esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Simulation of a Submerged Floating Tunnel: Validation and Analysis
## 3 Numerical Study of Shaking Table Model for Scaled-Down SFT  
The CEL method was utilized to simulate the dynamic response problem of an SFT under seismic action, and the accuracy of the CEL-calculated results was verified. The simplified model was established as follows: (1) the fluid was simulated by incompressible Eulerian elements; (2) the SFT tube body was simulated as a rigid body; and (3) the anchor cable was simulated using spring elements, which were subject to tension but not pressure, whereas the anchor cable mass was ignored. The accuracy of the model was verified by comparison with the results of underwater shaking table model tests reported by [PERSON] and [PERSON] (2008, 2012). Despite the referenced literature being from 2008, it represents a  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(V\) & \multirow{2}{*}{\(C_{D}\)} & \multirow{2}{*}{\(Re\)} & **Drag Force:** & **Drag Force:** & \multirow{2}{*}{**Relative Error**} \\ (m/s) & & & **Empirical Value** & & **Simulated Value** & **\%** \\  & & & **(N)** & & **(N)** & \\ \hline
0.5 & 1.08 & 10,000 & 2.7 & 2.9 & 7.4 \\ \hline
1 & 1.12 & 20,000 & 11.2 & 10.5 & 6.3 \\ \hline
2 & 1.13 & 40,000 & 45.2 & 44.4 & 1.77 \\ \hline
5 & 1.1 & 100,000 & 275 & 256.2 & 6.8 \\ \hline \hline \end{tabular}  
Relative error = ABS (\(\mid\) simulated solution \(-\) empirical solution\(\mid\)/empirical solution), \(C_{D}\) is the drag force coefficient and was taken from Figure 4, and \(V\) is the horizontal velocity of the column.  
\end{table}
Table 2: Comparison of empirically calculated and simulated values of drag force at various Reynolds numbers (\(Re\)).  
Figure 4: Empirical curve of the drag force coefficient of circular cylinder flow vs. Reynolds number (\(Re\)) [20].  
milestone, being the first study to conduct a shaking table test of a model for submerged floating tunnel vibrations, holding significant historical significance. On this basis, multiple sets of seismic wave conditions were simulated to analyze the coupled dynamic response of the SFT fluid-cable system under seismic action.
# Method of Feature Reduction in Short Text Classification Based on Feature Clustering
## 4 Experimental Validation
### Performance of the TSFC Algorithm
#### 4.2.2 Size of Similar Feature Clusters  
Within each sub-cluster of spectral clustering, multiple different similarity thresholds are used to connect the features. The word vector model calculates the cosine similarity between two features,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Testing Size** & **Training Size** & **Feature Size** & **Categories** \\ \hline China Mobile SMS & 23541 & 76001 & 7800 & 5 \\ Fudan News Data & 14332 & 36026 & 9537 & 6 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Dataset and feature size.  
Figure 3: Spectral clustering results. The time refers to the cost of constructing similar feature clusters in minutes. The baseline for evaluating the F1 score is obtained using the TF-IDF method.  
so the optimal similarity threshold is different when different word vector models are utilized. In order to verify the universal applicability of this method, the word vectors that are trained by Wikipedia's corpus are utilized instead of the specific word vectors in a certain field, which proves that our method has good portability.  
Figure 4 shows the results of similar feature clusters. Similarity thresholds of 0.6, 0.55, 0.5, 0.45, and 0.4 are used. Two important factors are plotted in the figure to reveal the effect of the clustering: the cluster size distribution and the total number of similar feature clusters. With the increase in similarity threshold, the size of the cluster of similar features decreases, but this reduction is not particularly obvious in the three intervals of 45%, 50%, and 55%. The cluster with a capacity that exceeds 20 is the smallest, and clusters with a capacity of 2-5 account for more than half of the total. However, when similar features are further increased, the size of feature clusters begins to decrease. The influence of feature clusters that are too large or too small on the classification results is also limited, which will be discussed later. When the threshold value is 40%, all of the features in the similar feature clusters cover about 50% of the feature word bags, which implies a very desirable feature reduction result. The essence of similar feature clusters is feature reduction and semantic extension. Determining the optimal similarity threshold depends on the actual task. Usually, the threshold is acceptable when similar feature clusters can cover 40%-50% of the total feature words.
