esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
In this research, a novel \(10N-9\) phase-shifting algorithm was derived. The new algorithm utilizes the Discrete Fourier Transform function and polynomial window function. The \(10N-9\) algorithm  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **PV (nm)** & **RMS (nm)** \\ \hline New \(10N-9\) & 513.587 & 2.05 \\ FUJINON interferometer & 524.376 & 2.33 \\ \hline Error & 10.789 & 0.28 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The comparison of different methods.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Phase-Shifting Algorithm** & **Standard Deviation (nm)** \\ \hline Synchronous detection & 27.61 [PERSON] (1984) \\ [PERSON]’s \(2N-1\) & 8.93 [PERSON] (1984) \\ [PERSON]’s \(2N-1\) & 8.19 [PERSON] (1984) \\ [PERSON]’s \(5N-4\) & 4.14 [PERSON] (1984) \\ New \(10N-9\) & 2.05 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Standard deviations of algorithms.  
can be applied to the surface profile measurement of the highly reflective silicon wafer because of the strong suppression ability of the coupling errors occurring between the phase shift error and the higher harmonics. The performance of the \(10N-9\) algorithm was represented in the frequency domain, and the sidelobe value was also calculated and discussed in comparison with other conventional algorithms. The error analysis results show that the new \(10N-9\) algorithm has a stronger error suppression ability than other algorithms. Finally, the surface profile of a four-inch silicon wafer was measured using the \(10N-9\) algorithm and a wavelength-tuning Fizeau interferometer. According to the experimental results, the repeatability measurement error of the surface profile was \(2.05\) nm, and the entire measurement uncertainty was \(23\) nm, which indicates that the new \(10N-9\) algorithm outperforms the conventional phase-shifting algorithm.  
Conceptualization, Methodology, Software, and Writing (original draft preparation), F.M.; Methodology, Investigation, Software, and Writing (original draft preparation), Y.K.; and Supervision and writing (review and editing), S.A. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, grant number 2017R1D1A1B03034853.  
The authors declare that there is no conflict of interest.
## 3 Results and Discussion  
### Oxidation Quantity vs. Oxidation Temperature  
A typical example of the protocol with thermogram used in our study is shown in Figure 2a. Changes in FID signals, transmittance, absorbance and temperature are relatively indicated and one can see that a split point is located between EC1 and EC2 fractions. The curve in Figure 2b was adopted from another study [21] in order to explain the oxidation quantity of carbon graphite contents increased with temperature. At low temperatures, between 400 \({}^{\circ}\)C and 500 \({}^{\circ}\)C, the oxidation quantity is very small, while the oxidation quantity increases from 500 \({}^{\circ}\)C to 800 \({}^{\circ}\)C, where EC1, EC2, EC3 and EC4 are quantified as shown in Figure 2a,b. EC losses may be more pronounced with NIOSH type protocols because the temperature of the inert He phase is higher than IMPROVE as can be seen in Table 1. OC and EC concentrations are affected by temperature profiles. For example, EC concentrations analyzed using IMPROVE were larger than those analyzed using NIOSH 5040 [22]. Particularly in He-Ox phase, the temperature profiles of IMPROVE is different from that of NIOSH 5040. The EC concentrations would result in larger values if IMPROVE protocol were employed in this study.  
Reducing the temperature of inert He phase may minimize split time issues, although OC does not completely evolve sometimes. It is difficult to determine which protocol is proper to quantify OC and EC because there is no standard reference materials for EC.  
Figure 2: **(a)** An example of relative profiles of temperature, transmittance, absorbance and FID signals used in this study; **(b)** Carbon oxidation quantity as a function of temperature. The curve was adopted from another study [21].
