esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Theory/Calculation  
### Ti Layer  
Titan, of 99.99% purity, was the material of the coating. The chosen properties of the Ti coating are listed in Table 2.  
The following information was observed during the experiment:  
* height of the measured place above the target H (mm) (Figure 4),
* tube radius R (mm) (Figure 4),
* the values of the \(\beta\) angles at which the titanium grains hit the coated surface (Figure 4),
* values of \(\alpha\) angles under which Ti particles evaporated and formed columnar grains (\(\alpha\)--angle of growth of columnar grains) (Figure 5),
* values of the thickness of the applied coating.  
The texture of the Ti layer (the dependence of the grain growth angle \(\alpha\) on the particle impact angle \(\beta\) and the height of the measured spot above the target) was evaluated on a Jeol 7000F electron microscope (Jeol, Tokyo, Japan). A brittle fracture perpendicular to the surface of the evaluated layer was evaluated at magnifications of 5000 times. The film separated from the substrate due to the fracture of the sample cooled on liquid nitrogen. A cross-sectional view shows the structure (the grain growth) of the deposited Ti film.  
The thickness of the Ti layer was measured on a Jeol 7000F electron microscope at the Institute of Material Research in Kosice. Figure 6 shows an example of the created Ti layer at a height of the measured spot above the target of 305 mm and a tube radius of 110 mm at a 5000-fold magnification.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Variables** & **Description** \\ \hline _Independent Variables_ & \\ Height of the measured place above the target (\(X_{1}\)) & from 305 to 425 mm, difference 30 mm \\ Radius of the tube (\(X_{2}\)) & from 100 to 140 mm, difference 10 mm \\ _Dependent Variables_ & \\ Angle of grain growth (\(Y_{1}\)) & Values in the interval from 40\({}^{\circ}\) to 61\({}^{\circ}\) \\ Thickness of the deposited layer (\(Y_{2}\)) & Values in the interval from 1.29 to 3.57 μm \\ Adhesion in the direction of grain growth (\(Y_{3}\)) & Values in the interval from 10 to 120 N \\ Adhesion against the direction of grain growth (\(Y_{4}\)) & Values in the interval from 8 to 110 N \\ Roughness of the deposited layer (\(Y_{5}\)) & Values in the interval from 0.40 to 0.49 μm \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of variables.  
Figure 4: Scheme of the experiment.  
Adhesion is an important property of layers. Adhesion measurement was carried out on a Scratch tester provided by a company called CSM Instruments (Needham Heights, MA, USA) under the following conditions: load force 0 to 120 N (macro), indenter movement speed 10 mm/min., maximum indenter travel 120 mm.  
The roughness of the evaluated surface was measured on a touch roughness meter (Surftest 301) from Mitutoyo (Kawasaki, Japan) before and after the formation of the Ti layer. Each roughness value was created as an arithmetic mean of five measurements.
# An Extensible Automated Failure Localization Framework Using NetKAT, Felix, and SDN Traceroute
## 4 Use-Cases
### Use-Case 1: Locating Layout Difference  
In our first test case, we simulated a simple change in the physical network compared to the one described by the specification, in order to emulate an undocumented or unwanted layout change in the network. We swapped the addresses of hosts _h4_ and _h5_ in the specification, as shown in Figure 12. If we compare the figure with Figure 11 that shows the physical network in this case, we can see that hosts _h4_ and _h5_ are still connected to switches _sw4_ and _sw5_, respectively. Since we introduced no other error in the network, we expect packet forwarding not to be disturbed. This also means that using only host monitoring to detect problems (i.e., applying only the Felix tool) would not reveal any issues and only the combination of path tracing and network specification can find out the problem.  
Figure 11: Tested network.  
Although here we demonstrate the problem on a simple case of wrong host configuration, similar results can arise from diverse causes. In a bottom-up direction, one such cause can be the failure of properly registering changes of the physical network on the level of specification. Examples here can be the permanent replacement of network equipment, changes in low level policies or hardware components for fixing temporary failures. Similar issues can manifest in a top-down direction as well when considering the introduction of new policy rules that have unexpected side effects.  
