esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 HCV proteins that attach to lipid droplets
### Properties of the HCV core protein  
As stated in Section 1.2, core protein forms the capsid within HCV virions. Maturation of core requires cleavage by two cellular proteases at a signal peptide between core and the adjacent E1 glycoprotein [57-59]. The first proteolytic event is performed by signal peptidase, which cuts between amino acids 191 and 192 in the ER lumen, thereby generating an immature form of core and the N-terminus of E1. The fully processed form of core requires further proteolysis by signal peptide peptidase within the signal peptide sequence at the C-terminus of immature core. The precise location of signal peptide peptidase cleavage remains unclear, but analysis of core protein isolated from cells indicates that its C-terminus lies between amino acids 177 and 182 [57,60].  
Mature core can be separated into two domains, termed D1 and D2 [61]. D1 consists of the N-terminal 117 amino acids of core and contains a high proportion of positively-charged residues. D1 is thought to interact with viral RNA but also associates with a variety of cellular components [62]. D2 begins at amino acid position 118 and terminates at the C-terminus of the protein. This domain is responsible for directing core to lipid droplets and does not require sequences in D1 for attachment. Indeed, D1 can be replaced by a heterologous protein with no disruption to lipid droplet-targeting by D2 [63]. D2 and its membrane-binding characteristics also contribute to folding of the entire core protein [64]. Structural studies on the domain have revealed that it contains two amphipathic \(\alpha\)-helices (Helix I and Helix II) that are separated by a non-structured segment with several hydrophobic residues, termed the hydrophobic loop (HL) [63]. Removal of segments from D2 that includes deletion of these structural features leads to loss of lipid droplet localization of core. Moreover, mutations at individual amino acids on the hydrophobic faces of HI and HII can abolish attachment to droplets. Such substitutions can also induce degradation of core by the proteasome [63]. Thus, it has been proposed that attachment of D2 to membranous surfaces is critical for folding of the domain, which in turn enables folding of the remainder of core, and disruption of membrane binding exposes core to cellular degradative processes.
###### Abstract
Besides, deep learning methods generally require a large number of labeled samples for training. For video sequences, it is very difficult and expensive to obtain a large number of labeled samples manually. As a special type of single-hidden layer feed-forward neural network, an extreme learning machine (ELM) has been extensively applied in several fields of machine learning [43; 44; 45; 46; 47; 48], in which the parameters are randomly generated and the values of parameters do not need to be tuned. [PERSON] and [PERSON] [48] proposed an online sequential extreme learning machine-based semi-supervised technique for moving cast shadow detection, which provided better generalization performance. Motivated by its fast learning, good generalization and universal approximation capability, we propose an effective moving shadow detection based on the extreme learning machine. The main contributions of this work are: (1) based on the shadow properties, a set of features consisting of pixel-level features and region-level features is extracted by considering the characteristics of pixels and the spatial correlations of neighboring pixels simultaneously. (2) A generalized model based on the extreme learning machine is constructed for classification, which is simple and efficient, without turning thresholds or parameters. (3) Extensive qualitative and quantitative evaluations on 13 various scenes demonstrate that the performance of the proposed method compared with some well-known methods. The rest of this paper is organized as follows. Section 2 describes the moving shadow detection based on ELM in detail, Section 3 presents the experimental results and analysis. The conclusions are given in Section 4.
# Mode Shape-Based Damage Detection Method (MSDI): Experimental Validation
###### Abstract  
The main principle of vibration-based damage detection in structures is to interpret the changes in dynamic properties of the structure as indicators of damage. In this study, the mode shape damage index (MSDI) method was used to identify discrete damages in plate-like structures. This damage index is based on the difference between modified modal displacements in the undamaged and damaged state of the structure. In order to assess the advantages and limitations of the proposed algorithm, we performed experimental modal analysis on a reinforced concrete (RC) plate under 10 different damage cases. The MSDI values were calculated through considering single and/or multiple damage locations, different levels of damage, and boundary conditions. The experimental results confirmed that the MSDI method can be used to detect the existence of damage, identify single and/or multiple damage locations, and estimate damage severity in the case of single discrete damage.  
