esa-sceva/satcom-chunk-collection huggingface dataset:
# Research Progress on Curved Plates in China: Applications in Architecture
## 4 Optical Characteristics of Curved Plates
### Natural Lighting Design of Auxiliary Elements
#### 4.2.2 Light Duct Lighting System  
As mentioned in Section 4.1.2, curved surface buildings that mainly rely on lighting from the top may lack effective natural lighting design in lower parts due to large height spaces or complex vertical structures. Therefore, a light duct lighting system represented by a tubular daylighting system (TDS) and an optical fiber daylighting system (OFDS) was developed. As shown in Figure 5(b1,b2), these two types of guided lighting systems mainly include three parts: the lighting area (I), transmission area (II), and diffuse reflection area (III) [105]. Among them, lighting areas and diffuse reflection areas are common between the two systems, and design research on lighting areas has developed rapidly in recent years [106], gradually developing from fixed system to solar tracking systems and tracking algorithms is constantly improving [107]. The remaining transmission area reflects the difference between the two systems. The TDS transmission area directly uses aluminum or silver as a reflection film material [108], which has a short transmission distance, but its advantage is that the construction technology is very low [109]. The OFDS, on the other hand, transmits light using fiber bundles [110] that can flexibly provide various focusing multiples (100-10,000). The transmission distance is very long, even up to more than 40 times that of the former, but the disadvantage is high technical difficulty [111]. As a result, the former is used in factories and gymasiums, while the latter is used in offices and classrooms. For example, the gymasium of the University of Science and Technology Beijing has adopted a TDS, and a total of 148 light tubes with a diameter of 530 mm are installed on the roof. With sufficient sunlight, relying only on natural light can fully meet any requirements of students in classes and sports training [112]. In addition, according to spectral analysis (Figure 5(b3)), the TDS is very similar to natural light, while the OFDS can filter infrared light and process natural light into a cold light source, which is more conducive to reducing the cooling load of air conditioning.
# Protecting Sensitive Data in the Information Age: State of the Art and Future Prospects
## 3 State-of-the-Art Privacy Measures
### Location Privacy
Weaknesses:  
The techniques are subject to many restrictions regarding the credibility of certain location information or trajectories (e.g., a person will most likely not be in the middle of the ocean), which limits their scope of action. Due to the sensor technology available in smart devices, more data sources are available to draw conclusions about location. This makes it easy to debunk a dummy location or a dummy trajectory.
# 3D Point Cloud Generation Based on Multi-Sensor Fusion
## 4 Results  
To verify the 3D point cloud generation method based on multi-sensor fusion, a test experiment was conducted at a track test site in Beijing. The test section was about 260 m and included straight and curved sections. As shown in Figure 18, there were multiple pairs of CPIII control point piles beside the experimental track line. In the experiment, a GNSS and IMU integrated navigation system was installed to obtain trajectory data, which were used to compare the deviation with the trajectory data calculated by the RTS smoothing algorithm. We used the integrated navigation method to generate 3D point cloud data and compared it with the multi-sensor fusion method to generate a 3D point cloud to verify the point accuracy and root mean square error.
## 2 Materials and Methods
### Methods
#### 2.3.2 Significance Test  
The Mann-Kendall test is a non-parametric test that can effectively avoid the effects of missing data and outliers on the results. It is usually used to test the significance of trends in long time series and has been widely used in meteorological, hydrological, and vegetation studies [61; 62; 63]. Its calculation method is as follows:  
Assuming that \(NPP=(NPP_{1},NPP_{2},\ldots,NPP_{n})\) is a time series variable, the test statistic \(Z\) is calculated as:  
\[Z=\left\{\begin{array}{ll}\frac{S-1}{\sqrt{Var(S)}},(S>0)\\ 0,(S=0)\\ \frac{S+1}{\sqrt{Var(S)}},(S<0)\end{array}\right. \tag{4}\]  
\[S=\sum_{i=1}^{n-1}\sum_{j=i+1}^{n}\text{sgn}(NPP_{j}-NPP_{i}) \tag{5}\]  
\[\text{sgn}(NPP_{j}-NPP_{i})=\left\{\begin{array}{ll}1,NPP_{j}-NPP_{i}>0\\ 0,NPP_{j}-NPP_{i}=0\\ -1,NPP_{j}-NPP_{i}<0\end{array}\right. \tag{6}\]  
\[Var(s)=\left[n(n-1)(2n+5)\right]/18 \tag{7}\]  
where \(n\) is the study period (20 years: 2001-2020), \(NPP_{j}\) and \(NPP_{i}\) are the sequence numbers of pixels, \(\alpha\) is the confidence level, and \(Z_{(1-\alpha)/2}\) is the standard normal variance. When \(|Z|\geq Z_{(1-\alpha)/2}\), the \(\alpha\) confidence level is set at 0.01, 0.05, and 0.1 to indicate that the trend of change passed the significance test [64].