During the network test, this is exactly what happens: Packet measurements cannot detect any problems on the hosts affected by the change of addresses. A video demonstration about the use-case is available at: [[https://youtu.be/~h1](https://youtu.be/~h1) XgMU0Y-k]([https://youtu.be/~h1](https://youtu.be/~h1) XgMU0Y-k). Packets can traverse the network between the two hosts using the same forwarding rules, even when the host addresses are swapped. Route tracing is able to discover the change, though. As Figure 12 shows, links _sw4-sw2, sw2-sw1, sw1-sw5_, and _sw5-sw6_ are identified as problematic ones. Our framework correctly identifies the wrong paths packets take to hosts _h5_ and _h4_ but does not mark the hosts as root causes in this case. Host to switch links do not show up as failed since neither of the used tools (the measurement service and SDN traceroute) are able to tell anything about these links. The use-case clearly shows that our method of integrating different tools in a unified framework that runs them automatically is beneficial for failure detection.
## 4 Example Analysis
### Multi-Objective Optimization Model Solution Results  
By substituting the data in Table 2 into Equation (8) and iteratively calculating with the help of MATLAB R2024a, the Pareto solution set after ISSA optimization can be obtained. In the simulation experiment, the number of sparrows is 100, the proportion of producers in the population is 20%, the proportion of early warners is 20%, the safety value is 0.8, and the number of iterations is 200.  
To verify the effectiveness of the ISSA in solving this problem, this paper compares the ISSA with NSGA-II, which is commonly used in multi-objective optimization problems. The algorithm parameters are the same: the population size is 100, the proportion of crossover is 80%, the proportion of variation is 20%, and the number of iterations is 200. The iterative convergence curves for the two sets of tests are shown in Figures 3-5, and the optimization results of the ISSA are shown in Figure 6.  
From Figure 3 to Figure 5, it can be seen that both the ISSA and NSGA-II have good convergence performance. For the quality objective, the search results of the ISSA and NSGA-II are maintained at around 0.9663; for the duration objective, the search ability of the ISSA is better than that of NSGA-II; and for the cost objective, the search ability of NSGA-II is better than that of the ISSA. To some extent, the results reflect the effectiveness and efficient search performance of the ISSA in solving the multi-objective optimization problem for construction solutions. The optimized Pareto solution set is shown in Table 3.  
Figure 4: Cost optimization iteration curves.  
Figure 3: Duration optimization iteration curves.  
Figure 5: Quality optimization iterative curves.  
Figure 6: Integrated duration–cost–quality optimization results.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scheme** & **Duration/Days** & **Cost/(CNY 10k)** & **Quality** \\ \hline A1 & 68 & 732.17 & 0.9334 \\ A2 & 69 & 716.50 & 0.9370 \\ A3 & 69 & 733.06 & 0.9614 \\ A4 & 70 & 735.59 & 0.9660 \\ A5 & 70 & 734.88 & 0.9617 \\ A6 & 70 & 717.78 & 0.9372 \\ A7 & 71 & 723.04 & 0.9623 \\ A8 & 71 & 719.26 & 0.9373 \\ A9 & 71 & 719.46 & 0.9403 \\ A10 & 72 & 723.81 & 0.9627 \\ A11 & 72 & 721.27 & 0.9603 \\ A12 & 72 & 738.60 & 0.9662 \\ A13 & 73 & 740.42 & 0.9663 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The optimized Pareto solution set of the ISSA.
## 4 Results
### On-Body Access Point Network Architecture
#### 4.1.2 Protocol Response to Dynamic Conditions  
A vivid demonstration of the ability of the proposed cost function to make use of the energy in the network as a distributed resource is presented in Figure 7. During a real-time experiment, a single ED was turned off for several seconds and then reconnected. As a result, the slope (energy consumption rate) of all other EDs was increased since the network lost an energy source. Because the ED did not consume power for several seconds, it became the one with the least energy used in the network. After being reconnected, it was immediately used as a relay by other nodes.  
Figure 7: Energy balancing in a dynamic network. Note the changing of slopes of the curves as network conditions change.  
This is expressed in a sharp increase in the slope of the reconnected ED versus a decrease in the slope of all other EDs. The excess energy of the reconnected ED was used by all EDs in the network to balance their energy consumption by more frequent relaying of packets through the reconnected ED. Relaying relaxes when the ED's accumulated energy equals other EDs in the network and the slope of all EDs is the same from that point on.
