esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Study on the Length of EVA in the Pantograph-Catenary System
### Influence of the EVA Length on the Real-Time Catenary Model Calculation Accuracy  
In this section, the existing real-time catenary model [18] is further considered to study the influence of the EVA length on its calculation accuracy and validate the appropriate length of EVA. The abovementioned pantograph-catenary system is further considered, and the corresponding pantograph-catenary contact force is calculated. The measurement data of the contact force obtained from [27] are used for validation purposes. Both the calculation results and measurement data are filtered in a 0-20 Hz span. Based on the test parameters, the train velocity is chosen as 222 km/h. Four different EVA lengths are chosen, which are 60 m, 120 m, 240 m, and an appropriate length of 215 m, and the EVA length is equal to the length of the real-time catenary model. The time histories of the contact force of the pantograph-catenary system are shown in Figure 8. It can be seen from Figure 8 that the length of the EVA has a great influence on the pantograph-catenary interaction dynamics. When \(d_{e}\) = 60 m and 120 m, the results from the real-time catenary model show a large difference between the measurement data. However, when the appropriate length of 215 m is chosen, the results from the real-time catenary model show good agreement with the measurement data. This means that the real-time catenary model can obtain accurate results with an appropriate length of EVA, and the appropriate length of EVA can be obtained based on the energy distribution of the catenary. In addition, it can be seen from Figure 8d that an EVA length longer than its appropriate value can also obtain accurate results.  
The mean contact force and standard deviation of the pantograph-catenary contact force with respect to different lengths of EVA are further calculated to validate the appropriate length of EVA, as shown in Figure 9, where the results are compared with those from the test results. Note that the minimum contact force of the test results cannot be fully consistent with those from the real-time catenary model; thus, it is not considered for validation. Figure 9 shows that the results from the real-time catenary model become closer to those from the test results with increasing EVA length. When the length of EVA becomes 210 m and higher, the mean contact force converges to 85.8 N, and the standard deviation converges to 18.9 N. Because these two values of the test results are 85.1 N and 18.2 N, the maximum relative difference between these results is not more than 1%. Based on  
Figure 7: Appropriate length of EVA with respect to different key parameters: **(a)** vehicle velocity and **(b)** contact wire tension.  
this, the appropriate length of EVA can allow accurate results to be obtained in a real-time catenary model.
## 1 Introduction  
Since the year 2004, graphene [1] has been a focus of condensed matter physics and materials science due to their superior physical properties, especially those showing potential applications in next generation electronics and optoelectronics. Recently, the twistronics, which refers to the studies of the electronic properties of two-dimensional layered structures with particular twist angles between the layers, has become a fast rising new branch of the field in regard to two dimensional (2D) material.  
The fascinating research on 2D materials experienced three sequential stages in its development, each represented by a new degree of freedom in the sense of material structure. Stage 1 is represented by a real 2D system, e.g., freestanding graphene, which allows the discovery of various properties superior to the 3D or quasi-2D systems; stage 2 is represented by tailor-made 2D heterojunctions, which give rise to a variety of van der Waals stacking systems and stage 3 is represented by a twist angle between the neighboring layers, which facilitates the fine tuning of electronics structures and magically induces strong correlation phenomenon at \"magic\" twist angles, e.g., twistronics.  
The concept \"twistronics\" was first introduced by [PERSON] et al. in a theory paper [2] to discuss general approaches to calculate the electronic structure of arbitrarily twisted bilayer 2D materials. Prior to that, researchers had already worked on this topic and discovered or predicted a number of interesting properties of twisted bilayer graphene (TBG), for example, van Hove singularity [3; 4; 5; 6], a possible flat band at \"magic\" angles [7; 8; 9; 10; 11]. The rise of twistronics was boosted in 2018, by [PERSON]'s group's experimental discovery of superconductivity [12] and Mott insulator [13] on the graphene device with almost the exact \"magic\" twist angle, which was fabricated by the so called \"tear and stack\"transfer assembly technique, as demonstrated in Figure 1a. Many other interesting properties, like a topological Chern insulator [14; 15; 16], local charge order [17; 18; 19; 20], as well as those of twisted multilayer 2D heterostructures [21; 22; 23; 11], have been reported soon after. Currently this field has become one of the hottest in the frontier. However, if the twistronics would one day come to practical electronics devices, it is worth putting effort into realizing such a material system by more feasible methods, like epitaxial growth.  
