esa-sceva/satcom-chunk-collection huggingface dataset:
# A Hybrid Mapping Method with Position and Stiffness for Manipulator Teleoperation
## 4 Stiffness Mapping Method for Compliant Teleoperation
### Stiffness Model at the Human Arm Endpoint  
In order to predict the stiffness at the human arm endpoint, starting with the muscle control of a single human arm joint, the motion control biomechanical model of a single joint is established. Then, the model of force and stiffness in Cartesian space is calculated. Generally, the motion of human joint is controlled by at least two muscles, which are called the extensor and the flexor. The extensor is activated to stretch the joint while the flexor is activated for flexion, as shown in Figure 5.  
Once the joint muscles receive motion commands from CNS through neurons, they are activated to generate the muscle force, which results in the moment \(T_{j}\) and stiffness \(K_{j}\) at the human arm joint. They can be obtained in Equations (10) and (11):  
\[T_{j}=F_{f}h_{f}-F_{e}h_{e} \tag{10}\]  
\[K_{j}=K_{f}+K_{e}=\frac{dF_{f}}{dl_{f}}+\frac{dF_{e}}{dl_{e}} \tag{11}\]  
where \(F_{f}\) and \(F_{e}\) represent the muscle forces of the flexor and extensor, \(h_{f}\) and \(h_{e}\) represent the moment arm of the flexor and extensor, \(K_{f}\) and \(K_{e}\) represent the stiffness of the flexor and extensor.  
The motion at the human arm endpoint is achieved by the multi-joints motion. Therefore, the force vector \(\mathbf{F}_{d}\) and stiffness matrix \(\mathbf{K}_{d}\) at the human arm endpoint can be calculated in Equations (12) and (13):  
\[\mathbf{F_{d}}=\boldsymbol{J}^{-T}(q)\boldsymbol{\tau_{q}} \tag{12}\]  
\[\mathbf{K}_{d}=\frac{d\mathbf{F}_{d}}{d\mathbf{x}}=\frac{d\left(\boldsymbol{ J}^{-T}(q)\boldsymbol{\tau_{q}}\right)}{d\mathbf{x}}=\frac{d\left( \boldsymbol{J}^{-T}(q)\right)}{d\mathbf{x}}\boldsymbol{\tau_{q}}+\boldsymbol {J}^{-T}(q)\frac{d\left(\boldsymbol{\tau_{q}}\right)}{d\mathbf{x}} \tag{13}\]  
where \(\boldsymbol{\tau_{q}}\) is the moment vector of human arm joints, and \(\boldsymbol{J}(q)\) is the Jacobian matrix of human arm.  
Especially, \(\boldsymbol{J}(q)\) changes very small if the joint displacement \(q\) changes little, then \(d\left(\boldsymbol{J}^{-T}(q)\right)\) can be neglected. Therefore, the stiffness matrix \(\mathbf{K}_{d}\) at the human arm endpoint in Equation (13) can be simplified as:  
\[\mathbf{K}_{d}=\boldsymbol{J}^{-T}(q)\frac{d\left(\boldsymbol{\tau_{q}} \right)}{d\mathbf{x}}=\boldsymbol{J}^{-T}(q)\frac{d\left(\boldsymbol{\tau_{q }}\right)}{d\mathbf{q}}\frac{dq}{d\mathbf{x}}=\boldsymbol{J}^{-T}(q)\mathbf{ K}_{d}\boldsymbol{J}^{-1}(q) \tag{14}\]  
where \(\mathbf{K}_{q}\) is the diagonal stiffness matrix of human arm joints.  
Figure 5: Muscle control model of a single joint.
# Localization of GNSS Spoofing Interference Source Based on a Moving Array Antenna
## 4 Simulations and Results
### Experiment 5: Spoofing Localization Effects Considering Compatibility at Different Frequency Points  
The GNSS navigation band is not a continuous broadband but consists of multiple narrow bands with fixed carrier frequencies, so we typically use a 6 + 1 model to cover a narrow GNSS band and a dual 7-element design to cover a dual GNSS band in practice. For example, an antenna array commonly used in our practical applications that covers the navigation frequency bands of GPS L1 (1575.42 MHz), BDS B1 (1561.098 MHz), and BDS B3 (1268.52) generally consists of two circles of array elements. The antenna elements in the outer circle are arranged with the 1/2 wavelength of the BDS B3 frequency point as the baseline, while the antenna elements in the inner circle need to consider both GPS L1 and BDS B1. To avoid the occurrence of integer ambiguity, we choose half of the shorter wavelength GPS L1 wavelength as the baseline for layout, so we should consider the compatibility of the above layout method at the BDS B1 frequency point.  