## 1 Introduction
Finally, the safety factor of each sample is calculated for each cluster obtained from each class, and each cluster of the class is oversampled or under-sampled according to the safety factor and the number of samples, so that a balanced data chunk is obtained and the original data distribution is maintained well. 2. A dynamic weighting method based on G-mean is investigated and proposed. The G-mean value of each classifier on the current chunk is used as the weight of each base classifier in the ensemble. As the data chunk arrives, if the weight of the base classifier is not lower than the threshold set by the user, it will be added to the ensemble directly; otherwise, the base classifier will be removed. The classifiers in the ensemble are constantly in a dynamic updating process as a way to adapt to concept drift. 3. The HSDW-MI algorithm proposed in this paper deals with imbalance and concept drift in data streams by hybrid sampling phase and dynamic weighting phase and is capable of handling data streams with extreme imbalance and variable class ratio. In addition, detailed experiments are conducted to demonstrate the effectiveness and feasibility of the algorithm, including parameter sensitivity experiments, ablation experiments, and algorithm comparison experiments, and the experimental results are comprehensively analyzed. The remainder of this paper is organized as follows. Section 2 provides an overview of the related work on handling multi-class imbalanced data and data streams with concept drift. Section 3 describes the workflow of the proposed HSDW-MI algorithm in detail. Section 4 presents the experiments and discussions. Finally, Section 5 concludes the paper.
###### Abstract  
In modern structural codes, the reference value of the snow load on roofs is commonly given as the product of the characteristic value of the ground snow load at the construction site multiplied by the shape coefficient. The shape coefficient is a conversion factor which depends on the roof geometry, its wind exposure, and its thermal properties. In the Eurocodes, the characteristic roof snow load is either defined as the value corresponding to an annual probability of exceedance of 0.02 or as a nominal value. In this paper, an improved methodology to evaluate the roof snow load characterized by a given probability of exceedance (e.g., \(p=0.02\) in one year) is presented based on appropriate probability density functions for ground snow loads and shape coefficients, duly taking into account the influence of the roof's geometry and its exposure to wind. In that context, the curves for the design values of the shape coefficients are provided as a function of the coefficient of variation (\(COV_{g}\)) of the yearly maxima of the snow load on the ground expected at a given site, considering three relevant wind exposure conditions: sheltered or non-exposed, semi-sheltered or normal, and windswept or exposed. The design shape coefficients for flat and pitched roofs, obtained considering roof snow load measurements collected in Europe during the European Snow Load Research Project (ESLRP) and in Norway, are finally compared with the roof snow load provisions given in the relevant existing Eurocode EN1991-1-3:2003 and in the new version being developed (prEN1991-1-3:2020) for the \"second generation\" of the Eurocodes.  
ground snow load; roof snow load; shape coefficients; wind exposure; design values; structural reliability; Eurocodes
# Motion Control and External Force Estimation of a Pneumatically Driven Multi-DOF Robotic Forceps
## 1 Introduction
This layout is considered to be optimal and orthodox for surgeries in narrow spaces because it considerably reduces the size of the end effector, and the output force is large enough for surgery. However, the forceps used in the Da Vinci system cannot realize external force estimation without force sensors due to its electrical actuation. Furthermore, motion interference between the forceps joints due to the wire drive mechanism is also an issue for accurate force estimation. Therefore, in this work, we propose a pneumatically driven robotic forceps using a rigid-link mechanism with less motion interference between the joints. The main contributions of this work are as follows. First, we built a motion controller for a pneumatically driven robotic forceps. Second, we proposed an external force estimation scheme by taking advantage of back-drivability of the pneumatic cylinders. Third, we implemented experiments to testify the performance of motion control and external force estimation. In Section 2, we briefly introduce the forceps mechanical design. Section 3 describes the control system including the kinematics and dynamics modeling, wherein the force estimation scheme is introduced. Section 4 demonstrates the performance of motion control and force estimation.
## Appendix A Derivation of the Asymptotic Secret Key Rate
 \tag{12}\]  
The mutual information \(I(A:B)\) can be expreseed as  
\[I(A:B)=\frac{1}{2}\log_{2}\frac{V_{B}}{V_{B|A}}, \tag{13}\]  
with \(V_{B}=(a+1)/2\), \(V_{A}=b\) and \(V_{B|A}=V_{B}-TZ_{d}^{\prime 2}/2V_{A}\). Meanwhile, the Holevo bound \(S(E:A)\) can be calculated as  
\[ \begin{split} S(E:A)&=S(E)-S(E|A)\\ &=S(AB)-S(B|A)\\ &=G[(\gamma_{1}-1)/2]+G[(\gamma_{2}-1)/2]-G[(\gamma_{3}-1)/2], \end{split}  \tag{14}\]  
where \(G(x)\) is the Von Neumann entropy that is given by  
\[G(x)=(x+1)\log_{2}(x+1)-x\log_{2}x, \tag{15}\]  
and \(\gamma_{1,2,3}\) are the symplectic eigenvalues derived from covariance matrices calculated by  
\[\gamma_{1,2}=\frac{1}{2}(A\pm\sqrt{A^{2}-4B^{2}}), \tag{16}\]with  
\[A =a^{2}+b^{2}-2c^{2}, \tag{10}\] \[B =ab-c^{2},\]  
and  
\[\gamma_{3}=\sqrt{a^{2}-\frac{ac^{2}}{b}}. \tag{11}\]  
Combining with Equation (6), we can obtain the asymptotic secret key rate.
