esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The performance of bathymetric measurements by traditional methods (using manned vessels) in ultra-shallow waters, i.e., lakes, rivers, and sea beaches with a depth of less than 1 m, is often difficult or, in many cases, impossible due to problems related to safe vessel maneuvering. For this reason, the use of shallow draft hydrographic Unmanned Surface Vessels (USV) appears to provide a promising alternative method for performing such bathymetric measurements. This article describes the modernisation of a USV to switch from manual to automatic mode, and presents a preliminary study aimed at assessing the suitability of a popular autopilot commonly used in Unmanned Aerial Vehicles (UAV), and a low-cost multi-Global Navigation Satellite System (GNSS) receiver cooperating with it, for performing bathymetric measurements in automated mode, which involves independent movement along a specified route (hydrographic sounding profiles). The cross track error (XTE) variable, i.e., the distance determined between a USV's position and the sounding profile, measured transversely to the course, was adopted as the measure of automatic control precision. Moreover, the XTE value was statistically assessed in the publication.  
Unmanned Surface Vessel (USV); multi-Global Navigation Satellite System (GNSS) receiver; bathymetric measurements; cross track error (XTE) +
Footnote †: journal: Sensors
# A Comprehensive Review of Developments in Electric Vehicles Fast Charging Technology
## 3 Advanced Infrastructure for DC Fast Charging for Electric Vehicles
### Charging Standards
#### 3.1.1 Organizations for Standardization  
International bodies like the International Electrotechnical Commission (IEC), the Society of Automotive Engineers (SAE), and the International Organization for Standardization (ISO) are frequently responsible for developing and maintaining charging standards [97]. These groups work with industry participants to establish technical requirements and charging protocol standards for EVs.
# Fault Diagnosis of Wind Turbine Gearbox Based on Mel Spectrogram and Improved ResNeXt50 Model
###### Abstract  
In response to the problem of complex and variable loads on wind turbine gearbox bearing in working conditions, as well as the limited amount of sound data making fault identification difficult, this study focuses on sound signals and proposes an intelligent diagnostic method using deep learning. By adding the CBAM module in ResNeXt to enhance the model's attention to important features and combining it with the Arcloss loss function to make the model learn more discriminative features, the generalization ability of the model is strengthened. We used a fine-tuning transfer learning strategy, transferring pre-trained model parameters to the CBAM-ResNeXt50-ArcLoss model and training with an extracted Mel spectrogram of sound signals to extract and classify audio features of the wind turbine gearbox. Experimental validation of the proposed method on collected sound signals showed its effectiveness and superiority. Compared to CNN, ResNet50, ResNeXt50, and CBAM-ResNet50 methods, the CBAM-ResNeXt50-ArcLoss model achieved improvements of 13.3, 3.6, 2.4, and 1.3, respectively. Through comparison with classical algorithms, we demonstrated that the research method proposed in this study exhibits better diagnostic capability in classifying wind turbine gearbox sound signals.  
fault diagnosis; wind turbine gearbox; Mel spectrogram; CBAM-Resnext50-ArcLoss; transfer learning Article
# Silk Protein-Based Membrane for Guided Bone Regeneration
## 3 Silk Fibroin Membrane  
### Silk Fibroin Only Membrane  
SF as natural biomaterial has been studied for use in the scaffolding of bone tissue engineering [35]. SF has been known as a biocompatible and biodegradable biomaterial [29]. However, SF is a high-molecule natural polymer and can induce an inflammatory reaction when implanted in the human body. Therefore, low molecular SF power is used as a bone graft scaffold [56]. Low molecular SF power shows favorable osteogenic cell properties in in vitro studies. It increases the expression of osteogenic genes, including alkaline phosphatase (ALP), collagen type 1, and transforming growth factor-\(\beta\)1, and the ALP activity in MG-63 cells [56]. In vivo, the low molecular SF power combined with Choukroun platelet-rich fibrin showed rapid bone regeneration compared with the empty defect in a rabbit calvarial defect and a rabbit tibia peri-implant defect [57; 58].  
