esa-sceva/satcom-chunk-collection huggingface dataset:
# The Rapid Detection Technology of Lamb Wave for Microcracks in Thin-Walled Tubes
## 2 Theoretical Principle
### Dispersion Characteristics of Lamb Waves  
In the process of Lamb wave flaw detection, different modes of the Lamb wave will be excited when different incident angles are used, and thus, different sensitivities can be obtained for defects at different locations. Therefore, theoretical analysis is needed to determine which mode is most sensitive to the axial and circumferential distribution defects before testing. The Rayleigh-Lamb frequency equation is used to describe the wave characteristics of Lamb waves [11]. They are expressed mathematically as follows.  
Symmetric mode:  
\[4 pq\tan\frac{\pi fd}{c_{p}}q+\left(p^{2}-1\right)^{2}\tan\frac{\pi fd}{c_{p}}p=0, \tag{1}\]  
Antisymmetric mode:  
\[\left(p^{2}-1\right)^{2}\tan\frac{\pi fd}{c_{p}}q+4 pq\tan\frac{\pi fd}{c_{p}}p=0, \tag{2}\]  
where \(p=\sqrt{\left(\frac{c_{p}}{c_{T}}\right)^{2}-1}\), \(q=\sqrt{\left(\frac{c_{p}}{c_{L}}\right)^{2}-1}\), \(c_{p}\) is the phase velocity of Lamb wave, \(c_{T}\) is the S-wave velocity, \(c_{L}\) is the P-wave velocity, \(f\) is the frequency of Lamb wave, and \(d\) is the thickness of the plate.  
In calculating the dispersion characteristics of the Lamb waves, the P-wave velocity \(c_{L}\) and S-wave velocity \(c_{T}\) of the steel plate were 5970 and 3200 m/s, respectively. The Rayleigh-Lamb frequency equation was solved numerically using Matlab, and the phase velocity \(c_{p}\) vs. frequency-times-thickness \(fd\) dispersion curves of the Lamb wave was obtained, as shown in Figure 2.  
Because the wall thickness of the thin-walled tubes varied from 0.8 to 1.2 mm, when a 5 MHz transducer was used for detection, the frequency-times-thickness was 4-6 MHz-mm, as shown by the red dotted line in Figure 2. As shown in the figure, the possible modes of the Lamb wave included the \(A_{0}\), \(S_{0}\), \(A_{1}\), \(S_{1}\), \(A_{2}\), and \(S_{2}\) modes. Because the phase velocities of the \(A_{1}\), \(S_{1}\), \(A_{2}\), and \(S_{2}\) modes vary greatly, the position of the defect echo signal is not fixed on the time axis, and it is difficult to identify the defect signal. Therefore, only the \(A_{0}\) and \(S_{0}\) modes were considered in the detection process.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Defect Types** & **Defect Position (Away from the Mouth)/mm** & **Defect Depth/mm** \\ \hline Inner circumferential distribution & 40 & 0.045 \(\pm\) 0.005 \\ Inner axial distribution & 60 & 0.045 \(\pm\) 0.005 \\ Outer axial distribution & 50 & 0.045 \(\pm\) 0.005 \\ Inner axial distribution & 80 & 0.045 \(\pm\) 0.005 \\ Outer axial distribution & 60 & 0.073 \(\pm\) 0.005 \\ Inner axial distribution & 80 & 0.073 \(\pm\) 0.005 \\ Outer axial distribution & 60 & 0.10 \(\pm\) 0.005 \\ Outer axial distribution & 80 & 0.10 \(\pm\) 0.