Experiment 5 tests the effectiveness of the proposed method for the localization effect of deception interference sources at GPS L1 frequency point and BDS B1 frequency point under the above antenna setting. The spoofing interference source settings, carrier phase measurement error settings, antenna positioning error settings, and the positioning accuracy statistical methods for deception interference sources in the experiment are consistent with Section 4.5. The numbers of observation points are set to 21 and 41, respectively. The experimental results are shown in Figure 14, where the horizontal and vertical coordinate settings are consistent with those of Figure 10.  
Simulation results show that under the proposed GPS L1 antenna design, the localization effect of the proposed method is essentially the same for both BDS B1 and GPS L1 frequency point spoofing interference sources, while the BDS B1 effect is slightly reduced. The proposed method is compatible with GPS L1 and BDS B1 frequency points, which enables efficient localization of spoofing interference at the aforementioned frequency points.  
Figure 13: Comparison of localization effects at varying numbers of observation points.
# Joint Power Charging and Routing in Wireless
## 3 Optimization Algorithms  
In this section, we propose two optimization algorithms to solve the joint charging and routing optimization problem. In both algorithms, we divide the optimization into two problems: the routing and the optimal charging. First, we propose the heuristic algorithm for optimal charging with a predefined routing tree, and then, the routing and charging are optimized in a combined manner. In this algorithm, the optimal tree is obtained by a GA, and the heuristic algorithm is used to evaluate the fitness of each individual in the GA and guide the evolution process.
# Influence of Thoracolumbar Mobility on Running Performance: A Comparative Study
## 4 Discussion
### Relationship Between Thoracolumbar Mobility and ROM: LD Mechanical Properties  
Larger-scale studies are required to gain a more comprehensive understanding of thoracic and trunk mobility. Few studies have investigated the coordination between the pelvis and thorax during running, and none have included the lumbar segment [36]. Coordination between the pelvis and spine facilitates arm movements, which are essential for balance and angular momentum [37]. Electromyography studies suggest that lumbar extensor muscles are active during foot contact and early stance phases [38]. In our study, lumbar extensor tightness correlated with trunk extensor ROM, with the highest values observed in the two-year running group and the lowest values in sedentary participants. Flexibility is a recognized determinant of ROM [33; 34]. However, foot strike patterns were not considered in this study. Previous research suggests that changes in lumbar spine ROM, particularly lumbar lordosis, are associated with increased low back pain. However, the impact of transitioning from rearfoot strike (RFS) to forefoot strike (FFS) on lumbar lordosis or low back pain remains unexplored. A study examining this transition found no statistically significant differences in lumbar extension or flexion between FFS and RFS. There was a significant difference between the two patterns in terms of perceived comfort, with RFS perceived as more comfortable and better at attenuating shock. There is a need for further research into foot strike patterns and their effects on thoracolumbar mobility and lumbar flexibility [39].  
Regarding thoracic mobility, lateral trunk flexion flexibility on both dominant and nondominant sides was greater in the running for the last two years group compared to both sedentary and running for the last six months groups. Dominant-side lateral trunk flexion ROM was highest in the two-year running group and lowest in sedentary individuals. This suggests that the regular performance of thoracic rotations and lateral flexions in sports may enhance muscle extensibility [40]. However, no significant differences were observed in trunk extensor flexibility across groups.
## 2 Materials and Methods
### Instrument Setup and Data Collection
#### 2.2.2 Wind Data  
Measurements of wind direction were carried out using ultrasonic anemometers (Windmaster Pro ultrasonic anemometer, Gill Instruments Limited, Lymington, Hampshire, UK) with a 1 Hz resolution. A total of nine ultrasonic anemometers were installed on the southern opening side of the barn (inlet for southern wind directions). The southern opening was divided into 9 partial areas, and each partial area was equipped with an ultrasonic anemometer, that was positioned as close as possible towards the center of the area, based on the findings of [PERSON] et al. [25]. On the western side above the roof of the barn (Figure 2), called the \"K point\", at an approximate height of 12 m, another ultrasonic anemometer was installed to measure wind speed and wind direction.  
