esa-sceva/satcom-chunk-collection huggingface dataset:
# Classification of Road Surfaces Based on CNN Architecture and Tire Acoustical Signals
## 2 Background Theory
### Convolutional Neural Network (CNN)
The last fully connected layer had as many nodes as the number of classes for classification. Deep neural networks used for classification tasks generally have a softmax output layer. This layer used the softmax function defined in Equation (5). \(o_{i}\) denotes \(i\)-th node's output value in the final fully connected layer. The output \(P_{i}\) is the normalized probability value for \(i\)-th class. As shown in the equation, each output value passes through an exponential function. Then, each scaled value is divided by the sum of all outputs. A neural network selects the output class that has the greatest possibility, as shown in Equation (6). \[P_{i}=\frac{\exp(o_{i})}{\sum_{n=1}^{k}\exp(o_{n})} \tag{5}\]  
\[\hat{y}=\max(P_{1},P_{2},\cdots,P_{k-1},P_{k}) \tag{6}\]  
The training process is performed using these layers. Softmax normalizes such that the output can be viewed as the probability that the input is within the class. Once the score vector is calculated, the loss function uses the vector and ground-truth labels to calculate the loss of the input image. Updates for every learnable parameter in a neural network are determined using backpropagation and gradient descent methods. These are the typical training processes for CNN [12]. The training algorithm was developed during the development of the CNN architecture. Currently, almost all training algorithms can be categorized into two groups. The first is to adjust the gradient direction. The second is to control the step size. The gradient-direction type is good at avoiding local minima. The step-size type enables the network to learn stable. Recently, an algorithm called Adam got a lot of interest for its good performance [15]. It is a hybrid-type algorithm that combines the advantages of gradient direction and step-size type. For efficient training, Adam is used in this study instead of a conventional stochastic gradient descent algorithm. Further information on CNN can be found in various books and papers [16; 17; 18]. The used software for this research is Matlab (MathWorks Co., Natick, MA, USA). Figure 3: Example of two typical pooling operations that filter size is \(2\times 2\) and stride is 2.
## 1 Introduction
Especially the possibility of doing SWH observations with GNSS aboard ships could provide useful validation data on the open ocean. After presenting the methodology in Section 2, the experimental datasets are explained in Section 3. A first experiment was carried out with a one-month dataset from a static antenna. For a second experiment, data from a three-month set of GNSS data, gathered aboard a moving ship, were used. In Section 4, the processing steps to derive the damping coefficient out of an SNR signal are described. Furthermore, antenna-related models of the SWH as a function of the damping coefficient are derived using in situ SWH data from wave buoys. In Section 5, we present a validation of the results using SWH data from an independent wave model. A summary of our findings is given in Section 6. Figure 1: SNR observations gathered under different SWH conditions (blue dots) and the fitted SNR model (red). (**a**) SNR observations during SWH of 0.3 m. (**b**) SNR observations during SWH of 1.5 m.
# Design of Surface Plasmon Nanolaser Based on MoS2
## 3 Simulation Analysis
### A Discussion of the Locality and Loss of Modes
#### 3.2.1 Discussion on Mode Locality of Different Materials  
The normalized mode area (Figure 5a) and effective propagation loss (Figure 5b) for different materials are shown in Figure 5. The structure in Figure 5 is consistent with that in Figure 3. As can be seen from Figure 5, with the increase of the thickness of the space layer, the normalized mode area of the nanometer laser structure which chooses \(\mathrm{MoS_{2}}\) as the space layer is smaller and smaller, and the effective propagation loss decreases first and then increases. However, the normalized mode area of nanolaser which selects \(\mathrm{MgF_{2}}\) and \(\mathrm{SiO_{2}}\) as the space layer is larger and larger, and the minimum value of normalized mode area is greater than the maximum value of normalized mode area of nanolaser using \(\mathrm{MoS_{2}}\) as the space layer.  
This difference is mainly because \(\mathrm{MoS_{2}}\) is a transition metal sulfide, with a part of metallic properties that can enhance mode locality to some extent. \(\mathrm{MoS_{2}}\) belongs to the hexagonal crystal system. It has a graphene-like structure, a large band gap [42; 43], more excellent photoelectric properties than \(\mathrm{MgF_{2}}\) and \(\mathrm{SiO_{2}}\), high electron mobility, and good thermal stability, and it can store energy, reducing energy loss as a result.  
Figure 4: The electric field distribution of nanolaser versus (**a**) the height of the air gap, (**b**) the radius of CdS and Ag.