damage detection; plate-like structure; operational modal analysis; mode shape; modal assurance criterion (MAC) matrix; mode shape damage index (MSDI); discrete damages +
Footnote †: journal: Applied Sciences
## 2 Synchronization of Two Coupled FHN Neurons without Time Delay
Since \(a_{i}^{2}\geq 0\), \(i=1\), \(2\), \(c-\alpha_{2}<0\), it leads to the synchronization condition:  
\[c<\frac{1}{2}(a+by)<\frac{1}{6}(1+a)^{2}. \tag{8}\]  
Next, we prove that synchronous spikes do not occur in Equation (2) under Equation (8) by analyzing the stability of the origin of Equation (2). Linearizing Equation (2) around its origin yields:  
\[\frac{dU}{dt}=DU, \tag{9}\]  
in which \(U=[u_{1},\mu_{2},u_{3},u_{4}]^{T}\) and:  
\[D=\left[ \begin{array}{cc}D_{1}&D_{2}\\ D_{2}&D_{1}\end{array} \right],\]  
where  
\[D_{1}=\left[ \begin{array}{cc}c-a&-1\\ b&-by\end{array} \right],D_{2}=\left[ \begin{array}{cc}-c&0\\ 0&0\end{array} \right]. \tag{10}\]  
The matrix \(D\) can be diagonalized as:  
\[\left[ \begin{array}{cc}D_{1}&O\\ O&D_{3}\end{array} \right],\]  
where \(O\) is a \(2\times 2\) zero matrix and \(D_{3}=D_{1}-D_{2}D_{1}^{-1}D_{2}\). The eigenvalues of matrix \(D\) consist of eigenvalues of matrixes \(D_{1}\) and \(D_{3}\). All eigenvalues of matrix \(D\) have negative real parts if and only if:  
\[ \begin{array}{l}c<a+by<\frac{(1+a)^{2}}{3},\,\,c<a+\frac{1}{\gamma}<\frac{(1 +a)^{2}}{3},\\ \frac{(a+\gamma+1)(by+a)-c(by^{2}+2\alpha y+1)}{(a-c)y+1}>0,\\ \frac{b(a+1)(\alpha-2c)y+1}{(a-c)y+1}>0.\end{array}  \tag{11}\]  
Recalling the parameter ranges defined in Equation (1), it is easy to check that all inequations in (11) hold if Equation (8) is satisfied. Therefore, the origin of Equation (2) is asymptotically stable, corresponding to the neural rest, if Equation (8) holds. This means that for any given coupling, strength \(c\) synchronous spikes in Equation (1) do not occur. To demonstrate the validity of the  
**Remark 1**. : _In this section, we employ the energy method to analyze the synchronization between two coupled FHN neurons without time delay in signal transmission (Equation (2)). By considering the change rate of energy difference between the two neurons, we analytically derive the synchronization condition in the case of no time delay. The stability analysis for Equation (2) shows that if the time delay of signal transmission is ignored, the two neurons can only maintain neural rest when synchronization is achieved between them._
## 4 Experimental Results and Analysis
### Experimental Design
#### 4.1.2 Results of Experimental Design for Summer Data  
The experimental data in summer do not meet the assumption of normality as shown in Figure 5; thus, the ANOVA can be used to analyze the variance of two factors. This study adopted [PERSON]'s test, which is a nonparametric method that does not need to satisfy the assumption of normality, to find the best parameter settings for DE and SSO. The results are shown in following Tables 12 and 13.  
Figure 4: Main effects plot for spring (autumn).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Source** & **DF** & **SS** & **MS** & **F** & **P** \\ \hline SSO & 2 & 0.0000312 & 0.0000156 & 0.56 & 0.573 \\ DE & 2 & 0.0001656 & 0.0000828 & 2.97 & 0.057 \\ Interaction & 4 & 0.0000184 & 0.0000046 & 0.79 & 0.955 \\ Error & 81 & 0.0022555 & 0.0000278 & & \\ Total & 89 & 0.0024708 & & & \\ \hline S = 0.005277 & & R-Sq = 8.71\% & R-Sq(adj) = 0.00\% \\ \hline \hline \end{tabular}
\end{table}
Table 11: Two-way ANOVA—transition.  
