esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 AdViSED Thresholds Analysis
After these considerations, we adjust the thresholds to find the best values to maximize the metrics. In the thresholds set in Table 2, just a single threshold type is changed, while the other threshold values are kept fixed. The set \(1A\) and the set \(1B\) are developed using \(THR_{overlap}=14\)_and_ 4; the set \(4A\) and the set \(4B\) are performed using \(THR_{sat}=0.6\)_and_ 0.1; the set \(3A\) and the set \(3B\) are obtained using \(THR_{forg}=0.16\)_and_ 0.04; the set \(2A\) and the set \(2B\) adopt \(THR_{area}=Area/3000\) and \(Area/1000\) and \(THR_{turb}=70\)_and_ 40; and the set \(5A\) and the set \(5B\) are developed using \(THR_{recentr}=1\)_and_ 0.7, \(THR_{convArea}=0.9\)_and_ 0.1, \(THR_{ext\_min}=0.1\)_and_ 0.9, and \(THR_{ext\_max}=0.4\); the set 6 in Table 2 is the optimal one. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Set** & **Sat** & **Foreig.** & **Area** & **Turb.** & **Overlap** & **Ecc.** & **ConvArea** & **Ext min** & **Ext max** \\ \hline
1A & 0.2 & 0.8 & 2000 & 55 & 14 & 0.99 & 0.5 & 0.2 & 0.95 \\
1B & 0.2 & 0.8 & 2000 & 55 & 4 & 0.99 & 0.5 & 0.2 & 0.95 \\
2A & 0.5 & 0.8 & 3000 & 70 & 7 & 0.99 & 0.5 & 0.2 & 0.95 \\
2B & 0.2 & 0.16 & 1000 & 40 & 7 & 0.99 & 0.5 & 0.2 & 0.95 \\
3A & 0.2 & 0.04 & 2000 & 55 & 7 & 0.99 & 0.5 & 0.2 & 0.95 \\
3B & 0.2 & 0.8 & 2000 & 55 & 7 & 0.99 & 0.5 & 0.2 & 0.95 \\
4A & 0.6 & 0.8 & 2000 & 55 & 7 & 0.99 & 0.5 & 0.2 & 0.95 \\
4B & 0.1 & 0.8 & 2000 & 55 & 7 & 0.99 & 0.5 & 0.2 & 0.95 \\
5A & 0.2 & 0.8 & 2000 & 55 & 7 & 1 & 0.9 & 0.1 & 0.99 \\
5B & 0.2 & 0.8 & 2000 & 55 & 7 & 0.7 & 0.1 & 0.4 & 0.80 \\
6 & 0.2 & 0.8 & 2000 & 55 & 7 & 0.99 & 0.5 & 0.2 & 0.95 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Different threshold set considered for testing, for all \(\tau_{\alpha}=\)10 s, \(\tau_{\beta}=16\) s, and \(N=2M=3\) s.  
Figure 4: Metrics comparison between different threshold sets.
# Large-Language-Model-Enabled Text Semantic Communication Systems
## 4 Numerical Results
### Effect of Beam Size  
Decoding performance heavily depends on the beam search width \(K\). We conducted simulations comparing various \(K\) values, averaging BLEU and sentence similarity performance at each SNR, as shown in Figure 5. Narrow beams (\(K=1\)) performed the poorest, akin to greedy search, resulting in the lowest BLEU and sentence similarity scores. Three key patterns emerged from the averaged results:  
1. Significant Gain from Small \(K\): Increasing \(K\) from 1 to 5 yields substantial improvements: * BLEU-1 increases by \(\Delta\)0.73 (from \(\sim\)0.05 to \(\sim\)0.78); * Sentence similarity increases by \(\Delta\)0.27 (from \(\sim\)0.31 to \(\sim\)0.58). This reveals beam search's critical advantage over greedy decoding (\(K=1\)).
2. Diminishing Returns Beyond \(K=10\): Further increasing \(K\) from 10 to 30 provides minimal gains: * BLEU-1 improves by only \(\Delta\)0.07 (from \(\sim\)0.85 to \(\sim\)0.92); * Sentence similarity improves by \(\Delta\)0.07 (from \(\sim\)0.71 to \(\sim\)0.78). The relative gain drops below 10% for \(K>10\), indicating rapidly diminishing returns.
