esa-sceva/satcom-chunk-collection huggingface dataset:
# Knowledge-Graph- and GCN-Based Domain Chinese Long Text Classification Method
## 4 Experimental Results and Analysis
### Experimental Result and Analysis
Compared with the method proposed in this paper, it lacks the focus on the key information brought by the attention mechanism and the features of the dependency and entity relationships, and they are less capable of handling long-distance text compared with GCN, so the classification effect is not as good as the model proposed in this paper. TextGCN uses the GCN to model the graph, which can capture high-order neighborhood information, establish the boundary between two word nodes using word co-occurrence information, establish the boundary between word nodes and document nodes using word frequency, and then transform the text classification problem into a node classification problem, so the effect is significantly improved compared with the TextCNN model. Combining BERT with GCN, the BERT model is trained with large-scale numbers to obtain dynamic word vectors with rich semantic features, which gives better performance and improved accuracy. Static word vectors are prone to word ambiguity problems, KGBGCN uses BERT to encode text to learn word dynamic word vectors, and BERT and GCN jointly calculate the loss function. To solve the problem of too many parameters of BERT and GCN models, which are prone to overfitting or difficult to converge, the vectors of the last four layers of BERT are stitched together. To solve the problem of lack of domain knowledge, entity-entity relationships, together with syntactic dependency information, are proposed as edges of the GCN to improve model performance with knowledge introduction, and dependency types and relationship types are added directly to each GCN layer to capture richer text features with graph structure. Applying the attention mechanism to each relational connection in the GCN layers allows the GCN in KGBGCN to encode dependency and entity relations more densely, so that they can be better utilized to guide text classification. To improve the  
Figure 4: Accuracy result comparison of comparison experiments. learning ability of the model for semantic dependencies between words, graph structure masking is proposed, which randomly masks the edge connections between two tokens and then allows the model to predict whether there is a connection relationship between two tokens and the type of connection. KGBGCN adds entity relations and dependency relations in GCN, and the GCN puts domain knowledge in the form of graph structures with pre-trained models The integration is a key factor to achieve the improvement of model performance. Among the three publicly available Chinese datasets, the IFLYTEK dataset has the smallest sample length, and the THUCNews dataset and the Fudan dataset are longer. In Table 3, we can observe that KGBGCN has a better classification effect for the latter two datasets, which means that KGBGCN has a better classification effect for longer texts, which can verify the effectiveness of our proposed model for classifying long Chinese texts.
###### Abstract  
To analyze the influence of the operator experience on the accuracy of orthodontic self-tapping micro-screws placement, a total of 60 orthodontic self-tapping micro-screws were randomly distributed into two study groups: Group A. Orthodontic micro-screws placement by an orthodontist with 10 years of experience (\(n=30\)); and B. Orthodontic micro-screws placement by an orthodontic-tist student without experience (\(n=30\)). Cone-beam computed tomography scans and intraoral scans were performed before and after the orthodontic self-tapping micro-screws placement and uploaded in 3D implant-planning software to analyze the deviation angle and the horizontal deviation measured at the coronal entry point and apical endpoint between orthodontic micro-screws planned and performed. In addition, intraoperative complications such as root perforations after orthodontic self-tapping micro-screws placement and fracture of the orthodontic self-tapping micro-screws during their placement were also analyzed. The paired \(t\)-test revealed statistically significant differences at the apical endpoint (\(p=0.004\)) of planned and performed orthodontic self-tapping micro-screws between the orthodonticist with 10 years of experience and the orthodontic student without experience. However, the paired \(t\)-test revealed no statistically significant differences at the coronal entry point (\(p=0.220\)) and angular deviations (\(p=0.602\)) of planned and performed orthodontic self-tapping micro-screws between the orthodonticist with 10 years of experience and the orthodontic student without experience. Furthermore, five root perforations were observed in the no experience study group and three orthodontic self-tapping micro-screws were fractured in each study group. In conclusion, the results show that the greater experience of the operator influences the accuracy of orthodontic micro-screws placement, resulting in less intraoperative complications.  
