esa-sceva/satcom-chunk-collection huggingface dataset:
# Probabilistic Condition Monitoring of Azimuth Thrusters Based on Acceleration Measurements
## 2 Materials and Methods
### Classification Tests
#### 2.3.2 Reference Methods  
Fault classification based on machine learning algorithms has been widely studied during the last decades. Some of the commonly applied classifiers were chosen as the reference methods here. They include k-Nearest Neighbor (k-NN), Linear Discriminant Analysis (LDA), Support Vector Machines (SVM) and AdaBoost M1. Each of these algorithms have been previously applied in machine diagnosis studies, such as in [47; 48; 49; 50],  
Figure 8: Relation between squared Mahalanobis distance and chi-square cumulative distribution function with three and five degrees of freedom (\(d\)). The distances that equal 10%, 5% and 0.1% probabilities are marked in the curves.  
respectively. The details of theoretical bases and implementations of the classifiers are reported in the previous literature and are, therefore, omitted here.  
The hyperparameter values of the classifiers were optimized based on the mean accuracy in the inner resampling loop of the nested cross-validation by using the exhaustive search. The tested values are shown in Table 6. The features shown in Table 3 were used as the input variables for the classifiers after normalizing them to unit variance and zero mean. The computations were done by using Matlab(r) 2019a.
## 1 Introduction
In the process of xylose preparation, corncob raw materials have to be pretreated in order to remove metal ions and soluble proteins that are not conducive to the separation and purification of the xylose product from corncobs [26]. However, studies on pretreatment of corncob raw material for effective eliminating soluble proteins and metal ions have rarely been reported. In our previous study [26], we suggested using an extremely dilute sulfuric acid solution as pretreatment liquid for processing corncob raw material. By exploiting optimum process conditions, the removal rate of soluble proteins and metal ions attained to 97.0% and 94.3%, respectively [26]. Interestingly, as the basic composition of the corncob, the contents of cellulose, hemicellulose and lignin after pretreatment exhibit negligible variations. We believe that this is attributed to the extremely low concentrations of sulfuric acid (0.1 wt.%) utilized for corncob pretreatment under comparatively moderate operating conditions, i.e., a 100 \({}^{\circ}\)C processing temperature in combination with an atmospheric pressure. Despite the afore-mentioned advantages, the acid treatment method is constrained since large amounts of waste acid and wastewater have been produced. The wastewater can be discharged only if it meets relevant discharge standards after treatment; meanwhile, the production efficiency is reduced and the production cost is increased. In industrial corncob pretreatment, the ratio between the mass of the corncob to the volume of the dilute acid solution is generally in the range of 1:10 to 1:5. However, a huge amount of waste acid are generated after pretreatment, which results in extra waste disposal processes. Although the amount of sulfuric acid and water utilized during corncob pretreatment hasbeen decreasing through a variety of means, reducing the consumption of both acid and water at an order of magnitude level is still an ongoing problem. In this paper, a new process for economic pretreatment of corncob by repeated using extremely dilute sulfuric acid as the pretreatment liquid is proposed. To evaluate the effect of corncob pretreatment, metal ions and soluble proteins in solution are analyzed respectively for different re-use rounds of the pretreatment liquid. In addition, basis for the selection of an optimal reuse round of the pretreatment liquid are suggested. To the best of authors' knowledge, no previous published work was found in which extremely dilute sulfuric acid as the pretreatment liquid has been reused on corncob pretreatment. The methodological approach presented in this study aims to provide the industrialists and researchers a guideline for design of a cost-effective pretreatment process on corncob conversion with significantly reduced water and sulfuric acid consumption.
# Application of Mastic Asphalt Waterproofing Layer in High-Speed Railway Track in Cold Regions
## 5 Conclusions  
In this paper, mastic asphalt was designed and practically validated for application as a waterproofing layer on the subgrade surface of high-speed railway tracks in cold regions. The overall performance of mastic asphalt mixture was first evaluated by laboratory tests, then a test section of MAWL was constructed as part of real high-speed railway line, and water content sensors were placed inside the subgrade of test section to monitor the performance of MAWL. Based on the field investigation results and monitoring data during the two years of operation, the following conclusions can be drawn:  
* Application of mastic asphalt significantly improves the waterproof performance of waterproofing layer beside the slab track system, and therefore helps to reduce freeze-thaw damage;
* MAWL outperforms conventional concrete waterproofing layer due to better cracking resistance, the observed cracks in MAWL are mostly short cracks caused by the expansion of construction joints in the concrete side slope;
* MAWL showed relatively high stability during the two years of investigation period, no obvious deterioration was observed in the test section;
* Construction joint failure is the key problem of existing discontinuous MAWL in slab track system, and further study is still needed to improve existing practices.  
