esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 SSA-CAE Based Abnormal Data Classification Techniques in Edge Intelligence Device
### Data Classification Method through Deep Learning Model  
A convolutional autoencoder (CAE) is a variation of the convolutional neural network used as a tool for unsupervised learning of convolutional filters. In general, it is applied to image reconstruction tasks to minimize reconstruction errors by learning optimal filters. It can be applied to all inputs to extract functions that have completed learning about this task. The convolutional autoencoder is a general-purpose function extractor different from a general autoencoder that does not consider a 2D image structure.  
In an autoencoder, an image must be rolled into a single vector and a network must be built according to the constraints on the number of inputs. Therefore, the number of data sets used in this experiment averaged 160, which is about 2 min and 40 s of data. To match the number of inputs, the 36 most preceding arrays of 160 were expanded at the end of the array and applied in two dimensions.  
The vibration signal, which served as an input value, is a one-dimensional signal of \(1\times 196\), the size of the input data was set to \(14\times 14\). The feature map was identified through the convolution kernel and maxpooling is used for down sampling. The sigmoid function was used as an activation function, and Adam was used as an optimization function. The detailed architecture of CAE can be found in Table 1.
# A New Image Oversampling Method Based on Influence Functions and Weights
## 3 The Proposed IFW-SMOTE
### Initial Image Sample Selection  
Let \(f(x,\theta)\) denote our pre-trained model  
\[\hat{\theta}_{\varepsilon,x} =\arg\min_{\theta}\left(\frac{1}{n}\sum_{i=1}^{n}L(y_{i},f(x_{i}, \theta))+\varepsilon L(y,f(x,\theta))\right)\] \[=\arg\min_{\theta}[R(\theta)+\varepsilon L(y,f(x,\theta))] \tag{4}\]  
According to Equation (4), the sensitivity of the sample weights to the model parameters can be obtained. The importance of this sample to the whole model can be measured by the sensitivity of this change as shown in Equation (5), and the Hessian Matrix expression is (6):  
\[I(x;\theta)=\left.\frac{d\hat{\theta}_{\varepsilon,z}}{d\varepsilon}\right|_{ \varepsilon=0}=-H^{-1}\triangledown_{\theta}L(y,f(x,\theta)) \tag{5}\]  
\[H\overset{\text{def}}{=}\frac{1}{n}\sum_{\theta}^{n}\
abla_{\theta}^{2}L(y_{ i},f(x_{i},\theta)) \tag{6}\]  
where \(I(x;\theta)\) is the derivative of the weights with respect to \(\varepsilon\), evaluated at \(\varepsilon=0\), which measures the rate of change in the parameter estimates as small \"weights\" are added to the data \(x_{i}\). \(\
abla_{\theta}^{2}\) is the second-order partial derivative of the loss function concerning the parameter \(\theta\), which measures each sample \(x_{i}\)'s contribution to the loss function's curvature.  
After finding the impact value of \(x_{i}\) for each minority sample, the sample score is calculated using Equation (7), and the samples are filtered through Equation (8):  
\[IFW(x_{i})=I(x_{i},\theta)w_{i} \tag{7}\]  
\[Select\quad IFW(x_{i})<\delta \tag{8}\]  
In Equation (8), \(\delta\) is a hyperparameter. According to the probability statistical function, the mean \(\overline{IFW}\) and variance \(S^{2}\) of the sample scores are calculated, and \(\delta=\overline{IFW}\pm 2S^{2}\), where \(\delta\) denotes the samples within the normal distribution range, and the large samples outside of this range are outliers and belong to the noise samples.
# Role of 4-Hexylresorcinol in the Field of Tissue Engineering
## 2 Biological Activity of 4-HR and Effect on Tissue Healing  
### Antimicrobial Activity  
4-HR is known to have antimicrobial and antiparasitic activities [17]. It was used as an antiparasitic and antiseptic agent in the medical field for the treatment of typhoid carriers in the 1920s [18; 19]. Recently, it has been used as an ingredient in topical antiseptics for the treatment of sore throats [20]. 4-HR has antioxidant and antigenotoxic properties and inhibits bacterial, fungal, and parasite growth [21]. Moreover, it has bactericidal activity against oropharyngeal organisms related to acute sore throats [2]. Since 4-HR is a derivative of phenolic lipid, its antimicrobial activity is related to the biological activity of phenolic lipids. Phenolic lipids have cytotoxic, antimicrobial, and antiparasitic activity, and interacts with the biological membrane and cellular metabolism. It affects the phospholipid cell membrane and changes the membrane structure and permeability, leading to disturbance of the membrane. Phenolic lipids also affect intercellular proteins and the DNA structure [21].  
