esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Analysis of Characteristics of Tool Forces by Cutting Conditions
#### 3.1.2 Effect of Penetration Depth and Spacing on Cutting Force  
The cutting force was generated parallel to the cutting plane and the cutting direction. It is directly related to the torque requirement of the excavation machine and is used to calculate the specific energy [29]. Several studies have reported that the cutting force increases as the penetration depth and spacing increase, regardless of the type of cutting tool used [12; 14; 16; 17; 30; 31; 32; 34].  
The variations in the mean cutting force with penetration depth and spacing are shown in Figure 7 for all rock specimens, and the details are listed in Tables 3-6. When the pre-cutting machine tool is used, the cutting force increased linearly with penetration depth and spacing. In addition, the cutting force, similar to the normal force, was influenced more by the spacing than the penetration depth.
## 2 Materials and Methods
### Data Analysis
#### 2.4.2 Classification Difference Analysis  
The MSG-GCN architecture was refined and optimised based on U-shape encoder and decoder models (U-Net and U-Net++). The MSG-GCN, U-Net, U-Net++, and RF classical machine learning methods were used to map the spatial distributions of correctly classified and misclassified areas. A confusion matrix was applied to the test dataset using the raster calculator of ArcGIS version 10.8 (Esri Inc., Redlands, CA, USA). The results are shown as I-J values, where I is the ground truth and J is the predicted forest type. This provides insight not only into the number of errors but also the type [100]. The percentage is the sum of the correctly classified and misclassified areas divided by the total area of each forest type and makes comparisons within and between forest types more intuitive. A DN is associated with the value of a given pixel for each forest type in the different spectral bands. We used ArcGIS software to extract the DNs for each forest type, which were then summed and divided by the total number of pixels for each classification type. To evaluate differences in mean DNs between classification types, we used [PERSON]'s least significant difference (LSD) test [101] and the 'boxplert' function of R software v 4.2.0 (R Development Core Team) to analyse significant differences between pairs of spectral DNs for correctly classified and misclassified forest types.  
The spatial positions of correctly classified and misclassified forests over the entire research site were visualised. The forest type tiles predicted by each model were merged into a 35,152 \(\times\) 41,152 map and overlaid with the ground truth using the ArcGIS raster calculator. The misclassification rates of multiple buffer zones (10-170 m) around the research site were extracted by ArcGIS [102] to analyse the spatial distribution of the misclassified forests. Given the limitations in data availability, all aerial photographs and composites of the three series were collected. Thus, the effects of seasons, solar radiation and weather conditions were included in the dataset. The areas of misclassified forests from the three districts were extracted to determine how the dataset affected classification.
# Line Segment Matching Fusing Local Gradient Order and Non-Local Structure Information
###### Abstract  
Line segment matching is essential for industrial applications such as scene reconstruction, pattern recognition, and VSLAM. To achieve good performance under the scene with illumination changes, we propose a line segment matching method fusing local gradient order and non-local structure information. This method begins with intensity histogram multiple averaging being utilized for adaptive partitioning. After that, the line support region is divided into several sub-regions, and the whole image is divided into a few intervals. Then the sub-regions are encoded by local gradient order, and the intervals are encoded by non-local structure information of the relationship between the sampled points and the anchor points. Finally, two histograms of the encoded vectors are, respectively, normalized and cascaded. The proposed method was tested on the public datasets and compared with previous methods, which are the line-junction-line (IJL), the mean-standard deviation line descriptor (MSLD) and the line-point invariant (LPI). Experiments show that our approach has better performance than the representative methods in various scenes. Therefore, a tentative conclusion can be drawn that this method is robust and suitable for various illumination changes scenes.  
