverse crime in the reconstruction problem. The training data contained up to two circular inclusions having a radius randomly chosen from 0.1 to 0.25 units. The objects were selected to have a random number between 10 to 20 units of conductivity, while the rest of the domain had 1 unit of conductivity. Our simulations shown below are based on circular in-homogeneity; however, we also investigated other shapes, such as elliptical in-homogeneity, and found similar results.  
These circular conductivity distributions were used to solve the forward problem and generate the voltage measurement data. Sixteen equally spaced electrodes were used on the boundary, and all the electrodes were chosen to have a contact impedance of 0.01 units. Adjacent alternate currents were injected with a driving current of 0.01 A. This consisted of 208 measurements in total. All of this was performed using EIDORS [34]. Thus, the rbf has an input array of 208 elements and an output array of 576 elements for training data. The input boundary voltage data for the rbf network are normalized. The total size of the generated training data set is 10,000. Five thousand are single inclusions and another five thousand are double inclusions. From these, 8000 randomly chosen models were used for training. The other 2000 were used for validation. Furthermore, 1000 triple inclusions are created by solving the forward problem and were used to test the generalization of the network to untrained scenario. Both input and output data are normalized.
# Software-Defined Heterogeneous Vehicular Networking: The Architectural Design and Open Challenges
## 4 Towards Software-Defined Heterogeneous Vehicular Networks
### Architectural Challenges
#### 4.2.4 Highly Dynamic and Distributed Behaviour of Vehicles  
One of the significant challenges for SDHVNets is the highly dynamic and distributed behaviour of vehicles, which makes it extremely difficult for the SDN control plane to maintain the run-time positions of the vehicles and their anticipated travelling trajectories. This, therefore, leads to network management overhead and a substantial amount of end-to-end delay for safety-critical and non-safety vehicular applications. Undoubtedly, the dynamic mobility and the distributed behaviour of vehicles are in fact the root cause for all the vehicular networking challenges and demands careful attention. Furthermore, it is indispensable to disseminate network packets depending on the geographical position of vehicles instead of their IP addresses, and highly intelligent and efficient localization and trajectory prediction mechanisms need to be designed in order to tackle such a challenge. This requires that the intelligence is passed to the edge of the network. Furthermore, a number of localization techniques have been proposed in the research literature for mobile ad hoc networks, and the same could be explored and subsequently optimized for SDHVNets.
## 4 Results and Discussion  
### One-Year-Out Cross-Validation with ROC Curve  
In order to evaluate the ensemble model and to compare it with the individual integrated and meteorological models, the ROC curves of one-year-out cross-validation (Figure 3a-f) and the entire years (Figure 3g) are depicted in Figure 3. Since a large weight (i.e., 80%) was applied to the integrated model when combining two models, it is not surprising to see similar ROC curves between the ensemble and integrated models. When the entire year results were summed, the AUC of the meteorological model was 0.8097, while the AUCs of the integrated and the ensemble models were 0.8434. The ensemble and integrated models outperformed the meteorological model. This means that the models benefited from the use of accessibility, fuel, and time-related variables. The AUC 0.8434 means that the ensemble model is good at distinguishing between high and low fire risk areas.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Index**} & \multicolumn{4}{c}{**Class**} \\ \cline{2-5}  & **Low** & **Moderate** & **High** & **Very High** \\ \hline HFRI & 0–0.49 & 0.5–0.66 & 0.67–0.83 & 0.84–1 \\ \hline DWI & 0–50 & 51–65 & 66–85 & 86–100 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Forest fire risk classes and their intervals for Hourly Forest fire Risk Index (HFRI) and Daily Weather Index (DWI).
###### Abstract  
The prevalence of \"building-bridge integrated\" structures in station design is increasing. However, in stations where the mainline speed exceeds 160 km/h, structural joints are typically incorporated to ensure the integrity and functionality of the integrated system. The inspection and maintenance of these joints, which are critical for the long-term performance of such structures, can be particularly complex. Therefore, it is important to explore the feasibility of designing such stations without structural joints on the mainline. To address this issue, two six-line railway bridge-type stations are selected. The vibration simulation analysis model of the train-track-station coupling system is established, considering two structural types of the \"building-bridge integrated\" system: the arrival-departure line \"building-bridge integrated\" and the mainline \"building-bridge integrated\". The vibration responses induced by trains passing through two types of \"building-bridge integrated\" station structures at speeds of 200-350 km/h on the mainline and 80 km/h on the arrival and departure tracks were simulated. The six-line operating conditions were selected as an example, and the influence of setting a structural joint on the mainline on the dynamic response of the \"building-bridge integrated\" station structure was analyzed. For both types of \"building-bridge integrated\" station structures, with and without a structural joint on the mainline, the dynamic responses of trains under operational loads show minimal differences. However, the structural joints on the mainline reduce the overall stiffness of the rail bearing floor slab and effectively isolate the train-induced responses transmitted to the platform slab during high-speed operation on the mainline. Therefore, the acceleration response of the platform slab is smaller in station structures with structural joints, while the acceleration and displacement response of the rail bearing floor slab is larger. Additionally, structural joints often lead to issues such as water leakage and seepage. Considering these factors, it is advisable to avoid setting structural joints on the mainline for such station structures.  