orthodontics; micro-screws; orthodontic anchorage; mini-implants; temporary anchorage devices +
Footnote †: journal: Applied Sciences
## 1 Introduction  
The progressive development of energy-intensive technologies requires the development and improvement of devices capable of storing electricity and quickly providing load. Strict requirements are placed on the ability of devices to operate hundreds of thousands of cycles without deteriorating energy performance. Supercapacitors (SC) are capacitive energy storage devices that are successfully used in switching electrical systems and electric vehicles. SC are widely known as ultracapacitors, ionizers or two-layer electrochemical capacitors [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17]. These elements occupy an intermediate area between conventional batteries and linear capacitors. The specific power of the SC is 23 times higher than the specific power of modern lithium-ion batteries [11], due to relatively low internal resistance. The capacitance of the SC can be from 1 to 10 KF, at nominal voltage (2.7 to 4 V). The voltage applied to the SC is limited by the voltage of electrochemical decay of the electrolyte, at which the electrolyte begins to decompose more intensely and the supercapacitor fails. For use at higher voltages, the SCs are connected in series and receive rated voltages of hundreds of volts.  
SCs can withstand up to a million charge-discharge cycles without destroying their structure (Table 1), in contrast to lithium-ion batteries, which are capable of operating at a maximum of several thousand such cycles [11, 16, 17]. Rechargeable batteries are best used for steady-state power consumption. Therefore, in the combined power sources of electric vehicles, supercapacitors provide pulse modes of high power consumption in the load,and the battery provides long-term operation of the electromechanical system in steady state [3; 5; 8; 9; 10; 11; 12].  
A detailed review of the results of the analysis of the energy characteristics of supercapacitors [2; 8; 9; 10; 11; 12] showed that research has been conducted without taking into account the loss of electricity in the circuits of charge, recharging and internal redistribution of supercapacitor charges. There was also no analysis of electricity losses in the charge circuits of supercapacitors from the battery under non-zero initial voltage conditions.  
The aim of this work is to compare the energy characteristics (energy transfer coefficient and energy loss) of a linear capacitor and supercapacitor, when charged by a battery, both at zero and non-zero initial voltage conditions, as well as to establish the influence of initial and final voltages on power losses in the charge circuits of linear capacitors and supercapacitors.
## 3 Simulation Analysis  
A finite element modeling of reinforced geopolymer concrete beam was constructed by ANSYS software [25] in order to analyze the flexural performance. The nonlinear elasticity model was adopted for the polypropylene fiber and steel fiber geopolymer concrete specimens. The response could be predicted numerically by using the eight-node element Solid 65, as shown in Figure 9. In this simulation analysis, the element with three freedom degrees was chosen for the structure beam and was suitable for considering cracking and flexural deformation.  
Figure 8: Flexural test.  
Figure 7: Experiment of Poisson’s ratio and elastic modulus.  
The supported beams with 400 mm effective length and a cross-section of \(100\times 100\) mm were taken for simulation analysis with the different volume fractions of each embedded fiber, as shown in Figure 10. The failure criterion of [PERSON] was used for the definition of the concrete failure, as was [PERSON]'s model [26]. The stress-strain relationship of specimens was determined from the experiment, as shown in Section 4.1.2, with the various dosages of fiber. Equation (1) shows how to measure the stress-strain relationship of the fiber-reinforced geopolymer concrete model with stress f at strain \(\varepsilon\) and ultimate compressive strength f\({}^{\prime}\)c at strain \(\varepsilon_{0}\)[27].  
\[\begin{array}{l}\text{f}=\frac{\text{E}_{\text{c}}\,\varepsilon}{1+\left( \frac{\varepsilon_{0}}{\varepsilon_{0}}\right)^{2}}\\ \varepsilon_{0}=\frac{2\text{E}_{\text{c}}}{\text{E}_{\text{c}}}\\ \text{E}_{\text{c}}=\frac{\text{f}}{\varepsilon}\end{array} \tag{1}\]  
The material properties of the fiber geopolymer concrete model were defined by using experimental results, as shown in Section 4.1.2. These data were measured from flexural tests. The geopolymer concrete (GC) without fiber had an elastic modulus of 25.40 GPa and a Poisson's ratio of 0.128, while the generated fiber had different properties depending on the volume fraction of fiber.  