3. Optimal Operating Point: \(K=10\) achieves >90% of the maximum observed performance for both metrics. This suggests \(K_{\rm opt}\approx 10\) provides the best accuracy-complexity trade-off for practical deployment.  
These findings accord with the theoretical expectation: LLM token distributions are typically concentrated on a few high-probability candidates. Beyond the top-\(K\) paths (where \(K\) matches the typical number of plausible continuations), additional exploration yields minimal benefits.  
In summary, a moderate \(K\) strikes a balance between efficiency and accuracy. Values around 10 optimize both performance and complexity by concentrating the search on the most probable sequences informed by language model statistics. This approach leverages the inherent knowledge of the language model to prune implausible decoding.  
Figure 4: The BER and TER performance of LLM-SC and hard demodulation.
###### Abstract  
Facioscapulohumeral muscular dystrophy (FSHD) is an autosomal dominant genetic disease, which is caused by the mistaken expression of double homeobox protein 4 protein 4 (DUX4) in skeletal muscle. Patients with FSHD are usually accompanied by degenerative changes in the face, shoulders, and upper muscles, gradually accumulating in the lower limb muscles. The severity of patients is quite different, and most patients end up using wheelchairs and losing their self-care ability. At present, the exploration of treatment strategies for FSHD has shifted from relieving symptoms to gene therapy, which brings hope to the future of patients, but the current gene therapy is only in the clinical trial stage. Here, we conducted a comprehensive search of the relevant literature using the keywords FSHD, DUX4, and gene therapy methods including ASOs, CRISPR, and RNAi in the PubMed and Web of Science databases. We discussed the current advancements in treatment strategies for FSHD, as well as ongoing preclinical and clinical trials related to FSHD. Additionally, we evaluated the advantages and limitations of various gene therapy approaches targeting DUX4 aimed at correcting the underlying genetic defect.  
facioscapulohumeral muscular dystrophy; genetic treatment; oligonucleotides; Crispr; DUX4  
are categorized as FSHD 1. In patients with FSHD1 (Figure 1), it is caused by contraction of a distal array of microsatellite repeats on chromosome 4q35; in normal individuals, these regions usually consist of 11-100 3.3 kb-sized D4Z4 repetitive units containing two exons and a double homeobox protein 4 protein 4 (DUX4) gene throughout the open reading frame (ORF), which is typically hypermethylated in healthy individuals, while the D4Z4 repeats shrinks to 10 or fewer units and methylation is disrupted in FSHD1 [4]. FSHD2, about 5% of the FSHD, shrinkage of the D4Z4 array is not the primary factor; the reduction of this array does not even occur (Figure 1). The pathogenic causes of FSHD2 are more related to gene mutations encoding D4Z4 array methylation proteins; for example, most FSHD2 patients carry mutations in SMCHD1, which mainly encodes proteins that inhibit chromatin structure, and mutations in DNMT3B, which encodes DNA methyltransferase [5; 6], resulting in hypomethyllation of D4Z4 sequences. Both FSHD1 and FSHD2 cause chromatin relaxation and misexpression of the DUX4 in skeletal muscle [7; 8] (Figure 1), ultimately leading to degenerative necrosis of skeletal muscle fibers.
# On IoT-Friendly Skewness Monitoring for Skewness-Aware Online Edge Learning
## 3 Three Versions of Running Skewness and Efficient Calculation Methods
### Cumulative Sample Skewness (CSS)
#### 3.1.3 Calculation of Cumulative Sample Skewness (CSS)  
By trying to minimize the amount of multiplication and exponentiation from the coding's perspective, we developed Equation System (4) to calculate CSS.  