mainline structural joints; building-bridge integrated; bridge-type station; vehicle-induced vibration; dynamic behavior +
Footnote †: journal: Accepted: 12 April 2025  
14 April 2025  
**Citation**: [PERSON]; [PERSON]; [PERSON] Research on the Dynamic Behavior of \"Building-Bridge Integrated\" Railway Bridge-Type Station with Setting the Structural Joints on the Mainline. _Appl. Sci._**2025**, 15, 4335
# Level of Agreement in Subjective Selection of Gingival Colour
## 1 Introduction
There has been a similar lack of research on the difference in the size of the \"chromatic errors\" (the colour difference between the colour coordinates of the subjectively selected shade tabs and the objective colour coordinates provided with electronic devices) made by men and women in the gingival colour space and whether such errors have clinical relevance in practice. This study had the following objectives: (1) to determine the chromatic errors made by a male observer and a female observer--both of whom were dentistry graduates--by calculating the difference between spectrophotometer readings and the colour they selected visually, using the Euclidean and CIEDE2000 formulae, and to assess whether these errors depended on the gingival zone (two papillae, free gingival margin, attached gingiva, and mucoginoyal line) and (2) to determine the CIELAB coordinates of the subjective gingival colour selections made by each observer in the various gingival regions and examine the level of agreement between them, identifying whether there were any statistically significant differences between the observers. The null hypotheses of this study are the following: (1) the chromatic error made in subjective colour selection is the same in all five gingival zones; (2) the level of agreement between the two observers for the same gingival zone is acceptable and the chosen CIELAB coordinates are homogeneous.
# A Spatial Prediction-Based Motion-Compensated Frame Rate Up-Conversion
## 2 Proposed SP-MCFI Algorithm  
Figure 1 shows the flowchart of the proposed SP-MCFI algorithm. First, a block classification is done to divide the frame into basic blocks and absent blocks. Second, the full search ME based on advanced SEA is utilized to track the MVs of basic blocks. Then, the spatial prediction method is used to achieve the MVs of absent blocks according to the motion information of basic blocks. Finally, the MVs of the two types of blocks are combined to compensate the image of the interpolated frame \(f_{t+0.5}\) with the pixel values of two reference frames \(f_{t}\) and \(f_{t+1}\).
## 5 Simulation Results and Water Tank Experiments
### Water Tank Experiment  
To validate the feasibility of rapid USV development using the proposed simulation system, the \"Noah\" USV developed by AMOVLab [20] was employed to conduct water tank experiments under the most comprehensive path-following mode. The experimental environment is shown in Figure 15.  
As in Section 5.1.3, the waypoint coordinates were set as P1 (\(-\)20, \(-\)44), P2 (\(-\)20, \(-\)60), P3 (\(-\)33.8, \(-\)68), P4 (\(-\)47.6, \(-\)60), P5 (\(-\)47.6, \(-\)44), and P6 (\(-\)33.8, \(-\)36). The USV's path-following trajectory and cross-track error curves are shown in Figure 16a and Figure 16b, respectively.  
Figure 14: Simulation result for path-following. (**a**) Trajectory in path-following mode. (**b**) Cross-track error variation curve. (**c**) Surge velocity and thrust variation curves. (**d**) Yaw angle and azimuth angle variation curves.  
As observed in Figures 14a,b and 16a,b, the trajectories in the simulation experiments are relatively smooth with minor cross-track errors, while trajectories in the water tank experiment exhibit slight oscillations around the desired path, with cross-track errors below 1 m. The consistency between simulation and experimental results demonstrates that the proposed simulation system effectively simulates the USV's motion characteristics and real-world scenarios, confirming its validity for control algorithm verification.
## 3 Comparative Analysis of Existing Evaluation Methods
### Overall Accuracy  
The predictive performance of the formula is assessed for accuracy. Accuracy is defined as the ratio of correctly predicted samples to the total number of samples, with detailed statistics further refined for the liquefaction and non-liquefaction cases.  
