esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion and Conclusions
### Conclusions  
In this study, a SHM system for a jacket offshore platform under construction was designed. The content and application of the whole SHM, the sensor subsystem, and the evaluation subsystem were analyzed. The results can provide reference for SHM of offshore platforms in terms of analysis methods, equipment selection and overall performance evaluation. Changes in the natural vibration frequency of the jacket structure under the influence of scouring, corrosion, growth of marine life, and varying temperature were analyzed. The main contributions of this paper can be summarized as follows.  
* According to the overall framework of the sensor subsystem, data reading and transferring subsystem, data administration subsystem, and assessment subsystem, a SHM system for jacket offshore platform under construction was designed.
* A method for safety monitoring and early warnings for an offshore platform based on static tests of the platform displacement, inclination, and loads of pile was presented.
* Overall performance evaluation based on frequency variation and damage identification based on strain modal using strain monitoring of platform was presented.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Frequency**} & \multicolumn{2}{c}{**Different Months of the Same Year**} & \multicolumn{2}{c}{**Same Month of Different Years**} \\ \cline{2-5}  & **Lower** & **Higher** & **Lower** & **Higher** \\  & **Orders** & **Orders** & **Orders** & **Orders** \\ \hline \textless{1}\% & T & T & M & N \\
1–3\% & T & T & M & C, M \\
3–5\% & T & T & S & M \\ \textgreater{5}\% & N & T & C & M \\ \textgreater{8}\% & N & N & C & C \\ \textgreater{10}\% & N & N & N & S \\ \hline \hline \end{tabular} Note: “T” represents temperature; “M” indicates marine life; “S” stands for scouring; “C” stands for corrosion; “N” indicates none. Lower orders are 1–3; higher orders are 4–8.  
\end{table}
Table 8: Main factors causing the change of frequency.  
* Taking an offshore platform in the East China Sea as an numerical case study, the application of the SHM system was discussed, which verifies its feasibility. At last, variation in the natural frequency of the model under the influence of scouring, corrosion, the growth of marine organisms, and temperature variations was quantitatively analyzed. The main factors causing frequency variations in different months of a given year and the same month in different years were obtained.  
H.Y.: writing--original draft, supervision, investigation, methodology. C.J.: data curation, formal analysis. F.Z.: methodology, resources. S.L.: methodology, validation. All authors have read and agreed to the published version of the manuscript.  
This research was funded by general project of military logistics scientific research fund of China under project No. 2019J013.  
Thanks for three anonymous reviewers for their detailed and constructive comments, which greatly improved the quality of the manuscript.  
The authors declare no conflict of interest.
## 2 Materials and Methods  
The research protocol was registered in the International Platform of Registered Systematic Review and Meta-Analysis Protocols (INPLASY) under the number 6973 (protocol ID: INPLASY2024100101; doi:10.37766/inplay2024.10.0101). The literature search for the present study followed the Preferred Reporting Items for Systematic Review and Meta-Analysis of Diagnostic Test Accuracy Studies (PRISMA-DTA) guideline. A comprehensive search has been performed in medical databases such as PubMed Central, Scopus, and the Cochrane Library, focusing on articles reporting on the use of EUS-RFA in PDAC patients. The following MeSH (medical subject headings) terms and keywords were used: \"Endoscopic Ultrasound\", \"EUS\", \"Endosonography\", \"Radiofrequency Ablation\", \"RFA\", \"Radiofrequency Therapy\", \"Pancreatic Neoplasms\", \"Pancreatic Cancer\", and \"Pancreatic Adenocarcinoma\". Specific search strings were constructed for each library to ensure thorough retrieval.  
