esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### Principal Component Analysis  
In order to explore possible differentiation among wine samples clarified by different technologies, principal component analysis (PCA) was applied to the data matrix containing the volatile compounds with OAV \(>\) 1 [20; 34]. Although the interaction between compounds may be partially lost, these volatiles are the major compounds that contributed to the overall aroma of the wine samples. As principal components 1 and 2 (PC1 and PC2) represented 53.51% and 25.1% of the total variance respectively, approximately 80% of the total variance has been represented (Supplement Figure S1). The relationships/correlations between wine samples and compounds could be observed from Figure 2, where the control wine was positioned on the positive side of PC1, while SP and BT wine samples were located on the negative side. Nevertheless, the CF and MF wines were located on the positive and negative sides of PC2, respectively. In addition, rose oxide and eugenol were located on the positive side of PC1 while \(\beta\)-damascenone, citronell, guaiacol, ethyl hexanoate, ethyl decanoate, octanoic acid, 1-hexnol, isoamyl acetate, ethyl butyrate, and hexanoic acid were located on the negative side (Figure 2 and Supplement Figure S2). In addition, these compounds played an important role in aromatic feature which distinguished the control wine from SP and BT wines. As for PC2, ethyl decanoate, ethyl hexanoate, eugenol, and guaiacol were located on the positive side, while octanoic acid, 1-hexnol, isoamyl acetate, ethyl butyrate, hexanoic acid, rose oxide, and geranyl acetone were located on the negative side (Figure 2 and Supplement Figure S2) which indicated that CF and MF wines could be separated by these compounds. [PERSON] et al. also found that most odor-active compounds were positively correlated with icewine cultivar and vintage combinations by PCA analysis [34].
# Dynamic Modeling of McKibben Muscle Using Empirical Model and Particle Swarm Optimization Method
## 5 Empirical Modeling of the McKibben Muscle
### Elasticity Model  
The McKibben muscle behaves asymmetrically at low pressure especially when the muscle was further stretched, even after reaching its maximum expansion; where the mechanism of McKibben muscle only consisted of braided thread and a rubber tube [18]. Thus, in this state, the McKibben muscle is considered to be an elastic body as the expansion reached zero internal pressure, as shown in Equation (12). This condition was implemented to modify the dynamic force of McKibben muscle; where there is no contraction force generated which caused no force to balance the vertical load.  
\[F_{elastic}=K(L_{0}-L(1-\varepsilon)^{3}),\quad\varepsilon\leq\frac{L-L_{0}}{L} \tag{12}\]
# Four-Port MIMO Antenna System for 5G n79 Band RF Devices
## 3 Results and Discussion
### Performance Parameters
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Ref**} & \multirow{2}{*}{\
\begin{tabular}{c} **Size (L \(\times\) W)** \\ **in mm** \\ \end{tabular} } & \multirow{2}{*}{**Frequency (GHz)**} & \multirow{2}{*}{
\begin{tabular}{c} **MIMO** \\ **Elements** \\ \end{tabular} } & \multirow{2}{*}{**Isolation**} & \multirow{2}{*}{**ECC**} & \multirow{2}{*}{**Gain (dBi)**} & \multirow{2}{*}{**Efficiency (\%)**} \\ \cline{5-5}  & & & & & & \\ \hline
[12] & 40 \(\times\) 40 & 2.3–3.0/5.4–5.6 & 4 & 14 & \textless{}0.2 & 3.1 & N/A \\ \hline
[14] & 80 \(\times\) 80 & 4.5–5.0 & 5 & 15 & 0.01 & 5.6 & N/A \\ \hline
[15] & 80 \(\times\) 80 & 20–40 & 4 & 20 & \textless{}0.2 & 10 & 70 \\ \hline
[18] & 30 \(\times\) 30 & 27–29 & 4 & 28 & \textless{}0.01 & 7 & 92 \\ \hline
[21] & 80 \(\times\) 80 & 3.3–3.8/5.5–5.5 & 4 & 18 & \textless{}0.2 & 4 & 70 \\ \hline
[24] & 30 \(\times\) 30 & 4.8–6.0 & 4 & 10 & \textless{}0.15 & 4.02 & 67 \\ \hline
[25] & 112 \(\times\) 112 & 5.15–5.35 & 4 & 22 & \textless{}0.15 & 4.8 & 89 \\ \hline
[33] & 30 \(\times\) 30 & 27.5–29.5 & 4 & 26 & \textless{}0.03 & 5.8 & 90 \\ \hline Proposed & 40 \(\times\) 40 & 4.7–5.1 & 4 & 25 & \textless{}0.01 & 2.8 & 70 \\ \hline \end{tabular}
\end{table} 
Table 2: Comparison table of proposed MIMO systems. Figure 10: Calculated channel capacity at different SNRs.