## 3 RISC-V Processor  
### Risc-V Isa  
RISC-V is an open-source ISA which has been widely accepted and adopted in many projects by communities. Many resources and tools, such as compilers and debuggers, have also been developed, forming a diverse open-source ecosystem. The RISC-V project started in 2010 at UC Berkeley. Compared to ARM and x86, an RISC-V processor has the following advantages:  
* Free: RISC-V ISA and its surrounding development projects are mostly open-source.
* Simple: RISC-V is much smaller than other commercial ISAs.
* Modular: RISC-V has a small standard base ISA with multiple standard extensions.
* Stable: The base and many extensions of the ISA are already standardized and frozen. No significant changes are expected.
* Extensibility: specialized instructions can be added, based on extensions.  
The based instruction sets defined by RISC-V are RV32I, RV64I, and RV128I, supporting 32-bit, 64-bit, and 128-bit, respectively, with around 40 instructions. RISC-V specifies the encoding, control flow, register sizes, memory, addressing, logic manipulation, and ancillaries for the processor. While RV64I is suited for large, sophisticated, complex systems, and RV128I can serve as a theoretical instruction set for a 128-bit processor in the future, RV32I is the most suitable for small embedded systems, such as IoT devices, for example. In addition, the architecture of the processor can be extended, advancing its specialization through standardized extended instruction sets such as M, A, F, D, Q, and G (general purpose--IMAFD). In recent years, many implementations of RISC-V processors have been published in response to the open-hardware movement. Some notable ISA implementation processors supported by communities include the RocketChip [33] from UC Berkeley, the BlackParrot [34], and the SiFive E31 [6].
# Improvement of Wheat Grain Yield Prediction Model Performance Based on Stacking Technique
## 3 Results
### Weight Results for the Secondary Models  
To determine the weight of each growth stage in the model stacking process, the regression coefficients of the prediction results of each growth period in the secondary model were statistically analyzed with and without coupling LAI, as shown in Figure 7.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Model** & \(\mathbf{t}\) & \(\mathbf{p}\)**Value** \\ \hline \multirow{4}{*}{Without LAI} & Ensemble vs. Joining & 51.153 & 0.000 \\  & Ensemble vs. Flagging & 39.902 & 0.000 \\  & Ensemble vs. Anthesis & 17.029 & 0.000 \\  & Ensemble vs. Grain filling & 15.711 & 0.000 \\ \hline \multirow{4}{*}{With LAI} & Ensemble vs. Joining & 60.18 & 0.000 \\  & Ensemble vs. Flagging & 39.092 & 0.000 \\  & Ensemble vs. Anthesis & 23.188 & 0.000 \\  & Ensemble vs. Grain filling & 16.646 & 0.000 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results from the paired sample _t_-test.  
Figure 6: The statistical distributions of prediction accuracy of the stacking regression when including four growth stages for predicting grain yield in the test phases; (**a**) distributions of R\({}^{2}\) without or with LAI coupling; (**b**) distributions of RMSE without or with LAI coupling.  
The results showed that when only the vegetation indices were used as the input factors for GY prediction model, the average regression coefficients of the jointing, flagging, anthesis, and grain filling stages in the integrated model were 0.04, 0.05, 0.49, and 0.66, respectively. In the case of vegetation indices coupled with LAI, a GY prediction model integrating multiple growth stages was constructed, and the average regression coefficients of the jointing, flagging, anthesis, and grain filling stages were 0.06, 0.23, 0.48, and 0.72, respectively. Overall, with the development of the growth period, the proportion of each growth stage in the integrated model showed a gradual upward trend, in which the average regression coefficient of the grain filling stage and the weight were the highest. Compared with only using vegetation indices for GY prediction, after coupling the vegetation indices with LAI, the weight of the jointing, flagging, and grain filling stages in the integrated model all increased, while the weight of the anthesis stage decreased slightly, and the weight of the flagging stage increased most evidently.
# UAV-BS Site Planning Based on Circular Coverage Strategy
## 6 Discussion  
In the process of communication network development, UAVs, as a new communication carrier, bring more possibilities for future network expansion. Meanwhile, their flexible mobility, deployability in the air, and other characteristics also make the network structure and its characteristics more complex, as well as the network design more challenging. In UAVCNs, as the channel complexity of UAV is higher, the power consumption is greater.  
Figure 8: Schematic diagram of traditional hexagon covering method.  
