esa-sceva/satcom-chunk-collection huggingface dataset:
# A GPU-Accelerated Method for 3D Nonlinear Kelvin Ship Wake Patterns Simulation
## 5 Numerical Simulations and Discussion
### Verification of the GPU Solver
#### 5.2.2 Efficiency  
To verify the efficiency of the GPU solver, numerical simulations are conducted on \(F=0.7\) and \(\epsilon=0.4\) with five mesh sizes, namely \(121\times 41\), \(181\times 61\), \(241\times 81\), \(301\times 101\), \(361\times 121\) and \(\Delta x=0.3,\Delta y=0.3\). The overall runtimes of the GPU solver are compared with those of the CPU solver proposed by [PERSON] et al. [12] (note that we keep the same calculation parameters as the GPU solver when running the CPU solver), as shown in Figure 12. It clearly shows that the overall runtimes of the GPU solver are much shorter  
Figure 11: A comparison of the centerline profiles for the simulation results of the CPU solver and GPU solver, which are computed on a \(361\times 121\) mesh with \(\Delta x=0.3,\Delta y=0.3,F=0.7\) and \(\epsilon=0.4\). The solid line represents the simulation result of the GPU solver, while the solid circles represent the simulation result of the CPU solver.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Mesh Size** & **Before** & \(\mathbf{b^{\prime}}\) & **After** & **Reduction Ratio** \\ \hline \(181\times 61\) & 0.91 GB & 19 & 0.28 GB & 3.2 \\ \(241\times 81\) & 2.9 GB & 24 & 0.88 GB & 3.3 \\ \(301\times 101\) & 6.9 GB & 33 & 2.3 GB & 3.0 \\ \(361\times 121\) & 15 GB & 38 & 4.6 GB & 3.3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The running memory usage before and after applying the banded preconditioner method.  
than those of the CPU solver. The clear accelerated-up ratios between the GPU solver and the CPU solver are shown in Table 4. The accelerated-up ratio on all cases are around 20.0. Therefore, the computation efficiency of the proposed GPU solver is much higher than that of the CPU solver.  
The proposed GPU solver has also been compared with another GPU solver proposed by [PERSON] [15] on these cases. The comparison of computation time between them is shown in Table 4. Obviously, the efficiency of the GPU solver proposed in this paper is higher than that of the GPU solver proposed by [PERSON] [15], and the advantage is more significant with the increase of the mesh size. The reason is that [PERSON] [15] only introduced the GPU acceleration technique in the boundary integral method rather than the whole processs, whereas this paper proposes a complete parallel computing framework including the parallel process for inverting the preconditioner matrix. Thanks to the process of inverting the larger mesh size and the heavier computational load, the advantage of the GPU solver proposed in this paper can be more significant.
## 1 Introduction
The change of mechanical properties of the structure changes the mechanical impedance of the structure, which may also cause the variation of electrical impedance of the PZT patch [60; 61]. By using the EMI method, [PERSON] et al. [62] investigated the grout compactness of concrete-filled fiber-reinforced polymer tubes and proposed the grout compactness index (CI) based on the root-mean-square deviation (RMSD), which could assess the grouting compactness precisely. However, many PZT sensors may be used in the long-span post-tensioned prestressed concrete structures [63]. Hence, it is impractical to measure the electrical impedance of a large number of sensors one by one through the EMI approach. In this paper, we proposed a multi-sensing technique based on EMI to monitor the grouting compactness. The experimental study of tendon duct specimens with five different grouting compactness levels (empty, 25%, 50%, 75%, 100% compactness) was carried out to verify the proposed technique. In this experiment, three different diameter PZT wafers, which have different resonant frequencies, were connected in series and mounted onto the different locations of the duct surface, so that the impedance signature at each frequency peak contains information about the grouting compactness of the duct at different locations. The impedance values of the PZT wafers were obtained by a single frequency sweep, which significantly simplifies the measurement procedure and reduces the data processing time. The serial connection also simples the wiring of the transducers. In addition, the 3-dB mean absolute percentage deviation (MAPD) based on MAPD was proposed to function as a quantitative index of grouting compactness. Finally, a finite element analysis was used to further understand the principle of the proposed method.
