esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
First-year athletes are generally the youngest on the team and need time to mature physically. In Division I football players, it was noted that the largest gains in body mass and vertical jump and decrease in percent body fat occurred during the athlete's first-year [17]. One study examined differences between class years in women's Division I soccer and found no significant differences in height, mass, vertical jump, or distance in Yo-Yo Intermittent Recovery Test Levels 1 (Yo-Yo IR1) and 2 (Yo-Yo IR2) [18]. This study measured the athletes one time, during their off-season, and did not examine first-year athletes prior to the start of their competitive season. Awareness of how the athletes begin the season is critical. Annual testing can also inform coaches on how each cohort is progressing through years of training. A longitudinal study has been conducted tracking female Division I basketball, swimming, volleyball, soccer, and track athletes over the course of multiple seasons, but did not include field hockey athletes [19]. Female Division I basketball players increased bench press, squat, incline press and vertical jump from their first year to senior year [20]. No study has examined changes in anthropometric or fitness measures across a season for Division I field hockey athletes. Fitness levels of athletes can vary by class year and it may be useful for coaches to understand those differences [17; 18; 20; 21; 22]. Testing of athletes prior to the season could determine whether incoming first-year athletes are physically fit for playing at the collegiate level. Currently, no study has examined differences in anthropometric and fitness levels, specifically, body mass, percent body fat, vertical jump, and Yo-Yo IR1 distance between class years in collegiate field hockey athletes. Therefore, the purpose of this study was to investigate how body mass, percent body fat, vertical jump, and Yo-Yo IR1 distance, vary between class years, and across a competitive season in a women's Division I field hockey team. It is hypothesized that first-year athletes will have a lower body mass, higher percent body fat, lower vertical jump height, and shorter Yo-Yo IR1 distance when compared to older athletes. It is also hypothesized that the vertical jump height and Yo-Yo IR1 distance will increase for all class years across the season.
# Experimental Study of Accuracy of High-Rate GNSS in Context of Structural Health Monitoring
## 3 Results and Discussion
### Evaluation of Accuracy of the 100-Hz Displacement Time Series  
#### 3.2.1 Stationary Test  
We first compared the 3D displacement results during the stationary period to study the noise level of different displacement time series computed with the PPP, PPP-AR, and RTK techniques based on different GNSS observations. The standard deviations (STDs) are calculated for quantifying the noise level of displacement and labeled in Figure 3. As depicted in Figure 3, the RTK displacement time series show more stability, followed by the PPP-AR solutions. Apparent oscillations can be observed in the displacement time series derived by the PPP technique, especially for upward direction, with the STDs being at 6.98 cm, 5.32 cm, 4.03 cm, and 2.92 cm for G, C, GC, and GCER observations, respectively. For all three positioning techniques, the GPS-only solutions have the highest noise level with STD values of up to 6.98 cm, 5.38 cm, and 0.60 cm for the PPP, PPP-AR, and RTK techniques, respectively. The main reason is that the limited GPS satellites are tracked during the experiment (about 5 G, Figure A1). With more satellite observations involved, the other three solutions (C, GC, GCER) show an improvement of up to 75% compared to the GPS-only solutions. When using four GNSS system observations, the RTK solutions can achieve a millimeter-level accuracy of 3D displacement (STD = 0.10 cm, 0.09 cm, and 0.22 cm for E, N, and U, respectively), followed by the PPP-AR that allowed millimeter-level accuracy in the horizontal direction and centimeter-level in the vertical direction.  
We also calculated the amplitude spectra of the displacement for the stationary test calculated by the Fast Fourier Transform (FFT) to identify the noise spectral characteristics, shown in Figure 4. According to the FFT results of the background noise analysis, more significant attenuation of the noise level can be found in the RTK-derived displacement solutions, especially for using the GCER observations. In the low-frequency band, results computed with GCER show better performance for all three positioning techniques.  