# Machine Failure Prediction Using Survival Analysis
## 3 Background
### Feature Analysis
#### 3.2.2 Rank2D  
The Rank2D model is a feature selection technique that ranks input features based on their pairwise correlation with the target variable. The model used in our study utilizes two types of ranking methods: Pearson ranking and covariance ranking. Pearson's ranking is based on the Pearson correlation coefficient, which measures the linear relationship between two variables. Covariance ranking, on the other hand, measures the linear relationship between two variables without standardizing their scale. The Rank2D model first computes the pairwise Pearson correlation coefficients and covariance coefficients between each input feature and the target variable. The features are then ranked based on their absolute value of the correlation coefficient or covariance coefficient. The top-ranked features are then selected for use in the classification model. The mathematical formula for Pearson ranking is given by:  
\[r(x,y)=\frac{n\cdot\sum(x\cdot y)-\sum x\cdot\sum y}{\sqrt{(n\cdot\sum x^{2}-( \sum x)^{2})(n\cdot\sum y^{2}-(\sum y)^{2})}} \tag{8}\]where \(r(x,y)\) is the Pearson correlation coefficient between input feature \(x\) and target variable \(y\), \(\Sigma\) is the sum of the values of the input feature or target variable, and \(n\) is the total number of data points. The mathematical formula for covariance ranking is given:  
\[cov(x,y)=\frac{\sum_{i}(x_{i}-mean(x))\cdot(y_{i}-mean(y))}{n-1} \tag{9}\]  
where \(cov(x,y)\) is the covariance between input feature \(x\), and target variables \(y\), \(x_{i}\), and \(y_{i}\) are the values of input feature \(x\) and target variable \(y\) for the \(i_{th}\) data point; \(mean(x)\) and \(mean(y)\) are the mean values of input feature \(x\) and target variable \(y\); and \(n\) is the total number of data points.  
Rank2D can be used to select the most relevant features for classification tasks and can improve the accuracy and efficiency of the classification model. The model can be particularly useful in situations where the number of input features is large and feature selection is required to reduce the complexity of the model.
## 2 Literature Review  
### Free-Form Panels Panelization and Optimization  
Free-form buildings are much more complex than traditional rectangular buildings due to the difficulty in forming and fabricating their curved facade. To form the curved facade, it should be divided into the shape and size that can allow production and fabrication, which is called panelization [2]. At the design stage of a free-form building, designers create very complex building shapes that reflect their design inspiration. Initial panel configurations to embody these shapes consist of free curves with curvatures that change in many ways, causing difficulty in production and fabrication. The designers need to adjust the initial panel configurations to secure constructability of the panels at reasonable cost while maintaining the desired aesthetic quality of free-form shapes and cluster line smoothness of the panels. The process to change the curved panels with complex curvature into planar panels or the panels with simple curvature without damaging the visual quality of the free-form facade is called panel optimization [3; 5].  
In general, three approaches are implemented to panelize and optimize free-form facade surfaces: (1) shaping the surfaces into faceted planar panels, which can be triangular, rectangular, or pentagonal; (2) shaping the surfaces into single-curved panels, i.e., smoothly bent strips; and (3) shaping the surfaces into smaller double-curved panels [13]. Research has been performed to develop various panel optimization technologies including the use of regular patterns of planar panel shapes, the application of developable surfaces, and the use of parts of the curves such as sphere, cone, torus, cylinder, paraboloid, and cubic [14; 15]. In this research, the three approaches were all illustrated in the panelization of the case study subject.