[PERSON] performed the laboratory tests, analyzed the data and wrote the draft paper. [PERSON] revised the manuscript and provided many valuable suggestions. [PERSON] directed this research work and improved the manuscript. [PERSON] and [PERSON] assisted the implementation of test section and collection of field data.  
This project has received funding from National Natural Science Fund of China (project number 51778136, 51778140), the Fundamental Research Funds for the Central Universities, and Jiangsu Innovation Program for College Graduates (project number KYLX16_0267).  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Data Collection
#### 2.3.5 Landing Error Scoring System  
To evaluate the quality of landing, we employed the test Landing Error Scoring System (LESS), a validated and reliable assessment tool for evaluating landing patterns during drop-jump tasks [20]. The LESS has been established as a promising screening tool towards the identification of athletes who are at high risk of ACL injury [34]. The guidelines by [PERSON] et al. [35] were followed for the configuration of the test and the evaluation of the landing technique. Participants jumped from a 30 cm platform with both feet, landing at a distance equal to half their height, before executing a maximal vertical jump. After 1-2 familiarization trials, three successful jump-landing attempts were recorded. Two standard cameras (Panasonic HC-V770 (Panasonic Corporation, Tokyo, Japan) and Sony HDR-CX625 (Sony Group Corporation, Tokyo, Japan) captured frontal and sagittal views of the trials. The recorded footage was stored on a computer and analyzed using Kinovea software (0.8.26 experimental version). A single examiner, the NIL author with clinical experience in sports injury assessment, rehabilitation, and proper use of the the LESS scoring instrument, rated all videos [17; 36; 37]. Additionally, two evaluators assessed a subset of 37 athletes' trials, demonstrating excellent intrarater reliability for both experienced (interclass correlation coefficient (ICC) = 0.95, 95% CI, 0.89-0.97; \(p<0.001\)) and novice raters (ICC = 0.95, 95% CI, 0.90-0.97; \(p<0.001\)), as well as very good to excellent interrater reliability for the first (ICC = 0.90, 95% CI, 0.77-0.95; \(p<0.001\)) and second (ICC = 0.86, 95% CI, 0.71-0.93; \(p<0.001\)) evaluations [36]. The average LESS score derived from three trials was used for the analysis.  
Figure 2: Strength test configuration with handheld dynamometer. (**a**) Quadriceps strength test; (**b**) hannstrings strength test; (**c**) abductors strength test.
## 4 Results and Discussions
### Acoustic Performance of Single-Vs. Double-Layered Structures  
The STL results for single- and double-layered (the double-layered structures were assembled by bonding two single-layers, which formed a panel-core-panel-core-panel structure) Nomex honeycomb sandwich structures are shown in Figure 16. On the whole, the performance of the double-layered honeycomb sandwich structure was superior to that of the single-layered structure. In the 1.6-2.4 kHz and 3.7-5.2 kHz frequency bands, the average STL value of the double-layered structure was 4 dB larger than that of the single-layered structure. The increase in STL was mainly in the ranges of 1.5-2.5 kHz and 4-5 kHz, which are in the stiffness region. [PERSON] et al. [34] found the stiffness of the composite multilayer structures increased with intermediate layers. So, increase in STL is affected by the increasing of stiffness of the multilayer structure.  
Figure 17 displays the simulated results for the shapes of the modes occurring in the D2-type panel. The shapes of first mode are the same as those in the previous simulations, and the vibration of the upper and lower aluminium face sheet lead to a significant decrease in the STL curve. For the simulation, there are few simulations available for impedance tube testing, especially for small impedance tubes in the test range of 1600-6300 Hz, and it is difficult to accurately model [19]. In the research mentioned above it can be seen that the STL results of impedance tube tests are consistent with the changes in the structure's stiffness and resonance region, indicating that the influence of the dimension parameters on the structure's STL can be studied using an impedance tube.  
Figure 16: Test results for single-and double-layered honeycomb sandwich structures.  
Figure 15: Simulated mode shapes for the C\({}_{2}\)-type panel: (**a**) mode at 5288 Hz; (**b**) mode at 6824 Hz; (**c**) mode at 6829 Hz.
