esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Subjects and Data Acquisition  
Ten juvenile participants (1 male, 9 females, age: 11.6 \(\pm\) 3.1 years, mass: 45.2 \(\pm\) 17.9 kg, height: 1.44 \(\pm\) 0.15 m, BMI: 21.01 \(\pm\) 5.09 kg/m\({}^{2}\), Table S1) were selected among those recruited as part of a previous study [15; 16; 17]. The study was conducted following the Helsinki's Declaration on Human Rights and approved by an ethical committee. Written informed consent was obtained by patients' guardians and lower-limb gait analysis data and medical resonance imaging (MRI) were then collected. Stereophotogrammetric data were collected across two different laboratories using a 6-camera optoelectronic system (BTS, Smart DX, MA, USA 100 Hz) with two force plates (Kistler, Winterhur, Switzerland, 1 kHz), and an 8-camera system (Vicon, MX, Oxford, UK, 200 Hz) and two force plates (AMTI, OR6, Albion, Australia, 1 kHz), respectively. For each participant, three walking trials at self-selected speed were used for the analysis. The marker set was a combination of the markers from the Vicon Plug in gait protocol (Vicon, Oxford, UK) and the modified Oxford Foot Model (mOFM) protocol [18], for a total of thirty-eight markers. All gait markers were marked on the skin of the participants and replaced with MRI-visible markers retained during the subsequent imaging acquisition for data registration.  
Three MRI sequences were acquired: a lower-limb e-THIRIVE scan with 1 mm in-plane resolution and a 1 mm slice thickness; a regional foot and ankle multi-slice multi-echo 3D Gradient Echo (3D_mFFE_WATS) scan with water-only selection in the sagittal plane with 1 mm slice thickness, 20.5 mm interslice gap and 0.5 mm in-plane resolution; a regional foot and ankle 3D short T1 inversion time inversion recovery fast field echo (T1w_TSE) scan in the sagittal plane with 2 mm slice thickness, 21 mm interslice gap and 0.6 mm in-plane resolution.  
Experimental data were collected from both limbs, however in one case data from one limb were discarded due to issues with the gait analysis acquisition, whereas in three other cases data from one limb were discarded due to the quality of the foot imaging, resulting in bilateral information being only available for 6 participants, hence leading to a total of 16 datasets.  
The 3D representations of the volunteers\({}^{\prime}\) talus and tibia were obtained from manual segmentation of the T1w_TSE (through MITK freeware software). The bones and the cartilages of the tibio-talar articulation were reconstructed and the attachment regions of the tibio-calcaneal (TiCa) and fibulo-calcaneal (FiCa) ligaments were also identified.
# Engineering the Future: Evaluation of Virtual Reality Across Project Lifecycle Stages
## 2 Literature Review
### VR in Education, Training and Communication
[50] reviewed 20 studies on VR-based soft skill development. They found that virtual environments were effective in teaching social interaction, safety behaviors, and professional competencies--many of which transferred to real-world behaviors. Beyond training, VR also reshapes communication and engagement practices within complex systems. In the context of public engagement and communication, [PERSON] et al. [52] demonstrated that immersive VR journalism evokes stronger empathy and emotional responses than traditional 2D media. Their findings underscore VR's unique capacity to enhance human perspective-taking, suggesting broader implications for team collaboration, stakeholder alignment, and decision-making in complex systems engineering environments. VR enhances user empathy through immersive simulations, supports creative design thinking [53], and improves risk management and operational safety [54]. These studies illustrate the multidimensional value of VR. From design to risk management, and from soft-skills training to stakeholder engagement, VR supports creativity, efficiency, and collaboration. [PERSON] et al. [55] argue that VR not only improves decision-making but also offers novel perspectives for multidisciplinary teamwork. As implementation increases, it becomes essential to evaluate not just technical efficacy but also psychological, educational, and organizational impacts. The present study builds on these foundations by assessing how professionals perceive the practicality of VR across various stages of the systems engineering lifecycle, identifying key contextual and personal predictors of value.
## 3 Results and Discussion
### Comparison with Linear Regression Model and BP ANN Model  
Table 8 shows the MSED of three predictive models, which were the RBF ANN model with the optimum performance constructed in this study, the BP ANN model, and the linear regression model. For each anthropometric measurement, the MSED of RBF ANN model was less than that of the other two models, which meant that the estimating performance of the RBF ANN model was better than the others.