The parameters of DE were set to level 3, that is, \(Y=0.9\) and initial \(F=0.15\) and the parameters of SSO were set to level 2, that is, \(c_{g}=0.5\), \(c_{p}=0.4\), and \(c_{w}=0.05\) when it is in progress for the experimental data of summer according to the analysis results obtained from Tables 12 and 13. The parameter configuration is used in the subsequent experiment for summer in Section 4.2.  
\begin{table}
\begin{tabular}{c c c c} \hline
**SSO** & **N** & **Est Median** & **Sum of Ranks** \\ \hline Level 1 & 30 & 0.89970 & 55.0 \\ Level 2 & 30 & 0.89979 & 64.0 \\ Level 3 & 30 & 0.89976 & 61.0 \\ \hline S = 1.40 & DF = 2 & \(p=0.497\) & \\ \hline \end{tabular}
\end{table}
Table 13: Friedman’s test, summer—SSO blocked by DE.  
Figure 5: Normality test for summer.  
Tables 14 and 15. The parameter configuration is used in the subsequent experiment for winter in Section 4.2.
## 5 Conclusions  
The measured results of the translational and torsional acceleration responses of the high-rise building in Haikou under the influences of typhoon Sarika were analyzed in detail in this paper, by which the vibration modes, natural frequencies, and damping ratios were obtained, and the variation laws of the first three-order frequencies and modal damping ratios were also investigated. On the basis of the measured data, the conclusions below are drawn:  
1. The measured value of the maximum instantaneous wind speed atop the building is 34.84 m/s, and that of mean wind speed is about 20.02 m/s when the basic time duration is 10 min. Besides, the peak value of the maximum angular acceleration at the top floor along the torsional direction is 0.031 rad/s\({}^{2}\).
2. The auto-power spectral density function, cross-spectral density function, phase spectrum, and coherence function can be applied to identify the first three-order modal frequencies of the building and to judge where different floors locate in the vibration shapes of resonance region. On the basis of phase spectrum and coherence functions, it can be judged whether the corresponding frequencies of the auto-power spectral and cross-spectral density functions at wave crests are true modal frequencies.
3. The first three-order vibration modal frequencies decrease linearly with the growth in mean wind speed when the mean wind speed is within the range of 0\(\sim\)20 m/s, which indicates that wind-induced vibration has significant influences on the high-rise buildings.
4. The existing codes can only estimate the fundamental frequency of high-rise buildings under static wind, but the frequency estimation of high-rise buildings under typhoon is not involved. This paper took the influences of wind-induced response on fundamental frequencies into consideration under the action of typhoons, and then the estimation formula of the modal frequencies of high-rise buildings considering the influences of different wind speeds was put forward, the formula can calculate the natural vibration frequency of high-rise buildings under strong wind in the design phase in a precise way.  
[PERSON] contributed to the overall study design, analysis, and writing of the manuscript. [PERSON] provided technical support and supervision. [PERSON] provided technical support and modification-polis. All authors have read and agreed to the published version of the manuscript.  
This research was financially supported by the Natural Science Foundation of Hunan Province, China (Grant No. 2020J)[5205], Scientific Research Project of Hunan Provincial Department of Education, China (Grant No. 21B0730) and the National Natural Science Foundation of China (Grant No. 91215302).  
Not applicable.  
The authors declare no conflict of interest.
## 3 Methods
### Graph DIVA Model
#### 3.3.1 Path Feature Generation Process  
In order to improve the perception range of path features by the path reasoning module, when generating the path feature matrix, the GraphDIVA model introduces a GraphSAGE algorithm to sample and aggregate the subgraph structure where the path is located. The process is shown in Figure 2.  