The inclusion criteria were as follows: (1) original research articles involving at least three human subjects (case series), written in English, and published (or available as \"online first\") up until 30 June 2024; (2) studies involving patients diagnosed with pancreatic adenocarcinoma; (3) studies reporting the use of EUS-RFA; (4) studies providing relevant outcome measures such as technical success rate, adverse events, overall survival (OS), and progression-free survival (PFS). Exclusion criteria were related to the type of manuscript--letter to the editor, case reports, conference abstracts, manuscripts written in languages other than English, and studies on animal subjects. For the initial review stages (screening and duplicate exclusion), we employed Rayyan software (available online at www.rayyan.ai, accessed on 1 July 2024). After initial retrieval, duplicates were automatically identified and manually verified before exclusion, followed by the eligibility assessment of papers based on their relevance to the role of EUS-RFA therapy in PDAC. This phase involved a careful review of titles and abstracts by two team members ([PERSON] and [PERSON]), who voted independently for each article; voting conflicts were resolved by discussion between the two members, and consensus decision was made to include 10 articles. Selected articles were retrieved for full-text screening, and all eligible studies were then processed for data extraction. Furthermore, the references and citations list of the selected papers were manually searched for potential additional relevant studies which were not detected by the initial search. Pertinent studies that came to the attention of the authors through research platforms were also included, if eligible. This last source contributed an additional article, adding to the 10 previously selected, generating a total of 11 articles included in the final analysis.  
The detailed process of study inclusion, based on the PRISMA methodology, is summarized in the following flow diagram (Figure 1).
# Short Landing for Flying-Wing Unmanned Aircraft with Thrust Vector
## 5 Short-Landing-Strategy Design
### Lateral and Vertical Guidance  
The lateral control's objective is to eliminate the CTE error by using the bank angle control. The bank angle command is calculated as follows:  
\[\phi_{c}=\arctan\Bigl{(}\dot{\psi}_{des}V_{g}/g\Bigr{)} \tag{54}\]  
where we have the following:  
\[\begin{array}{l}\dot{\psi}_{des}=\omega_{as}\Delta\psi\\ \Delta\psi=[(\psi_{des}-\psi_{l}+\pi)\bmod 2\pi]-\pi\\ \psi_{des}=\arcsin\bigl{(}\omega_{y}\Delta Y/V_{g}\bigr{)}\end{array} \tag{55}\]  
where \(\Delta Y\) is the CTE, \(V_{g}\) is the ground velocity, \(\psi_{l}\) is the heading angle, and \(\omega_{y}\) is the CTE control bandwidth. The course angle error, \(\Delta\psi\), is limited between \(-\pi\) and \(\pi\) and is fed  
Figure 19: Short-landing process.  
to a controller with proportional gain, \(\omega_{as}\), whose output is the desired rate of change of course angle.  
To improve the accuracy of the touchdown point, the sink rate command is adjusted online based on the distance from the landing point. The sink rate command is given by the following equation:  
\[\dot{h}_{c}=-\big{[}V_{g}\cos(|\psi_{rw}-\psi_{l}|)\big{]}\arctan(H/X_{des}) \tag{56}\]  
In Figure 20, the projection of the line of sight (LOS) in the direction of the runway is denoted as \(X_{des}\), the direction angle of the runway is \(\psi_{rw}\), and \(H\) is the altitude above ground level (AGL). It is important to note that when \(X_{des}\) approaches zero, the sink rate command tends to infinity according to Equation (56). In order to ensure the landing gear's strength, the sink rate command is limited to a range of \(-2\) to \(0\) m/s, and when \(X_{des}\) is less than \(5\) m, the sink rate command remains constant.
# A Deep Learning-Based Mapping Model for Three-Dimensional Propeller RANS and LES Flow Fields
## 4 Experiments
### Experiment of Feature Extraction Model for Flow Fields  
#### 4.3.1 Flow Field Customization Error Rate  
In accuracy evaluation for the results of flow field simulations, it is common to use the following error rate as an evaluation index:  
\[\text{err}=\sqrt[N]{\prod_{x\in X}\left|\frac{V_{x}-V_{x}}{V_{x}}\right|} \tag{6}\]  
where \(V_{x}^{{}^{\prime}}\) is the ground truth of the velocity value at the point \(x\), \(V_{x}\) is the corresponding predicted value, \(X\) is the set of all the points in the LES flow field whose velocity values are predicted, and \(|X|\) the number of points in \(X\). However, the accuracy evaluation for propeller flow field simulations has several challenges. One of the main issues is that the magnitude of the error rate changes with the magnitude of the true value. For example, if the error value is 0.01 and the true value is 0.001, the error rate would be 100%. However, if the true value is 0.1, the error rate would be 10%. Thus, there is a lack of objectivity in directly using this error rate as an evaluation index. To address this issue, we propose a comprehensive error rate as follows:  
\[\text{err}=\frac{\sum_{x\in X}|V_{x}-V_{x}^{{}^{\prime}}|}{\sum_{x\in X}|V_{x}|} \tag{7}\]  
where \(V_{x}\), \(V_{x}^{{}^{\prime}}\), \(X\) and \(|X|\) have the same meanings as those in Equation (6). Such a comprehensive error rate would not be affected by the fact that the real value of a certain point is too small.
