esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Simulation and Analysis of Heart-Aorta Fluid-Structure Interaction Based on S-ALE Method
## 2 Materials and Methods  
### S-ALE Method Fundamentals and Coupling Conditions  
The S-ALE method is an advanced FSI simulation that combines the structured fluid domain with the ALE algorithm, which enables the FSI analysis of structures in large deformations. \"Structured\" refers to an orthogonal fluid domain mesh created using an adaptive method. The ALE algorithm has the advantages of the Lagrangian and Eulerian algorithms. Figure 1 is a schematic diagram of the principle of FSI. The Navier-Stokes equations are used in the fluid domain, which are expressed as follows.  
Conservation of mass equation:  
\[\frac{\partial\rho_{f}}{\partial t}+\
abla\cdot(\rho_{f}\mathbf{u}_{f})=0 \tag{1}\]  
where \(\rho_{f}\) is the fluid density and \(\mathbf{u}_{f}\) is the fluid velocity.  
Conservation of momentum equation:  
\[\frac{\partial(\rho_{f}\mathbf{u}_{f})}{\partial t}+\
abla\cdot(\rho_{f} \mathbf{u}_{f}\otimes\mathbf{u}_{f})=-\
abla p+\sigma_{f}+\mathbf{f}_{f} \tag{2}\]  
where \(p\) is the fluid pressure, \(\sigma_{f}\) is the fluid viscous stress tensor, and \(\mathbf{f}_{f}\) is the fluid body force.  
Conservation of energy equation:  
\[\frac{\partial(\rho_{f}E)}{\partial t}+\
abla\cdot(\rho_{f}E\mathbf{u}_{f})=- \
abla\cdot(p\mathbf{u}_{f})+\
abla\cdot(\sigma_{f}\cdot\mathbf{u}_{f})+ \mathbf{f}_{f}\cdot\mathbf{u}_{f} \tag{3}\]  
here, \(E\) is the total energy per unit mass.  
For the case where the fluid density \(\rho_{f}\) maintains a constant value throughout the formulation (i.e., \(\partial\rho_{f}/\partial t=0\) and \(\
abla\rho_{f}=0\)), the governing equations characterize incompressible flow conditions.  
The governing equation of the structural domain is based on the momentum conservation equation of solid mechanics, as shown in formula 4:  
\[\rho_{s}\frac{\partial^{2}\mathbf{d}_{s}}{\partial t^{2}}=\
abla\cdot\sigma_{ s}+\mathbf{f}_{s} \tag{4}\]  
where \(\rho_{s}\) is the structural density, \(\mathbf{d}_{s}\) is the structural displacement, \(\sigma_{s}\) is the structural stress tensor, and \(\mathbf{f}_{s}\) is the structural body force.  
In FSI systems, fluid forces act on the interface of the structure and affect its motion, while the motion of the structure causes changes in the fluid computational domain and boundary conditions, thereby affecting the flow field. The coupling interface between the fluid domain and the structural domain needs to meet the following conditions.  
Figure 1: Schematic diagram of fluid–structure interaction principle.  
Velocity continuity:  
\[\mathbf{u}_{f}=\frac{\partial\mathbf{d}_{s}}{\partial t} \tag{5}\]  
Stress equilibrium:  
\[\sigma_{f}\cdot\mathbf{n}=\sigma_{s}\cdot\mathbf{n} \tag{6}\]  
where \(\mathbf{n}\) is the interface normal vector.