In this paper, we review the advances of this field, in the aspects of its band structure theory, transport properties and local electronic properties. Additionally, we will discuss the perspective of twistronics on epitaxial material systems, particularly epitaxial graphene on wide band semiconductor SiC (as in Figure 1b), aiming at promoting the application of twistronics in electronic devices based on the strong correlation effects, potentially useful in the next generation integrated circuits for quantum computing.
## 4 Experimental Results
### Performance Evaluation with Large-Scale Traffic
(**A**) A stacked bar chart comparing the average delay composition (Transmission Delay vs. Other Delays) and jitter (represented by error bars showing standard deviation). (**B**) A box plot comparing the distribution of the “Other Delays” (queuing, processing, etc.) for each algorithm. Both plots use a logarithmic y-axis to visualize the significant difference in magnitude. In conclusion, these results confirm that even with a large number of data streams and high network utilization, the proposed scheduling algorithm successfully maintains low-latency, low-jitter deterministic transmission. It significantly outperforms standard SP scheduling by virtually eliminating queuing delay and providing highly consistent and predictable performance. This ability to efficiently schedule hundreds of concurrent streams is a key enabler for deploying complex and scalable real-time IoT systems.
## 7 Experimental Demonstration
### Load Shedding Attack--Without a Governor  
Suppose in DRAS 1 area, 20% of generation loss (32.6 MW) occurs, and PU sends load shedding signals to reduce 30% of power consumption on load bus 5. An adversary leverages this information and performs irrelevant load shedding. In the first case, an adversary sends load shedding signals from compromised DRAS 1,2,3 to 70.88% of residential meters and 43.06% of commercial meters in DRAS 1, 44.41% in DRAS 2 and 58.98% in DRAS 3 to reduce 20% (additional load), 15% and 20% of the load, respectively. Figure 8 shows when generation loss occurs at 85 s, area frequency drops. However, relevant and irrelevant load shedding signals cause frequency to increase above the normal value but within the over-frequency threshold of 61.8 Hz. In this case, customers were forced to shed load when it was not required. This will cause dissatisfaction with this service and cause customers to start sending requests for power outage if it occurs regularly.  
In the second case, an adversary sends irrelevant load shedding commands from compromised DRAS 1,2,3 to reduce 20% (additional load), 45% and 60% of the load, respectively, with the main aim of destabilizing the system. DRAS 1 recovers load from 70.88% of residential meters and 43.06% from commercial meters in its area. DRAS 2 recovers 30 MW from 98.96% residential meters and 10.5 MW from 19.36% commercial meters, and DRAS 3 recovers 30 MW from 88.48% residential meters and 30 MW from 49.61% commercial meters. Figure 9 shows that when generation loss occurs at 85 s, area frequency drops. However, shedding commands cause frequency to increase beyond the over-frequency threshold of 61.8 Hz at 110 s. Once the area frequency goes beyond the secure threshold, generators trip and further generation loss occurs.  
Figure 8: Load shedding attack without a governor—within safe frequency limits.  
Figure 9: Load shedding attack—beyond safe frequency limits of 61.8 Hz.
## 4 Simulation Results and Discussion
### Discussion and Comparison  
The fault current was effectively limited by the r-SFCL in all investigated case studies. To understand the effect of shunt resistor values on the performance of r-SFCLs during fault scenarios, Figure 7a shows the fault currents in all cases, Figure 7b shows the corresponding voltage drops, and Figure 7c shows the temperatures of the investigated r-SFCLs. Table 3 summarizes the performance indices obtained from these studies.  