## 3 Results
### Results of Syntheses
#### 3.4.3 Sensitivity Analysis  
To assess the robustness of the synthesized results concerning the relationship between machine learning applications in transformer fault detection and classification based on DGA, a sensitivity analysis was conducted, specifically on the type of algorithms used. This analysis was crucial in determining the impact of each algorithm on the insights required to assess the application of machine learning in transformers. Figure 9 in Section 3.3 highlights the distribution of various algorithms used, with SVM, ANN, and kNN being the commonly used methods at 32%, 17%, and 12%, respectively. The other 39% is distributed between 23 other algorithms. The common application of SVM, ANN, and kNN in transformers may be linked to their high performance in solving binary and multi-classification problems.
###### Abstract  
Background: The aim of this study was to evaluate the adaptation of a resin-modified glass ionomer to the internal walls of dentin with different conditioning systems, with or without the application of silver diamine fluoride (SDF) and potassium iodide (KI). Methods: Cervical standardized cavities were prepared on the buccal and lingual sides of 15 extracted molars. Molars were then sectioned longitudinally in a buccolingual direction, obtaining two samples from each tooth with two cavities each (60 samples). Samples were randomly divided into three groups (\(n=20\)). Each group was divided into two subgroups. One subgroup did not receive dentin conditioning, one was conditioned with 25% polyacrylate acid--10 s (PA), and one with 37% orthophosphoric acid--5 s (OPA). In the other subgroup, dentin was treated with SDF/KI and not conditioned or containing with PA or OPA. All cavities were filled with Riva Light Cure(r) (RLC). The adaptation of the RLC to the cavity walls was evaluated by SEM at \(100\times\) magnification. The area of maximum interfacial gap was magnified at \(1000\times\) to measure its size. Kruskal-Wallis and Mann-Whitney U test were used for comparison. A significance level of \(\alpha=0.05\) was used. Results: No significant differences in the percentage of well-adapted samples were found in subgroups when SDF/KI was used, regardless of whether conditioner was used or not and whether PA or OPA was used (\(p>0.05\)). Regardless of the gap size, dentin treatment with SDF/KI did not negatively influence ionomer adaptation to dentin walls significantly (\(p>0.05\)), except for the subgroup conditioned with OPA (\(p<0.05\)). Furthermore, it improved the adaptation in the axial wall of the subgroup without dentin conditioning (\(p<0.05\)). Conclusion: According to the results of the present in vitro study, the use of SDF/KI did not affect RLC adaptation to the cavity walls. Subsequent use of a conditioner worsens the adaptation of the material to the cavity walls.  
definition; silver diamine fluoride; Riva Light Cure; orthophosphoric acid; polyacrylic acid; resin-modified glass ionomer cement
## 3 Spectral Scattering Imaging
### Quality Evaluation for Horticultural Products
#### 3.3.2 Other Horticultural and Food Products  
Research has also been carried out to evaluate quality attributes of other horticultural products by using hyperspectral scattering imaging. [PERSON] and [PERSON] [43] first reported on the use of hyperspectral scattering imaging to predict firmness of peach fruit. Scattering images were acquired from 'Red Haven' and 'Coral Star' peaches over the spectral region of 500-1000 nm, and a two-parameter Lorentzian function was used to extract scattering features for firmness prediction. The wavelength of 677 nm, corresponding to chlorophyll absorption, was the most correlated with firmness with \(r=0.69\) and the best predictions with \(r^{2}=0.58\)-0.77 were obtained by cascading the two function parameters at a combination of several wavelengths. [PERSON] et al. [63] used hyperspectral scattering imaging to predict glucose and sucrose contents of potatoes. They explored three types of datasets including the mean spectra, scattering profile features and their combination, and a number of calibration techniques including PLS and NNs, for model calibration. It was found that the mean spectra provided a more efficient and effective means than the features of scattering profiles, and good predictions for glucose were obtained by PLS with the best residual predictive deviation (RPD) of about 3.6, while poor sucrose predictions were obtained with RPDs of less than 1.2. Using mean spectra extracted from hyperspectral scattering images, [PERSON] et al. [64] built PLS models to predict the sucrose, soluble solids, and moisture content of sugar beet, with correlation coefficients of 0.88, 0.83 and 0.76, respectively.  