line segment matching; line feature; gradient order; non-local structure information +
Footnote †: journal:  
Article
## 6 Performance Analysis
### Computational Complexity Analysis
#### 6.1.2 Computational Complexity in the Batch Authentication Scenario
\begin{array}{rl}&Pr\{A\}=Pr\{i=1\}+\cdots+Pr\{i=10\}\\ &=&\frac{\begin{pmatrix}N_{all}-N_{ckd}\\ N_{req}-1\end{pmatrix}\begin{pmatrix}N_{ckd}\\ 1\end{pmatrix}}{\begin{pmatrix}N_{all}\\ N_{req}\end{pmatrix}}+\cdots+\frac{\begin{pmatrix}N_{all}-N_{ckd}\\ N_{req}-10\end{pmatrix}\begin{pmatrix}N_{ckd}\\ 10\end{pmatrix}}{\begin{pmatrix}N_{all}\\ N_{req}\end{pmatrix}}\\ &=&\frac{\begin{bmatrix}\Sigma_{i=1}^{10}\begin{pmatrix}N_{all}-N_{ckd}\\ N_{req}-i\end{pmatrix}\begin{pmatrix}N_{ckd}\\ i\end{pmatrix}}{\begin{pmatrix}N_{all}\\ N_{req}\end{pmatrix}}\end{array} \end{array} \tag{3}\]  
According to Equation (3), when there are only one or two invalid requests (\(i=1\) or \(i=2\)) in a batch, the probability of rebatch authentication is extremely small. However, in the case of DoS attacks by malicious UEs, rebatch authentication can protect a legitimate UE from authentication failure for a long time. The proposed scheme in the first authentication needs \(nT_{Exp}\) and \(1T_{Pairing}\) operation. The LEO calculates \(h_{i}(RAK_{i}\oplus H_{1}(B_{i}||T_{1}))\) for each UE in the first authentication phase, thus only the \(T_{Pairing}\) operation is required. Therefore, the computational cost of the worst and average cases of rebatch authentication is analyzed below:  
1. Worst case: According to the rebatch algorithm proposed by [PERSON] et al. (2015), we assume that the worst case is where invalid requests are always in the detected batch. Assuming there are \(n\) requests in a batch, it takes \(log_{2}n\) times the calculation in the worst case. Therefore, the worst-case total batch validation time for a valid request is \[T_{wst}=T_{fst}+2\times log_{2}n\times T_{reb}\] (4)  
Figure 8: Computational cost comparison of batch authentication in the verification phase. [PERSON] et al. (2011), [PERSON] (2012). where \(T_{wst}\) is the time required for the worst case, \(T_{fst}\) is the time required for the first batch verification, and \(T_{reb}\) is the time required for completion of the rebatch algorithm. 2. Average case: Calculating the total validation cost in all cases divided by the number of possible cases gives the validation time required for the average case: \[T_{ave}=T_{fst}+\frac{1}{log_{2}n+1}\sum_{i=2}^{log_{2}n}T_{reb}\] (5)  
Figure 9 shows the total validation complexity for the worst case, average case, and first batch authentication with 0 to 1000 UE requests. As can be seen from the figure, although the complexity of rebatch authentication in the worst case is relatively high, the cost of rebatch authentication in the average case is close to the complexity of the initial batch authentication, and the computational complexity does not increase rapidly with the increase of the number of requests. Considering the possibility of DoS attacks leading to large-scale UE authentication failure, rebatch authentication is necessary.
# Effect of Wheel Path in Raster Grinding on Surface Accuracy of an Off-Axis Parabolic Mirror
## 3 Result  
### Scallop Height  
The scallop height profile of the off-axis parabolic surface after rough grinding was measured, as shown in Figure 4. The measured value of the scallop height along the meridian direction is about 0.0964 mm, and the measured value of the scallop height along the arc vector direction is about 0.0933 mm. It was found that the scallop height of the raster path along the arc vector direction is smaller than that of the raster path along the meridian direction.
