esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Free Volatile Components of Okinawan Pineapple
The concentrations of these two compounds in 'N67-10' were 18.84 and 18.67 \(\mathrm{\SIUnitSymbolMicro g}\)/100 mL, respectively. Moreover, both cultivars also lacked the same esters, including ethyl propanoate, ethyl 2-methylpropanoate, 3-methylbutyl acetate, and ethyl pentanoate. Conversely, 'Yugafu' had a greater number  
Figure 1: (**A**) Content (\(\mathrm{\SIUnitSymbolMicro g}\)/100 mL) and (**B**) relative concentration (%) of free volatile components of Okinawan pineapple. Each value is expressed as the mean \(\pm\) standard deviation of three replicates. Means followed by the same letter are not significantly different at \(p<0.05\). of predominant ester compounds such as ethyl acetate, ethyl 2-methylbutanoate, methyl hexanoate, ethyl hexanoate, and ethyl 3-(methylthio)propanoate (12.58, 11.48, 11.40, 10.13, and 9.45 ug/100 mL, respectively). Except for methyl hexanoate, the concentrations of the other predominant esters of 'Yugafu' were significantly higher compared to 'N67-10' and 'Yonekura'. However, methyl (Z)-3-hexenoate, methyl (E)-3-hexenoate, methyl hep-tanoate, methyl 2-hydroxy-2-methylbutanoate, and dimethyl propanedio were lacking in 'Yugafu'. The second largest proportion of free volatile components were alcohols, which ranged from 3.57 to 5.08 ug/100 mL (Figure 1B; Table 2). 2-Furanmethanol was the main alcohol substance in all the cultivars (0.99-2.10 ug/100 mL), whilst ethanol was detected only in 'Yugafu' (1.31 ug/100 mL). The other alcohols were 1-hexanol, 1-heptanol, 2-ethyl-1-hexanol, 1-octanol, and menthol, and except for 1-heptanol, there were significant differences in the concentrations of these alcohol components among cultivars. For instance, both 'N67-10' and 'Yugafu' contained significantly higher 1-hexanol with the same concentration of 0.47 ug/100 mL, whilst 'N67-10' and 'Yonekura' had a significantly greater amount of 2-ethyl-1-hexanol than in 'Yugafu' (0.95 and 1.14 vs. 0.66 ug/100 mL, respectively). There were variations in terpene composition wherein 'N67-10' was comprised of six of eight identified compounds--namely eucalypt, \(\alpha\)-copaene, \(\beta\)-elemene, terpinen-4-ol, \(\gamma\)-muurolene, and \(\alpha\)-muurolene--that accounted for a total amount of 2.68 ug/100 mL. The highest significant amount of eucalypt was found in 'Yugafu' (0.67 ug/100 mL), compared to 0.38 and 0.29 ug/100 mL in 'N67-10' and 'Yonekura', respectively. Furthermore, eucalypt, \(\alpha\)-copaene, terpinen-4-ol, and \(\alpha\)-muurolene, along with solely detected (E)-\(\beta\)-ocimene, and \(\delta\)-cadinene, were accounted for in 'Yonekura', with a greater total concentration (5.76 ug/100 mL). 'Yonekura' also had remarkably higher concentrations of ketone components, such as 4-methoxy-2,5-dimethyl-3(2H)-furanone, 4-hydroxy-2,5-dimethyl-3(2H)-furanone, \(\gamma\)-hexalactone, \(\delta\)-octalactone, and methyl heptone, for a total concentration up to 24.46 ug/100 mL. Additionally, the concentrations of minor components were 1.12-4.61 ug/100 mL for carboxylic acids, 0.71-4.08 ug/100 mL for aldehydes, and 0.93-1.62 ug/100 mL for hydrocarbons.
## 3 Results
### Sample Characterization
Figure 11 depicts the fractography of samples in different magnifications. No residual layer structures introduced during the AM process were visible. Furthermore, no microcracks were observed, resulting in a smooth fracture surface. As a result of the successful consolidation process, no residual porosity was found (see Figure 11c). In order to inspect the minimum wall thickness, a highly macroporous gyroid-type design was printed. Figure 12 illustrates that sintered silicon nitride-based ceramics can be manufactured with a wall thickness of 265 \(\upmu\)m without visible crack formation. The scaffold designs with a wall thickness up to 100-300 \(\upmu\)m are commonly used for biomedical investigations [44]. For that reason, studies towards further reduction of minimum wall thickness to well below 200 \(\upmu\)m are currently underway. Figure 11: Scanning electron micrographs of biaxial flexural disc fracture surfaces of sintered silicon nitride-based ceramic specimens, in different magnifications with a scale bar of (**a**) 1 mm; (**b**) 200 \(\upmu\)m; (**c**) 20 \(\upmu\)m.  