\[\begin{split}& S_{n}=\frac{n\cdot\mathbb{S}_{n}}{(n-1)(n-2)\cdot \bar{s}_{n}^{3}},\quad\mathbb{S}_{0}=\mathbb{S}_{1}=\mathbb{S}_{2}=0,\ n>2\\ &\mathbb{S}_{n+1}=\mathbb{S}_{n}-3\cdot(\bar{x}_{n+1}-\bar{x}_{n} )\cdot\mathbb{V}_{n}+(\mathbb{V}_{n+1}-\mathbb{V}_{n})[x_{n+1}-\bar{x}_{n+1}-( \bar{x}_{n+1}-\bar{x}_{n})]\end{split} \tag{4}\]  
where \(S_{n}\) is the sample skewness of \(n\) observation values \(x_{1},x_{2},\ldots,x_{n}\). In this equation system, \(S_{n}\) is defined as a proportional function of the auxiliary variable \(\mathbb{S}_{n}\) that can be calculated in a recursive fashion, while \(\mathbb{S}_{n}\) essentially carries the crucial information of the third central moment, i.e., \(\sum_{i=1}^{n}(x_{i}-\bar{x}_{n})^{3}\).  
It should be noted that \((\bar{x}_{n+1}-\bar{x}_{n})\) and \((\mathbb{V}_{n+1}-\mathbb{V}_{n})\) can directly be obtained from the intermediate results in Equation (2) and Equation System (3), respectively. To our best knowledge, this is the most efficient way to calculate CSS, as derived in Appendix A.3 and validated in Section 4.3.
## 3 Methods  
### Algorithm Description of the DMP Model  
The CGLS-DMP model [37] uses the following equation for calculating Dry Matter Productivity except for the last efficiency, \(\epsilon_{\rm H_{2}O}\):  
\[\mathrm{DMP}=\mathrm{R}\times\epsilon_{p}\times\mathrm{fAPAR}\times\epsilon_{ \mathrm{RUE_{MAX}}}\times\epsilon_{\mathrm{T}}\times\epsilon_{\mathrm{CO_{2}}} \times\epsilon_{\mathrm{AR}}\times\left[\epsilon_{\rm H_{2}O}\right] \tag{5}\]  
where DMP is daily dry matter productivity (kgDM/ha/day). R is total shortwave incoming solar radiation (0.2-3.0 \(\upmu\)m) (GJ/ha/day). \(\epsilon_{\mathrm{p}}\) is the ratio of effective photosynthetically active radiation (PAR) (0.4-0.7 \(\upmu\)m) to the total incident radiation. Because the energy in the PAR band at the surface of the earth is approximately 48% of global radiation, a value of 0.48 has been used in the model [37]. fAPAR is the fraction of PAR absorbed by green vegetation and is estimated based on remote sensing [76]. \(\epsilon_{\mathrm{RUE_{MAX}}}\) is the maximum Radiation Use Efficiency (RUE). In order to make a distinction between C\({}_{3}\) and C\({}_{4}\) crops, we use 2.75 kgDM/GJ for wheat and 3.5 kgDM/GJ for maize [30]. \(\epsilon_{\mathrm{T}}\) is the normalized temperature effect [77] and indicates the role of air temperature in the photosynthesis efficiency [78]. \(\epsilon_{\mathrm{CO_{2}}}\) is the normalized CO\({}_{2}\) fertilization effect and takes into account the thermodynamic properties of the carboxylation/oxygenation reactions during photosynthesis [37]. \(\epsilon_{\mathrm{AR}}\) is the fraction kept after Autotrophic Respiration (AR). We introduced in Equation (5) the reduction factor \"\(\epsilon_{\rm H_{2}O}\)\" to account for water stress. The details of the changed parameters are explained in the following sections.
