esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### UHPLC-ESI-MS/MS Analysis  
UHPLC-ESI-MS/MS determinations were performed on a 1290 infinity ultra-high-performance liquid chromatograph connected to a 6460 triple quadrupole mass spectrometer (Agilent Technologies, Santa Clara, CA, USA) equipped with an electrospray ion source (Agilent Jet Stream, Santa Clara, CA, USA). Separation of the target compounds was achieved on a Zorbax Eclipse Plus-C18 RRHT column (2.1 mm \(\times\) 50 mm \(\times\) 1.8 \(\upmu\)m) integrated with a Zorbax Eclipse Plus-C18 Guard Column (2.1 mm \(\times\) 12.5 mm \(\times\) 1.8 \(\upmu\)m), both purchased from Agilent Technologies. The mobile phase consisted of 1% (\(v/v\)) formic acid in water (solvent A) and 1% (\(v/v\)) formic acid in acetonitrile (solvent B). The gradient program was as follows: 0-5 min--5% to 70% B; 5-7 min--5% B (column re-equilibration). The post run was 1 min. The column temperature and mobile phase rate were 25 \({}^{\circ}\)C and 0.3 mL/min, respectively. The injection volume was 5 \(\upmu\)L.  
The ion source was operated in the negative ion mode. The ionization parameters were as follows: nebulizer: 35 psi; gas temperature: 300 \({}^{\circ}\)C; gas flow: 10 L/min; sheath gas temperature: 325 \({}^{\circ}\)C; sheath gas flow: 10 L/min; capillary voltage: 4000 V. The ions were monitored in dynamic Multiple Reaction Monitoring (dMRM) mode. MRM transitions set for EA were 301 \(>\) 284 (quantifier, fragmentor: 100 V; collision energy: 28 eV) and 301 \(>\) 229 (qualifier, fragmentor: 100 V; collision energy: 28 eV), and for Q, they were 301 \(>\) 151 (quantifier, fragmentor: 140 V; collision energy: 20 eV) and 301 \(>\) 179 (qualifier, fragmentor: 100 V; collision energy: 16 eV). EA and Q signals appeared at a retention time (RT) of 2.6 and 3.5 min, respectively (Figure 1).  
For dMRM settings, the time window for each analyte transition was set to a \(\Delta\)RT of 1 min. The instrument control and data analysis were carried out using the MassHunter Acquisition software v.B.08 and the MassHunter Quantitative Analysis software v.B.07 (Agilent Technologies), respectively.  
Quantification was performed based on matrix-matched calibration curves, which were constructed for each type of plant material. Calibration solutions were prepared by fortifying plant extracts with EA and Q at different concentration levels (0.76-151.10 \(\mathrm{\SIUnitSymbolMicro g}\)). The calibration curves exhibited linearity with a coefficient of determination (R\({}^{2}\)) greater than 0.99. The limit of detection (LOD) and limit of quantification (LOQ) were estimated using the formula LOD or LOQ [\(\mathrm{\SIUnitSymbolMicro g}\)] = F \(\times\) SD/b, where F is a factor of 3.3 or 10 for LOD and LOQ, respectively; SD is a standard deviation of the intercept of the regression line; b is a slope of the regression line.  
Figure 1: Examples of UHPLC-ESI-MS/MS data for analysis of (**A**) EA standard solution, (**B**) Q standard solution, (**C**) chokeberry extract, (**D**) sea-buckthorn extract, (**E**) hawthorn extract, and (**F**) blackthorn extract.
###### Abstract  
The utilization of modal frequency sensors is a feasible and effective way to monitor the settlement problem of the transmission tower foundation. However, the uncertainties and interference in the real operation environment of transmission towers highly affect the accuracy and identification of modal frequency sensors. In order to reduce the interference of modal frequency sensors for transmission towers, a Kriging surrogate model is proposed in this study. The finite element model of typical transmission towers is created and validated to provide the effective original database for the Kriging surrogate model. The prediction accuracy and convergences of the Kriging surrogate model are measured and confirmed. Besides the merits in computational cost and high-efficiency, the Kriging surrogate model is proven to have a satisfied and robust interference reduction capacity. Therefore, the Kriging surrogate model is feasible and competitive for interference filtration in the settlement surveillance sensors of steel transmission towers.  
