esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Outcomes of Number of Oscillations (\(N\))  
The effects of the number of oscillations (\(N\)) on the streamline, isotherm, and nanoparticle distribution for 3-D and in the \(X-Y\) plane are illustrated in Figures 5 and 6, respectively. In this case, the wave amplitude (\(A\)), Rayleigh number (\(Ra\)), and nanoparticle volume fraction (\(\phi\)) are chosen as 0.1, \(10^{6}\), and 0.02, respectively. The flow, thermal, and concentration fields are more clearly visible in the cross-sectional plots as shown in Figure 6. The flow field is driven by the buoyancy force which consists of two symmetrical vortices near each wave as seen from the first columns of Figures 5 and 6. For every wave, the vortices are of equal and opposite strength. It is observed that, due to the increasing number of oscillations, the vortices become weaker. The value of stream function falls to 1 from 4 for raising the oscillations from 1 to 5. This is due to the fact that for fixed length of the bottom surface of the collector, increasing waviness of the upper surface the wave length reduces that leads the reduction of flow circulation.  
The middle columns of Figures 5 and 6 present the isotherm contours for various values of the \(N\). Here, the isotherms inside the collector are governed mainly by the convection due to the strong buoyancy effect. The isotherms are parallel to the bottom wall and take wavy shapes near the top wavy surface. For \(N=1\), the higher temperature gradients are restricted near the concave  
Figure 5: Variation of the 3D streamlines (**left**), isotherms (**middle**), and nanoparticle distribution (**right**) evolution by number of oscillations (\(N\)) for \(A=0.1\), \(Ra=10^{6}\), and \(\phi=0.02\).  
portion of the upper wall, which also indicates a region of higher heat transfer because the distance between the upper and lower surface is minimum at this area. For increasing the value of \(N\) to 5, the higher temperature gradient region is extended to the five concave portions of the top wall. For this reason, extra energy is conveyed from the upper wall to the bottom wall making the inside fluid hotter compared with other considered lower number of oscillations. The nanoparticle distributions for different \(N\) are presented in the last columns of Figures 5 and 6. The particle diffusion is governed by the Brownian motion and thermophoresis effect in addition to the convection current. Most of the iso-concentration lines appear near the collector walls and take the wall pattern. It is clearly seen from the figures, that at \(N=1\), higher and lower concentration gradients remain at the convex and concave regions, respectively. This trend continues up to the value of \(N=3\). However, further increment of \(N\) leads the different pattern of concentration gradient at the bottom wall. Though the same concentration was applied to the upper and lower boundary, increasing waviness causes the upper wall to have a larger surface area. This is why lower concentration lines appear at the bottom surface for higher values of \(N\).
## 2 General State-of-the-Art
### Point Cloud Processing  
Several approaches to point cloud processing exist in the literature. A very general division of point cloud segmentation and classification is given in [24], in which the existing algorithms are divided into either the use of geometric axioms and mathematical functions, or the use of machine learning techniques. This division is concurrent with ideas presented by [25], in which the former is mentioned as the use of geometrical, spatial, and contextual constraints. The authors in [26] mentioned a distinction between heuristic and machine learning techniques. Another attempt to classify the existing approaches was proposed by [27], in which the authors added region growing algorithms [28; 29], edge-based segmentation [30], and model fitting [31] as other possible segmentation approaches, while point cloud classification is divided into supervised (data-training), unsupervised, or interactive manner.
# Combined MvdXML and Semantic Technologies for Green Construction Code Checking
## 3 Classification of Code Checking for Green Construction  
With the development of green building certification, some national organizations and institutions further infiltrated the issues related to the construction phase to make up for the insufficiency of the requirements regarding green construction in green building certification. To reflect the environmental problems and guide the environmental management on the construction site, at present, some countries such as America and China released green construction-related standards and codes.  
The code checking of green construction standards can be categorized into the following four types based on the difficulty level to meet the requirements of the clauses.  
Class 1: Code checking based on simple defined attribute values in IFC schema;  
Class 2: Code checking based on the extension of IFC entities;  
Class 3: Code checking based on simple conceptual reasoning;  
Class 4: Code checking based on construction condition reasoning.  
This paper takes green construction standards and codes in China as an example.  
In China, sustainability was widely debated in the construction industry in recent years. In order to strengthen the \"green\" development during the construction phase, MOHURD organized research efforts to conduct green construction research, issuing the \"Evaluation Standard for Green Construction of Buildings\" in 2010 (GB/T506040), which was promulgated to promote green construction, and releasing the \"Code for green construction of building\" in 2014 (GB/T50905-2014), which provides directional guidance for green construction.  
