esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Experimental Evaluation
### Scenarios
#### 6.3.5 Scenario 5: Federated Workload Profiling Aiding in Edge Node Selection and FPSN  
Depending on the application, the edge nodes can be mobile or static in edge computing. Mobility reduces the efficiency with which edges can exploit their resources, which can result in the FWP deciding on which edge nodes to be removed. To keep resource utilization and performance at their peak, the algorithm also takes into account the reputation of each node, keeping only the most trustworthy edge nodes even if they are mobile. The  
Figure 11: Execution time before and after FWP.  
Figure 12: Effects of FWP on ML Tree Depth and execution time.  
response time; metric scores related to CPU, memory, network, sensors, processes, and storage; reputation score; mobility; etc., all factor into the weighted average score calculated at the server and used as a selection criterion for the EWP nodes. If two nodes have the same weighted average score, the server prioritizes the node with the highest reputation score, followed by the node with the lowest response time, the lowest mobility, and the best CPU and memory availability and selects the best n edges. Ultimately, the weight given to each of the factors depended on the specific requirements of the system and the trade-offs between aspects such as performance, scalability, and cost. It is important to continuously evaluate and refine the approach based on real-world performance to ensure optimal results.  
Because of its poor response time, limited mobility, excessive congestion, and low reputation, Edge Node 6 is removed after FWP is applied. Since time is of the essence in FPSN calculation, only the most resource-rich nodes were selected. To reduce the response time and to get a precise performance measurement while maintaining high accuracy, only trustworthy nodes were selected to use in our FPSN calculation by factoring in their reputation. It is evident that the performance measure accuracy has slightly improved after resource optimizations following FWP, which was applied at the nodes and used in FPSN calculations as shown in Figure 13.
## 5 Asynchronous Sample Encryption Validation and Context Flow Correlation
### Context DNS Flow Correlation  
DNS is a protocol that converts IP addresses and domain names. It is widely used in application protocols for communications based on domain names. DNS is not encrypted in most cases, so DNS traffic analysis is usually required in some management scenarios. Therefore, we propose the context DNS flow correlation module. It is worth noting that this module is optional, which is reflected in Figure 3.  
Experimental results show that on the end system, because the port call of DNS is generated by the OS, and the port time cycle of DNS flow is very short, the process port mapping cannot bind DNS traffic to an application process.  
In order to satisfy other studies that may require a truly complete data sample containing DNS, the DNS flow should be correlated with a specific domain-based application protocol flow, such as HTTP (HTTPS in encrypted traffic). Therefore, we propose a context DNS flow correlation method based on an asynchronous parameter pair. The correlation with the application protocol flow is established by retrieving the transaction ID field, the associated query, the timestamp, and the response packet.  
After finishing the three modules, we can obtain complete application-level or process-level encrypted traffic sample data with high confidence.
## 4 Experiments
### Datasets  
In this paper, we evaluate the datasets used in the studies [11; 12] to validate the proposed system by creating a combined video, as shown in Figure 7, and labeling the pedestrian misbehavior events and their onset and end times in the label file. Each combined movie contains ten clips of assault and fall events.  
Figure 6: Network structure of the fall event detection model.  
Figure 7: Structure of combined pedestrian abnormal behavior video and labels for experiments. The combined video contains assault, fall, and normal events, which are labeled with the start and end times in a JSON-formatted label file.  
Table 1 shows the datasets used in this study, and Table 2 shows information about the combined videos created. The assault videos were cut into clips from the datasets in [26; 27; 28; 29] and added to the combined video, while the fall videos were cut into clips from the datasets in [26; 30; 31] and added to the combined video. For videos with no events, we cut clips from the assaults and falls dataset during times at which no events occurred. The datasets used were as close as possible to the environment captured by the CCTV cameras and included daytime and nighttime footage. As shown in Table 2, only the AI Hub CCTV Abnormal Behavior dataset has 200 videos because it contains both assaults and falls, so we used 100 of each. The combined video data generated in this way were continuously streamed from 20 streaming servers to verify the efficiency and scalability of the proposed system by providing input to the system and further measuring the edge-only performance, server-only performance, and edge-server coupling performance.