Keywords:Kriging surrogate model; surveillance sensor; interference reduction; transmission tower +
Footnote †: journal: Applied Sciences
# Dynamic Impact Factor and Resonance Analysis of Curved Intercity Railway Viaduct
## 4 Numerical Analysis
### Parameter Analysis  
In order to systematically analyze the dynamic performance of bridges with varying spans, curve radius, and vehicle speeds, calculations were performed for 10-300 km/h speed (interval 2 km/h), curved radius of 400 m, 500 m, 600 m, 700 m, 800 m, and 900 m, respectively, and the bridge spans of 16-40 m (interval 0.5 m). The vertical and radial natural vibration frequencies of the bridges with different spans were 13.47 Hz to 2.16 Hz and 34.9 Hz to 5.60 Hz, respectively. The contour diagram of the vertical _IF_ of the bridge is shown in Figure 10. It can be seen that the contour diagrams of the vertical _IF_ of the bridge under different curved radii are almost the same, indicating the negligible effect of curved radius on the vertical _IF_ of the bridge within the calculation range. This also shows that within the calculation range, the curve radius has little effect on the vertical deformation of the bridge, which is also confirmed in [28]. Overall, the bridge _IF_ is within 0.6. When the first vertical natural frequency of the bridge is within the range of Equation (7), the _IF_ of the bridge can be effectively reduced (within 0.2).  
\[f_{1}^{\text{B}}=\left\{\begin{array}{l}\geq 2.16\text{ Hz}\\ \geq 0.02952v+0.1336\text{ Hz}\ (v<175)\\ \geq 0.04560v-0.1800\text{ Hz}\ (v\geq 175)\end{array}\right. \tag{7}\]  
The contour diagram of the radial _IF_ of the bridge calculated is shown in Figure 11. It can be seen that the contour diagrams of the radial _IF_ of the bridge under different curved radii are similar, which shows that the curved radius does not affect the radial _IF_ of the bridge within this calculation range. This is because although the centrifugal force will change with the radius of curvature, resulting in an increase in the dynamic deflection of the bridge, at the same time, the static calculated deflection of the bridge will also increase, and the two increases are of the same magnitude. Overall, the dynamic factor of the bridge is within 0.3. If the vehicle speed is between 135 km/h and 160 km/h, the second-order natural vibration frequency of the bridge avoids the range of 5.74 Hz to 6.90 Hz. If the  
Figure 10: Vertical _IF_ of the bridges under varying frequencies and curved radius: (**a**) \(R\) = 400 m; (**b**) \(R\) = 500 m; (**c**) \(R\) = 600 m; (**d**) \(R\) = 700 m; (**e**) \(R\) = 800 m; (**f**) \(R\) = 900 m.  
vehicle speed is between 180 km/h and 250 km/h, the second-order natural frequency of the bridge vibration avoids the range of 5.60 Hz to 7.744 Hz, which can effectively reduce the _IF_ of the bridge (within 0.2).
## 2 Materials and Methods
### Laser Welding  
In the welding process, a fiber continuous laser welding machine was used (Dongguan Sanhe, China, model: 3 HE-MF1500W, date of manufacture: 2018), and a continuous welding approach was applied. With a purity of 99.9%, argon was used as a shielding gas during welding. The weld parameters were optimized before the experiment with the Fe5 Co20 Ni20 Mn35 Cu20 HEAs powders. An ideal set of process parameters was determined and used for welding dissimilar metals of AA 6061 and UNS S33207 alloys, with a 3 mm laser spot diameter, 640 W laser power, and 8 mm/s scanning speed [39; 40]. Different parameter combinations were tested in trial runs before finalizing the best combinations [41; 42; 43]. Figure 1a shows the laser welding schematic with UNS S33207 hyper-duplex stainless steel (DSS) as the skin and AA 6061 aluminum alloy as the stinger with HEAs filler metal. Figure 1b shows the welded specimen. The laser beam was focused on the butting surface centerline of the lap-joint arrangement. This welding method prevents vaporization, which results in poor weld quality [44]. As a result, the heat generated when the laser beam fell on the HEAs area was effectively carried across the surface of the hyper-duplex steel.
## 4 Conclusions  
Evaluation of the ampacity of power cable lines placed in the ground without stabilized backfill is not a major problem. The situation changes when stabilized backfill materials with different thermal resistivity values to that of the native soil are used. In such cases, numerical calculations of the thermal states of power cable installations in these conditions are necessary. Installation users and designers are not always equipped with tools to determine precise calculations. The mathematical relationship proposed by the authors allows for the determination of the load/ampacity based on industry standards, supplemented by the known thermal and geometric parameters of materials involved in the heat exchange between cables and the environment. No specialized computer software is required to implement the correction factor. The presented results of these innovative calculations are characterized by good accuracy. The results related to the proposed calculation method can be used for other voltage levels/cable types, but under certain conditions--the cables must be of similar construction and must be laid in the same way (touching each other in a flat formation).  
