esa-sceva/satcom-chunk-collection huggingface dataset:
# Fast Implicit Surface Reconstruction for the Radial Basis Functions Interpolant
## 3 Implicit Function  
In implicit modeling, the implicit surface \(S\) is defined as the zero level set \(\left\{\mathbf{x}|f(x,y,z)=0\right\}\) of the implicit function \(f(\mathbf{x})\). To reconstruct the implicit surface \(S\), the implicit function \(f(\mathbf{x})\) should be first obtained by interpolating the domain constraints  
\[f(\mathbf{x}_{i})=f_{i},\quad i=1,2,\ldots,N \tag{1}\]  
where \(f_{i}\) are function values of the geometry domain and \(N\) is the number of constraints. These interpolation constraints are usually constructed from a set of sampling points.  
For the radial basis functions interpolant, the implicit function \(f(\mathbf{x})\) has the form [3]  
\[f(\mathbf{x})=f_{K}(\mathbf{x})+p(\mathbf{x})=\sum_{j=1}^{N}\omega_{j}K\big{(}\mathbf{x},\mathbf{ y}_{j}\big{)}+p(\mathbf{x}) \tag{2}\]  
where \(\mathbf{x}=(x,y,z)\), \(K\big{(}\mathbf{x},\mathbf{y}_{j}\big{)}\) is a kind of globally supported radial basis functions and \(p(\mathbf{x})\) are low-order polynomials. The unknown coefficients \(\omega_{i}\) can be determined by solving the linear system combined by the domain constraints. The first variable \(\mathbf{x}\) in \(K(\mathbf{x},\mathbf{y})\) is viewed as an evaluation point (or target point), and the second variable \(\mathbf{y}\) is viewed as a source point.  
Taking the polynomial part \(p(\mathbf{x})=c_{1}+c_{2}\mathbf{x}+c_{3}\mathbf{y}+c_{4}\mathbf{z}\) as an example, the smoothest interpolant satisfies the orthogonality conditions  
\[\sum_{i=1}^{N}\omega_{i}=\sum_{i=1}^{N}\omega_{i}\mathbf{x}_{i}=\sum_{i=1}^{N} \omega_{i}\mathbf{y}_{i}=\sum_{i=1}^{N}\omega_{i}\mathbf{z}_{i}=0 \tag{3}\]  
These orthogonality conditions, along with the domain conditions, lead to a linear system. The matrix form of the linear system can be written as  
\[\left[\begin{array}{ccccccccc}K_{1,1}&K_{1,2}&\cdots&K_{1,N}&1&x_{1}&y_{1}& z_{1}\\ K_{2,1}&K_{2,2}&\cdots&K_{2,N}&1&x_{2}&y_{2}&z_{2}\\ \vdots&\vdots&\ddots&\vdots&\vdots&\vdots&\vdots\\ K_{N,1}&K_{N,2}&\cdots&K_{N,N}&1&x_{N}&y_{N}&z_{N}\\ 1&1&\cdots&1&0&0&0&0\\ x_{1}&x_{2}&\cdots&x_{N}&0&0&0&0\\ y_{1}&y_{2}&\cdots&y_{N}&0&0&0&0\\ z_{1}&z_{2}&\cdots&z_{N}&0&0&0&0\\ \end{array}\right]=\left[\begin{array}{c}f_{1}\\ f_{2}\\ \vdots\\ f_{N}\\ c_{1}\\ c_{2}\\ c_{3}\\ c_{4}\end{array}\right]\]  
where \(K_{i,j}=K\big{(}\mathbf{x}_{i},\mathbf{y}_{j}\big{)}\), and the unknown coefficients \(\omega_{i}\) and \(c_{i}\) can be determined by solving the equation.  
There are several ways (e.g., Fast RBF method [30; 31]) to efficiently solve the interpolation equation for the RBF interpolant. In this paper, we focus more on the efficient reconstruction of the implicit surface. As mentioned earlier, the evaluation process and the reconstruction process are combined to improve the reconstruction efficiency.
## 8 Conclusions  
The present study shows that there are consistencies in people's sound-tracing to the melodic phrases used in the experiment. Our main findings can be summarized as:  
* There is a clear arch shape when looking at the averages of the motion capture data, regardless of the general shape of the melody itself. This may support the idea of a motor constraint hypothesis that has been used to explain the similar arch-like shape of sung melodies.
* The subjects chose between different strategies in their sound-tracings. We have qualitatively identified six such strategies and have created a set of heuristics to quantify and test their reliability.  