There were a total of nine specimens with volume fractions ranging from 0.5% to 1.5%--the same as the experimental test. The elastic modulus of specimens P1 and P2 reinforced with polypropylene fiber ranged from 21.32 to 26.10 GPa, and their Poisson's ratios ranged from 0.129 to 0.152. Meanwhile,  
Figure 10: Modeling of fiber-reinforced geopolymer concrete.  
Figure 9: Schematic of Solid65 element.  
for specimens H1 and H2, generated with steel fiber, the higher elastic moduli were 27.29-31.56 GPa, and Poisson's ratios of 0.140-0.163 were input.  
The boundary conditions were simulated exactly the same as the experiment set up, as shown in Figure 8. The meshing size was 5 mm with over 30,000 elements. In the middle span beams, the concentrated transverse load was applied to specimens and was divided into a load increments called load steps. The load-deflection relationship was obtained from the first crack load up to fully collapse. Additionally, the crack patterns of the geopolymer composites beams were also determined at the last converged load steps.
# Real-Time Object Tracking Algorithm Based on Siamese Network
## 2 Related Work
### Deep Feature-Based Tracking  
In recent years, deep features have greatly improved the performance of trackers, so they have been applied by many scholars. The work based on deep convolution networks is mainly divided into two parts. Firstly, a pre-trained object recognition network is used and built on a discriminant or regression model. The tracker combines the depth features with the correlation filter. For example, for the purpose of further enhancing the SRDCF algorithm, the convolutional features were introduced by [PERSON] et al. [9]. [PERSON] et al. [10] substituted HOG (Histogram of Oriented Gradient) features by depth-wise convolutional features and fused the confidence figures which are gained by the means of percolating three layers of features separately. [PERSON] et al. [11] put forward a tracking approach which combines depth-wise convolutional features with adaptive spatially regularized correlation filters. Another method uses classification or regression networks to introduce deep features. For example, [PERSON] et al. [12] put forward a tracker which consists of CNN and SVM. [PERSON] et al. [13] directly regress the target position by the means of training a neural network, which is considered as the first algorithm on the basis of deep learning to achieve 100 fps. The above approaches are computationally slow, difficult to track in real time, and difficult to achieve the highest performance for non-end-to-end training.
## 3 Methodology and Data
### Results of Qualitative Interviews
#### 3.2.3 Environmental Context  
The environmental context includes the external factors that may affect a company's decision on the adoption of innovative technology [28].  
* Competitive Pressure: Some respondents (A3, I1) in the adopter and initiator group have quite positive expectations of their future competitiveness because their competitors have yet to begin using broadband mobile applications. Some other respondents claimed that they have already felt pressure (A2) or received customers' responses (N1) because their competitors had already adopted the broadband mobile applications. N2 said they always attached great importance to other competitors on the market. Many of their competitors have already adopted a variety of broadband mobile applications. Nevertheless, the customer response is unfavorable, so there is no intention from N2's company to adopt broadband mobile applications for the time being.
* Business Partner: All respondents indicated that they have not received strong requests by suppliers or customers. Several respondents expressed great concern about the users' response to the broadband mobile applications if those users are customers or suppliers (A1-3, I1-2). I1 said that attracting younger customers is the most important reason that sparks interest in adopting broadband mobile applications within their company.
* External Support: Respondents P2 and P3 stand in the service provider's point of view. They reported that SMEs often consider the good solution provider as an important determinant for adoption. Goods providers can offer adequate services, shorten the implementation time, and prepare well for staff education and training. Most importantly, when an unexpected incident occurs, there must be sufficient support and manpower to promptly resolve it. Losses of SME, caused by abnormal situations, must be avoided. Respondent N2 expressed that good suppliers and support systems are expensive relative to their company's scale and simply difficult to afford. Respondent A1 said that the company's 24 h service cannot be disrupted, so the company attaches great importance to the quality of service providers and would rather pay a higher price to find a better service provider.