At first, GraphSAGE is used to randomly sample several statical data and the connection relationship for each entity in the path, and then the MaxPooling strategy is used to aggregate the sampled embedding vectors to obtain the feature representation of the path entities and relationships. The detailed calculation process is shown in (6). In (7), \([r_{v};e_{v}]\) indicates the relationship embedding vector and entity embedding vector at a certain step in the path, \(N(v)\) indicates that multiple adjacent entities are randomly selected from the path entity, \([r_{ui};e_{ui}]\) indicates the entity and connection relationship embedding vector adjacent to the path entity, and \(f(v)\) is the final output for the path feature vector. The final generated path feature matrix comprises multistep feature vectors in the path.  
\[AGGREGATE(v)=max(\{\sigma(W_{pool}[r_{ui};e_{ui}]+b_{pool}),\forall u_{i}\in N(v )\}), \tag{6}\]  
\[f(v)=W_{neighbor}AGGREGATE(v)+W_{self}[r_{v};e_{v}]+b_{pool}. \tag{7}\]
## 4 Factors Affecting Wound Healing Process
### Local Factors Influencing Wound Healing  
#### 4.1.1 Oxygenation  
Vascular disruption and high oxygen consumption in early wounds result in marked hypoxia due to reduced tissue oxygenation. Systemic factors like aging and diabetes further impair vascular flow, exacerbating the hypoxic state. This deficiency in oxygen supply is central to the pathophysiology of hypoxic and chronic wounds, as transcutaneous measurements indicate tissue oxygen tensions of 5-20 mm Hg in chronic wounds compared to 30-50 mm Hg in healthy tissue [36].  
Key contributors to wound hypoxia include peripheral vascular diseases that limit oxygen delivery, increased oxygen demand from healing tissue, and ROS production via respiratory burst and redox signaling. Additional factors--such as arterial hypoxia from conditions like pulmonary fibrosis, pneumonia, sympathetic pain responses, hypothermia, anemia, cyanide heart disease, or a high altitude--can further vary the degree of wound hypoxia, ranging from near-anoxia to moderate levels [37].  
ROS are pivotal regulators of wound healing, exerting antimicrobial effects, modulating immune signaling, and promoting angiogenesis, cell proliferation, and extracellular matrix formation. In particular, hydrogen peroxide (H\({}_{2}\)O\({}_{2}\)) plays a key role. However, dysregulated ROS levels due to excessive production or insufficient detoxification can lead to oxidative stress, impairing healing and contributing to chronic wound development, while also supporting endothelial function through HIF-1\(\alpha\) stabilization and VEGF signaling [38].  
Excess ROS prolong inflammation, promote infection, and disrupt cellular signaling, thereby contributing to chronic wound pathogenesis. Preclinical strategies include antioxidants (N-acetyl-L-cysteine), enzymes (superoxide dismutase), biocompatibility (Resolvin E1), and nanomaterials (cerium oxide nanoparticles) to balance oxidative stress. Oxygen therapies, both topical and hyperbaric, further reduce hypoxia and accelerate healing [39].  
Figure 3: The wound healing process phases. At first is hemostasis, followed by inflammation, proliferation, and remodeling. Obtained under Open Access Creative Commons License from [PERSON] et al. (2022) [26].
###### Abstract  
For decades, how to improve the resolution limit of seismic data has been a concern for seismologists. For this reason, several geoscientists have proposed various methods of improving the bandwidth of the data. Discovering an easy and scientifically reliable means of improving seismic data resolution would undoubtedly help geophysicists interpret more complex details of the subsurface geology. In this study, we transformed the bandlimited time-domain seismic data to the frequency domain using the Fourier analysis method, and a basis pursuit atomic algorithm was applied to decompose the real and imaginary parts of the spectrum into summations of cosines and sines. The resultant reflectivity spectrum (in the frequency domain) was deconvolved by a pre-estimated wavelet spectrum to obtain the true earth's reflectivity data spectrum and was subsequently extrapolated to beyond the original band limit. The result shows an extended bandwidth from 68 Hz to 161 Hz and 80 Hz to 170 Hz for both synthetic trace model and the main seismic data, respectively. Consequently, this improved vertical resolution of sub-seismic geologic features, such as crevasse splay, levee, barrier bar complex, lagoon inlet channels, alluvial fans, and fluvial channels, and shows subtle facies variations in Inas field.  