# Study on the Effect of Anchor Cable Prestress Loss on Foundation Stability
## 4 Comparison and Analysis of Calculation Results and Monitoring Data
### Horizontal Displacement Analysis of the Retaining Pile  
The calculated results of pile horizontal displacement in the cases of 0% and 10% prestress loss rates in the numerical calculation are extracted and compared with the measured values in the field, as shown in Figure 13. The development law of pile horizontal  
Figure 11: Maximum axial force of support under different prestress loss rates.  
Figure 12: Comparison and analysis of measured and calculated surface settlement under various working conditions.  
displacement during excavation is in good agreement with the numerical simulation results. After the completion of excavation, the horizontal displacement of the retaining pile basically presents a \"big belly\" distribution. The measured maximum horizontal displacement of the pile is 10.8 mm, which is about 13.7% less than the calculated maximum horizontal displacement without prestress loss (12.28 mm).
# Advances in the Noninvasive Diagnosis of Dry Eye Disease
## 6 In Vivo Confocal Microscopy  
In vivo confocal microscopy (IVCM) is a useful imaging technique for the in vivo microscopic observation of the corneal microstructure that enables clinicians to gain deep insight into the pathophysiology of ocular surface diseases [40]. Although it is a non/minimally invasive test, there are concerns about its routine use in clinical practice: (i) the examination requires direct contact with the ocular surface; (ii) a small field of view is obtained; (iii) only one z-plane is investigated; (iv) the eye tracker function is not feasible [41].  
IVCM of the corneal epithelium demonstrated significant alterations in DED patients, presumably due to increased desquamation of the superficial cell layer caused by hyperosmolarity of the tear film associated with increased tear evaporation and elevated inflammatory mediators. Furthermore, IVCM allows the evaluation of immune and inflammatory cells, corneal nerves, keratocytes, and MG structures on a cellular level [42].  
Several studies demonstrated a reduced density of superficial epithelial cells in [PERSON]'s and non-Sjogren's DED (respectively, PSDE and NSDE) [43]. [PERSON] et al. reported a reduction in stromal thickness in patients with PSDE as well as abnormal keratocyte hyperreflectivity [44]. [PERSON] et al. presented an increased density in anterior and posterior stromal cells in the PSDE group compared to the NSDE group, but without statistical significance [45].  
In accordance with the recent evidence of neural dysregulation in DED, studies regarding corneal nerve parameters reported an increased tortuosity and reflectivity of the corneal sub-basal nerve plexus and an increased number of bead-like formations in both PSDE and NSDE compared to healthy controls [41; 43]. The results obtained by [PERSON] et al. demonstrated that there was also immune dysregulation: in fact, while in healthy subjects dendritic and leukocytes epithelial cells of the cornea decrease from the periphery towards to the center, in DED they show the opposite trend [46].  
IVCM enables the visualization of the ultrastructure of MG [47; 48; 49]. [PERSON] et al. demonstrated an increased acinar dilatation, a higher reflectivity (grades 1-4) of meibum,a decreased diameter of MG orifices and increased inhomogeneity of the acinar wall in patients with SS and MGD compared to controls [47]. [PERSON] et al. demonstrated a reduction in the density and diameters of the acinar units in MGD [49]. [PERSON] et al., proposed a new classification of MGD based on IVCM findings (type 0 for normality, type 1 for meibum obstruction, type 2 for inflammation, and type 3 for fibrosis), and demonstrated a strong correlation between the IVCM score and the neibography scores [50]. In a cross-sectional study, [PERSON] found that DED symptoms were negatively correlated with IVCM parameters of MG and positively correlated with conjunctival inflammatory cells [51].
# Estimation of Time-Series Forest Leaf Area Index (LAI) Based on Sentinel-2 and MODIS
## 5 Conclusions  
Saihanba Machinery Forest Farm is the largest artificial forest farm in the world. The LAI is an important parameter to describe vegetation growth. Therefore, the acquisition of the LAI of forest land in a continuous time-series is very important for the statistics of the carbon storage and carbon sink of forest farms. In order to quickly obtain the forest land LAI data with high spatial and temporal resolution, this study proposes a reconstruction method based on Sentinel-2 data and MODIS LAI data. This study analyzes the accuracy of the LAI data of the reconstructed time-series forest land.  
The LAI curves of young, medium, near, adult and past forest ages of the coniferous forest in the study area extracted from the MODIS LAI products had little difference, indicating that the coniferous forest in the study area can use one curve to fit the annual changes.  
The remote sensing statistical estimation model of the LAI was developed using the atmospherically resistant vegetation index (ARVI), and the \(R^{2}\) between the estimated LAI and the measured LAI was 0.494.  
