esa-sceva/satcom-chunk-collection huggingface dataset:
# BER Minimization by User Pairing in Downlink NOMA Using Laser Chaos Decision-Maker
###### Abstract  
In next-generation wireless communication systems, non-orthogonal multiple access (NOMA) has been recognized as essential technology for improving the spectrum efficiency. NOMA allows multiple users transmit data using the same resource block simultaneously with proper user pairing. Most of the pairing schemes, however, require prior information, such as location information of the users, leading to difficulties in realizing prompt user pairing. To realize real-time operations without prior information in NOMA, a bandit algorithm using chaotically oscillating time series, which we refer to as the laser chaos decision-maker, was demonstrated. However, this scheme did not consider the detailed communication processes, e.g., modulation, error correction code, etc. In this study, in order to adapt the laser chaos decision-maker to real communication systems, we propose a user pairing scheme based on acknowledgment (ACK) and negative acknowledgment (NACK) information considering detailed communication channels. Furthermore, based on the insights gained by the analysis of parameter dependencies, we introduce an adaptive pairing method to minimize the bit error rate of the NOMA system under study. The numerical results show that the proposed method achieves superior performances than the traditional using pairing schemes, i.e., Conventional-NOMA pairing scheme (C-NOMA) and Unified Channel Gain Difference pairing scheme (UCGD-NOMA), and \(\varepsilon\)-greedy-based user pairing scheme. As the cell radius of the NOMA system gets smaller, the superior on the BER of our proposed scheme gets bigger. Specifically, our proposed scheme can decrease the BER from \(10^{-1}\) to \(10^{-5}\) compared to the conventional schemes when the cell radius is 400 m.  
Keywords:non-orthogonal multiple access (NOMA); user pairing; laser chaos decision-maker; bandit algorithm; system optimization; adaptive control; bit error; ACK; NACK +
Footnote †: journal: Electronics Letters
## 5 Computational Experiments
### Algorithm Parameter Settings  
In the HACO algorithm, several parameters significantly influence search performance and convergence quality. Due to the introduction of the pseudo-random proportional rule and \(\alpha=1\), the most impactful parameters are \(\beta\), \(\rho\), and \(q_{0}\). Therefore, this study determines the optimal values for these parameters through preliminary experiments. Other parameters, such as the number of ants (ant = 20), maximum iterations (300 or 100, depending on the scales of instances), power threshold (\(E_{th}=0.3\)), and heuristic information regulation factors (\(\gamma=0.4\) and \(\delta=0.6\)) are set as fixed values.  
Following the parameter tuning method described by [PERSON] et al. [45], the experimental ranges for the parameters are set as \(\beta\in\{2,3,4,5\}\), \(\rho\in\{0.1,0.2,0.3,0.4\}\), and \(q_{0}\in\{0.5,0.6,0.7,0.8\}\). Default values are assigned as \(\beta=3\), \(\rho=0.1\), and \(q_{0}=0.7\). For each experiment on C100, only one parameter is varied while keeping others at their default values. Each parameter combination is tested ten times, and the average results are compared. The cost optimal values in Table 2 have been highlighted in bold.  
As shown in Table 2, the HACO algorithm performs best when \(\beta=5\), \(\rho=0.2\), and \(q_{0}=0.6\). These values are adopted for subsequent comparative experiments.
# Calibration and Impact of BeiDou Satellite-Dependent Timing Group Delay Bias
## 2 BeiDou Timing Group Delay Bias from Precise Products  
### BeiDou Timing Group Delay  
Using the daily TGD values from the broadcast ephemeris from day of year (DOY) 001 to 180 in 2019, Figure 1 plots the TGD\({}_{1}\) variations of BDS-2 (C01-C16) and BDS-3 (C19-C37). It can be seen that the absolute TGD value of BDS-3 is much larger than that of BDS-2. However, this does not affect user positioning once it is corrected. Different from the daily updated DCB, the BDS TGD is set as a constant value on each satellite apart for some updating epochs. During the half-year period of 2019, the TGD of BDS-2 was updated on DOY 005, 032, and 151, while the TGD of BDS-3 was updated on DOY 087. For C28, there was a big update on DOY 167 due to the satellite reconfiguration that occurred on this date. Nevertheless, it is interesting to find that the TGDs of BDS-2 and BDS-3 were updated separately. Therefore, it can be inferred that the TGD updates on BDS-2 and BDS-3 on the BDS control segment may be determined independently.  