# Exploring the Potential of Sensing for Breast Cancer Detection
## 5 Biosensors for Breast Cancer Detection
### Flow Cytometry  
In the flow cytometry method, the physical scattering process is considered to determine the size, shape, and properties of a single cell in a combination of different cells [118; 119; 120]. Initially, the sample is mixed with saline solution in the cytometer and allowed to pass through a narrowing channel which finally causes a single cell to live in a single droplet. When this droplet with a single cell passes through the laser pulse, the droplet/cell interacts with the laser pulse [118]. The point at which the cell interacts with the laser pulse is known as the \"interrogation point\". After the laser pulse interacts with the droplet/cell, it scatters in different directions. The portion of the laser that moves forward in focus after scattering is known as forward scatter (FSC) [118; 119; 120], and the portion of the laser that moves in the sideways direction after scattering is known as side scatter (SSC) [118]. Experimental setup of flow cytometry can be seen in Figure 4.  
Forward scattering pulses provide information associated with the size of a cell, while sideways scattering pulses provide information associated with the shape and internal complexity of a cell [118]. The forward (sideways) scattering laser pulses are detected by detector 1 (detector 2) and converted into voltage pulses. These data are converted to a histogram plot according to the scattered light and cell number [118]. Researchers investigate both forward and sideways scattering data together and determine a cell's size, shape, and complexity [118; 119; 120].  
In [122], the authors applied the flow cytometric method to determine the number of cancer cells in peripheral blood. They studied the reactivity of several reagents, viz., anti-CK20, anti-CK7, anti-pan-CK, anti-CK8, anti-CK8/CK18, and anti-CK18, and found that this sensing system could detect a single cancer cell from \(10^{6}\) to \(10^{7}\) nucleated blood cells. In [123], authors saw different types of breast cancer markers, viz., ER, PR, HER-2/neu, (EGFR), and E-cadherin, by applying the flow cytometry method and reported the following: (a) a rigorous positive pattern had been observed for E-cadherin with an 85-92% marker expression; (b) in comparison with E-cadherin, a weakly positive expression pattern had been demonstrated by ER, PR, and HER-2/neu; (c) there was an inverse relation between HER-2/neu expression levels with MCF7 cell lines. So, flow cytometry is a powerful tool for detecting breast cancer in the primary stage.
# Free Vibration Analysis of a Thermally Loaded Porous Functionally Graded Rotor-Bearing System
## 1 Introduction
A vibration analysis of an FG rotor-bearing system whose FG shaft had a transverse crack and a surface crack was performed using finite element formulation [27; 28]. [PERSON] et al. [37] investigated the natural and whirl frequencies of a corroded FG rotor-bearing system using the finite element method (FEM). Even though a few works on the natural frequency analysis of cracked and corroded FG rotor systems are available [27; 28; 37] in the literature, to the best of the authors' knowledge, research on the dynamic analysis of a porous FG rotor system is rarely found. Recently, an attempt was made by [PERSON] and [PERSON] [38] to analyse the natural frequencies of a porous FG rotor-bearing system without thermal effects using ANSYS three-dimensional finite elements. However, this is an approximate analysis without any thermal effects and the model is not suitable for time dependent steady-state and transient dynamic vibration analyses. Since FG rotors are generally subjected to elevated temperatures to make use of FG material properties, a comprehensive and accurate study of the natural and whirl frequencies of a porous FG rotor system using efficient modelling is required, which is addressed in the present work. This literature review reveals that the research on the vibration analysis of porous FG rotor-bearing systems is extremely limited. Since the porosity affects the dynamic characteristics of an FG rotor system, it is important to analyse the dynamic properties, such as natural and whirl frequencies, to avoid rotor failures. Our main interest in the present work is to investigate the natural and whirl frequencies of a porous FG rotor-bearing system using a novel two-node porous FG shaft element. Dimensionless natural frequencies and whirl frequencies are computed for different power law indices (k) and volume fractions of porosity (\(\alpha\)) at different temperature gradients (\(\Delta\)T).
## 4 Conclusions  
The single-factor variable test method was used to examine the impacts of the repetition frequency of the femtosecond laser, the energy\(-\)flux density of the focused spot, and the scanning speed on the material removal rate for the diamond film surface.  