The regional daily LAI estimation method proposed in this study used MODIS LAI products to extract the LAI normalized growth curve and the traditional remote sensing statistical model to estimate the maximum LAI. The method of simply multiplying them to estimate the time-series LAI was applicable to small- and medium-sized regional scales.  
Compared with the measured values, the _RMSE_ of sample III was 0.268 and the _RMSE_ of sample IV was 0.48. This method was simple and fast in providing effective and high-resolution time-series LAI data for other studies at small and medium regional scales.  
This study shows that the combination of high spatial resolution (Sentinel-2 data) and high temporal resolution (MODIS LAI data) can accurately reconstruct the forest land time-series LAI in the study area. The method in this study can be used to reconstruct the LAI of time-series forest land with larch forests of different ages in cloudy areas. Therefore, it is of great significance to accurately count carbon stocks and sinks on a small- and medium-sized regional scale.  
Conceptualization, Z.Y. and W.L.; methodology, W.L.; software, Z.Y. and L.H.; validation, Z.Y. and Y.Q.; formal analysis, Z.Y.; investigation, Z.Y., H.L. and L.H.; resources, X.H.; data curation, Z.Y., Y.Q., H.L. and W.L.; writing--original draft preparation, Z.Y.; writing--review and editing, X.H. and W.L.; supervision, X.H. and W.L.; project administration, X.H. and W.L.; funding acquisition, X.H. and W.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the \"National Natural Science Foundation of China (Grant number 31700561)\" and the National Key R&D Program of China (Grant number 2022 YFD200503).  
Not applicable.  
All data generated or analyzed during this study are included in this published article.
# Dual-Band Single-Layer Fractal Frequency Selective Surface for 5G Applications
## 4 Design Parameters and Guidelines  
This section provides a guide to adjust the unit cell to be used with other frequencies. The four adjustable parameters in order of frequency tuning importance are \(w\), \(t\), \(dy\) and \(dx\), and are listed in Table 1 with their explanation. Designing a unit cell, such as the one purposed in this paper, starts with the general size of the element [9; 20]. This size is directly related to the resonance wavelength. Even more specific, in this design, the placement of the first resonance frequency. This can be concluded from the iteration results discussed in Section 3. On Figure 3 stage 1 only resonates at the lower of the two frequencies: it only has a valley around 800 MHz (and not the predetermined 700 MHz) and not around 3.5 GHz. All parameters have some influence in both low and high frequencies, and in the related bandwidths. Then, it is not possible to control both bands independently, i.e., there are no separate sets of parameters for each frequency band. However, the weights and thus the influence of those parameters vary significantly from one band to the other.  
The \(dx\) and \(dy\) parameters depict the distance between the individual bow ties, and are fixed at 3 and 2 mm, respectively. When these values are too low, the different bow ties will touch each other and the known response of the FSS will be lost. This also eases the way the simulations can be carried out in CST Microwave Studio.
## 5 Simulation Results
### Experimental Results of Phase II
Figure 6:1 Reliability assessment of the proposed routing strategy in terms of Packet Delivery Ratio (PDR) with respect to growing network size. (**a**) Variation of PDR with growing network size following LMM. (**b**) Variation of PDR with growing network size following SRMM. parameters (discount factor (\(\gamma^{\prime}\)), number of iteration) and output (utility value calculated for several iterations given a discount factor) subject to varying input conditions. In Phase II, the proposed approach is simulated. Here the strategy obtained from Phase I (in terms of optimal transmission power corresponding to each system state \(X_{t}\)) has been incorporated to the nodes before deployment of the network. Theoretical analysis of Phase I has been mapped to the simulated outcome of Phase II for the first two experiments of Phase II shown in Figures 8 and 9. The results in Figure 8 validate the prediction by MDP when the nodes are actually routing data to yield maximum utility (mapped to data packets received). Next experiment finds out the tunable values of threshold parameters as shown in Figure 6:2  
Subsequent experiments are conducted to find out the effectiveness of the transmission strategies with respect to routing data. Performance metrics such as data packets received by sink, consumed energy, heating ratio, and reliability in terms of PDR are observed with respect to time and increase in network size subject to relative node movement. Values of the tunable parameters for the overall experimental process are listed in Figure 6:3
# Hybrid Representation of Sensor Data for the Classification of Driving Behaviour
## 3 Hybrid Representation Approach  
The proposed hybrid representation approach employs NN-based (NN could be either CNN or RNN) time slice encoding, and rule-guided event detection. Both components have: (1) input formed by sensor measurements; (2) output encoded in the form of histograms representing frequency of occurrence. The two resulting histograms are merged, divided by route duration, and normalized to form feature vectors aimed at reflecting overall driving behaviour. Figure 1 summarizes the main stages of the proposed approach, whereas each component is described in the following subsections. It can be noted that rather than providing an alternative to CNN, LSTM, and GRU, each variant of the proposed approach incorporates one of these NN types as its data-driven component. Accordingly, CNN/LSTM/GRU models are just 'part of the story'.
