esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Performance Analysis of Proposed Schemes: MRS-ST-BL and MRS-SD-BL
### Results
#### 5.3.1 Impact of Different Battery Levels of Relays on Throughput
The key observation in Figure 6b is that M-Nearest-WFB (the dashed pink line) has highest throughput among all the schemes analyzed, when users in the network have strong social trust  
Figure 5: Average throughput of different schemes when Shape = 1.1 and Scale = 0.1 in a network having width of 100 m. (**a**) Different battery power levels not considered in relay selection; (**b**) Different battery power levels considered in relay selection. Figure 6: Average throughput of different schemes when Shape = 2 and Scale = 0.5 in a network having width of 100 m. (**a**) Different battery power levels not considered in relay selection; (**b**) Different battery power levels considered in relay selection. between them. Selecting a relay nearest to midpoint having battery power above battery threshold (\(B_{thres}\)) reduces throughput of the communication because a relay with longer link lengths is selected if the battery power of a device nearest to the midpoint is less than \(B_{thres}\). In this social trust scenario, our schemes perform worse than M-Nearest-WFB. The throughput of our proposed scheme MRS-ST-BL (the blue line with crosses) is greater than that of MRS-ST-WFB (the blue dashed line) for large values of search radius, but less than throughput of M-Nearest-WFB. The performance of our proposed scheme MRS-SD-BL is same as that of MRS-SD-WFB. In summary, from our analysis we see that average throughput of all schemes decreases when relay has varying battery power levels. Our proposed scheme MRS-ST-BL significantly improves the throughput compared to HRS in all of the analyzed network scenarios. MRS-ST-BL has higher throughput among all others, particularly when social trust among the users in the network are weak. This shows that throughput is improved when the battery power of device is also taken into consideration during relay selection, together with social information of users in the network. Next, we compare the average number of probes and average transmission power of relay of our proposed schemes with different other schemes. This shows how the limitation in number of probes and average relay power in MRS-ST-BL contribute to enhance throughput of D2D communications.
# Eye Fatigue Detection through Machine Learning Based on Single Channel Electrooculography
## 4 Method
### Feature Extraction
#### 4.2.3 Eye Movement Peak Ratio Feature Extraction  
The prominent patterns of eye movements are classified as moving to the left eye movements, right eye movements, and blinking. To simplify the complexity of the EOG measuring device and therefore improve the comfort when wearing, this paper designs a single-channel device with three electrodes. Electrodes are not placed on the upper and lower part of the eye for testing, so the blink signal is not detected. To extract the eye movement peak ratio feature, firstly, the baseline threshold THB is set and the signal is rectangular-sized (as shown in Figure 11). Then, use the differential thresholding algorithm to extract the peak and trough values of the eye movement signal. Finally, the ratio of the number of peaks and troughs to the number of points sampled is counted separately. As the eye movement peak ratio feature, this ratio is expressed in a physiological sense as the frequency of eye movements to the right and the frequency of eye movements to the left, respectively. Equations (1) and (2) show the calculation method.  
\[Ratio_{right} =\frac{\sum_{i=1}^{k}I[x2 rec(k)-x2 rec(k-1)>0\&x2 rec(k)-x2 rec(k+1)< 0]}{TotalN} \tag{1}\] \[TotalN =\sum\limits_{i=1}^{k}I[x2 rec(k)-x2 rec(k-1)>0\&x2 rec(k)-x2 rec(k+1 )\ \ <0]+i\] (2) \[=1 kI[x2 reck-x2 reck-1<0\&x2 reck-x2 reck+1>0]\]  
Figure 10: Welch spectral estimation of the denoised signal (Figure 7b).  
The \(Ratio_{right}\) above represents the percentage of right eye movement and the TotalN is the total number of eye movements to the right and the left. The percentage of right eye movement is calculated in the same way as in Formula (1).
# Compression-Aware Aggregation and Energy-Aware Routing in IoT-Fog-Enabled Forest Environment
## 5 Performance Evaluation
### Result Analysis
#### 5.3.5 Average Number of Parent Changes  
It specifies the stability of the network throughout the simulation time. Figure 8 illustrates the average number of parent changes in the proposed and existing RPL protocols. Two different scenarios of execution are done by varying the rate of transmission. One packet/min and 5 packets/min are the two cases taken for simulation. From Figure 8, it is observed that the average parent changes values are 0.12 and 0.32 for CAA-ERPL and E-RPL, respectively. The average number of parent changes is low when compared to the E-RPL. The reason behind this is aggregation in the network. Only the aggregator nodes participate in the DODAG, and henceforth, possibilities will be low for changing the parent frequently. From Figure 8, it is inferred that for the simulation of 5 packets/min, the average parent change is 0.25 and 0.6 for CAA-ERPL and ERPL, respectively.
## 3 Highlighting an Optimal Criterion Based on Fourier Number
### Application of the Criterion on a 3D Example  
To visualize the impact of the criterion, an example is illustrated by Figure 10. A representation of the \(\pi\)-plane vertical to the measuring surface that contains the theoretical heat sources is given in (a) and the resulting temperature at the surface in (b) at a certain time. In this example, all the sources have the same intensity, normalized to 1. The considered dimensions of the sample \(L_{x}\), \(L_{y}\) and \(L_{z}\) are all normalized in order to have \(x\) and \(y\) varying between \([-0.5,0.5]\) and \(z\) on the interval \([0,1]\). Others parameters are defined such as \(n_{x}=n_{y}=20\), \(n_{z}=25\) and \(n_{t}=300\).  
According to previous criteria, Fourier numbers are key parameters for the estimation of heat sources. Two cases are tested for this example in order to illustrate this criterion: the first one with \(Fo_{x}=Fo_{y}=Fo_{z}=0.1\) and the second with \(Fo_{x}=Fo_{y}=0.01\) and \(Fo_{z}=0.1\).  
The criterion given in Figure 9 enables comparing and verifying the results obtained for both cases. The first of the simulations is tested with Fourier numbers that, according to the criterion illustrated in Figure 9c, do not allow estimating the sources with a discretization higher than 20 in depth. The reconstruction obtained, illustrated by Figure 10d confirms this assertion: even if the  
Figure 10: Simulated field of heat sources (**a**), resulting temperature field at the surface at \(t/t_{f}=0.1\) (**b**), reconstructed source field for \(Fo_{x}=Fo_{y}=0.1\) and \(Fo_{z}=0.1\) (**c**) and reconstructed source field for \(Fo_{x}=Fo_{y}=0.01\) and \(Fo_{z}=0.1\) (**d**).  
sources closest to the surface are reconstructed, it is not the case for the deeper sources. Likewise, still according to this criterion, the Fourier numbers of the second simulation enable estimating the sources with thirty points of discretization in depth, which is confirmed by Figure 10c: all the sources are perfectly reconstructed.
# A Multiple-Choice Machine Reading Comprehension Model with Multi-Granularity Semantic Reasoning
## 2 Related Work
### Deep-Learning-Based MRC  
In 2015, [PERSON] et al. (2016) presented a large-scale fill-in-the-blank MRC dataset CNN/Daily Mail for the first time (about 1.26 million training data), and also proposed the ATTENTIVE READER neural network model for this dataset, which is based on the attention mechanism model and compared to the traditional ATTENTIVE READER neural network model, which is based on an attention mechanism and achieves a 12.9% improvement in accuracy compared to traditional NLP models. This marks the beginning of the DNN era for MRC. In 2016, [PERSON] et al. (2016) proposed SQuAD, an English dataset for extractive answer-based MRC tasks, which is the first canonical dataset containing large-scale natural language question-answer pairs in the MRC research community. Relying on the SQuAD dataset, [PERSON] et al. (2017) proposed the Match-LSTM and Answer Pointer models, which use a bidirectional LSTM model to encode questions and articles, and a one-way attention mechanism to perform semantic matching between article and question. [PERSON] et al. (2016) proposed QAnet, which uses multi-layer convolution and a self-attentiveness in the encoding module mechanism to integrate local and global interactions of articles and questions to improve the performance of the model. [PERSON] el al. (2017) use Longformer (2018), a long document transformer, to learn the abstract meaning of the context. It has been proved that deep learning-based MRC models have stronger text semantic representation ability and answer reasoning ability in English compared with traditional machine learning models.  
A set of large-scale datasets for different Chinese MRC tasks and datasets have also been proposed, such as ReCO (2018) for Chinese reading comprehension, ChID (2018) for the cloze-style task on Chinese idioms, CMRC2018 (2018) for the extractive task, and C\({}^{3}\)(2018) for the multiple-choice task. The proposal of a large number of high-quality MRC datasets has driven the development of deep neural MRC models. Knowledge-enhanced pretrained models, such as ERNIE 3.0 (2018) and Kepler (2018), are able to integrate factual knowledge into PLMs to achieve better performance. Instead of striving for better objective evaluation, [PERSON] et al. (2018) try to improve the explainability for MRC tasks on multiple-choice datasets. To the best of our knowledge, few studies have focused on semantic reasoning on different levels of granularity to address the Chinese MRC challenge. In this paper, we build on previous work to construct models that have greater comprehension and generalization capabilities for natural language in the field of MRC.
###### Abstract  
Fault diagnosis of rolling bearings is essential to ensure the efficient and safe operation of mechanical equipment. The extraction of fault features of the repetitive transient component from noisy vibration signals is key to bearing fault diagnosis. However, the bearing fault-induced transients are often submerged by strong background noise and interference. To effectively detect such fault-related transient components, this paper proposes a probability- and statistics-based method. The maximum-a-posteriori (MAP) estimator combined with probability density functions (pdfs) of the repetitive transient component, which is modeled by a mixture of two Laplace pdfs and noise, were used to derive the fast estimation model of the transient component. Subsequently, the LapGauss pdf was adopted to model the noisy coefficients. The parameters of the model derived could then be estimated quickly using the iterative expectation-maximization (EM) algorithm. The main contributions of the proposed statistic-based method are that: (1) transients and their wavelet coefficients are modeled as mixed Laplace pdfs; (2) LapGauss pdf is used to model noisy signals and their wavelet coefficients, facilitating the computation of the proposed method; and (3) computational complexity changes linearly with the size of the dataset and thus contributing to the fast estimation, indicated by analysis of the computational performance of the proposed method. The simulation and experimental vibration signals of faulty bearings were applied to test the effectiveness of the proposed method for fast fault feature extraction. Comparisons of computational complexity between the proposed method and other transient extraction methods were also conducted, showing that the computational complexity of the proposed method is proportional to the size of the dataset, leading to a high computational efficiency.  
fault diagnosis; feature extraction; MAP estimator; probability density functions; EM algorithm +
Footnote †: journal: Applied Sciences
# Single-Stage Step-Down Power Factor Corrector without Full-Bridge Rectifier
## 4 Design Considerations
### Design of Output Capacitor  
In the following, the current in the output capacitor \(C_{o}\), called \(i_{c}\)(t), can be expressed as  
\[\begin{array}{ll}i_{c}(t)&=\frac{V_{ln,avg,-I_{ln,rms}}}{V_{o}}\cdot(-\cos 2 \omega_{L}t)\\ &=-I_{o}\cos 2\omega_{L}t\end{array} \tag{23}\]  
The relationship between the output current capacitor \(i_{c}(t)\) and the output voltage capacitor \(v_{c}(t)\) can be expressed as  
\[v_{c}(t)=\frac{1}{C_{o}}\int i_{c}(t)dt \tag{24}\]  
By substituting (23) into (24), the output voltage ripple \(\widetilde{v}_{o}(t)\) can be expressed as  
\[\widetilde{v}_{o}(t)=\frac{-I_{o}}{2\omega_{L}C_{o}}\sin 2\omega_{L}t \tag{25}\]  
When the circuit operates in steady state, it can be seen that from (25), the output voltage ripple varies from \(\frac{-I_{o}}{2\omega_{L}C_{o}}\) to \(\frac{I_{o}}{2\omega_{L}C_{o}}\) and the frequency is twice the utility frequency.  
\[\Delta v_{o}=\frac{I_{o}}{\omega_{L}C_{o}} \tag{26}\]  
As shown in Table 1, the output voltage ripple is 3% of the output voltage. That is, the peak-to-peak value of the output voltage ripple \(\Delta v_{o}\) is  
\[\Delta v_{o}=3\%\cdot V_{o}=0.03\times 80=2.4\ \text{V} \tag{27}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Part No.** & \(A_{L}\)**(nH/N2)** & \(A_{c}\)**(cm2)** & \(B_{s}\)**(G)** \\ \hline CM270125 & \(157\) & 0.654 & 7000 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Characteristic parameters of powder iron core.  
Substituting (27) into (26) yields  
\[C_{o}=\frac{I_{o}}{\omega_{L}\Delta v_{o}}=\frac{1.125}{2\pi\times 60\times 2.4}=1243 \ \mu\text{F} \tag{28}\]  
Since the step-down power factor corrector has an input voltage lower than the output voltage, it is necessary to multiply the value of \(C_{o}\) by the conduction angle \(\pi-2\theta_{0}\) to obtain the value of \(C_{o,new}\).  
\[C_{o,new}=C_{o}(\pi-2\theta_{0})=1243\ \mu\times 1.78=2212\ \mu\text{F} \tag{29}\]  
Therefore, a 2200 \(\mu\)F/100 V Nippon Chemi-Con electrolytic capacitor connected in parallel with a 100 \(\mu\)F/450 V United Chemi-Con electrolytic capacitor is used as the output capacitor \(C_{o}\).
# Sustainable Agriculture and Soil Conservation
## 1 Introduction  
The ongoing global climate changes and the ever-increasing world population impose the adoption of agroecological or eco-sustainable production techniques to increase the resilience of the most fragile agrosystems [1]. For this reason, from many directions, a multidisciplinary scientific paradigm is increasingly suggested to better approach the complex problems concerning soil protection and sustainable agriculture [1, 2, 3].  
The development and adoption of sustainable agronomic practices able to preserve and enhance the physical, chemical and biological properties of soils and improve agroecosystem functions is a challenge for both scientists and farmers [4]. In recent decades, much has been done because, on one hand, scientists have improved and simplified the measurement methods in specific research sectors, and on the other hand, the process of transferring research outputs to farmers has been enhanced. The latter has been considerably extended thanks to research funding granted by the National and Supranational Political Institution, which increasingly involves farmers and stakeholders. From a scientific point of view, much progress has been achieved in the development of sustainable practices based on minimal soil disturbance, the use of cover crops, organic mulching, crop rotations, water and nutrient conservation, recycling crop residues and livestock manure for soil amendment, precision agriculture and so on, and many reviews have summarized the state of the art and traced the future trajectories (among others, refs. [5, 6, 7, 8, 9]). However,much remains to be explored, and Special Issues stimulating the scientific community on these main topics are desirable.  
The main goal of this Special Issue (SI) is to collect and present recent research on sustainable agriculture and soil conservation. A synthesis of the main results is reported in the following section.
# Iteratively Refined Multi-Channel Speech Separation
## 3 Experimental  
### Datasets and microphone Structure  
The effectiveness of our proposed method was evaluated using the spatialized version of the WSJ0-2 mix dataset [23]. This dataset contained 20,000 training data (about 30 h), 5000 validation data (about 10 h), and 3000 test data (about 5 h), respectively, as shown in Table 1. All utterances in the training and validation sets were either expanded or truncated to four seconds, and the sampling rate for all audio data was set to 8 kHz. This dataset comprised \"min\" and \"max\" versions: in the \"min\" version, the speech was truncated to match the duration of the shorter utterance, while in the \"max\" version, the speech was extended to match the longer utterance. The \"min\" version was used for training and validation, while the \"max\" version was used for testing, to maintain consistency with baseline methods. When mixing speech from two speakers, the signal-to-interference ratio (SIR) of the speech signals varied from \(-5\) dB to \(+5\) dB. Then, these adjusted speech signals were convolved with the RIRs to simulate the reverberation effect in real environments. The RIRs were simulated by the image method proposed by [PERSON] and [PERSON] in 1979 [24]. In the simulation, the length and width of the room varied from 5 m to 10 m, the height varied from 3 m to 4 m, the reverberation time varied from 0.2 s to 0.6 s, and the positions of the microphones and speakers were all randomly selected. The microphone array consisted of eight omnidirectional microphones placed inside a virtual sphere. The center of this sphere was located roughly at the center of the room and the radius of the sphere was randomly selected from 7.5 cm to 12.5 cm. The first four microphones were used for training and validation: two were symmetrically positioned on the surface of the sphere, while the other two were randomly positioned inside the sphere. The last four microphones were used for testing, and they were randomly positioned within the area defined by the first two microphones, to evaluate the performance of the model under unseen microphone configurations.
## 6 Conclusions  
Based on a finite element numerical simulation, this study constructed a three-dimensional numerical model of a composite foundation consisting of concrete piles, inter-pile soil, cushion layers, and the underlying rock-soil structure, and analyzed its stress-strain variations and the mechanical load-bearing mechanism of the pile-soil interaction. The main conclusions are as follows:  
1. The vertical stress of both the pile and the soil can be divided into three stages in the depth direction, from the pile's top to the pile's bottom. The stress diffusion of the pile undergoes a sharp increase, followed by a gradual decrease, and then a sharp decrease, while the stress diffusion of the soil is opposite to that of the pile.
2. The displacement of the pile can be divided by a critical depth. Above the critical depth, the displacement of the pile gradually decreases with depth, while below the critical depth, the displacement of the pile slightly increases with depth.
3. Under other identical conditions, end-bearing piles, due to their end-bearing effects and higher stiffness, exhibit larger vertical normal stresses and smaller deformations compared to friction piles, resulting in a higher bearing capacity for composite foundations.
4. The influence of the soil elastic modulus on the bearing characteristics of both the pile and the soil is very weak. In contrast, the bearing characteristics are mainly affected by the elastic modulus of the pile.
5. With an increase in soil depth, the stress diffusion effect of the soil becomes more pronounced. The additional vertical forces on all piles and inter-pile soil, as well as the ratio of additional vertical forces to the top load, gradually decrease. These trends are positively correlated with the elastic modulus of the pile and negatively correlated with the elastic modulus of the soil.  
Conceptualization, S.S., X.Z., W.L., Z.L., K.L., H.X. and P.H.; methodology, Z.L., W.L. and H.X.; software, X.Z., Z.L. and K.L.; validation, S.S., W.L., H.X., K.L. and P.H.; formal analysis, Z.L. and K.L.; investigation, Z.L., W.L., H.X. and P.H.; resources, S.S., W.L., H.X. and P.H.; data curation, W.L., H.X. and P.H.; writing--original draft preparation, K.L.; writing--review and editing, Z.L. and S.S.; visualization, W.L., H.X. and P.H.; supervision, X.Z., W.L. and Z.L.; project administration, W.L., H.X., P.H. and Z.L.; funding acquisition, Z.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant numbers 12262016 and 12162018, and the Yunnan Province Xingdian Talent Support Plan (XDYCCYCX-2022-0041).  
Not applicable.  
The original contributions presented in the study are included in the article, and further inquiries can be directed to the corresponding author.  
Authors [PERSON], [PERSON] and [PERSON] were employed by the company China Nonferrous Metals Industry Co., Ltd. All authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Parallel Image Completion with Edge and Color Map
## 3 Materials and Methods
### Architecture of PIC-EC
#### 3.2.2 Discriminators  
The discriminators \(D_{1}\), \(D_{2}\) and \(D_{3}\) in PIC-EC followed the \(70\times 70\) SN-PatchGAN [34] architecture with SN, which allows a larger receptive field to detect the fake images. SN-PatchGAN applied GAN loss for each point in the output feature maps and effectively modelled the image as a Markov random field, assuming independence between pixels separated by more than a patch diameter. It was fast and stable during training and produced high-quality completion results. As listed in Table 2, the discriminators consist of five \(4\times 4\) convolutional layers with 1 pixel zero padding. The first three layers have the stride of 2 and the rest are 1. LeakyReLU with slope 0.2 was employed after each layer except for the last one, which used a sigmoid function for the scores indicating whether each \(70\times 70\) overlapping image patch is real or fake. Therefore, the receptive field of each neuron in the last layer is \(70\times 70\), which can still cover the entire input image. In order to reduce the computational power and maintain the receptive field, \(4\times 4\) is minimum kernel size meeting this requirement. The configuration listed in Table 2 is a reasonable choice. IN was also used across all layers of these discriminators.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type** & **Kernel** & **Dilation** & **Stride** & **Channels** \\ \hline Convolution & \(7\times 7\) & 1 & \(1\times 1\) & 64 \\ Convolution & \(4\times 4\) & 1 & \(2\times 2\) & 128 \\ Convolution & \(4\times 4\) & 1 & \(2\times 2\) & 256 \\ ResBlock \(\times 8\) & \(3\times 3\) & 2 & \(1\times 1\) & 256 \\ Upsample (\(\times 2\uparrow\)) & - & - & - & 256 \\ Convolution & \(3\times 3\) & 1 & \(1\times 1\) & 128 \\ Upsample (\(\times 2\uparrow\)) & - & - & - & 128 \\ Convolution & \(3\times 3\) & 1 & \(1\times 1\) & 64 \\ Convolution & \(7\times 7\) & 1 & \(1\times 1\) & 1 or 3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Detailed architecture of the generators. After each convolutional layer, except the last one, there is a ReLU activation function. For \(G_{1}\), the output layer channel is 1, and for \(G_{2}\) and \(G_{3}\), the output layer channel is 3.