esa-sceva/satcom-chunk-collection huggingface dataset:
# SDBD Flexible Plasma Actuator with Ag-Ink Electrodes: Experimental Assessment
Numerous studies focus on the geometry-configuration optimization [10; 11; 12], the dielectric barrier specifications [13; 12], the material endurance [13; 14], the role of the driving voltage characteristics [15; 16; 17], the boundary layer separation control [18; 19], the laminar-to-turbulent transition delay [20], etc., just to name a few. As a contribution to the field, the present study is devoted to the implementation of a particular single dielectric barrier discharge (SDBD) [21] plasma actuator and its test under sinusoidal high voltage. Kapton(r) polyimide films are used as the dielectric barrier due to their ability to be mounted onto curved aerodynamic surfaces. On the other hand, silver (Ag) conductive paint is selected for the formation of the electrodes due to its provided feasibility to design electrodes of every shape with negligible thickness on a variety of surfaces. Both choices make the actuator fully flexible, and this is the main claim of the present communication. The actuator is probed by means of electrical and optical techniques. Electrical measurements are conducted to investigate the time evolution of the power consumption and the endurance of the actuator, over the time window of a day. High-resolution optical emission spectroscopy (OES) provides an insight into the emitting species of the plasma as well as an estimation of the temperature of the air above the actuator. Then, pitot-like tube and schlieren imaging give access to the induced fluid speed and the pattern of the relevant flow field, respectively. Eventually, the overall experimental results allow for a brief discussion on the physical mechanisms governing the aerodynamic specifications of this flexible plasma actuator.
# Effect of Surface Roughness on Early Stage Oxidation Behavior of Ni-Base Superalloy IN 625
## 3 Results
### Surface Roughness Evaluation
#### 3.1.2 Microroughness Evaluation Using Fractal Analysis  
The plot showing relative length as a function of scale (expressed in \(\upmu\)m) depicted in Figure 2 demonstrates the different shapes of the curves obtained for ground and polished surfaces of IN 625, namely, values of relative length for a given scale obtained for ground surfaces are higher as compared for the polished ones, especially for a smaller scale (see Figure 2). The parameters describing surface roughness based on fractal analysis, namely D, LSFC, and L\({}_{\text{R}}\), obtained for 1 \(\upmu\)m scale are presented in Table 3 and show a similar trend as standard roughness parameters, i.e., they are higher for ground surface than the polished one. In contrast, the SRC value is smaller for the ground than polished surface.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multicolumn{2}{c}{**Polishing (1 \(\upmu\)m)**} & \multicolumn{2}{c}{**Grinding (220 gri)**} \\ \cline{2-5}  & **Average Value** & **Standard Deviation** & **Average Value** & **Standard Deviation** \\ \hline R\({}_{\text{a}}\) & 0.039 & 0.007 & 1.067 & 0.065 \\ R\({}_{\text{z}}\) & 0.227 & 0.021 & 7.632 & 0.547 \\ R\({}_{\text{max}}\) & 0.288 & 0.056 & 9.189 & 0.800 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of samples surface roughness evaluation using conventional procedures.  
Figure 1: Surface roughness profiles performed by standard contact profilometer HOMMEL Werk T8000 on (a) polished (1 \(\upmu\)m); (b) ground (220 gri) IN 625.  
Figure 2: Relative length (L\({}_{\text{R}}\)) as a function of scale plots obtained for studied surfaces by fractal analysis using Sfrax 1.0 software [16].
