esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Deep Learning Models  
In the paper, 3 pretrained models--namely, AlexNet, GoogleNet, and Xception--are independently applied to classify 2D FBM. The AlexNet model is a network of 25 layers, consisting of 5 convolutional layers and 3 fully connected layers. The AlexNet model is suitable for problems with less training time and less overfitting. In addition, the AlexNet model successfully applied some tricks--such as ReLU, Dropout, and LRN--in the CNN for the first time. At the same time, the GPU was used for computing acceleration [35].  
The GoogleNet model is a network of 144 layers. It was proposed by the Google team and won first place in the 2014 ImageNet competition by reducing the error rate to 6.7% in the classification problem. The GoogleNet model was designed by using a special architecture--the Inception architecture--as its main core. The Inception architecture was first proposed by [PERSON] et al. [36] to replace the role of the original convolutional layers.  
It connected several convolutional layers and pooling layers with different filter sizes in the same layer, as well as stacked convolutional filters of different sizes. The Inception architecture can increase the richness of features extracted from input image data. The \(1\times 1\) convolutional filter added to the Inception architecture can extract only the channel information of the input images without the spatial information. It means that the \(1\times 1\) convolutional filter can separate out the channel features from the input images with no calculation of the spatial features. Another most commonly used purpose of the \(1\times 1\) convolutional filter is that it can perform dimensionality reduction. In addition, the original fully-connected layer is replaced with average pooling, and two auxiliary classifiers are added in the middle to avoid gradient vanishing. After that, the Inception architecture was improved many times, and it has now reached the InceptionV4 version [35; 36].  
The Xception model is a network of 170 layers. It was also proposed by the Google team in 2017 and developed based on the InceptionV3 version. Its main principle is to use depthwise separable convolution to replace the convolutional operation of the original network, and its depthwise separable convolutional operation layer consists of 2 layers--namely, the depthwise convolutional layer and pointwise convolutional layer. The performance of the model has been further improved while the network complexity basically remains the same. For example, the recognition rate for complex images by the deeper structure can often achieve better results than the shallower structure. That is, the purpose of Xception is different from that of Inception. The goal of Inception is to pursue the highest accuracy for classification tasks, resulting in an overly refined model. However, the goal of Xception is to design a model that is easy to transfer, requires less computation, can adapt to different tasks, and still has high accuracy [37].
## 3 Time-Domain Analytical Solution of the Received Signal
### Time-Domain Analytical Solution of the Received Magnetic Induction Intensity Signal  
#### 3.2.1 Full-Period Time-Domain Analytical Solution  
First, we decompose Equation (22) into \(R_{0}(p)+\sum\limits_{m=1}^{\infty}R_{m}(p)\). According to the frequency shift and scale transformation properties of the Laplace transform [29], and using the standard Laplace inverse transform formula [30], we can obtain:  
\[R_{0}(t)=\sqrt{\frac{4\mu_{r}^{2}\lambda_{i}^{2}}{\pi\tau t}}e^{-\frac{ \lambda_{i}^{2}t}{\tau}}-\frac{2\mu_{r}^{2}\lambda_{i}^{2}}{\tau}\mathrm{erfc} \left(\mu_{r}\sqrt{\frac{\lambda_{i}^{2}t}{\tau}}\right)-\delta(t) \tag{30}\]  
\[R_{m}(t)=\frac{\lambda_{i}^{2}e^{-\lambda_{i}^{2}t/\tau}}{\tau}\Bigg{[}\sum \limits_{j=0}^{2m}C_{2m}^{j}(-2)^{j+2}\mu_{r}^{j+1}\varphi_{j-1}\Bigg{(}\mu_{ r},2m\lambda_{i}d,\frac{\lambda_{i}^{2}t}{\tau}\Bigg{)}+\sum\limits_{j=0}^{2m-1}C_{2m- 1}^{j}(-2)^{j+2}\mu_{r}^{j+3}\varphi_{j+1}\Bigg{(}\mu_{r},2m\lambda_{i}d,\frac{ \lambda_{i}^{2}t}{\tau}\Bigg{)}\Bigg{]} \tag{31}\]  
where \(\tau=\mu_{0}\mu_{r}\sigma\), \(\varphi_{j}(a,k,t)=(4t)^{\frac{j}{2}}e^{ik+a^{2}t}i/\mathrm{erfc}\Big{(}a\sqrt {t}+\frac{k}{2\sqrt{t}}\Big{)}\). \(\mathrm{Erfc}(x)\) and \(\mathrm{erfc}(x)\) represent the error function and complementary error function, respectively, and \(i^{j}\mathrm{erfc}(x)\) is the \(j\)-th integral of the complementary error function.  
