esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Journal and Conference Distribution  
Table 3 presents the 15 most influential journals and conferences in the field of wearable technology research for VRU safety, providing a comprehensive overview of academic communication channels through citation counts, centrality metrics, and first publication years. Lecture Notes in Computer Science leads with 17 citations, demonstrating the foundational role of computer science in this interdisciplinary field since its first related publication in 2014. Following closely is Accident Analysis and Prevention, with 13 citations and a centrality of 0.27, indicating its emergence since 2016 as an important bridge connecting different research subdomains. Notably, centrality metrics reveal that some journals (despite not being outstanding in citation frequency) occupy key positions in the academic network. Human Factors (centrality 0.33), Foundation of Orientation and Mobility (centrality 0.36), and Ergonomics (centrality 0.32), published in 2007, 2000, and 2007, respectively, show high centrality values, suggesting these early publications provided theoretical foundations and methodological support for the entire research field, continuously influencing the direction of subsequent research.  
Figure 4: International collaboration network on wearable technologies for VRU safety.  
The diversity of publication types reflects the interdisciplinary nature of the field, ranging from computer science (Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE Access) to traffic safety (Transportation Research Part F: Traffic Psychology and Behaviour) and human-computer interaction (International Journal of Human-Computer Studies), demonstrating the comprehensive approach needed to integrate knowledge from multiple domains for addressing VRU safety issues. In terms of temporal distribution, these publications span from 2000 to 2023, with recent additions such as IEEE Access in 2023, indicating the field's continued attraction of new research interest and resource investment. Particularly noteworthy is the inclusion of multiple IEEE series conferences and journals, highlighting the significant contribution of engineering and technological perspectives in advancing wearable safety device development, while the preprint platform arXiv ranking eighth reflects researchers' increasing emphasis on open science and rapid knowledge sharing. This systematic analysis of major publication channels not only clearly presents the academic communication network structure of VRU safety wearable technology research but also deeply reveals the complete evolutionary trajectory of this field from early theoretical exploration to current multi-faceted practical applications.
## 3 Results
### Effect of Task on Interval-Specific RFD  
The RFD outcomes are summarized in Figures 2 and 3 for the ankle and knee extension tasks, respectively. As expected, there was a main effect of sex for all RFD variables. The effect sizes pertaining to sex differences were moderate or large for the ankle extension task (\(\eta^{2}\) = 0.12-0.21; \(p\) = 0.007-0.049) and large for the knee extension task (\(\eta^{2}\) = 0.38-0.55; \(p\) < 0.001). There were no sex \(\times\) contraction-type interactions (\(p\) = 0.149-0.608).  
The main effect of contraction type was present in the ankle extension task for the two latest intervals, 100-150 ms and 150-200 ms (\(p\) = 0.004-0.005; \(\eta^{2}\) = 0.22-0.23), as well as the RFD in the 0-150 ms interval (\(p\) = 0.044; \(\eta^{2}\) = 0.13). Pairwise comparisons in males showed that ballistic contractions produced a higher RFD than sustained contractions at the 100-150 and 150-200 ms intervals (mean difference: 386.3 N/s and 290.5 N/s; \(p\) = 0.035-0.46; Hedge's g = 0.51-0.55). Similarly, pairwise tests in females indicated a higher RFD at the 100-150 and 150-200 ms intervals in ballistic contractions compared to sustained contractions (mean difference: 172.5 N/s and 149.6 N/s; \(p\) = 0.003-0.007; Hedge's g = 0.58-0.61) (Figure 2).  
In the knee extension task, the main effect of contraction on the RFD was statistically significant at the 100-150 and 150-200 ms intervals (\(p\) = 0.004-0.024; \(\eta^{2}\) = 0.16-0.24), as well as the 0-200 ms interval (\(p\) = 0.002; \(\eta^{2}\) = 0.26). In males, there were no differences between sustained and ballistic contractions (\(p\) = 0.132-0.813). In females, ballistic contractions produced a statistically significantly higher RFD at the 100-150 and 150-200 ms intervals (mean difference: 454.3 N/s and 486.6 N/s; \(p\) = 0.001-0.003; Hedge's g = 0.58-0.73), and a statistically significantly higher RFD at the 0-100, 0-150 and 0-200 ms intervals (mean difference: 302.3 N/s, 250.1 N/s and 410.2 N/s; \(p\) = 0.003-0.016; Hedge's g = 0.42-0.71) (Figure 3).
