esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Method
#### 2.2.1 General Overview of the Method  
Consider a population of n positional errors \(E_{i}\) in 1D, 2D or 3D, and the observed distribution function (base model) of Figure 3. The horizontal axis represents the error size in metric units; it does not matter if they are meters, square meters, cubic meters, etc. On the other hand, it does not matter if the distance Euclidean distance or another kind distance. The vertical axis represents the accumulated frequencies. If a metric tolerance \(T_{1}\) is established within the range of the error distribution, logically there will be positional errors less than or equal to \(T_{1}\) for a given quantity n1 and other positional errors greater than \(T_{1}\) for a given quantity \(n_{2}\). The two proportions of errors are thus defined:  
\[\pi_{1}=P[E_{i}\leq T_{1}]\text{ and }\pi_{2}\ =1-\pi_{1}=P[E_{i}>T_{1}] \tag{1}\]  
This situation, which can be statistically modeled by a binomial distribution, is the basis of the error counting techniques referenced in the previous section. Now, consider Figure 4a, where the same base model as in Figure 3 is represented. In this case, two metric tolerances, \(T_{1}\) and \(T_{2}\), are considered. In this way, three categories of error and three proportions are defined as follows: \(\pi_{1}=P[E_{i}\leq T_{1}]\); \(\pi_{2}=P[T_{1}<E_{i}\leq T_{2}]\) and \(\pi_{3}=1-\pi_{1}-\pi_{2}=P[E_{i}>T_{2}]\). For the same base model, Figure 4b shows the case of three tolerances when four categories of errors and four proportions can be defined. This idea can be generalized for \(k\) metric tolerances that generate \(k+1\) categories of errors and \(k+1\) proportions such that \(\pi_{1}+\pi_{2}+\ldots+\pi_{k+1}=1\)  
Figure 3: Key elements of a control by proportions.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Infrastructure** & **Urban** & **Natural** \\ \hline Median & \(-0.026\) m & \(-0.064\) m & \(-0.097\) m \\ MAD & \(\pm 0.014\) m & \(\pm 0.060\) m & \(\pm 0.043\) m \\ Percentile 0.05 (\(5\%\)) & \(-0.052\) m & \(-0.216\) m & \(-0.248\) m \\ Percentile 0.25 (\(25\%\)) & \(-0.038\) m & \(-0.128\) m & \(-0.145\) m \\ Percentile 0.75 (\(75\%\)) & \(-0.011\) m & \(-0.006\) m & \(-0.057\) m \\ Percentile 0.95 (\(95\%\)) & \(0.009\) m & \(0.052\) m & \(-0.010\) m \\ Interpercentile range 50\% & \(0.028\) m & \(0.123\) m & \(0.088\) m \\ Interpercentile range 95\% & \(0.077\) m & \(0.304\) m & \(0.304\) m \\ \hline \end{tabular}
\end{table}
Table 5: Robust assessment for each study case.
## 4 Experimental Results and Analysis
### Visualization Analysis
#### 4.2.1 Stop Point Density Analysis  
Stop point density maps intuitively illustrate congestion distribution across the road network. Through spatial density analysis of trajectory points with zero speed, significant analytical results were obtained.  
According to the overall density distribution in Figure 11, congestion in the road network exhibits distinct spatial clustering characteristics. Intersections 1, 2, 3, 9, and 17 form several prominent high-density centers, representing critical nodes within the network. High stopping densities not only indicate frequent vehicle stops but may also lead to queue spillback, affecting normal operations at upstream intersections.  
Examining specific intersection levels reveals more detailed congestion characteristics. Congestion at intersection 1 primarily concentrates in the northbound direction, with stop point density maps indicating severe queuing in this direction. Similarly, the northbound approach at intersection 2 also exhibits high-density characteristics. This directional congestion distribution closely correlates with morning peak commuting patterns: substantial vehicular flow from southern residential areas toward northern commercial districts.  
Figure 10: Detailed OD matrix.  
Intersections 9 and 17 display relatively uniform stop density distributions, with congestion present to varying degrees in all directions. This likely results from their strategic positions within the network, handling multi-directional traffic conversion functions. For such intersections, balanced consideration across all approaches is necessary to avoid favoring one direction at the expense of others.  
Analysis of the intersection cluster comprising nodes 15, 16, 21, and 22 reveals another congestion pattern: stop densities at north-south approaches significantly exceed those at east-west approaches. This may relate to the area's road network structure and land use layout. Signal optimization should allocate more green time to north-south movements.