4-HR exhibits antimicrobial activity when incorporated into biomaterial. 4-HR incorporated into a silk disk and suture material releases from the silk in an aqueous medium and inhibits the growth of seven species of microbes in an anti-bacterial test [13]. Further, when 4-HR is an ingredient in a poly(lactic-co-glycolic acid) (PLGA) film, which is a biodegradable polymer and is used as a drug carrier, the 4-HR-loaded PLGA film exhibits antimicrobial activity against a panel of Gram-negative and Gram-positive bacteria, yeast, and fungi [10]. When 4-HR was incorporated into bovine bone, the bone disc had an antibacterial effect and inhibited the growth of two species of oral pathogens [15].
# Modal Strain Energy-Based Structural Damage Detection Using Convolutional Neural Networks
###### Abstract  
In this paper, a convolutional neural network (CNN) was used to extract the damage features of a steel frame structure. As structural damage could induce changes of the modal parameters of the structure, the convolution operation was used to extract the features of modal parameters, and a classification algorithm was used to judge the damage state of the structure. The finite element method was applied to analyze the free vibration of the steel frame and obtain the first-order modal strain energy for various damage scenarios, which was used as the CNN training sample. Then vibration experiments were carried out, and modal parameters were obtained from the modal analysis of the vibration signals. The experimental data were inputted into the CNN to verify its damage detection capability. The result showed that the CNN was effective in detecting the intact structure, single damage, and multi damages with an accuracy of 100%. For comparison, the same samples were also applied to the traditional back propagation (BP) neural network, which failed to detect the intact structure and multiple-damage cases. It was found that: (1) The proposed CNN could be trained from finite element simulation data and used in real frame structure damage detection, and it performed better in structural damage detection than BP neural networks; (2) the measured data of a real structure could be supplemented by numerical simulation data, and satisfactory results have been demonstrated.  
Keywords:structural damage detection; convolutional neural networks; modal strain energy; steel frame; finite element method; vibration experiment +
Footnote †: journal: Applied Sciences
## Nomenclature
\begin{tabular}{l l} \(\Delta p_{\text{GL}}\) & static pressure increase in the volute \\ \(\Delta p_{\text{GL},\text{loss}}\) & pressure loss volute \\ \(\Delta p_{\text{P}}\) & static differential pressure of the pump \\ \(Q\) & flow rate \\ \(\widetilde{Q}\) & flow rate of the impeller \\ \(Q^{*}\) & flow rate at BEP \\ \(Q_{\text{exp}}\) & measured flow rate \\ \(Q_{\text{Sp}}\) & leakage flow \\ \(r\) & radius \\ \(Ro_{\text{m}}\) & modified Rossby number \\ \(s\) & dimensionless streamline length \\ \(S_{\text{pro}}\) & entropy production \\ \(\widetilde{S}_{\text{pro}}\) & averaged entropy production \\ \(S_{\text{pro}}\) & turbulent part of entropy production \\ \(t\) & tuning factor \\ \(t\) & tuning vector \\ \(t_{\text{s}}\) & measured period \\ \(u\) & circumferential velocity \\ \(u_{1,\text{m}}\) & circumferential velocity mean meridian streamline impeller inlet \\ \(u_{2}\) & circumferential velocity impeller outlet \\ \(V\) & tank volume \\ \(v_{\text{ps}}\) & flow velocity pressure side \\ \(v_{\text{ss}}\) & flow velocity suction side \\ \(w\) & relative velocity \\ \(\underline{w}\) & vector relative velocity \\ \(w_{1}\) & relative velocity impeller inlet \\ \(w_{1,\text{m}}\) & relative velocity mean meridian streamline impeller inlet \\ \(w_{2}\) & relative velocity impeller outlet \\ \(w_{\text{m}}\) & mean relative velocity \\ \(w_{\text{s}}\) & shock velocity \\ \(Y\) & specific hydraulic work \\ \(\widetilde{Y}\) & specific impeller