## 4 Borehole Resistivity Monitoring of Overburden
### Comprehensive Analysis
#### 4.4.2 Analysis of Overburden Stress Characteristics  
The advancement of the coal mining working face is a dynamic process, and the deformation and failure of the overburden is also a dynamic evolution process. To capture the law of strata movement throughout the whole process, the monitoring starts from 393 m in front of the orifice to 120 m behind the orifice. According to Figures 8 and 10, during the monitoring period, the strata from initial stability to secondary equilibrium are well reflected in the resistivity profile. When the coal seam working face enters the control range of the borehole, it will lead to the development of overburden fracture under the influence of advance abutment pressure and periodic weighting. According to Figure 10c, when the working face is 87.1 m away from the orifice, the resistivity of the local area increases in a range of 20\(\sim\)70 m in the vertical height of 20\(\sim\)30 m in front of the orifice. This analysis is based on the influence of advance abutment pressure, and the influence range of advance abutment pressure is approximately 60 m. At the same time, fractures begin to develop at the interface of different lithologic combinations, in which fractures of hard roofs are less developed, and weak rock and the interface of soft and hard rock easily produce cracks, separation and other phenomena. The strata in the caving zone sustain significant damage and the resistivity is high. Fractures mainly develop during the process of overburden failure. The rock strata structure maintains good integrity in a range of 106-120 m control height of boreholes, and the development characteristics of the separated-layer fractured zone are found to be in the range of 120-127 m. The development height of the caving zone and the water-flowing fracture zone exhibits no obvious change from the working face to the 120 m borehole orifice, which indicates that the development height of overburden failure zone is related to the control of the lithologic combination.
# A Test Platform for Managing School Stress Using a Virtual Reality Group Chatbot Counseling System
## 3 Methodology
### Module 3 VR-Enabled Chatroom: A Group Therapy VR Interface with a Real Counselor  
The chatbot cannot replace humans, and especially not person-to-person interaction. People can expand their knowledge very effectively by sharing their experiences with others. People who share similar backgrounds can easily access and are more willing to listen to others' opinions about their emotions if phrased in friendly terms. This reflects the underlying structure of group therapy. People are not alone, and anyone can have daunting and life challenging problems. The therapist will conduct the therapy session and oversee the process of interaction to maintain a balance of collegiality. The same environment can be synthesized in a virtual environment and modified to enhance communication. People can be disguised as avatars to eliminate the fear of showing up and greeting people in reality. The virtual environment can be created in a classroom where casual encounters among the target suffering from stress maintain anonymity when discussing and seeking advice about ways to manage school stress.  
The test system uses the Photon engine as the tool for enabling communication between multiple members. The Photon engine is an online server solution that can be implemented in Unity. The formation of each avatar, the verbal interactions, the corresponding avatar movements are transmitted to the server of Photon engine to achieve real-time activities. Group therapy can be programmed using the same concept. The therapist and the students will all be directed to the same designated virtual room. The therapist and the students will be represented by avatars designed earlier in Unity. The therapist can then lead a group therapy session and are able to communicate at their will in real time. If the students prefer using text messages, the Photon engine supports sending and receiving text messages. This greatly benefits students who are reluctant to speak or those who are eager to respond to the therapist but do not have a chance to talk. With the use of Photon engine, group therapy sessions can be conducted in the forms of oral and written communication.
# A General Solution for the Errors in Variables (EIV) Model with Equality and Inequality Constraints
## 5 Conclusions  
In this paper, a general solution for the errors-in-variables (EIV) model with equality and inequality constraints is proposed based on the penalty function and the weight in measurement. The iteration formula of the general solution is similar to that of the least squares. It can maintain the EIV model with equality and inequality constraints on the one hand, and it can support the EIV model with equality and inequality constraints simultaneously on the other hand. Furthermore, it can also solve Gauss-Markov (G-M) model with equality and inequality constraints. Finally, three examples are testified, showing that the proposed general solution is effective and feasible.  
Conceptualization of the manuscript idea: [PERSON] and [PERSON]; methodology and software: [PERSON]; writing--original draft preparation: [PERSON] and [PERSON]; writing--review and editing: [PERSON]; supervision and funding acquisition: [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript. This research was funded by the Scientific Research Foundation for Doctor of Xiangtan University (No. 21 QDZ55), Key Laboratory of Geospace Environment and Geodesy, Ministry of Education, Wuhan University (No. 21-01-06). Not applicable. Not applicable. Not applicable. Not applicable. Not applicable.  