## 3 Construction of the Microseismic Monitoring System
### Dynamic Deployment of Sensors  
Six sensors were installed in both sidewalls in a symmetrical manner after tunnel excavation. If the distance between adjacent sensors is less than 35 m, the time difference for P-wave pickup is too small and the accuracy decreases. If the P-wave pickup time difference is too small, it is not conducive to the location of microseismic events. If the sensor is too far from the tunnel face, it is difficult for the sensor to receive fracture signals effectively. In order to achieve the best monitoring effect and avoid destruction to the monitoring cables caused by blasting, the distance between adjacent sensors was set to 40 m, and that between the tunnel face and the sensor closest to it was set to 30 m (as shown in Figure 4). Once the distance between the tunnel face and the sensor closest to it is 70 m, the two sensors farthest away from the tunnel face are moved to be close to it. With this dynamic adjustment, the rock fracture signals around excavation areas can be sufficiently captured.  
Figure 3: Topology diagram of the microseismic monitoring system in the diversion tunnel of the hydropower station.
# Study on the Influence of File Installation Disturbance on the Settlement of Composite Foundation
## 6 Conclusions  
Set against the backdrop of a deep and soft soil low-fill embankment project in the Zhongshan area, this study investigated the influence of CMP and PHC piles on settlement during the pile driving process. The main conclusions are as follows:  
1. Under low embankment load (0.5 to 2.5 m), the settlement of embankment treated with PHC pile (20 m) is only 20% less than those reinforced with CMP (8 m). This occurrence is attributed to the greater additional settlement induced by the former's pile driving process compared to the latter.
2. The disturbance caused by pile installation decreases with depth. For embankment fill heights ranging from 0.5 to 2.5 m, the average disturbance caused by the pile installation process of CMP and PHC pile are approximately 10% and 30%, respectively.
3. Compared to finite element analysis, disturbance settlement calculated through stress modified method aligns more closely with field-measured settlement. As the fill height increases, the proportion of disturbance settlement to total settlement gradually decreases. Hence, considering the impact of construction disturbance is more meaningful for low-fill embankment projects.  
Conceptualization, B.H. and Z.S.; methodology, X.L.; software, D.S.; validation, D.S., Z.S. and Y.Y.; formal analysis, Z.S.; investigation, Y.Y.; resources, B.H.; data curation, X.L.; writing--original draft preparation, Z.S.; writing--review and editing, G.W.; visualization, Z.S.; supervision, D.S.; project administration, B.H.; funding acquisition, X.L. All authors have read and agreed to the published version of the manuscript.  
This study was sponsored by the National Natural Science Foundation of China (Grant No. 51609071) and the Fundamental Research Funds for the Central Universities (Grant No. B200202087, B200204032).  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding authors.  
The authors' gratitude goes to the reviewers for their helpful comments and constructive suggestions in improving this paper.  
The authors declare no conflict of interest.
# Dentin Bond Strength of Calcium Silicate-Based Materials: A Systematic Review of In Vitro Studies
###### Abstract  
Background: Dentin adhesion is a basic aspect to consider in a root canal sealer. Calcium silicate-based cements are materials that have excellent biocompatibility and bioactivity. Bioactivity is closely related to dentin bond strength. One of the tests that is most used to evaluate the adhesive property of a sealing cement is the \"push-out bond strength\" test, which consists of applying tensile forces to the root to measure the resistance of the bonding of a material to root dentin. Aim: The aim of this systematic review is to perform a qualitative synthesis of available evidence on the adhesion of calcium silicate-based sealers to dentin. Methods: An advanced search of the literature was performed in five databases, limited to in vitro studies on human teeth published in the last 5 years. Results: 42 studies were eligible for the review, and data were collected according to the number of teeth studied, the canal preparation, the irrigant used, the mechanical test used, the root thirds and the type of sealer studied. A qualitative synthesis of the evidence is presented. Conclusions: TotalFill BC sealer and EndoSequence Root Repair Material appear as the calcium silicate-based materials with the highest bond strength to dentin. In addition, using 17% EDTA as a final irrigant increases the bond strength of calcium silicate-based sealers.  
calcium silicate-based sealer; calcium silicate-based cement; systematic review; push-out bond strength; dentin adhesion
## 3 Theoretical Part
### XR Technologies in Education
#### 3.2.1 Virtual Reality (VR)  
VR (Virtual Reality) technology allows users to immerse themselves in a fully synthetic, computer-generated environment using devices like VR glasses or headsets. In the field of education, VR is increasingly being leveraged to create experiences that provide students with access to environments and scenarios that would otherwise be inaccessible, such as historical landmarks, outer space, or microscopic worlds. One of VR's most powerful features is its ability to generate a strong sense of presence--making users feel as though they are truly \"inside\" the virtual environment. This immersion significantly enhances the learning experience, enabling students to engage with content in a more meaningful and memorable way. Studies have shown that this heightened sense of presence leads to improved knowledge retention and a deeper understanding of complex topics, making VR an effective tool for education [15].  