work \\ \(Z_{\text{BC}}\) & specific Borda-Carnot shock loss of the impeller \\ \(Z_{\text{GL},\text{R}}\) & specific friction and turbulence loss of the volute \\ \(Z_{\text{GL},\text{St}}\) & specific shock loss of the volute \\ \(Z_{\text{LN},\text{Ka}}\) & specific friction and turbulence loss of the impeller \\ \(Z_{\text{Rez}}\) & specific recirculation loss \\ \(Z_{\text{LN},\text{St}}\) & specific shock loss of the impeller \\ \(z\) & objective functional \\ \(z_{\text{L}}\) & number of impeller blades \\ \(\alpha\) & flow angle \\ \(\Delta\alpha\) & whirl angle \\ \(\Delta\alpha_{\text{pb}}\) & prewhirl body angle \\ \(\beta\) & blade angle angle \\ \(\beta_{2\text{B}}\) & blade angle impeller outlet \\ \(\gamma\) & performance factor \\ \(\eta_{\text{h}}\) & hydraulic efficiency of the pump \\ \(\eta_{\text{Rez}}\) & recirculation efficiency \\ \(\eta_{\text{Sp}}\) & volumetric efficiency \\ \(\rho\) & density of the pumped medium \\ \(\phi\) & norm of the objective functional \\ \(\phi_{\text{grad}}\) & norm of the objective functional of the gradient \\ \(\Delta\varphi\) & discretisation angle \\ \(\varphi_{\text{St}}\) & shock factor \\ \(\omega\) & turbulent eddy frequency \\ \(\underline{\omega}\) & vector angular velocity \\ \end{tabular}
## Methodology
#### 2.1.1 Characteristic Sections of Lava Tubes in the Canary Islands  
Lava tubes, or sub-basaltic tunnels, are characteristic caverns that are created inside basic basaltic lava flows (generally of the pahoehoe and aa types) during an eruption, which flow underground because the upper part of the flow hardens in contact with the atmosphere or under the cooler seawater, forming a shell. When the flow of lava from the source decreases, these pipes are gradually emptied of lava, constituting genuine galleries. These natural tunnels develop more longitudinally than transversally, with lengths ranging from a few metres to several kilometres. They also present a reduced slope; therefore, for engineering purposes, they may be considered as horizontal. According to the bibliography consulted about lava tubes in the Canary Islands [15; 16; 17; 18]), it may be concluded that, although the tubes of the Canary Islands are often classified as presented either in a single gallery (channel type) or in labyrinthine pipes, the existing typical simple cavities are quite similar to those shown in Table 1. The first entry in the table corresponds to the Corona volcano [17] in Lanzarote Island, representative of the channel type, whose dimensions are: 9 m wide and 12 m high and which will be approximated as an ellipse with axes of the same dimensions (width 2a = 9 m, height 2b = 12 m, and height/width ratio = 1.33) in the calculation model analysed. In the same table, the dimensions of the other characteristic sections of the labyrinthine type, such as the tubes of the caves of El Llano, El Viento and Don Justo [18], can also be observed, with height/width ratios that range between 0.33 and 0.50.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Type of Rock Mass**} & \multicolumn{3}{c}{**Cavern Geometry**} & \multirow{2}{*}{**Representative Real Case**} \\ \cline{2-2} \cline{4-5}  & **2a** & **2b** & **b/a** & \\ \hline CHANNEL type & 9 & 12 & 1.33 & Corona Volcano \\ \cline{2-5}  & & 12 & 4 & 0.33 & El Llano Cave \\ \cline{2-5}  & & 8 & 4 & 0.50 & El Viento Cave \\ \cline{2-5}  & & 6 & 3 & 0.50 & Don Justo Cave \\ \cline{2-5}  & & 4 & 2 & 0.50 & Don Justo Cave \\ \hline CARBONATE & 10 & 2.5 & 0.25 & Güerta Cave \\ Limestones and Dolomites & 7 & 5 & 0.71 & El Reguerillo Cave \\ \hline EVAPORITIC & 7 & 3 & 0.43 & South Community of Madrid \\ \cline{2-5} Gypsum & 12 & 2.5 & 0.21 & Sorbas massif \\ \hline \hline \end{tabular}
\end{table}
Table 1: Morphology of the most representative real cases of caverns.  
Figure 1: Geometry: a: horizontal semi-axis, b: vertical semi-axis, E: load eccentricity, H: cavity depth or cover thickness, B: foundation width.