Figure 10: Biaxial flexural strength of sintered silicon nitride-based ceramics. The thermal shock resistance of the silicon nitride-based ceramics generated by LCM was investigated by water quenching from 800 \({}^{\circ}\)C to room temperature. The microstructures of these test bars can be seen in Figure 13a (before water-quenching) and Figure 13b (after water-quenching). Despite the high thermal stress, no visible microcracks or other microstructural changes such as oxidation were observed. Thermal conductivity of conventional SiAlON grades typically varies between 20-40 Wm\({}^{-1}\)K\({}^{-1}\) for [3,43]. The thermal conductivity of the printed silicon nitride-based ceramic parts within this study was measured as 28.2 (\(\pm\) 0.4) Wm\({}^{-1}\)K\({}^{-1}\) using the laser flash method, which is a reasonable value for industrial applications, such as high frequency circuit substrates with ultimate thermal requirements. The XRD analysis of the silicon nitride-based ceramics is shown in Figure 14. According to diffractogram by far the largest part of the sintered ceramic consists primarily of \(\beta\)-SiAlON. The sample also contains traces of a pure Y\({}_{2}\)O\({}_{3}\) and pure Si\({}_{3}\)N\({}_{4}\). These findings are not surprising since yttria is the most prominent sintering aid responsible for the formation of the glass phase. Due to the nature of liquid phase sintering, it is possible to find traces of pure Si\({}_{3}\)N\({}_{4}\).
## 2 Materials and Experimental Procedures
### Fatigue Tests  
Fatigue crack propagation tests were carried out, in agreement with ASTM E647 standard [28], using 4 and 8 mm thick compact specimens (CT). The tests were performed under load control at room temperature using a 100 kN capacity servo-hydraulic Instron 1341, with a frequency within the range 15-20 Hz and stress ratios of \(R\) = 0.05 and 0.4. The specimen's geometry and dimensions are shown in Figure 1c. For both specimens' batches two types of tests were conducted: constant amplitude loading tests with the stress ratios \(R\) = 0.05 and 0.4 and variable amplitude loading tests in which periodic overload blocks of 300 cycles are applied with intervals of \(N_{int}\) cycles, as shown schematically in Figure 3. The main purpose of these tests is to obtain the \(a\)-\(N\) and \(da/dN\) curves as a function of the stress intensity factor range \(\Delta K\) to analyze the effects of the shot peening, specimen thickness and stress ratio.  
Figure 3: Scheme of variable amplitude loadings with periodic overloading blocks.  
Figure 2: Microstructure micrographs: (**a**) base material; and (**b**) shot peening (SP) sample.  
For the surface polished specimens, the surface crack length was measured using a travelling microscope (45\(\times\)) with 10 \(\upmu\)m accuracy. Crack growth rates under constant amplitude loading were determined by the incremental polynomial method using five consecutive points [28]. For the surface peened specimens, the crack length was obtained by using experimental calibration curves based on the compliance variation, previously obtained with the polished specimen's tests, considering the compliance (C) definition and ratio of displacement to load increment (Equation (1)).  
\[C=\frac{(u_{\text{max}}-u_{\text{min}})}{(P_{\text{max}}-P_{\text{min}})} \tag{1}\]  
where \(u\) and \(P\) are the axial grip displacement and the load, respectively, which were monitored during the test. From the non-peened specimen tests with constant amplitude loading, it was monitored a set of data for \(C\) calculation and the correspondent values of the crack length. The collected data are plotted in Figure 4, in terms of the crack length (\(a\) in mm) versus the compliance, and fitted by Equations (2) and (3) for specimens with 8 mm and 4 mm thickness, respectively, both with a 0.99 correlation factor. Equations (2) and (3) were afterwards used for the evaluation of the crack length in the tests with peened specimens and in the periodical overloading block tests.  