# Dynamics Analysis and Chaos Identification of Compound Pendulum Jaw Crusher with Joint Clearance
## 4 Validation Analysis
### Validation of Dynamical Model  
The primary components of the PE250 \(\times\) 400 crusher are imported into Adams software for virtual prototype simulation in order to confirm the correctness of the dynamic model of the working mechanism of the crusher with clearance. The simulation interface is shown in Figure 4, and the results obtained are compared with those of MATLAB. The simulation parameters are the same in MATLAB and Adams, where the clearance size at the revolute joint A is 0.06 mm and the system driving speed is 30 rad/s.  
After the mechanism has stabilized in operation, the displacement and velocity curves of the moveable jaw are shown in Figure 5a,b, respectively, and the collision force curve at the revolute joint A is shown in Figure 5c,d. Analysis of Figure 5a shows that the displacement curves obtained by Adams and those obtained by MATLAB are basically the same. A closer look at Figure 5b reveals that during motion, the velocity curve will exhibit noticeable fluctuations at the crest and trough. The fluctuation value of MATLAB is slightly larger, and the two curves essentially continue to overlap after the fluctuation. Analysis of Figure 5c,d shows that the collision forces at the revolute joint A obtained by Adams and MATLAB maintains the same waveform trend overall, although they exhibit complex nonlinear oscillations. There are slight differences in the curve fluctuations and peak magnitudes. In the time interval [12, 12.4], 12.099 s for the first time to reach the peak collision force of 37,796 N for Adams and 12.106 s for the first time to reach the peak collision force of 37,578 N for MATLAB.  
Figure 4: Adams virtual simulation interface.  
Figure 3: Flow chart for solving dynamic equation.  
In summary, the results from Adams and MATLAB are similar in terms of trends, which verifies the correctness of the dynamic model of the working mechanism of the crusher with clearance. The slight difference in the peak is analyzed for possible reasons [21]:  
1. Errors in the structural parameters of the virtual prototype. Adams is deficient in modeling capability, so a 3D model of the crusher is built in SOLIDWORKS and imported into Adams, while some structural simplifications are made to the model, such as merging the bearing rollers of the eccentric shaft and thickening the revolute joint where the elbow plate is connected to the moveable jaw, resulting in minor changes in mass, volume and other parameters.
2. The difference between the two solution methods. The normal contact force model and the tangential friction force model in the dynamic model are not fully consistent with the mechanical model in Adams. In Adams, the collision forces between the components are defined using the Contact tool. The normal contact force is calculated by the Impact function in the function library, and the tangential friction force is calculated by the Coulomb friction model.
# Influence of Ozone on the Biochemical Composition of Birch Sap
## 1 Introduction
Although ozone is a gaseous compound that occurs naturally in the atmosphere and is formed by lightning or high-energy ultraviolet radiation [27], both aqueous and gaseous ozone have been universally recognized as safe to be used by the food industry (GRAS--Generally Recognized as Safe) by the U.S. Food and Drug Administration (FDA) [28; 29]. Researchers [PERSON] et al. [29], [PERSON] et al. [30], and [PERSON] et al. [31] confirmed that the use of ozone as an antimicrobial compound in the form of gas or as water solution is safe for food treatment and storage, drinking water, and juice treatment. According to the United States Department of Agriculture, food treated with ozone can be considered as \"100% organic\" or \"organic\" [32]. Ozonation is becoming widely accepted in the worldwide food industry as an organic technology [33]. Due to its high oxidizing capacity, ozone inactivates most microorganisms, thus prolonging the shelf life and not damaging the product [26; 34; 35]. Ozone treatment adds up to energy conservation because it does not require thermal energy [36]. However, studies on the ozone concentrations used in sap treatment as well as the effect of ozone on changes in sap quality indicators are scarce. The aim of this study is to determine the effectiveness of ozone at reducing microorganisms in birch sap and the effect of ozone on the composition of sap.
# Rateless Coded Uplink Transmission Design for Multi-User C-RAN
## 3 Rateless Coded Uplink Transmission Scheme  
In this section, we present the detailed uplink transmission scheme based on rateless code. Firstly, we show the encoding process at the users. Then, the quantization scheme at the RRHs is given. Finally, we propose the iterative detecting and decoding algorithm at the BBU pool.