## 4 Results and Discussion  
### Performance Metrics  
Precision, recall, and accuracy are the parameters that describe the correctness of classification problems. Precision measures all the real positive outputs from the positively predicted ones, and recall is the measure of correctly predicted positive outputs from all positive outputs. The formula of the precision, recall and accuracy are shown in Equations (8)-(10), respectively.  
\[\text{Precision}=\
icefrac{{TP}}{{FP}}+\
icefrac{{TP}}{{FP}} \tag{8}\]  
\[\text{Recall}=\
icefrac{{TP}}{{FP}}+\
icefrac{{FN}}{{FN}} \tag{9}\]  
\[\text{Accuracy}=(\
icefrac{{TP}}{{TN}})/(\
icefrac{{TP}}{{TN}}+\
icefrac{{FP }}{{TP}}+\
icefrac{{FN}}{{FN}}) \tag{10}\]  
The output that is predicted as positive and is positive is known as true positive (TP), and the output predicted as positive and is negative is known as false positive (FP) and vice versa. There are four activities included in the current study. A confusion matrix to describe all the possible outcomes for the third activity, \"hauling\" is shown in Table 3.  
The outcomes in the diagonal are always true negative except for one. The third row shows the actual count of the \"hauling\" while the third column shows the predicted count of \"hauling\". Therefore, all of the which values fall in the third row, except diagonal, are wrongly classified as negative and so are named as \"false negative\". Similarly, all of the values which fall in the third column, except diagonal, are wrongly classified as positive and so are named as \"false positive\".  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline Learning Rate & 0.001 & 0.0001 & 0.0005 & 0.00001 \\ \hline Validation & 58.73\% & 93.55\% & 69.84\% & 76.20\% \\ Accuracy & 55\% & 88\% & 51\% & 73\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Validation accuracy at different learning rate.
# Method of Suppressing Rayleigh Waves Based on the Technology of Time-Domain Differential Detection
###### Abstract  
Seismic exploration is widely used in shallow engineering applications, yet extracting reflected wave information remains challenging due to contamination from Rayleigh waves. To overcome this, we propose a common shot point time-domain differential method that leverages the distinct velocity contrast between slow Rayleigh waves and faster P-wave reflections. These waves exhibit lower velocity and minimal dispersion in the radiation direction under the same seismic source excitation. This study establishes two closely spaced track records termed \"far main and near slave\" along the direction of the measurement line to counteract this interference. This method employs the difference in travel time between Rayleigh waves and subsurface interface reflection waves for time-domain differential analysis. The interference is minimized while preserving the reflected wave signal by conducting slight amplitude compensation on the far-field Rayleigh wave signal and subtracting the master and slave records. The application of time-domain differential detection technology in shallow engineering seismic exploration and marble plate thickness detection experiments demonstrated that this method effectively eliminates the influence of Rayleigh surface waves and enhances the resolution of reflection signals from anomalous bodies. Additionally, this study examines the impact of boundaries on time-domain differential technology. Without relying on long array shot records, this approach provides a promising result for Rayleigh wave suppression and offers broad potential in elastic wave exploration.  
Keywords:seismic exploration; Rayleigh wave suppression; records under the same seismic source excitation; time-domain differential detection
## 4 Discussion
### Latency  
None of the other publications provides values for the latency although that is a very important parameter for PID controlled closed-loop applications. As the camera frame rate plus processing time form a lower boundary for the latency of image processing systems, it is obvious that it must be longer than the 2 ms of our approach in all cases. This is also true for the commercial systems by Correlated Solutions [34], Instron [4], and Zwick Roell [35] with frame rates of 250, 490, and 160 Hz, respectively. Unfortunately, none of these companies specifies the conditions necessary to achieve these sampling rates. In our case, the image processing adds only 0.6 ms to the image reading time which is quite at the lower boundary of what preemptive multitasking systems like Windows require without GPU. Therefore, there is no significant additional latency due to the GPU. Shorter latencies for image processing systems in the range of 0.1 ms require different hardware architectures like FPGA or specialized sensor-processor hardware like cellular neural networks [26; 36].
