esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion  
### Results  
The total reflection characteristics of apparent channel waves are analyzed by calculating the critical angle. Taking the y component of the 33 rd channel of shot 1 as an example, the wave was recorded at 360 ms at a distance of 400 m. The velocity was calculated as 1100 m/s when the incident angle was equal to the critical angle. The refracted direct S-wave is 2300 m/s, corresponding to a time of 180 ms. According to Equation (8), the time difference between the arrival of the refracted direct S-wave and that of the apparent channel waves is 184 ms. When the incident angle is equal to the critical angle, the propagation of channel waves exhibits total reflection, with a phase velocity of 2300 m/s, which is equal to the refracted direct S-wave.  
Next, we employ the apparent channel wave velocity for imaging (Figure 11), in which the pink semicircular area along the upper roadway represents a collapse column. Analyzing the lower roadway reveals a fault or fissure zone that extends into the working face and intersects the lower roadway at a large angle. The error range of the imaging is less than 5 m, and the results are geologically verified.
## 2 Materials and Methods
### Deep Learning/CNN Architectures for Classification  
In deep learning (DL), which is one of the most popular approaches in recognition and classification applications, a model can learn to perform tasks directly from data such as text, sound, or image with high accuracy achievements [32; 33]. In DL models, some machine learning terms such as network design, number of layers, number of neurons, the architecture of the neural network, and optimization algorithm have gained more importance. DL uses multiple layers of nonlinear processing units for feature extraction and transformation. The most common scenario is that each node receives as inputs the outputs of every node from the previous layer in the structure [34]. Datasets used in deep learning can primarily consist of texts, images, audio, videos, or numerical data points. For instance, the image dataset consists of different features such as per-pixel density values, edge patterns, and special shapes. Some of these features better represent the original image data [35].  
A convolutional neural network (CNN) is a network architecture for deep learning that learns directly from two-dimensional image input. As an end-to-end diagnostic model, it is understood from the literature that it achieves a higher performance at a lower cost [36]. A one-dimensional CNN (1D-CNN) has a less complex network structure and lower computational complexity than a two-dimensional CNN (2D-CNN) and accepts one-dimensional raw data as input directly without any preprocessing.  
According to model structures and performance algorithms, one-dimensional LeNet (1D-LeNet), one-dimensional AlexNet (1D-AlexNet), and one-dimensional VGG16 (1D-VGG16) architectures are the most popular ones in 1D-CNN networks. In comparison to other types of algorithms, 1D-LeNet [37], 1D-AlexNet [38], and 1D-VGG16 [39] are used in several real engineering areas since they have simple model structures and fewer configuration parameters. In addition, 1D-LeNet, 1D-AlexNet, and 1D-VGG16 can process information more quickly and are also appropriate for fault classification. These deep learning architectures also demonstrate good performance for time series data classification [40; 41].  
Due to LeNet, AlexNet, and VGG16 networks having relatively simple structures and powerful classification capabilities, this paper employs these deep networks for PdM fault diagnosis. In the next section, these three types of convolutional neural network structures, called 1D-LeNet, 1D-AlexNet, and 1D-VGG16 classifiers, are presented for automatic feature extraction and fault identification of the dataset.
# GAO-FCNN-Enabled Beamforming of the RIS-Assisted Intelligent Communication System
## 4 Numerical Results
As the training period is progressed, the loss value of the optimized learning is decreased rapidly at first, then more gradually, and the optimal performance at epoch 64 is ultimately achieved. To prevent an increasement of loss values, the corresponding optimization strategy is stopped when the training process is reached at 64 epochs. In comparison to the two learning methods (i.e., the fixed noise power and the adjustable noise power), the optimized learning is converged to the expected performance in just nine cycles, although it is started with a higher loss value. That is to say, only when the smaller standard deviations and faster training speeds are obtained, then the stable results can be remained. In the RIS-assisted wireless communication system, the BER performance can be effectively optimized by training the GAO-FCNN algorithm. The system performance can be gradually improved by adjusting noise power levels. And the parameters can be adjusted dynamically during the whole training process. Finally, attention should be paid to satisfy the size of the standard deviation and the training speed should be ensured to obtain stable results.