## 3 Applications  
There are two data sets that were used for the applications of the presented method. The first one is from a shallow crustal Earthquake that occurred on the 23 August 2014 Mw 6.2, Chile, and the second one is from a deep Earthquake that occurred on the 21 February 2017 Mw 6.5, Bolivia. The performance to redetermine the focal depth for these two data sets will illustrate that the method can redetermine the focal depth accurately. All data have the same sampling rate of 0.01 s. After data pre-processing for the raw data (mean and trend removing, filtering, picking etc.), the inner core phase PKIKP and pPKIKP were well identified for the performances of this method, and the redetermined focal depth will be compared to the results from four different agency, CEA, GFZ, ISC-EHB, and USGS.
## 4 Results and Discussion  
### \"Real Scenario\" vs. \"LiDAR Scenario\" Bathymetric Differences  
The use of the MAE index on a random sample of 3000 points (located in the river channel, independent of the samples obtained in the floodplain) made it possible to estimate the error in the bathymetry data between the \"real scenario\" and the \"LiDAR scenario\". The MAE index adopted a value of 1.57 m. This was not surprising if we consider the type of riverbed that the Douro River had in the study area under analysis. However, when transferring the MAE index value to the average width of the river's cross-section, we obtained a reduction in the cross-sectional area of 353.25 m\({}^{2}\). This reduction in the cross-section could cause a reduction in the flow capacity of the channel of 353.25-529.87 m\({}^{3}\) s\({}^{-1}\) at times when the flow was medium-low and its average speed could be considered to be between 1-1.5 m s\({}^{-1}\). At times of flooding, when the average speed of the water could rise to values of 3 m s\({}^{-1}\) or more, the reduction in the flow capacity in the channel could rise to values of 1059 m\({}^{3}\) s\({}^{-1}\) or even more.  
The absolute error in bathymetric data between the \"real scenario\" and the \"LiDAR scenario\" ranged from 0-4.115 m at the Douro River reach, and it showed a clear trend from the river banks to its centerline (Figure 4A). At the same time, a more irregular pattern was observed in the downstream direction, where the application of a grouping analysis over the spatial distribution of errors in bathymetry did not show clear trends when no spatial constraints were used (compare with spatial constraints, such as K-nearest neighbors or Delaunay triangulation techniques, which show artificial grouping due to the requirements of spatial constraints between points in the same group). When no spatial constraints were used, the grouping of the random sample points could be related to the \"riffle and pools\" longitudinal profile shape (Figure 4B) of natural rivers.  
All previous data pointed out the importance of bathymetric data for the correct hydrodynamic modelling of the flow event flooding area, as well as the uncertainty of flood parameter results when using LiDAR data without a bathymetric data improvement. In this sense, [PERSON] and [PERSON] [29] previously pointed out this problem when they showed a reduction of between 5 and 20% in flooded areas when bathymetric data were combined with LiDAR data, with this reduction range being based on the channel type and hydrodynamic model (1D or 2D). In the same way, [PERSON] et al. [53] highlight that, in the context of determining water levels at a specific location, the difference in bathymetry could be very important. Furthermore, they also noted how this difference may be more evident when we use DEMs or remote sensing techniques to identify river geometry at the regional scale since it is difficult to obtain an accurate bathymetric river representation. Other assessments [23] pointed towards the same conclusion, but there is no quantification of the dependence between bathymetric errors and flooded area differences.