## 5 Results
### Changes in Front Position  
Changes in the front positions of the main and the west branch are shown as changes in their area in Figures 2f and 3c respectively. Although both the branches retreated during 2011-2016, their seasonal and interannual frontal changes differ considerably. In total, the west branch lost area much faster (9.30 km\({}^{2}\)) compared to the main branch (6.45 km\({}^{2}\)) in this period. The main branch repeatedly went through a retreat sequence during summer\(-\)fall, however, its terminus advanced during winter-spring. In terms of area changes, these repeated sequences of retreat and advance of the main branch were at the order of \(\sim\)1 km\({}^{2}\) during 2011-2013. However, it lost area about 2 km\({}^{2}\) and 3 km\({}^{2}\) in the summer-fall of 2014 and 2015, respectively, with relatively less area gain in the consecutive winter-spring. The west branch retreated significantly during summer-fall and maintained its front position (or minute advance) during winter-spring of 2011-2016 except 2014-2015. After the frontal retreat during summer-fall of 2013, the west branch continued to retreat throughout 2014 and lost 2.75 km\({}^{2}\) of area until the spring of 2015 with a total area loss of \(\sim\)6 km\({}^{2}\).  
Figure 3: Temporal variation of **(a)** glacier surface velocities; **(b)** vertical change of the scattering phase center in regard to 06/18/2011; **(c)** area change since 06/18/2011, observed at different elevations (locations indicated in Figure 1) on the west branch of Columbia Glacier, Alaska. The glacier at elevation points W\({}_{50}\), W\({}_{150}\), W\({}_{200}\) calved off at the respective times, as indicated in panel **(c)**. The green circles at the bottom axis indicate the time when the PC depicts actual surface elevation.
# Effects of the 12 May 2021 Geomagnetic Storm on Georeferencing Precision
## 3 Results

Table 2: Percentage of 868 stations around the globe with East, North, Up, and 3D position errors using the common noise filter. Figure 6: Time variations of percentage of stations around the globe (**a**) East Position Error \(\geqslant\) 5 cm. (**b**) North Position Error \(\geqslant\) 5 cm. (**c**) Up Position Error \(\geqslant\) 10 cm. (**d**) 3D Position Error \(\geqslant\) 10 cm. \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Error Intervals** & **East** & **East gs** & **North** & **North gs** & **Up** & **Up gs** & **3D** & **3D gs** \\
**[cm]** & **[\%]** & **[\%]** & **[\%]** & **[\%]** & **[\%]** & **[\%]** & **[\%]** & **[\%]** & **[\%]** \\ \hline \(<\)5 & 98 & 70 & 98 & 72 & 55 & 13 & 50 & 9 \\
5–10 & 2 & 16 & 2 & 19 & 39 & 33 & 43 & 32 \\
10–20 & 0 & 11 & 0 & 6 & 5 & 35 & 6 & 38 \\
20–40 & 0 & 2 & 0 & 2 & 1 & 15 & 1 & 15 \\
40–60 & 0 & 1 & 0 & 1 & 0 & 1 & 0 & 2 \\
60–100 & 0 & 0 & 0 & 0 & 0 & 2 & 0 & 2 \\ \(>\)100 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 2 \\ \hline \end{tabular}
\end{table} 
Table 3: Percentage of 325 stations in South America with East, North, Up, and 3D position errors using the common noise filter. Chile (South America). One of the Chilean stations is located in Las Vizcachas (vzch, 33.6\({}^{\circ}\)S, 70.51\({}^{\circ}\)W) nearby to Santiago, and the second one is located in Vegas de Itata (36.42\({}^{\circ}\)S, 72.86\({}^{\circ}\)W), 400 km south of Santiago. The selection of the stations is based on the results of previous studies of the storms of March 2017 and August 2019 [57; 61]. The August 2019 geomagnetic storm [61] was a moderate one that produced a extreme positive ionospheric storm with strong TEC perturbations over Europe. However, this work did not study positioning errors. Thus, we selected a station in Europe to be compared with the two Chilean stations, which are located in South America and in the area of interest for this work. In addition, the stations in Chile are both required since each of them was operative for different storms (see Table 4). [PERSON] et al. [57] studied the position error produced by the moderated geomagnetic storm of March 2017 but in broad latitudinal ranges, calculating the maximum positioning errors per component for each of these ranges but not per station. Figure 7 presents the time series of TEC related indexes and the position per component for the Madrid and Vizcachas stations. The left series of images show the data for the 5 August 2019 storm. The right series of images show the data for 12 May 2021. Table 4 summarized the positioning variations at each of the three stations produced by three different geomagnetic storms.