## 3 Results
### Analysis of the Pressure Shock Process  
By analyzing the shape, stress, and contact pressure of the sealing ring under the action of impact pressure in the pre-compressed state, we can identify the causes and locations of leaks occurring on the sealing surface. This analysis provides a theoretical basis for the design of sealing structures.  
2.1 Stress Nephogram of Sealing Ring under Different Compression Rates and Different Pressure Impacts  
As shown in Figure 12, when comparing the compression of the sealing ring under different compression ratios, we found that the stress of the sealing ring increased with the increase in impact pressure under the same shrinkage ratio; from left to right, the peak impact pressures are 5, 10, 15, 20, 25, and 30 MPa. For example, at a compression ratio of 10%, the maximum stress at the lip of the step seal ring increases from 32 MPa to 72 MPa. At a compression ratio of 15%, the maximum stress at the lip increases from 38 MPa to 80 MPa. At a compression ratio of 20%, the maximum stress at the lip increases from 49 MPa to 88 MPa. At a compression ratio of 25%, the maximum stress at the lip increases from 70 MPa to 109 MPa. It is worth noting that the magnitude of the increase in maximum stress from the 20% to 25% compression ratio is greater than the increase observed at 10% and 15% compression ratios.  
Figure 11: Contact pressure on sealing surfaces at different compression ratios: (**a**) contact pressure of the sealing surface I, (**b**) contact pressure of the sealing surface III.  
Figure 12: _Cont._
###### Abstract  
This paper presents the design of an autonomous dynamic adaptability system (ADAS) for maintaining the irradiance levels of a steady-state xenon arc lamp solar simulator (SS). The solar simulator is used to carry out indoor testing and accelerated age tests on photovoltaic (PV) cells at the Fort Hare Institute of Technology (FHIT). The ADAS was designed primarily for two reasons: Firstly, to maintain a set irradiance level, irrespective of external effects which may cause unintended irradiance drift or fluctuations, while carrying out indoor tests. Secondly, to achieve the solar simulator set point quicker, thus reducing temperature build up on the target area. At a cold start, the SS runs at 20% of its rated current (145 A). At 20% of 145 A, the simulator gave an irradiance of 145.97 Wm\({}^{-2}\) with a non-uniformity of 1.02%, and a cell surface temperature of 24.9 \({}^{\circ}\)C. At 50%, the simulator produced irradiance of 501.30 Wm\({}^{-2}\), with a non-uniformity of 1.53% and a cell surface temperature of 25.0 \({}^{\circ}\)C. The irradiance of 1000 Wm\({}^{-2}\), with a non-uniformity of 3.26% and a cell surface temperature of 25.9 \({}^{\circ}\)C, was achieved at 90% of the rated current. From the results obtained, the ADAS demonstrates that it can reliably operate the SS with very minimal human-machine interaction. Through the autonomous dynamic adaptability, set irradiance levels are maintained in a steady-state solar simulator once the user supplies operational set points via the supervisory control and data acquisition (SCADA) interface.  
indoor testing; photovoltaic cells; SCADA; solar simulator set point; autonomous dynamic adaptability
## 4 Empirical Results
### Results of Classical and Spatial Panel Data Models
For the sake of completeness, we summarize the results of both the fixed effects and random effects models in Table 3.  