\[a=39,515\times C^{5}-53,199\times C^{4}+27,962\times C^{3}-7278.2\times C^{2}+ 980.36\times C-23.559 \tag{2}\]  
\[a=2905.2\times C^{5}-6399\times C^{4}+5393.9\times C^{2}+2275.3\times C^{2}+50 2.82\times C-16.62 \tag{3}\]
## 3 Numerical Results
### Impact of the Backhaul Capacities  
We analyze the impact of the backhaul capacities on the algorithms' performance in Figure 2, meanwhile illustrating the average power consumption and average delay cost. As expected, increasing backhaul capacities not only reduces average power consumption but also decreases average delay cost. We observe that if the number of active MECSs equals to 0, all contents will be downloaded with the rate \(C_{o}\) from the CN, so \(T_{Aver}=\frac{U(t)L}{C_{b}}\). If the number of active MECSs equals to \(\infty\), the MECSs are enough to ensure that each content can be downloaded from the MECS, so the transmission rate is always \(C_{MEC}\), and thus \(T_{Aver}=\frac{U(t)L}{C_{MEC}}\). On the other hand, for a low value of backhaul capacities increasing the MECS's power consumption, we can reduce the average delay cost effectively when the system is in overloaded conditions, as the MECS can serve more requests. However, if excessively active MECSs cache a lot of unpopular content, it brings about a bad performance in effectively reducing average delay cost. Figure 2 explicitly demonstrates the trade-off between the average delay cost and the average power consumption for given backhaul capacity.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Number of MECS & \(M_{server}=15\)\(\sim\)20 \\ Number of MU & \(MUL=10\)\(\sim\)50 [25] \\ Radius of the MEC range & \(R=100\) m \\ Number of alternative contents in total & \(F=1000\) \\ Storage capacity of one MECS & \(C_{z}=3\)\(\sim\)10 GB \\ Size of each content & \(L\)\(=50\)\(\sim\)100 Mb [23] \\ Maximum transmission rate of MEC to MU & \(C_{MEC}=2\) Mbps \\ Maximum transmission rate of CN to MU & \(C_{Back}=1\) Mbps \\ The backhaul capacity of BS & \(C_{bmax}=15\)\(\sim\)30 Mbps \\ Noise power & \(\delta^{2}=-102\) dBm [7; 33] \\ BS transmit rate requirement & \(C_{o}=2\) Mbps \\ Contents Request Pattern & \(\alpha=05\)\(\sim\)16 [7; 10; 31; 32] \\ Power of system & \(P_{Op}=800\) W [35] \\ Power of one MECS & \(P_{Server}=200\) W \\ Transmit power of BS & \(P_{Trans}=20\) W \\ Transmit bandwidth W & 10 MHz \\ Path-loss exponent \(\alpha\) & 4 [10] \\ Trade-off weight & \(\omega=0.5\)[34] \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of main simulation parameters.  
Figure 2: System joint performance versus backhaul capacity.
# Experimental Study of the Vibration of the Spot Welding Gun at a Robotic Station
## 2 Related Work
Hence, the analysis of dynamics in the flange and tool area is essential, as it provides insights into the impact of loads and tool design on the robot-tool system's dynamic lifespan. This kind of study can allow for minimizing power consumption. Doingso is one of the postulates of Industry 4.0, which sees the decrease of energy consumption and the growth of smart production being taken into consideration in the designing of state-of-the-art production systems [21]. Improvements in the efficiency of resource usage can be achieved by analyzing energy consumption, equipment reliability, maintenance frequency and operating costs, in addition to life cycle evaluation [22; 23]. Several research studies have analyzed the effect of vibrations on the dynamic performance of robotics manipulators. Among the topics is the modal analysis of robot manipulators; in [24], the effect of flexibility on the dynamics of a two-arm planar manipulator with two revolute joints is considered, while, in [25], authors focus on robotic manipulators with rigid links and flexible joints. Researchers are also expanding issues related to the control of robot manipulators with flexible elements [26; 27; 28; 29; 30; 31]. Another solution, as shown in [32; 33; 34], is trajectory optimization, which leads to a reduction in robot vibration. Such research is being conducted not only by researchers [35; 36], but also by robot manufacturers, as exemplified by FANUC. They are proposing a solution, based on removable sensors that allow for optimization of the welding process through vibration analysis. A potential solution to the problem presented could be the use of trajectory smoothing through trajectory planning methods. An example of such a solution can be the S-curve method, based on a segmented sigmoidal function [37]. According to the author of [38], a method to reduce residual vibrations by up to 90% is to shape the input signal. The publication [39] also confirms the desirability of conducting this type of research. The problem of vibrations of industrial robot manipulators exists (despite the efforts of manufacturers), and ongoing research work shows that it is possible to improve the quality of technological processes by reducing them. Most scientific publications focus on machining processes (cutting, deburring). Optimization of the robot's path of motion in a given technological process is essential for productivity, and it is necessary to carry it out in every new application. Due to the nature of the problem, publications tend to focus on one of the mentioned aspects and deal with different processes. Although the number of publications on the welding process is limited, the problem exists as confirmed by the robot manufacturers and integrators responsible for installing new robotic stations.