# A Raisin Foreign Object Target Detection Method Based on Improved YOLOv8
## 2 Materials and Methods
### Improvement
#### 2.4.3 Loss Function  
Figure 8 illustrates the incorporation of CIoU loss into the regression loss of the YOLOv8 framework. The CIoU loss \(L_{CIoU}\) is computed through Equations (1) to (5), with A representing the estimated box, B the actual box, and C the minimal rectangle encompassing both. The dimensions are denoted by width (\(w\)) and height (\(h\)), while \(IoU\) measures the overlap between A and B. The centers' separation is given by the Euclidean distance \(\rho(b^{A},b^{B})\). The diagonal of C is \(c\), and \(R_{CIoU}\) adjusts for aspect ratio discrepancies. The coefficient \(\alpha\) modulates the weight, and \(v\) indicates the aspect ratio variance.  
Figure 7: RFAConv structure diagram.  
\[L_{CIoU}=1-IoU+\frac{\rho^{2}\left(b^{A},b^{B}\right)}{c^{2}}+R_{CIoU} \tag{1}\]  
\[R_{CIoU}=\alpha v \tag{2}\]  
\[IoU=\frac{|A\cap B|}{|A\cup B|} \tag{3}\]  
\[\alpha=\frac{v}{(1-IoU)+v} \tag{4}\]  
\[v=\frac{4}{\pi^{2}}\left(\arctan\frac{w^{B}}{h^{B}}-\arctan\frac{w^{A}}{h^{A} }\right)^{2} \tag{5}\]  
Although the CIoU loss builds upon the DIoU loss [20] by incorporating a penalty for the aspect ratio discrepancy between the estimated and actual boxes, the \(v\) component within the \(R_{CIoU}\) term merely indicates the variance in aspect ratio, not the specific interplay of width and height for both boxes. In order to tackle this concern, [PERSON] et al. [21] proposed the EIoU loss based on CIoU loss, as shown in Figure 9. The EIoU loss \(L_{EIoU}\) calculation formula is shown in Equations (6) to (8), where \(w^{C}\) is the width of the smallest bounding rectangle, and \(h^{C}\) is the height of the smallest bounding rectangle. EIoU loss refines the CIoU loss by breaking down the aspect ratio penalty term \(R_{CIoU}\) into separate penalties for the width and height discrepancies between the estimated and actual boxes. This not only resolves the previously mentioned issue but also improves the convergence rate and accuracy of the loss function. Therefore, using EIoU loss helps improve the efficiency and accuracy of raisin foreign matter detection.  
Figure 8: CIoU loss.  
Figure 9: EIoU loss.  
\[L_{EIoU}=1-IoU+\frac{\rho^{2}\left(b^{A},b^{B}\right)}{c^{2}}+R_{EIoU} \tag{6}\]  
\[c^{2}=\left(w^{C}\right)^{2}+\left(h^{C}\right)^{2} \tag{7}\]  
\[R_{EIoU}=\frac{\rho^{2}\left(w^{A},w^{B}\right)}{\left(w^{C}\right)^{2}}+\frac {\rho^{2}\left(h^{A},h^{B}\right)}{\left(h^{C}\right)^{2}} \tag{8}\]
# State-Of-The-Art and Prospects for Peer-To-Peer Transaction-Based Energy System
## 5 Frameworks for TE  
The framework for energy trading and sharing to achieve the desired outcome can be described in terms of structure, operation mechanism and operation optimization. The structure of energy trading and sharing reported in the literature can be categorized into two major groups: distributed control and centralized control. Distributed energy sharing and trading involves prosumers that trade energy directly with each other in a P2P manner, while the centralized structure requires the prosumers to exchange energy through a central entity.