## 4 Experiments, Results, and Discussion
### Experimental Results  
#### 4.3.1 Horizontal Object Detection  
In this experiment, four different models, YOLOX-s, YOLOX-m, YOLOX-l, and YOLOX-x, were used in the YOLOX model cluster, as well as the improved models in this paper, DCE_YOLOX-s, DCE_YOLOX-m, DCE_YOLOX-l, and DCE_YOLOX-x. The training batch size was set to 32, and the performance metrics were tested after every 10 epochs of training were completed, and a total of 100 periods of baseline and improved models were trained. Table 2 shows the comparison experiments of the four versions, and the best results are highlighted in bold. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Configuration** \\ \hline Operating System & Ubuntu 20.04.2 LTS \\ CPU & AMD Ryzen 5 3600 \\ GPU & GeForce RTX 3060 Ti \\ Languages & Python \\ Platform & CUDA11.1,CuDNN8.0 \\ Framework & Pytorch 1.7.0,Torchvision 0.8.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Hardware and software. As can be seen from Table 2, among the eight different YOLOX models, the improved model has some improvement in performance indexes compared with its corresponding initial model, among which the improved X-x has the largest improvement, with a 5.72% improvement in mAP, and the mAPs of X-s, X-m, and X-l have improved by 5.16%, 3.51%, and 4.06%, respectively. Therefore, it can be proved that the improved network model has better detection performance than the original network model. In addition, the cross-sectional comparison of the four improved models shows that DCE_YOLOX-x has the best performance. We compared the performance and speed of the model before and after improvement, including mAP_0.5, mAP_0.5:0.95, Speed, and FLOPs, where Speed is the sum of average forward time, average NMS time, and average inference time. DCE_YOLOX sacrifices part of the detection speed when improving the detection accuracy, and the detection speed and Floating point operations are inferior to the original model. Among them, the optimal mAP_0.5 and mAP_0.5:0.95 of the improved model are 4.44% and 5.52% higher than the original model, respectively, but the optimal Speed and FLOPs are 12.78 ms and 41.6 G lower compared to the original model. The comparison results are shown in Table 3, and the best results are highlighted in bold. Three sets of representative images from the dataset were selected for testing, and the before-and-after comparison graph shown in Figure 6 was obtained, which further shows the improved performance of the improved network for small object detection.
## 2 Materials and Methods
### Polar Plot for Visibility and Back Trajectory Modelling  
Next, the effects of local wind and long-range transport on visibility degradation were investigated. Wind advection carries air masses to a receptor area or site. They can absorb airborne constituents emitted from or existing at places/areas during migration. Such constituents can be either clean or polluting, and either dry or moist. The long-range transport of airborne constituents is known as a physical mechanism at a regional scale, which can potentially impact local conditions. In view of visibility, moisture and air pollutants can be absorbed and carried along in air masses moving through areas before reaching a receptor. The effect of local wind on visibility degradation has been analyzed using polar plots [50] looking at the climatological patterns of visibility as a function of local wind speed and direction for the three main stations. For analyzing the effect of long-range transport, we performed back-trajectory modeling, which is a useful tool to examine such impacts [51; 52]. Here, daily kinematic trajectories were generated using FLEXTRA (version 5) ([[http://flexpart.eu](http://flexpart.eu)]([http://flexpart.eu](http://flexpart.eu))) [53] and driven by the six-hourly 0.5\({}^{\circ}\)-resolution wind fields of the CFSR data [54]. Each back trajectory that represents a daily air mass was tracked backward in time for five days for the dry season. The latitude, longitude, and height of the air mass were recorded along its migration. The arrival time was 1300 LT when mixing height was generally well developed and well mixed throughout the atmospheric boundary layer. The arrival height was set as 500 m AGL, approximately the middle of the atmospheric boundary layer. A conventional \(K\)-means clustering method was used to divide all finally obtained back-trajectories into clusters. The objective of the \(K\)-means method is to minimize the sum of the square of Euclidean distance of within-cluster members from their center [55]. Here, the \(K\)-means clustering was carried out in the R computing environment [56], in which scree and silhouette diagrams were employed to guide the optimal number of clusters. Since stations CB and LC are in the same province and the distance between them is not far (33 km) compared to the CFSR grid spacing, the back-trajectory modeling was performed only at CB and RY.