The data used for validation in this paper are from literature [19].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **IC-Fang** & **IC-EIV** & **EIC-Fang** & **EIC-EIV** \\ \hline \(\mathbf{X_{1}}\) & \(-0.10000\) & \(-0.10000\) & \(-0.10000\) & \(-0.10000\) \\ \(\mathbf{X_{2}}\) & \(-0.10000\) & \(-0.10000\) & \(-0.10000\) & \(-0.10000\) \\ \(\mathbf{X_{3}}\) & \(0.16870\) & \(0.16870\) & \(0.63333\) & \(0.63333\) \\ \(\mathbf{X_{4}}\) & \(0.39961\) & \(0.39961\) & \(-0.09432\) & \(-0.09432\) \\ TSSR & \(0.13974\) & \(0.13974\) & \(0.21074\) & \(0.21074\) \\ \(\mathbf{D}(\mathbf{X_{1}})\) & \(0.09069\) & \(0.04\times 10^{-6}\) & \(0.06845\) & \(0.04\times 10^{-6}\) \\ \(\mathbf{D}(\mathbf{X_{2}})\) & \(0.10011\) & \(0.04\times 10^{-6}\) & \(0.07127\) & \(0.04\times 10^{-6}\) \\ \(\mathbf{D}(\mathbf{X_{3}})\) & \(0.08969\) & \(0.056\) & \(0.06866\) & \(0.03\times 10^{-6}\) \\ \(\mathbf{D}(\mathbf{X_{4}})\) & \(0.09747\) & \(0.063\) & \(0.09309\) & \(0.3\times 10^{-6}\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Adjustment results of equality and inequality constraints.
## 6 Experiment
### Simulation Setup
#### 6.1.1 Mobility Model  
One of the features that makes the simulation more realistic is the model that governs the movement of the nodes in the vehicular network, providing coordinates, speeds, and pause times for the nodes. Popular models include random waypoint, map-based movement, and shortest path map-based movement [34]. We used the latter for the simulation, which constrains the node movement to predefined paths, using [PERSON]'s shortest path algorithm to find its way through the map area. Under this model, once one node has reached its destination, it waits for a pause time, then another random map node is chosen, and the node moves there repeating the process.
## 4 Results and Discussion
### Flexural Strength  
The results of the flexural strength tests, according to the types of reinforcement fibers of RCLMFRRHCC for concrete pavement emergency repair, are shown in Figure 8. In this study, the flexural strength standard was based on the 4-h traffic open standard of 3.5 MPa and the 28-day curing standard of 4.5 MPa. The test results satisfied both standards. When latex and reinforcement fiber were added, they influenced the tensile force and flexural strength rather than the compressive strength [1; 2; 3; 5]. Latex film has the effect of increasing the binding force between the materials when the concrete receives flexural or tensile load. Thus, it increases tensile force and flexural strength [5; 11; 12]. Furthermore, the reinforcement fibers increase the tensile force of the concrete itself and increase flexural strength by restraining crack generation [16; 17; 18; 19; 20]. In the flexural strength tests, the case using macro synthetic fibers showed the best performance, while natural Jute fiber and PVA fiber mixes exhibited nearly the same flexural strength. These results showed that the macro synthetic fiber, a structural fiber, improved the structural performance of the concrete [22; 23]. Thus, the macro synthetic fiber appeared to serve a different purpose than PVA fiber and jute fiber, which are used to improve the durability by controlling crack formation in concrete.  
Figure 7: Compressive strength of RCLMFRRHCC.
## 3 Experiments and Results
### Experimental Introduction  
The experimental deployment based on \(\Phi\)-OTDR system is shown in Figure 8, where the system included a continuous wave laser, an optical coupler, an acoustic-optical modulator, an erbium-doped optical fiber amplifier, a circulator, photodetector, a data acquisition card, and a personal computer. The center wavelength of the laser was 1550.12 nm, the linewidth was 3.7 kHz, the pulse width of the detection pulse was 100 ns, the repetition period was 1 ms. The length of the fiber was 550 m and was buried at a depth of 20 cm. There was a nearly 20 m experimental area after 200 m. The experimental area was on the campus with pedestrians, bicycles, and other interferences 10 m away.  
Figure 7: Standard signal location map based on AMNBP.  
In the experimental area, three kinds of typical interference events were introduced, i.e., a standard knock, walking, and manual excavation. The relative initial noise was also recorded at a quiet time. The beat frequency signal was continuously recorded by the digital acquisition card, the sampling frequency was 10 kHz, and 30 groups of four kinds of data were collected.