With the more accurate DCB values from the DLR and CAS (ftp:/igs.ensg.ign.fr/pub/igs/products/mgex/dcb/), we can assess the precision of the TGD. In this study, the daily DCB products provided by DLR were adopted. While the MGEX DCB products are commonly aligned to a satellite zero-mean condition, the basis of the TGD is usually referred to a calibrated reference receiver [5]. For the comparison of the TGD and DCB, the systematic bias should be removed. In this study, the TGD and DCB are realigned to the zero-mean of the 33 BDS satellites, as they were all available throughout the test period.  
Figure 2 gives the difference between the BDS TGD\({}_{1}\) and the DLR DCB\({}_{\rm B1I/B3I}\) during DOY 001-180 in 2019. The difference is not as stable as the TGD variation in Figure 1, due to the daily jump of the DCB values and the TGD update. Despite that, a considerably constant difference on each satellite can still be observed, which implies that a constant bias of the DCB and TGD does exist on each satellite. What is more, it is clear that there is a more significant bias between BDS-2 and BDS-3. The average bias is approximately 3.24 ns, which is close to the value of 3.8 ns as reported by [PERSON] et al. [9]. This systematic bias may be attributed to the independent receivers employed at the monitoring stations for BDS-2 and BDS-3 [9]. Given that the DCB is determined by commercial receivers at MGEX stations, this TGD bias between BDS-2 and BDS-3 should be corrected when similar receivers are used for positioning.  
Figure 1: TGD\({}_{1}\) variation of BDS-2 (**left**), (C01–C16), and BDS-3 (**right**), (C19–C37), from Day of Year (DOY) 001 to 180 in 2019.  
Figure 2: TGD\({}_{1}\) and DCB\({}_{\rm B1I/B3I}\) difference for BDS-2 (**left**), (C01–C16), and BDS-3 (**right**), (C19–C37), from Day of Year (DOY) 001 to 180 in 2019.
# Three-Dimensional Force Characterizations in Maximly Molar Distalization: A Finite Element Study
###### Abstract  
Class II malocclusion is a very common condition in orthodontic patients. The reaction force and moment on the teeth induced by a maxillary segmental distalizer (MSD) are essential for understanding tooth movement, tipping, and rotation. This work quantified the three-dimensional (3D) reaction force and moment on canine and molar teeth induced by three different MSDs: the JVBarre (JVB), Carriere Motion 3D (CM3D), and CM3D Clear. A patient-specific mandibular model was reconstructed based on cone-beam computed tomography (CBCT) images. Each of the three MSDs was implanted using finite element analysis (FEA). The reaction force and moment were obtained. The results show that the JVB induced less extrusion force (15% less), tipping (90% less), and rotational moment (70% less) on the canine, compared with the other two CM3 Ds. However, the JVB induced a relatively larger extrusion force, tipping, and rotational moment on the molar due to the hook location changing from the end to the middle of the bar. These observations were consistent with the 3D stress distribution of the MSDs. The mechanical understanding from this work may shed light on the optimal design of MSDs.  