## 3 Results and Discussion
### Scanning Electron Microscopy  
The first analysis of the ruptured cross-section of the samples after the tensile test is made is that for the different printing conditions there are smooth layer displacements regarding the material deposition. It is possible to notice in each of the individually selected PB of the five assays performed for samples with infills of 15%, 25%, 50%, 75%, and 100%, small displacements in the lines referring to the inner fill and shells after PB rupture, as shown in Figure 14 for 15% and 25% of infill. Another  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Group**} & **Strain** & **Yield** & **Modulus of** & **Breaking** \\  & **Maximum** & **Stress** & **Elasticity** & **Elongation** \\  & **(MPa)** & **(MPa)** & **(MPa)** & **(\%)** \\ \hline
1 (15\%) & 11.53 & 7.36 & 232.91 & 9.55 \\
2 (25\%) & 20.68 & 14.19 & 552.54 & 6.92 \\
3 (50\%) & 22.05 & 15.32 & 583.77 & 7.58 \\
4 (75\%) & 23.80 & 16.33 & 598.00 & 7.13 \\
5 (100\%) & 37.78 & 25.63 & 798.05 & 10.35 \\ \hline \hline \multicolumn{5}{c}{**Standard Deviation**} \\ \hline
1 (15\%) & 2.90 & 2.76 & 21.29 & 2.52 \\
2 (25\%) & 1.51 & 0.76 & 42.27 & 0.68 \\
3 (50\%) & 0.97 & 0.79 & 32.21 & 1.13 \\
4 (75\%) & 2.09 & 2.00 & 39.40 & 0.74 \\
5 (100\%) & 2.40 & 3.68 & 77.98 & 1.89 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Average and standard deviation results obtained from tensile test.  
Figure 13: Engineering stress–strain curves for the different internal fill levels of 15%, 25%, 50%, 75%, and 100%.  
difference is the final rupture region. For the samples with the highest percentage of infill this region is gently offset from the central region of the cross section. This differentiation of the final rupture region between the samples, besides being caused by the uniaxial load applied by the tensile testing equipment, may also be caused by the different internal fill percentages.  
Moreover, it is possible to notice also, in the microstructure images, the internal lattices varying according to their concentrations. This is due to its internal fill density. Furthermore, it is possible to verify through the generated images the displacement of the lattices that constitute the internal region of the cross section, that is, due to the plastic deformation (permanent deformations) generated in the structure as a result of the axial load value applied by the test machine, as shown in Figures 14-16 for 15%, 25%, and 100% of infill. Figure 16 shows the absence of reticulations because for infill equal to 100% fill the sample is printed as a fully solid part.  
Figure 14: Microstructure images for 15% sample infill: (**a**) the deposition lines of the layers, shell, and top/bottom layer and (**b**) the internal reticulations.  
Figure 15: Microstructure images for 25% sample infill: (**a**) the deposition lines of the layers, shell, and top/bottom layer and (**b**) the internal reticulations.
## 1 Introduction  
Social media are increasingly used not only as a form of entertainment, but also for learning purposes [1]. Among students of all specialties, the use of social media for learning has become a very popular method of enhancing both theoretical and practical knowledge [2]. Social media include web tools and applications designed to facilitate online interaction and instant information sharing [3, 4]. YouTube, one of the most commonly used social media applications, has gained popularity among dental students and has changed their way of learning [2, 4]. YouTube has become a source of general information, with the majority of students using YouTube videos to prepare for clinical procedures that they have never encountered, including some surgical procedures [4, 5, 6].  
Several studies on dental and medical education have been conducted worldwide and have reported that YouTube is the electronic resource most frequently used by health-professional students [1, 3, 4, 5, 6]. A cross-sectional study found that 90% of survey participantsreported using videos to prepare for surgical cases, of whom 95% were medical students and residents; moreover, YouTube was the video source most widely used in the preparation for surgical procedures [5]. Another study, which included 657 students from 23 different Saudi Arabian medical schools, found that YouTube is the website most commonly used by medical students [7]. Additionally, a recently published study including third- and fourth-year dental students at five United States dental schools revealed that dental students are very likely to use YouTube as a resource to learn and prepare for clinical dental procedures [1].  