Figure 7: Current limiting behavior for all investigated cases: (**b**) voltage drops in five cases; (**c**) fault response without and with r-SFCL without shunt resistor. (**a**) Fault current; (**b**) voltage drop; (**c**) temperature of four r-SFCLs.  
In terms of the fault current limiting capability, the r-SFCL without a shunt resistor had the best performance, as shown by the green line in Figure 7a. However, this meant that its temperature reached 499 K, as shown by the purple line in Figure 7c, which was the highest temperature seen in all cases (>400 K) and put the r-SFCL at the highest risk of damage.  
Among the other three cases, the r-SFCL with the 16 \(\Omega\) shunt resistor exhibited the best current limiting capability, with a current peak of 2.44 kA, while it remained within acceptable temperature limits and avoided permanent damage, reaching 384 K. The associated voltage drop was also the lowest of the three at 14.49 kV\({}_{\text{rms}}\). Meanwhile, the worst current limiting capability was observed with the 4 \(\Omega\) shunt resistor, leading to the highest current peak of the three at 3.9 kA (62.4% of the prospective current) and the lowest system voltage at only 8.061 kV\({}_{\text{rms}}\), as shown in Figure 7a,b, respectively. However, this also resulted in the lowest r-SFCL temperature of 195 K, as shown in Figure 7c.  
Based on Figure 7a-c and Table 3, there was a clear trade-off between the value of the shunt resistor, the fault current limiting ability, and the temperature of the r-SFCL. The higher the shunt resistor value, the higher the fault current limiting capability, but the higher the temperature, such as in case 3 (16 \(\Omega\) shunt resistor).
###### Abstract
However, despite the efforts to provide a remedy to some drawbacks of DIAL-Lidar systems (in terms of costs, complexity of the setup and maintenance, portability and integration time), the aforementioned systems had severe limitations such as the need of a topographic target to perform the measurements of CO\({}_{2}\), which were not range-resolved (only path averaged gas concentrations were retrieved). In addition, the volcanic CO\({}_{2}\) flux computed by OPS was affected by a large uncertainty [14]. Therefore, it can be concluded that BILLI was the first range-resolved DIAL system used to remotely measure both CO\({}_{2}\) concentration and flux in a volcanic plume. In particular, as will be shown in the following paragraphs, BILLI has been deployed to retrieve range-resolved profiling in wide plumes, thus providing spatio-temporal dispersion concentration maps and high-resolution CO\({}_{2}\) flux time-series. It has opened unprecedented possibilities in measuring volcanic CO\({}_{2}\): BILLI made it possible to clearly detect an excess of a few tens of ppm over a distance of more than 4 km, considering a spatial and temporal resolution of 5 m and 10 s, respectively [42]. Quantifying the CO\({}_{2}\) output from Pozzuoli Solfatara (October 2014) [37; 21; 38] and Stromboli (June 2015) [39; 40; 41] allows for the interpretation, and if possible, the forecast, of the upcoming development of the volcano scenario, with huge benefits to the population living nearby. These examples show the huge social values of lidar systems in monitoring gas emissions. Moreover, observations at densely populated volcanic regions will have a high societal impact throughout Europe. In this regard, the potential of our system has also been confirmed by the results acquired during the last experimental campaign, carried out between July and August 2016 at Mt. Etna [42], retrieving a plume peak beyond 4 km. The main goal of these experimental campaigns was to measure the exceedance of in-plume CO\({}_{2}\) concentration and flux to provide useful information to volcanological research for a precocious alert to the population in the event of eruptions. In this paper, after a description of BILLI, special emphasis will be given to data processing and analysis of the experimental results, which will be thoroughly discussed. This was made possible by means of automatic software procedures written in MATLAB and based on the newly designed BRIDGE DIAL data processing technique. Significant CO\({}_{2}\) concentration profiles, dispersion maps, and time-series of CO\({}_{2}\) flux will be shown. Moreover, our monitoring was assessed by conventional data and local meteorological measurements. Finally, thanks to the BILLI system and from a complete time-resolved plume evolution, it was also possible to retrieve the wind velocity (Stromboli field campaign, 2015), a useful parameter for the CO\({}_{2}\) flux calculation and for the mitigation of volcanic hazards, especially in the field of air traffic security.