SF can be manufactured as a membrane-type scaffold for use as a GBR membrane [59]. GBR membranes should have several properties, such as biocompatibility, mechanical properties for space maintenance, cell occlusion of connective tissue cells, and biodegradability without inflammatory reaction [10]. SF membranes can be made by an electrospinning and casting technique [27]. SF membrane has shown cell and tissue compatibility and the increase of osteogenic cell proliferation (Figure 2) [17]. The electrospinning SF nanofiber membrane increased osteoblast-like cell attachment and proliferation according to the culture period, and the expression of osteoclast and ALP activity were increased [38]. SF nanofiber membrane in a rabbit calvarial defect showed significantly increased new bone regeneration compared with the empty defect without inflammatory reaction [38]. Compared with commercial collagen membranes, SF nanofiber membranes have shown satisfactory mechanical stability and new bone regeneration ability in a rat calvarial defect [39]. In clinical application, SF nanofiber membranes have shown similar bone regeneration ability compared with polylactic acid/poly(lactic/glycolic) acid (PLA/PLGA) membranes in tooth extraction defects [60; 61].  
The SF membrane manufactured by the casting technique showed higher new bone regeneration ability than the uncovered defect in a rabbit calvariat defect (Figure 3) [19], and compared with the collagen membrane, it shows comparable new bone regeneration without inflammatory reaction [40]. However, the casting SF membrane has weak mechanical properties and is brittle in its dry state [19]. Despite their low mechanical properties and difficult handling, SF membranes can be suitable barrier membranes for GBR due to their low cost and biocompatibility [40]. In the previously reported study, the silk-based membranes showed their potential as GBR membranes. They have favorable osteogenic cell compatibility and new bone regeneration ability with low immune reaction.
# Binary Cockroach Swarm Optimization for Combinatorial Optimization Problem
## 3 Binary Cockroach Swarm Optimization
#### Solving TSP with BCSO  
The TSP comprises a salesman and a set of cities. The salesman visits each city beginning with the first city and returning to the same city at the end of his trip. The goal of the salesman problem is to minimize the total cost or length of the trip. TSP can be described as a complete graph \((G,f,t):G=(V,E)\), where \(f\) is a function \(V\times V\to Z\), \(t\in Z\), and \(G\) is a graph that contains a traveling salesman tour with a cost that does not exceed \(t\).  
The main idea of this work is that BCSO is able to search for an acceptable solution in the space using cockroach operators. The operators enhance local and global searches. The weakness of the chase swarming operation is being trapped in a local optimum. This is strengthened by other operations, such as dispersion and hunger operations. The dispersion operation encourages a high level of diversity, as cockroaches rapidly disperse across the search space and start exploiting and exploring new areas. Likewise, the hunger operation perturbs the search space, as cockroaches travel from one point to another looking for food in the strategic food locations within the search space.  
The CSO is extended to solve TSP in order to make use of its advantages in the binary version. The binary version is adapted to TSP based on the re-interpretation of the terminologies of the basic CSO operations.  
In the adaptation of BCSO to TSP, the 2-opt and nearest neighbor methods were used for perturbation to change the order of cities visited. 2-opt and the nearest neighbor methods were used respectively for tour construction and improvement. In a combinatorial optimization problem, a solution neighborhood is generated by the smallest perturbation, which causes the minimum number of changes in the solution [21]. 2-opt is a good technique for perturbation, because, for a new solution, two is the minimum number of contiguous edges that can be deleted.  
Steps for the 2-opt method:  
1. Take two pairs of nodes (A, B) and (C, D) respectively from a tour and check if the distance ABCD is longer than ADBC.
2. If true, swap A and C, that is, reverse the tour between the two nodes.
3. Continue with the improvement process by returning to Step 1; otherwise, stop if no improvement is possible.  
Steps of the nearest neighbor method:  
1. Start on an arbitrary vertex as the current vertex.
2. Find out the shortest edge connecting the current vertex and an unvisited vertex V.
3. Set the current vertex to V.
4. Mark V as visited.
5. If all of the vertices in the domain have been visited, then terminate the sequence.
6. Go to Step 2.  
The result of the algorithm is the sequence of the vertices visited.  