Table 3 confirms that the spatial autoregressive coefficient \(\rho\) of the fixed effects spatial lag model (Model 3) is significant and positive, indicating the existence of spatial interaction effects of GTFP. This finding is in line with [PERSON]'s I test results. On the one hand, it may exhibit significant spatial spillovers of GTFP. On the other hand, increases in the GTFP scores of neighboring provinces contribute to the GTFP growth of a given province. Furthermore, the coefficient \(\rho\) of the random effect model (Model 4) is highly insignificant, further supporting the conclusion that fixed effects models should be chosen. However, the spatial lag model merely incorporates the spatial lag of the dependent variable into the model, but ignores the spatial lags of the explanatory variables; this aspect of the model could lead to biased estimates. To address the omitted-variable bias, the fixed effects spatial Durbin model is estimated with results shown in column four (Model 5). We observed that the spatial autoregression coefficient \(\rho\) is also significant and positive, but much lower than in the spatial lag model (Model 3), implying that the upward bias is corrected. Therefore, the fixed effects spatial Durbin model is the best-fitted model for our study. However, [PERSON] and [PERSON] (2014) advise that the estimation results of the spatial Durbin models should not be explained directly; following the suggestions of [PERSON] and [PERSON] (2014) we therefore calculated the direct and indirect effects of these explanatory variables. In particular, the direct effect refers to the impact of an explanatory variable of the given province on the GTFP of the local province, while the indirect effect refers to the impact of an explanatory variable of neighboring provinces on the GTFP of the given province, implying a spatial interaction effect. The total effect is the summation of direct and indirect effects. The direct, indirect, and total effects are summarized in Table 4. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Year** & **GTFP** & **\(p\) Value** & **Aging** & **\(p\) Value** \\ \hline  
Average & 0.188 & 0.017 & 0.220 & 0.026 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of [PERSON]’s I test from 2002 to 2018.
## 3 Proposed Method
### Interference Mitigation  
Clustering can effectively improve the management ability of the network to reduce the inter-cell interference. We use graph-based k-means [15] downlink clustering for interference elimination.  
Let us redefine the aggregated inter-cell interference at the \(i\)-th user served by \(m\)-th BS from other users assigned to the same sub-channel and served by other BSs  
\[I_{m,i,l}^{inter}=\sum_{n=1,n\
eq m}^{\mathcal{M}}v_{n,i,l}Y_{n,i,l}p_{n,i,l}d_ {n,i,l}^{-\alpha} \tag{8}\]  
To build up the potential interference relationship between the users in the different base stations, we construct an interference graph. We define the points representative of the downlink of each base station to its associated user.  
Let us define our graph as \(\mathbb{G}=(\mathcal{V},\mathbb{E})\), where \(\mathcal{V}\) represents the vertices which are the communication devices and \(\mathbb{E}\) represents the set of edges that correspond to the interference level received by the \(i\)-th user from other BSs, this interference level is calculated by (8) after the initial assignment of power.  
The benefit of cluster is that no matter the complexity of the environment, we consider the relationship between users who interfere with each other. According to the strength of interference received by the user's and with setting up a specific threshold, the network is divided into numerous interference areas similar to Reference [16], which correspond to the coverage areas of each base station. For convenience, we will call interference areas as clusters. Once we finish cell association and since our proposed power allocation scheme increasingly allocates power and as a consequence that leads to increment in interference, nevertheless, any interference value above the threshold can be neglected. Consequently, the potentials of NOMA to manage the interference can be employed.
## 1 Introduction
### Organization  
The rest of this paper is organized as follows. Section 2 describes the signal model as well as the data processing methods we use and provides detailed information about the network. Section 3 reports the performance analysis of our approach and presents the results of comparative simulations. Section 4 concludes the paper.
# Molecular Mechanisms of Biofilm Formation in Helicobacter pylori
## 4 Mechanism of Biofilm Formation
### Biofilm Maturation  
Biofilm maturation in _H. pylori_ involves a significant morphological transformation accompanied by an increase in extracellular polymeric substance (EPS) synthesis [45]. This maturation process is closely linked to the formation of the extracellular matrix, which plays a crucial role in biofilm development [46]. Studies have shown that there is a notable difference in biofilm biomass production between 24 and 72 h, indicating a time-dependent maturation process [47]. Mutants with impaired motility or flagella production exhibit reduced biofilm formation capabilities, partly due to flagellar filaments within the biofilm structure [7].  