Subsurface imaging; spectral inversion; seismic interpretation; harmonic extrapolation; reservoir geophysics +
Footnote †: journal: Applied Sciences  
1
Footnote 1: institutetext: Department of Physics, University of California, Berkeley, CA 94062-8955, USA
# Physics-Informed Graph Neural Networks for Attack Path Prediction
## 2 Materials and Methods
### Path Prediction Training  
All blocks are trained end-to-end, meaning that for a given problem set, the graph convolution and path prediction (or node classification) blocks are optimized simultaneously. All models are trained using the Adaptive Moment Estimation (ADAM) optimizer [100], a standard approach in deep learning [64]. The optimizer computes the gradients of the model loss \(\mathcal{L}(\cdot)\) with respect to its parameters. The path prediction architecture is trained by optimizing its parameters using a physics-informed loss function. The node classification architecture, in contrast, is trained with a regular (non physics-informed) loss function.  
To train the path-prediction architecture, we define the following loss function:  
\[\mathcal{L}=\Phi\mathcal{L}_{\text{data}}+\Psi\mathcal{L}_{\text{pim}} \tag{2}\]  
where \(\mathcal{L}_{\text{data}}\) is a masked-weighted binary cross-entropy (MWBCE) loss (see Section 2.5.1), and \(\mathcal{L}_{\text{pim}}\) is a physics-informed loss (see Section 2.5.2). The hyperparameters \(\Phi\) and \(\Psi\) balance the contribution of each loss term to the total loss.
# Slip Estimation Model for Planetary Rover Using Gaussian Process Regression
## 3 Data Collection
### Ground Tests' Site  
Figure 4a displays the indoor test site used for ground tests. The rover used in the tests was the validator of Zhurong; as such, its design and functions are essentially the same as the Zhurong rover on Mars. The test sites were equipped with a low-gravity simulation device to provide a simulated Martian gravity environment for the rover. The vertical pressure of each wheel always stayed consistent with the values that the rover would experience on Mars. An indoor global positioning system (iGPS) was equipped for dynamic tracking and measurement of the position and attitude of the rover; 4 laser transmitters were arranged in the field, and a receiver was installed on the rover, and the measurement accuracy of position and attitude are better than 1 mm and 0.1\({}^{\circ}\), respectively.  
The tests site was covered with three kinds of simulated soil: TYII-2 simulated lunar soil, JLU Mars-2 simulated Martian soil, and JLU Mars-3 simulated soil. As shown in Figure 4b, the TYII-2 and JLU Mars-2 were laid in a horizontal area, the simulated soil was naturally laid to form gentle terrain undulations, and the slope variation range was small (0-5\({}^{\circ}\)). Research has demonstrated that the slope of most areas within 50 km of the Zhurong landing area are below 5\({}^{\circ}\)[24]; the data collected in the horizontal area were used in this paper. In addition, the JLU Mars-3 data will be used to analyze the influence of  
Figure 4: (**a**) The indoor test site of ground tests; (**b**) the distribution of simulated soil.  
Figure 3: Research framework.  
slope on slip data in our subsequent work. Table 1 presents the mechanical parameters of TYII-2-simulated lunar soil and JLU Mars-2-simulated Martian soil [25; 26].
## 1 Introduction
By doing so, we sought to enhance the convenience and accuracy of the diagnostic process, ultimately maximizing the effectiveness of hearing impairment treatment and rehabilitation. Furthermore, this research is expected to contribute significantly to expanding the applicability of deep learning and machine learning in medical data analysis. The study's key contributions are as follows:  
* Proposes a deep learning- and machine learning-based multi-class classification model for predicting speech audiometry results using pure-tone audiometry data and compares the performance of the multilayer perceptron (MLP), recurrent neural network (RNN), XGBoost, and gradient boosting models. * Deep learning and machine learning models effectively learn the nonlinear patterns of pure-tone audiometry data. * The performance of each deep learning and machine learning model was evaluated using accuracy, loss, and a confusion matrix. The results indicate that the recurrent neural network model slightly outperformed the multilayer perceptron model, and the gradient boosting model demonstrated superior performance to XGBoost. This paper consists of the following sections. Section 2 discusses the relevant background research on PTA data, speech audiometry data, MLP, RNN, gradient boosting, XGBoost, and performance evaluation metrics. Section 3 reviews deep learning and machine learning models for PTA data classification and speech audiometry prediction. Section 4 presents an overview of the benchmark dataset, evaluation metrics, and comparative analysis. Finally, Section 5 concludes the study and suggests directions for future research.