It parameterized two conditions that compared a healthy control hip (an alpha angle of 40\({}^{\circ}\)) and a hip with Cam FAI (an alpha angle of 74\({}^{\circ}\)). The proposed FE model's cartilage component was created with a poroelastic, orthotropic material in order to simulate biphasic properties. As in previous parametric studies [9; 10], stand-to-sit actions and walking were simulated. Thus, the peak contact pressures on the normal hip occurred in the superior cartilage while walking (2.87 MPa) and also in the posteromedial cartilage while engaging in stand-to-sit movements (3.58 MPa). The peak pore pressure in the control model (0.42 MPa in the posterior cartilage) and the FAI model (3.76 MPa in the anterosuperior cartilage) differed noticeably. A recent study of mechanical factors that enable Cam-type deformities to develop [14] was conducted. A proximal femur model was constructed from CT data that had been parameterized with three different growth plate shapes. It simulated four activities, which were internal rotation, external rotation, hip flexion, and normal walking. In vivo instrumented prostheses provided the loading data. The growth plate was modeled with a constant elastic modulus. Heterogeneous femur bone material properties were provided by CT data. This enabled the varying densities to better reflect reality. External rotation produced an osteogenic index of 0.7 MPa. This index was noticeably higher during external rotation and flexion. The objective of the current study was to analyze and compare the von Mises stress and displacements of the cartilage of the healthy and damaged (with CAM) hip joints of a patient while considering an alpha angle of 50\({}^{\circ}\) and the patient's body weight in both cases. The healthy hip joint was obtained when the patient (male) was 22 years old and 80 kg in weight, whereas the damaged hip joint was obtained five years later, when the cartilage was severely damaged. The paper is organized as follows. Section 2 introduces the terminology that is used throughout this paper and describes the different types of hip joint with femoroacetabular impingement. Section 3 describes the CT scan acquisition, the segmentation, the image reconstruction process and the proposed FE models. Section 4 presents an analysis of the detailed the results for the healthy and Cam-type femoroacetabular impingement hip joint FE models. Finally, Sections 4 and 5 provide the discussion and conclusions.
# An Improved Multi-Sensor Fusion Navigation Algorithm Based on the Factor Graph
## 3 System Model for the Navigation System
### Measurement Model of the System
#### 3.2.1 GPS Measurement Equation  
The GPS receiver measures the MUAV's velocity and position. Hence, the GPS measurement equation is described as Equation (3).  
\[\begin{array}{l}\mathbf{p}_{gps}=\mathbf{p}+\mathbf{\omega}_{p}^{gps}\\ \mathbf{v}_{gps}=\mathbf{v}+\mathbf{\omega}_{v}^{gps}\end{array} \tag{3}\]  
where \(\mathbf{p}_{gps},\mathbf{v}_{gps}\) are measurements of the GPS receiver; and \(\mathbf{\omega}_{p}^{gps}\), \(\mathbf{\omega}_{v}^{gps}\) are the gauss stochastic measurement noise terms.
## 3 Results and Discussion
### Parametric Effect on Heat Transfer
#### 3.2.3 Effect of Pressure  
Generally, for the single-phase fluids, there was no big difference in the heat transfer coefficient among the various cases under different pressures. However, for the supercritical fluid, the convective heat transfer may present some differences. Figure 10 shows the effect of pressure on the heat transfer coefficient of supercritical CO\({}_{2}\) under various heating conditions (\(Re_{\text{in}}=5100\)-\(5300\)). To exhibit the differences in the supercritical and subcritical pressure, heat transfer behavior at 7 MPa was introduced in Figure 10.  
Figure 10: Variations in inner-wall temperatures with pressure. (**a**) Heat flux (\(q\)) = 35.5 kW/m\({}^{2}\); (**b**) \(q=45.5\) kW/m\({}^{2}\).  
Figure 9: Variations in inner-wall temperatures and heat transfer coefficients for various bulk enthalpies at different mass fluxes and the pressure of 8.0 MPa. (**a**) Temperature; (**b**) Heat transfer coefficient.  
As seen in Figure 10, when the heat flux was 35.5 kW/m\({}^{2}\), the inner-wall temperature profiles presented incredibly similar variations under supercritical pressure. These profiles were also similar to those obtained under subcritical pressure in the \"vapor\" region (x/D > 100). However, when the heat flux increased to 45.5 kW/m\({}^{2}\), the inner-wall temperature discrepancy was affected by pressure. This is mainly due to the drop in heat capacity at high pressures, which also significantly decreased the transportation of added heat. Therefore, the inner-wall temperature was always higher at high pressures than at low pressures. Interestingly, at the pressure of 7 MPa, inner-temperature was kept constant at first, which is different from supercritical pressure. Since the latent heat disappears in supercritical pressure, the phase transition from \"liquid-like\" to \"vapor-like\" is instantly completed; thus, the temperature is always higher than that in the two-phase flow region at subcritical pressure. At a certain x/D, temperature suddenly increased to a higher temperature and became similar to the \"vapor\" temperature at the supercritical pressure in the downstream.