We assumed, based on previous calculations [14; 24; 28] that there is a prevailing wind flow in the experimental barn that transports the exterior and interior concentrations throughout the barn and out of the outlet. Based on this assumption, we identified the prevailing volume flow (90\({}^{\circ}\) south) and used only northern and/or southern sampling lines (depends on the strategy) to measure the inflow and outflow concentrations. For those cases, we also considered the wind speed values.  
Figure 1: View on the southern (inlet) side of the experimental barn. Marked with red lines are partial areas on the opening area. The numbers in the white circles (1–9) denote the individual partial areas and the position of the respective Windmaster Pro ultrasonic anemometers. The red point above the roof of the barns marks the position of the anemometer used for the measurements of wind speed and wind direction (“K point”).
# Free Space Optics Communication for Ultra-High-Speed Train Running in Evacuated Tube
## 1 Introduction
Hence, a 100 km distance requires 500 BSs to cover it, and the data rate is adversely affected due to the wide divergence angle used. From the above discussion, it can be noted that the increasing expansion and sophisticated growth of high train systems necessitate the provision of high-speed Internet services, which the existing radio frequency technology cannot readily offer, due to the time of handover mechanism and shortest time connectivity, especially with the ultra-high-speed future trains that would be run in an evacuated tube. Moreover, the existing techniques that use free-space optics suffer from the high number of base stations that need to be used (high expenditures). Therefore, this work proposes a unique strategy for ultra-high-speed trains in evacuated tubes based on FSO that achieves high speed, high reliability, low latency, high data rate, and fewer base stations required. Hence, in this paper, the number of base stations is significantly reduced, and the divergence angle could be adaptive depending on the distance between the train's transceiver and the stationary BS transceiver. In addition, the power consumption will be less than the other techniques depending on the distance and the divergence angle, and thus might indeed be adaptive as well. Moreover, the data rate can be fixed at 1.25 Gbps (with reduced power consumption, and changeable divergence angle) or possibly a dynamic data rate increases more than 1.25 up to 4 Tbps or more depending on the situation. Furthermore, a new handover method is proposed and addressed in this work. The remainder of this article is structured as follows. First, the system model is presented in Section 2, where the FSO technique is proposed for ultra-high-speed trains in evacuated tubes to provide excellent dependability and low latency. The handover mechanism is discussed in Section 3. Then, simulation results are presented and analyzed in Section 4. Finally, conclusions are presented in Section 5.
## 1 Introduction
The arrangement of this article is as follows: Section 2 briefly reviews the quasi-static field model of a galvanic-type IBC in order to obtain the channel system transfer function up to 1 MHz and also improve the transfer function model semi-empirically by the MMSE technique; Section 3 investigates the channel characteristics in the time domain via pulse response in both baseband and passband transmissions; Section 4 evaluates different possible modulation schemes over IBC. Finally, the conclusions of this paper can be found in Section 5.
## 2 Materials and Methods
### Continuous Wavelet Transform Based on Acoustic Emission Signals  
The continuous wavelet transform (CWT) is a signal processing technique which extracts the time domain content of acoustic emission signals [45]. The CWT has been successfully used to characterize damage modes in materials and structures. [PERSON] et al. performed structural health monitoring (SHM) using AE and wavelet analysis [46]. [PERSON] et al. monitored damage in metal panel damage using AE and the adaptive enhancement variational mode decomposition wavelet packet transform [47]. [PERSON] et al. detected damage in concrete subjected to bending using AE and wavelet entropy [48]. [PERSON] et al. detected wear using the acoustic emission wavelet analysis of AE [49].  
The CWT is described by Equation (1).  
\[CWT_{f}(a,b)=\int_{-\infty}^{\infty}f(x)\overline{\xi_{a,b}(x)}dx,a\succ 0, \tag{1}\]  
\[\zeta_{a,b}(x)=\frac{1}{\sqrt{a}}\zeta\bigg{(}\frac{t-b}{a}\bigg{)},\ \ \ a,b\in R,\ a\
eq 0 \tag{2}\]  
where the mother wavelet described by Equation (2) is a parameter that determines the size of the wavelet. b sets the shift along the time axis and is decomposed into wavelet coefficients. [PERSON] et al. provided more details of CWT [50]. In this paper, the Implementation CWT algorithm is performed on the MATLAB(r) (2022a) environment [45].  