## 2 System and Channel Models
### One-Hop RF SublinkSubsystem under Various Weather Conditions  
In the one-hop RF sublink under the Nakagami-m fading channel, based on [33], the PDF of the SNR \(\gamma_{x,y}^{RF}\) is:  
\[\begin{split} f_{\gamma_{x,y}^{RF}}(\gamma_{x,y}^{RF})& =\Bigg{(}\frac{m_{x,y}}{\gamma_{x,y}^{RF}}\Bigg{)}^{m_{x,y}}\frac{ \gamma^{m_{x,y}-1}}{\Gamma(m_{x,y})}\exp\left(-\frac{m_{x,y}\gamma_{x,y}^{RF}} {\gamma_{x,y}^{RF}}\right)\\ &=\Bigg{(}\frac{m_{x,y}}{\gamma_{x,y}^{RF}}\Bigg{)}^{m_{x,y}} \frac{\gamma^{m_{x,y}-1}}{\Gamma(m_{x,y})}G_{01}^{1,0}\Bigg{[}\frac{m_{x,y} \gamma_{x,y}^{RF}}{\gamma_{x,y}^{RF}}\bigg{|}\begin{array}{c}-\\ 0\end{array}\bigg{]},\end{split} \tag{6}\]  
where \(\Gamma(\cdot)\) is a Gamma function, \(G_{\cdot,\cdot}^{\cdot,\cdot}(\cdot)\) is a Meijer-G function, \(m_{x,y}\) is the fading parameter (\(m_{x,y}\geq 0.5\)) of the one-hop RF link, and \(\overline{\gamma}_{x,y}^{RF}=\frac{p_{x,y}^{RF}g_{x,y}^{RF}}{\left(g_{x}^{RF} \right)^{2}}\) is the average SNR [36]. Herein, \(P_{x,y}^{RF}\) is the transmittedtransmission power, and \(\left(\sigma_{x,y}^{RF}\right)^{2}\) is the noise variance. At the frequency of 60 GHz (MMW-RF), the atmospheric loss of the RF link \(g_{x,y}^{RF}\) can be expressed as [36]:  
\[g_{x,y}^{RF}[dB]=G_{x,y}^{t}+G_{x,y}^{r}-20\text{log}_{10}\Bigg{(}\frac{4\pi L _{x,y}}{\lambda_{x,y}^{RF}}\Bigg{)}-L_{x,y}\Big{(}\omega_{x,y}^{Oxg}+\omega_{ x,y}^{Rain}\Big{)}, \tag{7}\]  
where \(G_{x,y}^{t}\) and \(G_{x,y}^{r}\), respectively, represent the transmitter and receiver antenna gains of the RF channel, \(\lambda_{x,y}^{RF}\) represents the RF carrier wavelength, and \(\omega_{x,y}^{Oxg}\) and \(\omega_{x,y}^{Rain}\) [dB/km],  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Weather Condition** & \(\mathbf{C_{n}^{2}[m^{-2/3}]}\) & \(\mathbf{\omega_{x,y}^{FSO}}\) [dB/Km] & \(\mathbf{\omega_{x,y}^{Rain}}\) [dB/Km] & \(\mathbf{\omega_{x,y}^{Oxg}}\) [dB/Km] \\ \hline Clear air & \(5\times 10^{-14}\) & 0.43 & 0 & 15.1 \\ \hline Haze & \(1.7\times 10^{-14}\) & 4.2 & 0 & 15.1 \\ \hline Light fog & \(3\times 10^{-15}\) & 7.7 & 0 & 15.1 \\ \hline light rain (2.5 mm/h) & \(6\times 10^{-15}\) & 1.98 & 1.50 & 15.1 \\ \hline \end{tabular}
\end{table}
Table 1: Atmospheric Channel Parameters [33; 34; 35; 30].  