# Electrospinning of Essential Oils
## 1 Introduction
In the second enzymatic step, cinnamate 4-hydroxylase (C4H) catalyses the hydroxylation of cinnamic acid to \(p\)-coumaric acid, which is converted into coumaroyl-coenzyme A (coumaroyl-CoA) by 4-coumaroyl CoA ligase (4 CL). Coumaroyl-CoA is the intermediate for the generation of various phenylpropanol compounds, such as eugenol, myristicin, methyl cinnamate, chavicol, methyl clavicol, and estragole [11; 23]. The chemical components of essential oils are volatile and susceptible to easy degradation due to temperature, light, oxygen, and moisture. Hence, encapsulating systems, such as micro- and nano-particles, capsules, droplets, cyclodextrin complexes, and fibres, are required to deliver still-functional EOs to a specific target and in a controlled fashion [24; 25; 26]. This review analyses the current state of the art on the release of EOs from polymeric fibres produced by electrospinningning. After giving an overview of the electrospinning process, the review discusses in detail the research conducted so far on the incorporation of essential oils of cinnamon, oregano, peppermint, clove, thyme, and lavender in polymeric electrospun fibres. These EOs have been selected because the properties of the resulting fibres have been widely discussed in the literature.
# A Large Bridge Traffic Operation Status Impact Assessment Model Based on AHP-Delphi-SVD Method
## 4 Theory and Method of Traffic Operation Status Evaluation
### Ahp  
The Analytic Hierarchy Process (AHP) decomposes elements related to decision-making into levels such as goals, criteria, and plans, and makes decisions based on these levels. The method is concise and flexible. The Analytic Hierarchy Process (AHP) is a comprehensive evaluation method that combines qualitative and quantitative analysis. Its main idea is to decompose complex problems into several levels and compare the importance of each two indicators, create a judgment matrix, and calculate the weight values of different factors in the system by calculating the eigenvalues and eigenvectors of the judgment matrix [15].  
In the impact assessment of bridge traffic operation status, according to the theory of AHP, the influencing factors of the bridge components to be evaluated can be divided into \(n\) different levels of factor sets, \(U=\{u_{1},u_{2},\ldots,u_{n}\}\). The corresponding weight set is \(W=\{w_{1},w_{2},\ldots,w_{n}\}\).  
In addition, in order to effectively evaluate the overall impact of the bridge on traffic, based on the importance of the impact on traffic, the overall traffic impact of the bridge is divided into two categories: the results of the main structure inspection and the results of the bridge deck system inspection. As shown in Table 8:  
Establish a comment set \(V=\{v_{1},v_{2},\ldots,v_{n}\}\) based on the above table. Based on the factor set and comment set, define the evaluation result set \(R=\{r_{1},r_{2},\ldots,r_{n}\}\), which includes:  
\[r_{i}=\sum_{j=1}^{n}\sum_{k=1}^{m}v_{ij}w_{jk} \tag{6}\]  
In the formula, the selection of weight vector \(W\) is carried out using the Delphi method.
## 2 Materials and Methods
### Data Acquisition and PAT Calculation  
All data were acquired by the BioSign HRV-Scanner plus Study version 3.05 (BioSign GmbH, Ottenhofen, Germany). The ECG, PPG, and respiratory signals were stored in 16-bit raw files. The signals were sampled at 1 ms (ECG), 2 ms (PPG), and 20 ms (breathing signal) temporal resolution. The data pre-processing and the computation of the PATs and HRA parameters were performed by the HRVScan_Merge v3.2 software. This software was developed by the last author ([PERSON]), and it has been used in previous studies [5; 7]. The signals were cubic spline interpolated to 1 ms resolution, then automatically filtered with a 21 ms wide, single pass moving average filter and a 1001 ms trend removal filter, which subtracts the moving average of the previous and succeeding 500 points [34]. Applying adequate filters is especially important to reduce noise and baseline fluctuations, thus increasing the detection accuracy.  
We used the \(\frac{1}{2}\)-amplitude height of the ascending R-waves (R1/2) on the ECG, as the starting point of the PAT calculation. The detection accuracy is superior at this point, compared to the peak detection methods [35]. We defined eight different points on the PPG signal as a reference for PAT calculation. The software detected the local positive peaks (T\({}_{3/3}\)), the local minima (T\({}_{\rm LM}\)), and the 1/3-1/2-2/3 amplitude heights (T\({}_{1/3}\), T\({}_{1/2}\), T\({}_{2/3}\)) between the previous two points. The program computed the extrapolated base points by the intersection of the actual linear regression line on the T\({}_{1/3}\), T\({}_{1/2}\) and T\({}_{2/3}\) points and the heights of the preceding local minima. Additionally, the PATs were calculated at the peaks of the first derivative (T\({}_{\rm diff}\)) as well as the smooth derivative (T\({}_{\rm smdiff}\)) [36] of the PPG signal (Figure 3).  
\begin{table}
\begin{tabular}{c c c c c} \hline n & Age (Years) & Gender & Body Height (cm) & Body Weight (kg) \\ \hline
35 & 25.4 (\(\pm\)2.9) & 21 female/14 male & 174.4 (\(\pm\)9.5) & 67.2 (\(\pm\)13.2) \\ \hline \end{tabular}
\end{table}
Table 1: The characteristics of the included subjects. Values presented as mean \(\pm\) standard deviation, if applicable.  