[PERSON] et al. [44; 71; 72] initiated a study on monitoring the changes of moisture content, color, SSC and hardness of food products during the drying process by using laser light backscattering imaging. In a preliminary study on monitoring the moisture content of banana slices [44], the authors observed a significant relationship between changes in moisture content and backscattering area that was produced with laser diode emitting at 670 nm. [PERSON] and [PERSON] [73; 74] developed a laser-light backscattering image (LLBI) technique, operating with two laser modules at 660 nm and 785 nm, to investigate light propagation in fruit tissues. Later, [PERSON] et al. [75] explored the technique for detecting chilling injuries in bananas by analyzing scattering profiles that were fitted with the MLD function, with the resultant detection errors of 0.67%-8%. In two separate studies, [PERSON] et al. [46; 76] further explored LLBI with five laser modules at different wavelengths (532, 660, 785, 830 and 1060 nm), for detecting early decay in citrus fruit. The authors used the Gaussian-Lorentz cross product function with five parameters to characterize scattering profiles, and LDA combined with different features selection methods to build classification models, yielding the best overall detection accuracy of 93.4% [76].
## 2 Method
### Data Analysis
#### 2.2.1 Continuous Wavelet Transform  
We use the CWT method to map MI-EEG signals into two-dimensional image signals and extract the mu and beta rhythms from these image signals. The expression of the continuous wavelet transform is shown in Equation (2) [39].  
\[W_{\mathrm{s}}(a,\tau)=\frac{1}{\sqrt{a}}\int s(t)\phi^{*}\left(\frac{t-\tau}{a} \right)dt \tag{1}\]  
where \(s(t)\) is the input signal, \(a\) is the scale of wavelet transform, \(\phi\) is the wavelet basis function, and \(\tau\) is the time shift. In order to better extract the local and global features of MI-EEG in time and domain, we select the Morlet wavelet as the wavelet function [40]. Its expression is as follows:  
\[\phi(t)=\left(\frac{2}{\pi T^{2}}\right)^{\frac{1}{4}}\exp\left(-\frac{t^{2}}{ T^{2}}+jw_{c}t\right). \tag{2}\]  
The expression of frequency is:  
\[\Phi(w)=\left(\frac{T^{2}}{2\pi}\right)^{\frac{1}{4}}\exp\left(-\frac{(w-w_{c })^{2}}{4T^{2}}\right) \tag{3}\]  
where \(\phi(t)\) is the time domain expression after CWT and \(\Phi(w)\) is the frequency domain expression after CWT, after the Morlet wavelet is determined as the analysis wavelet. By analyzing the MI-EEG data, \(T\) and \(w_{c}\) of the wavelet function are determined. A large amount of literature indicates that the energy of MI-EEG is mainly concentrated in the low frequency band below 30 Hz (the mu rhythm is 8-12 Hz and the beta rhythm is 18-26 Hz) [41]. The Morlet wavelet's center frequency \(f_{c}\) is 0.8125, and T is 0.04. The minimum scale \(a_{\mathrm{min}}\) is 1 and the maximum scale \(a_{\mathrm{max}}\) is 250.  
The sampling frequency is 250 Hz, so each trial has 1000 time sample points. In order to prevent the loss of effective features, the frequency range of mu and beta rhythms is appropriately extended (mu is 4-15 Hz, beta is 19-30 Hz). Feature images with the same size as (22, 1000) the mu and beta rhythm were extracted. The length of the sample points of time is 1000, and 22 is the length of the sample points of the frequency. These two feature pictures are combined to form (44, 1000). Through analyzing the signals of the three electrodes C3, Cz, and C4 (\(N_{C}\) = 3), the time-frequency feature images obtained are (3@44\(\times\)1000). To reduce the number of time sample points, we take an average of every five points on the time axis of the feature image. The feature images of the three electrodes are shown in Figure 3 (\(N_{C}\)@\(N_{fr}\)\(\times\)\(N_{t}\)). The horizontal axis of the feature image is the time sample points, and the vertical axis is the frequency. A training sample (3@44\(\times\)200) consists of feature images of the three electrodes.  
Figure 2: The flowchart of the MI-EEG signal processing in this study. The black arrows represent the flow of signals and the dashed box represents the proposed algorithm. Continuous wavelet transform (CWT) and simplified convolutional neural network (SCNN).