# Cooperative Guidance Law for the Mother-Cabin of the Anti-UAV Cluster Mother-Son Missile
## 3 Multi-Missile Cooperative Guidance Law Design
### Multi-Missile Cooperative Guidance Structure  
[PERSON] et al. designed a two-layer cooperative guidance structure [7] composed of two different control strategies. The upper layer is the coordination layer, which receives the coordination variable information based on the centralized or distributed communication network topology and obtains the expected coordination variable based on the coordination strategy. The lower layer is the control layer, which combines with the expected coordination variables generated by the coordination layer to form the local guidance law of each missile. The coordination variables refer to the minimum information required to achieve the collaborative tasks. The guidance structure can achieve multi-missile coordination by converging the coordination variables of each missile to the expected coordination variables. It has the advantages of a clear structure and good universality and can be better applied to the multi mother-cabin guidance without a leader-follower relationship, as discussed in this paper.  
The cooperative guidance structure in this paper is designed based on the two-layer cooperative guidance structure, as shown in Figure 3. The coordination strategy (Equation (7)) in this paper cannot obtain the expected coordination variables, but directly obtains the control output \(u_{ri}\) that can control the coordination variables \(t_{go,i}\) and \(v_{i}\) to achieve consistency. \(u_{ri}\) is transferred to the control layer, where it is combined with the guidance law \(u_{qi}\) with zero LOS constraint of each missile to form the local guidance law \(u_{i}\) of each missile.
## 2 Materials and Methods
### Data Fusion
#### 2.4.2 Selection of Features  
Several distinctive features for both the ultrasonic and radiographic experimental data were used as input for data fusion. Previous work on the reliability of detecting debonding defects with ultrasonic techniques in adhesive joints showed that in addition to the amplitude of the ultrasonic echo, the attenuation and the amplitude in frequency domain play important roles in the defect contrast [2], allowing higher probability of defect detection. Therefore, in this study, in addition to the peak-to-peak amplitude, we also selected signal attenuation, frequency domain maximum amplitude, and absolute energy as features of the ultrasonic inspection results, as shown in Table 2. The features are extracted  
Figure 9: Workflow of data fusion.  
by applying the specified formula to 3D ultrasonic data (\(x\), \(y\), \(t\)), hence the dimensions are reduced to 2D (\(x,y\)). On the other hand, we applied 2D image processing techniques to obtain 2D (\(x\), \(y\)) X-ray radiography results to create additional features, namely, FAST (features from accelerated segment test), Harris corner detection, and Sobel edge detection.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**NDT Technique** & **Feature** & **Formula** \\ \hline Ultrasonic & Peak-to-peak & \(\max(A(t))-\min(A(t))\) \\ Ultrasonic & Amplitude & \(\frac{(\max(A(t_{0}))-\min(A(t_{0})))-(\max(A(t_{0}))-\min(A(t_{0})))}{\max(A(t_ {0}))-\min(A(t_{0}))}\) \\ Ultrasonic & Frequency domain & \(\max(FFT(A(t)))\) \\ Ultrasonic & maximum amplitude & \(\sum\limits_{t}A(t)^{2}\) \\ Ultrasonic & Absolute energy & none \\ X-ray & Amplitude & \(S_{p\to x}=\left\{\begin{array}{ll}d,&I_{p\to x}\leq I_{p}-T\\ s,&I_{p}-T<I_{p\to x}<&I_{p}+T\\ b,&I_{p}+T\leq I_{p\to x}\end{array}\right.\) \\ X-ray & Harris & \(E(u,v)=\sum\limits_{x,y}w(x,y)\) [\(I(x+u,y+v)-I(x,y)\)] \\  & Detection & \(G_{x}=\left[\begin{array}{ll}1\\ 2\\ 1\end{array}\right]*([+10-1]*A)\), \\ X-ray & Sobel Edge & \(G_{y}=\left[\begin{array}{ll}+1\\ 0\\ -1\end{array}\right]*([12\;1]*A)\), \\ & \(\Theta=atan2\big{(}G_{x},\;G_{y}\big{)}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Different features extracted from the measured data.  
