esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Contribution of Human Activities and Climate Change to LDD  
As shown in Figure 5a, human factors have a stronger impact on the changes in the LDD process than climate change factors. In most regions, human activities are responsible for over 60% of the alterations in LDD. Among them, the highest contribution of human activities to the changes in LDD is in GX (85.31%), followed by LN (81.3%) and TJ (79.3%), and the lowest is in FJ (50.73%). To further quantitatively analyze the impact of human factors and climate change on the LDD process, the LDD process is divided into eight types (Table S3). As shown in Figure 5b, the LDD process affected by human activities accounts for about 25.76% of the study area. Specifically, the majority of the LDD process in the study area (about 21.61% of the total study area) is attributed to land development, concentrated in the east of GX, HB, and the west of GD and LN. On the other hand, the distribution of land degradation is limited to only 4.15% and can be found in fragmented areas, including the PRD, YRD, and northeast of LN. The LDD process affected only by climate change accounts for about 7.42% of the study area, with land development and land degradation accounting for 7.38% and 0.04%, respectively. The LDD process affected by the joint impact of climate and human factors accounts for 30.94% of the study area, with the majority attributed to land development (30.64%) and mainly distributed in FJ, northern JS, SD, and HB. Although 30.94% of the LDD process is affected by the joint impact of climate and human factors, human factors are the primary factor affecting this LDD type (Figure 5a). At the same time, about 4.56% of the study area experienced natural degradation or development, and about 31.31% showed no significant change.  
Figure 5: Effects of human activities on LDD processes. (**a**) The spatial distribution of human contributions, the bar chart displaying the contribution rate (unit: %) of human activities (HA) and climate change (CC) to the LDD process in each province. The red and blue bars represent CC and HA, respectively; (**b**) the LDD types impacted by HA and CC.
# Predicting User's Measurements without Manual Measuring: A Case on Sports Garment Applications
## 2 Materials and Methods
### Materials
#### 2.3.2 Styku 3D Scanner  
The Styku scanner is a commercially available scanning system that is mainly oriented on the fitness, health, and wellness sectors [30]. It rotates the subject on a turntable and scans using a Microsoft Kinect V2 scanner, as seen in [31,32]. Its proprietary software requires data input such as body weight and stature, and then scans one full rotation. Data are processed by the proprietary software, and provides a reliable [28] an easy-to-understand overview of common measurements on the body, as well as volume and outline traces [30]. This setup is included as it could be a commercial cost-effective method for receiving relatively accurate measurements with a simplified interface and mode of operation [31,32]. Thus, it can deliver fast (around two minutes) and cheap measurements in a retail or professional environment. However, it has the downside of requiring the subject to be almost naked (underwear) or wearing skin-tight garment (i.e., being almost as time consuming as fitting the real garment in the first place), and a few other restrictions are mentioned in the literature [33, 34, 35].
# Device Modeling of Quantum Dot-Organic Light Emitting Diodes for High Green Color Purity
## 3 Results and Discussion  
### Emission Spectrum with Different G-QD Densities  
The emission spectrum according to the wavelength was first investigated using a 15 nm spiro-DPVBi, which is a fluorescent blue material. Figure 4 shows that the blue material's emission spectrum analysis indicates the maximum peak in the 445 nm wavelength band. In addition, as a result of the blue color coordinated analysis using CIE1931 to investigate the color purity, we can observe that the blue color coordinate in the CIE1931 color space chromaticity diagram indicates that Bx is 0.15 and By is 0.12. Additionally, according to the wavelength, the emission spectrum was investigated while changing the QD volume density from 1% to 40%. From the results shown in Figure 5, the thickness of the G-QD layer was fixed at 10 \(\upmu\)m, a condition that minimizes light leakage in the blue wavelength band and obtains a sharp peak in the green wavelength band. As the blue OLED incident light passes through 1% of the G-QD density, most of the emission spectrum occurs only in the blue wavelength band and is only weakly observed in the green wavelength band. This result is thought to be due to the low G-QD density in the G-QD layer, so that blue incident light cannot be sufficiently scattered through G-QD, resulting in a low conversion rate to green color. It can be seen that when the G-QD density increases to 5%, the blue spectrum rapidly decreases, and the green spectrum increases. This result can be assumed to occur because the blue incident light is actively scattered through G-QD when the G-QD density is 5% or more. However, even in this condition, the full width at half maximum (FWHM) at a green wavelength is still large, at about 59.62 nm. When the G-QD density is more than 20%, the FWHM decreases rapidly in the green wavelength band, and sharper peaks are obtained. When G-QD is over 30%, blue light leakage in the blue wavelength band becomes very weak, the peak in the green wavelength band becomes very sharp, and the FWHM is reduced to 40.82 nm, showing a saturation behavior. Therefore, it can be seen that in order to minimize blue light leakage and obtain a sharp green peak with a narrow FWHM, the G-QD density should be at least 30%.