## 2 Results and Discussion
### Impact of Cluster-Zone Leaf Removal on Grape Phenolic Profiles
#### 2.2.1 Anthocyanins
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Vintage and** & **Treatment** & **Weight of** & **Total Soluble** & **pH Value** & **Titratable** \\
**Variety** & **Treatment** & **100 Grapes (g)** & **Solids (\({}^{\circ}\)Brix)** & **pH Value** & **Acidity (g/L)** \\ \hline \multirow{6}{*}{Cabernet Sauvignon} & LR1 & 128.40 \(\pm\) 4.04 & 20.43 \(\pm\) 0.5 & 3.37 \(\pm\) 0.02 ab & 7.26 \(\pm\) 0.19 ab \\  & LR2 & 134.60 \(\pm\) 12.15 & 19.97 \(\pm\) 0.12 & 3.4 \(\pm\) 0.03 a & 6.82 \(\pm\) 0.11 b \\  & LR3 & 139.93 \(\pm\) 5.99 & 20.03 \(\pm\) 0.45 & 3.39 \(\pm\) 0.01 a & 7.13 \(\pm\) 0.34 ab \\  & None & 133.60 \(\pm\) 7.92 & 20.4 \(\pm\) 0.44 & 3.32 \(\pm\) 0.03 b & 7.51 \(\pm\) 0.14 a \\  & \(p\) value & ns & ns & 0.0147 & 0.0252 \\ \hline \multirow{6}{*}{Cabernet Sauvignon} & LR1 & 118.29 \(\pm\) 2.76 & 20.3 \(\pm\) 1.31 & 3.23 \(\pm\) 0.02 & 4.38 \(\pm\) 0.05 b \\  & LR2 & 126.09 \(\pm\) 2.31 & 21.23 \(\pm\) 0.42 & 3.26 \(\pm\) 0.01 & 4.27 \(\pm\) 0.08 b \\ \cline{1-1}  & LR3 & 119.88 \(\pm\) 3.39 & 20.97 \(\pm\) 0.78 & 3.24 \(\pm\) 0.01 & 4.29 \(\pm\) 0.01 b \\ \cline{1-1}  & None & 126.13 \(\pm\) 6.37 & 21.43 \(\pm\) 0.75 & 3.21 \(\pm\) 0.03 & 4.53 \(\pm\) 0.06 a \\ \cline{1-1}  & \(p\) value & ns & ns & ns & 0.0018 \\ \hline \multirow{6}{*}{Cabernet Sauvignon} & LR1 & 112.70 \(\pm\) 3.62 & 21.4 \(\pm\) 0.36 & 3.37 \(\pm\) 0.03 & 6.75 \(\pm\) 0.11 b \\  & LR2 & 113.00 \(\pm\) 1.22 & 21.63 \(\pm\) 0.4 & 3.34 \(\pm\) 0.03 & 6.94 \(\pm\) 0.09 ab \\ \cline{1-1}  & LR3 & 105.33 \(\pm\) 4.4 & 21.07 \(\pm\) 0.47 & 3.34 \(\pm\) 0.03 & 6.82 \(\pm\) 0.11 b \\ \cline{1-1}  & None & 106.93 \(\pm\) 8.77 & 21.53 \(\pm\) 0.06 & 3.35 \(\pm\) 0.02 & 7.17 \(\pm\) 0.36 a \\ \cline{1-1}  & \(p\) value & ns & ns & ns & 0.0124 \\ \hline \hline \end{tabular} Note: Lowercase letters indicate significant differences at \(p\) < 0.05 according to [PERSON]’s test, and “ns” indicates no statistical differences. The stages of leaf-removal treatment include the following: LR1 (cluster-zone leaf removal at grape-berry pea size), LR2 (cluster-zone leaf removal at the beginning of veraison), LR3 (cluster-zone leaf removal about 25 days after veraison) and None (no leaf removal).  
\end{table} 
Table 1: Physicochemical parameters of grapes at harvest from vines at different stages of leaf-removal treatment from 2021 to 2022.