###### Abstract  
DNA N4-methylcytosine(4 mC) plays an important role in numerous biological functions and is a mechanism of particular epigenetic importance. Therefore, accurate identification of the 4 mC sites in DNA sequences is necessary to understand the functional mechanism. Although some effective calculation tools have been proposed to identifying DNA 4 mC sites, it is still challenging to improve identification accuracy and generalization ability. Therefore, there is a great need to build a computational tool to accurately identify the position of DNA 4 mC sites. Hence, this study proposed a novel predictor XGB4 mcPred, a predictor for the identification of 4 mC sites trained using an extreme gradient boosting algorithm (XGBoost) and DNA sequence information. Firstly, we used the One-Hot encoding on adjacent and spaced nucleotides, dinucleotides, and trinucleotides of the original 4 mC site sequences as feature vectors. Then, the importance values of the feature vectors pre-trained by the XGBoost algorithm were used as a threshold to filter redundant features, resulting in a significant improvement in the identification accuracy of the constructed XGB4 mcPred predictor to identify 4 mC sites. The analysis shows that there is a clear preference for nucleotide sequences between 4 mC sites and non-4 mC site sequences in six datasets from multiple species, and the optimized features can better distinguish 4 mC sites from non-4 mC sites. The experimental results of cross-validation and independent tests from six different species show that our proposed predictor XGB4 mcPred significantly outperformed other state-of-the-art predictors and was improved to varying degrees compared with other state-of-the-art predictors. Additionally, the user-friendly webserver we used to developed the XGB4 mcPred predictor was made freely accessible.  
N4-methylcytosine; eXtreme gradient boosting; site identification; feature selection Article
# The Role of Powered Surgical Instruments in Ear Surgery: An Acoustical Blessing or a Curse?
## 4 History of Otological Surgical Procedures
### TEES Procedures  
While MES is still, by far, the common surgical approach employed in ear surgery, transcanal endoscopic ear surgery (TEES) has emerged as a viable alternative in the last twenty years. The microscope is not used in TEES, but instead an endoscope is employed to access and visualize the internal anatomy of the ear through the external auditory canal instead of the more invasive mastoidectomy approach. Even though the endoscope has long been a standard part of surgery in other surgical fields, ear surgeons have had to face a unique set of anatomical and technological challenges that delayed the use of the endoscope within the ear. Endoscopes equipped with cameras were first used together with the microscope when performing mastoidectomies in the 1990s as a way to get a better view of the internal anatomy of the ear [32]. The first surgeries performed completely via the external auditory canal with the endoscope alone were reported on by Dr. [PERSON] in 1997 [33] and once again in 1999 [34]. TEES really took off after 2008 with the further development of the 3-charged-coupled device (CCD) camera connected to high definition (HD) monitors, which resulted in high-resolution images during surgery of the tiny structures of the ear [35; 36]. Figure 5a illustrates how the endoscope and forceps can be simultaneously inserted into the external auditory canal to perform TEES, and Figure 5b shows the typical set up of the operating room and positioning of surgeons when performing a TEES procedure.  
TES offers a number of advantages over MES, including no need to perform an invasive mastoidectomy which requires bone loss, better visualization of the surgical field, the ability to see into deep recesses within the ear, no disfiguring retroauricular scarring, and a quicker recovery time. Moreover, most TEES procedures are performed entirely without powered surgical instruments, which eliminates the potential for sensorineural hearing loss resulting from noise levels or vibrations. However, the \"conventional\" or what we have unofficially dubbed \"non-powered\" TEES can be performed only so far into the middle ear, but a subset of TEES procedures use the Sonopet(r) Omni to remove bone within the middle ear and enable access to the arturn which we call powered TEES. Figure 6 illustrates the internal anatomy of the middle ear (Figure 6a); the scope of the indications for non-powered TEES which can only reach into the inferior portion of the attic (Figure 6b); and the scope of the indications for powered TEES that can reach into the arturn (Figure 6c). This figure underscores that the Sonopet(r) Omni is reaching deep into the middle ear and closer to the ossicular chain and cochlea, which raises the specter of sensorineural hearing loss resulting from noise levels or vibrations, and is addressed below.  
Figure 5: Transcanal endoscopic ear surgery (TEES) **(a)** Accessing the internal anatomy of the ear with an endoscope via the external auditory canal. **(b)** A surgeon performing TEES.