The performance of the proposed BCSO algorithm is evaluated in Section 4 against some benchmark function problems, as well as against some instances of TSP.
# An Efficient Bidirectional Point Pyramid Attention Network for 3D Point Cloud Completion
## 3 Methods
### Point Feature Encoder  
The size of the incomplete point cloud input was N = 2048 with its 3-dimensional coordinates. The encoder network was utilized to extract features from the incomplete input. Motivated by PointNet, we designed an Attention Combined Multi-Layer Perceptron (ACMLP) as our point cloud feature encoder. As shown in Figure 4, the structure of the encoder comprised a shared multi-layer perceptron (MLP) with LeakyReLU activation, which consisted of five layers with neuron sizes 64, 128, 256, 512, and 1024. Different dimensions of MLP can extract low, mid-, and high-level features, each of which contains rich point cloud information. In order to utilize these features effectively, we used max pooling to obtain the global latent representation \(f_{i}\) of the output of the last four layers, whose size was \(f_{i}:=128,256,512,1024\), for \(i\) = 1,...,4, and concatenate \(f_{i}\) to produce a 1920-dimensional feature vector. Then, we adopted a channelwise attention module for the 1920-dimensional features to obtain the weighted multilevel features \(\widetilde{f}\) that contained low-, mid-, and high-level feature information, and could help in generating effective features for point cloud prediction. Since we used max pooling for the 1920-dimensional feature vector, we did not take the pooling layer into the channelwise attention module. Eventually, we generated the final feature vector F\({}_{1}\) through a fully connected layer.
## 5 Conclusions  
In this study, to solve the problems of small sample size in a remote sensing scene data set, as well as simple scene structure and distortion caused by direct generation, a scene reconstruction method was proposed based on random element transformation and GAN-based texture synthesis. This method can be applied to generate remote sensing scene data set with labels, in which the random transformation of elements can be used to reconstruct the scene structure and the texture synthesis can be used to fill the spectrum of the reconstructed scene. Experimental results demonstrate that the RSSI generated by our method achieved a better visual effect than a GAN model. In addition, the performance of CNN classifiers was reduced on the enhanced data set, which is partly attributed to the complexity of the generated samples. Our work can generate a large number of scene images with complex structures and high fidelity. Thus, the existing remote sensing scene data sets can be expanded, alleviating the accuracy saturation issues. In addition, pre  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Network** & **20\% Training Set** & **40\% Training Set** & **80\% Training Set** \\ \hline AlexNet & 70.42 \(\pm\) 4.77\% & 80.50 \(\pm\) 2.86\% & 82.30 \(\pm\) 3.01\% \\ VGGNet & 53.88 \(\pm\) 9.06\% & 80.44 \(\pm\) 2.36\% & 79.62 \(\pm\) 7.00\% \\ ResNet & 95.23 \(\pm\) 0.62\% & 99.21 \(\pm\) 0.82\% & 100.00 \(\pm\) 0.00\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Accuracy results for the data generated by our approach (256 \(\times\) 256).  
trained classification models for RSSIs can also be constructed based on the enhanced data sets.  
In the future, we expect to generate more images with complex scene structures and realistic descriptions, to promote the development of scene image generation in remote sensing.  
Conceptualization, Z.L. and X.L.; methodology, Z.L.; validation, R.G. and J.H.; investigation, Z.L.; resources, X.L.; writing--original draft preparation, Z.L.; writing--review and editing, X.L. and W.C.; visualization, Z.L.; supervision, X.L.; project administration, X.L.; funding acquisition, X.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Natural Science Foundation of China (No. 42071430) and College Students' Innovative Entrepreneurial Training Plan Program (No. 202010491040).  