* Government's Support: Respondents in the provider group believed that the role of the government is very crucial. Respondent P2 suggested that the government's promotions of both the construction of infrastructure, as well as the popularization of broadband mobile signal reception, are needed. Respondents P1 and A2 reported that it may be of more direct help for SMEs if the government has a subsidy policy, and the two companies themselves have already benefited from such a policy. Respondents I2 and I3 claimed that for the popularization of an innovative technology, such as broadband mobile applications, the government should entrust more professionals to educate SMEs employees.
# Method of Multi-Angle Transmission Radiowave Tomography of Dielectric Objects
## 2 Method of Forward Task Solution and Numerical Modeling
On the basis of the obtained field in the measurement region, \(A(x,y,\alpha,\omega)\), in the frequency band (in the numerical model from 16 to 20 GHz, 16 frequencies) for different rotation angles, it is necessary to find the distribution of dielectric heterogeneities. For real experiment function, \(A(x,y,\alpha,\omega)\) needs to be represented as an array of complex numbers that corresponds to the magnitude and phase of the radiowave field passed though the test object and measured at certain points with a fixed spatial step. Figure 3: The result of modeling the field of diffraction of a plane wave on rectangular objects with a permittivity of 1.5 and 2 at a frequency of 20 GHz. (**a**) The given distribution of inhomogeneities with the gray rectangle showing a refractive index of 1.5 and the black rectangle, a refractive index of 2; (**b**) Field of diffraction.
## 1 Introduction
It is reported that less than 10% of the methane potential is being captured and utilized [35]. Landfill gas at a high concentration also causes the risk of explosion [36] and odor problems [37]. Due to the heterogeneous nature of MSW, unreliable data on production rate, diversity in collection and treatment, and the complex biological processes involved in an accurate estimation of GHG, methane emission is quite complicated. Saudi Arabia is the largest country in the Middle East, which occupies about four-fifths of the Arab Peninsula, with a land area of around 2,149,690 square kilometers [38]. According to the United Nations [39], the total population of Saudi Arabia in 2019 was 34 million, which makes it the most populous country in the Middle East. Due to significant annual growth rate in population (2.3% between 2010 and 2015), total SW generation is expected to increase proportionally. Financial indices such as GDP or Gross National Product (GNP) and per capita income not only have a direct correlation with per capita SW generation rate, but also with the composition of SW. It is reposted that the per capita income of the kingdom increased at 5.26% for the period 1996-2013. This growth was also accompanied by improvement of other human indicators such as education, life expectancy, infant mortality rate, etc. The effect of all these factors is not always synergistic on SW generation. For example, studies showed that people with higher education levels generated less waste [40]. During the past three decades, the education sector in the kingdom made significant improvements and the literacy rate was reported to be 94% in 2013. Thus, the effects of the increase in per capita income and education level will counteract each on per capita SW generation. The above literature review suggests that the main driving factors of the type and the quantity of solid waste generation include urbanization, GDP, employment rate, literacy, gender, lifestyle, household size, population density, old population, economic growth, industrial growth, etc. Emissions from solid waste management systems mainly depend on solid waste management practices, climatic conditions, share of recycling, and emission management system. Many of the driving factors have been following an increasing trend in the Kingdom of Saudi Arabia. Therefore, there is a need to analyze the driving factors and their impact on the greenhouse gas emissions from solid waste management practices. In this study, we adopted a causality-based analysis to trace the impact of selected driving factors on emissions. To support the policy-making process, this study generated a few possible emission scenarios along with suitable climate change mitigation opportunities.