## Materials and Methods
### Analytical Set-Up for the Detection of CB Nano-Particulates in the Abrasion
#### 2.3.2 AF4 and MALLS Measurements  
The AF4 measurements were carried out with an \"AF2000 MT Series mid temperature\" (Postnova Analytics, Landsberg, Germany). The system was equipped with a 350 \(\upmu\)m channel and a cellulose membrane (cut-off: 5 kDa). For the determination of the particles size distribution, a 21-angle-MALLS detector \"PN3621\" (Postnova Analytics, Landsberg, Germany) was used.  
CB nano-particulates of all four dispersions were separated using the following AF4 method. At an injection flow of 0.2 mL/min 1 mL of the respective CB dispersion was injected and focused into the AF4 system for 10 min. The optimized AF4 conditions for the CB dispersions are as follows. The cross-flow was kept constant at 2.5 mL/min (start conditions). Within a transition time of 1 min, the focusing of the sample was terminated and the elution of sample started. The cross-flow was then rapidly reduced linearly to 1.5 mL/min within 0.4 min, followed by a parabolic decrease (power gradient 0.20) to 0.1 mL/min in 10 min, and another linear reduction to 0 mL/min in another 10 min. The channel was flushed at the end by the detector flow for 10 min without any cross flow to empty the channel completely. The detector flow was kept constant at 0.7 mL/min for the entire run. The channel was maintained at a temperature of 40 \({}^{\circ}\)C.  
To correlate the MALLS detector outputs with the injected mass of CB, the diluted reference dispersions were used for MALLS calibration. For all CB dispersions, a series of dilutions from a fresh 100 mg/L stock dispersion were prepared at 0 (blank), 5, 10, 25, 50, 75, 100, and 125 ng/mL in 96% ethanol. The AF4 fractograms were integrated at all detection angles (except the 7\({}^{\circ}\) angle) of the MALLS detector to obtain the peak areas that are directly proportional to the injected mass.  
The CB material was completely dispersed in 96% ethanol with no sediment, thus the weighed mass of CB corresponds to the mass of CB in the dispersion. The CB standard dispersions can therefore be used for calibration of the MALLS output by the peak areas.  
Furthermore, the results of the light scattering experiment were used to calculate the aggregate size of the dispersed CB through the radius of gyration, \(\mathrm{r_{g}}\).
## 5 Experiment
### Strategy Performance Analysis  
In this section, we will compare the performance of Trinity with other baselines from different perspectives including runtime, peak communication, peak memory, and search time. Our experiment uses the CIFAR-10 and PTB datasets and tests the Inception, NMT, NASNet (large), and PNASNet (large) models.  
The experimental results are shown in Figures 6-8 and Table 4 below. We show the performance comparison with Trinity and GPU only, layered expert, and Hierarchical from three dimensions: (1) runtime: the model completes a single forward propagation and derivation; (2) peak communication: the maximum communication cost among all hardware devices; (3) peak memory: the maximum memory occupation among all hardware devices. In the legend of Figures 6-8, the numbers (2 or 4) represent the number of GPUs. Expert, Hierarchical and Trinity are the baseline and the method proposed in this paper, respectively. Single step means that the model completes a single forward propagation and derivation.  
Compared with Google's Hierarchical, the InceptionV3, and GNMT networks, the runtime of the parallel strategy searched by Trinity is similar, but it has a better performance in communication and memory. For large-scale networks, such as NASNet-L and PNASNet-L, Trinity has a better balance of performance. It is worth noting that the Hierarchical method cannot search for a suitable model parallel strategy for NASNET-L based on 4 GPU, but Trinity can. It only takes 5.21 s to execute the parallel strategy. Trinity has less runtime than layered expert methods.  