Figure 3: 100-Hz 3D displacement time series from G, C, GC, and GCER observations, respectively, and PPP, PPP-AR, and RTK techniques. The upper, middle, and lower panels represent displacements in the East, North, and Up directions, respectively, while the left, middle, and right columns represent results from PPP, PPP-AR, and RTK, respectively. The standard deviation (in cm) of each time series is given in the corresponding color.
# Mechanical Design and Gait Optimization of Hydraulic Hexapod Robot Based on Energy Conservation
## 3 Robot Modeling
### Foot Trajectory Planning Analysis  
The straight motion with triangle gait is used to analyze the energy consumption of the HHR. The foot trajectory is designed with a supporting phase and a swing phase: (1) In the supporting phase, the foot contacts the ground and the robot moves at a constant speed; (2) In the swing phase, the foot gets off the ground and swings at a certain height. The swing phase of the foot trajectory is designed based on sextic spline interpolation, and the supporting phase is a straight line. The duty cycle is set to 0.5, and the time of both phases is _T/2_. When the two phases are switched, it is essential to ensure the continuity of position, velocity, and acceleration of the foot trajectory. The coefficient of the swing phase can be obtained according to the limiting conditions, as shown in Table 2. \(T\) is the time cycle of the foot trajectory, \(H_{0}\) is the vertical height of the root joint, \(s_{i}\) is the offset of the foot trajectory of leg \(i\), and \(w\) is lifting height of the foot.  
According to the above limits, the foot trajectory can be written in equation, and its curve is shown in Figure 7.  
\(\bullet\): swing phase (\(0<t<T/2\))  
\[\begin{array}{c}P_{x,i}(t)=\Big{(}a_{6}t^{6}+a_{5}t^{5}+a_{4}t^{4}+a_{3}t^{3 }+a_{2}t^{2}+a_{1}t+a_{0}\Big{)}S+s_{i}\\ P_{z,i}(t)=\Big{(}b_{6}t^{6}+b_{5}t^{5}+b_{4}t^{4}+b_{3}t^{3}+b_{2}t^{2}+b_{1}t+ b_{0}\Big{)}w-H_{0}\end{array} \tag{15}\]  
where  
\[\begin{array}{c}\left[\begin{array}{cccccc}a_{6}&a_{5}&a_{4}&a_{3}&a_{2}&a _{1}&a_{0}\\ b_{6}&b_{5}&b_{4}&b_{3}&b_{2}&b_{1}&b_{0}\end{array}\right]=\left[\begin{array} []{cccccc}0&\frac{384}{T^{5}}&-\frac{480}{T^{4}}&\frac{160}{T^{3}}&0&-\frac{2} {T}&-\frac{1}{2}\end{array}\right]\\ \left[\begin{array}{cccccc}\frac{8192}{T^{6}}&-\frac{9216}{T^{5}}&\frac{307 2}{T^{4}}&-\frac{256}{T^{3}}&0&0&0\end{array}\right]\end{array}\]  
\(\bullet\): supporting phase (\(T/2<t<T\))  
\[\begin{array}{c}P_{x,i}(t)=\Big{(}1-\frac{t}{T}\Big{)}S+s_{i}\\ P_{z,i}(t)=-H_{0}\end{array} \tag{16}\]  
\begin{table}
\begin{tabular}{c c c} \hline
**Items (Time)** & **x Direction** & **z Direction** \\ \hline position (0) & \(-S/2+s_{i}\) & \(-H_{0}\) \\ position (T/4) & \(s_{i}\) & \(-H_{0}+w\) \\ position (T/2) & \(S/2+s_{i}\) & \(-H_{0}\) \\ velocity (0) & \(-2S/T\) & 0 \\ velocity (T/2) & \(-2S/T\) & 0 \\ acceleration (0) & 0 & 0 \\ acceleration (T/2) & 0 & 0 \\ \hline \end{tabular}
\end{table}
Table 2: The limiting conditions of the swing phase (0–T/2).  
