esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials Furthermore, Methods
### Hysteresis Description and Reference Design  
Common excitation signals that represent a suitable hysteresis graph in PEAs are triangular and sine waves [5]. The triangular waveform is a complex input for the PEA, as it not only implies that sharp slope changes are required to be followed, but also this wave is generated from high-frequency harmonics [30]. Therefore, we developed a triangular reference of a 145V amplitude, which is lower than the maximum allowed voltage so that the PEA lifetime is not affected; additionally, we chose a period of 4 s for this signal.  
With the previously described triangular waveform as an output, we acquired the displacement of the PEA where the outcome is the one shown in Figure 2. The mechanics and features of the curve are developed as follows from the beginning of the applied voltage:  
* **Initial point:** We calibrated the PEA so that it initially starts at a zero displacement from this.
* **Curve (1):** This is known as the initial ascending curve, which begins from the previously described point and ends at the upper target point. As the figure shows, the non-linearity is present along this path.
* **Upper target point:** At this place, the PEA reached the correspondent displacement to the specified amplitude of the triangular waveform.
* **Curve (2):** This, known as the _second ascending curve_, shows that the PEA has an asymmetric hysteresis, which is a phenomenon that creates difficulties when mathematical models need to be found to reflex.
* **Lower converging point:** Ideally, the final position could have been at the initial point when the applied voltage is null. However, in this case, the lower converging point is not the same as the initial point.
* **Curve (3):** Provided that amplitude and period are the same along the experiment, then this curve will be equal for the following ascending cycles.
* **Curve (4):** As with the curve (3), this course will be the same provided that the reference configuration is constant.  
Figure 1: Hardware and software workflow.  
Taking into account the previous analysis, we designed the reference considering the applied voltage in the PEA. As a first consideration, the initial point within the first ascending curve is negligible to evaluate the performance of the PEA in a long-term experiment. Therefore, we outlined a reference between the lower converging and upper target point as linear Equation (1) reflects. In this mathematical expression, we defined \(X\) as the displacement, \(\alpha\) the slope between the two points of interests and \(b\) the vertical offset in terms of the lower converging point.  
\[X[\mu\text{m}]=\alpha\cdot V+b \tag{1}\]
# The Survey of Lava Tube Distribution in Jeju Island by Multi-Source Data Fusion
###### Abstract  
Lava tubes, a major geomorphic element over volcanic terrain, have recently been highlighted as testbeds of the habitable environments and natural threats to unpredictable collapse. In our case study, we detected and monitored the risk of lava tube collapse on Jeju, an island off the Korean peninsula's southern tip with more than 200 lava tubes, by conducting Interferometric Synthetic Aperture Radar (InSAR) time series analysis and a synthesized analysis of its outputs fused with spatial clues. We identified deformations up to 10 mm/year over InSAR Persistent Scatterers (PSs) obtained with Sentinel-1 time series processing in 3-year periods along with a specific geological unit. Using machine learning algorithms trained on time series deformations of samples along with clues from the spatial background, we classified candidates of potential lava tube networks primarily over coastal lava flows. What we detected in our analyses was validated via comparison with geophysical and ground surveys. Given that cavities in the lava tubes could pose serious risks, a detailed physical exploration and threat assessment of potential cave groups are required before the planned intensive construction of infrastructure on Jeju Island. We also recommend using the approach established in our study to detect undiscovered potential risks of collapse in the cavities, especially over lava tube networks, and to explore lava tubes on planetary surfaces using proposed terrestrial and planetary InSAR sensors.  
lava tube; InSAR; machine learning; detection; Jeju Island 1
## 3 Experimental Results and Discussion
### Crack Pattern and Failure Mode  
Figure 7 shows the crack development of each specimen traced continuously throughout the four-point load test until ultimate capacity was reached. Three types of failure mode were observed in the thirteen specimens as follows: Type 1: Specim CS showed pure shear failure dominated by diagonal tension failure; Type 2: All three of the FP series specimens showed flexural failure followed by sudden diagonal tension cracking; and Type 3: All nine of the HP series specimens showed flexural failure followed by gradual diagonal tension cracking. Figure 8 summarizes these three types of crack development.  
For specimen CS, the first crack was initiated by a diagonal tension crack that occurred near the support plate when a load of 115 kN was reached. The diagonal tension crack propagated diagonally upwards towards the loading plate as the load was increased incrementally to 165 kN. At a loading of 145 kN, the first flexural cracks were observed at locations L1 and L3 where the LVDT were installed, developing slowly as the loading was increased. After a loading of 190 kN was reached, the diagonal tension cracks propagated rapidly. Upon reaching the failure load, the cracks passed through the entire depth and width of the beam. Diagonal tension failure was observed when an ultimate load of 240 kN was reached.  