* There is a clear gender difference for some of the strategies. This was most evident for Strategy 4 (representing small objects), which women performed more than men.
* The 'obscure' strategy of representing melodies in terms of a small object, as is typical in Hindustani music, was also found in participants who had no or little exposure to this musical genre.
* The data show a tendency of moving within a shared'social box'. This may be thought of as an invisible space that people constrain their movements to, even without any exposure to the other participants' tracings. In future studies, it would be interesting to explore how constant such a space is, for example by comparing multiple recordings of the same participants over a period of time.  
In future studies, we want to investigate all of these findings in greater detail. We are particularly interested in taking the rest of the body's motion into account. It would also be relevant to use the results from such studies in the creation of interactive systems,'reversing' the process, that is, using tracing in the air as a method to retrieve melodies from a database. This could open up some exciting end-user applications and also be used as a tool for music performance.  
The following are available online at Available online: [[http://www.mdpi.com/2076-3417/8/1/135/s1](http://www.mdpi.com/2076-3417/8/1/135/s1)]([http://www.mdpi.com/2076-3417/8/1/135/s1](http://www.mdpi.com/2076-3417/8/1/135/s1)), supplementary archive consists of data files of the following nature: segmented motion tracings of 26 participants annotated with participant number, melody traced, and hand strategy used. The melodies are from 1 to 16, and the pitch data and sound stimuli are separately provided as well. More information about the same and code can be provided upon request.  
This work was partially supported by the Research Council of Norway through its Centres of Excellence scheme, Project Number 262762.  
[PERSON] and [PERSON] both conceived and designed the experiments; [PERSON] performed the experiments and analyzed the data; both authors contributed analysis tools, and wrote the paper.  
The authors declare no conflict of interest.
# Adhesion Performance between Solid Waste and Bitumen Based on Surface Energy Theory
## 2 Materials
### Mechanical Properties  
The mechanical properties of the three aggregates were tested in terms of two aspects of the crushing and wearing values. The crushing value of the aggregate represents its capability to resist external loads, and the wearing value represents its capability to resist being worn down. A grain size of each aggregate was selected between 9.5 and 13.2 mm in the crushing test, and each group of samples was approximately 3000 g. Meanwhile, the grain size of aggregates in the abrasion test was between 4.75 and 9.5 mm and between 9.5 and 16.0 mm, each group of samples weighing approximately 2500 g. The screening status of each material in the preparation stage of the test is shown in Figure 5, and the machines for the two tests are shown in Figure 6. The crushing value is equal to the ratio of the mass of fine aggregates, passing the 2.36 mm standard sieve from the sample after the test to the initial mass of the sample. The wearing value is equal to the ratio of the mass of fine aggregates, passing the 1.70 mm standard sieve from the sample after test to the initial mass of the sample. The crushing value and the wearing value are expressed by Equations (1) and (2), respectively. The test results are shown in Figure 7, and the two mechanical properties' standards in China are shown in Table 3.  
\[Q_{a}=\frac{m_{a}}{m_{0}}\times 100\% \tag{1}\]  
where: \(Q_{a}\): Crushing value of aggregate, %; \(m_{0}\): Initial mass of the sample, g; \(m_{a}\): Mass of fine aggregates passing the 2.36 mm standard sieve from the sample after test, g.  
\[Q_{b}=\frac{m_{b}}{m_{0}}\times 100\% \tag{2}\]  
where: \(Q_{b}\): Wearing value of aggregate, %; \(m_{0}\): Initial mass of the sample, g; \(m_{a}\): Mass of fine aggregates passing the 1.70 mm standard sieve from the sample after test, g.  
Considering crushing value, glass is the highest, followed by ceramics and then limestone; limestone and ceramics can be used in all grades of roads. Considering wearing value, the order of the three aggregates is similar to that of the crushing value, but only  
Figure 5: Raw material preparation.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Mechanical Properties** & **High-Grade Highway** & **Low-Grade Highway** \\ \hline Crushing value & \(\leq\)26 & \(\leq\)30 \\ Wearing value & \(\leq\)28 & \(\leq\)35 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Local standards of mechanical properties.  
limestone can be used in all grades of roads. Combining the two indicators, the particle size of waste glass and waste ceramics should be treated as little as possible in the actual usage, and using these materials in high-grade highways is not recommended.