JVBarre; carriere motion 3D; reaction force; reaction moment; orthodontics; class II malocclusion; finite element method; maxillary segmental distalizer (MSD); dentistry 2023
## 2 Related Work
Then, attack detection was performed using a deep learning model based on Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM). The results showed that deep learning outperformed in attack detection [14]. [PERSON] et al. emphasized that the controller in SDN architecture is the main target of attacks. LSTM and CNN-based deep learning models were used to detect DDoS attacks, and the performance of these models was compared with traditional machine learning algorithms. The study concluded that deep learning models achieved higher success rates than traditional algorithms [15]. In their study, [PERSON], [PERSON], and colleagues proposed a real-time anomaly defense framework called ERT (Extremely Randomized Trees)-EDR (Edit Distance on Real sequence) to detect and prevent TCP-targeted low-rate DoS (LDoS) attacks in SDN-based networks. ERT-EDR consists of three modules: collecting traffic statistics, detecting LDoS attacks using the ERT algorithm, identifying compromised ports, and mitigating the attack using the EDR algorithm. Experiments showed that ERT-EDR effectively detected LDoS attacks with an accuracy of 96.47% and successfully mitigated the attacks [16]. [PERSON], [PERSON], and colleagues proposed a deep learning model called TabNet to detect DDoS attacks in SDN-based VANETs. TabNet is a model that works on tabular data and outperforms traditional machine learning models. The performance of the model was enhanced with Adam optimization, and comparative evaluations with other algorithms achieved 99.42% accuracy. The proposed method provides a better solution in terms of accuracy and efficiency for detecting DDoS attacks in SDN-based VANETs compared to traditional techniques [17]. In their study, [PERSON], [PERSON] et al. focus on detecting DoS attacks in IoT networks that use the MQTT protocol. They evaluate six supervised classification methods combined with PCA for feature reduction, achieving high detection accuracy. Models like Multi-Layer Perceptron and Decision Trees achieved AUC values exceeding 99%. This study offers a robust approach for securing MQTT-based IoT systems and suggests potential extensions to other protocols and types of attacks [18].
# Assessing External Peak Physical Demands in Under-19 Years and Professional Male Football
## 2 Materials and Methods  
### Participants  
A non-probabilistic convenience sampling method was employed for this study. Performance data were collected from players belonging to a single professional football club. A total of 30 match observations were recorded during the 2023/2024 season, including 15 matches from the Spanish U19 First Division (the highest national league for this age group) and 15 matches from the Spanish Second Professional Division. Both squads belonged to the same club and ranked within the top five positions of their respective competitions. To avoid potential overlapping and ensure data independence, no players were shared between the two teams during the analyzed matches.  
Each team was composed of 22 players. The U19 team had a mean age of \(17.6\pm 1.0\) years, while the professional team averaged 26.9 \(\pm\) 4.1 years. Players were classified into five positional groups: central defenders, full-backs, central midfielders, wide midfielders, and forwards [11]. Figure 1 provides details on the number of players included, the tactical formation employed, and the total number of match records per position.  
As exclusion criteria, matches with weak satellite tracking signals resulting in incomplete data were excluded, along with those where errors occurred during data collection. Players who participated for fewer than 45 min or switched positions during the match were also excluded (i.e., if a player started as a central defender, and later moved to full-back, they were omitted from the study). Additionally, goalkeepers were excluded due to their low high-speed running (HSR) demands [18]. Matches in which either team finished with a numerical advantage were also removed. These criteria align with previous studies, as match intensity and physical performance values could be affected [19; 20].
# The Effectiveness of Unimodal and Multimodal Warnings on Drivers' Response Time: A Meta-Analysis
## 1 Introduction
### Multimodal Warning Systems and Related Theories
#### 1.3.1 Redundant Signal Effect  
According to the redundant signal effect (RSE), a facilitatory effect can be found when it comes to humans' perception and reaction to multiple signals as compared to one signal [30]. The RSE can be represented by two models: the coactivation model and the race model [31]. The coactivation model believes that information from multiple sensory modalities merges into a single criterion before being further processed by the brain; whereas the race model holds the opinion that information would not be interfered with by one another, but elicit motivation to each modality to give rise to the facilitatory effect. With the development of neuroscience, researchers found that even though the information processing of the individual sensory modality lies within different brain regions, multisensory information processing appears to execute in the independent superior colliculus region [32; 33]. This suggested that information processing from multiple sensory modalities is mostly in line with the coactivation model.  
From the perspective of multisensory redundant signal effect (RSE), the multisensory integration effect (MIE) allows multimodal driving warning systems to elicit quicker information processing in drivers' brains [26; 34; 35], reducing drivers' response time in avoiding potential risks (see left panel of Figure 1). Moreover, the use of multimodal driving warning systems provide drivers with supplementary information if one modality fails to carry its function [36], assisting them with additional support from other modalities.  
Figure 1: Illustration of multisensory Redundant Signal Effect (RSE) and Yerkes-Dodson Law (YDL).