## 5 Conclusions  
This paper was devoted to analyzing the response characteristics of the CCS for oil cut measurement in the horizontal oil-water two-phase flow. By using the finite element method, the static response characteristics of the CCS, CYS, and COS were obtained. The simulation results show that the CCS was better than the other two sensors in measuring the water cut for different flow patterns. The temperature and pressure calibration experiments of the CCS were conducted. The calibration results show that the CCS has a good stability under different temperatures and pressures. The dynamic experiment was carried out on the horizontal oil-water two-phase flow experimental facility under different working conditions. The flow pattern distribution of the horizontal oil-water two-phase flow with different flow rates and water cuts was obtained by high-speed camera. The response values of the CCS were obtained under different flow pattern conditions, which show that the CCS has good response characteristics in the range of flow rate 3-60 m\({}^{3}\)/d. It can be deduced that the CCS can be effectively applied in the water cut measurement in the horizontal oil-water two-phase flow.  
Conceptualization, L.L. and B.X.; methodology, X.L. and L.K.; software, Y.W. and F.Z.; validation, X.F. and W.K.; formal analysis, B.X.; investigation, L.L., B.X. and X.F.; resources, X.L.; data curation, W.K.; writing--original draft preparation, B.X.; writing--review and editing, L.L. and B.X.; visualization, L.L.; supervision, L.K.; project administration, X.L.; funding acquisition, L.K.  
This research was funded by the National Science and Technology Major Project of the Ministry of Science and Technology of China, grant number 2017 ZX05019001-011, the Project funded by China Postdoctoral Science Foundation, grant number 2018M631763, the Yanshan University Doctoral Foundation, grant number BL18010, Research of Yanshan University for Youths, grant number 15 LGA009 and the Natural Science Foundation of China, grant number 51774092.  
This work was supported by Key Laboratory of Computer Virtual Technology and System Integration of Hebei Province, Qinhuangdao 066004, Hebei, China.  
The authors declare no conflict of interest.  
Figure 23: Standard deviation results of the CCS in the horizontal oil–water two-phase.
## 3 Results and Discussion
### Precision  
System precision studies were evaluated by five replicate measurements at three concentrations (intra-day) and on two different days over a week (inter-day). The % RSD was found to be less than 1% for both substances, which indicates high precision of method (Tables 4 and 5). In practical analysis, that means that average results are statistically equivalent [30]. RSD values were lower than results shown in two other mentioned methods [20; 21].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Level (\%)** & **Concentration of Standard Solution (mg/mL)** & **Obtained Concentration (mg/mL)** & **Recovery(\%)** \\ \hline
50 & 0.0550 & 0.0556 & 101.8 \\
100 & 0.1100 & 0.1089 & 99.0 \\
125 & 0.1375 & 0.1356 & 98.6 \\ Mean recovery (\%) & & 99.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Accuracy results for acetylsalicylic acid.  
Figure 4: Linearity plot for acetylsalicylic acid.  
Figure 5: Linearity plot for clopidogrel.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Level (\%)** & **Concentration of Standard Solution (mg/mL)** & **Obtained Concentration (mg/mL)** & **Recovery(\%)** \\ \hline
50 & 0.0400 & 0.0406 & 101.6 \\
100 & 0.0800 & 0.0802 & 100.3 \\
125 & 0.1000 & 0.1000 & 100.0 \\ Mean recovery (\%) & & & 100.6 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Accuracy results for clopidogrel.
## 5 Conclusions  
The unknown and changeable underwater environment inevitably makes accurate positioning hard for MEMS-grade INS carried in AUV. It is difficult for traditional algorithms to obtain state information. The performance of the proposed SRAKF is deduced theoretically and tested through lake trials. The experiment results demonstrate that the SRAKF can calculate the position of AUVs more accurately than the other two traditional algorithms, even if the noise measurements are enlarged and changeable. The position of the AUV can be obtained with higher accuracy using the proposed SRAKF algorithm. Moreover, not only are accuracy and efficiency achieved by the SRAKF, but the SRAKF has better robustness in terms of position estimation.  