# Timing Performance Testing and Regularity Analysis of eLoran System
## 3 Measuring Equipment and Tests
### Experimental Design  
The test equipment and overall experimental design of this study are illustrated in Figure 2.  
Figure 1: Image of the test equipment.  
To quantify the timing accuracy and stability of the two eLoran navigation timing monitoring receivers, we examined their performance with both real and simulated signal sources. The following sections provide a detailed analysis of their timing performance under various conditions.
# Optimized Clustering Algorithms for Large Wireless Sensor Networks: A Review
## 3 Optimized Hierarchical Based Routing Protocols
### Fuzzy Logic
#### 3.1.1 Fch  
The Cluster-head Election using Fuzzy Logic (FCH) is presented in [31]. It integrates a FL approach for the election of the CH based on the energy, node concentration, and centrality during computation. CH is elected by the BS in each round by defining the chance each node has to become the CH, helped by the fuzzy descriptors (energy, concentration, and centrality). The model of FL consists in four steps: fuzzification for the input descriptors, rule evaluation, aggregation of rules, and the defuzzification. LCH is compared to the famous LEACH protocol, it ameliorates the FND better than in LEACH and then increases the network lifetime in relation to LEACH.
## 6 Discussion  
Table 1 highlights the crucial role of well-defined communication strategies between human workers and collaborative robots (cobots). Well-defined communication enhances efficiency and safety in collaborative environments. Clear communication protocols, such as defining names for places, objects, actions, and trajectories, are essential for effective bidirectional communication.  
A key finding is the importance of cobots conveying predetermined path trajectories to human workers. This ensures workers are aware of cobot movements, which is important for all cobot activities. Creating a comprehensive workstation map detailing locations for both humans and cobots is also vital for successful communication.  
Establishing a common language and naming objects and locations fosters a shared understanding of the work environment. This shared understanding optimizes cobot-assisted tasks and allows workers to anticipate cobot movements, enhancing collaborative efficiency.  
The strategies in Table 1 contribute to a structured communication framework, ensuring clarity and reducing misunderstandings. These strategies create a sense of order and predictability, further enhancing safety and productivity.  
Human workers, unlike cobots, rely on memory for retrieving names, places, objects, maps, and trajectories. Therefore, comprehensive training is imperative before they begin working with cobots. Training should cover technical aspects and the nuances of effective communication and collaboration.  
AR applications offer promising tools for communication and collaboration, providing an alternative to traditional workstation maps. AR can simulate collaborative scenarios, preparing workers for real-world interactions with cobots and offering immersive training experiences.  
Safety measures are paramount in human-cobot collaboration. Addressing issues such as collision avoidance, force sensing, and emergency stop mechanisms is crucial to prevent accidents and ensure a secure working environment. Implementing safety standards and regulations is essential for governing human-cobot interactions and promoting a safe and productive collaborative workspace.
## 2 Materials and Methods
### Data Recording and Analysis  
EMG activity was recorded from the six muscles on the upper limb (DA, DP, BB, TB, FCR, and ECR) using surface wireless bipolar electrodes (Delsys Trigno EMG system, Natick, MA, USA). EMGs were pre-amplified and filtered (bandwidth of 20-450 Hz). Angular displacements in the two upper limb joints (shoulder and elbow) were recorded using potentiometers attached laterally to each joint. The kinematic and EMG data were sampled at 1000 Hz.  