# From Transactions to Transformations: A Bibliometric Study on Technology Convergence in E-Payments
## 4 Result Analysis
### Network Analysis
#### 4.2.3 Citation Network
A common methodological approach across these papers is structural equation modeling (SEM), a powerful statistical technique that analyzes complex relationships between observed and latent variables. This approach is instrumental in unpacking the nuanced influences on consumer adoption and use of mobile payment services. Through SEM, researchers have validated theoretical models incorporating various factors, including perceived ease of use, usefulness, trust, security perceptions, and the impact of social influence. Trust and security emerge as central themes, underscoring their pivotal role in accepting and adopting mobile payment technologies. These papers collectively argue that trust in the technology and the provider is paramount for users to feel comfortable engaging in mobile transactions. Security concerns, particularly related to personal and financial data, significantly affect user willingness to adopt mobile payments. Addressing these concerns through robust security measures and clear communication about privacy protections is essential for fostering trust and encouraging wider adoption. Cultural and social factors also play a critical role, as highlighted by the studies' findings on the impact of social influence and cultural values on technology adoption. The research indicates that individual decisions about mobile payment usage are not made in isolation but are influenced by the behaviors and attitudes of peers, the perceived norm within one's social circle, and broader cultural attitudes towards technology and financial transactions. This suggests that mobile payment providers consider these social dynamics in their strategies, potentially leveraging social networks to enhance adoption and acceptance. Furthermore, the papers point to the importance of user experience, including ease of use and the compatibility of mobile payment systems with users' lifestyles and existing payment habits. Mobile payments' convenience and added value, such as reducing transaction times or providing integration with other services, are significant factors in user adoption decisions. The literature presents a detailed landscape of mobile payment adoption, underpinned by a complex web of factors that include trust, security, social influence, cultural values, and technological attributes. The methodological rigor of these studies, particularly SEM, provides a solid foundation for understanding the intricate dynamics at play. As mobile payment technologies continue to evolve, this body of work offers valuable insights for academics and practitioners in designing user-centric payment solutions that address potential users' multifaceted needs and concerns. Future research directions include exploring emerging technologies, deeper dives into cultural and social influences across different regions, and the long-term impacts of mobile payment adoption on consumer behavior and financial systems.
# Influence of the Approach Direction on the Repeatability of an Industrial Robot
## 2 Conditions and Procedure for Hypothesis Verification  
* According to the authors' assumption, there is a dependence between the direction of the robot arrival at the target and the repeatability that can be achieved at this target. Based on the summary stated above, the authors claim that this influence is not considered in the case of robot repeatability increasing attempts done by other researchers.
* In the first phase, only robot drives resolver data will be used for verification. These data should already prove the dependence of the direction of approach on the repeatability of the robot in the measured target. For this reason, the joint variables will be converted (according to the forward kinematics task) to the endpoint deviation (x, y, z, and the total deviation). Since the data from the revolvers do not include the error that occurs in the gearbox, arm deflection, thermal expansion, and also does not include the influence of manufacturing tolerances and deviations of individual parts of the manipulator mechanical structure, the second phase of measurement will follow.
* In the next phase, the target position and orientation will be measured by the 3D DIC method, and the deviation in the repeatability spectrum will be evaluated with high precision.
* As a result of the measurement, there will be a graphical representation of the measured target vicinity and the degree of deviation valid for its directions of approach.
* The result of this experimental measurement will be valid for a specific industrial robot, with a defined solution of installation in the workplace and for a specific target.
# Shearer-Positioning Method Based on Non-Holonomic Constraints
## 2 Positioning Algorithm of Shearer  
### Calculation Process  
Figure 1 shows the execution flow of the positioning algorithm of the shearer proposed in this paper: firstly, the information on shearer's attitude, speed, and position are obtained by the output of the inertial measurement unit installed in the center of the shearer's body through mechanical arrangement. The odometer output installed on the traction gear of the shearer is used to calculate the instantaneous speed of the shearer. Next, the mechanical arrangement results of the INS, the output of the odometer, and the non-holonomic constraints are fused through the Kalman-filter algorithm. The results obtained after the Kalman filter are used as the error feedback to modify the mechanical arrangement results of the INS to obtain the optimal estimation of the attitude, speed, and position information of the shearer. These three kinds of information reflect three different positions, and a unified coordinate system is required for a unified solution.