###### Abstract  
This study investigated the efficacy of Augmented Reality (AR) in the rehabilitation of lower limb amputees by comparing movement accuracy and subjective satisfaction with and without AR. Ten individuals with unilateral lower limb amputations participated. Joint range of motion during exercises was measured using a 3D motion capture system. Subjective satisfaction was assessed via a 5-point Likert scale. Movement accuracy was evaluated by analyzing changes and variability (standard deviation) in joint angles across eight selected movements. Results showed that AR feedback significantly increased average joint angles in specific movements: standing arm raise to the side, standing trunk lateral flexion, and standing knee flexion to maximum height. Furthermore, AR feedback led to a significant reduction in the standard deviation of joint angles for most exercises, indicating improved movement consistency. Subjective satisfaction scores for interest, motivation, exercise effectiveness, movement accuracy, and overall satisfaction were significantly higher with AR.  
augmented reality; lower limb amputation; rehabilitation training; movement accuracy; subjective satisfaction 2020 IEEEexample: date / Accepted: 13 June 2025  
10 10 2025
# Human-Robot Collaboration: An Augmented Reality Toolkit for Bi-Directional Interaction
## 6 Experiments
### Analysis of the Results
#### 6.3.2 SUS Results  
The effectiveness of the proposed platform can be distinctly assessed through the analysis of the SUS, whose main results are reported in Figures 17 and 18.  
Statistically significant differences have been found in the SUS overall score between the two interfaces: the AR system (\(M=80.83\%\), \(SD=13.65\%\)) surpasses the kinesthetic system (\(M=74.61\%\), \(SD=16.24\%\)) by 6.22\(\%\) in terms of usability, \(t(44)=2.09\), \(p=0.043\), \(d=0.3984\), as depicted in Figure 17. The overall SUS score has corroborated that the Franka Emika Panda robot is designed to be a collaborative robot with a user-friendly kinesthetic teaching interface, which is much more intuitive than the industrial robots' teach pendant. Hence, it is noteworthy that the improvement in usability achieved by the proposed AR platform could be further increased in industrial scenarios.  
In this regard, the results concerning the first SUS statement (_\"I think that I would like to use this system frequently\"_), shown in Figure 18a, prove that users' preference for the HoloLens platform increases by about 16\(\%\) compared to the classical programming interface (\(M=4.71\), \(SD=2.17\) for the AR system, \(M=3.91\), \(SD=1.98\) for kinesthetic interface, \(t(44)=3.769\), \(p=0.0005\)), with a large effect size of \(d=0.7916\).  
Furthermore, the proposed AR interface satisfies the requirements of efficiency and consistency. This is evidenced by the scores of statements 5 (_\"I found the various functions in this system were well integrated\"_), Figure 18b, and 8 (_\"I found the system very cumbersome to use\"_), Figure 18c. Indeed, statistically significant differences have been found for the statement 5 (\(M=4.44\), \(SD=0.659\) for the AR system, \(M=4.089\), \(SD=0.90\) for kinesthetic interface, \(t(44)=2.701\), \(p=0.01\), \(d=0.4329\)) and the statement 8 (\(M=2.00\), \(SD=1.24\) for the AR system, \(M=1.58\), \(SD=0.81\) for kinesthetic interface, \(t(44)=2.06\), \(p=0.045\), \(d=0.3863\)).  
Although it does not represent a statistically significant difference, the statement 9, concerning users' confidence during task execution, reveals that each participant felt safer by the HoloLens platform of about 4.4\(\%\). To this purpose, it has been experienced that visualizing the trajectory in the AR environment as a set of way-points significantly boosts users' confidence.