# Diabetic Retinopathy Diagnosis Based on RA-EfficientNet
## 3 Experiments
### Experimental Results and Discussion
#### 3.2.1 Task 1: 2-Grade Classification
When trained on RA-EfficientNet, we observe that the total number of correctly predicted images is 722, demonstrating the advantages of our diagnostic model. \begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Parameter** \\ \hline InceptionResNetV2 & 54,354,954 \\ Xception & 20,873,770 \\ MobileNetV2 & 2,265,666 \\ EfficientNet & 4,057,253 \\ RA-EfficientNet & **4,272,070** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of parameters for different CNN networks for Task 1. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy (\%)** & **Precision (\%)** & **F1 (\%)** & **Kappa (\%)** \\ \hline InceptionResNetV2 & 97.54 & 97.55 & 97.54 & 95.08 \\ Xception & 97.81 & 97.85 & 97.81 & 95.63 \\ MobileNetV2 & 97.68 & 97.68 & 97.68 & 95.36 \\ EfficientNet & **97.95** & **97.96** & **97.95** & **95.90** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of different CNN networks for Task 1. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy (\%)** & **Precision (\%)** & **F1 (\%)** & **Kappa (\%)** \\ \hline InceptionResNetV2 & 97.95 & 97.95 & 97.95 & 95.90 \\ Xception + RA & 98.22 & 98.23 & 98.22 & 96.45 \\ MobileNetV2 + RA & 98.09 & 98.11 & 98.09 & 96.18 \\ RA-EfficientNet & **98.36** & **98.37** & **98.36** & **96.72** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of different CNN networks with RA block for Task 1. To better understand the representative of our RA-EfficientNet, we compare it with existing methods in the literature [11, 14]. In Table 5, we can see that the proposed method achieves 98.36% accuracy, which performs significantly better than existing methods.
# Optical Effects Induced by Bloch Surface Waves in One-Dimensional Photonic Crystals
## 7 Conclusions  
In conclusion, we considered optical and magneto-optical effects observed in photonic crystals during the Bloch surface wave excitation and propagation. Some effects taking place at the light reflection and scattering from the photonic crystal surface were described. We discussed the enhancement of the Goos-Hanchen effect caused by the BSW. The magneto-optical Faraday effect induced by the BSW was reviewed. The research concerning the optical forces the BSW exerts on the dielectric micro-objects were described. The optical effects induced and controlled by the BSW that are reviewed here have values comparable or superior to the analogues currently used in prototypes and commercial devices. With the undoubted merits of BSWs, such as low material absorption and good control of the spectral-angular resonance position, BSWs are a good candidate for use in optoelectronic integrated devices implementing spatial light modulation, control of the polarization of light, optical trapping and mechanical control of micro-objects. BSWs have a great potential to become widespread in future photonic applications.  
Figure 9: (**a**) Experimental angular dependences of the photonic crystal (PC) reflectance (solid curve) and the magnitude of the BSW-generated force acting on a trapped microparticle at a PC/particle gap of approximately 300 nm (dots). Angle \(\theta\) is the angle of incidence in the prism. The peak value corresponds to the BSW resonance angle; (**b**) \(x\)- (red dots) and \(z\)-coordinate (black dots) projection values of the BSW-generated force as a function of the distance between the trapped microparticle and the PC surface. Reprinted with permission from [PERSON], [PERSON], [PERSON], [PERSON], Opt. Lett. 40, 4883 (2015). (c) 2015 Optical Society of America [75].  
**Acknowledgments:** This work was partially supported by the Russian Ministry of Education and Science (Grant No. 14.W03.31.0008) and the the Russian Foundation for Basic Research (Grants No. 17-08-01716, 15-32-70012, 18-02-00880) and the Moscow city Government.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Education-to-Skill Mapping Using Hierarchical Classification and Transformer Neural Network
## 3 Results and Discussion
### Experimentation
#### 3.1.3 Hierarchical Classification  
The exploratory analysis revealed that classes used for the classification task are unbalanced. For example, the largest of first digit (first hierarchical level) LPK occupation set \"2\" has 128,713 cases (aprox. 49 percent of total cases), and the least number cases belongs to set \"6\" LPK group with 523 cases. The metrics of class imbalance per level were as follows: for the first digit \(\rho=254\), for the second \(\rho=3924\), for the third \(\rho=22\),919, and for the last level, as it was stated previously: \(\rho=18\),041. Thus, even in the most shallow level, data is extremely unbalanced. Therefore, standard Binary cross-entropy loss was customized, applying \(\gamma\) parameter of Focal loss.  