Figure 7: _Cont._  
Figure 8: Development of cracking. (**a**) Specimen CS (**b**) FP series specimens (**c**) HP series specimens.  
Figure 7: Crack development of specimens.
## 2 Materials and Methods
### Techno-Functional Properties Characterization
#### 2.5.4 Size Distribution  
The emulsion particle size distribution was measured using a Mastersizer 3000 (Hydro EV, Malvern, Worcestershire, UK). Briefly, the emulsion was added to the Hydro EV unit dropwise until it reached an 8-12% laser obscuration. For the dispersed phase (olive oil), the refractive index was adjusted to 1.46, and for the dispersant (water), it was set to 1.33. The dispersions were measured five consecutive times at 25 \({}^{\circ}\)C in the diffractometer. Mastersizer software (version 3.60, Worcestershire, UK), was used to characterize the particle size of each sample in terms of the volume distribution (%) vs. droplet diameter; the surface-weighted mean diameter (\(d_{[3,2]}\); Equation (6)); the volume-weighted mean particle diameter (\(d_{[4,3]}\); Equation (7)); and the polydispersity index (PDI; Equation (8)).  
\[d_{[3,2]}=\frac{\sum n_{i}d_{i}^{3}}{\sum n_{i}d_{i}^{2}} \tag{6}\]\[d_{[4,3]}=\frac{\sum n_{i}d_{i}^{4}}{\sum n_{i}d_{i}^{3}} \tag{7}\]  
where \(d_{i}\) is the droplet diameter and \(n_{i}\) is the number of oil droplets having a diameter \(d_{i}\).  
\[PDI=\frac{d_{90}-d_{10}}{d_{50}} \tag{8}\]  
\(d_{10}\), \(d_{50}\), and \(d_{90}\) are the diameters at 10, 50, and 90% of the cumulative droplet distribution, respectively [19].
## 6 Results
### Scenario-3  
In Scenario-3, the efficiency of the learning algorithms was tested in a dynamic environment, where packet-loss changes occur at a higher rate, i.e., every 2 min. Packet-loss percentages on the links connecting servers to the client are given in Table 8. Servers \(s_{1}\) and \(s_{3}\) were configured with the lowest packet-losses. Figure 6a shows that the average delay obtained with SLA was higher than the other three algorithms in serving GET requests, and except SLA, all of them showed equivalent performances. Figure 6b indicates that Q-Learning produced the lowest average delay when serving POST requests. With GET requests, the average delay of RR was \(\approx\)15% and \(\approx\)25%-40% better than Q-Learning and SLA. The average delay with HSLA was similar to RR, whereas Q-learning was \(\approx\)20% better than SLA, and HSLA was \(\approx\)10% better than Q-Learning. When sending POST requests, average delays of HSLA, Q-Learning, and SLA were \(\approx\)30%, \(\approx\)40%, and \(\approx\)20% better than RR, and Q-learning was \(\approx\)20% better than HSLA and \(\approx\)30% better than SLA.  
Figures 6c,d depict the request-loss ratios obtained with the algorithms. All learning algorithms show better performances, and Q-Learning shows the best performance. When sending GET requests, the request-loss ratios of HSLA, Q-Learning, and SLA were \(\approx\)60%, \(\approx\)90%, and \(\approx\)50% better than RR, and Q-Learning was \(\approx\)90% better than both SLA and HSLA. When sending POST requests, the request-loss ratios of HSLA, Q-Learning, and SLA were \(\approx\)60%, \(\approx\)90%, and \(\approx\)60% better than RR. Q-Learning was \(\approx\)75% better than both SLA and HSLA.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **s1** & **s2** & **s3** & **s4** \\ \hline
**RR** & 515 & 515 & 515 & 514 \\
**QL** & 518 & 16 & 841 & 18 \\
**SLA** & 598 & 171 & 666 & 176 \\
**HSLA** & 581 & 49 & 757 & 59 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Server (feeder link) selection count for Scenario-1 (GET).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **s1** & **s2** & **s3** & **s4** \\ \hline
**RR** & 416 & 415 & 413 & 413 \\
**QL** & 776 & 24 & 727 & 24 \\
**SLA** & 526 & 159 & 749 & 148 \\
**HSLA** & 687 & 68 & 727 & 68 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Server (feeder link) selection count for Scenario-2 (POST).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Packet-Loss** \% & **s1** & **s2** & **s3** & **s4** \\ \hline
**First 2 min** & 20 & 30 & 1 & 30 \\
**second 2 min** & 1 & 40 & 20 & 40 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Emulated packet-loss percentages for Scenario-3.