## 4 System Operation in the Optimal Zone: Analysis of Various Dependencies
### The Dependencies of Frequency, Steady-State Current, and Power Factor in Relation to Wind Speed
The maximum wind energy that the turbine can capture in a time interval of 3500 s, \(E_{\text{W-MAX}}(3500)\), is obtained by integrating the maximum power \(P_{\text{WT-MAX}}\):  
\[E_{W-MAX}(3500)=\int\limits_{0}^{3500}P_{\text{WT-MAX}}\cdot dt=\int\limits_{ 0}^{3500}55\cdot V^{3}\cdot dt=\int\limits_{0}^{3500}55\cdot\left(9+\sin 9.43 \cdot 10^{-2}\cdot t\right)^{3}\cdot dt=1.4321\cdot 10^{8}[J] \tag{68}\]  
The electric energy supplied to the grid \(E_{E}\) is derived from the integration of electrical power:  
\[E_{E}(3500)=\int\limits_{0}^{3500}P_{\text{EC}}\cdot dt=\int\limits_{0}^{3500} 33\cdot 9^{3}\cdot dt=0.841995\cdot 10^{8}[J] \tag{69}\]  
Upon comparing the energy values, it is evident that  
1. The amount of electric energy supplied to the grid is 41.206% lower than the maximum wind energy that can be harnessed by the turbines: \[\frac{E_{W-MAX}(3500)-E_{E}(3500)}{E_{W-MAX}(3500)}\cdot 100=\frac{1.4321 \cdot 10^{8}-0.841995\cdot 10^{8}}{1.4321\cdot 10^{8}}\cdot 100=41.206\%\] (70)
2. The wind energy harnessed by the turbine at \(P_{\text{EC}}\) = 24,057 W is 41.17% than the maximum wind energy that the turbine is capable of capturing: \[\frac{E_{W-MAX}(3500)-E_{W}(3500)}{E_{W-MAX}(3500)}\cdot 100=\frac{1.4321 \cdot 10^{8}-8.425\cdot 10^{7}}{1.4321\cdot 10^{8}}\cdot 100=41.17\%\] (71)
# Experimental Study of Influence of Plant Roots on Dynamic Characteristics of Clay
## 2 Experimental Materials with Tested Material  
### Experimental Materials  
The test soil was obtained from the red clay of the railway embankment slope near Zhuzhou West Station in Hunan, China, as illustrated in Figure 1. The particle size distribution curve of the soil is presented in Figure 2. The physical parameters of the soil are detailed in Table 1. The soil particle gradation curve is relatively smooth without significant abrupt changes (the particle sizes at cumulative passing rates of 60%, 30%, and 10% are d\({}_{60}\) = 1.0214, d\({}_{30}\) = 0.3520, and d\({}_{10}\) = 0.0955, respectively). The uniformity coefficient (Cu) is 10.70, and the curvature coefficient (Cc) is 1.27, indicating that it is well-graded soil [25].  
Figure 1: Clay in Zhuzhou city.  
Figure 2: Particle size grading curve of soil.  
The experiment employed the root systems of Cynodon dactylon, using grass roots with a diameter of 0.5-1 mm and maintaining the length of the grass roots between 8 mm and 10 mm. To quantify the number of roots added to the soil, we introduced the concept of root mass density.  
\[\rho_{g}=\frac{m_{g}}{V} \tag{1}\]  
The following terms are defined as follows: \(\rho_{g}\) represents the root mass density, \(m_{g}\) stands for the total mass of roots within the soil sample, and \(V\) signifies the volume of the soil sample.
# Special Issue on \"Advances and Applications in Computational Fluid Dynamics\"
## 2 Turbomachinery  
This section features five papers centered on the analysis and optimization of pumps, turbines, and engines using CFD. [PERSON] et al. (Contribution 2) increased centrifugal pump efficiency by implementing a counter-rotating pumping stage with impeller trimming, which nearly doubled the pressure head from 127 to 270 m. By integrating CFD modeling and the Moore-Penrose method, the authors not only improved energy efficiency but also achieved lighter, more compact pump designs. [PERSON] et al. (Contribution 4) compared various CFD methods--mixing plane, frozen rotor, and sliding mesh--for simulating a counter-rotating dual-rotor wind turbine. Their findings revealed that sliding mesh best captured transient flow, the frozen rotor was optimal for analyzing wake effects, and the mixing plane was well-suited for kinetic and kinematic studies.  
Three papers in this category explore aspects of gas turbine performance. [PERSON] et al. (Contribution 12) developed a comprehensive model for microgas turbines by incorporating secondary flow and combustion efficiency. Their model, validated against JetCat P300-RX engine test data, achieved an error margin of less than 5% in key metrics. [PERSON] et al. (Contribution 14) enhanced turbomachinery flow analysis in OpenFOAM by correcting temperature-related issues in foam-extend-4.1, producing results consistent with those of commercial software. [PERSON] and [PERSON] (Contribution 16) used 2D axisymmetric CFD simulations with a porous media model to accurately predict brush seal performance. By applying a porosity correction, they improved flow resistance calculations and achieved leakage flow rate predictions within a 5% error margin across various pressure ratios.