respectively, represent attenuation caused by oxygen absorption and rain, as shown in Table 1. The CDF of the SNR \(\gamma_{x,y}^{RF}\) can be obtained through integration,  
\[F_{\gamma_{x,y}^{RF}}(\gamma_{x,y}^{RF})=\frac{1}{\Gamma(m_{x,y})}G_{1,2}^{1,1} \bigg{(}\frac{m_{x,y}\gamma_{x,y}^{RF}}{\gamma_{x,y}^{RF}}\bigg{|}\begin{array} []{c}1\\ m_{x,y},0\end{array}\bigg{)}. \tag{8}\]
## 2 Materials and Methods
### Measurement of Ferric Reducing Antioxidant Power (FRAP) Activity  
To measure the FRAP activity of the SF and SSF extracts in different solvents, the FRAP solution was prepared by mixing 300 mM sodium acetate (pH 3.6), 10 mM TPTZ in 40 mM HCl, and 20 mM FeCl\({}_{3}\) in a 10:1:1 ratio. To 150 uL of FRAP solution, 10 uL of 10 ug/uL SF and SSF extracts were added and allowed to react at 37 \({}^{\circ}\)C for 30 min. Then, the absorbance was measured at 593 nm [32]. Ascorbic acid was used as the positive control, and the FRAP activity was calculated as follows:  
\[\text{FRAP activity (\%) = (1 - B/A) \times 100} \tag{4}\]  
A: Absorbance of the sample group  
B: Absorbance of the negative control
## 4 Security Analysis and Discussion
### External Attacks
#### 4.1.3 Hardware Vulnerability Exploitation  
In our proposed QKA protocol, the decoy photon mechanism effectively defends against hardware-level vulnerability exploitation attacks. The effectiveness of this protection is demonstrated in the following aspects:  
* Active Probing Defense: The random insertion of decoy photons prevents attackers from determining which are key photons and which are decoy photons. When attackers attempt to inject probing signals through hardware defects (such as fiber bypass or detector control), these operations inevitably affect the decoy photons, thereby being detected during the security verification phase.
* Hardware Manipulation Detection: Decoy photons utilize four non-orthogonal quantum states \(\{|0\rangle,|1\rangle,|+\rangle,|-\rangle\}\), ensuring that any attempt to manipulate measurement devices through hardware vulnerabilities will alter the quantum states of these photons, subsequently causing statistical deviations in measurement results.
* Reverse Channel Blockage: Physical hardware vulnerabilities in quantum communication often involve creating covert channels by exploiting reflection properties of optical components. The decoy photon mechanism effectively monitors such reverse channel attacks by inserting photons of known quantum states at random positions. Any probing attempt exploiting hardware defects will disturb these photon states.
* Real-time Monitoring Capability: The protocol requires both communicating parties to perform immediate measurements of decoy photons and compare results, a process that continuously evaluates the integrity of the quantum channel and associated hardware, ensuring that any hardware-level anomalies can be detected promptly.  
Through the decoy photon mechanism, our protocol can effectively resist hardware vulnerability exploitation attacks such as Trojan horses without relying on additional hardware protection measures, ensuring secure transmission of sensitive information in medical environments. The statistical properties of decoy photons ensure that even if attackers gain partial hardware control, they cannot extract sufficient information to obtain the final protocol key without being detected.
## 5 Case Study  
### UWB Positioning System Design  
In this study, the functional structure of the UWB positioning system is designed, as shown in Figure 9. The architecture of a complete positioning system in a workshop can be divided into four layers, perception, transport, analysis, and service, as shown in Figure 10. Each layer plays a relatively independent role in the positioning system. The perception layer is the sensing process of UWB signal sending to receiving. Before the base station communicates with the label, the respective parameters need to be configured to guarantee desired positioning results. The transmission layer is data transmission between the base station and the server, which can be realized through the wire, the base station and PC are connected to the transport layer and the service layer. The service layer includes a UWB location server, which visualizes the resolved location data through UWB location engine software.  
As the hardware topology of the UWB positioning system is shown in Figure 10, the POE switch can realize the transmission of positioning data in the same network. The main implementation of the analysis layer is to parse the internal hexadecimal message through the server, including the time of sending and receiving data, transmission frequency, etc.
## 7 Design Applications
### Fatigue Life Assessment  
In addition to ultimate static/dynamic forces considered for the design of highway structures, there are other dynamic effects like fatigue that need to be considered. To design these vital structural systems for dynamic and fatigue effects, appropriate design rules/graphs have to be defined for practicing engineers [42]. In the study herein, a comprehensive simulation scenario table was defined to take into account the shapes (truck, double-decker bus, and so on), conditions (with\(\backslash\)out air deflector, among others), and speeds of HSVs for gantry designs. The results of this comprehensive verified study were used to verify design rules for vehicle buffeting effects (e.g., British BD94/07 [43] code of practice as well as interim advice note IAN 86/07 [44]).  