## 3 Methodology
### Modal Analysis Theory  
Multiple-degree-of-freedom (MDOF) systems can be expressed by the following equation [33]:  
\[[\mathbf{M}\mathbf{\ddot{x}}(t)+\mathbf{C}\mathbf{\dot{x}}(t)+\mathbf{K} \mathbf{x}(t)=\mathbf{f}(t)] \tag{2}\]  
with \(M,C\), and \(K\) are the dynamic mass, damping, and stiffness matrices, and \(x(t)\) is the displacement of the MDOF system as a function of time.  
Transforming Equation (2) to the Laplace domain yields the transfer function matrix \(\mathbf{H}(s)\) between displacement and force vectors, \(\mathbf{X}(s)=\mathbf{H}(s)\mathbf{F}(s)\):  
\[\mathbf{H}(s)=[\mathbf{M}s^{2}+\mathbf{C}s+\mathbf{K}]^{-1}=\frac{\mathbf{N} (s)}{d(s)} \tag{3}\]  
where common-denominator polynomial \(d(s)=det(\mathbf{M}s^{2}+\mathbf{C}s+\mathbf{K})\), also known as the characteristic polynomial.  
When the damping is small, the roots of the characteristic polynomial \(d\)(s) are complex conjugate pole pairs, \(\lambda_{m}\) and \(\lambda_{m}^{*}\), \(m=1\),..., \(N_{m}\), where \(N_{m}\) is the number of modes in the system. The transfer function \(H(s)\) can be rewritten in a pole-residue form, i.e., the so-called \"modal\" model:  
\[H_{ik}(\omega)=\sum_{=1}^{N_{m}}\frac{R_{r}}{j\omega-\lambda_{r}}+\frac{R_{r} ^{*}}{j\omega-\lambda_{r}^{*}} \tag{4}\]  
with complex conjugate residues \(R_{r}\), \(R_{r}^{*}\), and poles:  
\[\lambda,\lambda^{*}=-\sigma\pm j\omega. \tag{5}\]  
The pole is a complex number with numerical value of its real part (\(\sigma\)), the damping value, and its imaginary part, the damped natural (modal) frequency (\(\omega\)).  
Figure 7 shows a typical frequency response function measured on the bicycle frame. For a frequency range between 1 and 6000 Hz, 10 modes are selected. The modal parameters are estimated [26]. The estimated resonance frequencies of the performed measurements can be found in Table 2.
# Link Quality Estimation for Wireless ANDON Towers Based on Deep Learning Models
## 3 Materials and Methods
### Transceiver  
The ANDON tower uses the low-cost NRF24L01 [36] transceiver to send the experimental data from the sensor node to the coordinator node, with a computer connected to the coordinator node to store the data in a CSV file. The transceiver works in the 2.4 GHz free band and allows setting the transmission power at \(-18\) dBm, \(-12\) dBm, \(-6\) dBm, and \(0\) dBm. The transmission rate setting options are 250 kbps, 1 Mbps, and 2 Mbps. The transceiver uses the SPI protocol for communication with the ANDON tower micro-controller. Its maximum range is 30 m, which is sufficient for the maximum distance in our experimental scenario of 16 m. The communication tests were realized by sending a payload of 2, 4, 8, 16, and 32 bytes.
## 2 Materials and Methods
### Optimum Design of the Steel Profile via Topology Optimization  
The aim of optimum design is to use less material and prove a more rigid design. In topology optimization, it is aimed at removing inefficient areas, that is, materials with low stress, from the system [16]. Before the optimum design, the boundary and loading conditions of the problem are determined, and then the optimization problem is analyzed. Different objective functions, such as minimizing stress and volume or maximizing stiffness, can be selected depending on structural properties [17]. Topology optimization has been used in many civil engineering problems, such as truss structures, buildings, and reinforced concrete elements [18].  