# Identification of Wiener Box-Jenkins Model for Anesthesia Using Particle Swarm Optimization
## 3 System Identification
### Wiener Box-Jenkins Model Structure  
The Wiener Box-Jenkins model is shown in Figure 1, where it consists of a linear dynamic subsystem, \(G(z^{-1})\), in cascade with a static non-linearity, \(m\big{(}\centerdot\big{)}\). The noise model \(H(z^{-1})\) is fed by a white noise sequence \(e(t)\) to yield a colored noise \(v(t)\)[30; 31].  
The output of the linear process part is given by  
\[x(t)=G(z^{-1})u(t) \tag{3}\]  
where \(u(t)\) is the model input. The model output \(y(t)\) is expressed as  
\[y(t)=y_{d}(t)+v(t)\]  
where the colored noise \(v(t)\) is given as  
\[v(t)=H(z^{-1})e(t) \tag{4}\]  
and \(y_{d}(t)\) is the undisturbed process output calculated as  
Figure 1: The proposed Wiener Box-Jenkins model structure.  
\[y_{d}(t)=m\big{(}x(t)\big{)}=\sum_{i=0}^{M}\beta_{i}x^{i}(t). \tag{5}\]  
where the coefficients, \(\beta_{i},i=0\dots M\), are the parameters of the polynomial nonlinearity \(m\big{(}\cdot\big{)}\) of order \(M\). Other types of basis expansions, such as orthogonal polynomials [32] or splines [33], could also be used.  
The linear filters \(G(z^{-1})\) and \(H(z^{-1})\) are defined as:  
\[G(z^{-1})=\frac{B(z^{-1})}{F(z^{-1})}, \tag{6}\]  
\[H(z^{-1})=\frac{C(z^{-1})}{D(z^{-1})}, \tag{7}\]  
where the numerators and denominators, \(B(z^{-1})\), \(F(z^{-1})\), \(C(z^{-1})\), and \(D(z^{-1})\), are polynomials in the discrete shift operator \(z^{-1}\) as follows:  
\[\begin{split} B(z^{-1})&=b_{0}+b_{1}z^{-1}+\dots+b _{n}z^{-nb}\\ F(z^{-1})&=1+f_{1}z^{-1}+\dots+f_{nf}z^{-nf}\\ C(z^{-1})&=1+c_{1}z^{-1}+\dots+c_{n}cz^{-nc}\\ D(z^{-1})&=1+d_{1}z^{-1}+\dots+d_{nd}z^{-nd}\end{split} \tag{8}\]  
It is assumed that the maximum degree of the elements \(nb\), \(na\), \(nc\), and \(nd\), and \(M\) are selected a priori and the noise filter \(H(z^{-1})\) is stable and inversely stable. For systems with time delay of \(l\) samples, the leading numerator parameters, \(b_{0},b_{1},\cdots,b_{l-1}\), would be zero. Detailed formulations of the Wiener Box-Jenkins model are available in [34].
# Hunting Molecules in Complex Matrices with SPME Arrows: A Review  
[PERSON]  
1  
[PERSON]  
1 Restek Corporation, Bellefonte, PA 16823, USA; colonton.myerschromatography, analyte introduction onto the instrument is typically performed via the direct thermal desorption of the SPME device.  
SPME, originally conceived and patented by researchers at the University of Waterloo (UW) in the late 1980s, was licensed and commercialized at the beginning of the 1990s by Supelco Inc. (now Millipore-Sigma, Bellefonte, PA, USA). The first peer-reviewed manuscript, published in 1989 [7], brought to light the most know configuration of SPME: \"the fiber\" [8]. As shown in Figure 1, a traditional SPME device is composed of the following parts: a color coded screw hub (A), a sealing septum (B), a septum piercing needle (C), a fiber attachment needle (D), and a coated fused silica fiber (E). Though multifarious SPME devices have been developed since the mid-1990s for thermal, liquid, and inclusive laser desorption [1; 4; 9], the thin cylindrical geometry described by [PERSON] et al. [7] is the most well-known and the leader in sales worldwide. Indeed, GC coupled to several detection systems (e.g., ECD, FID, and MS) is the most commonly used instrument to interface SPME devices.  