Figure 10: Measured data preparation for data fusion.
# Numerical Analysis of the Beam-Column Resistance Compared to Methods by European Standards
## 3 Verification According to European Standards
### Resistance of Beam-Columns with RHS 200/100/10 Cross-Section
#### 3.2.2 Axial Compression and Major-Axis Bending without Lateral Restraints  
The behaviour of beam-columns subjected to bending moments about the major axis and susceptible to flexural buckling about the perpendicular axis (minor axis) was also observed. Therefore, no lateral restraints were modelled. Moreover, no local instabilities were caused by transversal loads and no stiffeners were needed.  
The comparison of analytical methods and FEM simulations is presented on the Figures 38-42.  
Figure 37: Interaction curves for laterally restrained beam-columns with RHS cross-section.  
Figure 38: Interaction curves for beam-columns with RHS cross-section without lateral restraints.  
Figure 41: Interaction curves for beam-columns with RHS cross-section without lateral restraints.  
Figure 40: Interaction curves for beam-columns with RHS cross-section without lateral restraints.  
Figure 39: Interaction curves for beam-columns with RHS cross-section without lateral restraints.  
The methods of Eurocode 3 were not able to provide such accurate results, as in the previous case of the IPE-profile. The resistances according to these methods were lower compared to the FEM curves.  
The method for verification of beam-columns according to Eurocode 9 provided too conservative values of resistances for these loading conditions. The deviations increased with growing slenderness ratio and were significantly lower than deviations according to the methods of Eurocode 3.
## 8 Conclusions and Extensions
The \(FI\) scores indicated the influence of the higher order AR coefficients, e.g., AR 7 and 6, and some lower order coefficients, e.g., AR 1 and 2. Therefore, it is important to engage _all ARIMA coefficients_ as damage features for the accurate and stable performance of the proposed ARIMA-ML framework. Even though the proposed ARIMA-ML framework achieved promising results in the studied experiments, there still exist certain drawbacks that require future study. These drawbacks and the corresponding future suggestions are summarized as follows:  
1. The presented framework was validated using limited shaking table tests of a TCBBF, where the structural type and damage scenarios were not sufficient due to the high expense in conducting other full-scale experiments. More validations using physical tests or detailed finite element analyses are needed in the future. Moreover, using transfer learning techniques [40] to only train on simulated data but with fine-tuning and tests from real experimental data has high potential for more efficient and practical research directions related to ML adoption in SHM. 2. The TS models and ML classifiers need to fit the available data with the possible risk of underfitting or overfitting, e.g., when using inappropriate orders of the ARIMA model or poorly tuned parameters in the ML classifiers. Even though selecting several candidate models instead of using one single \"best\" model provides robustness of the framework, this is a subjective and costly approach, which requires further investigation. 3. Substructure idealization is suggested for the improvement in damage localization tasks of framed structures, where more advanced structural idealization techniques beyond SDOF and MDOF approaches, Figure 7, can be explored. Conceptualization and methodology, Y.G., K.M.M., and Y.C. ([PERSON]); Software and validation, Y.G. and K.M.M. ; Formal analysis and interpretation, Y.G., K.M.M., and Y.C. ([PERSON]); Data curation, Y.G., Y.C. ([PERSON]), W.W., and Y.C. ([PERSON]); Original draft preparation, Y.G., K.M.M., and Y.C. ([PERSON]); Supervision and final revision, K.M.M. ; Funding acquisition, K.M.M., W.W., and Y.C. ([PERSON]). All authors have read and agreed to the published version of the manuscript. This research received funding support from Tsinghua-Berkeley Shenzhen Institute (TBSI), China; California Department of Conservation, California Geological Survey, Strong Motion Instrumentation Program agreement 1019-016; [PERSON] Chair of Civil Engineering, University of California, Berkeley; and California Department of Transportation (Caltarms) for \"Bridge Rapid Assessment Center for Extreme Events (BRACE2)\" project (TO 001), part of the PEER-Bridge Program agreement 65A0774. The shaking table test was funded by the Natural Science Foundation of China (Grant No. 51578403 and 51820105013). Not applicable. Data Availability StatementSome or all data, models, or code that support the findings of this study are available from the authors upon reasonable request. The authors declare no conflict of interest.