# Search and Rescue in a Maze-like Environment with Ant and Dijkstra Algorithms
## 2 Background
### Wireless Sensor Networks (WSN) in SAR for Swarms of Drones
#### 2.2.2 Mobile Wireless Sensor Networks (MWSN) in SAR  
The use of mobile infrastructure is not restricted to delivering communication services, as e.g., surveillance infrastructure (e.g., CCTV cameras or crowd sensors) is commonly deployed alongside the above-mentioned communication infrastructure. In case of an emergency situation, especially in SAR operations, fast, cost-efficient and simple deployment is a crucial factor when it comes to saving lives and reducing economic damage [48]. When the environment under considerations may have been altered or have suffered damage, the ability to deploy sensing capabilities alongside other equipment may facilitate real-time planning based on the gathered information. Generally speaking, the more adaptive and reactive a deployment process into such an environment is, the more efficient and safe it can be. In the literature, the principle of self-organization is increasingly applied to such networks due to the fact that decentralized operations further reduce the need for communication infrastructure and speed up the decision making.
## 6 Evaluation of the USN-Platform Semantic Extension
### Performance of the Observation Storage including a Semantic Web Repository  
The experimental infrastructure used to test the performance of the semantic extension of the Observation Storage (described in Section 5), was based on a Java Servlet Container (specifically, Version 6.0 of Apache Tomcat) that supports the Sesame Java API. This container was installed in a USN-Platform back-end with an Intel Core i7 CPU 3.20 GHz processor, 12 GB of RAM and a 1.5 TB hard disk, running Windows 7 Professional x64 Service pack 1 and using Java SDK version 1.6.0_31. Additionally, we used a laptop (Intel Core i7-2640M CPU @ 2.80 GHz, 6 GB RAM, 500 GB hard disk, Windows 7 Professional x64) to simulate HTTP requests coming both from smart devices (_i.e._, publishing observations) and USN-Platform applications (_i.e._, semantic queries to the Observation Storage). All of the performance metrics were obtained using the capabilities that Apache JMeter ([[http://jmeter.apache.org/](http://jmeter.apache.org/)]([http://jmeter.apache.org/](http://jmeter.apache.org/))) provides, with the same Java SDK.  
Using this experimental infrastructure, two complementary sets of performance tests were undertaken. First we evaluated storage performance when managing O&M-OWL observations received from smart devices. In particular, we measured the time needed to publish observations from the electric vehicle. Second, we evaluated the response times of the USN-Enabler when it provided information to applications accessing the USN-Platform; we simulated several semantic queries sent from different applications to get information related to smart car observations.
# A Walkway from Crayfish to Oligochitosan
## 3 Results and Discussion
### Chitin Deacetylation and Preparation of Chitosan  
Chitin deacetylation is usually carried out in 30-60% (_w/v_) NaOH solution at an elevated temperature (80-150 \({}^{\circ}\)C) and leads to chitosan that is soluble in acidic aqueous media at pH \(<\) 6 [1,41]. The origin of chitin plays a significant role in choosing the conditions of chitin deacetylation since the \(\alpha\), \(\beta\), and \(\gamma\) chitin present in different animals differ in their ability to deacetylate [42]. Depending on the origin of chitin and its concentration and temperature, commercial chitosan obtained has a MW ranged from 20 to 1000 kDa and DD 70-95% [43].  
As is mentioned above, the leakage of heavy metals (Fe, Cr, Ni) from stainless steel in harsh alkaline media makes stainless steel reactors unapplicable for the preparation of chitosan, as the content of heavy metals results in chitosan that fails to meet pharmaceutical requirements. For this reason, and for better deproteination and deacetylation, crayfish chitin was deacetylated by using 40% NaOH (_w/v_) solution at 120 \({}^{\circ}\)C and polytetrafluoroethylene-made reactor. Like other crustaceans, crayfish shell chitin has an \(\alpha\)- structure, which is hard to deacetylate, and requires a high concentration of sodium hydroxide and a high process temperture [29,41].  