# Incorporating Grey Total Influence into Tolerance Rough Sets for Classification Problems
## 3 Grey-Total-Influence-Based Tolerance Rough Sets
### Illustrative Example
#### 3.5.2 Testing Phase  
During the testing phase, \(\mathbf{Z}_{11}\) and \(\mathbf{Z}_{12}\) need to be generated. For \(\mathbf{Z}_{12}\), when we use \(\mathbf{x_{4}}\) as the reference pattern, \(\Delta_{\max}\) and \(\Delta_{\min}\) are 27 and zero, respectively. For \(\gamma(\mathbf{x_{1}},\mathbf{x_{4}})\), \(\xi_{1}(\mathbf{x_{1}},\mathbf{x_{4}})\) can be computed as follows:  
\[\xi_{1}(\mathbf{x_{1}},\mathbf{x_{4}})=\frac{0+0.5\times 27}{0.9+0.5\times 27}=0.938 \tag{37}\]  
Furthermore, \(\xi_{2}(\mathbf{x_{1}},\mathbf{x_{4}})\), \(\xi_{3}(\mathbf{x_{1}},\mathbf{x_{4}})\), and \(\xi_{4}(\mathbf{x_{1}},\mathbf{x_{4}})\) are 0.622, 0.351, and 0.517, respectively. \(Y(\mathbf{x_{1}},\mathbf{x_{4}})\) can thus be computed as:  
\[Y(\mathbf{x_{1}},\mathbf{x_{4}})=\
icefrac{{1}}{{4}}(0.938+0.622+0.351+0.517) =0.607 \tag{38}\]  
In a similar manner, \(Y(\mathbf{x_{2}},\mathbf{x_{4}})\) and \(Y(\mathbf{x_{3}},\mathbf{x_{4}})\) can be computed as 0.596 and 0.612, respectively. Finally, \(\mathbf{Z}_{12}\) is as follows:  
\[\mathbf{Z}_{12}=\left[\begin{array}{cccccc}0.607&0.572&0.559\\ 0.596&0.574&0.571\\ 0.612&0.594&0.567\end{array}\right] \tag{39}\]  
Therefore, the grey direct influence matrix \(\mathbf{Z}\) is generated as follows:  
\[\mathbf{Z}=\left[\begin{array}{cccccc}0&0.712&0.671&0.607&0.572&0.559\\ 0.630&0&0.558&0.596&0.574&0.571\\ 0.589&0.558&0&0.612&0.612&0.567\\ 0&0&0&0&0&0\\ 0&0&0&0&0&0\end{array}\right] \tag{40}\]  
As \(\lambda=3.121\) occurs in the first row of \(\mathbf{Z}\), the normalized matrix \(\mathbf{X}\) derived from \(\mathbf{Z}\) is:  
\[\mathbf{X}=\left[\begin{array}{cccccc}0&0.228&0.215&0.194&0.183&0.179\\ 0.202&0&0.179&0.191&0.184&0.183\\ 0.189&0.179&0&0.196&0.190&0.182\\ 0&0&0&0&0&0\\ 0&0&0&0&0&0\\ 0&0&0&0&0&0\end{array}\right] \tag{41}\]  
The grey total influence matrix \(\mathbf{T}\) can be easily generated by \(\mathbf{X}(\mathbf{I}-\mathbf{X})^{-1}\):  
\[\mathbf{T}=\left[\begin{array}{cccccc}0.118&0.308&0.295&0.334&0.318&0.310\\ 0.272&0.108&0.257&0.315&0.302&0.298\\ 0.260&0.256&0.102&0.315&0.304&0.294\\ 0&0&0&0&0&0\\ 0&0&0&0&0&0\\ 0&0&0&0&0&0\end{array}\right] \tag{42}\]
## 2 Status and Literature Review
### Domestic and Overseas Design-for-Safety Review
#### 2.3.3 Singapore--Design-for-Safety, ConSASS  
Singapore introduced the concept of design-for-safety and distributes related guidelines to achieve safety management during the life cycle of buildings through risk assessment at each design stage. To this end, they provide checklists and risk assessment templates for each GUIDE process, i.e., GUIDE-1, 2, and 3, and conduct integrated risk assessment through qualitative and quantitative risk assessment through checklists [17; 18]. Consistent risk assessment is performed by providing information on tasks required during safety management, such as methods and items for recognizing hazards. Safety management of construction projects is performed by specifying the subject of risk management, such as reduction due to design changes and countermeasures against contractors. In addition to the safety management method that uses the existing risk assessment model, there is a separate safety assessment system known as ConSASS [19; 20]. Twenty-three types of hazards that can occur in construction sites are provided, and safety is scored by assigning weights to each control measure. In addition, safety management of construction projects is implemented by compulsory submission of scoring results according to the ConSASS checklist during the licensing process. To ensure safety management occurs in the design stage, integrated safety management of construction projects is achieved through the development and introduction of conventional risk assessments such as design-for-safety and evaluation methods specific to the Singapore construction environment such as ConSASS. In other words, Singapore has implemented a safety assessment model unique to Singapore in addition to the existing safety management measures to achieve a consistent risk assessment by complementing each other.