## 4 Results and Analysis  
### Comparison of Apple Orchard Extraction Results for Different Feature Combinations  
To compare the classification results of different feature combinations, a total of 12 groups of feature combinations designed in Section 3.4 were used to conduct RF classification experiments, compare the classification results of these 12 feature combinations, and produce a graph of the classification results in the study area, as shown in Figure 9.  
Comparing the classification results of G1 to G12, the G1 classification results (Figure 9a) could better identify apple orchards. However, identifying artificial surfaces was poor, and there was slight confusion between water bodies and apple orchards. The classification results for G2 (Figure 9b) identified apple orchards. However, there was a clear \"Salt and pepper phenomenon\" when the other land types were identified. G3 (Figure 9c) did not distinguish apple orchards from water bodies. G4 (Figure 9d) showed a significant im  
Figure 9: Comparison of classification results for different combinations of features. (**a–l**) are the results of G1-G12 in Table 3.  
provement in the confusion between apple orchards and water body boundaries compared to G1 and improved the recognition of artificial surfaces. However, apple orchards and other cultivated land could not be distinguished.  
G5 and G6 (Figure 9e,f) could identify apple orchards and still exhibited the phenomenon of mixing apple orchards and cultivated land. However, there was still a lack of accuracy in identifying apple orchards and croplands, and the identification of artificial surfaces was insufficient. G7 (Figure 9g) identified the types of features contained in the thin cloud-covered areas of the study area, although there was still a clear mixing of apple orchards and water bodies. Compared to G5, G6, and G7, G8 (Figure 9h) was better at identifying the features contained in the apple orchards and the thin cloud-covered area in the study area and better recognized other features. Nevertheless, its recognition was still more fragmented.  
The classification of G9 to G12 (Figure 9i-l) still contained fragmented image elements. However, the number of fragmented image elements in G12 was lower and more consistent with the range of features obtained by visual interpretation in Section 3.1. The above analysis shows that the extraction of apple orchards by combining RGB Bands, NDVI, textural features, GLCM, and improved spectral angle features is optimal and can be applied to the extraction of apple orchards in the Zhaoyang District, Zhaotong City.
# Polar Cap Patches Scaling Properties: Insights from Swarm Data
###### Abstract
Among these, the European Space Agency (ESA) Swarm satellite constellation, which was launched in 2013 and is still in orbit, provides the opportunity to analyze new high-quality data in order to better understand PCP properties and dynamics through a product that identifies PCPs. So, we will use this Swarm product and, taking advantage of the studies on the role of turbulence in the formation of ionospheric irregularities at low-latitudes (e.g., [18; 19; 20] and references therein) and at mid- and high-latitudes [20; 21], we will try to understand if turbulence plays a role also in the formation of PCPs. In detail, by estimating the first- and second-order scaling exponents and intermittency of electron density (\(N_{\rm e}\)) fluctuations, we examine whether \(N_{\rm e}\) measurements taken inside PCPs are characterized by values of these quantities that are different from those taken outside PCPs. This is performed for two different levels of geomagnetic activity, i.e., for quiet and disturbed conditions, based on the values of the auroral electrojet (AE) geomagnetic index. We investigate also the possible relation between the occurrence of PCPs and GPS LoL events. Section 2 is devoted to the description of all data and method used and to a brief characterization of the time and space distributions of PCPs included in the dataset. Section 3 discusses the obtained results, while conclusions are drawn in Section 4.
## 4 Discussion
### Future Directions and Limitations  
The studies highlight the significant potential of virtual reality (VR) technology in managing the health of older adults. However, they also expose several limitations and suggest key directions for future development. A major concern is the generalizability of the findings, with many studies suffering from small sample sizes. For instance, studies by [PERSON] and [PERSON], as well as [PERSON] et al. [16; 24], involved insufficient sample sizes that limit the broader applicability of their conclusions. Additionally, [PERSON] et al. [17] utilized a single-participant design, which provided valuable insights but cannot be generalized to a wider population. Furthermore, some studies have predominantly focused on healthy older adults [26], neglecting those who are physically or cognitively impaired, which limits the findings' relevance for a broader aging population.  