The ultimate principle is to apply the continuous wavelet transform (CWT), extract the time-frequency spectrograms of the acoustic emission signals, and then use these spectrograms as an image to train a convolutional neural network (CNN) for classification [43; 44]. In this work, the characteristics of the acoustic emission signals collected during the tensile testing of the AlSi10 Mg samples are also analyzed in time-frequency using CWT. A comparison of CNN architectures is performed. A new, simplified architecture is built in order to achieve similar performances in a shorter time.
## 4 Simulation Results
### Rotor-Position Simulation Results  
The performance of the proposed observer for determining the rotor position can be observed in Figure 12b-e. The position responses are presented at the four speeds of 0.18, 0.32, 0.48, and 0.65 p.u-speed, respectively. Overall, the positions of the observer and encoder were almost identical; the average position error was close to zero (0.0127 p.u-position).  
Figure 12b, for example, presents the results for 0.18 p.u-speed (738 rpm). The simulation result reveals that the time required for one electrical position cycle was 0.0203 s, and the time for one mechanical cycle was 0.0812 s. The rotation frequency was thus 12.4376 Hz, or 738.92 rpm. The other three speed calculation results are presented in Table 2.
## 2 Materials and Methods
### HPO-KNN-SRU Construction of the Predictive Model  
According to the above description of the HPO-KNN and HPO-SRU, the implementation process for the proposed HPO-KNN-SRU model is as follows. The dynamic optimization process of the hunter/prey position in the HPO algorithm is used to achieve efficient outlier processing by optimizing the KNN parameters and then optimizing the SRU model parameters to improve prediction accuracy.  
The structural framework of the HPO-KNN-SRU prediction model is shown in Figure 8.  
The HPO-KNN-SRU algorithm mainly comprises four modules: the HPO module, SRU module, KNN module, and Data module. The HPO module describes the detailed process of the hunter/prey optimization algorithm. The KNN module describes the detailed algorithm for K-Nearest Neighbors outlier handling. The SRU module describes the detailed algorithm for the SRU network. The Data module serves to supply the raw data.  
The main steps of the HPO-KNN-SRU model for ultra-short-term photovoltaic power prediction are as follows:  
1. Initialize the HPO algorithm population.  
Figure 8: Structural framework of HPO-KNN-SRU prediction model.  
Determine the n_neighbors and contamination of the KNN and the hidden size, learning rate, network layers, and batch size of the SRU, which need to be solved by the HPO algorithm.
3. Train and test the HPO-KNN, use different parameters to process the data as outliers. The SRU model is used as the control experiment, and the Mean Absolute Error (MAE) is returned to the HPO to update the optimal solution of the population. Finally, obtain the minimum fitness value achieved by the HPO-optimized model and process the data as outliers.
4. Train and test the HPO-SRU model, and use different parameters to train and validate the exception processed data. MAE is returned to the HPO as the fitness value to update the best solution to the population. Finally, under the HPO, the optimization model is obtained and the optimal parameter combination model is obtained.
5. Construct the HPO-KNN-SRU model for final prediction.
## 4 Results and Discussion
Clear eye diagrams can be observed at data rates up to 180 Mbit/s. Figure 6: Photographs of the LDOF (**a**) without, (**b**) with the red light, and (**c**) with the green light launchings. Figure 7: Experimental photographs of the CO. Red arrow—red light; Green arrow-green light; APD—avalanche photodetector; DSO—digital sampling oscilloscope; DM—dichroic mirror. Figure 8: Experimental photographs of the client side at different viewing angles. Red arrow—red light; APD—avalanche photodetector; Col.—collimator; LDOF—light-diffusing optical fiber. Figure 10: Received DL OOK eye diagrams. Figure 9: DL BER measurements via the LDOF at the client side. Figure 11 shows the UL BER measurement from data rates of 100 Mbit/s to 1000 Mbit/s measured at the CO. From a data rate of 100 Mbit/s to 600 Mbit/s, it is error-free. The BER starts to increase at a data rate of 700 Mbit/s, and the BER is 1.14 \(\times\) 10\({}^{-6}\). The BER of 2.15 \(\times\) 10\({}^{-3}\) is measured when the UL data rate is 850 Mbit/s, satisfying the 7% pre-FEC threshold. Figure 12 shows the corresponding received UL OOK eye-diagrams at different data rates. Clear eye diagrams can be observed at data rates up to 800 Mbit/s. Figure 12 shows the DL BER measurement curves at different data rates and at different positions of the 100-cm LDOF optical antenna. We can observe that at data rates of 100, 150, and 190 Mbit/s, error-free detection can be achieved at positions from 10 to 90 cm even when the light is not quite uniform along the LDOF, as shown in Figure 4. At data rates of 200 and 210 Mbit/s, we can observe that pre-FEC BER detection (BER = 3.8 \(\times\) 10\({}^{-3}\)) can be achieved for the whole range of LDOF, with the highest BERs measured at 90 cm locations with BER = 7.60 \(\times\) 10\({}^{-5}\) and 1.30 \(\times\) 10\({}^{-3}\), respectively. In Figure 13, we can  
Figure 11: UL BER measurement at the CO.  