According to different arrangement of the legs, the coordinate of the foot can be expressed as  
\[\mathbf{P_{i}}=\begin{cases}\left[\begin{array}{ccc}P_{x,i}&0&P_{z,i}\\ \end{array}\right]^{T}&i=1,2\\ \left[\begin{array}{ccc}-&P_{x,i}&0&P_{z,i}\\ \end{array}\right]^{T}&i=3,4,5,6\end{cases} \tag{17}\]
# The Impact of Antenna Height on 3D Channel:
## 2 Methods and Numerical Results  
### Intelligent Ray Launching Algorithm (IRLA)  
Radio wave propagation prediction modeling is critical in wireless network planning and optimization [20]. Types of propagation models have been presented in decades which can be classified as empirical models and deterministic models [21; 22; 23]. On the one hand, the channel characteristics in empirical models are determined by empirical factors, such as the carrier frequency and the distance between the BS and the MS. These models are time-efficient but with lower accuracy. On the other hand, deterministic models mainly consider the environmental information, such as the position and the reflection factor of the buildings. Thus, they are time consuming, but with higher accuracy [24]. The IRLA in our research can be categorized as a deterministic approach. The IRLA is used to compute all the possible reflections and diffractions of the emitting rays from the BS. Once the rays undergo reflections or diffractions, the additional loss will be added to the corresponding signal strength [25].  
In [26], the authors originally presented the IRLA model, which relays on the cubic data obtained from discretization of the environment. The building walls, ground, and trees can be regarded as a cube element. Each cube is associated with a set of information. Then the authors proposed and implemented the parallel IRLA based on parallel object-oriented programming in C++ [27].  
The IRLA contains three main components: light-of-sight (LOS), vertical diffraction (VD) and horizontal diffraction and reflection (HDR). The LOS component is used to calculate the path which is visible to emitter. The VD component is responsible for finding the roof-top diffractions rays. The HDR component is the most complicated part which launches a complete set of combination of diffraction and reflection rays. In [28], the authors implemented the angular dispersion of ray launching to improve its accuracy. Moreover, the model was extended to indoor scenarios in [29] and the combination with other methods was given in [30; 31]. The correction of the IRLA model is justified in [29] and [32; 33; 34]. A measurement test in [29] showed the indoor IRLA model is promising with accurate results and time saving. Meanwhile, the accuracy of the model under an indoor-to-outdoor scenario and outdoor-to-indoor scenario were validated in measurements [32; 33]. Recently, analysis and comparison between the simulation results and measurements showed that the ray-launching methods are efficient and accurate for massive MIMO channel modelling [34].
## 3 Performance Evaluation: Settings
### Real Data  
As for the captured data, the same datasets used for tests presented in [17] were used, since they were made available by the authors. Data consist in the latencies measured when visiting Internet home pages of 760 universities, with 1361 measured latencies for each home page. Latencies are measured in milliseconds, and are provided in the form of a 1361 by 760 matrix, where each column represents the latency measured in collecting a data sample from one of the webpages. Table 1 presents an excerpt of the data in the form of a 5 by 5 submatrix; the full data set can be downloaded from [24].  
These data, even if captured through a single cabled network, can represent the performance that different wireless networks offer; they are therefore particularly interesting for the test of the muMAB model, whose aim is to better fit real world scenarios. Further details about the data and the capture process can be found in [17].  
Given the generic \(i\)-th latency sample for the \(j\)-th website \(l\left(i,j\right)\) two different functions were considered in order to obtain the corresponding non-negative reward \(r\left(i,j\right)\) required by the muMAB model:  
1. \(r\left(i,j\right)=l_{max}-l\left(i,j\right)\) (linear conversion);
2. \(r\left(i,j\right)=\frac{log\left(l_{max}\right)-log\left(l\left(i,j\right) \right)}{log\left(l_{max}\right)}\) (logarithmic conversion),  
where \(l_{max}\) is the maximum latency in the entire data set.  