Dental students, specifically during instructor-led, pre-clinical, and clinical sessions, primarily gain their clinical competency by studying and practicing on mannequins and patients. To further facilitate learning, it is useful for them to also have multiple learning aids in various forms, such as YouTube videos [8; 9; 10]. Studies have shown the benefits of using videos during the learning process, especially in transforming cognitive input into long-term memory, which is indicative of learning [9; 11; 12]. Considering the benefits and convenience of YouTube videos, it must be explored how widely this learning modality has been used by dental students, as well as its accuracy for providing certain knowledge. Although students often utilize videos when preparing for clinical procedures, they encounter difficulties related to the lack of certainty regarding the quality and accuracy of the content uploaded on YouTube [1; 5; 6].  
It is presently unknown how commonly YouTube videos are used among Saudi dental students to prepare for their clinical dental procedures, when compared to other learning modalities, such as reading a textbook. In this study, therefore, we aimed to assess the dependency on YouTube videos as a modality for learning clinical procedures among dental students in Saudi Arabia. An additional objective was to assess whether the responses were impacted by participants' gender and academic institutions.
# Optimal Control Method of Semi-Active Suspension System and Processor-in-the-Loop Verification
###### Abstract  
This study presents an implementation of a proportional-integral-derivative (PID) controller utilizing particle swarm optimization (PSO) to enhance the compromise on road holding and ride comfort of a quarter car semi-active suspension system (SASS) through simulation and experimental study. The proposed controller is verified with a processor-in-the-loop (PIL) approach before real-time suspension tests. Using experimental data, the magnetorheological damper (MR) is modeled by an artificial neural network (ANN). A series of experiments are applied to the system for three distinct bump disturbances. The algorithm performance is evaluated by various key metrics, such as suspension deflection, sprung mass displacement, and sprung mass acceleration for simulation. The phase plane method is used to prove the stability of the system. The experimental results reveal that the proposed controller for the SASS significantly improves road holding and ride comfort simultaneously.  
semi-active suspension system; PID; PSO; ride comfort; road holding; PIL verification
# Optimal Order of Time-Domain Adaptive Filter for Anti-Jamming Navigation Receiver
## 4 Optimal Filter Order
### Demand Analysis of Anti-Jamming Filter
However, the anti-jamming performance will worsen because of the loss of navigation signal and additive noise in the interference suppression frequency band, and the ultimate performance will also be reduced in the meantime.
## 4 Discussion
### Patterns, Trends, and Insights  
#### 4.2.1 Shift from Reactive to Proactive Safety Management  
A significant trend across sectors is the shift from reactive to proactive safety management enabled by AI. Predictive analytics and real-time monitoring allow organizations to anticipate and mitigate risks before incidents occur, enhancing overall safety outcomes.
## 4 Solution Problem  
In the proposed scenario, we consider a network of UAV-BSs that supports the conventional network suffering from congestion based on spatial and temporal characteristics, together with computational intelligence to determine approximately the location and time when the macrocell needs help to relieve the instantaneous traffic. In particular, the system considers two important steps, LPA and UCPA, to perform the management of UAV-BSs. In the LPA step, the load history generated by the mobile network is used as an input variable into the predictive model to determine which regions are congested and the specific times of each macrocell that need assistance. In this step, we use two models known in the literature for time series prediction. One is the random forest (RF), a model that uses a series of random decision trees to generalize the model operations, which minimizes the overfitting of each model to improve the prediction accuracy. The other model is the generalized regression neural network (GRNN), a variation of the traditional neural network with an excellent solution for dynamical systems and time series predictions [46].  
GRNN is often used on nonlinear datasets because it has better behavior compared to other models.  
This section presents the intelligent system methodology for the next-generation communications network. The LPA subchapter shows how the data collection, processing, and preparation are performed, as well as the network utilization prediction models, which facilitate finding the congested regions and times. The UCPA subchapter shows how the number of UAV-BSs is calculated by using two strategies used in this work and calculating the ideal positioning for each device requested to support the congested macrocells.  