To be clear, a single GPU is the strongest baseline, which incur no cross-device communication cost. Model parallelism is suitable for scenarios in which the AI model scale is too large and cannot be trained on a single GPU. So, Trinity and single GPU training are applicable to different scenarios. Trinity is not trying to surpass, but hopes to be closer to the runtime under a single GPU.  
We compute the improvement of the performance indicators compared with Hierarchical in Table 5 and compare the time required for the Hierarchical and Trinity methods in the case of 100 search iterations. The table analysis shows that the trinity will exchange part of the memory overhead for communication optimization. The overall performance is more balanced, and the runtime is also reduced to varying degrees.  
Due to the introduction of the execution simulator in Trinity, it can achieve up to a 40% increase in parallel strategy search speeds, in most cases. It can be proven that the introduction of the execution simulator can greatly reduce the time required for search and sampling. Although Trinity introduces a simulator to simulate the execution process, the performance evaluation using these experimental data is performed in a real distributed environment.
## 4 Computational Study  
### A Three-Dimensional Model of a Wind Turbine with Three Asymmetrical Blades  
The choice of an appropriate turbulence model in numerical simulations affects the obtained results. In study [30], it was shown that the k-omega SST turbulence model provides more accurate Cp values compared to the Realizable k-epsilon model.  
The asymmetrically shaped blade is shown in Figure 8 as a three-dimensional (3D) sketch. The asymmetrical shape of the profile enhances the lift component on the upper surface, ensuring efficient conversion of wind energy into mechanical work, especially at low wind speeds. The blade is optimized for operation at different angles of attack, which expands the range of operating conditions of the wind turbine.  
The computational study was performed using the Ansys Fluent software package based on the solution of the Navier-Stokes averaged equations.  
A numerical model of a wind turbine with three asymmetric blades was developed to study its aerodynamic characteristics. Numerical modeling of the flow around a wind wheel with asymmetric blades is a complex task that requires an accurate description of the geometry and consideration of the features of the interaction of the flow with the surface of the blades. To perform the numerical analysis, a high-precision geometric model of the wind turbine was created using 3D Compass 3D v20 software, which made it possible to take into account all the design features and conduct a detailed study of the aerodynamic parameters. Figures 9 and 10 show two design options for the wind turbine. Figure 9 illustrates a model of a wind turbine with asymmetric blades supplemented with plates. Figure 10 shows a model of a wind turbine with an asymmetric blade shape without plates.  
Figure 8: Three-dimensional (3D) sketch of an asymmetric blade.  
Figure 7: Breakaway torque curve.  
Figure 11: Mathematical model of a wind turbine with three blades.  
Figure 10: Model of a wind turbine with an asymmetric blade shape (without a plate).  
Figure 9: Model of a wind turbine with asymmetric blades (with plates).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Meaning** \\ \hline Air type & Incompressible \\ Flow type & Isothermal \\ Gas density & 1.1691 kg/m\({}^{3}\) \\ Gas viscosity & 1.84 \(\times\) 10\({}^{-5}\) kg/m\(\times\)s \\ Viscosity mode & Turbulent \\ Turbulence (Reynolds Averaging) & Implemented k-epsilon \\ \hline \hline \end{tabular}
\end{table}
Table 2: Initial parameters for the numerical simulation of the flow around wind turbine blades.