_H. pylori_ mutants with impaired motility (AmotB) or flagella production (\(\Delta\)_fiM_, \(\Delta\)_fiA_) exhibit reduced ability to form mature biofilms, partly because of the presence of flagellar filaments in the biofilm structure [46]. Adjusting _H. pylori_'s phenotype during biofilm growth involves metabolic changes and the transformation of flagella into adhesive structures, further emphasizing the role of motility in biofilm development [48]. Biofilm formation in _H. pylori_ has been associated with increased antibiotic resistance, with mature biofilms showing significantly higher resistance levels compared to planktonic cells [7]. This ability possessed by mature biofilm is contributed by the structure that consists of:
# Multi-View Network Representation Learning Algorithm Research
## 4 Experiments and Evaluations
### Link Prediction  
The link prediction algorithm can predict the future link probabilities of non-existing edges between vertices. First, we need to give a reasonable score for each vertex pair. We see \(r_{i}\) and \(r_{j}\) as the representations of the vertex \(i\) and vertex \(j\), we calculate the similarity score based on the cosine similarity \((r_{i}\cdot r_{j})/(||r_{i}||_{2}\cdot||r_{j}||_{2})\). We use AUC to evaluate the performance of link prediction. We remove 30%, 20% and 10% edges of Citeseer, Cora and Wiki as test set and we use the remaining edges to learn the network representations. We also use some baseline algorithms of link prediction to make some comparison analysis with MVNR proposed in this paper. We set the \(K\) value as 3 and 6. The results are shown in Table 6.  
As shown in Table 6, The MVNR algorithm proposed in this paper is compared with the 8 kinds of link prediction algorithms presented in Table 6. The experimental results show that the proposed MVNR algorithm always achieves the better performance under the three kinds of training proportions on Citeseer, Cora and Wiki datasets.
# Feature-Based Image Watermarking Algorithm Using SVD and APBT for Copyright Protection
###### Abstract  
Watermarking techniques can be applied in digital images to maintain the authenticity and integrity for copyright protection. In this paper, scale-invariant feature transform (SIFT) is combined with local digital watermarking and a digital watermarking algorithm based on SIFT, singular value decomposition (SVD), and all phase biorthogonal transform (APBT) is proposed. It describes the generation process of the SIFT algorithm in detail and obtains a series of scale-invariant feature points. A large amount of candidate feature points are selected to obtain the neighborhood which can be used to embed the watermark. For these selected feature points, block-based APBT is carried out on their neighborhoods. Moreover, a coefficients matrix of certain APBT coefficients is generated for SVD to embed the encrypted watermark. Experimental results demonstrate that the proposed watermarking algorithm has stronger robustness than some previous schemes. In addition, APBT-based digital watermarking algorithm has good imperceptibility and is more robust to different combinations of attacks, which can be applied for the purpose of copyright protection.  