Figure 7: The influence of attenuation index on the transmitted power of the system.  
UAV-BS station site planning has attracted much attention. This study focuses on UAVCN communication energy consumption and the full coverage of users in the target area. The circle coverage strategy is used for modeling, and the proportional relationship between the small circle radius and number is fitted by a power function with a given large circle radius, aiming to minimize power loss under the condition of full coverage. Newton iteration is used to give the number of UAV-BSs required to meet the target and the optimal coverage radius to determine the optimal UAV-BS site planning strategy.  
The traditional hexagon strategy and the minimum power loss strategy can also achieve full coverage. However, due to its own limitations, the hexagon strategy can achieve full coverage while the power increases sharply. However, its simple deployment is also worth considering. In contrast, the method proposed in this paper can fully cover ground users and ensure the minimum communication power consumption under the same number of systems, but there are still some limitations to be considered. First, the method proposed in this paper requires each UAV-BS to provide omnidirectional and undifferentiated signal coverage to ground users, without considering the service differences of different users and their individual requirements on network performance. Second, this study does not involve dynamic environments, such as changes in UAV-BS position or user mobility, which may affect the coverage effect and system performance. The strategy primarily aims at optimizing power consumption, yet it does not encompass the allocation and management of spectrum resources. We recognize the significance of additional parameters including user experience quality, throughput, resource utilization, and computational complexity, which could offer a more thorough assessment of the strategies.  
However, it should not be ignored that the method proposed in this paper can cover all points in the target area without discrimination, ensure that users at any location can access the network, meet the UAV network transmission power target, and effectively reduce the system transmission power, which has a lot of reference value for solving UAV-BS site planning problems.
# IFF-Net: Irregular Feature Fusion Network for Multimodal Remote Sensing Image Classification
## 3 Results
### Results and Analysis on Augsburg Data
The proposed IFF-Net classifier fully integrates spectral and spatial information, and achieves the best classification results, especially for forest (C1), industrial area (C2), and low vegetation (C4), which are close to the real scenes. In addition, the classification effect of SAR remote sensing data by the proposed IFF-Net classifier alone is noisy, and the classification effect is not good, especially for the commercial area (C6) and water (C7). Through intuitive comparative analysis, the classification map generated by the proposed IFF-Net can provide more realistic land cover, and the classification effect performs the best.
# An Analysis of Commercial Noni Juice: LC-MS/MS Phenolic Profiles and Cytotoxic Activity
## 3 Results and Discussion
### AChE Inhibitory Activity  
As shown in Table 5, all of the noni juice brands showed relatively low activity against AChE, ranging from 9.6% inhibition for DHN to 30.2% inhibition for CIN. This indicates that, in contrast the results found by [PERSON] et al. [24], commercial samples of noni juice are unlikely to provide significant AChE inhibitory activity, particularly not at the level required to alleviate neurological conditions such as Alzeimer's disease or dementia.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Analyte** & **FN** & **LHN** & **DHN** & **TON** & **CIN** \\ \hline TPC (mg GAE L\({}^{-1}\)) & 1761 \(\pm\) 64 \({}^{\text{a}}\) & 1391 \(\pm\) 47 \({}^{\text{b}}\) & 1106 \(\pm\) 6 \({}^{\text{c}}\) & 1642 \(\pm\) 12 \({}^{\text{a}}\) & 1345 \(\pm\) 58 \({}^{\text{b}}\) \\ FRAP (mg TE L\({}^{-1}\)) & 817 \(\pm\) 29 \({}^{\text{a}}\) & 468 \(\pm\) 34 \({}^{\text{b}}\) & 311 \(\pm\) 2 \({}^{\text{c}}\) & 496 \(\pm\) 10 \({}^{\text{b}}\) & 196 \(\pm\) 2 \({}^{\text{d}}\) \\ Ascorbic acid (mg L\({}^{-1}\)) & 20.1 \(\pm\) 2.9 \({}^{\text{a}}\) & 15.2 \(\pm\) 0.1 \({}^{\text{ab}}\) & 12.2 \(\pm\) 0.7 \({}^{\text{b}}\) & 19.7 \(\pm\) 0.3 \({}^{\text{a}}\) & 16.1 \(\pm\) 0.1 \({}^{\text{ab}}\) \\ Crude crystals & & & & & \\ TPC (mg GAE 100 g\({}^{-1}\)) & 5060 \(\pm\) 23 & 4786 \(\pm\) 521 & 5393 \(\pm\) 298 & 4980 \(\pm\) 391 & 4962 \(\pm\) 81 \\ FRAP (mg TE 100 g\({}^{-1}\)) & 4235 \(\pm\) 118 \({}^{\text{a}}\) & 873 \(\pm\) 88 \({}^{\text{c}}\) & 3244 \(\pm\) 566 \({}^{\text{c}}\) & 4312 \(\pm\) 23 \({}^{\text{a}}\) & 6389 \(\pm\) 49 \({}^{\text{b}}\) \\ \hline \hline \end{tabular} Abbreviations: FN = Fjian Noni; LHN = Life Health Noni; DHN = Dynamic Health Noni; TON = Tahitian Organic Noni; CIN = Cook Island Noni.  