To obtain the time-domain expression for the reflected magnetic induction intensity of the Hall sensor under actual conditions, we first use MATLAB 2023b to perform the fast Fourier transform (FFT) on \(i(t)\) and \(R_{0}(t)+\sum\limits_{m=1}^{\infty}R_{m}(t)\), respectively. After multiplying the two results, we apply the inverse FFT (IBFT) to the product. Finally, the inverse transform result is combined with the remaining part through series operations.  
\[B_{ref}(t)=\sum\limits_{i=1}^{\infty}\frac{\mu_{0}e^{-\lambda_{i}x_{h}}Y( \lambda_{i})}{h^{2}I_{0}^{2}(\lambda_{i}h)}\cdot\frac{1}{2\pi}\int_{-\infty}^ {+\infty}\Bigg{[}R_{0}(\omega)+\sum\limits_{m=1}^{\infty}R_{m}(\omega)\Bigg{]} \cdot I(\omega)e^{j\omega t}d\omega \tag{32}\]
# A Review of Medical Diagnostic Video Analysis Using Deep Learning Techniques
## 10 Advancing Medical Diagnostic Videos: Limitations and Future Work
### Transfer Learning  
Some models, such as VGG-16 and GoogleNet, are already trained on one million images and can be used for video data processing. Our discussion shows that such heavily labeled data is not readily available for medical cases, so researchers are exploring transfer learning to resolve this problem. Several studies, such as [70; 73; 75], have exploited transfer learning to solve the problem of limited data and have achieved good results. The pre-trained models are further fine-tuned to be applied to medical problems. Another form of transfer learning is yet to be explored in the medical field. As we know, deeper models need a large number of parameters to be trained. However, there is a possibility to divide the data into low-level and high-level features and then freeze the features that are similar in all videos. This way, the number of learnable parameters can be reduced; thus, fewer data will be required. The low-level features can also be transferred by another pretrained model which is already trained on similar data.
## 2 Materials and Methods
### Process Design
#### 2.2.2 Laser Engraving  
In this paper, fabrics are woven with fine and soft POFs which usually have an end-glowing effect, but not a side-glowing effect. At present, the commonly used methods to improve the POF's side-glowing effect are destroying the POF's core-skin structure and bending the POF. The principle of both methods is to destroy the total reflection of light  
Figure 3: Structural design of the fabric’s sensing area: (**a**) Schematic (before pressing); (**b**) POF fabric sample; (c) schematic (after pressing).  
Figure 2: Sample of the double-layer POF fabric: (**a**) Face layer; (**b**) back layer; (**c**) light source coupling; (**d**) electrical schematic.  
in the POF, so that the light is well scattered from the side of the POF, to improve the side-glowing effect of the POF [15].  
Currently, laser engraving technology has been widely applied in the textile industry [16]. Based on the structural characteristics of POFs, laser engraving can be applied to the surface of POF fabrics to enhance the side-glowing effect of POFs and create bright luminescent patterns on the fabric by destroying the core-skin structure [17]. In order to achieve better interactivity of this smart textile, combined with the aforementioned fabricating process, the patterns and positions for laser engraving are designed.  
As shown in Figure 4a, a \"sun\" pattern with a size of 5 cm \(\times\) 5 cm is laser-engraved on the sensing area-1 of the fabric, and a \"mountain peak\" pattern with a size of 12 cm \(\times\) 6 cm is laser engraved in the middle area of the fabric. Because the laser engraving of the sun pattern is on the press-sensitive area of the fabric, the electrical control function is given to the \"sun\" pattern. Through the subsequent circuit module design, this pattern design could help the fabric achieve the interactive effect of pressing the \"sun\" pattern to control the luminescent color of the \"mountain peak\" pattern.  
This pattern design scheme was implemented on the Feihong Laser Inkless Printing Equipment of Suzhou BeaM Optoelectronics Technology Co., Ltd., Suzhou, China. The equipment uses a CO\({}_{2}\) laser and is based on the flying marking technology to perform high-speed laser marking on the surface of POF fabrics. The optimal power for laser engraving is 30 W, the laser wavelength is 9.3 \(\upmu\)m, the engraving speed is 2000 mm/s, and the filling density is 0.2 mm. Figure 4b shows the effect after laser engraving on the POF fabric surface with the \"mountain peak\" pattern.
# Hard Fusion Based Spectrum Sensing over Mobile Fading Channels in Cognitive Vehicular Networks
## 2 System Model and Problem Formulation
### Channel Model  
With the far-field assumption, the distances between the PU and SVUs are wide apart in comparison with the range of CVN. As a result, we can assume that the distances from the PU to the SVUs are approximately identical. We will consider only the small-scale fading.  