The application of VR in education spans a variety of disciplines, including history, geography, science, engineering, and more. For instance, history students can virtually \"visit\" ancient civilizations and examine artifacts within their original contexts, offering a richer and more interactive approach than traditional textbooks or static images. Similarly, science students can explore the inside of cells, observe molecular structures in three-dimensional space, and interact with dynamic models of complex systems, all of which help to make abstract concepts more tangible and understandable [16]. Research indicates that VR-based learning not only increases student engagement but also facilitates a deeper understanding of intricate concepts, especially when combined with other interactive learning tools such as simulations and real-time collaboration [17].  
Despite the significant advantages of VR in education, there are still several challenges that need to be addressed to maximize its potential. These include the high cost of VR equipment, the technical requirements for running VR-based applications, and the need to adapt curricula to fully integrate VR's interactive features. Additionally, there are concerns regarding accessibility and the need for educators to develop the skills required to effectively use VR in teaching. However, despite these hurdles, VR remains one of the most promising technologies for revolutionizing education in the 21 st century. As the technology matures and becomes more affordable, its potential to transform how we teach and learn will only continue to grow, offering new opportunities for personalized and immersive educational experiences [18].
## 3 Main Findings  
### Results of the Systematic Literature Review  
The initial dataset consisted of 820 items. After screening, 101 texts were selected. The removal of duplicates left a final total of 72 items: eight book chapters, 14 reviews, and 50 research papers. Figure 1 summarizes the selection process, which is consistent with the PRISMA statement. Figure 1a-c show results for ScienceDirect, PubMed, and the Web of Science, respectively.
## 8 System Feasibility Discussion
### Feasibility of the Aircraft-SB System for Flights above or near Tropopause Height  
As most of the atmospheric losses occur in the troposphere, communication between an aircraft and SB above tropopause level seems very attractive and feasible even at 5G mm-wave frequencies above 26 GHz. Cloud heights are rarely extended above 12 km heights and therefore, fog/cloud and rain losses do not exist, and the only dominant losses are the FSL and atmospheric gaseous attenuation. According to the data shown in Figure 13, a maximum atmospheric gaseous attenuation of about  
Figure 18: Fog/cloud attenuation in dB for two aircraft-SB communication scenarios.  
20 dB for the 66 GHz frequency is expected for an aircraft performing tangential handover at 11 km high where the LOS distance is approximately 520 km between the aircraft and SB. Below 66 GHz and for the same tangential LOS handover scenario, all frequencies are subjected to very low gaseous attenuation where it is only of maximum 3 dB at 50 GHz which is reduced to less than 1 dB for the other lower frequencies. However, for the maximum LOS handover scenario, large gaseous attenuation occurs for frequencies above 40 GHz due to the re-insertion of the LOS path into the troposphere. Therefore, the system could be feasible and applicable at the new mm-wave frequencies of the 5G systems subjected to the flight altitude above the tropopause height during cruise and the handover is performed at the tangential LOS case. One major advantage of operating the 5G frequencies at these heights only is reduced interference with the future terrestrial 5G networks wave bands where the troposphere in this case provides an attenuation barrier between the aircraft-SB network and terrestrial 5G networks especially for frequencies above 40 GHz where the gaseous loss is very high. In addition, an aircraft could be served at low altitudes with terrestrial networks initially while near cruise levels, a vertical handover between the terrestrial 5G networks and the 5G SB aerial network could be performed to gain more bandwidth and provide higher bit rate services to onboard passengers during flight.