# Key Technologies for 6G-Enabled Smart Sustainable City
## 5 Summary and Outlook  
In this paper, we explored the main technical challenges and potential benefits of prime technologies essential for creating 6G-enabled smart sustainable cities. We have identified several key enabling technologies and their roles in improving the quality of life of humans and the efficiency of urban services. With these disruptive technologies,6G-enabled SSC is envisioned to seamlessly connect intelligent things such as UAM, IoT sensors, robots, and mobile devices without limitations on the data rate, latency, and coverage. In Figure 7, we represent the overall 6G-enabled SSC architecture by highlighting the key enabling technologies. We anticipate that this will bring a truly hyper-connected society to achieve ubiquitous connectivity, sustainability, and enhanced security. Further, we have put forth urban practice cases for 6G-enabled SSC. We have addressed how the potential benefits of prime technologies enable various scenarios and applications. While we find these technologies to be quite promising and relevant for 6G-enabled SSC, in the future, we will explore more technologies, and our perspectives on the significance and effectiveness of various technologies will naturally change. An important future direction is to consider the concept of smart rural to achieve balanced development between rural areas because overcrowding in SSC leads to numerous and serious environmental issues, as well as disparities between urban and rural environments.  
**Author Contributions:** Conceptualization, N.K., G.K. and B.L.; methodology, G.K. and N.K.; validation, S.S., J.S. and B.L.; investigation, N.K., G.K., S.S. and S.J.; resources, S.S. and S.J.; writing--original draft preparation, G.K., N.K., S.S. and S.J.; writing--review and editing, J.S. and B.L.; Visualization, S.S., S.J., N.K. and G.K.; supervision, B.L. and J.S.; project administration, B.L. and J.S.; funding acquisition, B.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the Incheon National University Research Grant in 2022.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflicts of interest.  
Figure 7: Overall 6G-enabled SSC architecture.
## 5 Results and Discussion
### Results of Pre-Trained CNN Selection Experiments  
During the process of selecting pre-trained model parameters, it was found that a more complex model does not necessarily yield better performance. In fact, the model's performance depends on various factors, including its structure, the number of parameters, and its adaptability to specific tasks.  
In the experiments, DenseNet201 demonstrated superior performance on unseen datasets. The validation loss and testing loss of DenseNet201 were lower compared to other pre-trained models, indicating that this model has higher generalization capability when handling new data. The gap between training loss and validation loss for DenseNet201 was smaller, suggesting lower overfitting. Overfitting refers to a situation where a model performs well on training data but poorly on validation data. Lower overfitting indicates that DenseNet201 can better balance performance between training data and unseen data, thus enhancing its reliability in practical applications.  
Based on these observations, this paper decided to choose DenseNet201 as the backbone architecture for the model. Specifically, this paper utilizes parts of DenseNet's structure, combined with improvements, to achieve optimal performance for the specific task in this study. Tables 2 and 3 show the experiment results of the pre-trained model for questions 12 and 13, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Epoch** & **Training Loss** & **Validation Loss** & **Validation Accuracy (\%)** & **Testing Loss** & **Test Accuracy (\%)** \\ \hline ResNet50 & 46 & 0.0227 & 1.8164 & 93 & 0.7265 & 91 \\ ResNet101 & 13 & 0.1031 & 0.6472 & 88 & 0.7629 & 88 \\ DenseNet201 & 1 & 0.0889 & **0.1358** & **96** & **0.1247** & **95** \\ InceptionResNetV2 & 30 & 0.0065 & 0.1597 & 96 & 0.6192 & 93 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Pre-trained model experiment results for question 12.