In each run, five random arms among the 760 ones were picked up; then, for each selected arm in each run, the 1361 available values were randomly sorted, and then repeated in order to reach the required \(n_{TH}\) time steps.
## 3 Simulation Model and Simulation Methods
### Mesh  
Given the complex and irregular area of the internal channel of the brake disc, a tetrahedral mesh is used. Tetrahedral meshes are selected for their ability to handle the complex geometry of the brake disc, particularly the irregular internal channels. While hexahedral meshes generally offer better accuracy and convergence for computationally demanding scenarios, their generation for such complex geometries can be challenging. Therefore, this paper selects tetrahedral meshing to ensure compatibility with simulation tools and maintain a good balance between accuracy and computational cost. For the cylindrical fluid domain, the encrypted mesh is used to improve the mesh quality and calculation accuracy, as shown in Figure 3b. For the rectangular fluid domain, the mesh size is increased without affecting the mesh quality, the number of meshes is reduced, and calculation time is saved [15]. A growth ratio of 1.2 was applied in our calculations to ensure smooth transitions between mesh elements and accurate heat flow analysis. The number of meshes is 739,236, and the average quality of the mesh is 0.84. The average orthogonal quality of the mesh is 0.78 and the average ortho-skew quality of the mesh is 0.22. The mesh division of the entire calculation area is shown in Figure 3a.
# Enhancing Street-Crossing Safety for Visually Impaired Pedestrians with Haptic and Visual Feedback
## 2 Related Works
### Visual Feedback for Low-Vision and Blind Users
#### 2.3.2 Harnessing Light Perception for Visual Assistance  
Although many visual assistive technologies require users to have functional residual vision, which makes it difficult for legally blind individuals to use these devices, studies have shown that approximately 90% of legally blind individuals still retain light perception abilities [2; 3]. While such residual visual function is insufficient for performing fine visual tasks, it is sufficient for visually impaired individuals to perceive changes in light sources in their environment [28]. This basic light perception ability holds significant value in the daily activities of visually impaired individuals. For example, a study by [PERSON] et al. found that visually impaired individuals can use environmental light sources (such as light at the end of a corridor) to assist with orientation and pathfinding [79]. Based on this finding, [PERSON] et al. developed a wearable navigation device using LED light strips. Experimental results demonstrated that this system significantly improved navigation efficiency (with the average completion time reduced by 28.5%) and accuracy (with an average deviation of less than 4\({}^{\circ}\)) [27]. In real-world environment testing, the collision rate of users employing this visual feedback device (0.25 collisions per 100 m) was significantly lower than the rate observed when using tactile feedback systems (1.58 collisions per 100 m) [27]. These findings provide important evidence for exploring new pathways in information acquisition for visually impaired individuals, indicating that well-designed light signal feedback systems can effectively leverage their light perception capability toprovide environmental awareness support. Assistive technology applications for visually impaired individuals are summarized in Table 1.  
Building upon the aforementioned research, this study aims to explore the comparative effectiveness of both haptic and visual feedback in street-crossing scenarios for visually impaired individuals. Specifically, we propose to design a light-based visual cue system [27] and a vibration-based tactile feedback device [29; 67] to systematically evaluate the effectiveness of these two feedback modalities in conveying traffic signal states and surrounding vehicle activity information. By systematically comparing the distinctive characteristics of light and tactile perception in visually impaired individuals, we aim to determine which feedback method provides optimal support in complex traffic environments. The findings are expected to provide reliable theoretical foundations and practical guidance for developing safer and more intuitive assistive systems for visually impaired individuals.