## 6 Experimental Tests
### Model Validation and Comparison Results
However, the results show that this problem has a good performance neither for coverage nor the number of located facilities and modules. The problem MCLP-SCLP has good performance for the only one size problem that it could solve, and for the rest of the problems, it was either infeasible or no solution was found by the solver. Among all three variants, MCLP-MCLP is the only problem that was successful in obtaining solutions. Although the coverage percentage is not better than HCLP, it has an overall acceptable performance. However, this problem was not able to solve larger problems, and the general-purpose solver required higher computational effort. Concerning the optimization criteria mentioned above, the results obtained from test problems show that, in terms of both equity (coverage percentage and efficiency (elapsed time), the problem HCLP outperforms other variants and can provide better coverage in a reasonable time for different size of test problems. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{\(i=j\)} & **Demand** & \multirow{2}{*}{\(p_{[T]}\)} & \multicolumn{3}{c}{**HCLP**} & \multicolumn{3}{c}{**MCLP–MCLP**} & \multicolumn{3}{c}{**MCLP–SCLP**} & \multicolumn{3}{c}{**SCLP–SCLP**} \\  & & & **\%** & **Z** & **Y** & **T** & **\%** & **Z** & **Y** & **T** & **\%** & **Z** & **Y** & **T** & **\%** & **Z** & **Y** & **T** \\ \hline \multirow{4}{*}{50} & Low & 3 & 86 & 3.3 & 4.3 & 6 & 79 & 3 & 3.8 & 2 & 82 & 3 & 5.5 & 4 & 0 & 3 & 4.6 & 0.8 \\  & Low & 4 & - & - & - & - & 89 & 3.3 & 4.2 & 7 & 91 & 4 & 6 & 11 & & & & \\  & High & 3 & 65 & 3.6 & 4.3 & 2 & 53 & 3 & 3.8 & 2 & 59 & 3 & 5.6 & 2 & 0 & 3 & 4.2 & 0.7 \\  & High & 4 & - & - & - & - & 61 & 3.3 & 4.3 & 4 & 74 & 4 & 7 & 11 & & & & \\ \hline \multirow{4}{*}{100} & Low & 3 & 84 & 3.6 & 6.5 & 213 & 75 & 3 & 5.3 & 219 & & Inf & & 41 & 100 & 197 & 1006 \\  & Low & 4 & - & - & - & - & 82 & 4 & 6 & 904 & & Inf & & & & & \\  & High & 3 & 62 & 3.6 & 6.6 & 255 & 50 & 3 & 5.3 & 268 & & Inf & & & 42 & 100 & 197 & 1006 \\  & High & 4 & - & - & - & - & - & 59 & 4 & 6.3 & 180 & & Inf & & - & - & - & - \\ \hline \multirow{4}{*}{150} & Low & 8 & 86 & 8 & 10.7 & 10.103 & & \multirow{4}{*}{RE} & \multirow{4}{*}{NS} & 39 & 150 & 301 & 1015 \\  & High & 7 & 62 & 7.6 & 11.2 & 10.473 & & & & & & & & & & & \\ \hline \multirow{4}{*}{200} & Low & 7 & 30 & 7 & 6 & 1064 & & & & & & & & & & & \\  & High & 7 & 22 & 7 & 6 & 1050 & & & & & & & & & & & \\ \hline \multirow{4}{*}{250} & Low & 22 & 22 & 22 & 5.3 & 1583 & & & & & & & & & & & & \\  & High & 22 & 14 & 22 & 5.3 & 1505 & & & & & & & & & & & & \\ \hline \hline \end{tabular}  
* Inf: infeasible; RE: resource exceeded; and NS: no solution.  
\end{table} 
Table 6: Computational results to compare different variants for three regions. HCLP: hybrid covering location problem.
## 3 Near-Space Passive Radar Remote Sensing  
Passive radar is a receiver-only radar that usually dissociates the receiving antenna at a different location from the transmitter [19]. The interest in bistatic radar, particularly synthetic aperture radar (SAR) has rapidly increased in recent years [22, 23, 24, 25, 26, 27, 28]. This is based on the specific advantages of bistatic radar configurations in comparison to monostatic systems like increased information with regard to feature extraction and classification. This could be worthwhile, e.g., for topographic features, surficial deposits, and drainage, to show the relationships that occur between forest, vegetation and soils. Even for objects that show a low RCS in monostatic radar images, one may find distinct bistatic angles to increase their RCS to make these objects visible in the final radar image. At the same time, there has been a resurgence of the interest in passive bistatic radar systems [29, 30, 31], partly because they do not require fielding expensive transmitters and partly fueled by technology developments in digital signal processors and processing algorithms [32, 33].  
Figure 2: Ground coverage area as a function of looking-down angle for different flying altitude.