## 4 Anomaly Detection
### Hidden-State Representation  
Hidden-state space modeling of multivariate time series is one of the most important tasks in representation learning by dimensional reduction. In this work, we propose that the anomaly detector is a thresholding marginal log-likelihood with Bayesian non-parametric HMM, which leads to tackling the anomaly detection problem in a more natural way to meet the need for real-world applications.  
As discussed above, the sHDP-VAR-HMM interpreted each observation \(y_{t}\) by assigning it to a single hidden state \(z_{t}\), where the hidden-state value is derived from a countably infinite set of consecutive integers \(z_{t}=k\in\{1,2,3,\ldots,K\}\). We denote \(\Theta_{s}\) to represent all the parameters of the trained sHDP-VAR-HMM from nominal demonstrations, including the hyperparameters for learning the posterior and the parameters for the observation model definition. Our anomaly detector performs anomaly detection by comparing the marginal log-likelihood \(log\ p(y_{t}|\Theta_{s})\) based on current executing skill of the observation \(y_{t}\) with a learned threshold \(\rho(z)\) that derives from the most likely estimated hidden state given current observations.  
\[z=\operatorname*{arg\,max}_{1,\ldots,K}\ p(z_{t}|y_{t},\Theta_{s}). \tag{16}\]  
Here, the \(z\) would be included variable values; that is, we should synchronously update the threshold in the same skill period. Let us say the definition of an anomaly data point at any time during the manipulation is one that \(logp(y_{t}|\Theta_{s})<\rho(z)\), otherwise it deems the manipulation to be nominal.  
The proposed method generates the mapping pairs of marginal log-likelihood and the most likely hidden state at any time. In specific hidden state \(z\), we calculated the fixed log-likelihood threshold from testing the nominal demonstrations by subtracting half the range between maximum and minimum from the minimum. Unlike the anomalous log-likelihood deviated by a certain standard deviation from the mean in [9; 28], that is \(\rho(z)=\mu-c\sigma\), where the symbol \(c\) is a constant value for adjusting the sensitivity of the detector, \(\mu\) and \(\sigma\) are the estimated mean and standard deviation of the log-likelihood. Our implementations do not need to take the constant \(c\) into consideration for each skill or hidden state.
# Recent Advances in Plasmonic Sensor-Based Fiber Optic Probes for Biological Applications
## 6 FOPS Geometrical Configuration and Techniques
### Specialty Fibers  
The specialty fiber configurations of this group are non-conventional fibers. In this line, the polarization-maintaining optical fibers support two linearly polarized modes, namely, slow and fast polarizations. When one of the polarized modes is aligned with the metal layer, the other modes tend to exhibit the SPR mode. A polarization mode fiber sensor has been demonstrated with refractive index resolutions better than 4 \(\times\) 10\({}^{-6}\) RIU [69]. Over the years, in order to enhance the sensitivity of the fiber sensors, there has been continuous engineering done on the geometry of the fiber. This has led to notable advances in sensor technology.  
Recently, microstructured optical fibers (MOFs) have attracted more interest due to their usefulness in design engineering. MOFs are mostly modeled with air holes in the cladding region surrounded with metal over the core region, as shown in Figure 7g.h [70]. The several SPR-based FOPs reported so far have been classified into five categories, namely, nanowire based, micro-fluidic slotted based, internal metal coated, external sensing type, and D-shaped. In the mentioned techniques, the sensitivity has been achieved by coating the metal in the air holes and filling the analyte selectively in the cladding air holes. By engineering the geometric structure of the MOFs with various structural parameters, a maximum sensitivity of 21,700 nm/RIU has been achieved [71]. In this line, several different configurations, namely, three-hole, multi-core, grapefruit, and higher-order asymmetric MOFs have been demonstrated [72; 73; 74; 75; 76; 77; 78; 79].  
Furthermore, polymer plastic MOFs have been used for plasmonic generation in achieving the biocompatibility [80]. The U-shaped plastic optical fiber (POF) with long period grating exhibits better sensing performance of 1130%/RIU with a resolution of 8.44 \(\times\) 10\({}^{-4}\) in the RI range of 1.33 to 1.41 [81].