## 5 Conclusions  
In this study, we first proposed effective measures to block the infiltrating rainwater in the subgrade through a capillary barrier. Secondly, using the numerical simulation method, we conducted studies on the mechanism and stability of the capillary barrier used in the subgrade to control distress against wetting. The subgrade with a capillary barrier could effectively use the different permeability coefficients of the different types of soil to achieve an anti-seepage effect, which minimized the deformation of the subgrade and increased stability. Using the numerical simulation method, this paper analyzed the pore-water pressure distribution, settlement, and safety factor of the conventional subgrade and the subgrade protected by the capillary barrier. Through a comparison of the numerical results of the two subgrades, the following conclusions were drawn:  
1. The pore-water pressure of the subgrade protected by the capillary barrier was hardly affected by rainfall. Compared with the conventional subgrade, the pore-water pressure was reduced by 180%, and the pore-water pressure distribution was close to the hydrostatic pressure distribution. The moisture content of the conventional subgrade was effectively reduced by approximately 18% by using a capillary barrier;
2. The settlement of the subgrade using a capillary barrier was uniform and small, and is only 1 cm. Compared with the conventional subgrade, the settlement was reduced by 94%. The safety factor of the subgrade using a capillary barrier was almost unchanged. Compared with the conventional subgrade, the safety factor increased by 15%;
3. In humid climates, it is feasible to use subgrades with a capillary barrier to reduce the deformation and stability problems caused by humidification.
## 3 The Proposed LeBERT Model  
In deep learning, the BERT model is one of the current word embeddings and text representation models under study for sentiment analysis. BERT, unlike other word embedding algorithms, can effectively read series of words in either direction of the input text, and since it uses the attention mechanism to assign a word, its vector depends on the surrounding words, and it is efficient in word vectorization [39]. Although BERT considers the context of a word when assigning the vector, it does so for all the words in the input text, which leads to a resultant vector with high dimensionality. Second, word vectors built from BERT do not contain semantic information, which is critical in sentiment classification. Compared with BERT, the sentiment lexicon can be used to identify sentiment words in a text and assign specific sentiment polarity to the words. However, sentiment lexicon cannot generate representative word vectors, hence leading to high data sparseness. Thus, to improve sentiment classification, this paper proposes the LeBERT model, which combines sentiment lexicon, N-grams, and BERT algorithms.  
The design idea of the LeBERT model is to first use N-grams to split the input text into sections, and then use a sentiment lexicon to identify a section or sections that contain a sentiment word. It is worth noting that text reviews, such as social media posts, contain short text, and characteristically, semantic features in short texts are concentrated in a certain part [39]. Thus, extracting features from such parts will lead to efficient and effective text representation. The words of the identified section(s) are then converted into a vector by BERT. The output word vector is then used as the input into a CNN model with a fully connected layer where features from the vector are obtained. The features extracted are then integrated by the dense output layer, and finally the sentiment class of the text is performed by a SoftMax classifier. The architecture of the proposed LeBERT model is shown in Figure 1.  
Figure 1: Architecture of the proposed sentiment analysis model.  
As shown in Figure 1, the sentiment lexicon, N-grams, and BERT algorithm are used in the embedding layer to build the word vector. The overall sentiment analysis model using the LeBERT model is presented in Figure 2.
# Secure Delivery Scheme of Common Data Model for Decentralized Cloud Platforms
## Materials and Methods
### Materials
#### 2.1.5 Blockchain Application in CDM Environment  
Medical data is defined as personal sensitive information by law, so its use is limited. In order to use the various healthcare data that have been established, it is limited to de-identification of personal information data, or individual consent is required, resulting in restrictions on data linkage/utilization. In addition, for the safe use of medical data, problem-solving for security threats must be preceded.  