Different variations of HSV dimensions with different speeds were simulated to take into account the size and shapes of real-world HSV traffic under the gantry. The variations of peak calculated pressure against the height above the centerline of the HSV were calculated and its smoothed design curves are extracted. The dynamic and fatigue simulations (using the spectral Eigen technique) of the gantries are then conducted based on global and local  
Figure 12: (**a**) Graphical representation of the calculated scatter plot for genetically-driven function and its derivation tree, (**b**) design curves for maximum exerted pressure in terms of speed and gap distance, and (**c**) resulting functional from genetical algorithm symbolic regression for maximum pressure in terms of vehicle speed \(v\) and distance gap \(d\).  
analyses concept using the prescribed multi-resolution zonal technique. Figure 13 shows the global spectral Eigen model of two gantry systems with and without traffic signs/boxes along with their mode shapes.  
The global dynamic analyses can initially be performed using the damped spectral Eigen technique for buffeting and gust pressure wave loading conditions. The calculated stresses on the gantry members and joints are post-processed to flag out the areas prone to fatigue failures. The local fatigue analyses were also performed using the high-resolution zonal technique to calculate the stress cycles and fatigue life, where 3D-detailed shell models of the members/joints were created using high-resolution mesh. Figure 14 shows the global CAD geometry model along with the 3D-detailed computational model (high-resolution zones) of the high-stress zones for the fatigue life assessment.  
Figure 13: Global model of portal gantry (**a**) without and (**b**) with signs\(\backslash\)signal boxes, and (**c**) gantry mode shapes without dynamic effects of traffic signs and (**d**) with traffic signs effects.
## 4 Results
### Temporal Variation and Trends of Annual GPP  
During 2000-2012 (Figure 4), the annual mean GPP varied from 529.6 to 854.7 g C m\({}^{-2}\) for the whole region and was 1231.6-1809.7, 953.1-1406.5, 390.8-712.4, and 428.8-701.4 g C m\({}^{-2}\) for the forestland, shrubland, cropland, and grassland areas, respectively. This result indicates variation between years. The trend analyses indicate that variation in the re  
Figure 3: Spatial distribution of the mean GPP during 2000–2012 (g C m\({}^{-2}\) yr\({}^{-1}\)).  
gion's mean GPP experienced a significant increasing trend. The variation increased by 11.7 g C m\({}^{-2}\) yr\({}^{-1}\) or 1.63% per year, but for the four land use areas, the change trend was different. In the cropland areas, the mean annual GPP showed a significant increasing trend at a 95% confidence level (\(Z=2.26\)) with a rate of 2.62% per year, and in the grassland and shrub areas, a nonsignificant increasing trend (\(Z=1.4\) and \(Z=1.1\)) was observed. In the forestland, no trend was detected (\(Z=0.43\)) at a confidence level above 50%.  
Figure 5 presents the spatial distribution of annual GPP trends, categorized as follows: decreased significantly (\(Z\leq-1.96\)), decreased nonsignificantly (\(Z=-1.96\) to \(-0.675\)), no trend (\(\,|\,Z|<0.675\)), increased nonsignificantly (\(Z=0.675\) to 1.96), and increased significantly (\(Z\geq 1.96\)). It can be seen that the annual GPP experienced an increasing trend in the majority of the region. The statistical results indicate that in 80.2% of the pixels, the annual GPP presented an upward trend, while in a small area (1.8%), the annual GPP experienced a downward trend. Nearly half (47.8%) of the area showed a significant increasing trend at a 95% confidence level (\(Z\geq 1.96\)), which was mainly distributed in the gully areas of the middle and north areas (Figure 5), with the land use mainly including cropland in 2000 (Figure 2). The presence of change trends in annual GPP was not observed at above a 50% confidence level (\(\,|\,Z|<0.675\)) in an area of 18.0%, which was mostly distributed in the southern forestland areas, dry northwestern, and northeastern grassland areas, where the dry climate is not suitable for forest vegetation.  