Figure 2 shows the flowchart of the intelligent system. We start by mining the data collected by the open platform. These data are used to determine the location of the user in the simulation environment, revealing the patterns and behavior of the real world. Then, the network simulation is performed to create the dataset with the network utilization history based on the user behavior collected and processed in the previous step (see flowchart). Next, the network history dataset is used as the basis for training two machine learning models to predict the time and location of potential network congestion. With this information, the resources (UAV-BSs) can be managed more efficiently, and then the system can perform the calculations to define the number of resources needed to support the compromised network. This starts with defining the strategy needed to determine the parameters of the K-means algorithm. Then, the number and positioning of UAV-BSs in the region suffering from congestion are calculated. Finally, the simulation is performed by adding the UAV-BSs to support the congested regions, and the results show the performance of the network.  
Figure 2: Flowchart of the proposed system.
# Object-Tracking Algorithm Combining Motion Direction and Time Series
## 2 Related Works
The STARK algorithm [25] uses an attention mechanism to encode temporal and spatial features of the target template and search image, and predicts the coordinates of the upper left and lower right corners of the target. The STARK algorithm is trained using L_1 loss and GIoU [30] loss. For template updating, it employs a fully connected network to determine the reliability of the tracking results, controlling the template update process. Recently, the STARK algorithm has demonstrated state-of-the-art performance on various benchmarks.
# Application-Aware SDN-Based Iterative Reconfigurable Routing Protocol for Internet of Things (IoT)
## 4 Heterogeneous Network Model of Proposed Model
### DSDN-IRIoT Infrastructure Layer Network Modeling  
The initial settings of the infrastructure layer of the DSDN-IRIoT network model defines the pattern of periodic reconfigurations requirements throughout network operation. In the initial configuration, BS is located at the network outer boundary. Whereas, the DSDNs controllers resides at the BS with the assumption of extensive heterogeneity and processing capacity. Meanwhile, IoT devices are scattered with different heterogeneity levels and categorized into normal nodes, advanced nodes, and super nodes with ascending energy order.
## 3 Results
### Results of Topological Indicators
#### 3.3.2 Results of the Topological Indicators Describing the Network  
The average degree of the vegetation ecospatial network in the YRB was 6.22, which means that each ecological node in the network had an average of 6.22 corridors. On the other hand, the diameter of the network was 9, the average path length was 3.014, and the average clustering coefficient of the network was 0.547. The clustering coefficients of the network in this study were larger compared with other studies, which indicated that the aggregation of the network in this study was relatively good, and the distribution of some ecological nodes was more concentrated [55]. In addition, the small average path length and the large average clustering coefficient indicate that the ecospatial network constructed  
Figure 8: The distribution graph of the degree of ecological nodes, representing a topology simplification of the ecospatial network. The size of the node in the graph represents its degree. The number on the node represents its serial number.  
in this study had the characteristics of a small world. The small-world characteristics of complex networks are closely related to the information dissemination in the network. In small-world networks, information transmission is fast, and the performance of the network can be greatly improved with a small change of a few connections [56; 57]. This showed that the ecos spatial network of the YRB has great potential for optimization in the future.  
Through the community division algorithm, the ecos spatial network was divided into three communities (Figure 10). Different communities contained different numbers of nodes. Community 1 had the largest number of nodes, which was 12. Community 2 contained eight nodes, and Community 3 contained seven nodes. We usually consider networks to have a distinct community structure when their modularity is between 0.3 and 0.7 [58]. The modularity of the network in this study was calculated to be 0.327, which is a good result for community division. Combining the results of the clustering coefficient of the nodes, we found that the nodes with higher clustering coefficients were all in Community 1 (Figures 9 and 10). The clustering coefficients of nodes were significantly stratified across communities, and the average clustering coefficients of nodes in Community 1 were larger than those in Community 2 and Community 3.  
Figure 9: The results of five topological indicators of ecological nodes.