## 3 Results and Discussion
### Adsorption Removal
[PERSON] et al. [54] examined two mesocosm-scale constructed wetland systems operating in parallel. In one system, the filling material was zeolite, and in the other, it was gravel. Each system consisted of two CWs. The concentration of orthophosphate was measured at the inflow and outflow of both systems. The results showed that the orthophosphate removal efficiency was 70% in the zeolite medium, which was statistically greater (\(p<0.05\)) than the results obtained from the gravel medium. The limited orthophosphate removal observed in our system (Figure 1b) is likely attributed to the absence of artificial aeration and, generally, to the different environmental conditions compared to those of constructed wetlands. More specifically, in a study by [PERSON] et al. [54], phosphorus removal efficiencies of approximately 70% were achieved using clinoptiolite-based zeolite; however, this was under continuous or cyclic artificial aeration. In addition, the presence of macrophytes in constructed wetlands can further enhance phosphorus removal through direct uptake by plant tissues and through rhizosphere-mediated processes, where plant roots provide oxygen and exudates that stimulate microbial activity and promote phosphorus immobilization. Figure 1: Effect of time on removal of (**a**) ammonium and (**b**) orthophosphate. Error bars indicate the standard deviation.
## 2 Materials and Methods

Table 2: Rotational speed, feed speed, and cut height for the nine cutting conditions. Also listed is the corresponding average chip thickness. The whole experiment was carried out on an experimental single-shaft circular saw, which is located in the laboratory of the KVAT Faculty of Technology in the development workshops of the Technical University in Zvolen. The main purpose of using this machine is the production of wood semi-finished products and experimental research. The machine is of a compact construction; the main supporting part of the whole machine is a frame, which is welded from a plate steel material. The circular saw-cutting mechanism assembly is attached to the frame by screw connections. The high-speed part of the cutting mechanism (shaft) is mounted in radial ball bearings in the bearing housings. The feeding mechanism of the circular saw consists of a belt conveyor and an upper roller feeder MW 102 (TOS Svitavy a. s., Czech Republic). The feed speed of the belt conveyor is continuously variable in the range of (4-40) m/min. The feed speed of the upper roller feeder is gradually variable by means of a gearbox in the range of (4-34) m/min. The moving parts of the machine are mounted in plain bushes or ball bearings. The drive of the circular saw shaft is ensured by an electric motor, pulleys, and V-belts. The electric motor has a power of 5.5 kW and speed of 2910 rev/min. A smooth change of shaft speed is ensured by using a frequency converter (VONSCH s.r.o Brezno, Slovakia). The circular saw was connected to a mobile sawdust extractor type U 1500. The power measurement was realized by the frequency converter, while the active power input without losses and the power of the electric motor were evaluated from the current, voltage, and efficiency of the electric motor. Recording of measured power quantities could be read, displayed, and saved on an external computer via VDS software (Vonsch Drive Studio, Brezno, Slovakia). This measuring device generated 20 power values per second. Individual cuttings of the samples lasted more than 3 s. All measurements have already considered the values during idling before and after cutting. Cutting power is calculated based on the relationship:  
\[P=P_{C}-P_{C0}\ (W), \tag{2}\]  
where \(P_{C}\) is total power during sawing (W) and \(P_{C0}\) is power during idling (W), calculated as:  
\[P_{C}=U_{C}\ I_{C}\cos\varphi\ (W), \tag{3}\]  
and:  
\[P_{C0}=U_{C0}\ I_{C0}\cos\varphi\ (W), \tag{4}\]  
where \(U_{C}\) and \(U_{C0}\) are voltages during sawing and idling (V), \(I_{C}\) and \(I_{C0}\) are currents during sawing and idling (A), and \(\cos\varphi\) is the power factor. The temperature distribution in the circular saw blade is determined by the following factors: friction in the cutting process and heat dissipation from the saw blade surface.