Conceptualization, S.S. and S.C.; methodology, S.S., S.C. and A.T.; software, S.S. and A.T.; validation, S.S., S.C. and A.T.; formal analysis, S.C. and H.T.; investigation, S.S. and S.C.; resources, S.S. and A.T.; data curation, S.S. and A.T.; writing--original draft preparation, S.S. and A.T.; writing--review and editing, S.S., S.C. and H.T.; visualization, S.S., S.C. and A.T.; supervision, S.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(z_{s}\) & \(\rho_{\beta^{\prime}}\) & \(\rho_{zz}\) & \(I_{\rm A-bb}\), A & \(I_{\rm A-bb}\) = (\(F_{\rm Ic}\)\(I_{\rm A}\)), A & **Relative** \\
**cm** & **(K**-**m)/W** & **(K**-**m)/W** & **(from Table 3, ANSYS & **(Correction Factor** & **Error,** \\
30 & 1.0 & 0.75 & 508.8 & 493.1 & 3.1 \\
30 & 2.0 & 0.75 & 428.4 & 397.3 & 7.3 \\
30 & 2.0 & 1.0 & 410.4 & 387.1 & 5.7 \\
30 & 1.0 & 0.75 & 515.2 & 506.2 & 1.7 \\
30 & 2.0 & 0.75 & 450.8 & 436.3 & 3.2 \\
30 & 2.0 & 1.0 & 424.6 & 415.7 & 2.1 \\
30 & 1.0 & 0.75 & 521.6 & 519.4 & 0.4 \\
30 & 2.0 & 0.75 & 468.6 & 475.2 & 1.4 \\
30 & 2.0 & 1.0 & 436.0 & 444.4 & 1.9 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summary of the comparison results of the calculated ampacity values for a power cable line with a cross-sectional area of 240 mm\({}^{2}\).
# A Review of Milk Frauds and Adulterations from a Technological Perspective
## 3 Results and Discussion
### Salt
#### 3.1.5 Adulterations Involving Species Substitutions  
Adulteration caused by the replacement of milk with higher nutritional value (e.g., goat, sheep, buffalo), including exotic species such as donkey and camel, with cheaper cow milk is explained by seasonal fluctuations and lower yields of more expensive milk. This process has an overall negative economic impact and could represent an issue from an ethical, religious, or cultural perspective.  
In certain EU countries, especially those in the Mediterranean area, Eastern Europe, and the Middle East, a variety of cheeseses are prized as traditional products made from goat's milk, sheep's milk, their milk in a mixture, or buffalo milk. These sorts of cheeses generally have higher market prices and are therefore more prone to adulteration of the raw material from which they are made. There is now a growing market for non-cow's milk in some countries, particularly goat's milk, but also for other aspects such as its superior digestibility. In addition, according to studies, goat's milk may be a possible alternative to cow's milk as it is considered to be hypoallergenic in comparison with cow's milk.  
However, because the proteins are very similar, people allergic to cow's milk proteins can be affected by milk from any species, demonstrating the importance of correct labeling. Therefore, milk from any species should be listed on food labels, according to the legal requirement, since it could pose a health risk to consumers who are allergic. [3; 41; 42].  
The composition of milk from different types of farm animals differs significantly in physico-chemical indicators such as the mass fraction of proteins and fats, minerals, vitamins, enzymes, etc. An important identification criterion for the type of milk is also the polymorphism of caseins, the constituent technological components of raw milk that determine its possibility of industrial processing. In order to avoid possible fraudulent substitution of goat and sheep milk by cow's milk, it is necessary to develop analytical procedures capable of detecting such frauds and protecting consumers against misleading mislabeling [13; 41; 43].
## 5 Conclusions  
A microring resonator was demonstrated on the cost-effective polymer platform. An MMI structure which has good fabrication tolerance was designed to couple the light between the microring and the straight waveguide. The polymer material, OrmoCore, which has a low absorption loss at  
Figure 8: The schematic of the measurement setup.  
Figure 9: (a) Optical transmission spectrum of the fabricated microring resonator under TE mode; (b) Lorentzian fit of the single resonance peak.  
short wavelengths was utilized. The radius and the structure of the MMI coupler were simulated and optimized using 3D BPM in order to reduce the total loss of the microring resonator. The microring resonator was prepared using a UV Soft NIL which is considered as low cost, with high throughput and high resolution. A new material, PFPE, which has very low surface energy was utilized to fabricate the soft mold instead of the PDMS material. The thickness of the residual layer was further reduced by optimizing the proportion of OrmoCore and its thinner maT. The fabricated microring resonator worked at about 890 nm, where the water absorption loss was low, and the lasers and the detectors were also cheap. The transmission was measured by a tunable laser and a power meter. The spectrum with 335 pm FSR and 11.6 dB ER was obtained. The Q factor as high as \(2.3\times 10^{4}\) has been shown.  