Figure 2: Composition of the final study group.  
The beat-to-beat time intervals between the corresponding T\({}_{1/3}\) and T\({}_{2/3}\) points were computed (T\({}_{1/3}\)T\({}_{2/3}\)), which are inversely proportional to the steepness of the ascending slope (Figure 3). The derivative as the slope of the PPG signal would not give comparable values since the PPG amplitude is not calibrated or standardized. The HRVScan_Merge v3.2 software automatically calculated the eight different PATs (between the R1/2 and the given PPG reference points) and the T\({}_{1/3}\)T\({}_{2/3}\) intervals at each cardiac cycle.
# Application of Digital Image Analysis for Assessment of Star Content and Distribution in Potatoes
## 3 Results and Discussion
The analysis of the range values did not allow the distinguishing of any homogeneous groups. This is not surprising, as it means that the potato tubers contain starch-rich areas and areas devoid of starch. The range of pixel brightness in all the samples is nearly identical and varies from 51.9 to 63.2. An analysis of skewness shows that the starch distribution in potatoes is right-skewed and skewness values are positive and range from 0.18 for cv. Melody to 0.36 for cv. Lord. This coefficient enabled us to distinguish four homogeneous but overlapping groups, so it cannot be used to distinguish between the potato cultivars. The most interesting results concern the coefficient of variation. This value is associated with the pattern of starch distribution along the measurement lines, irrespective of the total starch content. The analysis of this coefficient allows the distinguishing of all the studied cultivars except Satina and Allianz, which formed a homogeneous group. This suggests that the patterns of starch distribution in potato tubers are cultivar-specific. Figure 3 shows some light-colored areas in the tuber interior, where less starch is accumulated than under the skin. The shape of the light-colored area is cultivar-specific. A similar study was performed by [PERSON] et al. using images taken with ImageJ software [27]. We also investigated the correlation between the starch content assessed chemically and the mean pixel brightness (Figure 5). The coefficient of correlation was high and reached \(-\)0.93, and the formula for estimating the starch contents of potatoes on the basis of the mean pixel brightness is:  
\[S=28.84-0.26\cdot Px \tag{1}\]  
where:  
\(S\) = starch content in potato [%]  
\(Px\) = mean pixel brightness determined by digital image analysis. An ANOVA was performed for the basic statistics of pixel distribution along the measurement lines, depending on the potato storage conditions (Table 5). All the analyzed coefficients significantly differed between the two groups. The very high mean, median, and range values allow the distinguishing of frozen potatoes from cold-stored potatoes. Figure 5: Dependence of starch contents of potatoes on mean pixel brightness assessed using digital image analysis, R\({}^{2}\) = 0.86. The storage of potatoes in a freezer results in a significant decrease in the starch contents of the tubers, so the mean values and medians of the starch contents of the potatoes allowed us to distinguish two homogeneous groups. It is noteworthy that the freezing of potatoes does not cause a uniform transformation of all the starch deposits into simpler sugars [22], but influences the coefficient of variation of the starch granule distribution along the measurement lines. This means that in some parts of the tubers, starch is decomposed faster to simpler sugars than in other areas.
## 4 Environment Perception, Mapping, and Representation for Locomotion
### Other Relevant Sensor Technologies for Navigation in Non-Snake Robots  
Multiple examples of robots with shapes and propulsion mechanisms others than snake robots, and which use different sensor modalities from the ones introduced above, are common in literature. Structured light sensors based on temporal coding are seldom used for navigation because of their limitations to cope with movement, either due to dynamic elements in the environment or with a moving sensor. However, an example of a moving robot for search-and-rescue applications is introduced in [71]. The work in [72] focuses on improving the performance of SLAM using RGB-D sensors in large-scale environments. The use of stereovision for mapping and navigating in very challenging outdoor scenarios is shown in [73]. A single camera together with an IMU is demonstrated to be enough to produce a map and calculate a pose estimate at 40 Hz in a micro aerial vehicle (MAV) in [74]. One of the most relevant limitations in MAVs is the constraints to the size, power, and weight of the payload. These constraints are addressed by [75] by designing and building a radar sensor based on ultra-wide band (UWB), used for mapping and obstacle detection.  