1. The diamond film's ablation width and depth at various repetition frequencies do not fluctuate much due to the ablation equilibrium, and the material removal rate tends to be constant when the energy \(-\)flux density of the focused point and the scanning speed are known.
2. After determining the femtosecond laser repetition frequency and scanning speed, the focused spot energy \(-\)flux density increases, increasing the femtosecond laser's etching intensity. This increases the etching width and ablation depth significantly, as well as the rate at which material is removed.
3. The spot overlap rate reduces as the scanning speed increases, but the laser energy density does not change; therefore, the etching width does not change significantly when the energy \(-\)flux density and repetition frequency of the focused spot are known. Due to the plasma shielding effect and the decreased spot overlap rate, the etching depth of the film is greatly lowered. The volume of material removed increases and subsequently falls as the scanning speed increases; the material removal rate is highest at \(1\) mm/s.  
Conceptualization, L.R. and Y.M.; methodology, Y.M.; software, H.C.; validation, L.R., Y.M. and X.Z.; formal analysis, H.C.; investigation, Y.H.; resources, C.W.; data curation, H.C.; writing--original draft preparation, H.C.; writing--review and editing, C.W.; visualization, Y.H.; supervision, L.R.; project administration, Y.M.; funding acquisition, L.R. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of Anhui Province, grant number 1208085 ME63, 1908085 ME129, and Natural Science Foundation of Anhui Education Department, grant number KJ2015 A050, KJ2015 A013, and Humanities and Social Sciences Research Program of Higher Education Institutions in Anhui Province SK 2019 JD 21.  
The authors declare no conflict of interest.
## 3 Structural Optimization and Actuation Design
### Actuation Design
#### 3.2.2 Actuator Verification  
To explore the influence of the clockwork spring on the hopping performance of the robot, as well as the above theoretical analysis, dynamics simulations were performed for the hopping robot HR-A (with special actuator) and hopping robot HR-B (only with the rigid motor).  
Figure 10 shows the deformation velocity of the tension spring and the angular velocity of the crank-gear-bar \(P_{1}P_{3}\) of the HR-A. The X-axis is the simulation time. The blue dashed line represents the deformation velocity of the tension spring (corresponding to the right Y-axis), and the red solid line represents the angular velocity of the \(P_{1}P_{3}\) (corresponding to the left Y-axis).  
In the early and middle phases of the hopping cycle, the deformation velocity of the tension spring increases slowly as the angular velocity of the crank-gear-bar \(P_{1}P_{3}\) grows gradually. However, in the late stage, the deformation velocity of the tension spring and the angular velocity of the crank-gear-bar \(P_{1}P_{3}\) both sharply increase, indicating that the effect of the clockwork spring in the actuator is effective in storing elastic energy for hopping.  
To further investigate the performance of the proposed actuator, the deformation velocities of the tension springs in HR-A and HR-B are compared, as shown in Figure 11a. Where the blue dashed line represents the deformation velocity of the tension spring in HR-A, and the red solid line represents the deformation velocity of the tension spring in HR-B. For HR-A, the deformation velocity of its tension spring increases slowly before \(\mathfrak{t}=0.521\) s. After this time, the energy of the clockwork spring is released, then the deformation speed of the tension spring increases rapidly, reducing the energy storage time to \(0.63\) s. However, the deformation velocity of tension spring in HR-B only grows and decreases steadily in its whole energy storage phase, which reaches the length of \(0.704\) s. Key positions in the simulation of HR-A and HR-B are compared in Figure 11b.  
Figure 10: Simulation results of the HR-A. The deformation velocity of the tension spring and the angular velocity of the crank–gear–bar \(P_{1}P_{3}\).