Medical data have conflicting action plans to protect and utilize personal data. As the openness of public data is activated, related personal information leakage and re-identification cases are increasing, and data provision is limited due to issues such as information security and personal information protection. Due to the nature of medical data, the scope is only medical information, not non-medical data, and such data are differentiated from the existing traditional database. Rather than implementing high performance, it is necessary to develop a technology and build an environment that requires a high level of security, considering the characteristics of medical information. As a result, it is necessary to propose a more reinforced system that can solve the information security system problems such as medical data leakage and re-identification, and this is presented in this paper.  
In this paper, we present a study on blockchain technology for handling authority and management of medical data and a data exchange model for data linkage between institutions. In the case of blockchain, it is possible to protect personal information and strengthen self-determination through secure data exchange or smart contract. As for the data-exchange model, there is a CDM method developed for the purpose of exchange and analysis of distributed and stored data. Research that applied blockchain technology to the CDM environment is in the early stage of research, and specific implementation cases have been rarely published.
## 4 Conclusions  
The purpose of this study is to measure the harmfulness of an asphalt mix plant based on PAH concentration. The study area is a village near an asphalt mix plant where inhabitants showed about a 25% cancer rate after the asphalt mix plant was established. PAHs in PM\({}_{2.5}\) ranged from 0.51 to 60.73 ng/m\({}^{3}\), with an average of 11.54 ng/m\({}^{3}\). The diagnostic ratios between PAHs indicate that the major source of PAHs could be incomplete combustion of petrogenic origin. Seasonal \(\sum\)BaP\({}_{\text{TEF}}\) in winter is 2.284 ng/m\({}^{3}\) followed by lower spring and autumn values. The TEF value is about 6.5 times higher than the concentration in the BI background area and more than three times higher than in the capital city, Seoul, in the Republic of Korea. In conclusion, the primary emissions source for the rural village could well be the asphalt mix plant. It is necessary to investigate methodologies of reducing the PAHs emitted from asphalt mix plants. Continuing research on asphalt mix plants is necessary.  
[PERSON] contributed experimental measurements, data analysis and manuscript preparation. [PERSON] and [PERSON]-H.O. contributed meteorological data analyses and manuscript preparation. M.-S.B. contributed to experimental planning and data analysis. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Rural Development Administration, National Institute of Agricultural Sciences (Grant Number PJ01490002). We greatly appreciated using the Convergence Research Laboratory (established by the MNU Innovation Support Project in 2019) to conduct this research.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **Sampling Site** & **BaA** & **Chry** & **BbF** & **BkF** & **BaP** & **DBA** & \(\Sigma\) \\ \hline \multirow{3}{*}{This study} & Suburban Residential Area & 0.060 & 0.0185 & 0.184 & 0.054 & 0.37 & 0.19 & 0.8765 \\  & BI (background) & 0.004 & 0.0027 & 0.021 & 0.014 & 0.08 & 0.01 & 0.1317 \\  & Seoul (Capital) & 0.009 & 0.0043 & 0.033 & 0.020 & 0.13 & 0.09 & 0.2863 \\ \hline \multirow{3}{*}{Previous study} & China, Wuhan & 0.149 & 0.0308 & 0.486 & 0.140 & 1.91 & 0.22 & 2.9358 \\  & Saudi Arabia, Riyadh & 0.005 & 0.0005 & 0.097 & 0.068 & 3.19 & 0.00 & 3.3605 \\ \cline{1-1}  & USA, Minnesota & 0.011 & 0.0019 & 0.016 & 0.007 & 0.08 & 0.20 & 0.3159 \\ \cline{1-1}  & Taiwan, Taichung & 0.014 & 0.0020 & 0.005 & 0.026 & 0.24 & 0.066 & 0.353 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of BaP\({}_{\text{TEF}}\) toxicity in the study area and in previous studies.