# The Analysis of Periodic Order in Monolayers of Colloidal Superballs
## 1 Introduction
The recent developments in synchrotron sources, X-ray detectors, and X-ray optics paved the way to millisecond SAXS data acquisition, micro-radian angular resolution, the possibility to scan samples with a submicrometer-sized beam and the application of coherent X-ray scattering techniques [23; 24]. Due to all these achievements, contemporary SAXS and SANS became truly complementary techniques. In this work we have studied the ordered monolayer structures of colloidal superballs similar in size but with different shapes, ranging from spherical to almost cubic, prepared using the unidirectional rubbing method. Since these structures are static, the contrast is high, and because high angular resolution is required due to the large structure period, SAXS is the technique of choice in the present work. The small-angle scattering from a periodic arrangement of particles does not only depend on their mutual spatial arrangement, but also on the shape of the individual particles. The latter is described by the form factor. For spherically symmetric particles the form factor is isotropic because the X-rays are scattered equally in all directions and the form factor can be calculated analytically. For non-spherical particle shapes, however, the form factor is no longer isotropic, which complicates the analysis of the scattering patterns. It was previously shown by [PERSON] et al. that the form factor of hollow silica superballs with a thin shell and distinct cubic shape become cross-like with little intensity in the corners of the diffraction patterns [11]. These effects in the form factor will result in Bragg peaks in these locations to be less visible, which complicates the structural analysis. As we faced in the present study, the presence of minima in the form factor of hollow superball particles can make many important diffraction peaks invisible, which made the structure characterization complicated if not impossible. Here, we present a strategy for the measurement and analysis of the SAXS diffraction patterns of monolayers of colloidal silica superballs prepared using the unidirectional rubbing method. We show that by performing the measurements under a range of rotations, a detailed analysis can be performed of both the form factor and structure factor. In this case the \(q\)-values of the Bragg peaks can be shifted away from the minima in the form factor. The effect of sample rotation is first illustrated using spheres for which the form factor is isotropic and can be calculated analytically. We then show for the more cubic superballs, for which the form factor cannot be calculated analytically, that this rotation allows us to visualize and assign all Bragg diffraction peaks for a full structural analysis. Finally, we present a model for the phase transition observed in the lattices of colloidal superball monolayers prepared using the unidirectional rubbing method upon varying the shape of the colloidal superballs from spherical to almost cubic.
# Plant Electrical Signal Classification Based on Waveform Similarity
## 4 Results
### Classification Results of SVM  
As described in Section 4.4, we used PCA method to reduce the features dimension. Then we used the SVM tool provided by Matlab 2014a. Two-thirds of the dataset are selected randomly to train the SVM, and one-third of the data are for testing. The 10-fold cross validation was used to train the data.  
Firstly, we used the linear kernel in SVM for classification. Table 7 shows the classification results with different components.  
We just select the first three principal components for simplification, and the accuracy is 75.8%. Figure 9 shows the first two principal components scatter plot and 3-D scatter plot of first three principal components of AP and non-AP. The red letter \"+\" represents the non-AP features, and blue circle \"o\" represents the AP features. From the figures, we see that two kinds of data are mixed and not easily divided by the linear classifier.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Units Number** & **Accuracy** & **Run Time** \\ \hline
20 & 79.7\% & 2704 s \\
16 & 84.8\% & 5340 s \\
12 & 81.5\% & 34,727 s \\
8 & 84.1\% & 65,664 s \\
4 & 83.5\% & 20,687 s \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance of three-layer BP neural network with different hidden units.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **First Principle** & **Second Principle** & **Third Principle** & **Fourth Principle** & **Fifth Principle** \\  & **Component** & **Component** & **Component** & **Component** \\ \hline Accuracy & 55.6\% & 66.7\% & 75.8\% & 76.7\% & 77.3\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Results of different components for SVM classification.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **First Principle** & **Second Principle** & **Third Principle** & **Fourth Principle** & **Fifth Principle** \\  & **Component** & **Component** & **Component** & **Component** & **Component** \\ \hline Eigenvalue & 0.2700 & 0.0770 & 0.0285 & 0.0128 & 0.0078 \\ Percentage & 65.89\% & 18.80\% & 6.96\% & 3.13\% & 1.91\% \\ Accumulated & 65.89\% & 84.68\% & 91.65\% & 94.78\% & 96.69\% \\ percentage & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Features reduction by PCA.  
In addition, we also tested other nonlinear kernel functions. As shown in Table 8, we found that the sigmoid kernel function has the best performance in the classification with 78.2% accuracy.  
To get best parameters, we set the range of every parameter at increasing intervals and then trained the dataset with them.