## 4 Case Study and Analysis of Vibration Control Effect
### Design of FST  
According to structural dynamics, the natural frequency of the FST is mainly determined based on the stiffness of the steel spring under the slab and the mass of the slab. The damping of the steel spring influences the peak vibration of the track. The stiffness and damping of the steel spring were decreased and increased, respectively, to broaden the vibration isolation frequency band and to reduce the resonance peak of the FST. The slab thickness was increased to increase the track mass. The parameters of the adjusted FST (called the special FST [SFST]) and existing conventional FST (CFST) are listed in Table 4. The typical reaction forces of the steel spring for the two types of FSTs, calculated using the 3D train-FST coupled model in the time domain, are shown in Figure 9. It should be noted that, since the mathematical mode superposition method is used to represent and solve the displacement response of the rails and floating slab, and the floating slab is simulated using a thin plate model, it is necessary to determine the number of modes required for calculation. In the frequency range of less than 100 Hz that the environmental vibration problems focus on, when the rail mode is set as 10 and the thin plate modes along, and perpendicular to, the direction of train travel are, respectively, set as 20 and 10, the calculation accuracy requirements can be satisfied [55]. Compared to the CFST, the range of change of the steel spring reaction force under the SFST decreased, which can improve the fatigue resistance of the steel spring and reduce the impact of vibration on the surrounding environment from the vibration source.  
Figure 8: Comparison between model calculation and on-site measurement results of ground vibration acceleration.  
Figure 10 shows a comparison between the model calculation results for the ground vertical vibration acceleration under ordinary track conditions and the two types of FST conditions. Both types of FSTs significantly decreased the ground vibration acceleration, and the RMS value of vibration acceleration is reduced by one order of magnitude. The vibration control effect of the SFST is better, because the steel spring support reaction forces under this condition are smaller than the condition with the CFST, which can be seen in Figure 9. In addition, the ground vibration exhibited amplification near the natural vibration frequency of the FST. After adopting the SFST, the natural vibration frequency of the track decreased from 10 to 8 Hz, widening the effective vibration reduction frequency band of the track. Moreover, the ground vibration near the natural vibration frequency of the FST decreased by approximately 5-8 dB. However, the amplification of the ground vibration at the natural frequency of the FST did not change.
# On the Local and String Stability Analysis of Traffic Collision Risk
## 3 Introduction of Traffic Collision Risk Stability
### Definition of Collision Risk String Stability  
Traffic string stability studies the disturbance propagation along the vehicle fleet or platoon. Inspired by the definition and criterion of traffic string stability, the criterion of risk string stability in the time domain can be formulated as  
\[\frac{||\Delta r_{n}(t)||_{p}}{||\Delta r_{n+1}(t)||_{p}}>1 \tag{12}\]  
which implies the \(p\)-norm of risk disturbance of vehicle \(n+1\) is smaller than that of vehicle \(n\), and risk disturbance is compressed along the vehicle fleet. Regularly, 2-norm or \(\infty\)-norm is adopted for exploration, where 2-norm indicates the disturbance energy and \(\infty\)-norm implies the maximum disturbance. In this study, similar to the traffic string stability analysis, we focus on the 2-norm string stability.  
In the frequency domain, the block diagram of traffic and risk string stability are shown in Figure 3. The legend is the same as Figure 2. Away from the equilibrium states, \(X_{n}^{eq}(s)\) and \(X_{n+1}^{eq}(s)\), the biases of the position for vehicle \(n\) and \(n+1\) are derived as \(\Delta X_{n}(s)\) and \(\Delta X_{n+1}(s)\), respectively. The evaluation of traffic string stability aims to compare the \(p\)-norm of \(\Delta X_{n}(s)\) and \(\Delta X_{n+1}(s)\). If the p-norm of \(\Delta X_{n+1}(s)\) is smaller than that of\(\Delta X_{n}(s)\), the platoon is considered traffic-string-stable. To demonstrate risk string stability, \(R(s)\) calculators are introduced to the diagram. The inputs of \(R(s)\) calculator for vehicle \(n\) are the position, velocity, acceleration of vehicle \(n\) and vehicle \(n+1\), according to Equation (9). The velocity and acceleration are obtained from \(X_{n}(s)\) through the differentiation operator \(s\). After the initial state, the deviation of collision risk for vehicle \(n\) and vehicle \(n+1\) are \(\Delta R_{n}(s)\) and \(\Delta R_{n+1}(s)\).  
In contrast to traffic string stability, which examines the relationship between \(\Delta X_{n}(s)\) and \(\Delta X_{n+1}(s)\), the focus of risk string stability is on the relationship between \(\Delta R_{n}(s)\) and \(\Delta R_{n+1}(s)\).