Conceptualization, H.L.; methodology, Y.L.; software, H.L.; validation, Z.W., and X.H.; formal analysis, H.L. and Y.L.; investigation, M.Z.; resources, G.M. and M.Z.; data curation, H.L.; writing--original draft preparation, H.L.; writing--review and editing, Y.L. and G.M.; visualization, H.L.; supervision, Y.L.; project administration, Z.W.; funding acquisition, M.Z.  
This work was supported by the International Science and Technology Cooperation Program of China (2014 DFG32590), National High-Tech R&D Program (2012 AA040406), Natural Science Foundation of China (61405198, 61875028), National Pre-Research Foundation of China (61404130403), Dalian Science and Technology Innovation Foundation (2018J11 CY006), and Fundamental Research Funds for the Central Universities (DUT18 ZD106, DUT18 GF102, and DUT18 LAB20).  
All authors declare that they have no conflict of interest.
# Safe Path Planning of Mobile Robot Based on Improved A* Algorithm in Complex Terrains
## 4 Path Planning in Complex Terrains
### Evaluation Function  
In a single terrain environment, the robot moves at a constant speed. A shorter path length means a reduced movement time of the robot. But in complex terrain environment, we need to take the shortest movement time as the goal of path planning under the premise of ensuring the safety of the robot. Moreover, the improved evaluation function proposed in Section 3.2 has two parameters: distance cost and safety cost. The physical meanings and units of the two parameters are different so the evaluation function, which is the weighted sum of the two parameters, lacks physical meaning and is hard to understand. Therefore, the two parameters are converted into time cost so that the units can be unified and their physical meanings are clear as well.  
The distance of the path is related to the speed and movement time of the robot; the distance cost of the path can be expressed as a function of speed and time cost. The safety cost of the path is inversely proportional to the distance between the path and obstacles. If a robot is moving along the path, when it nears obstacles it needs to slow down to ensure safety. Accordingly, the safety threat from obstacles affects the speed and movement time of the robot; namely, the safety cost can be converted into time cost. Thus, the actual path cost from \(X\) to \(S\) can be expressed as  
\[\mathrm{g}(X)=\sum_{i=1}^{n}{(\frac{l_{i1}}{v_{i1}(1-p_{i1})}+\frac{l_{i2}}{v _{i2}(1-p_{i2})})}, \tag{7}\]  
where, \(v_{i1}\) and \(v_{i2}\) respectively indicate the speeds of the robot moving at the two states where the path section locates. The speeds are only relative to the terrains that the states are in; \(p_{i1}\) and \(p_{i2}\) indicate the speed reduction ratios of the robot affected by the safety cost when moving on the \(i\)-th section of the path. When the minimum distance between a state and obstacles exceeds the safe distance, the speed reduction ratio of the state is set to zero.  
The heuristic function can be expressed as  
\[h(X)=\frac{l(X)}{v}=\frac{|x-y|+\min\{x,y\}\times\sqrt{2}}{v}, \tag{8}\]  
Figure 9: Complex terrain environment model: (**a**) Terrain model; (**b**) Obstacle model.  