Conceptualization and Supervision, H.H.; Data curation, J.T.; Resources, B.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Fundamental Research Funds for the Central Universities (B20020163, B210203010) and the Postgraduate Research and Practice Innovation Program of Jiangsu Province (KYCX21_0480), the National Natural Science Foundation of China (61703098), the Natural Science Foundation of Jiangsu Province (BK20160699), and the Fujian Provincial Key Laboratory of Coast and Island Management Technology Study (F)CIMITS2019-03).  
Not applicable.  
The authors declare no conflict of interest.
## 3 Results  
### Color Measurement (L, a, and b Values)  
#### 3.1.1 L Values  
The results of the statistical analysis of the lightness, darkness, and luminosity values (L) are shown in Figure 4. The three crown materials, E-max, Lisi, and Celtra, presented different degrees of luminosity. There was a significant difference in the luminosity of Celtra compared to E-max and Lisi (\(p<0.05\)). Regarding the color measurements at baseline, it was observed that the L values of all materials decreased after immersion in the saliva, resulting in a darker color (\(p>0.05\)). After brushing, the L values of all crown materials did not change significantly (Figure 4A).  
Figure 4: Mean \(\pm\) SD of L values (**A**) at the starting point, following immersion in artificial saliva and following brushing; (**B**) at the starting point, following immersion in coffee and following brushing. Different letters denote statistically significant differences. The comparison of different crown materials at the same measurement time point is indicated by upper-case letters. The comparison of the same crown material at different measurement time points is indicated by lower-case letters.  
Figure 3: Using the compare tool to automatically calculate the color coordinates and color change before and after immersion.  
Regarding the color measurements at baseline, it was observed that the L values of all materials decreased after immersion in coffee, resulting in a darker color (\(p<0.05\)). There was a significant difference in the luminosity of Celtra compared to E-max and Lisi (\(p<0.05\)). Nevertheless, after brushing, the L values of all crown materials were analogous to the baseline values (Figure 4B).
## 3 Results
### Chloride Ion Diffusion  
The results of the chloride penetration depth test of pavement concrete using the ACS aggregate are shown in Figure 4. The chlorine ion penetration depths were 12.82, 19.37, and 15.15 mm for plain, GG 100%, and GS 50%/GG 100% mixes, respectively. The chloride ion penetration depth increased with the ACS aggregate's substitution ratio, being 1.00, 1.51, and 1.18 for plain, GG 100%, and GS 50%/GG 100% mixes, respectively. The chloride ion diffusion coefficient test results are shown in Figure 5; the diffusion coefficient values depended on the substitution ratio of the ACS aggregate, being 5.13 \(\times\) 10\({}^{-12}\), 5.45 \(\times\) 10\({}^{-12}\), and 5.27 \(\times\) 10\({}^{-12}\) m\({}^{2}\)/s for plain, GG 100%, and GS 50%/GG 100% mixes, respectively. The test result showed that the diffusion coefficient of the mixture to which the ACS aggregate was applied was slightly larger. In addition, the diffusion coefficient was slightly larger in the case of applying GG 100% than in the case of applying the GS. Therefore, the diffusion coefficient and penetration depth of the GS 50%/GG 100% mix with the GS and GG applied  
Figure 3: Test results of flexural strength.  
decreased compared to the GG 100% mix with only the GG applied. According to previous studies, coarse slag aggregates have relatively more voids compared to natural coarse aggregates [11; 12; 13]. Therefore, when the GG of rural-road pavement concrete is applied, the penetration depth of the concrete increases due to an increase in the volume of voids. In addition, the ACS aggregate is composed of components similar to the fine powder of blast furnace slag, so it expresses a similar pozzolanic reaction and makes the structure of the concrete dense in the long- term. In particular, it can be seen that this effect is more effective than the GG because of the smaller particle size of the GS.  
Figure 4: Test results of chloride ion penetration depth.
## 3 Review of Existing Channel Models for Underground Mines
### Full-Wave Model  
The full-wave model is an effective scheme to estimate channel characteristics for underground mines. This approach is considered valid for analyzing the degrading effects of reflection, diffraction, and refraction in tunnels with a complete solution for signal propagation in complex areas [28]. It is considered an alternative approach to compute Maxwell equations with the aid of a numerical technique that comprises the Finite-Difference Time Domain (FDTD). The weakness of this model is the computational complexity of the FDTD beyond the normal capacity for large-size tunnels and higher UHF-band frequencies [7].