# Experimental and Numerical Study on Dynamics of Two Footbridges with Different Shapes of Girders
## 2 Materials and Methods
### Eigenvectors and Eigenvalues
#### 2.6.2 Example 2  
The first 16 eigenvectors for the footbridge above the University Route in Bydgoszcz together with the corresponding eigenfrequencies are listed in Table 3. The modes of vibration for the structure are shown in Figure 14. In this case, many eigenmodes are typically associated with the arch deflection and low participation of the deck deformation. It can be seen from the Table 3 that eigenvectors B_La1-B_La3, B_La5-B_La7 are rather purely lateral with the movement of steel girder only, while eigenvectors B_La4, B_La8, and B_La9 have significant vertical component as well. Five eigenmodes B_Vd1-B-Vd5 are closely related to the vertical movement of the platform and relatively large deflection of the steel girder have been identified. Among the vertical modes, B_Vd1 have the largest lateral modal component of the steel girder. The two torsional eigenvalues B_Td1 and B_Td2 show the significant bending deflection of the deck. In this case, the platform generally have small horizontal deflection compared to other components for all mode shapes. Despite the use of various excitation techniques during field tests (Figure 10), it was not possible to excite all the vibration frequencies that were obtained in the numerical model.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Number**} & \multirow{2}{*}{**Mode Type**} & \multirow{2}{*}{**Frequency**} & \multicolumn{4}{c}{**Modal Component Ratio**} \\ \cline{3-8}  & & & & **Deck** & & **Arch** \\ \cline{3-8}  & & **Hz** & **Vd** & **Ld** & **Td** & **Va** & **La** \\ \hline
1 & B\_La1 & 0.94 & 0.02 & 0.00 & 0.00 & 0.04 & 1.00 \\
2 & B\_La2 & 1.12 & 0.08 & 0.00 & 0.00 & 0.01 & 1.00 \\
3 & B\_La3 & 2.30 & 0.05 & 0.01 & 0.00 & 0.13 & 1.00 \\
4 & B\_Vd1 & 2.35 & 1.00 & 0.01 & 0.00 & 0.66 & 0.73 \\
5 & B\_La4 & 2.65 & 0.43 & 0.00 & 0.00 & 0.51 & 1.00 \\
6 & B\_La5 & 3.39 & 0.07 & 0.01 & 0.00 & 0.20 & 1.00 \\
7 & B\_Vd2 & 3.58 & 1.00 & 0.02 & 0.00 & 0.89 & 0.40 \\
8 & B\_La6 & 4.01 & 0.13 & 0.00 & 0.00 & 0.08 & 1.00 \\
9 & B\_La7 & 4.17 & 0.09 & 0.01 & 0.00 & 0.14 & 1.00 \\
10 & B\_Vd3 & 4.82 & 1.00 & 0.01 & 0.00 & 0.72 & 0.12 \\
11 & B\_Td1 & 4.95 & 0.50 & 0.01 & 1.00 & 0.33 & 0.11 \\
12 & B\_Td2 & 5.18 & 0.49 & 0.03 & 1.00 & 0.42 & 0.24 \\
13 & B\_La8 & 5.26 & 0.29 & 0.01 & 0.00 & 0.14 & 1.00 \\
14 & B\_La9 & 5.27 & 0.31 & 0.02 & 0.58 & 0.14 & 1.00 \\
15 & B\_Vd4 & 5.56 & 1.00 & 0.03 & 0.00 & 0.97 & 0.16 \\
16 & B\_Vd5 & 5.90 & 1.00 & 0.01 & 0.00 & 0.51 & 0.17 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Modes and eigenvalues identified from the FEM model of the arch footbridge above the University Route in Bydgoszcz.  
Figure 13: FEM model eigenvectors and eigenvalues of the arch footbridge above expressway S8.
## The Proposed Method
### Network Architecture  
This part describes in detail the architecture of the network, the model used in the experiments. Except for the input layer, the model is comprised of 12 layers, all of which contain trainable parameters, as shown in Figure 3. All convolution layers use the same convolution operation, so that more information of the image can be retained. For convenience, let \(C_{x}\), \(S_{x}\), and \(F_{x}\) denote convolutional layers, sub-sampling layers, and fully-connected layers, respectively, where \(x\) is the index of each layer.  
Layer \(C_{1}\) is a multi-scale kernel convolutional layer which can expand to the receptive filed. The convolution operation is carried out with convolution kernel of the size of \(1\times 1\), \(3\times 3\), and \(5\times 5\). Each convolution module has 4 kernels and the output feature maps are concatenated after they pass through a rectified linear unit (ReLU) function.  
Layer \(S_{2}\) is a max pooling layer with 12 feature maps. Since the \(2\times 2\) receptive fields do not overlap, the number of rows and columns of the feature map in \(S_{2}\) is half of the feature map in \(C_{1}\).  