The NWPU-RESISC45 data set can be obtained through [[http://www.escience.cn/people/JunweiHan/NWPU-RESISC45.html](http://www.escience.cn/people/JunweiHan/NWPU-RESISC45.html)]([http://www.escience.cn/people/JunweiHan/NWPU-RESISC45.html](http://www.escience.cn/people/JunweiHan/NWPU-RESISC45.html)), accessed on 24 February 2022.  
The authors declare no conflict of interest.
## 4 Graphene Nanoplatelets for Strain Sensors and Stretchable Electronics
### Smart Sensing Outlook  
In summary, GnP-based smart sensors have been extensively investigated to date and are already considered mature for large-scale production. In particular, strain sensing is a more promising application, considering the high gauge factor obtained using GnPs. Such nanoflakes are an excellent candidate for applications which demand high sensitivity to tiny deformations (like structural and health parameter monitoring), broad sensing range, or a combination of the two. To date, PDMS-GnPs strain sensors are the most studied. Another promising approach for stretch sensing is functionalizing textiles with conductive materials to obtain fibrous strain sensors. So far, piezoresistive devices were investigated much more, but some examples using capacitive feedback mechanism are present in the literature. GnP-based stretchable and deformable sensors were also employed for pressure sensing and robotic tactile sensing, obtaining remarkable results.
# Focus on Point: Parallel Multiscale Feature Aggregation for Lane Key Points Detection
## 3 Methodology
### Parallel Multi-Scale Feature Aggregation Network
#### 3.1.3 Analysis of Architecture  
In the global perspective, the proposed network is a DCNN (deep convolutional neural network), which takes images or videos as the input, and conjectures lanes accurately by predicting multiple key points sets. The unique design of the framework not only makes full use of the feature extraction capability of the network, but also does not need to repeat the process of high-to-low and low-to-high several times for boosting the performance, which greatly reduces the amount of computation costs and information redundancy. In the local perspective, the network is the parallel connection structure of multiple spatial scales. The multi-scale fusion strategy can help the network integrate spatial information from each scale and enrich global features by other local clues under complex or challenging driving scenarios. In addition, the parallel connection could be regarded as an extended residual connection or skipping connection, which is helpful for coping with the information loss problem in the sampling process of the series structure to a certain extent.
# Developing a Generalized Regression Forecasting Network for the Prediction of Human Body Dimensions
## 3 Methodology of the GRFN
### Hybrid Prediction Process
#### 3.2.1 Step 1: Data PreProcessing  
The data are subjected to min-max normalization, resulting in a range of \([0,1]\):  
\[x_{i}^{\prime}=\frac{x_{i}-x_{\min}}{x_{\max}-x_{\min}}, \tag{12}\]  
where variable \(x_{i}\) denotes the original data, and \(x_{i}^{\prime}\) represents the normalized data, whose sequences are bounded by location boundaries of \(x_{\max}\) and \(x_{\min}\). Then, the length and perimeter factors with large contribution rates (i.e., height, waist circumference, hip circumference, waist circumference height, and hip circumference height) are used as key feature inputs, and randomly assigned to the training and testing sets.
## 2 Modelling and Methods
### Test Cases Examined  
Part of this study's unique contribution is to examine the impact of varying degrees of input data quantity and quality on the performance of the ANN model. An attempt was made to isolate and assess the influence of data by keeping the same algorithm architecture and gradually altering the amount of information provided for training. This provided a level ground for comparing the algorithms, without introducing inconsistencies due to hyperparameter and architecture variations. Table 4 summarises the input data used for training each of the 4 ANN models. The original algorithm (ANN 1) was developed using the full dataset, comprising the entirety of the data obtained through the FE analysis, as described in detail in previous paragraphs. Each subsequent algorithm was trained with a subset of the original input information. Specifically, the cases examined include:  
* ANN 1: As mentioned above, this uses the complete dataset for training and testing purposes. * ANN 2: The second algorithm was developed using only the extreme values of insulation thickness. As such, the wall assemblies considered included the non-insulated ones and those insulated with 100 mm of EPS internally and externally. * ANN 3: Only the extreme values of the emissivity coefficient were used for the development of the third algorithm. Wall assemblies with \(\varepsilon=0.5\) were disregarded and only those with \(\varepsilon=0.1\) and \(\varepsilon=0.9\) were included in the dataset. * ANN 4: This was the most input data-deprived algorithm--a combination of the previous two cases. Only the extreme cases of insulation and thermal emissivity coefficient were offered to the algorithm at the training stage, considerably reducing the density of the offered input data.