According to the above classification of code checking, some example clauses in GB/T50905-2014 are shown in Table 1.  
In the case study, for each type of rule, an example from these standards with logic formulas was given using mvdXML and semantic technologies to specify its semantics.
# Seismic Performance Evaluation of Highway Bridges under Scour and Chloride Ion Corrosion
## 2 Earthquake and Scour Combination in the Failure Probability of Bridge
### Application for a Practical Project
#### 2.2.7 Determination of Combined Partial Coefficient of Scour Action  
The reasonable scour depth \(H\) for the joint probability density between scour depth and seismic action were both found to be 4.89 m when the average failure probability and the average reliability index were substituted into the fitted non-linear relationships between the scour depth and failure probability and between the scour depth and reliability index, respectively, which verified the sufficient credibility of both non-linear relationships. According to the probability distribution of scour depth, 98% quantile value in the cumulative probability density distribution of the maximum scour depth at a 100-year design reference period was taken as the design value of scour depth, which equaled 6.15 m [16]. The combinatorial coefficient of scour action was defined as the ratio of reasonable scour depth corresponding to the average failure probability under combined action of scour and earthquake to design value of the scour depth during the design reference period, which is derived as  
\[\gamma_{\rm S}=\frac{\overline{H}}{H_{\rm d}}=\frac{4.89}{6.15}=0.795 \tag{15}\]  
where \(\overline{H}\) is the reasonable scour depth corresponding to the average failure probability under the combined action of scour and earthquake; \(H_{\rm d}\) is the design value of the scour depth during the design reference period.  
Further, the combined expressions for effects of the combined action between earthquake and scour can be developed as  
\[S_{\rm d}=S_{\rm G_{k}}+S_{A_{\rm Ek}}\text{;}0.795H_{\rm d} \tag{16}\]  
where \(S_{\rm d}\) is the design value of the combined effect; \(S_{\rm G_{k}}\) is the standard value for the effect of the dead load effect; \(S_{A_{\rm Ek}}\) is the standard value of the seismic effect.
## 3 CFD Setup
### Numerical Setup  
The flow inside the turbine is computed using the Ansys(r) CFX v17.2 software [53]. The CFX solver uses an element-based finite volume method with a co-located grid. The Rhie-Chow discretization is used for the pressure-velocity coupling [56]. The system of equations is solved using a coupled formulation and a multigrid accelerated Incomplete Lower Upper (ILU) factorization technique. For the steady state simulations, a pseudo-time step is used to reach convergence, whereas for the unsteady simulations, the second order backward Euler scheme has been chosen. Regarding the convection terms, a high order scheme [57] is used for the mean transport equation and a first order scheme for the turbulence transport equations, except for the simulations with the SAS SST \(k-\omega\) model, for which a high order scheme is specified. The fluxes at the interfaces between the stationary and rotating domains are computed using a General Grid Interface (GGI) algorithm. A frozen formulation is used for the steady simulations, whereas a fully-transient approach is used for the unsteady simulations.  
Regarding the boundary conditions:  
* no-slip walls are specified at the solid walls.
* the mass flow rate or the total pressure is set at the inlet of the spiral case.
* the surface averaged pressure is set at the outlet through an opening boundary condition.
* the rotational speed is imposed in the rotating domain.