Layers \(C_{3}\)-\(C_{9}\) are convolutional layers with \(3\times 3\) kernels. Two residual blocks are added to the network which can attain higher classification accuracy with deeper layers. The last convolutional layer \(C_{9}\) outputs 32 feature maps.  
Figure 3: The overall architecture of the proposed Multi-scale ResNet network. Concat is the operator of concatenating feature maps produced by \(C_{1}\).  
Figure 2: The process of spectral–spatial fusion to form a sample, where \(c_{r}\) is assumed to be 25.  
Layer \(F_{10}\) and layer \(F_{11}\) are fully-connected layers with 120 and 84 units, respectively. To decrease the risk of overfitting, the dropout method is conducted.  
The last layer \(F_{12}\) is also a fully connected layer, which is also the output layer of the model. The number of neuron units are related to the number of classes. Since it implements a multi-classification task, Softmax regression is used in this layer.
## 4 Experimental Results and Discussion
### Computational Time  
The time required for watermark embedding and extractions process in the DIW scheme is termed as computational time. The computational time of the major algorithmic steps of the proposed scheme is provided in Table 8. Considering only major algorithmic steps and the most expensive operations, the proposed adaptive watermarking scheme has cubic time complexity as shown below:  
\[\begin{split} Computational\ time=O(2 MN)+O(2 MN)+O(2 MN^{2}+2N^{3})\\ +O(2M2N)+O(MN)+O(MN)+O(log(logm))=O(N^{3})\end{split} \tag{15}\]  
Table 9 shows the computational time (in seconds) for the proposed scheme. From Table 9, it can be studied that the average embedding time for grayscale and color images is 1.527 s and 1.382 s, respectively, whereas the average extraction times for grayscale and color images are 0.995 s and 0.870 s, respectively. For all images, extraction time is less than the embedding time since pseudo random key generation is not done during extraction. There is no significant difference between embedding and extraction time for grayscale and color images. Computational time of the proposed scheme is optimum as the watermark is embedded and extracted in less than 2 s and 1 s, respectively.
# Analytic Form Fitting in Poor Triangular Meshes
## 3 Methodology
### Primitive Fitting from Triangle Set  
This section addresses the determination of the parameters of a given analytic form (cylinder, cone, sphere) of type \(\widehat{S}\), which the user wants to fit to a triangle set \(S\). The strategy used in our algorithm is to locally _probe_ the triangle set, progressively determining parameters of the form (radius, center, axis, apex, etc.). This strategy was chosen instead of _statistical_ fitting, which requires statistically meaningful (dense, large, homogeneous, isotropic) triangle set inputs. Our implementation does use quadratic form fitting for the purpose of determining minimal and maximal curvature directions on specific and small neighborhoods of the triangle set \(S\).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Edge** & **Vertex 1** & **Vertex 2** & **Counter-EDGE** & **Face** \\ \hline
1 & 1 & 2 & 4 & 1 \\
2 & 2 & 3 & 5 & 1 \\
3 & 3 & 4 & - & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: EDGE table. If counter-EDGE cell is empty, indicates border.  
Figure 5: Input triangle set \(M\) with inconsistent FACE orientation. Output B-Rep of \(M\). (a) and (b) displayed with culling based on the triangle orientations.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Border Loop** & **Vertex 1** & **Vertex 2** & **Vertex 3** & **Vertex 4** & **Vertex 5** & **Vertex 6** & \\ \hline
1 & 4 & 7 & 8 & 91 & \textless{}End-of-loop\textgreater{} & \\
2 & 10 & 71 & 31 & 11 & 1 & 90 & \textless{}End-of-loop\textgreater{} \\
3 & 63 & 83 & 47 & 121 & 9 & \textless{}End-of-loop\textgreater{} & \\ \hline \hline \end{tabular}
\end{table}
Table 5: BORDER table.  
The fitting of the \(g()\) analytic form to set \(S\subset M\) is indifferent to the manner in which triangle subset \(S\) is determined. The description of the fitting follows.  
**Input:**  
1. \(S\): triangle support set (\(S\subset M\)) to fit the analytic form \(g()\).
2. \(\widetilde{\mathscr{T}}\): primitive type, selected by the user from the set {Cylinder, Sphere, Cone}  
**Output:**  
1. \(g()\): The analytic form of the chosen primitive, fit to subset \(S\).  
For each analytic form, we implement an approach for the identification of its parameters.