## 3 Other Applications
### Environmental Monitoring Other Than Rainfall Estimation
#### 3.1.1 Monitoring Phenomena Related to Water Vapor  
The method of monitoring water vapor using CMLs that are operating at the water vapor absorption band around 22 GHz was first proposed by [PERSON] et al. [99], whereas [PERSON] et al. [100] and [PERSON] et al. [101] also suggested the use of phase delay and normalized differential spectral attenuation to measure absolute humidity, respectively. Subsequently, [PERSON] et al. [102] generated daily moisture fields in Israel, and the results were in better agreement with ground-based observations than the current weather prediction product. With the expansion of microwave frequencies, the potential of E-band CMLs to monitor near-surface water vapor was demonstrated in [103]. In addition, deep learning methods have also been applied to water vapor estimation by CMLs. [PERSON] et al. [104] trained an SVM model using prior data from RSL and water vapor density to mitigate the estimation error introduced by the process of extracting water vapor attenuation from RSL. [PERSON] et al. [105] achieved high temporal resolution (5-min) water vapor retrieval based on E-band CMLs using an LSTM deep learning model. More studies using CMLs for water vapor estimation can be found in the literature [106; 107; 108; 109]. In addition to these, CMLs have been shown to be valid in recognizing and measuring fog [110; 111; 112] and dew [113].
# Multi-Source Image Fusion Based Regional Classification Method for Apple Diseases and Pests
###### Abstract  
Efficient diagnosis of apple diseases and pests is crucial to the healthy development of the apple industry. However, the existing single-source image-based classification methods have limitations due to the constraints of single-source input image information, resulting in low classification accuracy and poor stability. Therefore, a classification method for apple disease and pest areas based on multi-source image fusion is proposed in this paper. Firstly, RGB images and multispectral images are obtained using drones to construct an apple diseases and pests canopy multi-source image dataset. Secondly, a vegetation index selection method based on saliency attention is proposed, which uses a multi-label Relieff feature selection algorithm to obtain the importance scores of vegetation indices, enabling the automatic selection of vegetation indices. Finally, an apple disease and pest area multi-label classification model named AMMFNet is constructed, which effectively combines the advantages of RGB and multispectral multi-source images, performs data-level fusion of multi-source image data, and combines channel attention mechanisms to exploit the complementary aspects between multi-source data. The experimental results demonstrated that the proposed AMMFNet achieves a significant subset accuracy of 92.92%, a sample accuracy of 85.43%, and an F1 value of 86.21% on the apple disease and pest multi-source image dataset, representing improvements of 8.93% and 10.9% compared to prediction methods using only RGB or multispectral images. The experimental results also proved that the proposed method can provide technical support for the coarse-grained positioning of diseases and pests in apple orchards and has good application potential in the apple planting industry.  
multi-source images; apple diseases and pests; vegetation indices; feature fusion; deep learning +
Footnote †: journal: Medical Image Analysis  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Multi-Source Image Fusion Based Regional Classification Method for Apple Diseases and Pests. _Appl. Sci._**2020**, _14_, 7695. [[https://doi.org/10.3390/app14177695](https://doi.org/10.3390/app14177695)]([https://doi.org/10.3390/app14177695](https://doi.org/10.3390/app14177695))
# A Study of Order Modification for Reactive Power Support by Wind Farm during Communication Failure
## 3 Case Studies
### Simulation Results
#### 3.2.3 Imposing Detachment Situation  
In situations where the detachment of several wind turbines occur, due to unexpected faults and communication errors, this modification has to be maintained. Figures 11 and 12 represent the case C analysis: As the first wind turbine in array 1 is detached due to a fault, the entire reactive power supply capability of array 1 is significantly reduced. Figure 11b shows an increase in the previously measured voltage drop in array 1, case B. This situation can be resolved if the modification method independently changes its proportion. Figure 12a illustrates this changed reference, using the supplied reactive power quantity. After the first reference is induced, each wind turbine takes additional references, in comparison with the first state. The previous reactive power reference for the first wind turbine is then distributed to the remaining wind turbines. Hence, further reliable results are derived in terms of reactive power supply. This result is intended to be a loss minimization technique that can lead to a continuous management process, irrespective of whether unexpected communication errors occur or not.  