# Research on Grain Moisture Model Based on Improved SSA-SVR Algorithm
## 4 Results
### Other Model Training Results
#### 4.2.2 MLP Model Results  
To establish a Multi-Layer Perceptron (MLP) model using the MLP regressor from scikit-learn, first create an instance of the MLP regressor with two hidden layers specified. We set the hidden_layer_sizes = (7, 3), indicating that the model has two hidden layers--the first layer contains seven neurons, and the second layer contains three neurons. The model is trained using the Adam optimizer, which is an adaptive learning rate stochastic optimization algorithm known for achieving good performance. Due to the small-scale dataset in this paper, one to two hidden layer structures are sufficient to avoid overfitting [44,45]. The activation function is initially set to the Relu function:  
\[f(x)=max(0,x) \tag{16}\]  
The Rectified Linear Unit (ReLU) function outputs the positive part of the input, which is max(0, x). Its purpose is to set all negative input values to 0 while leaving positive input values unchanged. It is a simple non-linear transformation that provides non-linearity  
Figure 8: (**a**) Shows the training graph of the alpha = 0.1 model; (**b**) training graph for alpha = 10 model.  
in practice, avoids the vanishing gradient problem, and is computationally efficient. The characteristic of the ReLU function maintaining active gradients in the positive range helps alleviate the vanishing gradient problem during training and can improve runtime speed.  
This model has a large deviation, and the curve is not stable. In subsequent identical training sessions, significant differences in the model graphs are observed. When modifying parameters by changing hidden_layer_sizes from (7, 3) to (10, 5) and using the tanh function as the activation function, the following is achieved:  
\[f(x)=\frac{e^{x}-e^{-x}}{e^{x}+e^{-x}} \tag{17}\]  
By changing the maximum number of iterations to 10,000, we obtained a relatively optimal model. This model has a correlation coefficient of 0.96, a determination coefficient of 0.95, and a root mean square error of 2.69. The training time for this model was 4.02 s. The training model is shown in Figure 9.  
The reasons for selecting MLP as a comparative algorithm to establish a model are as follows:  
(1) Strong nonlinear modeling capability: MLP is a fundamental deep learning model capable of capturing complex nonlinear relationships in data, making it suitable for handling problems that are nonlinearly separable.  
(2) High flexibility: by adjusting the number of network layers and neurons in each layer, MLP can be designed to adapt to various tasks of different complexities.
## 1 Introduction  
Countries where sustainable economy is developed repeatedly use (recycling) asphalt from the old pavements of transport infrastructure. Old asphalt grains are properly prepared for further recycling, called reclaimed asphalt (RA) or reclaimed asphalt pavement (RAP). Grains are extracted from asphalt layers by cold milling or breaking them, followedby crushing old pavement blocks and screening. Reclaimed asphalt (RA) is asphalt reclaimed by milling asphalt road layers, by crushing slabs ripped up from asphalt pavements or lumps from asphalt slabs, and asphalt from surplus production [1].  
Used asphalt grains of the determined maximum size (RAP) are dosed at the plant during the production of hot-mix asphalt (HMA) and warm-mix asphalt (WMA) mixtures (hot-mix recycling). In some cases, they are cold mixed with or without virgin mineral materials and binders and paved in the base layer (cold-mix recycling). In both operations, economic and environmental benefits are targeted as virgin resources and energy are saved, and production waste is not generated. One of the most important goals in the road construction industry is increasing the amount of RAP, i.e., the target is that all or almost all RAP available in the country is reused as rationally as possible.  
High-quality material milled from a street, aircraft runway, or vehicle parking lot pavement, the lower or upper asphalt layer course, is most beneficial for repeated use in the production of recycled hot and warm asphalt mixtures (RHMA and RWMA). The higher the percentage of RAP used, the lower the cost of the recycled asphalt mix [2]. The aim is that the recycled asphalt mixture has no worse properties than a mixture produced only from virgin materials. For this purpose, it is necessary to use homogeneous RAP, to separate the RAP removed from different pavement's layers, to assess its properties with sufficient accuracy, to be able to choose the mass ratio between it and virgin materials, to choose a suitable renewing material (rejuvenator) or a more liquid bituminous binder, and to accurately dose and mix all materials. It is especially important to produce a recycled mixture of HMA and WMA in an asphalt mixing plant (AMP) that corresponds to its job mix formula (JMF), strictly controlling the required temperature and completely mixing its components. In the process of its production, storage, and transportation, it is necessary to reduce the segregation, oxidative aging of the bituminous binder, and decrease in temperature.  
The aim of this paper is to investigate the ratio of the amount of RAP used in the production of HMA and WMA mixtures in individual countries to the total amount of prepared RAP available in the country, to systematize the factors (criteria) that increase this recycling rate and, after using expert assessments, four MCDM methods to determine the weights of these criteria and analyze them.