## 3 Methodology
### Loss Function Design
By default, YOLOv8's total loss combines these sub-losses with respective weights, as shown in Equation (9):  
\[L_{\text{YOLOv8}}=\lambda_{\text{cls}}\,L_{\text{cls}}+\lambda_{\text{box}}\, L_{\text{CIoU}}+\lambda_{\text{DFL}}\,L_{\text{DFL}} \tag{9}\]  
where \(\lambda_{\text{cls}},\lambda_{\text{box}}\), and \(\lambda_{\text{DFL}}\) are the weights for classification, CIoU, and DFL losses, respectively. By default, \(\lambda_{\text{cls}}=0.5\), \(\lambda_{\text{box}}=7.5\), and \(\lambda_{\text{DFL}}=1.5\). Conventional pixel- or boundary-level losses often struggle to adequately constrain the network's focus on the intricate textures and boundaries of small defects, especially under noise from oil or rust. In response, this paper integrates a perceptual loss term to enhance the network's attention to subtle texture details within defect regions by aligning predicted and ground-truth bounding boxes in high-level feature space. The implementation employs Region of Interest Align (RoAl Align) to accurately sample the feature regions corresponding to predicted and ground-truth boxes on the backbone's deep feature maps. Bilinear interpolation removes quantization artifacts and maps floating-point coordinates onto a uniform grid of aligned feature patches, preserving the fragile edges and textures of defects. Let \(\phi\left(I_{\text{pred}}\right)\) and \(\phi\left(I_{\text{true}}\right)\) denote the high-level feature representations of the predicted and ground-truth boxes post-RoAlign, respectively. The perceptual loss is calculated in Equation (10):  
\[L_{\text{perceptual}}=\frac{1}{WH}\sum\limits_{x=1}^{W}\sum\limits_{y=1}^{H} \left\|\phi\left(I_{\text{true}}\right)_{x,y}-\phi\left(I_{\text{pred}}\right) _{x,y}\right\|_{2}^{2} \tag{10}\]  
where W and H are the widths and heights of the extracted feature patches, and \(\phi(\cdot)\) represents the backbone's deep feature mapping function. By compelling the network to maintain consistency in the semantic structure within defect regions--rather than merely relying on pixel- or geometry-level similarity--this perceptual loss bolsters the detection of small, ambiguous boundaries. Incorporating this perceptual loss term into YOLOv8's overall objective (Equation (11)) establishes a joint training target:  
\[L_{total}=L_{YOLOv8}+\lambda_{perc}\,L_{perceptual} \tag{11}\]  
where the hyperparameter \(\lambda_{perc}\) controls the balance between the detection and feature alignment tasks. The architecture implementing perceptual loss is illustrated in Figure 6.  
By compensating for the shortcomings of pixel- and boundary-level losses regarding detail preservation, perceptual loss maintains superior texture and shape consistency in smaller defect areas. Because it focuses on the divergence of high-level features, the network becomes more attuned to internal textures and edges, enhancing accuracy in complex or multi-defect scenarios. While rail surfaces often exhibit non-defect noise, the perceptual loss guides the model to ignore extraneous interference, yielding greater robustness for various defect types, including cracks, spalling, and corrosion.
## 3 In-Vehicle Engine Order Sound Fitting and Verification
### Engine Order Sound Synthesis Theory
#### 3.2.3 Analysis and Selection of Window Function Characteristics  
Commonly used window functions include rectangular window, Hanning window, Hamming window, Blackman window, Kaiser window, etc.  
Key parameter indicators that affect the performance of window functions mainly include: main lobe width, side lobe peak value, and stopband minimum attenuation. The key parameter pairs of frequency response characteristics of each window function are shown in Table 6[39].  
In the process of increasing engine speed, the requirement of frequency resolution changes from high to low. At the same time, the higher the engine order frequency, the shorter the time period, and the faster the engine order frequency changes with time at high speed.  
Kaiser window function has high flexibility, which can adjust the width of the main lobe and the peak value of the side lobe by controlling the shape parameter \(\beta\) value and the window length \(N\), and is widely used in time-varying signal analysis in various engineering fields. The larger the value, the smaller the peak value of the side lobe and the larger the width of the main lobe. The larger the window length, the smaller the main lobe width, and the window length has no effect on the side lobe peak value. Eight typical Kaiser window functions with \(\beta\) values are shown in Table 7.  