Figure 2 shows an example of a topology optimization application on a cantilever beam. While one end of the cantilever beam was fixed support, a load was applied to the other end. By analyzing the cantilever beam, which was divided into finite elements (meshing), regions with low stress were removed, and the final design was obtained.  
The example of a cantilever beam has dimensions of 2000 mm \(\times\) 3000 mm, and a 350 kN load was applied. The steel material used for cantilever beam has a modulus of elasticity of 2 \(\times\) 10\({}^{5}\) and a yield strength of 235 MPa. Also, the finite element model has a 40 mm mesh size, 3750 elements, and 7752 nodes.  
In the first case, the cantilever beam does not have any opening, and the maximum stress occurred at 61 MPa. Then, topology optimization was applied, and the material amount of the cantilever beam decreased to nearly 30%. In the new design, a maximum stress of 201 MPa occurred. This example shows the effectiveness of topology optimization with material savings.  
Topology Optimization of the Steel Profile Used in the Composite Column  
In this study, linear analysis of a composite column under the influence of axial force and bending moment was performed, and elements with low stress in the steel profile body were removed from the system. Figure 3 shows the steel profile body region where topology optimization was performed. At the beginning of the analysis, successive small  
Figure 2: An example of topology optimization for a cantilever beam.  
The sinusoidal-like opening shapes are arranged as shown in Figure 6 in order to be suitable for production. Figure 7 shows the manufacturing stages of the steel profile with an optimum opening shape. First of all, the steel profile is cut from the body in accordance with the opening shape. Then, the two parts are shifted and welded. This simple manufacturing process increases the moment-bearing capacity of the steel section. The manufacturing process could be undertaken simply by computer numerical control operators.
## 2 Doppler Power Spectrum (DPS) and Coherence Time (CT) as Radio Channel Metrics
### Doppler Power Spectrum  
In order to obtain an appropriate Doppler power spectrum (DPS) model to assess the effects of mmWave over 5G radio channels, we analyze the mathematical approaches of [PERSON], [PERSON], [PERSON], and IEEE, defined in [1,3], respectively; under mmWave frequency range.  
Based on the above, and considering the frequency shift (Doppler Shift) caused by the user mobility, which depends on the signal angle of arrival \(\alpha\); the receive signal frequency \(F_{r}(\alpha)\) and Doppler frequency \(f_{D}(\alpha)\), are defined as follow:  
\[F_{r}(\alpha)=f_{c}+f_{D}(\alpha) \tag{1}\]  
\[f_{D}(\alpha)=\frac{f_{c}v}{c}\cos\alpha \tag{2}\]  
where the carrier signal frequency \(f_{c}\), in [GHz], is shifted by the effects of Doppler frequency \(f_{D}(\alpha)\), in which the parametric values of the light speed \(c\) and the receiver speed \(v\), both in [m/sec], are considered. Additionally, the extreme values of \(f_{D}(\alpha)\), define the frequency interval of interest as, \(-f_{D}<f<f_{D}\), as was suggested in [16,18].  
From the antenna radiation pattern perspective, only the proposals of the [PERSON] and [PERSON] mathematical approaches has the following assumptions: with the [PERSON] proposal, in [16], an omnidirectional antenna radiation pattern (monopole antenna) is supposed, and the receive signal angle of the arrival has a uniform distribution in a range of \([-\pi,\pi]\), where the azimuthal gain is constant. Meanwhile, [PERSON], in [18], defines the DPS as:  
\[S(f)=\frac{G_{0}}{f_{m}\sqrt{1-\left(\frac{f}{f_{m}}\right)^{2}}}, \tag{3}\]  
where \(f_{m}\) is the maximum Doppler frequency, defined as \(f_{m}=\frac{f_{c}v}{c}\), \(G_{0}\) is the receiver antenna gain with a directive beam. The receiver antenna gain is taken when the following condition is accomplished:  
\[G(\alpha-\xi)=\left\{\begin{array}{ll}G_{0},&\text{if}\\ 0,&\text{otherwise}\end{array}\right.\left|\alpha-\xi\right|<\beta/2 \tag{4}\]  
where the difference between the arriving signal angle (\(\alpha\)) and the direction of the antenna beam (\(\xi\)) is limited by half of the beamwidth of the receiver antenna \(\beta\).