Even though the SPME patent did not thwart academia/industry from conducting research on SPME devices and extraction phases [10; 11; 12], it categorically prevented corporations from commercializing improved versions of the \"traditional\" fiber (e.g., enhancements to the substrate to make a more robust technology). The expiration of intellectual property a few years ago enabled commercial vendors to not only offer the \"traditional\" SPME fibers, but to also mechanically and chemically enhance versions of this technology. An example of these enhancements includes the first large volume SPME fiber developed by CTC Analytics AG for GC applications, known as the SPME Arrow (see Figures 1 and 2) [13].  
In parallel with the development of the SPME Arrow, other green chemistry technologies aiming to overcome the drawbacks of the traditional SPME fiber have also appeared [14]. Among them, one can highlight the thin film microextraction (TFME), the stir bar sorptive extraction (SBSE), and the in-tube extraction (ITEX). As recently reviewed by [PERSON] and [PERSON] [9; 14], most of these technologies offer better analytical features over the traditional SPME fiber. However, some of them are harder to automate [15] or are not compatible for direct immersion experiments [16]. Thus, the focus of the current review article is to summarize the fundamental and operational aspects of SPME Arrows, as well as recent developments and future directions [17; 18].  
Figure 1: Solid phase microextraction (SPME) Arrows and a traditional SPME fiber; (**1**) 1.5 mm SPME Arrow; (**2**) 1.1 mm SPME Arrow; and (**3**) 23 gauge traditional SPME fiber. d1: Support tubing; d2: septum piercing needle; d3: phase diameter; d4: phase support tubing diameter; l3: phase length; a3: phase area; and v3: phase volume.
## 4 Case Study
### Optimal Selection Analysis of FLSP  
The numerical simulation results used to obtain the relationship curve between the vault settlement and the spring stiffness are summarized in Figure 13. It can be seen from the figure that the vault settlement decreased gradually with the increase in spring stiffness, indicating that the bearing force provided by the FLSP increased gradually. Under the no-spring condition, the vault settlement was greater than the control settlement value of 16.5 cm, so it was necessary to restrict and control the settlement of the arch vault with the FLSPs. The original design adopted four \(\phi\)42 \(\times\) 4 mm FLSPs. Because the corresponding spring stiffness was 4088 kN/m, and the steel arch spacing was 75 cm, the corresponding stiffness in the plane model was 5450 kN/m. The corresponding vault settlement calculated by the numerical simulation was 18.7 cm more than the vault settlement control value. Therefore, it was necessary to optimize the design of the FLSP. As can be seen in Figure 11, when the vault settlement was 16.5 cm, the corresponding spring stiffness was approximately 9860 KN/m, and the required stiffness of FLSP for each steel arch frame was 7400 kN/m.
## 4 Numerical Examples
### Results
#### 4.2.3 Sensitivity Analysis  
A sensitivity analysis refers to an analysis of the effect of a parameter change by substituting all the possible values of the parameter when it is uncertain in a model. We conducted a sensitivity analysis  
\begin{table}
\begin{tabular}{c c c c} \hline
**Cost Coefficient** & **EC(T)** & **T*** & **Reliability** \\ \hline Base & 19,157.92 & 102.6 & 0.8564 \\ C1 = 2.5 & 18,907.92 & 129.1 & 0.9074 \\ C1 = 7.5 & 19,395.72 & 89.2 & 0.8135 \\ C2 = 80 & 15,496.43 & 104.3 & 0.8608 \\ C2 = 120 & 22,819.4 & 100.9 & 0.8518 \\ C3 = 1500 & 19,078.56 & 92.6 & 0.8260 \\ C3 = 2500 & 19,233.28 & 111.2 & 0.8767 \\ C4 = 200 & 19,141.58 & 100.4 & 0.8504 \\ C4 = 400 & 19,174.25 & 104.7 & 0.8618 \\ \hline \end{tabular}
\end{table}
Table 10: Optimal release time with expected total cost and the software reliability according to cost coefficient.  
Figure 15: Expected total cost for the baseline case.