where \(v\) indicates the speed of the robot moving on flat land. According to Equations (1), (7), and (8), the evaluation function of the improved A* algorithm can be expressed as  
\[f(X)=\sum_{i=1}^{n}\left(\frac{l_{i1}}{v_{i1}(1-p_{i1})}+\frac{l_{i2}}{v_{i2}(1- p_{i2})}\right)+\frac{|x-y|+\min\{x,y\}\times\sqrt{2}}{v}. \tag{9}\]
## 2 Pattern Reconfigurable Antenna Structure and Design
### RF MEMS Switch Structure and Design
(**a**) 3D model; (**b**) Bias line and pad. When the position of the switch beam reaches the point \(\frac{2g_{0}}{3}\), the increase of the electrostatic force is greater than the increase of the restoring force, resulting in a rapid drop-down of the beam. The actuating voltage corresponding this position is maximum. To fully pull the switch down, the actuating voltage must be equal to or greater than the maximum voltage. The actuating voltage of the switch can be evaluated by formula  
\[V_{p}=\sqrt{\frac{2k_{e}}{\varepsilon_{0}W_{b}L_{d}}\frac{g_{0}}{3}\left( \frac{2g_{0}}{3}+\frac{t_{e}}{\varepsilon_{r}}\right)^{2}} \tag{2}\]  
where \(k_{e}\) is the effective elastic coefficient, \(\varepsilon_{0}\) is the dielectric constant in free space, \(g_{0}\) is the air gap between the RF MEMS switch beam and the Si\({}_{3}\)N\({}_{4}\) dielectric, \(\varepsilon_{r}\) is the relative dielectric constant of Si\({}_{3}\)N\({}_{4}\) dielectric, \(t_{e}\) is the thickness of the RF MEMS switch beam, \(W_{b}\) is the width of the MEMS switch beam, and \(L_{d}\) is the length of the Si\({}_{3}\)N\({}_{4}\) dielectric, respectively. The actuating voltage (approximately 7.5 V) is evaluated by using Formula (2). However, the measured actuating voltage is 15.8 V--approximately twice the calculation value using Formula (2). This is mainly caused by the incomplete release of polyimide and the inhomogeneity of thickness. When the manufacture process has a good release and flatness, the actuating voltage will decrease to the theoretical value. The quality factor [15] of the RF MEMS switch is \(Q=[4\rho t_{e}^{2}E^{-1/2}/\mu(W_{b}L_{d})^{2}]g_{0}{}^{3}\approx 1\). Thus, the pull in time of the RF MEMS switch is \(t_{s}\approx(27{V_{p}}^{2})/(4\omega_{0}Q{V_{s}}^{2})\approx 12.6\)\(\upmu\)s, where \(E\) is Young's modulus, \(\mu\) is the air viscosity coefficient between the MEMS beam and the Si\({}_{3}\)N\({}_{4}\) dielectric, and \(\omega_{0}\) is the mechanical resonant frequency, respectively. The overall structure of the antenna and the RF MEMS switches is fabricated on a 400 \(\upmu\)m-thick high resistivity silicon substrate, and then 0.3 \(\upmu\)m thickness of SiO\({}_{2}\) is formed by thermal oxidation as an insulating layer. To form the Coplanar Waveguide (CPW) transmission lines, 0.2 \(\upmu\)m thickness of Al is deposited and patterned to define DC bias pads afterward. Thin SiAl (\(\sim\)0.05 \(\upmu\)m) is patterned by lift off to form the bias lines after deposition. Plasma Enhanced Chemical Vapor Deposition (PECVD) 1500 A thickness of Si\({}_{3}\)N\({}_{4}\) is patterned on top of the bottom electrode and bias lines. One point five micron thickness of Al is evaporated as the anchors. Polyimide (as the sacrificial layer) was cut down from 5 \(\upmu\)m to less than 1.3 \(\upmu\)m by chemical mechanical polishing (CMP). The beam uses 0.6 \(\upmu\)m of SiAl. Finally, the wafer is released in a plasma dryer to avoid collapsing the membrane.
# Current Challenges Supporting School-Aged Children with Vision Problems: A Rapid Review
## 3 Overall Context of Vision Screening  
As we described earlier, there are vision problems influencing the quality of life and development of individuals. Presently, vision problems are detected by traditional and instrumental screening, often by professionals. Most detected vision problems can be resolved by corrective lenses (spectacles or contact lenses), vision training, or eye surgery [36]. Children's vision is essential when starting school; 70% to 80% of all tasks in an educational setting require good vision, yet children's vision is seldom screened at this stage [37; 38]. Many children may be disadvantaged by not enjoying the same learning conditions as children with no vision problems, or those having earlier recognized vision problems. The level of income, parental education, the policies of countries, and ethnicities are identified as factors influencing whether school-aged children benefit from vision screening or not [28; 35; 39].  
Vision screening is usually carried out using a visual attention task that requires children to judge how much of the visual world they see in clinical and non-clinical settings. During the task, the children's visual attention may drop, and they may not be able to maintain their focus [40]. If the children cannot keep their attention, they may stop looking at the given task and become bored. Consequently, their responses become less accurate and sometimes they can be misdiagnosed [41]. Occasionally, attention may drop due to other problems, such as ADHD or dyslexia [42], problems associated with vision difficulties [43; 44; 45]. Vision problems are often associated with hearing or other motoric problems, such as balance [24; 46].  
Generally, vision screening tests are carried out in primary care clinics by practitioners for checking eye health in children. Some of the tests can be supplemented by technologies. [PERSON] et al. [47] identified three categories for vision screening, namely, conventional (traditional or manual), instrumental, and computer software (see Figure 2). Her categories can be extended for vision training as well, and the second and the third categories can also overlap each other. In this paper, an additional category is differentiated (Section 3.4) for web and smartphone-based software tools; the discrimination of this category often overlaps with other computer-based programs. However, this category points to newer, more affordable, and portable possibilities that appear today.