image watermarking; scale-invariant feature transform (SIFT); all phase biorthogonal transform (APBT); singular value decomposition (SVD); robustness; copyright protection +
Footnote †: journal: Pattern Recognition  
_Future Internet_**2017**, 9, 13; doi:10.3390/f89020013
## 3 Results and Discussion
### SPME Performance of CDs/g-C\({}_{3}\)N\({}_{4}\) Composites  
The extraction performances of pristine g-C\({}_{3}\)N\({}_{4}\) and CDs/g-C\({}_{3}\)N\({}_{4}\) coatings with various doped-CD amounts were investigated. As shown in Figure 4a, the extraction efficiencies of CDs/g-C\({}_{3}\)N\({}_{4}\) display a distinct improvement towards eight CBs, in comparison to pristine g-C\({}_{3}\)N\({}_{4}\). The extraction efficiencies increase gradually with the enlarged amounts of CD-doping, and the maximum extraction capacity are achieved with CDs/g-C\({}_{3}\)N\({}_{4}\)-3. The further enlargement of CD amounts lead to a diminishment in extraction efficiencies, especially for 135-TCB, 124-TCB, and 123-TCB. Furthermore, the comparisons of extraction capacities for CBs with commercial SPME coatings were also studied to verify the advantages of the CDs/g-C\({}_{3}\)N\({}_{4}\) coating (Figure 4b). Results show that the CDs/g-C\({}_{3}\)N\({}_{4}\) coating presents superior performance, compared to the PA, PDMS, and PDMS/CAR/DVB coating. The enhancement in extraction capacities for CDs/g-C\({}_{3}\)N\({}_{4}\) coating can be ascribed to the following reasons. Firstly, the adsorption of CBs on CDs/g-C\({}_{3}\)N\({}_{4}\) composites mainly depends on the chemical group binding, \(\pi\)-\(\pi\) stacking, and hydrophobic interactions. Secondly, abundant nitrogen-containing and oxygen-containing groups on CDs/g-C\({}_{3}\)N\({}_{4}\) are beneficial for bonding with compounds containing polar groups. The addition of CDs introduced more polar groups for composites. However, excessive CD amounts also decrease the hydrophobic interactions between analytes and materials, especially for trichlorobenzenes.
## 3 Results and Discussion
### In-Vitro Study
#### 3.3.2 Corrosion Rate Results by ICP-MS  
The relative amount of Zn\({}^{2+}\) (in ppm) released into the culture medium after stent immersion is related to the corrosion rate of the metal. Figure 15 presents a comparison of released Zn\({}^{2+}\) from uncoated and polymer-coated stents, before and after expansion. The Zn\({}^{2+}\) concentrations, shown in Figure 15, were significantly affected by the coating (two-way ANOVA, \(p\) = 0.033 for coating, (n) = 3), but not by expansion (\(p\) = 0.099). However, when individual comparisons were made using a Holm-Sidak post-hoc multiple comparisons test, there were no differences between the four conditions at the significance level of 0.05. Therefore, the coating made a difference, but only if the expanded and unexpanded samples were considered together. Note that measurements of Zn\({}^{2+}\) in the medium can be complicated by the fact that Zn\({}^{2+}\) can be sequestered in precipitates on the metal and therefore would not have been released into the medium, which makes the levels of Zn\({}^{2+}\) potentially less accurate than the weight measurements. However, the combined effects with both measurements did show that Parylene C coating slowed down the corrosion rate of the devices.  
Figure 14: Comparison of corrosion rates between uncoated and Parylene C-coated Zn stent samples before and after expansion. (*) Significance level \(p\) = \(<\) 0.001.
## Abstract  
Remote sensing is recognized as a valuable tool for flood mapping due to its synoptic view and continuous coverage of the flooding event. This paper proposed a hybrid approach based on multiple endmember spectral analysis (MESMA) and Random Forest classifier to extract inundated areas in Yuyao City in China using medium resolution optical imagery. MESMA was adopted to tackle the mixing pixel problem induced by medium resolution data. Specifically, 35 optimal endmembers were selected to construct a total of 3111 models in the MESMA procedure to derive accurate fraction information. A multi-dimensional feature space was constructed including the normalized difference water index (NDWI), topographical parameters of height, slope, and aspect together with the fraction maps. A Random Forest classifier consisting of 200 decision trees was adopted to classify the post-flood image based on the above multi-features. Experimental results indicated that the proposed method can extract the inundated areas precisely with a classification accuracy of 94% and a Kappa index of 0.88. The inclusion of fraction information can help improve the mapping accuracy with an increase of 2.5%. Moreover, the proposed method also outperformed the maximum likelihood classifier and the NDWI thresholding method. This research provided a useful reference for flood mapping using medium resolution optical remote sensing imagery.  
keywords: flood mapping; spectral mixture analysis; random forest; medium resolution imagery +
Footnote †: journal: Journal of chemical and chemical chemical engineering