A mobile Rayleigh fading channel is usually used to characterize the channel time variations. We can model the channel vector as a zero-mean, unit-variance, complex Gaussian random vector with the land-mobile correlation fading model (Table 2.1 in [21]). Let \(\rho(\tau)\) denote the correlation coefficient between two samples separating \(\tau\) time interval, which satisfies  
\[\rho(\tau)=\mathcal{J}_{0}(2\pi F_{d}T_{s}\tau), \tag{1}\]  
where \(\mathcal{J}_{v}(\cdot)\) is defined by the \(v\)th-order Bessel function of the first kind, and \(F_{d}T_{s}\) represents the normalized Doppler shift, \(F_{d}T_{s}=\frac{f_{c}\tau}{c}T_{s}\), which may relate to a vehicle speed \(v\), a carrier frequency \(f_{c}\) and the sampling interval \(T_{s}\). Note that \(\rho=1\) means a time-invariant channel, \(\rho=0\) means a completely random time-variant channel.  
In general, the channel correlation characteristic is mainly dependent on \(F_{d}T_{s}\). Based on the different \(F_{d}T_{s}\), we can employ a suitable model, which is convenient to analyze the fading characteristic of sensing channel. For instance, three different models, \(\mathcal{M}_{1}\), \(\mathcal{M}_{2}\) and \(\mathcal{M}_{3}\), are given as follows, respectively  
\(\mathcal{M}_{1}\): When \(F_{d}T_{s}\) is relatively smaller (\(<\)0.001), the channel process is nearly time-invariant.  
\(\mathcal{M}_{2}\): When \(F_{d}T_{s}\) is small (\(<\)0.03), the channel process is correlated (\"slow\" fading).  
\(\mathcal{M}_{3}\): For larger values of \(F_{d}T_{s}\) (\(>\)0.03), the channel process are almost independent time-varying (\"fast\" fading).  
It should be pointed out that \(\mathcal{M}_{1}\) and \(\mathcal{M}_{3}\) are two extreme cases of temporally correlated Rayleigh fading channel.
# Identifying Growth Patterns in Arid-Zone Onion Crops (Alium Cepa) Using Digital Image Processing
## 2 Materials and Methods
### Data Processing
#### 2.2.4 Weed Detection  
To perform weed detection in the CIELAB, color-space-based segmentation (CIE 1976 \(L\)_a b_) was implemented as a model used to describe all colors the human eye can perceive. It is described in Equation (4), an international standard for color measurement specified by CIE in 1976 [24]. This color space consists of an \(L\) brightness layer with a black-white ratio from 0 to 100, with the chromaticity layer \(a\) indicating where the color falls along the green-red axis, with negative values for green and positive for red. The chromaticity layer \(b\) indicates the blue-yellow axis with negative values for blue and positive values for yellow.  
\[\begin{split} L&=116\,f\bigg{(}\frac{Y}{Y_{0}} \bigg{)}-16\\ a&=500\,\bigg{[}f\bigg{(}\frac{X}{X_{0}}\bigg{)}-f \bigg{(}\frac{Y}{Y_{0}}\bigg{)}\bigg{]}\\ b&=200\,\bigg{[}f\bigg{(}\frac{Y}{Y_{0}}\bigg{)}-f \bigg{(  
\(f\) is described by Equation (6):  
\[\begin{array}{c}f(q)=\sqrt[3]{q};\\ f(q)=7.787q+\frac{16}{16}\end{array}q\leq 0.008856 \tag{6}\]  
and the CIE XYZ are the tristimulus values of the reference white point. For this study, ill-minant D65 was used, where \([X0\quad Y0\quad Z0]=[0.9504,\quad 1.0,\quad 1.0888]\), simulating midday light with a correlated color temperature of 6504 K.  
In the color space in this representation, \(L\) represents the luminosity of an object, \(a\) represents the variation from green to red, and \(b\) represents the variation from blue to yellow. With this color transformation, a sample region was selected for each color. The average color of each sample region was calculated in space \(ab\), using Equation (4). These samples were used as color markers to classify each pixel in the image. Once the color label was found, the near-neighbors technique was implemented, which is a machine-learning method that classifies an unknown sample according to its neighbors [25]. Each pixel is classified based on calculating the Euclidean distance between that pixel and each color marker, as indicated by Equation (7).  
\[d(x,y)=\sqrt{(a-marker1)^{2}+(b-marker2)^{2}} \tag{7}\]  
where \(a\) and \(b\) are the CIELAB color channels \(a\) and \(b\) while \(marker1\) and \(marker2\) are the values of the markers found for the pixels. The smallest distance denotes the match with a particular color label. The label corresponding to the green color was selected, and the erosion and dilation operations were applied to extract the weed areas.