# The Role of Artificial Intelligence in Sports Analytics:
## 4 Discussion
### AI Performance
Most Popular Metrics in AI-Based Sports Analysis  
The studies reviewed predominantly employed metrics such as posture analysis, action recognition, and various forms of performance prediction as primary indicators of athletic performance. These metrics quantify critical biomechanical variables and movement dynamics, providing a granular assessment of technique and efficiency across different sports. In addition, a range of supplementary metrics, spanning physical performance indices, team and individual player analyses, and sport-specific evaluative techniques were frequently reported. This diverse combination of performance indicators reflects the multidimensional nature of sports performance, where kinematic precision, tactical execution, and physiological efficiency all play crucial roles. Recent studies underscore the importance of these performance indicators. The following examples are provided:  
* [PERSON] et al. [7] developed an AI-driven, pose-based sports activity classification framework that accurately captures athletes' dynamic postures across multiple disciplines, demonstrating improved biomechanical assessment over traditional methods. * [PERSON] et al. [10] integrated advanced sensing modalities with convolutional and recurrent neural networks in volleyball training, enabling real-time action recognition and delivering immediate, data-driven feedback to both athletes and coaches. * [PERSON] [14] applied supervised machine learning to player movement trajectories for defensive strategy analysis in basketball, showing how AI models can reveal tactical patterns and support in-game decision-making. * [PERSON] et al. [16] employed ensemble machine learning techniques on player statistics to derive composite key performance indicators in football, blending individual and team metrics to refine talent evaluation. * [PERSON] et al. [1] conducted a systematic review of AI applications in sports, mapping out implementation contexts from training optimization and performance monitoring to health management and injury risk prediction. The widespread use of these metrics across sports suggests that AI applications are moving toward a more holistic approach, balancing biomechanics, tactical assessment, and physiological data integration.
# Cyber-Secure IoT and Machine Learning Framework for Optimal Emergency Ambulance Allocation
## 2 Background
### Technological Innovations in Ambulance Deployment
Similarly, for stroke patients, portable brain imaging can be performed during transport, with the results transmitted to the receiving hospital to enable early preparation for treatment. Such telemedicine systems play a crucial role in stabilizing patients before they arrive at the hospital and significantly improve the continuity and speed of emergency care [26; 27]. In addition to in-ambulance telemedicine, drone technology has emerged as a valuable tool for supporting emergency medical services in areas that are difficult to access by road. In mountainous regions or areas experiencing severe traffic congestion, ambulances may be unable to reach the scene promptly. Drones can be deployed to deliver essential medical supplies such as medications, defibrillators, and blood products directly to the site. Their rapid mobility and small size make drones particularly effective in bypassing physical obstacles and delivering critical resources ahead of ambulance arrival. Despite their promise, several technological challenges remain for the widespread adoption of drones in emergency services, including regulatory restrictions, flight safety concerns, and limitations in battery endurance. Active research is underway to address these barriers, and drone-assisted emergency medical delivery systems are expected to become an increasingly important component of prehospital care in the future [28].
# Low-Complexity Synchronization Scheme with Low-Resolution ADCs
## 3 Analysis of Synchronization Algorithm  
### Training Sequence Design  
Superframe is the basic unit of the physical layer signal in the OFDM-UWB system. The head part of the superframe is filled with a preamble sequence which contains two identical beacon sequences. Each OFDM symbol consists of a cyclic prefix (CP), an FFT block, and a guard interval (GI). The superframe structure is shown in Figure 2.  
The training sequence used in the system is a PN sequence, which is the longest nonlinear shift register sequence with excellent autocorrelation properties. The beacon sequence is a band-limited pseudo-random signal whose expression can be written as  
\[x_{B}=\frac{1}{N_{c}}\sum_{i=0}^{N_{c}}X_{B}[i]\exp[j2\pi i(\Delta f)_{c}n],0 \leq n\leq N, \tag{9}\]  
where \(x_{B}[i]\) represents the training sequence in the frequency domain, \(N_{c}\) is the number of subcarriers, \((\Delta f)_{c}=f_{s}/N_{c}\) denotes the subcarrier spacing, and \(f_{s}\) is the sampling frequency.