# Precise Feature Removal Method Based on Semantic and Geometric Dual Masks in Dynamic SLAM
## 5 Time Evaluation  
In practical applications, real-time performance is a key metric for evaluating SLAM systems. We tested the average computation time of the main modules under different datasets. The experiments used the pretrained YOLOv11-seg.pt model for semantic segmentation. The results shown in Table 8 indicate that the semantic segmentation processing speed with GPU hardware acceleration meets real-time requirements (KITTI:  
Figure 5: ATE and RPE results for SLAM systems on the fre3_sitting_xyz dataset.  
Figure 6: Map of ATE and RPE for SLAM systems on the fre3_sitting_xyz dataset.  
15.79 ms/frame, TUM: 13.36 ms/frame). For the geometric consistency check phase, we utilized the existing PnP nonlinear optimization module in ORB-SLAM3, where the process involves matching outliers from the previous frame to the current frame to achieve geometric consistency detection. This is a simple modification, but proves to be both practical and cost-effective. Therefore, the additional time at this stage comes only from the calculation of the retained outliers, with an increase in processing time of less than 1 ms.  
Next, we conducted a comparison with DS-SLAM. In the geometric consistency detection phase of DS-SLAM, optical flow is used to quickly match feature points, then the fundamental matrix is computed to complete the epipolar geometry constraint process for detecting feature point anomalies. The DS-SLAM experiments were conducted on a computer equipped with an Intel i7 CPU, P4000 GPU, and 32 GB of memory, with the process taking 29.51 ms. The semantic segmentation stage of DS-SLAM [28] uses a neural network called SegNet, which takes 37.57 ms. These data are taken from the DS-SLAM team's experimental report.  
Next, we conducted a comparison with DynaSLAM. The geometric consistency detection phase in DynaSLAM uses a multi-view geometry method to detect dynamic objects by analyzing the depth and angle between feature points in RGB-D images. DynaSLAM took 235.98 ms on the fre3_walking_rpy sequence from the TUM dataset. These data were taken from the DynaSLAM team's experimental results [29]. The semantic segmentation stage of DynaSLAM uses a Mask R-CNN neural network. [PERSON] et al. [35] reported that the running time of Mask R-CNN with an Nvidia Tesla M40 GPU was 195 ms per image.  
Although both DS-SLAM and DynaSLAM were evaluated based on ORB-SLAM2 and the method proposed in this paper was implemented on ORB-SLAM3, the processing time of dynamic detection stages such as geometric consistency checks and semantic segmentation remains comparable across systems. Notably, the method proposed in this paper accomplishes these tasks with significantly lower computational cost and reduced processing time.
## 4 The Application of Tikhonov Regularization in THz SAR
### Imaging Algorithm Based on Tikhonov Regularization  
By using the proposed method in this paper, the azimuthal wavenumber spectrum of the SAR echo signal can be reconstructed from Equation (32). The received echo signal is an \(N_{m}\times N_{k}\)-dimensional data matrix. This means that we need to solve \(N_{k}\) linear equation systems with the same coefficient matrix \(A\). Under the condition of SVD of \(A\), the solution process is relatively simple and the computational complexity is mainly concentrated on the SVD of \(A\). Additionally, for two-dimensional SAR echo data collected in the same time period, the noise level of the received signal is the same. Therefore, in the process of solving \(N_{k}\) linear equation systems, only one regularization parameter needs to be selected. For this purpose, we calculate the weighted average value \(s_{f}\) of these \(N_{k}\) linear equation systems, which is obtained by setting  
\[s_{f}=\frac{1}{N_{k}}\sum_{k=1}^{N_{k}}s_{f_{k}} \tag{33}\]  
By applying L-curve criterion, we solve for the regularization parameter of the linear equation system corresponding to \(s_{f}\), which represents the regularization parameter selected for \(N_{k}\) linear equation systems.The proposed algorithm offers an effective solution for the reconstruction of non-uniformly sampled signals. It exhibits superior efficiency compared to interpolation-based [31], iterative [32] when handling two-dimensional non-uniformly sampled data, such as in SAR applications.  
Based on the above analysis, we can combine the proposed algorithm with a Range-Doppler Algorithm (RDA) to process SAR echo data with azimuth non-uniform sampling. The corresponding algorithm flowchart is shown in Figure 7, which will be used for the simulation and imaging experiments of THz SAR in Section 5. Compared to the conventional RDA, the algorithm shown in Figure 7 transforms the processing of azimuth echo data into a wavenumber domain and replaces azimuth FFT operation with the algorithm proposed in this paper. Therefore, it can also be combined with the Range Migration Algorithm (RMA) to process SAR azimuth non-uniform sampling data, making it applicable to any imaging geometry.