## 5 Local Strengthening through FRP Jacketing
#### Beam-Column Joint Strengthening
The plates selected to be used have a thickness of \(\mathrm{t_{o}}=1.4\) mm, modulus of elasticity \(\mathrm{E_{f}}=205\),000 MPa, ultimate tensile stress \(\mathrm{f_{fu}}=3200\) MPa and ultimate strain \(\mathrm{\varepsilon_{fuk}}=0.017\). The design tensile strain \(\mathrm{\varepsilon_{fd}}\) in the FRP layer shall not exceed the limit: \(\mathrm{\varepsilon_{fd}}=\mathrm{\varepsilon_{fuk}}/\gamma_{f}=0.017/3=0.0056\) where \(\gamma_{f}=3\) for FRP anchored on brittle substrate. According to fib Bulletin 90 [4] the allowable design value of FRP tensile strain shall not be taken higher than 0.004. Hence, \(\mathrm{\varepsilon_{fd}}=0.004\). The thickness, \(\mathrm{t_{f,h}}\), is estimated from \(\mathrm{t_{f,h}}=\gamma_{\mathrm{Rd}}\times\mathrm{V_{jh}}/(\mathrm{f_{b}} \cdot\mathrm{E_{f}}\cdot\mathrm{\varepsilon_{fd}})\) as \(\mathrm{t_{f,h}}=1.6\) mm \(>\mathrm{t_{o}}=1.4\) mm. Because the calculated value is very close to \(\mathrm{t_{o}}\), given the increased conservatism the Approach 1 has, one layer of CFRP L-shaped plate is required by covering the full height of the beams (detailing as shown in Figure 6b).
# Blockchain-Based Evidence Trustworthiness System in Certification
## 4 Secure Evidence Trustworthiness System
* Assurance of origin: Tracing the provider of evidence is essential, and permissioned Blockchain guarantees this through signed transactions. * Adaptability: The audit trail solution can be adapted to the particularities of each application domain. * High Usability: Trustworthiness systems must be user-friendly for non-technicians involved in certification processes. The Blockchain monitor included in the design to access evidence records via a browser makes the use of Blockchain transparent. However, there are some aspects that need to be improved in the certification domain. * Confidentiality: Evidence is usually considered sensitive information that should not be directly recorded on the Blockchain to avoid privacy issues. Integration with existing databases should be enabled. * Automation: Auditors often require automatic processes to facilitate the certification process. For these reasons, the audit trail solution considered has been adapted and extended to cover the particularities of an evidence trustworthiness system for certification. As a reminder, the Blockchain network considered by this audit trail solution is a private Quorum deployment in which smart contracts with administrative and auditing functionalities are provided. Quorum is a permissioned implementation of Ethereum, designed to support data privacy and alternative consensus mechanisms [39]. Although EBSI is based on Hyperledger Besu, implementations over Quorum are also consistent with EBSI. Quorum was specifically tailored for enterprise needs with a modular and secure design and optimized performance for private transactions. Hyperledger Besu,on the contrary, is more generalist and has a broader focus, which may lead to lower performance [40; 41]. Additionally, Blockchain events are automatically emitted every time a piece of information is recorded. These events feed the Blockchain Monitor, based on Elastic stack, providing a mechanism for external users to access the information recorded on the Blockchain. This mechanism improves the usability of the Blockchain technology, but still requires manual interaction. The architecture of the audit trail solution considered has been updated according to Figure 2, where the previously implemented components are shown in gray, while the extensions are shown in green.
## 2 Materials and Methods
### Anonymization and Information Loss Estimation
#### 2.2.3 Mondrian Algorithm  
The Mondrian is a greedy anonymization algorithm that implements k-anonymization through multidimensional generalization and is applicable to categorical as well as numeric data [23]. According to this method, a k-anonymization of a given dataset is achieved in two stages, the first one focusing on the recursive partitioning of the dataset in a number of multidimensional regions covering its domain space, a process similar to the KD-tree construction method. The second stage focuses on the mapping of generalized values to each dataset partition by applying re-coding functions using summary statistics from each region. The time complexity of the Mondrian algorithm is logarithmic, outperforming other optimal algorithms implementing k-anonymity, whose worst-case complexity is exponential [23].