Figure 4: GPP variation of different vegetation and mean values (g C m\({}^{-2}\) yr\({}^{-1}\)).
## 3 The Proposed Clustering-Based Bubble Meshing Method
### Procedures of the Proposed Bubble Method
#### 3.2.2 Procedure 2: Initial Bubble Placement  
Initial positions and radii of bubbles are essential for the procedure of iteration in this method because a good initial position can reduce the convergence time of the relaxation. We devised an initial bubble determination combining the box technique with the result of clustering in Section 3.2.1. This method subdivides the inputted domain by using the groups of points based on the clustering result. Bounding boxes of points are created in each group. Then the bounding box is divided into several boxes. The length of the boxes is half of the sum of maximum and minimum length of surface bubbles.  
It is also important to determine suitable initial radii of bubbles for the high quality of mesh efficiently in three-dimensional domains. A procedure based on the distance of nearby bubbles to obtain the initial size of bubbles. First, the initial radius will be given the value of half of the sum of max and min radii of surface bubbles. If the sum of the radius of the initial point and the nearby point is 1.5 times smaller than the distance between the two points, this adjacent point is called the weight point. The weighted point will affect the determination of the initial point radius. Then the closer the surface vertices, the greater the weight for the bubble near the surface, see Equation (4). Our experience has shown that the proposed method leads to better shapes of initial mesh.  
\[\begin{split}\lambda_{i}&=\frac{1/ri}{\sum_{i=1}^{n }1/ri}\\ R_{i}&=\sum_{i=1}^{n}\lambda_{i}*r_{i}\end{split} \tag{4}\]  
where \(r_{i}\) is the radius of weight point, \(\lambda_{i}\) is the weight of the weight bubble, \(n\) is the number of weight points, \(R_{i}\) is the computed radii by its neighboring bubbles.  
We construct initial conditions including the clustering of point set of given surface meshes and generating initial internal bubbles. First, we adopt internal clustering validation measures [26; 30] to evaluate the clustering validation of initial bubble of given surface vertices. In general, CH value [31] and Silhouette value [26] correspond to the same optimal number with the small the SSE value. The result of clustering is relatively reasonable. Second, each group of vertices based on clustering results is divided several boxes for initial bubble placement, the distance of the bubbles within the range of force. Therefore, process of iteration is easier to converge and the initial conditions are relatively good.
## 6 Conclusions  
An improved singular spectrum decomposition method was developed by adopting a novel waveform matching continuation method to improve its end effects. The improved singular spectrum decomposition method was combined with Hilbert transform to perform time-frequency analysis on rotor vibration signal. A novel index called characteristic frequency band energy entropy was introduced to replace the time-frequency entropy to extract the fault feature of the time-frequency spectrum. The state of rotor can be automatically classified by SVM using the fault features of characteristic frequency band energy entropy. By analyzing the results of simulation and experimental signals, we can get the following conclusions:  
(1) ISSD can overcome the drawbacks of EMD, such as mode mixing and meaningless components. Moreover, the ISSD method improves the end effects of SSD. Therefore, ISSD is more suitable for extracting the sub-component signals of rotor vibration signals.  
(2) The proposed method can identify rotor faults accurately. Compared with EMD-based characteristic frequency band energy entropy method, EMD-based time-frequency entropy method and ISSD-based time-frequency entropy method, the presented method has higher recognition accuracy.  
[PERSON] conceived the method and completed the paper writing; [PERSON] and [PERSON] performed the experiment; [PERSON] provided guidance.  
**Funding:** This research received no external funding.  
Figure 23: The classification results of SVM using the ISSD-based time–frequency entropy.  