# Seismic Performance Evaluation of Highway Bridges under Scour and Chloride Ion Corrosion
## 3 Effect of Cl\({}^{-}\) Corrosion on the Seismic Performance of Bridge Column
### Comparison of Quasi-Static Test Results of Columns before and after Cl\({}^{-}\) Corrosion
#### 3.3.4 Energy Dissipation Capacity of Specimen  
The energy dissipation level of ductile bridge columns is represented by the enclosed area of hysteresis curve, which is a comprehensive index to measure the seismic performance of columns. The index of cumulative hysteretic energy dissipation is defined as the sum of hysteretic loop areas at all levels of displacement amplitudes with quasi-static loading on the structure or component until failure, which reflects the overall energy dissipation level of the specimen in its entire life cycle.  
\[E_{\text{AD}}=\sum_{i=1}^{n}\Delta W_{\text{i}} \tag{17}\]  
where \(E_{\text{AD}}\) is the cumulative hysteretic energy dissipation of components; \(n\) is the total number of laps loaded by the test; and \(\Delta W_{\text{i}}\) is the area of hysteresis loop corresponding to the \(i\)-th displacement amplitude.  
In the three groups of specimens, the relationship curve between the area of three hysteresis loops at the \(i\)-th level of displacement amplitude and the displacement before and after corrosion is shown in Figure 29. A comparison of three sets of energy consumption-displacement curves shows that an increase in both ultimate displacement and reinforcement ratio will enhance the energy dissipation capacity of specimen to a certain extent. In addition, for specimens with different column heights and reinforcement ratios, the energy consumption level of specimens subjected to Cl\({}^{-}\) corrosion was significantly lower than those of the corresponding un-corroded specimens. The values of cumulative hysteretic energy dissipation for each specimen from the beginning to the end of loading are listed in Table 5. It shows that the cumulative hysteretic energy dissipation of specimens LLE, MME, and MHE only occupied 43.65%, 20.38%, and 24.8% of the corresponding original un-corroded specimens, respectively.  
Figure 29: Comparison of energy dissipation–displacement curves of specimens.
# Extended Depth-of-Field of a Miniature Optical Endoscope Using Wavefront Coding
## 2 Principle and Concept  
In this section, we show the fundamental theory of wavefront coding that enables the DOFs of incoherent optical imaging systems to be extended [11; 15]. This approach involves a phase plate to create blurred images that are almost invariant to defocus. On the basis of Fourier optics theory, wavefront coding technology combines the ambiguity function, from the radar field to optical transfer functions, and the method of stationary phase, normally used to estimate oscillatory integrals.  
The generalized pupil function takes the form  
\[P(x,y,\varphi)=p(x,y)e^{i\left[f(x,y)+\varphi(x^{2}+y^{2})\right]} \tag{1}\]  
where  
\[p(x,y)=\left\{\begin{array}{cc}1&x,y\text{ {in the pupil}}\\ &0\text{ {others}}\end{array}\right. \tag{2}\]  
In Equation (1), \(\varphi\) represents defocus aberration, and \(f(x,y)\) denotes the modulation factor.  
Being the impulse response of a focused optical system, the point spread function (PSF) of the optical imaging system can be calculated by a 2D Fourier transform of the pupil, which is in the form  
\[h(x^{\prime},y^{\prime},\varphi)=cF[P(x,y,\varphi)]F^{*}[P(x,y,\varphi)] \tag{3}\]  
where \(c\) is the constant. As a complex-valued function of spatial frequency, the optical transfer function (OTF) is defined as the Fourier transform of the point spread function  
\[H(u,v,\varphi)=F[h(x^{\prime},y^{\prime},\varphi)] \tag{4}\]  
In addition, the OTF can be seen as a combination of two real-valued functions--modulation transfer function (MTF) and phase transfer function (PTF), which can be defined as  
\[H(u,v,\varphi)=M(u,v,\varphi)\exp(-j\Phi(u,v,\varphi)) \tag{5}\]  
where \(M(u,v,\varphi)\) and \(\Phi(u,v,\varphi)\) are the modulus and phase of the OTF, respectively.  