## 4 Results and Discussion
### Trajectory Planning for AUV
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \(\mathbf{SN_{i}}\) & i = 1 & i = 2 & i = 3 & i = 4 & i = 5 & i = 6 & i = 7 & i = 8 & i = 9 & i = 10 & i = 11 & i = 12 \\ \hline EI & 1 & 2 & 2 & 4 & 6 & 4 & 3 & 6 & 7 & 6 & 7 & 7 \\ \hline L (Kbits) & 150 & 190 & 160 & 190 & 220 & 220 & 130 & 180 & 150 & 190 & 190 & 150 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The sensor nodes’ information. Vol objective results in the AUV not attaining its optimal performance with minimal energy expenditure. In contrast, the proposed modified ACO method prioritizes energy-efficient trajectory planning by constructing the most effective order for visiting the sensor nodes; for instance, it recommends visiting sensors 12, 10, and 2 in a sequence that minimizes overall energy consumption, as depicted in Figure 5d. This represents a distinctive contribution when compared with the other two aforementioned methodologies. The proposed modified ACO creates a tour to visit some specific sensor nodes, e.g., sensors 12, 10, and 2, in a way that minimizes energy consumption. It is important to note that while trajectory planning directly affects energy efficiency, it does not directly degrade the Vol at this point. In this way, the present approach optimizes the AUV's journey by suggesting the most beneficial order in which to visit the sensor nodes in terms of the information collected while taking into account low energy consumption. Our method achieved a VoI = 53.2663, which is a significant enhancement over the two aforementioned competitors. Figure 6 shows the VoI of the three competitors. This increase in the VoI by the proposed method is a consequence of the optimal sensor node visitation order and enhanced energy efficiency. This means that the energy efficiency of the AUV is maximized, and the order of the sensors visited is intelligently planned--it outperforms both competitors in terms of the Vol. The VoI obtained by the proposed method is improved by 6.9% and 6.8% compared with the methods presented in [24; 26], respectively. This improvement reveals that the proposed modified ACO not only achieves energy conservation but also increases the Vol to the maximum value and thus outperforms its competitors. The proposed optimization algorithm or the navigation path of underwater cruisers can be improved by introducing recent intelligent techniques, neural network structures, and deep learning models [27; 28; 29; 30; 31; 32; 33; 34; 35]. Moreover, other optimization methods can be involved to enhance the navigation path of UAVs, such as the Bee algorithm, the Genetic Optimization Algorithm, Particle Swarm Optimization, the Butterfly Optimization Algorithm, and the Pelican Optimization Algorithm [36; 37; 38; 39; 40].
## 3 Motion Compensation Method
### Proposed Motion Compensation Algorithm
Similarly, if the positions of channel 10 and channel 19 are the same, the positions of channel 19 and channel 28 are the same, and the positions of channel 28 and channel 1 in the next frame (represented as channel 33) are the same, then the increase rates of the delays (\(\Delta\tau_{2}\), \(\Delta\tau_{3}\), and \(\Delta\tau_{4}\)) of the remaining three sections can be calculated as:  
\[\Delta\tau_{2}=[S_{10}(t)\otimes S_{19}(t)]/9 \tag{5}\]  
\[\Delta\tau_{3}=[S_{19}(t)\otimes S_{28}(t)]/9 \tag{6}\]  
\[\Delta\tau_{4}=[S_{28}(t)\otimes S_{33}(t)]/5 \tag{7}\]  
The increase rate of the echo delay can be used to compensate for the delay in the echo signal measured by each channel in each section. The compensated signal \(\widetilde{S}_{j}(t)\) can be described as:  
\[\widetilde{S}_{j}(t)= \begin{cases}\textit{slide}[S_{j}(t),(\texttt{n}1-1) \Delta\tau_{1}],\,\texttt{n}1=1,\ldots,9\\ \textit{slide}[S_{j}(t),(\texttt{n}2-10)\Delta\tau_{2}+T_{1-10}],\,\texttt{n} 2=10,\ldots,18\\ \textit{slide}[S_{j}(t),(\texttt{n}3-19)\Delta\tau_{3}+T_{1-19}],\,\texttt{n} 3=19,\ldots,27\\ \textit{slide}[S_{j}(t),(\texttt{n}4-28)\Delta\tau_{4}+T_{1-28}],\,\texttt{n} 4=28,\ldots,32\end{cases}  \tag{8}\]  
where \"\(\textit{slide}[\,]\)\" is a shift function which can shift the original signal according to the corresponding time delay [1]. \(T_{1-10}\), \(T_{1-19}\), and \(T_{1-28}\) are the time difference between the overlapping channels 10, 19, and 28, and channel 1, respectively, which can be obtained using the convolution formula. \[ \begin{split}\textit{slide}[S_{j}(t),\Delta\tau]=\\ \left\{\begin{array}{c}[\mathbb{S}_{j}(\Delta\tau:\text{end}),\mathcal{S}_{j}(0:\Delta\tau)],\text{when}\,\,\Delta\tau\geq 0\\ [\mathbb{S}_{j}(\text{end}+\Delta\tau:\text{end}),\mathcal{S}_{j}(0:\text{end }+\Delta\tau)],\text{when}\,\,\Delta\tau<0\end{split}  \tag{9}\]  
The flowchart of the proposed compensation algorithm is given in Figure 8. The compensated signal \(\widetilde{S}_{j}(t)\) is used for 3D TWI. The modified Kirchhoff 3D TWI algorithm is employed in the system, which has been described in detail in [10]. Figure 7: The switching sequence of the channels of the improved array. The algorithm is implemented on Matlab 2020a, and the pseudocode of the proposed motion compensation method is shown in Figure 9.