# Thermal Effect of the Back Radiation from Disk to Head after Laser Heating in HAMR
## 3 Results and Discussion  
### Materials in HAMR  
The magnetic recording layer in HAMR is made out of magnetic materials, which is in progress along with the development of the magnetic recording technology. Until now, FePt has become the next generation of high-density magnetic recording media material. FePt has significant advantages in magnetic recording because it has several excellent characteristics, such as high anisotropy, similar saturation magnetization, small superparamagnetic critical size, and the ability to keep high thermal stability when the size of the particle is reduced to 3-4 nm [31; 32; 33; 34]. To meet the practical requirement of a magnetic recording medium, the alloy needs to be modified by doping. But, in theoretical calculations, the modifying of the material can be ignored [32; 35]. Several studies on the properties of alloys show that the dielectric function of thin alloy films of different alloy ratios basically fluctuates in a similar trend and within a small range [36; 37]. To simplify the calculation, the parameters of a single metal material can be used instead of a complex alloy material.  
\[\epsilon=1-\frac{\omega_{p}^{2}}{\omega^{2}+i\gamma_{e}\omega} \tag{20}\]  
where \(\omega_{p}\) and \(\gamma_{e}\) stand for the equivalent plasma frequency and damping factor of the material, respectively. The two parameters of metal Fe are \(\omega_{p}=0.62204\times 10^{16}\) rad/s and \(\gamma_{e}=0.27709\times 2\pi\times 10^{14}\) rad/s. In the radiation band, the susceptibility of the metal is very small so that the permeability can be regarded as constant by ignoring the influence on the radiative heat flux contributed by the magnetism.  
The permittivity of the material of the magnetic head for calculation, Al\({}_{2}\)O\({}_{3}\), is approximated by the following:  
\[\epsilon=\varepsilon_{\infty}\prod_{j}\frac{\Omega_{jLO}^{2}-\omega^{2}+i \gamma_{jLO}\omega}{\Omega_{jTO}^{2}-\omega^{2}+i\gamma_{jTO}\omega} \tag{21}\]  
where \(\Omega_{jLO}\) and \(\Omega_{jTO}\) are longitudinal and transverse resonance frequency respectively, \(\gamma_{jLO}\) and \(\gamma_{jTO}\) are longitudinal and transverse damping factor respectively. These parameters are related to the temperature, as discussed in Ref. [38].
## 3 Experiments and Results
### Prediction Results Based on Non-linear Supervised Learning Methods
The best method is painted in bold for each performance measure. Table 2 shows the merit figures for estimated EV models for different regions or countries when either only endogenous operational normalized variables, or both endogenous operational normalized and exogenous variables were incorporated. In general terms, for the different markets, we found that bagging tree and SVR methods, better estimated when only endogenous variables were considered. Moreover, GPR and the SVR methods, can yield better results when both type of variables were included.
## 2 AE Monitoring at the 105-C Reactor Facility  
### Acoustic Emission Sensing Systems  
Two separate AE systems were utilized for remote monitoring at the 105-C Reactor Facility. These systems are referred to as a 'wired' AE system and a 'wireless' AE system. All acoustic emission system components and software were manufactured by Mistras Group, Inc. of Princeton Junction, New Jersey. The wired system utilized both R6I (peak resonance near 55 kHz) and WDI (relatively broadband) sensor types (calibration sheets for both sensor types are available in the manufacturer's website). Both sensor types utilize integral pre-amplifiers within the stainless steel sensor housing. The resonant sensors are more sensitive to damage in reinforced concrete structures than the broadband sensors. However, the broadband sensors provide higher fidelity frequency data, which can be useful for data reduction and interpretation. The sensors were connected to a 16-channel DiSP acoustic emission data acquisition system that utilizes four high speed data acquisition boards specifically designed and manufactured for the acquisition and processing of acoustic emission data as well as specialized software (AEWin).  
The wireless acoustic emission system (type 1284) includes 4 channels and utilizes low power PK6I acoustic emission sensors. These sensors are resonant in the vicinity of 55 kHz and utilize integrated preamplifiers within stainless steel housing. The sensors were connected to the 1284 system where preliminary processing of the data is performed. The data is transmitted through an antenna and is received through a base station module that is connected to a conventional laptop computer. Specialized wireless acoustic emission software (AEWin for Wireless) is used for controlling the data acquisition. This system was powered through solar panels connected to 12V DC batteries.