General parameters of rhythmic arm movements included the following: cycle duration, angular joint oscillations, and mean EMG activity. The kinematic data were low-pass-filtered with a zero-lag 4 th-order Butterworth filter (4 Hz cut-off), and cycle duration and amplitudes of angular motions in different joints were evaluated as the parameters averaged over 8-10 cycles. Raw EMG data were rectified, low-pass-filtered with a zero-lag 4 th-order Butterworth filter (10 Hz cut-off), and time-interpolated over a time base of 100 points for individual arm movement cycles (max shoulder angle as the cycle's onset), and the mean amplitude of EMG waveforms was computed for each cycle and averaged across cycles. To prevent potential flooding caused by muscle vibrostimulation, the signal was also filtered with a 6 th-order Butterworth bandpass filter at the power supply (50 Hz) and vibration (60 Hz) frequencies. MEP intervals were excluded from the calculation of mean EMG activity in TMS cycles.  
The MEP was measured by calculating the peak-to-peak amplitude of the EMG signal over the period from 10 to 40 ms following stimulation (Figure 1C left panel) and normalized to the maximum MEP (MEPmax) across all probes of non-voluntary and voluntary movement conditions. Only those MEPs that fulfilled the following criteria were considered: the average amplitude of the MEP was at least three times greater than the amplitude of the background activity of this muscle before the stimulus was applied. The background activity was calculated as the mean value of rectified EMG in the interval from 110 to 10 ms before the stimulus (Figure 1C). The MEP's latency was measured when the EMG response exceeded three times the background EMG activity. The total number of MEPs we analyzed in each subject was on average 115 \(\pm\) 17 (mean \(\pm\) SD) per muscle during voluntary movements and 126 \(\pm\) 20 during vibration-induced rhythmic arm movements. Since the MEP value is dependent on the phase of movement [16], we separated the arm movement cycle into eight equal intervals (Figure 1C right panel) and averaged the MEPs for each of the eight stimulation intervals.
# A Novel Approach for Acute Mental Stress Mitigation Through Adapted Binaural Beats: A Pilot Study
## 2 Materials and Methods
### Adaptive \(f_{BB}\) Stimulation  
Stress index values were at the basis of the adaptive stimulation approach. Indeed, they were used to estimate a stress predictor (\(S\)) described in the next lines. For every four values of the stress index while stimulating at a certain \(f_{BB}\), \(S\) was calculated considering the average of the last two index values (the first two are discarded as their effect could be affected by a transient) and \(f_{BB}\) was modified to minimize \(S\). Initially, three \(f_{BB}\) were investigated: \(f_{BB_{0}}\) and \(f_{BB_{0}}\pm\Delta f_{BB}\), with \(\Delta f_{BB}\) = 1 Hz. Thereafter, the \(f_{BB}\) value corresponding to the minimum \(S\) was identified. Then, \(\Delta f_{BB}\) was halved and the frequencies \(f_{BB}\pm\Delta f_{BB}\) were explored, choosing the stimulation frequency for which \(S\) was smaller. The previous steps were then iterated. The \(f_{BB}\) research started with a beat frequency of 6.0 Hz, and \(\Delta f_{BB}\) could not be lower than 0.125 Hz. The beat frequencies remained within the theta range (4.0-8.0 Hz), and if an extreme was reached, \(f_{BB}\) was varied by 0.25 Hz in the opposite direction to move away from the approached extreme. The number of stress index values used to adjust \(f_{BB}\) was determined experimentally, along with the number of respiratory cycles taken into account by the regressor. It is important to mention that the duration of the stimulation at a specific \(f_{BB}\) is not fixed and depends on the time required to estimate the four stress indexes for evaluating the predictor S (related to the breath rate). Once initialized, the \(f_{BB}\) adaptation continues for the whole duration of the cognitive task.