# Effect of COVID-19 on Selected Characteristics of Life Satisfaction Reflected in a Fuzzy Model
###### Abstract
In the case of fuzzy sets, a feature value may only belong to the set to some degree, allowing for a more tailored description of the value of diagnostic uncertainty. In this way, descriptive information typical for humans is converted into numerical information (e.g., about the represented range of numbers) that is possible to process by computational systems. The process of converting values from the domain of real numbers to values from the domain of fuzzy sets is called fuzzification, and the reverse process is defuzzification. It allows a certain freedom of value operation and information exchange in the system-human relationship. From the perspective of constructing a fuzzy system, the following steps are crucial:  
* definition of the task and how it can be accomplished using fuzzy sets;
* definition of linguistic variables and their fuzzy equivalents;
* definition of membership functions;
* definition of a set of fuzzy rules for these variables;
* choice of defuzzification method. Thus, a clinical decision support system based on Mamdani-type fuzzy data using clustering and pivot tables can help to make clinical decisions concerning five groups of diseases [34] or musculoskeletal disorders [35]. The current study is a step toward developing computational patient models to support medical professionals in diagnosing burnout. Digital patient twins and artificial intelligence-based optimization can play a key role in improving the accuracy, efficiency, and safety of such computational tools and supporting the fulfillment of constraints in patient-specific solutions [36; 37]. The novelty of the proposed approach lies in the combination of the use of data from validated clinimetric tests with the collection of data from characteristic time points and the way in which they are analyzed using fuzzy logic through transparent and scalable hierarchical models. To date, this approach is unique and has no equivalent in the literature. The aim of this study is twofold: (1) to develop a fuzzy model reflecting changes in life satisfaction test scores as influenced by the COVID-19 pandemic, and (2) to develop guidelines for further research on more advanced models that are clinically useful.
# Cellulose Nanofibers and Other Biopolymers for Biomedical Applications. A Review
## 2 Nanocellulose and Other Cellulose Derivatives
nanocellulose-based bioinks can be based purely on nanocellulose [39] or by combining it with different biopolymers, such as polyethylene glycol [40], gelatin methacrylate [41] or alginate [42]. The use of nanocellulose in biomedical applications requires a full evaluation of its biocompatibility, including parameters, such as cytotoxicity, skin irritation, skin sensitisation and genotoxicity. [PERSON] et al. [43] reported extensively on the biological impact of nanocellulose to analyse the potential hazard of nanocellulose. In this review, the cytotoxicity, inflammation, oxidative stress and genotoxicity were evaluated. The conclusion obtained was that there is a disparity between the different studies, as some authors manifest the benign nature of nanocellulose, and others emphasise the potential for adverse effects. These differences can be attributed to the variation of different studies in cell systems, starting material, treatment, characteristics and concentration of nanocellulose. Several authors reported the low toxicity potential and the benign nature of these materials, and therefore, their suitability for biomedical application, such as tissue engineering [44; 45; 46; 47; 48]. On the other hand, other authors have reported the toxic nature of nanocellulose when present at high concentration (\(>\)250 \(\upmu\)g/mL) or when it is chemically modified [49; 50; 51]. Therefore, it is concluded that several types of nanocellulose (BNC and various types of CNF and CNC) do not present cytotoxicity or genotoxicity against a series of cell lines. However, chemically modified nanocellulose (e.g., carboxymethyl and periodate oxidation) can induce inflammation and may cause chronic complications [52]. The cytotoxicity of these nanocellulose can be reduced by subjecting the cellulose fibers to an alkaline autoclave treatment to obtain an ultrapure nanocellulose with an endotoxin level of \(<\) 50 endotoxin units/g [53]. The initial inflammatory and coagulation responses of ultrapure nanocellulose have been evaluated using a lepirudin-based human whole blood model showing a coagulation, complement and cytokines activation improving the selection possibilities in several treatments' situation of wound healings.
# Fiber Orientation Reconstruction from SEM Images of Fiber-Reinforced Composites
## 2 Materials and Methods
### Methods for Describing Fiber Orientation  
As shown in Figure 3, the fiber orientation information comprises two angles [28]: the fiber deflection angle \(\phi\) and the azimuth angle \(\varphi\), where the deflection angle is the angle  
Figure 2: SEM-based 3D reconstruction method for fiber orientation measurement.  
between the fiber axis and the reference plane vertical line in the range of \(0^{\circ}\leq\phi\leq 90^{\circ}\). The azimuth angle is the angle between the projection of the fiber axis on the reference plane and the horizontal direction in the range of \(-90^{\circ}\leq\varphi\leq 90^{\circ}\) (counterclockwise is positive).  
As observed in Figure 3, the abovementioned fiber deflection angle \(\phi\) and azimuth angle \(\varphi\) represent the fiber orientation information in general. In a Cartesian coordinate system, fiber orientation information may alternatively be given by the unit vector \(t=(t_{x},t_{y},t_{z})\) in the fiber's axial direction, where  
\[\left\{\begin{array}{l}t_{x}=\sin\varphi\cos\phi\\ t_{y}=\sin\varphi\sin\phi\\ t_{z}=\cos\varphi\end{array}\right. \tag{1}\]