Another ongoing challenge is the issue of cyber motion sickness and technology adaptation. Although progress has been made, these issues remain significant barriers to VR adoption. Most studies have shown that the severity of motion sickness has decreased over time. For instance, [PERSON] et al. [19] reported that participants experienced only mild motion sickness during a virtual tour task. In contrast, [PERSON] et al. and [PERSON] et al. [23, 25] emphasized the need for acclimatization training, noting that discomfort from initial exposure to VR diminishes quickly. [PERSON] et al. [18] further suggested that different age groups adapt to VR technology at varying rates. Future VR designs should therefore prioritize user-friendly interfaces and step-by-step instructions to ensure smoother technology adaptation for older adults.  
Recent years have seen a shift from traditional 2D video training to more immersive 3D VR content, aimed at enhancing cognitive, physical, and psychological well-being in older adults. For example, a study by [PERSON] et al. [23] found that VR gaming significantly improved concentration and engagement compared to 2D video. Similarly, multitasking VR games [26] combining cognitive and physical training have been shown to improve balance and memory, while socially collaborative VR games [12] have demonstrated potential in enhancing motor engagement and emotional states. These advancements indicate that 3D immersive VR experiences are becoming increasingly popular, and their potential for promoting healthy aging is expanding.  
In terms of future directions, VR research should focus on making devices lighter and more affordable to promote widespread adoption in homes and communities. More research should also be conducted in real-life settings to improve the ecological validity of studies [16, 25]. Long-term longitudinal studies could provide valuable insights into the lasting effects of VR interventions, such as changes in cognitive functions and emotional health. By optimizing VR devices, expanding application scenarios, and fostering greater collaboration, VR technology has the potential to become an essential tool in advancing healthy aging.
## 4 Results
### Data-Driven Modeling Results
#### 4.2.1 Multiple Linear Regression (MLR)
The MSLE (Mean Squared Logarithmic Error) is 0.063, and the RMSLE (Root Mean Squared Logarithmic Error) is 0.252, showcasing the model's performance in handling logarithmic differences. The \(R^{2}\) value, a measure of how well the model fits the data, is 0.809 for the training dataset, indicating a reasonably good fit. Similarly, in the testing database, the model performs well with an MAE of 32.260 kPa, MSE of 2026.062, and RMSE of 45.012. The MSLE is 0.116, and RMSLE is 0.341. The \(R^{2}\) value for the testing database is 0.837, suggesting a good fit. Overall, the table demonstrates that the MLR model provides reasonably accurate predictions for the ultimate bearing capacity \begin{table}
\begin{tabular}{c c c} \hline \hline
**Metric** & **Training Database** & **Testing Database** \\ \hline MAE & 33.938 & 32.260 \\ MSE & 2189.854 & 2026.062 \\ RMSE & 46.796 & 45.012 \\ MSLE & 0.063 & 0.116 \\ RMSLE & 0.252 & 0.341 \\ \(R^{2}\) & 0.809 & 0.837 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Overall performance of the best MLR model to predict the ultimate bearing capacity of shallow foundations for both training and testing databases. Figure 5: Results of proposed MLR based on (**a**) training database and (**b**) testing database. of shallow foundations in both the training and testing databases. While some discrepancies exist, the model generally performs well in capturing the relationships between the input variables and the target output. Although the linear model exhibits relative accuracy, the performance of the frame model continues to improve.