As it can be seen from Table 3, hierarchical classification does not improve classification metrics in the most detailed level compared to simple classification model: Recall, Precision and F1 metrics are similar. In shallower levels, F1 scores improves by 6-28 percent. A larger network (1000 neurons per layer) and medium drop-out achieved the best performance.  
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline
**Model Name** & **Units** & **Drop-Out** & **Loss** & **Acc** & **Pr** & **Re** & **F1** & **Data** & **Param. No.** \\ \hline Baseline & 1 & 0 & 0.014 & 0.998 & 0.00 & 0.00 & 0.00 & Sparse & 3709 \\ \hline Simple multi-label with Binary crossentr. & 1000 & 0.5 & 0.012 & 0.997 & 0.41 & 0.13 & 0.20 & Sparse & 4,277,433 \\ \hline Simple multi-label with Focal loss & 1000 & 0.5 & 0.002 & 0.996 & 0.22 & 0.23 & 0.23 & Sparse & 4,277,433 \\ \hline Hierarchical classification (default weights) & & & 0.090 & & & & & & \\ \hline I level & & & 0.062 & 0.859 & 0.42 & 0.61 & 0.50 & \\ \hline II level & 1000 & 0.1 & & 0.020 & 0.964 & 0.35 & 0.40 & 0.37 & Sparse & 14,460,612 \\ \hline III level & & & 0.007 & 0.989 & 0.30 & 0.26 & 0.28 & \\ \hline IV level & & & 0.002 & 0.997 & 0.28 & 0.19 & 0.22 & \\ \hline BERT embeddings & 1000 & 0 & 0.002 & 0.997 & 0.29 & 0.16 & 0.20 & Embedd. & 2,203,433 \\ \hline Full & & & & 0.057 & & & & & \\ \hline I level & & & 0.036 & 0.856 & 0.42 & 0.61 & 0.49 & \\ \hline II level & 1000 & 0 & 0.013 & 0.965 & 0.35 & 0.39 & 0.37 & Embedd. & 12,386,612 \\ \hline III level & & & 0.006 & 0.989 & 0.30 & 0.24 & 0.27 & \\ \hline IV level & & & 0.002 & 0.997 & 0.30 & 0.16 & 0.20 & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of best performing models on test set. In table different models, the data structure, number of parameters and information about parameters are shown, as well as metrics of Loss, Accuracy (abbr. Acc), Precision (abbr. Pr), Recall (abbr. Re), F1.  
Despite the inability to improve metrics for accuracy in hierarchical classification, the model predicts smoothly on different levels of output hierarchy. This feature is important for practical applications of the model.
## 4 Experiment Validation
### Case 2: Bearing Fault Diagnosis Based on XJTU Bearing Datasets  
#### 4.2.1 Experiment Setup and Data Description  
The test platform of XJTU consists of the rotating shaft, motor speed controller, support and test bearing, AC motor, and other parts; the test platform can be seen in Figure 12. Two unidirectional sensors were fixed in the vertical direction and horizontal direction of the test bearing by the magnetic base to obtain two sets of sensor data. The experimental bearing is LDK UER 204 rolling bearing, and the experiment has a 25.6 KHz sampling frequency. BPFO of the bearing is 115.62 Hz, BPFI is 184.38 Hz, and BSF is 144.66 Hz. The parameter information of the bearing is listed in Table 4.  
The selected fault types include five fault types: inner race, cage, outer race, composite fault of the inner and outer race (CFIO), and composite fault of the inner race, cage, and rolling body (CFICR). By taking 1024 data as a sample, a total of 2000 samples were selected from two sets of sensor data, and the sample number of each set of sensor data is 1000. In order to verify the method proposed in the paper, two samples were selected from two sets of sensor data, respectively, and the two selected samples were merged into a matrix with two rows and 1024 columns. That is, the new number of samples is 1000, of which 80% were used for training and 20% for tests. A detailed and specific description of the data used in the experiment of XJTU is given in Table 5.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter Names** & **Value** & **Parameter Names** & **Value** \\ \hline Diameter of inner & & & \\ ring raceway/mm & 29.30 & ball diameter/mm & 7.92 \\ \hline Diameter of outer & & & \\ ring raceway/mm & 39.80 & Number of balls & 8 \\ \hline Basic static & & & \\ load rating/KN & 6.65 & Contact angle/(\({}^{\circ}\)) & 0 \\ \hline Basic Dynamic & & & \\ Load Rating/KN & 12.82 & Pitch diameter/mm & 34.55 \\ \hline \hline \end{tabular}
\end{table}
Table 4: LDK UER 204 rolling bearing parameters.  