After deacetylation of chitin-BSiG, ICP-MS analysis of chitosan (Figure 3a) showed a presence of a low concentration of Fe (3.7 ppm). EDX analysis found the rest of the residual Si \(<\) 0.1% and a low content of residual proteins in the concentration of 0.11% (Table 1; Figure 1, samples 4 and 8).  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Compound** & **Proteins (\%)** & **Fe (ppm)** & **Cr (ppm)** & **Ni (ppm)** \\ \hline Crude waste & 10 \(\pm\) 2 & 23.0 & 0.4 & 0.3 \\ \hline Chitin-AlSiG & 2 \(\pm\) 0.5 & 2.5 & Nf * & Nf * \\ \hline Chitin-BSiG & 2 \(\pm\) 0.5 & 3.0 & Nf * & Nf * \\ \hline Chitosan & 0.11 \(\pm\) 0.03 & 3.7 & Nf * & Nf * \\ \hline Oligochitosan & \textless{}0.01 & 2.8 & Nf * & Nf * \\ \hline \end{tabular}  
* Nf—not found.  
\end{table}
Table 1: Content of residual proteins, heavy metals, and arsenic in chitin, chitosan, and oligochitosan. USP 34-NF29 limits: proteins \(\leq\)0.2%; Fe \(\leq\) 10 ppm, Cr \(\leq\) 1 ppm, Ni \(\leq\) 1 ppm, Pb \(\leq\) 0.5 ppm, Cd \(\leq\) 0.2 ppm, Hg \(\leq\) 0.2 ppm, and As \(\leq\) 0.5 ppm.  
Figure 2: (a)—EDX spectrum of chitin-AlSiG; (b)—EDX spectrum of chitin-BSiG.
# Influence Analysis of Traveling Wave Effect on Rail Interaction of Long-Span Suspension Bridge
## 5 Response of Beam-Rail System under Traveling Wave Effect
### Earthquake Response of Bridges
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Apparent Wave Speed** & **200 m/s** & **500 m/s** & **1000 m/s** & **2000 m/s** & **4000 m/s** & **8000 m/s** & \(\infty\) \\ \hline
**PPMCC** & 0.76 & 0.72 & 0.74 & 0.78 & 0.81 & 0.81 & 0.82 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Numerical value of PPMCC of displacement and stress at point A. Therefore, depending on the size of the wave velocity, the internal force of N2, S2 and the bottom of the main tower will be significantly affected. Table 10 shows the moment and magnitude when the bottom of the 3# main tower reaches the absolute maximum value of shear force and bending moment at different apparent wave velocities. The analysis shows that the internal force response of the main tower bottom has a certain oscillation at a low apparent wave velocity, and tends to be stable as the apparent wave velocity increases; as the apparent wave velocity decreases, the moment when the main tower bottom reaches the maximum internal force response gradually increases. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Apparent Wave Speed** & **Time (s)** & **Absolute Maximum Value of** & **Time (s)** & **Absolute Maximum Value of** \\
**(m/s)** & & **Shear Force (kN)** & & **Bending Moment (kN-m)** \\ \hline
200 & 9.94 & 37,658 & 21.52 & 2,571,377 \\
500 & 6.02 & 24,596 & 21.78 & 1,540,600 \\
1000 & 5.08 & 43,257 & 24.72 & 1,673,810 \\
2000 & 4.90 & 48,575 & 8.12 & 2,146,168 \\
4000 & 4.74 & 40,321 & 4.76 & 1,752,818 \\
8000 & 4.68 & 41,705 & 4.72 & 1,866,733 \\ \(\infty\) & 4.62 & 43,313 & 4.66 & 1,965,863 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Absolute maximum of internal force at the bottom of 3# main tower at different visual wave speeds. Figure 23: The absolute maximum value of the bending moment at different apparent wave velocities. Figure 22: The absolute maximum value of shear force at different apparent wave speeds. Table 11 gives the Pearson product moment correlation coefficients of the bending moment time course curves and shear time course curves at the base of the 3# main tower for seven different sets of apparent wave velocities. Figures 24-26 shows the comparison of the bending moment time course curves and shear time course curves of 3# main tower base at 200 m/s, 8000 m/s and unanimous excitation, and the shear force value is increased by 50 times for a clearer comparison. The analysis shows that the bending moment time course curve and shear time course curve of 3# main tower base under high apparent wave speed and consistent excitation are very similar, and the peaks and valleys almost correspond to each other, that is, when the bending moment reaches the peak, the shear force also reaches the peak and is approximately synchronous change, and will not keep synchronous change under low apparent wave speed.