According to the theory of [PERSON], the defocus aberration \(\varphi\) of an standard optical system is represented by the  
\[\varphi=\frac{\pi D^{2}}{4\lambda}(\frac{1}{f}-\frac{1}{d_{o}}-\frac{1}{d_{i}})=kW _{20} \tag{6}\]  
where \(D\), \(\lambda\), \(f\), \(d_{o}\), \(d_{i}\) represent the diameter of the lens aperture, focus length, object distance and image distance, respectively. \(k\) and \(W_{20}\) stand for the wave number and traditional defocus aberration constant, respectively.  
The OTF of an optical imaging system can be written as a properly scaled autocorrelation of the pupil function  
\[\begin{split} H(v,\varphi)=\int Q(u+\frac{v}{2})Q*(u-\frac{v}{ 2})du\\ =\int[P(u+\frac{v}{2})e^{j(x+\frac{v}{2})^{2}\varphi}]\cdot[P^{* }(u-\frac{v}{2})e^{-j(x-\frac{v}{2})^{2}\varphi}]du\end{split} \tag{7}\]  
where \(u\), \(v\) represent the normalized spatial frequency, respectively.  
For the sake of clarity, our following discussion is 1-D. The 1D OTF with defocus aberration \(\varphi\) can be rewritten as  
\[H(u,\ W_{20})=\left\{\begin{aligned} &\frac{sin(2 kW_{20}u\cdot(1-\frac{|u|}{ 2}))}{2 kW_{20}u}& u\ \
eq\ 0\\ & 1& u=0\end{aligned}\right. \tag{8}\]  
Meanwhile, the normalized MTF is in the form  
\[|H(u,\ W_{20})|=|\frac{sin(2 kW_{20}u\cdot(1-\frac{|u|}{2}))}{2 kW_{20}u}|\ \ u \
eq\ 0 \tag{9}\]
# Queue Spillover Management in a Connected Vehicle Environment
## 5 Conclusions and Future Work  
This paper presents a method to solve the problem of intersection overflow based on vehicle networking technology. The theoretical method proposed in this paper is described mainly through the following steps:  
1. Establish vehicle travelling model;
2. Calculate the maximum control distance according to the model;
3. Queuing overflow logic judgment;
4. Optimal solution of queue overflow;
5. Simulation analysis.  
According to the analysis of the simulation results, it can be concluded that the method of preventing the overflow of an intersection is feasible, which can effectively prevent the overflow of an intersection in a timely and effective manner. It can alleviate traffic to a certain extent, pressure, and is suitable for use in the traffic volume of the main road traffic control.  
The simulation results show that the method can improve the efficiency of trunk line transportation and has a certain practicality and feasibility. This method can be applied to the management and control of local traffic congestion in urban road networks. The research of this paper can promote the application and promotion of the technology, in the field of traffic engineering in the future. Of course, the method of this paper still needs to be further tested, and the problems such as redundant control and emergency disposal are expanded and improved to adapt to the more complicated traffic environment.  
In the future, we will extend from the control of traffic signals at adjacent intersections to the control of signals at regional intersections, to realize the coordinates of vehicles, roads and people in the region. In addition, we can improve cloud computing capability through the emerging automobile network cloud technology, such as mobile edge computing [39]. The rapid development of communication technology provides a strong guarantee for the calculation and sharing of big data in the vehicular cloud environment.  
Conceptualization and Methodology, C.R.; Formal analysis and Writing-original draft, W.Z.; Writing-review and editing, L.Q.; Data curation, B.S.  
This research was funded by [Science and Technology Project of Shandong transportation department] grant number [No. 2017B08]\" and [Key Research and Department project of Shandong Province] grant number [No. 2016 GGX105013].  
The authors declare no conflicts of interest.