## 4 Results and Discussion
### Characteristics of Turbulence Intensity Distribution along the Flow Direction
By comparing the turbulence intensity of the three conditions with the control experiment (0-5) focusing on the same areas where the artificial reefs are located, the longitudinal and vertical turbulence intensity increased by 53.6% and 76.8%, respectively. Table 2 displays the average values of longitudinal and vertical turbulence for cases 0-5, 1-5, 2-5, and 3-5. It is possible to notice that the real scenario, 2-5, has lower values of turbulence in comparison with the spacing configurations suggested with 1-5 and 3-5. Therefore, configurations suggested by the authors (1 and 3) could be more beneficial to fish and the ecology of the reefs, because stronger turbulence could provide more food and nutrients within the entire area under investigation [75]. \begin{table}
\begin{tabular}{c c c} \hline
**Test ID** & **Average Longitudinal Turbulence** & **Average Vertical Turbulence** \\ \hline
0-5 & 0.075 & 0.046 \\
1-5 & 0.117 & 0.086 \\
2-5 & 0.110 & 0.073 \\
3-5 & 0.118 & 0.084 \\ \hline \end{tabular}
\end{table} 
Table 2: The average longitudinal and vertical turbulence intensity for cases 0-5, 1-5, 2-5, and 3-5.
## 3 Results  
### Differences in Lumbar Intramuscular Properties of the AS (or Right) and NAS (or Left)  
Table 2 shows the differences in lumbar intramuscular properties of AS and NAS within the amputee group.  
Mean values of stiffness and frequency were greater in NAS than in the AS for both upper and lower lumbar regions; the upper and lower lumbar stiffness increased by 8.6% and 5.2%, and the upper and lower lumbar frequency increased by 5% and 1.2%, respectively (statistical significance was only in the upper lumbar). The decrement values were less in the NAS than in the AS in both upper and lower lumbar regions (decreased by 6.2% and 1.6%, respectively); however, there was no significant difference.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multirow{2}{*}{**Lumbar Part**} & \multicolumn{3}{c}{**Amputee Group (_n = 13_)**} & \multirow{2}{*}{**SE**} & \multirow{2}{*}{**t-Value**} \\ \cline{3-3} \cline{5-6}  & & **AS** & & & \\ \hline \multirow{4}{*}{**Stiffness (N/m)**} & Upper & 303.2 \(\pm\) 51.3 \({}^{\lx@math@degree}\) & 329.1 \(\pm\) 51.2 & 8.57 & \(-\)3.026 \({}^{\ast}\) \\ \cline{2-6}  & Lower & 225.9 \(\pm\) 51.2 & 237.6 \(\pm\) 61.3 & 8.73 & \(-\)1.348 \\ \cline{2-6}  & SE & 16.89 & 17.92 & - & - \\ \cline{2-6}  & t value & 4.579 *** & 5.105 *** & - & - \\ \hline \multirow{4}{*}{**Frequency (Hz)**} & Upper & 16.5 \(\pm\) 1.9 & 17.4 \(\pm\) 1.5 & 0.30 & \(-\)2.704 \({}^{\ast}\) \\ \cline{2-6}  & Lower & 14.0 \(\pm\) 1.4 & 14.1 \(\pm\) 1.5 & 0.33 & \(-\)0.503 \\ \cline{2-6}  & SE & 0.60 & 0.58 & - & - \\ \cline{2-6}  & t value & 4.318 *** & 5.590 *** & - & - \\ \hline \multirow{4}{*}{**Decrement (Ratio)**} & Upper & 1.25 \(\pm\) 0.24 & 1.17 \(\pm\) 0.17 & 0.06 & 1.379 \\ \cline{2-6}  & Lower & 1.15 \(\pm\) 0.23 & 1.13 \(\pm\) 0.23 & 0.04 & 0.496 \\ \cline{2-6}  & SE & 0.06 & 0.06 & - & - \\ \cline{2-6}  & t value & 1.881 & 0.744 & - & - \\ \hline \hline \end{tabular}  
* AS, amputated side; NAS, non-amputated side; SE, standard error; \({}^{\lx@math@degree}\) mean \(\pm\) standard deviation; \({}^{\ast}\)_p < 0.05_, ***_\(p\) \(<\) 0.001.  
\end{table}
Table 2: Comparison of lumbar intramuscular properties between AS and NAS sides in the amputee group.  
Table 3 shows the differences in intramuscular properties of the right and left sides within the CG.  
The right and left stiffness values were not significantly different in either upper or lower lumbar regions. Frequency values were greater on the left side for both upper and lower lumbar regions (increased by 3.4% and 2.0%, respectively); however, statistical significance was found only in the lower lumbar region. Conversely, decrement values were greater on the right side in both upper and lower lumbar regions, with 12.4% and 1.5% increase, respectively (statistical significance was only in the upper lumbar region).