# A Three-Dimensional Electrochemical Process for the Removal of Carbamazepine
However, the costs associated with the addition of chemicals, the separation of catalysts [18], and sludge management, as well as the possibility of generating toxic byproducts, need to be considered. Adsorption, another treatment option, is one of the most promising due to its efficiency, simple design, and low cost. Furthermore, it does not lead to the formation of toxic intermediate compounds [29]. One of the most crucial factors in this process is the adsorption capacity of the used material [29], and therefore, activated commercial carbon is commonly used because of its high adsorption capacity. The use of granular and powdered activated carbon provides efficient CBZ removal, ranging from 90 to 99% [30; 31]. Nevertheless, it is necessary to overcome the restrictions associated with the high price of activated carbon and the limitations associated with its regeneration [32]. Recently, biochars (waste biomass pyrolysis products) have drawn attention as alternative adsorbents to activated carbon due to their physicochemical properties and widespread availability [33]. Besides adsorption, electrochemical processes have gained increasing interest in recent years to treat polluted waters, and were applied by [PERSON] et al. [34] to remove CBZ from wastewaters. Electrochemical systems offer several advantages, such as simple operation (at ambient temperature and pressure), robust performance, the capability to adjust to variations in the influent composition and flow rate [35], and viability in degrading nonbiodegradable pollutants [36]. In contrast with the conventional two-dimensional (2D) process, in which there are only two electrodes (cathode and anode), in a three-dimensional (3D) process, a third electrode can be utilized. If this electrode is composed of particles, an extraordinarily expansion of its specific surface area is achieved. Under an electric field, these particles with specific shapes and sizes can be polarized,mimicking charged microelectrodes and delivering higher electrocatalytic efficiency [37]. The removal of pollutants in 3D systems is 10-50% higher than in 2D systems, while simultaneously reducing energy consumption [35]. [PERSON] et al. [34] applied a 3D electrochemical process for CBZ degradation employing two aluminum electrodes, as both anode and cathode, and powdered activated carbon as the particulate electrode, achieving a removal efficiency of 89.8%. In this context, the aim of the present work was to apply a 3D electrochemical process to the degradation of CBZ, considering the influence of the anode and cathode materials, type of carbon-based adsorbent (biochar and commercial activated carbon), and other operational parameters, such as current density, reaction time, electrolyte concentration, distance between the electrodes, and CBZ concentration. The use of biochar may prove to be a key and accessible input for sustainability, as it has many potential socio-economic and environmental benefits in the framework of the circular economy.
## 5 Case Study: COTS-based Embedded Real-Time Control of a Time Mash Delivery Machine
### The COTS-Based Controller  
The design and implementation flow of the control system development follows the described COTS-based development process and involves the initial specification of COTS hardware and software modules and units (COTS node), their applicability analysis, their modeling and simulation-based verification, the actual programming and implementation steps, and finally, real-time experimentations of the applied control system for performance validation purposes.  
The objective was to replace the custom-built controller by a COTS-based, easily customizable, reusable, low-cost, low-power, and open-source embedded control system. For this purpose, a Raspberry Pi3 Model B microcontroller board was chosen as the development platform (RPi3 microcontroller integrates an ARMv8 Cortex-A53 quad-core processor unit within a Broadcom BCM2837 SoC [42]). The choice of RPi3 is based on the fact that it has been extensively used for embedded low-powered control applications due to its computing power andcomparative price, its ease of use with peripheral equipment, and low-energy consumption [43]. In addition, research shows that such a system is capable of supporting adequate real-time timing for most measurement purposes [44]. The system runs UBUNTU 18.04 LTS Linux patched with PREEMPT_RT for real-time processing. This architectural upgrade brings several advantages regarding reconfigurability, versatility, availability, and cost. All the specific operation constraints that apply in operating the lime machine under control, are handled by this COTS component, the RPi3.