# Deep-Learning-Based Framework for PET Image Reconstruction from Sinogram Domain
## 3 Experiments
### Real Patient Experiments  
To test the feasibility of the proposed method on human datasets, the method was also evaluated using a human brain PET dataset from nine real patients. The tracer injected into the patient was 18F-FDG. For each patient, 93 slices of reconstructed images and corresponding sinogram images were obtained. The reconstruction method was OSEM. The sizes of the images were set to 320 \(\times\) 320. The last two objects were selected as the testing dataset, and the other seven objects were used as the training dataset. In addition to using rRMSE, structure similarity (SSIM) was used to evaluate the quality of the results.  
\[\text{SSIM}(x,y)=\frac{(2u_{\hat{x}}u_{x}+c_{1})(2\sigma_{x\hat{x}}+c_{2})}{( u_{\hat{x}}{}^{2}+u_{x}{}^{2}+c_{1})\big{(}\sigma_{x}{}^{2}+\sigma_{y}{}^{2}+c_{2} \big{)}} \tag{11}\]  
where \(u_{x}\) is the mean value of \(x\), \(u_{\hat{x}}\) is the mean value of \(\hat{x}\), \(\sigma_{x}{}^{2}\) is the variance of \(x\), \(\sigma_{x}{}^{2}\) is the variance of \(\hat{x}\), and \(\sigma_{x\hat{x}}\) is the covariance of \(x\) and \(\hat{x}\).  
Here, \(c_{1}=(k_{1}L)^{2}\), \(c_{2}=(k_{2}L)^{2}\), \(k_{1}=0.01\), and \(k_{2}=0.03\); both \(c_{1}\) and \(c_{2}\) are constants used to maintain stability. In addition, \(L\) is the dynamic range of the pixel values.
# Object Detection with Low Capacity GPU Systems Using Improved Faster R-CNN
###### Abstract  
Object detection in remote sensing images has been frequently used in a wide range of areas such as land planning, city monitoring, traffic monitoring, and agricultural applications. It is essential in the field of aerial and satellite image analysis but it is also a challenge. To overcome this challenging problem, there are many object detection models using convolutional neural networks (CNN). The deformable convolutional structure has been introduced to eliminate the disadvantage of the fixed grid structure of the convolutional neural networks. In this study, a multi-scale Faster R-CNN method based on deformable convolution is proposed for single/low graphics processing unit (GPU) systems. Weight standardization (WS) is used instead of batch normalization (BN) to make the proposed model more efficient for a small batch size (1 img/per GPU) on single GPU systems. Experiments were conducted on the publicly available 10-class geospatial object detection (NWPU-VHR 10) dataset to evaluate the object detection performance of the proposed model. Experiment results show that our model achieved a 92.3 \(m\)AP. This is a 1.7% \(m\)AP increase when compared to the best results in the models using the same dataset.  
deep learning; object detection; remote sensing; deformable convolutional network; weight standardization +
Footnote †: journal: Applied Physics Letters
## 4 Results and Analysis
### Process of the Usability Experiment
#### 4.3.5 Measuring the Membership Functions  
In order to determine the participants' subjective degree of satisfaction on these four design ideas, the membership values of all items need to be measured. The definition of the fuzzy evaluation set is as follows:  
\[\underbrace{V}_{\text{Strongly disagree}}=\left\{\frac{0}{\text{Strongly disagree}},\frac{0.25}{\text{Disagree}},\frac{0.5}{\text{Neither agree nor disagree}},\frac{0.75}{\text{Agree}},\frac{1}{\text{Strongly agree}}\right\} \tag{3}\]  
After that, the average membership values of each child item on the design ideas can be obtained from the values that were calculated from the questionnaire. The results are shown in Table 8.  