Figure 4:0 Received UL OOK eye diagrams. observe that BER = 1 \(\times\) 10\({}^{-9}\) can be measured by our BER tester (Anritsu(r), MP1800A) at data rates from 100 Mbit/s to 190 Mbit/s. BER = 1 \(\times\) 10\({}^{-9}\) can be considered error-free. Here, we would like to illustrate that if we use a low DL data rate of \(<\)190 Mbit/s, error-free detection can be achieved for the whole 1-m long LDOF. If we want to use higher data rates of 200 Mbit/s and 210 Mbit/s, FEC performance (BER = 3.8 \(\times\) 10\({}^{-3}\)) can be guaranteed. However, when the data rate is increased to 220 Mbit/s, only half length of the LDOF can satisfy the FEC transmission.
## 5 Simulation Results
Specifically, under the same condition of being disturbed by wind in the X direction, it can be found from Figure 7a that the trajectory tracking effect of the yellow curve is better than the blue curve, which shows that the proposed TD3-LADRC method is better than the traditional LADRC method, and this conclusion can also be further verified in Figure 9a. Observing the response of the proposed TD3-LADRC to different disturbances, it can be found that the thrust output and flap deflection will also fluctuate when they are perturbed, and they will eventually stabilize to a constant value, as illustrated in Figure 8. Furthermore, the proposed method can overcome the effects of disturbance and coupling to achieve trajectory tracking control. It can be seen from Figure 10 that the parafoil does not lose control of its attitude during flight and can fly smoothly. The selection of parameters in Figure 11 is determined based on the reward value in Figure 9b. As state changes result in fluctuations in reward values, the controller parameters experience more frequent adjustments in the presence of disturbances compared to disturbance-free scenarios. Furthermore, when subjected to the same disturbance, the trajectory tracking effect optimized by the TD3 algorithm proves superior, as evident from Figure 9a. By examining the change process of the steering gear, thrust output, and controller parameters, as illustrated in Figures 8 and 11, we observe that the system responds differently to various wind disturbances to achieve precise trajectory tracking. This observation further supports the trajectory tracking approach presented in this paper. Figure 11: Controller parameters by TD3. Figure 10: Euler angles and speed change process.
# WiFi FTM, UWB and Cellular-Based Radio Fusion for Indoor Positioning
## 1 Introduction
To validate the proposed method, we use a real location deployment with ranging information based on time measurement from UWB and WiFi and received signal strength from a LTE network as a back-up. To the best of our knowledge, no system unifies all these three technologies and brings them into a real scenario to show the performance of real-time localisation. We also test several search methods for the MLE, to compare the advantages in location precision and computational time of each one. The contribution of this paper is listed as follows:  
* Proposition of a fusion method in trilateration based on the work presented in [10], with a dynamic weighting with MLE that improves the robustness of location accuracy;
* Validation of the proposed method with a real-world setup with several different scenarios;
* Comparison between different MLE search methods for finding the best for resolving over-determined location problems. The rest of this paper is organised as follows: Section 2 provides an overview of the different location technologies explaining features of the technologies used in this work. Section 3 explains the proposed method and the algorithm of multi-technology fusion and the MLE as a weighting technique for outliers. Section 4 describes the experimental setup with two scenarios and Section 5 presents the results obtained from three different cases deployed in the two scenarios. Section 6 discusses the results presented in the previous section. Finally, Section 7 presents the conclusions of this work. The acronyms in this paper are listed in the Table 1 as follows: