esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Literature Review  
#### 2.2.1 Search Strategy and Papers Collection  
Figure 2 shows the three-step procedure entailed by PRISMA flow diagram: (i) identification; (ii) screening; (iii) inclusion. Step (i) was performed with five keyword combinations, again using the Boolean operator \"AND\": (1) \"Acoustic Emission\" AND \"Grain\" AND \"Pine\", (2) \"Acoustic Emission\" AND \"Pine\", (3) \"Acoustic Emission\" AND \"Softwood\", (4) \"Acoustic Emission\" AND \"Grain\" AND \"Wood\" AND \"Fracture\", (5) \"Acoustic Emission\" AND \"Earlywood\" AND \"Latewood\".  
The search was limited to the published paper in the time interval January 2000-June 2021 and conducted in slightly different ways according to the internal differences among the three databases. The identification step was applied in Google Scholar, sorting the documents by relevance and considering the first three pages viewed (corresponding to the first 30 documents) having all the keywords present either in the title or in the brief subtitle description. The same was applied in Scopus, looking at the research keywords in the \"Abstract, Title, Keywords\" section. In Web of Science, the documents were first sorted by relevance and then selected by conducting the keywords research in the \"Topic\" section. Duplicates were removed. The research initially provided a total number of 158 documents, shown separately for each of the different keyword combinations in Table 1. All the included documents were written in English and available online. According to the criteria adopted in the selection of records, the keyword combinations in the considered abstract and citation databases, might not capture that do not fit any combination of the search terms. For example, the combination \"Acoustic Emission\" AND \"Softwood\" was not able to capture papers focused on the AE detection on spruce samples, if the term \"Softwood\" was not explicitly indicated in the keywords or in the title and abstract. However, we focused our search on papers concerning Scots pine.  
Figure 2: PRISMA flow diagram for new systematic reviews showing the number of documents obtained after each evaluation-step.  
Step (ii) was carried out by reading 105 documents (i.e., the abstract in case of journal articles or the brief introduction in case of other type of documents, such as chapters of books or conference publications). This led to exclude 74 documents.  
In step (iii), a list of 31 papers were included in the review, among them 26 articles in peer reviewed journals, 4 conference papers and 1 book chapter.  
Among these 31 documents, 18 were obtained from Scopus/Web of Science (hereinafter named only as Scopus if duplicate) and 13 from Google Scholar.
# Support Vector Machine-Based Transmit Antenna Allocation for Multiuser Communication Systems
## 3 SVM-Based Antenna Allocation
### Task 1: Designing a Training Sample Set  
We need to manipulate the matrix form of the channel gain samples into training data with a suitable form for input into the learning system. Three procedures are performed to obtain the training data for the machine learning system (not necessarily in sequence): (i) design training data from the channel gain matrices, (ii) design the key performance indicator (KPI), and (iii) declare the corresponding label based on the KPI, i.e., labeling.
# Dual-Band Multiple-Element MIMO Antenna System for Next-Generation Smartphones
## 4 Simulation Results  
The simulated S-parameters response of the proposed MIMO antenna system is shown in Figure 5. The simulation of the proposed MIMO antenna was performed in CST Microwave Studio. Due to the symmetry, only one side of the MIMO system is considered. Figure 5a shows the reflection coefficient response of Ant 1 to Ant 4. From the figure, it can be seen that all the antennas are resonating well at the desired bands of interest, as shown in Figure 5a. The isolation between the antenna elements is shown in Figure 5b. The isolation is noted to be \(>\)18 dB for the 3.5 GHz frequency band, and \(>\)12 dB for the 5.4 GHz frequency band. Figure 6a,b shows the radiation and total efficiency of the MIMO antenna system. The radiation efficiency of Ant 1 and Ant 4 fluctuates between 62% and 71% (see Figure 6a), while for Ant 2 and Ant 3, the radiation efficiency varies from 58% to  
Figure 4: Effect of design parameter (**a**) D, (**b**) H, and (**c**) Z on antenna’s reflection coefficient.  
74%, as shown in Figure 6b. The total efficiency for Ant 1 and Ant 4 ranges over 51-60% for both frequency bands, as shown in Figure 6a. For Ant 2 and Ant 3, the total efficiency varies in the range of 48-58% (see Figure 6b).
## 1 Current Role of CCTA in Clinical Practice and Guidelines  
### Chronic Coronary Syndromes  
CCTA constitutes a first-line non-invasive tool in the diagnostic process of patients with stable chronic coronary syndromes (CCS). Nevertheless, its role in this category of patients went through a significant evolution. Indeed, the 2012 American Heart Association/American College of Cardiology (AHA/ACC) guidelines for the diagnosis and management of patients with stable ischemic heart disease (SIHD) reported that CCTA should be used as a first-line test for risk assessment only in patients with SIHD who were unable to exercise to an adequate workload (class IIa) [6]. The 2016 United Kingdom (UK) guidelines of National Institute for Health and Care Excellence (NICE) [7] recommend CCTA as the first-line diagnostic test in patients with atypical and typical angina (or electrocardiogram abnormalities suggestive of CAD in the absence of symptoms). More recently, the 2019 European Society of Cardiology (ESC) guidelines recommend CCTA (class IB) in suitable patients with low-to-intermediate clinical probability of CCS (Table 1) [8]. Indeed, the PROMISE trial demonstrated that CCTA is superior to functional testing in predicting cardiovascular events in patients with chest pain suspicious for coronary artery disease and reduces the rate of coronary angiography showing non-significant coronary artery disease [9]. Moreover, the SCOT-HEART trial in the same group of patients confirmed that using CCTA in addition to standard care resulted in a lower risk of death for CAD and reduced incidence of non-fatal myocardial infarction [10].
## 2 Materials and Methods
### Data Acquisition and Processing
#### 2.3.5 Meteorological Data  
Meteorological data from 2001 to 2018 were obtained from the National Meteorological Information Center of the China Meteorological Administration ([[http://data.cma.cn](http://data.cma.cn)]([http://data.cma.cn](http://data.cma.cn)),  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Year**} & \multicolumn{5}{c}{**Classification Accuracy Evaluation**} & \multicolumn{3}{c}{**Bamboo Forest Area (10\({}^{4}\) ha)**} \\ \cline{2-7}  & **Bamboo Forest Samples** & **Correctly** & **Incorrectly** & **User’s Accuracy (\%)** & **Estimate** & **Inventory** \\ \hline
2003 & 387 [42] & 309 & 78 & 79.84 & 486.56 & 495.32 [42] \\
2008 & 414 [42] & 328 & 86 & 79.23 & 545.14 & 548.73 [42] \\
2014 & 536 [42] & 435 & 101 & 81.16 & 639.22 & 610.65 [42] \\
2018 & 525 & 402 & 123 & 76.54 & 669.83 & 656.08 [41,42] \\ \hline \hline \end{tabular} Note: the results of the ninth National Forest Resources Survey do not have data from Taiwan, so the bamboo forest area of Taiwan is based on the results of the eighth National Forest Resources Survey.  
\end{table}
Table 2: Extraction accuracy evaluation and the comparison of estimated and inventory bamboo forest area of China.  
Figure 3: The optimal decision tree.  
accessed on 7 August 2020), and mainly included minimum temperature (T\({}_{\rm min}\)), maximum temperature (T\({}_{\rm max}\)), precipitation (Pre) solar radiation, and relative humidity. These meteorological factors are the main environmental factors in the carbon-water cycles [63]. The inverse distance weighting method was used to interpolate the data of 824 meteorological sites observed in the study area (Figure 1a) into spatial data with a 1 km resolution to obtain the grid cells of daily scale meteorological data of the study area. They are shown in Figure 1d-f. Among them, the temperature was corrected by the digital elevation method, and it was assumed that the temperature decreased by 6.5 \({}^{\circ}\)C for each per-kilometer increase in altitude. Solar radiation was simulated based on the measurements of sunshine duration at each site, following the methods of [PERSON] et al. [64]. Monthly and annual meteorological data based on interpolated daily scale meteorological data were obtained.
## 5 Conclusions  
(1) Organic pores within deep-marine shale of the Wufeng-Longmaxi Formation showed a variety of shapes, mainly macropores larger than 50 nm. They had good connectivity in three-dimensional space and acted as the major storage area and permeable space for natural gas. Many types of inorganic pores were developed, including intraparticle-dissolved pores of calcite and feldspar and interparticle pores between different minerals.  
(2) Within the deep-shale interval, the pore structure of Sections 1 and 3 was the best, and the pore morphology corresponded to the ink-bottle-shape beneficial to the adsorption and storage of natural gas. Meanwhile, Sections 1 and 3 had the largest pore volume, dominated by pores above 10 nm, and a large proportion of macropores.  
(3) Pore fluids in deep-marine shale could be classified into four parts: (1) capillary-bound water; (2) free, moveable water; (3) clay-bound water; and (4) closed-pore fluid. Pores smaller than 5 nm were clay-bound water and closed-pore bound water, which are not recoverable. Pores ranging between 5 nm and 25 nm corresponded to the capillary-bound water portion, which can be recovered by a certain fracturing mining process. Pores larger than 25 nm were movable water parts that can be directly developed. Sections 1 and 3 had the highest free-flowing and capillary-bound water content and the best development conditions.  
Conceptualization, G.C. and Y.G.; methodology, G.C.; validation, Y.G., Y.J. and Z.W.; formal analysis, G.C.; investigation, G.C. and Y.G.; data curation, Z.W.; writing--original draft preparation, G.C. and Y.G.; writing--review and editing, G.C. and Y.G.; visualization, Z.W.; supervision, Y.J.; project administration, Y.J. 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 (No. 42272171) and the Science and Technology Cooperation Program of CNPC-SWPU Innovation Alliance (No. 2020 CX020104).  
**Institutional Review Board Statement:** Not applicable.  
Figure 10: Pore fluid proportions of O\({}_{3}\)w–S\({}_{1}\)1 deep-marine shale. (**A**) Well A; (**B**) Well B; (**C**) Well C; (**D**) Well D.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data are available upon request due to privacy/ethical restrictions.  
**Conflicts of Interest:** The authors declare that they have no known competing financial interests or personal relationships that might have influenced the work presented in this article.
## 4 Materials and Methods
### Segmentation Model  
The segmentation model utilized for the pipeline in our experimentation is a fork of a building segmentation model [30] adapted from the U-Net architecture [31] as part of the SpaceNet data challenge [32]. The architecture for this is a fully connected network [33]. Therefore, the images inserted into this network are down-sampled to semantically classify the most salient features in the image and then up-sampled to semantically segment all the pixels in the image from those salient features. The down-sampling happens through a series of convolutional, pooling, and activation layers common to all CNNs, but is then followed by a series of deconvolutional and unpooling layers to provide the final semantic segmentation. The output of this process will provide a mask that is a prediction provided by the network as to whether each pixel in the image is part of a building or part of the background. This prediction is evaluated by the intersection over union (IOU) of the prediction and ground truth, as shown in Equation (2). The IOU is used to determine the loss of the network for each round of training and backpropagated through the network using stochastic gradient descent.  
\[IOU=\frac{area(prediction\ \cap\ ground\_truth)}{area(prediction\ \cup\ ground\_truth)} \tag{2}\]
## 2 Requirements of the Robot Patient
### Simulation of STS Movement by Robot Patient
#### 2.2.3 Range of Motion and Required Torque during STS Movement  
The required range of motion (ROM) of the lower limb during STS transfer was determined by referring to basic human anatomy [44] and the phases of STS movement [43] shown in Figure 1. The angle of hip joint flexion-extension without rotating the pelvis ranges from 0\({}^{\circ}\) to 120\({}^{\circ}\), whereas the angle of knee joint motion ranges from 0\({}^{\circ}\) to 150\({}^{\circ}\). Lastly, the angle of ankle joint dorsiflexion and plantar flexion ranges from 90\({}^{\circ}\) to 120\({}^{\circ}\). The robot's lower limbs were developed in accordance with these anatomical observations. The required torques were computed by analyzing the postures adopted during STS movement, within the designated ROM. The values determined through this analysis are presented in Table 1.  
For the STS movement, the required torques were calculated based on the robot mass, the lengths and weights of the body segmentation, and the joint angles. The maximum required torques during the STS process occur at the joint angles indicated in Figure 2. The  
Figure 1: Body configuration in sit-to-stand (STS) movement (**a**) bending of the trunk followed by extending of the lower limbs by correct STS transfer skill (**b**) extending of the lower limbs without bending of the trunk by incorrect STS transfer skill.  
mass of the upper body from the head to the hip joint is 20 kg, from the head to the knee joint is 30 kg, and from the head to the ankle joint is 34 kg. The COM is measured through the distribution of the robot's weight. Accordingly, the calculated maximum required torques are 19.6 Nm (at 120\({}^{\circ}\)) for each hip joint, 35.2 Nm (at 90\({}^{\circ}\)) for each knee joint, and 24.9 Nm (at 120\({}^{\circ}\)) for each ankle joint. These values represent the torques required for the robot patient to stand up without any assistance, that is, the torques required to complete the STS movement independently. Therefore, the required joint torque with the assistance of a nurse would be lower than these values.
## 4 Results and Discussion
### Impact Mechanism of Urban Spatial Form on Outdoor Environmental Performance
#### 4.2.2 Impact Mechanism of Urban Spatial Form on UTCI-W  
In Model 2, the results of explainable machine learning (Figure 11a) show that the variables influencing UTCI-W include AH, VAR, FAR, SC, PO, and others, while shape indicators such as CVH, OSR, SCD, and SV have little impact on UTCI-W. The SHAP summary plot (Figure 11b) reveals that spatial form indicators such as AH, VAR, and FAR significantly promote UTCI-W, while SC and PO significantly suppress UTCI-W.  
These results demonstrate the impact of urban spatial form on winter outdoor thermal comfort. Specifically, the higher the porosity and form factor of the block, the lower the UTCI value in winter, resulting in a poorer overall thermal environment. This may be because increasing the block's porosity and form factor reduces the ability of the block to block cold air in winter, leaving the outdoor area exposed to cold winds and low temperatures, thereby decreasing outdoor thermal comfort for the elderly in winter.  
On the other hand, the higher the average building height, the larger the volume density and the floor-area ratio (FAR), and the higher the UTCI value in winter. This may be because a more concentrated layout helps the block withstand more cold winds in winter, causing heat to accumulate and therefore raising the surrounding environment temperature, thus improving thermal comfort for the elderly.  
Comparing Figures 10 and 11, it can be seen that the requirements for urban block spatial form in winter and summer are somewhat contradictory. In the summer, a high building density and floor-area ratio suppress natural ventilation due to the higher temperatures. However, in winter, increasing the building density enhances the cohesion of the block and reduces heat loss caused by cold winter winds, creating a more pleasant outdoor thermal environment. This presents higher demands for the climate-adaptive design of urban block spatial form.
## 3 Control of HDV RNA Editing
### Mechanisms for Controlling Levels of amber/W Site Editing  
The rate and level of amber/W site editing achieved during replication are critical because HDAg-L is the limiting factor for virus particle production, but inhibits viral RNA replication. Comparison of HDV editing with other editing substrates indicates that amber/W site editing occurs much more slowly and at lower levels. Most cellular substrates for editing are pre-mRNAs that are not only frequently edited at high levels (\(\sim\)100% in some cases), but must be edited prior to splicing, which occurs within minutes following transcription. By contrast, only about 4% of HDV RNAs are edited at the amber/W site four days post transfection [7,12], and editing levels slowly increase to the maximum level. We have determined that this reduced level of editing is achieved by several mechanisms. First, for both HDV types 1 and 3, the amber/W site is sub-optimal for efficient editing. Second, both types 1 and 3 limit the availability of the RNA for editing by ADAR1, using distinctly different mechanisms. Third, because editing occurs on newly synthesized antigenome RNA, production of HDAg-L, which occurs as a result of editing and inhibits replication, eventually prevents accumulation of excessive editing levels.
# An Advanced SAR Image Despeckling Method by Bernoulli-Sampling-Based Self-Supervised Deep Learning
## 2 The Proposed Method
### Multiscale Despecking Network
#### 2.2.1 Main Network Architecture
\begin{table}
\begin{tabular}{c c c} \hline
**Main Parts** & **Subparts** & **Configurations** \\ \hline \multirow{5}{*}{PB} & Preprocessing & Conv (c = 64, k = 3, s = 1, p = 0) + PReLU \\  & & Conv (c = 128, k = 3, s = 1, p = 0) + PReLU \\  & & Conv (c = 64, k = 3, s = 1, p = 0) + PReLU \\  & DRB \(\times\) 2 & Conv (c = 64, k = 3, s = 1, p = 0) + PReLU \\  & & Conv (c = 128, k = 3, s = 1, p = 0) + PReLU \\ \hline \multirow{5}{*}{EB-i (i = 1, 2, 3)} & Downsampling & Conv (c = 128 \(\times\) 2\({}^{\text{i}}\), k = 3, s = 2, p = 1) + PReLU \\  & & Conv (c = 128 \(\times\) 2\({}^{\text{i}}\)\({}^{-1}\), k = 3, s = 1, p = 0) + PReLU \\  & DRB \(\times\) 2 & Conv (c = 128 \(\times\) 2\({}^{\text{i}}\)\({}^{-1}\), k = 3, s = 1, p = 0) + PReLU \\  & & Conv (c = 128 \(\times\) 2\({}^{\text{i}}\)\({}^{-1}\), k = 3, s = 1, p = 0) + PReLU \\ \hline \multirow{5}{*}{DB-i (i = 3, 2, 1)} & Upsampling & TConv (c = 128 \(\times\) 2\({}^{\text{i}}\)\({}^{-1}\), k = 2, s = 2) + PReLU \\  & & Dropout + Conv (c = 128 \(\times\) 2\({}^{\text{i}}\)\({}^{-2}\), k = 3, s = 1, p = 0) + PReLU \\  & DRB with dropout \(\times\) 2 & Dropout + Conv (c = 128 \(\times\) 2\({}^{\text{i}}\)\({}^{-2}\), k = 3, s = 1, p = 0) + PReLU \\  & & Dropout + Conv (c = 128 \(\times\) 2\({}^{\text{i}}\)\({}^{-1}\), k = 3, s = 1, p = 0) + PReLU \\ \hline \multirow{5}{*}{OB} & & Dropout + Conv (c = 128, k = 3, s = 1, p = 0) + PReLU \\  & & Dropout + Conv (c = 64, k = 3, s = 1, p = 0) + PReLU \\  & & Dropout + Conv (c = 1, k = 3, s = 1, p = 0) \\ \hline \end{tabular}
\end{table} 
Table 1: Detailed configuration of our proposed MSDNet. Figure 4: Architecture of our proposed MSDNet. convolution kernel consistent, the stride (s) and the padding (p) were set to 1 and 0, respectively. For the strided convolutions, to achieve 2-times downsampling, in other words, to make the size of the feature map change to half of the original ones, the stride (s) and the padding (p) were set to 2 and 1, respectively. In particular, to achieve 2-times upsampling, in other words, to make the size of the feature map change to the 2-times the original ones, the kernel size (k) and the stride (s) of TConv were set to 2 and 2, respectively. Furthermore, for the output channels of each convolution, in general, more channels of the feature map can enhance the expressive ability of deep neural networks. However, due to the GPU memory limitation, in this work, the number of the out channels was set to be a multiple of 64, such as 64 and 128.
# A Fast Point Clouds Registration Algorithm for Laser Scanners
## 2 Proposed Method
### 3D Shape Context  
The basic idea of 3 DSC [17] feature descriptor is to count the context information of each point in two point sets and compare whether they are similar to obtain a most approximate arrangement, and find the corresponding points between the two point clouds. The feature space of 3 DSC is shown in Figure 6.  
Figure 4: Local reference frame (LRF) of basis point.  
Figure 5: ISS feature points.  
The description steps of 3 DSC algorithm are as follows:  
Step 1, normals calculating. For each query point \(p_{i}\) in the point cloud, take \(p_{i}\) as the center of the sphere, \(R_{3 dsc\_frame}\) as the radius, and use the surface normal \(N_{i}\) as the direction of North Pole to establish a local supporting area. Figure 7 shows the normals of the point cloud after down sampling.  
Step 2, sub-regions dividing and radiuses calculating. On the radius \(R_{3 dsc\_frame}=\left\{R_{0}\ldots R_{I}\right\}\), divide \(J+1\) intervals by logarithm, divide \(L+1\) intervals along azimuth angle \(\psi=\left\{\psi_{0}\ldots\psi_{L}\right\}\), and \(K+1\) intervals along pitch angle \(\theta=\left\{\theta_{0}\ldots\theta_{K}\right\}\) equally; thus \(J\times K\times L\) sub-regions in total are divided. As the intervals are divided logarithmically on the radius, if the area near the sphere center \(p_{i}\) is too small, it will be disturbed by noise easily. Setting \(R_{0}\) as the minimum radius \(r_{min}\), and \(R_{J}\) as the maximum radius \(r_{max}\), the radius \(R_{j}\) is calculated by  
\[R_{j}=\exp\left\{ln(r_{min})+\frac{j}{J}ln\left(\frac{r_{max}}{r_{min}}\right) \right\}. \tag{4}\]  
Step 3, sub-regions weighting. Calculate the weights \(\omega(p_{i})\) of each point in the area by Equation (5). Here \(V(j,k,l)\) represents the volume of the area for the \(j\)-th radius, the \(k\)-th azimuth direction, and the \(l\)-th elevation direction. \(\rho_{i}\) is the density of the corresponding local point.  
\[\omega(p_{i})=\frac{1}{\rho_{i}\sqrt[3]{V(j,k,l)}} \tag{5}\]  
Step 4, feature vector composing. Then, make a statistic of the values of \(\omega(p_{i})\) to compose a feature vector \(\overset{\rightarrow}{f_{p_{i}}}\),  
\[\overset{\rightarrow}{f_{p_{i}}}=\left\{\omega_{1},\omega_{2},\ldots,\omega _{k}\right\} \tag{6}\]  
where \(k=\left\{1,2,\ldots,n\right\}\), \(n=J\times K\times L\).This vector contains the shape context information around the basis point.  
Figure 6: Three-dimensional shape context (3 DSC) feature space division.  
Figure 7: The normals of the point cloud.