Formulae (7) and (18) utilize the equivalent corrected \(V_{s1,cs}\), compared to Formulae (8) and (11). Figure 3 illustrates the binary classification effectiveness of each formula at liquefaction probabilities of 15%, 50%, and 85%. Due to the complex interleaving of liquefaction and non-liquefaction points, delineating clear curves is challenging.  
Figure 3: Curves for separating liquefaction and non liquefaction cases using various formulae under different probabilities. **(a)** Equation (7), **(b)** Equation (8), **(c)** Equation (11), and **(d)** Equation (18).  
At 15%, all formulae effectively separate the liquefaction points. However, with increased seismic intensity, significant differences in the curve trends emerge. Formula (11) is the most conservative, whereas Formula (7) exhibits steeper trends, better distinguishing the non-liquefaction cases. Both consider an upper \(V_{s}\) limit of 200 m/s for liquefaction, causing rapid curve rises around this threshold.  
At 50%, Formula (8) encompasses more liquefaction and non-liquefaction points, indicating a high evaluation accuracy but a higher risk of false positives, making it more conservative overall.  
At 85%, the formulae can effectively separate non-liquefaction points, but the overall performance is less effective than at 15%. [PERSON]'s study does not provide a recommended safety factor for an 85% probability; thus, an \(F_{S}\) value of 0.83 is adopted, resulting in a conservative estimate. Formula (7) shows the ideal performance, encompassing more liquefaction points while separating the non-liquefaction cases.  
To assess these formulae's performance, a 50% liquefaction probability is used as a binary evaluation threshold and the accuracy is statistically evaluated using the database. Here, accuracy refers to the proportion of cases where the evaluation matches the actual situation. Figure 4 presents the accuracy rates for each formula.  
Overall, the accuracy of each formula is around 80%. Formulae (7) and (18) exhibit the highest accuracy, approximately 3% higher than formulae (8) and (11). Notably, formula (11) has a balanced accuracy in both liquefaction and non-liquefaction areas.  
The Bayesian and logistic regression models demonstrate close overall accuracy, suggesting a high consistency and reliability in liquefaction evaluation, suitable for constructing and optimizing liquefaction probability methods.
## 3 Results
### Sensitivity Analyses and Calibration Procedure
As expected, higher GW level occurs during the winter season, then dropping to lower levels during the summer season with increasing ET. In general, the coupled model underestimates the GW levels during the high recharge periods, but the performance of the model was not equally comparable at all the observation locations. Due to the rather shallow GW tables, there is a strong relationship spatially and temporally between the precipitation and resulted simulated GW levels. GW elevations react dynamically to local precipitation events. Boreholes located in the vicinity of the climate station \"Demmin\" showed that the average simulated GW elevations across the middle and downstream section varies up to around 0.15 m during an extra ordinary precipitation event that occurred in October 2017. Coupled model performance was evaluated by using mean absolute error, root mean square error, correlation coefficient, and standard deviation residuals. Initially climate parameters were adjusted according to the basin climate conditions and after that the calibration process was focused mainly on horizontal and vertical conductivity and specific storage of the SZ parameters. As a result, the calibration process includes seasons with different GW dynamics due to variation in rainfall patterns and resulted varied GW levels. Specific yield and GW gradients turned out to be the most adjusted parameters, as boundary gradients normally determine the GW influx, while specific yield defines the dynamic response of the GW system, hence both have an impact on estimated water balance. Figure 9: Selected boreholes used for groundwater level calibration. Figure 10: _Cont._ \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Groundwater Well ID** & **MAE (m)** & **RMSE (m)** & **R (Correlation)** & **STDres** \\ \hline HyBorr 105/1985-LOCID \# 466 & 1.467 & 1.508 & 0.845 & 0.352 \\ HyStav 2/2005-LOCID \# 42 & 1.159 & 1.166 & 0.749 & 0.131 \\ Hy Borr 106/1985-LOCID \# 467 & 0.478 & 0.557 & 0.786 & 0.351 \\ HyStav 7/2005-LOCID \# 47 & 2.137 & 2.139 & 0.500 & 0.096 \\ HySzw 141/1989-LOCID \# 670 & 1.080 & 1.157 & 0.868 & 0.414 \\ Hy Kris 1/2008-LOCID \# 37 & 5.987 & 5.998 & 0.435 & 0.439 \\ Hy Top 1/2008-LOCID \# 67 & 1.500 & 1.555 & 0.646 & 0.411 \\ \hline \hline \end{tabular}  
* MAE = “mean absolute error in level m”; RMSE = “root mean square error in level m”; R = “correlation”; STDres = “standard deviation residuals”.  
\end{table} 
Table 6: Statistics of the objective function for groundwater level calibration.