## 3 Results
### Fourth-Order Kuramoto-Sivashinsky (KS) Equation  
The Kuramoto-Sivashinsky (KS) equation is a model of diffusion instabilities in a laminar flame front. It is defined as:  
\[u_{t}+uu_{x}+u_{xx}+\sigma u_{xxx}+u_{xxxx}=0. \tag{18}\]  
For the initial condition:  
\[u(x,0)=c+9-15(\tanh(k(x-x_{0}))+\tanh^{2}(k(x-x_{0}))-\tanh^{3}(k(x-x_{0}))) \tag{19}\]  
its exact solution is given as:  
\[u(x,t)=c+9-15(\tanh(k(x-ct-x_{0}))+\tanh^{2}(k(x-ct-x_{0}))-\tanh^{3}(k(x-ct-x_ {0}))). \tag{20}\]  
Following [PERSON] et al. [57], who solved Equation (18) using the LDG method, we set \(c=6\), \(\sigma=4\), \(k=0.5\), and \(x_{0}=-10\). Similarly, as in the 3 rd-order KdV equation, the solution of the KS equation is the wave traveling along the 'x'-coordinate and has the form of a solitary wave. Thus, the solution (20) is not periodic in real space. Its period is imaginary and with the selected coefficients it equals \(2i\pi\). Nevertheless, we treat the problem (18) as periodic along the 'x'-coordinate. To minimize the impact of the errors due to the lack of  
Figure 8: Third-order KdV equation: the exact and numerical solutions at the selected time instances with the grey line representing the local error at \(t=0.5\) (**a**), the overall solution errors (**b–d**) for various combinations of \(M\) and \(N\). (**a**) Exact (lines) and numerical (symbols) solutions, (**b**) \(M=1\) and various \(N\), (**c**) \(M=2\) and various \(N\), (**d**) \(M=4\) and various \(N\).  
the real periodicity, which will be revealed in a long-lasting simulation (\(t=4\)), we assume a large computational domain \(x\in[-30,30]\). Figure 9a shows the analytical and numerical solution of (18) obtained on the mesh with \(K=257\) nodes at time \(t=0.0,\,2.0,\,4.0\) using the CD scheme with \(M=1\) and \(N=13\). The dependence of the solution errors on the mesh density for various \(M\) and \(N\) combinations at time \(t=4.0\) is presented in Figure 9b-d. It can be seen that for large \(M+N\) values, the error lines do not show the improvement of the solution accuracy and for the meshes with \(K>161\), the errors remain on the same constant level \(\mathcal{O}(10^{-7})\). However, the presented results are an order of accuracy better than the best solution obtained in [57] using the LDG method. On a very dense mesh (\(K=320\)), and at an early simulation time (\(t=1.0\)), the error obtained was one order of magnitude higher (\(2.75\times 10^{-6}\)) than found here already on the twice coarser mesh.
# Stochastic Analysis of a Large-Span Continuous
## 4 Conclusions  
This work presented a stochastic seismic analysis of a large-span CGHSR bridge subjected to fully non-stationary seismic excitation using the PEM and MCS methods. A POD-based method is proposed to determine the pseudo static displacement for PEM under fully non-stationary seismic excitation. For the purpose of comparison, a modulated non-stationary ground acceleration model corresponding to the fully non-stationary one is derived based on the energy conservation principle. Taking into consideration the wave-passage effects, the critical response of the structural key positions under both non-stationary seismic excitations were analyzed and contrasted. The following conclusions are drawn accordingly:  
1. The proposed POD-based method can determine the structural pseudo static displacement for PEM under fully non-stationary seismic excitation. The structural dynamic and quasi-static displacement contributes almost the same to the absolute displacement.
2. The last arrived surface waves can also cause a relatively large displacement. The frequency non-stationarity of the earthquake ground motion should be considered to avoid underestimating the structural quasi-static displacement caused by the surface waves.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Pier** & **1\# Pier** & **2\# Pier** & **3\# Pier** & **4\# Pier** \\ \hline Fully non-stationary & 3.730 & 17.535 & 11.729 & 9.431 \\ \hline Modulated non-stationary & 3.122 & 14.687 & 9.828 & 7.900 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Maximum RMS of shear force at the bottom of piers (\(\times 10^{5}\) kN).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Pier** & **1\# Pier** & **2\# Pier** & **3\# Pier** & **4\# Pier** \\ \hline Fully non-stationary & 2.237 & 8.593 & 6.574 & 4.098 \\ \hline Modulated non-stationary & 1.873 & 7.196 & 5.506 & 3.432 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Maximum RMS of bending moment at the bottom of piers (\(\times 10^{6}\) kN\(\cdot\)m).  
The structural stochastic response under fully non-stationary seismic excitation is larger than that under equivalent modulated non-stationary seismic excitation.  
Note that the stochastic seismic analysis is conducted, here, in the frequency domain and it is difficult to consider the geometric nonlinearity, particularly for long-span bridges, which requires further analysis.  
Conceptualization, C.X. and H.W.; methodology, C.X.; formal analysis, C.X., Z.X. and R.L.; writing--original draft preparation, C.X.; funding acquisition, H.W. All authors have read and agreed to the published version of the manuscript.  
Not applicable.  
The authors declare no conflict of interest.