For the in-vehicle engine order sound, the calculation formula of the harmonic frequency difference \(\Delta f\) between two adjacent engines is:  
\[\Delta f=\frac{k\times n_{e}}{2\times 60}-\frac{(k-1)\times n_{e}}{2\times 60}= \frac{n_{e}}{120} \tag{10}\]  
When the engine speed rises from 750 rpm to 6000 rpm, the fundamental frequency of the engine order sound is \(f_{1}=\Delta f\). When the main lobe width of Kaiser window function is \(B\leq f_{1}\), all engine order sound frequencies can be clearly identified.  
The step length between adjacent frames also affects the results of short-time Fourier transform analysis. Usually, there is a certain overlap rate between adjacent frames, the step length of adjacent frames is less than the length of the frame itself, and the overlap rate is usually selected from 50% to 66.7%.
## 4 Results and Analysis
### Heat Transfer Characteristics of Target Surface
In the range of \(x/D\) = 0.5-0.6 in the B region, the vortex of the free shear boundary layer in the jet space disturbs the adherent flow, resulting in the suddenly thinning of the temperature boundary layer in this interval, causing a \(Nu\) peak, and then the jet enters the jet region of the wall. The temperature boundary thickness increases rapidly, resulting in a rapid decay of the instantaneous \(Nu\) along the direction of increasing \(x\). In Figure 17a, the flow of the 45\({}^{\circ}\) spiral hole is similar to that of the 0\({}^{\circ}\) straight hole in the range of \(x/D\) = 0-0.2. Near \(x/D\) = 0.2, the wave peak of \(Nu\) appears in the A region because of the influence of the shear boundary layer with a center hole area of 45\({}^{\circ}\) spiral hole. Then, under the action of  
Figure 16: The distribution of \(Nu/Nu_{st}\) in the \(x\) direction. the tangential airflow of the spiral groove, the jet begins to fluctuate gradually near the temperature boundary layer at x/D = 0.3. At x/D = 0.6, tilt and scour of the tangential flow of the spiral groove (B region of 45\({}^{\circ}\) spiral hole) causes the boundary layer to become thinner, corresponding to the instantaneous \(Nu\) peak in the position. In Figure 17b, the turbulent flow gradually forms after the 0\({}^{\circ}\) straight hole enters the jet region of the wall, causing small pitch fluctuation in the temperature boundary layer in the range of x/D=0.7-1.2, corresponding to the first and the second \(Nu\) peaks in the C interval. The fluctuation of the temperature boundary layer increases in the range of x/D = 1.4-1.8, resulting in two \(Nu\) peaks at x/D = 1.4 and x/D = 1.8, corresponding to the two larger \(Nu\) peaks behind the C interval. In the range of x/D = 1.8-2.6, the temperature boundary layer of the near wall is sharply thickened, and the fluctuation amplitude of the boundary layer increases, resulting in two distinct \(Nu\) peaks in the D region. In Figure 17b, the boundary layer of the wall of the C and D regions with a 45\({}^{\circ}\) spiral hole is obviously thinner, and the place where the \(Nu\) peak appears is the thinner part of the temperature boundary layer. It can be seen that the thickness of the temperature boundary layer and its change directly determine the strength of the heat transfer of the target surface, and both are strongly correlated.
## 3 Numerical Calculation Settings
### Calculation Domain and Grid Settings  
Before carrying out the prediction for propeller TVC inception based on the aforementioned models, we need to determine the size of entire calculation domain and perform the appropriate calculation setup. The calculation domain is a cylinder coaxial with the propeller, as illustrated in Figure 2, where the left is the velocity inlet boundary and the right is the pressure outlet boundary. The blade surface adopts non-sliding wall conditions and other wall surfaces adopt free wall conditions. The distance from the propeller disk surface to the velocity inlet and the pressure outlet are 4D and 10D, respectively, and the distance between the side boundary and the propeller center is 2.5D. We divide the calculation domain into two parts--a stationary domain and a rotational domain--using sliding grids. Moreover, the low pressure and larger radial pressure gradient at the tip vortex core concern the suction, growth, and collapse processes of bubbles. Therefore, it is necessary to conduct a grid refinement in the tip vortex region to reduce the numerical discrete error effectively and simulate the low pressure and the larger radial pressure gradient