# Stability of Reinforced Retaining Wall under Seismic Loads
## 5 Parametric Study
### Influence of Seismic Acceleration Coefficients  
Variation of the normalized tensile force \(K\) with friction angle was also evaluated for various seismic acceleration coefficients. \(\gamma\) is fixed as 20 kN/m\({}^{3}\), while is \(c\) is treated as 0. The horizontal seismic acceleration coefficient \(k_{h}\)[36] is calculated by:  
\[k_{h}=\frac{1}{2}(\frac{\alpha_{\max}}{g}), \tag{24}\]  
while \(k_{h}\) and \(k_{v}\) need to fulfill the relationship of:  
\[q\textgreater\tan^{-1}(\frac{k_{h}}{1-k_{v}}) \tag{25}\]  
Figures 4 and 5 show the influence of seismic acceleration coefficients on the normalized tensile force, while Figure 6 illustrates the influence of seismic acceleration coefficients on the inclination angle. It can be identified that normalized tensile force increases with the increase of seismic acceleration coefficient, especially when \(k_{h}\) is increased from 0.1 to 0.2. Comparing Figure 4; Figure 5 shows that \(k_{v}\) also contributes to \(K\), but it only has limited effect. This implies that the horizontal seismic load is the dominant force for a reinforced retaining wall. It also shows that when the friction angle increases, the normalized tensile force will decrease. This means that the use of a well-graded backfill material with a large friction angle will be beneficial to the stability of the reinforced retaining wall under seismic loads [37]. Figure 6 shows that the slip surface is almost linear from the toe to the middle part of the wall, since the variation of the inclination angle for each slice is very small, while from the middle to the top surface of the wall, the slip surface is a vertical line parallel to the wall. It also shows that the slip surface of the retaining wall moves toward the wall side with the increasing seismic load.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Characteristics** & **Value** & **Average Value** \\ \hline Reinforced retaining wall height (\(H\)) & 6 m & — \\ Soil density (\(\gamma\)) & 16–21 kN/m\({}^{3}\) & 18.5 kN/m\({}^{3}\) \\ Soil cohesion (\(c\)) & 0–15 kN/m\({}^{2}\) & 10 kN/m\({}^{2}\) \\ Internal angle of soil friction (\(\varphi\)) & 28–38\({}^{\circ}\) & 33\({}^{\circ}\) \\ Coefficient of horizontal seismic acceleration (\(k_{h}\)) & 0, 0.1, 0.2, 0.3 & 0.15 \\ Coefficient of vertical seismic acceleration (\(k_{v}\)) & 0, 0.05, 0.1, 0.15 & 0.075 \\ \hline \hline \end{tabular}  
* **Note:** The effect of soil’s relative density on the friction angle has not been taken into account.  
\end{table}
Table 1: Geometric and geotechnical characteristics of the wall used for the parametric study.  
Figure 3: Geometry of the reinforced retaining wall.
# A Survey of Information Extraction Based on Deep Learning
## 2 Entity Relationship Extraction Based on Deep Learning
### Supervised Learning--Joint Extraction  
Compared with the pipeline method, combining named entity recognition and relationship extraction into a single task can achieve better performance. Unlike the pipelined approach, the joint extraction framework focuses on extracting entities and relationships using a single model to capture the inherent linguistic dependencies between relationships and entity parameters, thereby solving the error propagation problem. Based on the different models, the joint extraction model can be divided into parameter-sharing-based, sequence-annotation-based, knowledge-enhancement-based, and graph-based methods.