Figure 11: Reactive power supply from array 1 and voltage condition in case C—conventional algorithm, with (**a**) reactive power supply, and (**b**) voltage variation.
## 3 Detection of GFPS Condition Based on the Laplace Transform
Furthermore, for any \(\eta>0\), there must exist a constant \(\rho(\eta)>0\) such that  
\[|H(t,A_{1})-H(t,A_{2})|\leq\rho(\eta)|A_{1}-A_{2}|,\quad(|A_{1}|,|A_{2}|\leq\eta) \tag{20}\]  
From the results given by [PERSON] [47], if the above conditions are satisfied, the successive approximations  
\[\omega_{0}(t)=0,\quad\omega_{n+1}(t)=B(t)+\int_{0}^{t}T(t-\tau)H(\tau,\omega_ {n}(\tau))d\tau,\quad n=0,1,2,\cdots \tag{21}\]  
will uniformly converge to the unique continuous solution \(A(t)=\omega(t)\) of Equation (19). Comparing Equations (17)-(19), it is easy to check that \(F_{1,2,3}\), which are defined in Equation (10), are smooth enough to satisfy condition (20). From Equation (21), \(e_{1j}=0\), \(j=1,2,\cdots,n\), \(e_{2j}=0\), \(j=1,2,\cdots,m\), \(e_{3j}=0\), \(j=1,2,\cdots,l\), are the unique continuous solutions of Equations (17) and (18). According to Theorems 1 and 2, we have the following result. **Theorem 3**. : _GFPS in systems (1) and (3) can be achieved if the matrix \(M\) defined in Equation (13) only has eigenvalues with negative real parts._
## 3 Results and Discussion
### Model Verification and Adequacy  
The residual plots (Figures 4-7) were analyzed to check the developed model's adequacy. The plot of the normal probability versus the internally studentized residuals is shown in Figure 4. As portrayed in the figure, the internally studentized residuals do not deviate considerably from the normal probability line as they are distributed along vertical trajectories, suggesting that they were normally distributed in the model response. Thus, indicating the model's accuracy in describing the relationship between the independent and dependent variables. A suitable model can also be determined based on the distribution of data points around the mean of the response variable. Figure 5 displays the internally studentized residuals versus the predicted values. From the figure, a random distribution of the residuals between \(\pm\)3.0 is noted. Thus, indicating that the approximation of the fitted model to the response is reasonably valid with no data recording high error; hence the model is good and reliable in predicting the response. Figure 6 illustrates the relationship between the values of the predicted response and the actual values. It is observed from the figure that the predicted values are in good agreement with the actual values in the operating variable range since most of the values lie along the diagonal line. Therefore, the model is accurately describing the experimental data. Figure 7 portrays the internally studentized residuals versus the run number to assess the satisfactory fit of the developed model. An arbitrary pattern was detected in the residuals vs. run plot, and all the data points fell within the range of control limits (i.e., \(\pm\)3.0). This indicates the experiments were conducted randomly, thus minimizing any chance of errors and ensuring a good fit. From the observed pattern in Figures 4-7, it is evident that the established model can reliably predict the experimental data.  
To demonstrate the correlation between the response (hydraulic conductivity) and the independent variables, a perturbation chart (Figure 8) was generated to evaluate the individual and shared effects on the response. It can be observed from Figure 8 that, by increasing the contents of the independent variables, the hydraulic conductivity of the carbonated specimen decreases gradually. Nonetheless, the hydraulic conductivity reduction with increasing carbonation pressure (C) was not very pronounced relative to the other two factors (olivine content (A) and carbonation time (B)). This is also confirmed with a relatively high \(p\)-value for carbonation pressure (0.0072) relative to the olivine content (<0.0001) and carbonation time (<0.0001) from the ANOVA analysis.  
Figure 4: Normal probability plot of internally studentized residuals.  
Figure 5: Plot of studentized residuals vs. predicted response.  
Figure 6: Plot of predicted response vs. actual value.  
Figure 7: Plot of studentized residuals vs. run number.