## 2 Methods
### Calculation Model and Parameter Selection  
In actual engineering, the height of steep cataclastic rocky slopes without reinforcement was not likely to be too high, therefore, the slope (height \(H\) = 20 m) was assumed in this paper. According to the research results of [PERSON] et al. [32] on the calculation accuracy of the safety coefficient of slope stability, the two-dimensional calculation model was determined as shown in Figure 1. The left boundary of the calculation model was 1.5 \(H\) from the toe of the slope, the right boundary was 2.5 \(H\) from the shoulder of the slope, and the bottom of the slope extends downward by \(H\). The slope angle \(\beta\) was 45\({}^{\circ}\). Calculation model boundary conditions were: the bottom boundary of the slope was a fixed constraint, the left and right boundaries were constraints in the normal direction, and the ground surface of the slope was a free boundary. The calculation model consists of cataclastic rock mass and muddy interlayer; the dip angle of the interlayer was \(\theta\). Both cataclastic rock mass and muddy interlayer were simulated by solid unit, and the thickness of the interlayer was 0.05 m. In order to improve the calculation accuracy, the cell size was set to the minimum value specified by the calculation program. Both the cataclastic rock mass and the muddy interlayer are considered as ideal elastoplastic materials, and the yield criterion was adopted as the conventional Mohr-Coulomb yield criterion. The Mohr-Coulomb criterion can be expressed in terms of the invariants \(I_{1}\), \(J_{2}\), and \(\theta_{\sigma}\), as shown in Equation (3):  
\[\frac{1}{3}I_{1}\sin\,\varphi\,+\,\left(\cos\,\theta_{\sigma}-\frac{1}{\sqrt{ 3}}\sin\,\theta_{\sigma}\!\sin\,\varphi\right)\sqrt{J_{2}}-\,\cos\,\varphi =0 \tag{3}\]  
where \(I_{1}\) is the first invariant of the stress tensor; \(J_{2}\) is the second invariant of the stress bias; and \(\theta_{\sigma}\) is the stress Lodder angle.  
The finite element numerical calculation was executed by the geotechnical engineering professional software PLAXIS 2D 2020, which does not consider the influence of pore water pressure and ignores the shear expansion angle (\(\psi\) = 0), i.e., the non-associated flow law was used, and the calculation results were conservative [32]. The physical and mechanical parameters of the cataclastic rock mass and muddy interlayers were determined based on the geotechnical investigation report [35]. The calculated parameters were presented in Table 1.
# Tire Defect Detection via 3D Laser Scanning Technology
## 3 Experiments and Discussion  
In this section, we first evaluated the performance of our method compared with state-of-the-art tire defect detection approaches. A new 3D tire dataset was introduced, which was the first dataset using 3D data for the detection of low-visibility tire defects. Then, we compared our proposed 3D pipeline with existing defect-detection methods based on X-ray images. Afterward, based on the 3D tire dataset, we used four different data configurations to demonstrate the effectiveness of our proposed pipeline: point clouds, RGB images, depth images, and rendered images. Notably, in actual detection pipelines, our method used rendered images for detecting tire defects. Moreover, we provided several ablation studies on rendered images to analyze the important designs of our detection model. Finally, we analyzed some failure cases.
## 2 Driving Theory Analysis
### Stability Analysis  
#### 2.3.1 Uphill Rearward Tilt Limit Angle Analysis  
Before the stability analysis, it is necessary to analyze the position of the center of mass of the track, according to the SolidWorks software [17] tool. To analyze the position of the center of mass of the assembly, when fully loaded, the height of the center of mass from the ground \(h=205\) mm, the distance from the center of the front support wheel \(L_{1}=554\) mm,the distance from the center of the rear support wheel \(L_{2}=446\) mm, and the track gauge of the track chassis \(B=800\) mm.  
When the slope is too steep, the prototype will tip and roll over, or slip due to insufficient adhesion, so it is necessary to analyze the prototype's uphill rearward limit tipping angle \(\alpha_{1}\), downhill forward limit tipping angle \(\alpha_{2}\), and lateral limit side tilting angle \(\delta\), as well as the slip stabilization angle \(\phi\).  
Regarding the prototype climbing process, if the slope gradient is great enough, backward instability will occur. Assuming that the prototype driving uphill and downhill at a constant speed, the force analysis is shown in Figure 5.  
If the prototype is destabilized by backward tilting, it is tilted from point A. Taking the moment at point A, the moment equilibrium equation can be obtained:  
\[\mathrm{F_{n}\cdot a+Gh\sin\alpha_{1}-GL_{1}cos\alpha_{1}=0} \tag{11}\]  
\[\mathrm{a}=\frac{GL_{1}cos\alpha_{1}-Gh\sin\alpha_{1}}{\mathrm{F_{n}}} \tag{12}\]  
where a is the width of the non-pedestrian side.  
If the slope angle \(\alpha_{1}\) increases, the support force of the slope on the prototype will then move backward until it coincides with the rear support wheel grounding point A, at which time the prototype will be rearwardly tilted and destabilized. The uphill rear tilt limit angle is given by Equation (13):  
\[\alpha_{1}\leq\tan^{-1}\frac{\mathrm{L_{1}}}{\mathrm{h}}=69.68^{\circ} \tag{13}\]