After that, the influence of the efficiency factors (accuracy of music source button, promptness of message reception, clarity of message reception, time for taking/placing CD) at the lower level on the efficiency factors at the higher levels. The influence of the four items on the four prototype stereo systems can be determined by the same approach from the lower levels up to the higher levels.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Idea 1** & **Idea 2** & **Idea 3** & **Idea 4** \\ \hline Precision of the audio source button (W11) & 0.650 & 0.517 & 0.783 & 0.333 \\ \hline Message reception promptness (W12) & 0.692 & 0.683 & 0.658 & 0.625 \\ \hline Message reception clarity (W13) & 0.583 & 0.525 & 0.708 & 0.525 \\ \hline Fast taking/placing CD (W14) & 0.525 & 0.358 & 0.533 & 0.842 \\ \hline Visibility of button design (W21) & 0.683 & 0.425 & 0.733 & 0.500 \\ \hline Complexity of interface design (W22) & 0.183 & 0.450 & 0.350 & 0.492 \\ \hline CD-ROM’s location and external style and dimensions (W23) & 0.333 & 0.608 & 0.308 & 0.267 \\ \hline CD-ROM’s way of mechanical operation (W24) & 0.517 & 0.758 & 0.383 & 0.383 \\ \hline Other ways of placement (W31) & 0.517 & 0.588 & 0.677 & 0.333 \\ \hline Adequacy of the product’s spatial arrangement (W32) & 0.300 & 0.642 & 0.783 & 0.475 \\ \hline Angle of product’s speakers and the consistency of sound (W33) & 0.483 & 0.792 & 0.567 & 0.525 \\ \hline Easy moving (W34) & 0.242 & 0.567 & 0.525 & 0.467 \\ \hline Novelty of style (W41) & 0.300 & 0.842 & 0.417 & 0.508 \\ \hline Preference of style design (W42) & 0.533 & 0.755 & 0.617 & 0.422 \\ \hline Audible added value of the style (W43) & 0.300 & 0.383 & 0.333 & 0.442 \\ \hline Enhanced overall atmosphere when listening to music (W44) & 0.608 & 0.667 & 0.683 & 0.533 \\ \hline Proportion and arrangement of the product’s screen and speakers (W51) & 0.642 & 0.583 & 0.667 & 0.408 \\ \hline The frequency of using other functions in addition to CD (W52) & 0.533 & 0.658 & 0.700 & 0.492 \\ \hline Durability (W53) & 0.700 & 0.400 & 0.733 & 0.683 \\ \hline Cleanness (W54) & 0.617 & 0.567 & 0.658 & 0.525 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Average measured values from the 30 questionnaire copies.  
4. Emotional factors:  
5. Satisfaction factors:
## 1 Introduction  
Alveolar bone consists of cortical and trabecular compartments in varying percent composition resulting in different bone qualities [1, 2]. When dental implants are placed, primary stability reducing micromotion at the implant-bone interface in order to ensure osseointegration, has to be achieved. In this context, a clinical study showed that implants with low initial stability bear a higher risk of failure [3].  
With shortened treatment protocols, implant manufacturers strive for maximizing primary stability by defining drill protocols depending on the hard to evaluate bone quality, and by designing implants often causing compression especially in the cortical layer of bone [4, 2, 5, 6]. The exact implications of the factors mentioned are not yet well known with partially contradictive results presented in literature. In an animal study comparing different drill protocols, a correlation between insertion torque and radiographic bone loss after 6 weeks of healing was found with implants achieving greater torque showing more marginal bone loss [7]. On the other hand, a clinical study showed that microstructural bone characteristics had no effect on changes in marginal bone level and implant stability [1]. A further clinical study found that implant survival was associated with intermediate bone types; the authors concluded that both, very sparse or very dense bone are critical [8].  
With marginal bone resorption causing problems with both, esthetics and maintenance, several potential factors, including vertical positioning and implant abutment connection [9; 10], governing bone response to dental implant placement have been investigated. Besides establishing biologic width, initial marginal bone loss after implant placement has been described as the result of an adaptive bone response to surgical trauma and implant loading [11]. Based on a recent clinical study including 22 implants, the extent of initial marginal bone loss between implant insertion and prosthetic restoration averages 0.41 mm (SD = 0.45 mm) [12].  
As a potential solution to mechanically overloading alveolar bone during implant insertion, implants with a triangular cross-section have been introduced. Following the creation of a round osteotomy, the insertion of a triangular implant with a flat surface oriented bucally would relieve the buccal bone plate. However, two clinical studies did not identify major differences with circular cross-section neck implants [13] apart from somewhat reduced crestal bone loss [14].  
It was the goal of this ex vivo experiment to quantify primary implant stability and strain development on the surface of the buccal bone plate during insertion of two current bone-level implant types and to examine potential correlations with histologically assessed bone damage.