Figure 9: Comparison of recognition accuracy between the different types input.  
Figure 10: Visualization result of t-SNE dimensionality reduction. (**a–c**) are the t-SNE visualization results of the weighted data of the base sensor, FE sensor and DE sensor respectively, (**d–f**) are the t-SNE visualization results of the unweighted data, (**g**) is the t-SNE visualization result of the weighted data of all sensors.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Label** & **Fault Type** &
\begin{tabular}{c} **Radical** \\ **Force (KN)** \\ \end{tabular} & **RPM** & **Train** & **Test** \\ \hline
0 & CFICR & 12 & 2100 & 160 & 40 \\ \hline
1 & Inner race & 11 & 2250 & 160 & 40 \\ \hline
2 & Outer race & 11 & 2250 & 160 & 40 \\ \hline
3 & Cage & 11 & 2250 & 160 & 40 \\ \hline
4 & CFIO & 10 & 2400 & 160 & 40 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Experimental datasets of XJTU.
# Transactive Energy to Thwart Load Altering Attacks on Power Distribution Systems
## 7 Conclusions  
In this paper, an attack-thwarting system was proposed that can halt the progress of load altering attacks in harming power grids. The proposed system provokes adjustment in power consumption of the flexible loads to correct frequency disturbances that are caused by the cyber attacks. It was shown that, by employing the proposed system, the frequency of sinusoidal voltages is maintained within a safe range even if a load altering attack is ongoing in the power grid. It was discussed that for thwarting an attack, there is a need for real-time energy exchanges between the flexible loads and the power distribution systems. Furthermore, it was argued that the energy exchanges should be in-proportion to the frequency disturbances that are caused by the attack. To fulfill the above requirements, a transactive energy framework was deployed in the proposed attack-thwarting system. Through the numerical simulations on an IEEE standard 33-bus system, it was shown that the proposed system is effective at thwarting the load altering attacks.  
**Author Contributions:** Investigation, M.G. and S.Y. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Research on a Comprehensive Evaluation Method of Train Anti-Slip System Performance
## 2 Anti-Slip Performance Evaluation Indexes
### Adhesion Coefficient Utilization Index  
The actual adhesive coefficient refers to the adhesive coefficient value actually utilized during the train braking process. In practical train testing, both the maximum adhesive coefficient available and the actual adhesive coefficient cannot be directly measured, and can only be estimated through instantaneous train deceleration or other parameters, making it difficult to obtain an accurate value. However, in the semi-physical simulation platform test, the adhesive coefficient is usually simulated by a numerical simulation model. Therefore, the wheel-rail adhesive coefficient during the entire train braking and anti-skid process and the actual adhesive coefficient can be easily obtained, making it possible to accurately calculate the adhesive coefficient utilization under anti-slip control.  
Assuming that the anti-slip system can always maximize the utilization of adhesion during the braking process--that is, the adhesive coefficient is always the maximum--from this, the theoretical shortest braking distance is calculated, and the actual braking distance is obtained through testing. Therefore, the adhesive coefficient utilization during the entire braking process of the train is as follows:  
\[\eta=\frac{s_{\text{min}}}{s_{\text{real}}}\times 100\% \tag{1}\]  
Due to the different dimensions and magnitudes of different evaluation indexes, it is necessary to normalize them so that the index results are in the same order of magnitude for comprehensive evaluation in conjunction with the analytic hierarchy process. Here, the deviation standardization method is used to map the index results to 0-1. The calculation method for standardization of dispersion is as follows:  
\[x^{*}=\frac{x-x_{\text{min}}}{x_{\text{max}}-x_{\text{min}}},x^{*}=1-\frac{x-x _{\text{min}}}{x_{\text{max}}-x_{\text{min}}} \tag{2}\]  
Formula (2) represents two forms of deviation standardization, both of which are used in this article. \(x^{*}\) represents the standardized deviation index, \(x_{\text{max}}\) represents the maximum value of the index, \(x_{\text{min}}\) represents the minimum value of the index, and \(x\) represents the calculated value of the index.  
For the adhesion coefficient utilization index throughout the braking process, take the minimum value \(\eta_{\text{min}}=0\) to represent the wireless extension of braking distance. The maximum value \(\eta_{\text{max}}=100\%\) indicates that the anti-slip system can fully utilize the adhesion coefficient and has the shortest braking distance. The normalized index is as follows:  
\[x_{1}=\frac{\eta-0}{100\%-0} \tag{3}\]  
Figure 1: Evaluation indexes of anti-slip performance.