## 4 Bond-Slip Relationship Model at High Temperature
### Parameter Solving and Model Validation  
According to the test results, splitting failure of the NC specimens occurred below 400 \({}^{\circ}\)C. Therefore, the NC specimens were not fitted.  
The relationships between parameters \(\varphi\) and \(\eta\) and \(T\) and \(V_{f}\) are used in the following equations.  
\[\varphi=\ln(1.210-0.021V_{f})+e^{\frac{1}{0.168V-1.246}-0.024T^{\prime}+0.007T^ {\prime 2}} \tag{8}\]  
\[\frac{{\eta_{V_{f}}}^{T}}{{\eta_{V_{f}}}^{20}}=\ln(\lambda+\gamma V_{f})+e^{-2. 255-0.236T^{\prime}+0.020T^{\prime 2}} \tag{9}\]  
\[\left\{\begin{array}{l}\lambda=3.163-0.710e^{0.274T^{\prime}}\\ \gamma=-0.049\left(1-e^{0.717T^{\prime}}\right)\end{array}\right. \tag{10}\]  
where \(V_{f}\) is the fraction of volume by steel fiber, \(V_{f}\)= 0.5, 1.0, 1.5; \(T^{\prime}\) is the temperature influence coefficient, \(T^{\prime}=T/100\); \(T\) is the maximum test temperature, \(T\) = 20, 200, 400 and 600 \({}^{\circ}\)C; \({\eta_{V}}^{T}\) is the fitting parameter of the falling section; \(\lambda\) and \(\gamma\) are temperature-dependent coefficients.  
The parameters \(\varphi\) and \(\eta\) obtained by fitting are shown in Table 6.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Specimen ID**} & \multicolumn{4}{c}{**BPE Model**} & \multicolumn{4}{c}{**Guo Model**} \\ \cline{2-10}  & \(\boldsymbol{\varphi_{T}}\) & \(\boldsymbol{R^{2}}\) & \(\boldsymbol{\varphi_{C}}\) & \(\boldsymbol{\varphi_{C}}\)/\(\boldsymbol{\varphi_{T}}\) & \(\boldsymbol{\eta_{T}}\) & \(\boldsymbol{R^{2}}\) & \(\boldsymbol{\eta_{C}}\) & \(\boldsymbol{\eta_{C}}\)/\(\boldsymbol{\eta_{T}}\) \\ \hline SF0.5T20 & 0.595 & 0.967 & 0.603 & 1.014 & 2
# Refined 3D Solar Temperature Field and Effect Simulation of Ultra-High Steel Bridge Pylon
###### Abstract  
In order to accurately calculate the sunshine-induced temperature effect of an ultra-high bridge pylon, a refined numerical simulation model for the 3D bridge temperature field was established based on the proposed automatic sunshine shadow recognition method and pylon-height-related convection modification method. A suspension bridge H-shaped pylon with a height of 280 m was taken as an example, and the temperature field and corresponding thermal stress and deformation were calculated under typical meteorological conditions in spring, summer, autumn, and winter. The results show that the maximum temperature differences between the outer surfaces of the pylon can reach 19 \({}^{\circ}\)C and 16 \({}^{\circ}\)C for north-south walls and east-west walls, respectively, and exceed the recommended value of \(\pm\)5 \({}^{\circ}\)C in the Chinese Specification. The maximum displacements can reach 370 mm and 110 mm at the top of the bridge pylon in the longitudinal and transverse directions of the bridge, respectively. After the modification of the convective coefficient of the outer surfaces with different wind speeds at different pylon heights, the temperature gradually decreased from the top to the bottom of the pylon, with a temperature difference of 8 \({}^{\circ}\)C. The significant influence of the sunshine shadow was shown on the temperature field and temperature effect of the bridge pylon. By considering the shadow effect, the maximum temperature difference can reach 12 \({}^{\circ}\)C between adjacent sunlt and shaded areas and can reach 14 \({}^{\circ}\)C between two pylon columns. A significant mutation of thermal stress existed in the shaded area, and the maximum stress could be reduced by 13 MPa compared with the adjacent sunlt area. Obvious asynchronous deformation was shown between two pylon columns, and the maximum asynchronous displacement at the top of the pylon can reach 18 mm and 45 mm in the longitudinal and transverse directions, respectively.  
bridge engineering; ultra-high steel bridge pylon; 3D solar temperature field; thermal effect; refined simulation; shadow recognition; convective coefficient +
Footnote †: journal: Applied Sciences