\end{table}
Table 4: Phytochemical composition and ascorbic acid content of the noni juice samples (n = 2 replicates for each sample). Different superscript letters in the same row indicate significantly different results according to _post hoc_ Tukey testing at \(\alpha\) = 0.05.  
Given the low inhibitory values, dose-response experiments were not conducted on the samples.
# Detection and Classification of Bearing Surface Defects Based on Machine Vision
## 2 Related Work
### Defect Feature Selection and Classification  
To classify bearing surface defects, one must first extract the features of the gray-scale image of the segmented defect, and then perform appropriate dimensionality reduction according to the dimension of the extracted defect features. A feature data set suitable for the pattern recognition classifier is then formed, and the defect is finally classified using the pattern recognition classifier.  
Many investigators have extracted features of research targets by combining geometric features, gray-scale features, texture features, and projection features. For example, [PERSON] et al. [36] extracted a total of 25 features of wood defect images, including geometry, region, and texture, and moment invariants, to achieve the detection of wood defects. [PERSON] et al. [37] propose a bearing defect classification network based on an autoencoder to enhance the efficiency and accuracy of bearing defect detection. [PERSON] et al. proposed a support vector machine recursive feature elimination (SVM-RFE) method that is capable of lowering the overfitting probability and improving feature selection efficiency [38; 39; 40; 41] by fully utilizing the information in the training set. Similarly, [PERSON] and [PERSON] [42] proposed a curvelet-transform-based global and local embedded algorithm for nonlinear feature extraction of weldment defects. Its classification accuracy was shown to be better than that of the principal component analysis (PCA) and locally linear embedding (LLE) algorithms. [PERSON] et al. [43] extracted defect images on the surface of woven fabric based on the textures of the gray-scale co-occurrence matrix and used the K-nearest-neighbor algorithm to classify the defect images. Further, [PERSON] et al. [44] extracted the spatial and color features of the color images of fruit defects and used the K-means clustering algorithm to detect and classify fruit defects. [PERSON] et al. [45] used PCA to extract the main components of weld defect images and used support vector machines to detect and classify weld defects.  
In summary, our search on feature extraction and classification and recognition methods revealed that different feature extraction and classification and recognition methods all have their advantages and disadvantages, and the classification performance of the pattern recognition classifier that researchers pay attention to depends on the feature extraction algorithm, the classification algorithm, and the number of samples. Thus, there is no general feature extraction and classification algorithm for different types of defect data. Therefore, this research is suitable for the feature extraction and classification algorithm for online detection of bearing surface defects. Based on specific characteristics of the defects, the number of extracted defect features, and the number of samples, and considering the actual detection cost, the goal of this study was to realize an efficient feature extraction and classification algorithm for online detection and classification.