**Remark 3**: _Knowledge about the environment and its properties, in addition to its geometric representation, can be successfully exploited for improving locomotion performance for obstacle-aided locomotion._  
While knowledge about the environment's geometry might seem an obvious requirement for obstacle avoidance, other kinds of interaction with the environment--including obstacle-aided locomotion--require some further task-relevant knowledge about the environment. From a cognitive perspective, this has been acknowledged by the robotics community by the creation of _semantic maps_[90], which capture higher-level information about the environment, usually linked or grounded to knowledge from other sources. For the \(T^{2}\) Snake-2s robot [64], the authors also claim that for planning the trajectory, they require the nature of the surrounding obstacles to be considered, as contact with some elements (e.g., fragile, high heat, electrically-charged, or sticky obstacles) might pose a safety risk to the robot and must then be completely avoided. However, safety is not the only reason. The biologically-inspired hexapod robot in [91] represents a good example of how knowledge about the environment is exploited for enhanced navigation. Information about certain terrain characteristics is captured in the environment model, and later adopted as part of the cost function used by the RRT* planner [92]. This way, the planned trajectory considers factors such as terrain roughness, terrain inclination, or mapping uncertainty.
## 4 Comparative Analysis of the Bending Moment of the Pile Body
### Comparison of the Bending Moment between the Straight and Inclined Pile Group  
The moment envelope of the straight and inclined pile group in non-liquefied sand is analyzed, as shown in Figure 14. The bending moment of the middle part of the pile body is small, but the bottom and top of the straight and the inclined pile groups have a large bending moment; both of which have similar laws. However, according to the distribution law of the overall bending moment, the maximum positive and negative bending moments at each section of the inclined pile group are slightly different, while the bending moment distribution of the straight pile group is obviously different. The bending moments at the top of the straight and inclined pile groups are the same, but the bending moment of the straight pile group at the bottom of the pile is obviously greater than that of the inclined pile group. Therefore, the straight pile group may be more prone to damage under an external load in the non-liquefied sand.  
Figure 15 shows the moment envelope of the straight and inclined pile groups in the 300 mm saturated sand. The bending moment of the middle part of the pile body is small, and the bottom and top of the straight and the inclined pile groups also have a large bending moment, both of which also have similar laws. However, in terms of the distribution law of the overall bending moment of the pile body, the bending moment envelope diagram of the straight pile group is \"X-shaped\", and that of the inclined pile group is in the shape of a vase. Their bending moments are essentially similar in the middle part of the pile body. At the top of pile, the bending moment of the inclined pile group  
Figure 14: Comparison of the bending moment envelope diagrams of straight oblique pile groups in non-liquefied sand: (**a**) No. 1 pile; (**b**) No. 2 pile.  
is slightly larger than that of the straight pile group, while that of the straight pile group bottom is obviously larger than that of the inclined pile group bottom. By a comprehensive comparison of the No. 1 pile and No. 2 pile, the bending moment of the straight pile group is 3-4 times of that of the inclined pile group at the bottom section of the pile.  
Figure 16 shows the moment envelope of the straight and inclined pile groups in the 380 mm saturated sand. The upper half of the bending moment of the straight pile group is generally larger than that of the inclined pile group, but the difference is not particularly obvious. The change trend is essentially same, and the most obvious section is still located at the bottom of the pile. The No. 2 pile is the most obvious. At the bottom section of the No. 2 pile, the maximum bending moment of the straight pile group is 33.08 N-m, while the inclined pile group is only 5.26 N-m. This shows that the inclined pile group has a better bending resistance under the same lateral dynamic load.