Figure 22: The classification results of SVM using the EMD-based time–frequency entropy.  
**Acknowledgments:** This work was supported by the Fundamental Research Funds for the Central Universities (No. 2017X5134), and the National Natural Science Foundation of China (Grant No. 51777074, 51475164).  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# BIM-GIS-Based Approach for Quality Management Aligned with ISO 9001
## 5 Results
### Creation of Qfield App  
For the on-site quality inspections, a mobile tablet equipped with GPS and Wi-Fi connectivity was used to run the QField application. The selected device met the necessary requirements for screen size, real-time location, and compatibility with QFieldCloud. A summary of its technical specifications is provided in Table 12.  
To begin the inspection process, the field inspector accessed the application using their QFieldCloud account (Figure 9a), selected the assigned project (Figure 9b), and synchronized the latest updates (Figure 9c). Once the project was loaded, the house floor plan was displayed, and the inspector's position was shown in real time using the device's integrated GPS. This spatial reference facilitated navigation and inspection alignment on-site. These steps ensure proper access to the most recent inspection forms and maintain consistency between mobile data collection and the central GIS environment. Their correct execution guarantees synchronization between field observations and digital project data, which is fundamental for the traceability and operability of the proposed workflow.  
The inspector initiated the on-site inspection by activating the editing mode and selecting the location of the element directly on the floor plan displayed in the application. A new control point was added, and the relevant fields of the inspection form were completed using the mobile interface. This process is illustrated in Figure 10a, which shows the creation of the inspection point on the map. Figure 10b presents the inspector performing the quality control task in front of the wall under inspection. Once the form was filled out, the inspection point was marked on the map with a green indicator, allowing the inspector to confirm its completion visually. Figure 10c shows the finalized form after completing the inspection. This stage is key in linking physical inspection activities with their corresponding georeferenced records in the digital environment. It ensures that data collected on-site are directly associated with specific building elements, supporting the traceability, consistency, and accuracy required for ISO 9001:2015-aligned quality documentation.  
Figure 10: Execution of georeferenced quality inspections using QField: (**a**) creation of an inspection point linked to a construction element, (**b**) visual confirmation and data entry on-site, and (**c**) completed inspection form.  
Figure 9: Key steps to initialize QField for quality inspections: (**a**) user authentication via QFieldCloud, (**b**) selection of the assigned project, and (**c**) synchronization of the latest project data.  
Existing inspection points could be edited to update information or complete additional control phases. Once the inspection round was finished, the inspector uploaded the changes to the cloud using the \"Upload changes\" option in QFieldCloud (Figure A5), ensuring the data would be available for processing in QGIS.
## 3 Experimental Results and Analyses
### Horizontal Deformation
#### 3.3.1 Time-History Curves of Horizontal Deformation
Figure 20: Time-history curves deducting accumulative deformation. (**a**) The locations of nine measurement points and the distribution (red line) of the specimen deducting accumulative deformation. (**b**) D1. (**c**) D2. (**d**) D4. (**e**) D5. (**f**) D6. (**g**) D7. (**h**) D8. (i) D10. (**j**) D13. For the measurement points on the abutment, the time-history curve of horizontal deformation is basically consistent with that of displacement load and symmetrical with time axis, as shown in Figure 20b. For the measurement points on the pile, the time-history curves of horizontal deformation of D5, D6, D7, D8, D10 and D13 are symmetrical. Their deformation values are not too large and all of them are smaller than that of pile head (D4), as shown in Figure 20e-j. The horizontal deformations of D4, D5 and D6 are +6.70 mm, +6.33 mm and +3.85 mm under the displacement of +16 mm. By comparison on Figure 18 that the accumulative deformations of D5 and D6 are +13.63 mm and +16.71 mm respectively under the displacement load of +16 mm, they are dramatically larger than the that accumulative deformation. Therefore, the influence of accumulative deformation of pile should be considered in practical IAJB when the movement of abutment is larger than 10 mm.