## 3 Results and Discussion
### Micromechanical Properties  
Table 7 shows the elastic modulus and hardness of the microcapsules characterized by nanoindentation tests. The elastic modulus of WM1 and WM2 are 0.55 GPa and 2.02 GPa, respectively. The hardness of WM1 and WM2 are 4.89 MPa and 72.54 MPa, respectively. The results show that the elastic modulus and hardness of WM2 are higher than those of WM1. The mechanical properties of microcapsules are mainly determined by their shell materials. After the nano-CaCO\({}_{3}\) is blended with cceresine wax, the nano-CaCO\({}_{3}\) acts as a physical cross-linking point, and the molecular chains of cceresine wax will be wrapped around the nano-CaCO\({}_{3}\) particles. When one of the cceresine wax molecular chains is under stress, the nano-CaCO\({}_{3}\) can transfer the stress to the other molecular chains, so that the stress is dispersed. Even if the network chain is broken at one place, due to the \"cross-linking-like\" effect of nano-CaCO\({}_{3}\), the other molecular chains can still withstand the stress and do not rapidly endanger the whole, reducing the possibility of fracture and playing a strengthening role [27].  
Figure 7 demonstrates the force-displacement curves of the microcapsules. A 1980 nm displacement for WM2 and 10,998 nm displacement for WM1 indicates that the displacement of WM2 is much smaller than that of WM1. The ability of microcapsules to resist plastic deformation induced by pressure is determined by the elastic modulus and hardness. In general, materials with high elastic modulus and hardness are able to produce smaller elastic deformation when subjected to external forces [28]. The addition of nano-CaCO\({}_{3}\) to  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Microapsules** & **Elastic Modulus (GPa)** & **Hardness (MPa)** \\ \hline WM1 & 0.55 & 4.89 \\ WM2 & 2.02 & 72.54 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Elastic modulus and hardness of microcapsules.  
Figure 6: FTIR spectra of nano-CaCO3, cceresine wax, E-44 epoxy resin, N, N-dimethylformamide and WM2.  
the microcapsule shells significantly increased their elastic modulus and hardness, thus reducing the displacement of the microcapsules.
## 3 Results
### Convergence Time
For the BRDM solution using GPS-only observations, the average convergence time was approximately 3.17 min for the vertical component (U), 3.35 min in 2D, and 3.13 min in 3D, with a relatively high variability between sessions (standard deviations of \(\pm\)2.04 min for 2D, \(\pm\)1.91 min for 3D, and \(\pm\)2.04 min for U). The percentage of sessions achieving the required thresholds was noticeably lower, amounting to only 80.7% for U, 27.4% for 2D, and 35.9% for 3D positioning. Using Galileo-only signals resulted in slightly worse average times--3.51 min for U, 3.92 min in 2D, and 3.66 min in 3D--but the variability was considerably smaller (\(\pm\)0.69 min for 2D, \(\pm\)0.78 min for 3D, and \(\pm\)0.69 min for U), and the percentage of sessions successfully reaching convergence improved to 92.0% for U, 64.6% for 2D, and 70.8% for 3D. A moderate improvement was observed when combining GPS and Galileo signals, with average convergence times of 2.86 min for U, 3.10 min in 2D, and 2.91 min in 3D, although the percentage of sessions successfully converged remained relatively modest. The HAS solution, benefiting from additional correction data, generally outperformed BRDM, although the differences were not uniform across all configurations. When using GPS-only observations, the convergence performance was noticeably worse, with average times of 2.59 min for U, 3.10 min in 2D, and 2.89 min in 3D, accompanied by a large variability between sessions of \(\pm\)2.07 min for U, \(\pm\)2.07 min for 2D, and \(\pm\)2.00 min for 3D. In this case, the percentage of sessions achieving the required convergence thresholds was very low, amounting to only 45.1% for the vertical component (U), 22.2% for 2D positioning, and 26.05% for 3D. A clear improvement was observed when using Galileo-only signals, where the convergence times decreased to 1.47 min for U, 2.94 min for 2D, and 2.25 min for 3D, with a relatively low variability (\(\pm\)0.80 and \(\pm\)0.83 min for 2D and 3D and \(\pm\)0.80 min for U), and success rates exceeded 70%. The best performance for the HAS solution was again obtained when combining GPS and Galileo signals, achieving average convergence times of 1.39 min for U, 2.91 min in 2D, and 2.16 min in 3D, with a relatively small variability (\(\pm\)0.76 and \(\pm\)0.75 min for 2D and 3D and \(\pm\)0.76 min for U) and with over 70% of sessions meeting the required accuracy thresholds. These results indicate that although HAS corrections significantly enhance the positioning performance compared to relying on broadcast data alone (BRDM), the convergence is still slower and less stable than what can be achieved with post-processing (CODE) or with real-time precise products (CNES). Detailed convergence results for each coordinate component are available in Appendix A.