# Context-Aware End-User Development Review
## 4 Application Building Techniques
### Visual Language-Based Flows  
Visual language-based flows augment the interaction for connecting components in sequence or parallel. End-users create flows through graphical components rather than using only commands or actions. The development tool includes an enhanced visual representation of each component (e.g., a block representing a programming construct). It also enhances visual aids for flow validation, such as shapes for compatible components, connections, or actions.  
MNFL (the Monitoring and Notification Flow Language), presented by [PERSON] [58], is a graphical flow language to build monitoring applications, i.e., an application that uses data from sensors and cameras to detect and notify situations of interest. The semantics include building blocks to represent sensing (e.g., audio/video, number, button), computing (e.g., opposite, combine, math), and actuating (e.g., send a message, display number, beeper). The language lets us connect building blocks with inputs (at left for one variable or top otherwise) or outputs (at right). Inputs/outputs types of graphical representation include both shapes for Booleans (triangle) and numbers (trapezoid); and icons for pictures and sounds. The language also includes blocks representing data duration types, i.e., snapshot, stream, and snippet types, to represent instant, continuous, and portion-time. Diverse colors facilitate the development/execution, e.g., a red triangle means \"no\" (i.e., Boolean false), green when yes, and yellow when it is an error/not present. The language implements other visual supports, such as showing the current number when the cursor is over a trapezoid and an \"\(i\)\" icon to open the block's configuration window.  
Puzzle, presented by [PERSON] et al. [66], is a framework to build, edit, and run end-user applications on mobile devices. End-users combine visual blocks in a jigsaw-based visual editor through drag and drop. The editor supports the developer with a color help system, hints, and options [82]. A new set of interactions includes deleting by dragging a jigsaw into a trash can, intuitive taps for configuration, and further helps and simplified menus [66]. A jigsaw piece includes an input (inner circular shape on the left) and an output (outer circular shape on the right) to receive and send information. Two pieces can connect when a piece's output color matches with the next piece's input color.
# Modeling Soil Temperature with Fuzzy Logic and Supervised Learning Methods
## 2 Materials and Methods
### Machine Learning and Fuzzy Logic Models for Soil Temperature Estimation  
In this study, two approaches for predicting soil temperature at different depths (5, 10, 20, 50, and 100 cm) using air temperature and soil depth were tested: (i) fuzzy logic systems (MFIS and ANFIS); (ii) supervised machine learning algorithms (MLP, SVR, RF, KNN, and XGB). For comparative purposes, an MLR model was also included in the analysis.
## 2 Materials and Methods
### Assessment of 5-LPO and MMP Activities, PGE\({}_{2}\) Level Using Assay Kits  
These evaluations were performed on supernatants from chondrocyte cultures and homogenized joint tissue samples. After measuring each knee's maximum extension angle, we meticulously isolated sections of the femoral and tibial articular cartilage (AC) and the synovial membrane (SM). Tissue homogenates were prepared using a bead beater (Model Taco(tm)Pre, GeneResearch Biotechnology Corp., Taichung, Taiwan), an ultrasonic cell disruptor (Model KS-750, Madell Technology Corp., Ontario, CA, USA), and a radioimmunoprecipitation assay buffer (Sigma-Aldrich, USA). The supernatants obtained were then centrifuged at 16,582\(\times\)\(g\) and 4 \({}^{\circ}\)C in a cryocentrifuge (Labocene 1236 MGR, Gyrozen, Daejeon, Korea) for subsequent analysis of PGE\({}_{2}\) levels, 5-LPO, MMP-2, and MMP-9 activities. All tissue homogenates were stored at \(-150\)\({}^{\circ}\)C in an ultra-deep freezer (Model MDF-1156, Sanyo, Tokyo, Japan) pending further analysis. For the quantification of PGE\({}_{2}\) levels and the activities of 5-LPO and MMPs, we employed commercial assay kits: the PGE\({}_{2}\) assay kit (SKGE004B, Parameter(tm), R&D Systems, Minneapolis, MN, USA), the Lipoxygenase Inhibitor Screening Assay kit (760700, Cayman Chemical, Ann Arbor, MI, USA), and MMP ELISA kits (MBS494797, MBS722532, Mybiosource, San Diego, CA, USA), analyzed with a microplate reader (Sunrise; Tecan, Mannedorf, Switzerland). Results were obtained following the protocols provided with the assay kits.