# Generation of Orbital Angular Momentum Modes Using Fiber Systems
## 3 Basic Concepts and Theories of OAM Beams Generation and Detection
### Generation of OAM Beams  
Recently, all-fiber OAM generation methods have received increasing interests owing to the advantages of the lower insertion loss and better compatibility with the optical fiber communication links. The all-fiber system used to generate OAM beams can be summarized as shown in Figure 4. The system is divided into three parts: mode couple module, field control module, and polarization separation module.  
Figure 3: Sketch of the four-dimension complex space model and the transformation among CV mode, LP mode, and OAM mode bases of \(l\)th azimuthal order modes.  
Due to the difference of the effective refractive index (ERI) between different order modes being sufficiently large that mode coupling between different mode groups cannot occur by perturbations, such as fiber bending, twisting or extrusion. The mode couple module is used to couple the fundamental mode to a specific \(l\)th-order CV mode. It is usually composed of the fiber gratings [31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44] and the fiber couplers [45; 46; 47; 48]. As mentioned above, the \(l\)th-order CV modes are composed of four degenerated modes. The mode couple module couples the fundamental mode to the \(l\)th-order CV modes, which can be seen as the superposition of the initial four degenerated modes with random amplitudes and phases in reality. This typically can't be used to generate pure OAM beams.  
The field control module, the polarization controller (PC) usually used in the fiber system, is applied to redistribute the generated random state of \(l\)th-order CV modes. Due to the ERIs of the four degenerated modes with the same order are approximately the same, the four degenerated modes with the same order will be strongly coupled to other degenerated modes in the same mode group when passing through the PC because of the bending, twisting, and extruding of the few-mode fiber (FMF) provided by the PC. This will change the relative amplitudes and phases among these four degenerated modes. In some particular situation, the pure OAM modes can be generated, such as \(\hat{\sigma}^{\pm}OAM_{\pm l}=HE_{l+1,m}^{even}\pm iH_{l+1,m}^{odd}\).  
The polarization separation module is used to separate two orthogonal polarized OAM modes after the polarization control module. It is composed of a QWP and a polarizer with a particular angle. The angle depends on the mode distribution prior to the polarization separation module. If the field control module generates an electric field of two orthogonal polarized OAM modes which carry different TCs, such as \(\phi^{+}OAM_{-l}+\phi^{-}OAM_{+l}\), the two polarized orthogonal OAM modes can be separated through a polarization separation module. A polarization separation module is not necessary if the electric field after the polarization control module is carrying pure TC.
## 2 Materials and Methods  
### Modelling Approach  
In this section, the numerical model is introduced. As mentioned in the introduction, the model in this work is a reduced fidelity model, in order to gain computational speed. The model is set up in the commercial finite element (FE) software suite ANSYS Academic Research Mechanical, release 2019 R2, using the built-in AM capabilities. The following section will go through the modelling methodology used for this module and the coupling with the subsequent heat treatment. To speed up the simulations, the elements used for this work are cubical. Features with a size smaller than the cubic element size cannot be simulated, but part-scale deformations can be simulated with relatively high resolution.
# A Review on Ultrafast Laser Enabled Excellent Superhydrophobic Anti-Icing Performances
###### Abstract  
Fabricating and developing superhydrophobic anti-icing surfaces have been a research hotspot for eliminating undesired icing issues. Among various fabricating strategies, ultrafast laser micro-nano fabrication is regarded as a greatly promising technique owing to its advantages of high geometric accuracy, highly flexible microstructure or dimension availability, no contact, and no material limitation. A number of diverse micro-nanostructured superhydrophobic surfaces have been developed by ultrafast lasers and demonstrated extraordinary anti-icing properties. They are collectively known as ultrafast laser-fabricated superhydrophobic anti-icing surfaces (ULSASs). In this article, we reviewed the recent advances in ULSASs from micro-nano structure fabricating to anti-icing performances and to potential applications. The surface wettability and mechanisms of ultrafast laser micro-nano fabrication are first introduced, showing the strong ability of ultrafast laser for fabricating superhydrophobic surfaces. Then the deepened understanding of the relationship between superhydrophobicity and icephobicity is discussed in detail, including Cassie-Baxter stability, surface durability and environmental adaptability. Eventually, the passive anti-icing technique, the passive/active combined anti-icing technique and their practical applications are presented together with current challenges and future prospects.  
ultrafast laser; superhydrophobic; anti-icing +
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