## 4 Discussion
### Verification of Optimal Mix Proportions  
The optimal mix proportions were selected to fabricate EAC slab specimens, and their surface texture characteristics, SRNR performance, mechanical properties, and workability indicators were experimentally verified. Meanwhile, to compare the high SRNR performance of EAC with the optimal mix proportions, grooved cement concrete slab specimens with a texture width of 6 mm, depth of 3 mm, and texture spacing of 15 mm were fabricated as a control group. The corresponding test results are shown in Table 6. A spectral analysis comparison of the A-weighted tire/surface noise between EAC and grooved concrete was conducted, and the results are shown in Figure 11.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Test Indicators**} & \multicolumn{4}{c}{**Factors**} \\ \cline{2-5}  & **A** & **B** & **C** & **D** \\ \hline Pendulum value & 0.46 & 31 & 42 & 70 \\ Noise value & 0.43 & 28 & 42 & 50 \\
28-day compressive strength & 0.40 & 34 & 44 & 50 \\
28-day flexural strength & 0.40 & 31 & 40 & 70 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Optimized combination results.  
As can be seen from Table 6 and Figure 11, compared with grooved concrete, the pendulum value of EAC increased by 31.5%, and the noise value decreased by 6.48 dB. The frequency range of noise reduction was mainly concentrated between 1.5 and 4.0 kHz, which falls within the mid-to-high frequency range from 1.0 to 5.0 kHz that is more sensitive to the human ear [39]. This indicates that the optimized EAC, due to its rich surface texture, has superior SRNR performance compared to ordinary concrete, and can serve as a reference for engineering applications.
# VoI-Based Optimization of Structural Assessment for Spatially Degrading RC Structures
## 4 Case Study: A Simply Supported RC Girder Bridge
### Approximation of the Posterior Distribution by MAP Estimates  
As pointed out in Section 3.1, approximate methods can be used to estimate the posterior distributions of the model parameters instead of the computationally expensive MCMC sampling. Before using these approximations of the posterior distribution of the model parameters in the Vol calculations, it should be investigated whether the approximation is accurate enough. For the considered bridge, the MAP estimate of the posterior distribution of the diffusion coefficient and the corrosion rate is compared with the posterior distribution found when applying MCMC for the three monitoring strategies. For monitoring strategy 2 implemented at 35 years, the resulting posterior distributions are visualized in Figures 3 and 4. Here, it can be seen that the approximation is quite good when considering the mean and uncertainty of the distribution.  
Figure 3: Posterior distributions of the diffusion coefficient based on strain mode shapes at all 1 elements. The dashed areas correspond to the 68% confidence intervals.  
The influence of the use of approximate posterior distributions based on MAP estimates instead of MCMC sampling on the resulting VoI is also investigated. When the VoI is calculated accounting for monitoring strategy 2 implemented at \(t=35\) years, the difference in posterior costs found based on the MAP estimates and MCMC calculations is equal to \(\epsilon 844.88\) or \(0.4\%\) of the total bridge value. In addition, other results were compared, and it was found that, for this specific case, the difference in VoI by applying MAP estimates instead of full MCMC does not influence the decisions made based on the resulting VoI, and the MAP estimates can be used to speed up the calculations of the VoI. Whether an error on the VoI is acceptable depends on the order of magnitude of the error and the order of magnitude of differences between the VoI's of the different considered monitoring strategies. Hence, before using the approximate posterior distributions, a few checks should be made on the performance of the approximate method by evaluating the posterior distribution both with the approximate method and with MCMC simulations. In addition, the influence on the VoI should be checked at least once. This example shows how the computational demand can possibly be limited since the error on the VoI by using approximate distributions can be small. However, it is advised to at least check this once for individual cases.
## 3 Results and Discussion
### Cooling Load Reduction by the Air Insulation Wall  
Figure 17 shows a comparison of the cooling load of the building with and without an insulating wall during the year. It can be seen from the figure that the cooling load reduction of the building without the insulating wall for the entire year was 56551.03 kWh. When the air insulation wall was installed, the reduced cooling load was 55417.33 kWh. The difference between them was the cooling load reduction of the air insulation wall, i.e., 1133.7 kWh.  
Figure 16: Comparison of the heat transfer with and without the glass roof patio in winter.