## 3 Tissue Engineering Approach
### Conventional Fabrication Techniques
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Fabrication Technique** & **Scaffold Structure** & **Cell Type** & **Research Stage** & **Reference** \\ \hline Solvent casting & Non-porous film & Human fetal RPE cells & In vitro & [47] \\ Solvent casting & Non-porous film & Human cell line ARPE-19 & In vitro and in vivo & [48] \\ Solvent casting & Non-porous film & Human cell line ARPE-19 & In vitro and in vivo & [49] \\ Solvent casting & Non-porous film & Human cell line ARPE-19 & (rabbit) & [49] \\ Solvent casting & Non-porous film & Human cell line ARPE-19 & In vitro & [50] \\ Solvent casting & Non-porous film & Human RPE cells & In vitro & [51] \\ Solvent casting & Non-porous film & Human iPSC- RPE cells & In vitro & [52] \\ Solvent casting & Non-porous film & Human cell line D407 & In vitro & [53] \\ SC/PL & Porous membrane & Human cell line ARPE-19 & In vitro & [56] \\ SC/PL & Porous membrane & Human fetal RPE cells & In vitro & [57] \\ SC/PL & Porous membrane & Human ESC- RPE cells & In vitro & [58] \\ SC/PL & Porous membrane & Human or pig RPE & In vitro & [59] \\ Silicon Mold & 3D hydrogel with & Rabbit RPE cells & In vitro & [63] \\  & cells encapsulated & Human cell line ARPE-19 & In vitro & [64] \\ Petri dish Mold & 3D hydrogel with & Human iPSC- and & & \\ Mold & cells encapsulated & ESC-derived embryoid & In vitro & [65] \\ Custom or well plates & Thin hydrogel & Human iPSC- RPE cells & In vitro & [66] \\ mold & Thin gel film & Human cell line ARPE-19 & In vitro & [67] \\ Custom mold & 3D hydrogel & Rat RGCs and amacrine cells & In vitro & [68] \\ Well plate mold & 3D hydrogel with & Rat dental pulp stem cells & In vitro & [69] \\  & cells encapsulated & Human conjunctiva & In vitro & [70] \\ Well plate mold & 3D hydrogel with & mesenchymal stem cells & In vitro & [70] \\  & cells encapsulated & Human cell line ARPE-19 & In vitro & [76] \\ TIPS & Porous membrane & Rat retinal progenitor cells & In vitro and in vivo (rats) & [77] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A summary of scaffolds produced by conventional methods and used for retinal tissue engineering. This table highlights the fabrication technique, the structure of the scaffold and the cell type used. SC/PL: solvent casting-particle leaching; TIPS: thermally induced phase separation; RPE: retinal pigment epithelium; ESC: embryonic stem cells; iPSC: induced pluripotent stem cells; RGC: retinal ganglion cells.
## 3 Results and Discussion
### Mass Loss under Acid Freeze-Thaw  
An electronic balance with a precision of 0.01 g was used for mass weighing. Before weighing, the specimens needed to be dried. After the specimens were dried, they were weighed and recorded before and after the specimens treatment. By comparing the drying quality of grout-reinforced specimens before and after treatment, the mass loss rate of grout-reinforced specimens with different crack dip angles can be obtained.  
The mass loss rate is calculated by the formula:  
\[S=\frac{m_{\mathrm{g}}-m_{\mathrm{s}}}{m_{\mathrm{g}}}\times 100\% \tag{1}\]  
In the formula:  
\(S\)--specimens mass loss rate after treatment, %;  
\(m_{\mathrm{g}}\)--dry weight of specimens before treatment, g;  
\(m_{\mathrm{s}}\)--dry mass of specimens after treatment, g.  
The mass loss rate of the test block is classified according to different degrees of fracture penetration and different angles of fracture inclination. The mass loss rate of each test block is shown in Figure 7. In terms of the degree of fracture penetration, the mass loss rate of grout and solid in the full-thickness fracture is greater than that in the half-thickness fracture. In terms of fracture angle, the mass loss rate of grout-increasing angle.  
The mass loss rate of the test block was classified according to different degrees of fracture penetration and different angles of different freeze-thaw cycles fracture inclination. The mass loss rate of each test block is shown in Figure 8. In terms of fracture penetration degree, the mass loss rate of grouting with solid in the full-thickness fracture was greater than that in the half-thickness fracture. In the angle of fracture, the mass loss rate of grout-reinforced specimens increases as the angle increases. The reasons for the deterioration in quality are as follows: firstly, the corrosion resistance of the grouting material is weaker than that of the sandstone, and secondly, the combination of grout-reinforced specimens increases with the increase in the inclination angle. From the analysis of fracture penetration  
Figure 6: The sample surface changes after different freeze–thaw cycles: (**a**) fully penetrated specimens with a crack angle of 90\({}^{\circ}\); (**b**) semi-penetrated specimens with a crack angle of 30\({}^{\circ}\).  
degree, the mass loss rate of full-thickness fracture grout-reinforced specimens is obviously larger than that of half-thickness fracture grout-reinforced specimens, which is similar to the reason why the mass loss rate changes with the fracture. The area of the reinforcement surface of full-through fracture grout-reinforced specimens is obviously greater than that of semi-through fracture grout-reinforced specimens. The mass loss rate of grout and solid is proportional to the area of the grouted reinforcement surface. The larger the area of the grouted reinforcement surface, the larger the area in contact with the acid solution and the corresponding mass loss rate.