esa-sceva/satcom-chunk-collection huggingface dataset:
These data were exploited to create a velocity map to highlight the possible subsidence phenomenon induced by excavations. Then, entropy-energy representations were applied to single buildings and building complexes. Finally, the authors concluded that future developments and the continuous increase in the quality of satellite data may allow for the practical application of such information for SHM, leading to a low-cost automated process for the study of large urban areas. [PERSON] et al. [6] proposed a methodology to approach the identification of interconnected diaphragmatic structures using a simplified analytical model (i.e., spring-masses model). The simplified model is exploited to aid the identification of a significant case study, represented by the Pavilion V, designed by [PERSON] as a hypogeum hall in the Turin Exhibition Center composed of three interconnected blocks with joints. Not only does the presence of these joints result in modal complexity but also in very high sensitivity of the stiffness parameters, especially when the joints are fully effective. This complexity also affects the design of the experimental setups, which are often unable to capture the whole-body dynamics. As the main result, light was shed on the contribution of the stiffness of the joints to the global dynamic behavior of structures composed of interacting diaphragms and, in particular, on the effectiveness of the joints of Pavilion V.  
[PERSON] et al. [7] evaluated the damage severity of cantilever beams by means of an optimization algorithm known as Stochastic Hill Climbing (SHC). This is applied to the deflections of fixed-free structural elements with both open and closed cracks at different locations. The algorithm, which was implemented in a Python application named PySHC, was validated via an experimental test. It was found to be capable of estimating the location and depth of the crack with minimal error (respectively, 1.1% and 0.3 mm). [PERSON] et al. [8] presented a critical review of bridge monitoring methods and ML algorithms for scour detection. This is an extremely relevant topic, as foundation scour is one of the first causes of total or partial bridge collapse. Furthermore, its effects are mainly located underwater and are, thus, not detectable from visual inspection only. They also depend on underground soil conditions that are even more difficult to investigate from above ground. The authors present and detail the techniques and the main outcomes of 36 studies, divided into two broad categories: conventional-monitoring-based studies and advanced Machine-Learning-based studies to detect scour. In conclusion, this Special Issue collected high-quality contributions on various SHM applications in applied sciences, and it also provided a solid state-of-the-art reference in this research area. This Special Issue collected the efforts of all the authors, reviewers, and members of the _Applied Sciences_ Editorial Office. We would like to thank all the professional contributions to this publication. The Special Issue benefited from the coordination efforts and support from [PERSON]. The authors declare no conflict of interest.
## 2 Eddy Current Magneto-Optical Imaging System Settings
### Eddy Current Magneto-Optical Defect Detection System  
The scanning ECMO imaging defect detection system with a small excitation current and high magnetic field responsivity is built; the system settings are shown in Figure 2. The collimated probe beam is generated by the 2 mW He-Ne laser incident to the polarizer after passing through the optical isolator, and the light polarization direction of the polarizer is set  
Figure 1: Principle of the magneto-optical detection and imaging system.  
at a 90-degree angle with the analyzer. The nonpolarized beam splitter (NPBS) does not affect the polarization state of the light but only changes the transmission direction of the light. The linearly polarized light from the polarizer passes through the NPBS and is then converged by the Plano convex lens and incident on the sensor. The signal processing system provides an excitation signal of 1 mHz to 1 MHz to the excitation coil. The sample under the excitation coil generates EC. The presence of defects in the sample will affect the distribution of EC, resulting in different secondary magnetic fields, which are superimposed with the excitation magnetic field. The superimposed magnetic field affected by the defect is detected by the MO sensor. An electromagnet or permanent magnet is used to provide a bias magnetic field. When there is no external excitation magnetic field, the probe light passes through the sensor and is reflected by the reflective layer, and it rotates towards 45 degrees under the effect of the bias magnetic field. The 90-degree polarizer and analyzer angle setting can eliminate the influence of reflected light on the sensor surface, and the rotation caused by the bias magnetic field puts the magnetic field detection in a highly sensitive response range. (_TmBi_)\({}_{3}\) (_FcGd_)\({}_{5}\)\(O_{12}\) film is grown on a 0.5 mm-thick Gadolinium Gallium Garnet (GGG) substrate by the liquid phase epitaxy method, and the germanium coating is used as a reflective layer to reflect back the probe light. When passing through the sensor, the polarization direction of the probe light will rotate under the influence of the magnetic field in the vertical sample direction. The reflected light will return to NPBS and be reflected horizontally and land on the analyzer. The light intensity through the analyzer is converted into an electrical signal by the photodetector. The lock-in amplifier filters and amplifies the detected electrical signals. The imaging and signal process system controls the 2D moving platform to move the sample and reads the output of the lock-in amplifier for imaging.  
Eddy Current Magneto-Optical Imaging Theoretical Analysis and Simulation of Carbon Fiber Reinforced Polymer Cracks
## 2 Numerical Model Development and Experimental Validation
### Numerical Model
#### 2.2.1 Lamb Waves Simulation  
To simulate LWs, it was first necessary to observe how the vibrations are induced on the real SLJs. In this work, piezoelectric actuators were used. Thus, the overall excitation occurs on the surface of the substrate, radially from the actuator. To approximate this behavior, the forces were discretized and placed on the edges of the actuator, represented in yellow in Figure 7. Through empirical testing, it was seen that this discretization was enough to mimic experimental tests and, thus, was deemed accepted.  
The LWs were generated with Hanning window pulse forces with the same characteristics as in the experiment setup. There are multiple ways of creating and representing these  
Figure 6: The LW of two samples 6678 N and 6680 N, obtained during the tensile test, shows that even very similar tensile strengths generate completely different signals.  
Figure 7: Illustration of a piezoelectric sensor with the forces being discretized at the tips of the square that encompasses the sensor.  
waves in Abaqus, the one chosen in this work was to create a Python script that would apply a variable deformation on the surface of the part simulating the sinusoidal pattern of the Hanning window. Furthermore, the simulation program Abaqus discretizes the vibrations received into the three primary Cartesian components, U1, U2, and U3, as shown in Figure 2. This will be further analyzed in Section 2.3.2 to determine what component or combination of components is most suited and similar to the real signal.
# Energy Management of a Multi-Source Power System
## 3 Methodology
### Heuristic Approach
If the state-of-charge of the battery is between 47% and 100%, the system will use the fuel cell, photovoltaic cell, and battery to meet the demand of the drone. If the state-of-charge of the first battery is less than 47%, the system will move to check the state-of-charge of the second battery and so on. The range of 47% to 100% was chosen because at the ratings used for simulation (Supercapacitor ratings: 24 V, 200 A, time constant 0.36; Battery ratings: 22.4 V, 165 A; Fuel Cell ratings: 21 V, 10 A; Photovoltaic Cell power rating: 120 W), using a battery for one-time instance reduces the state-of-charge of the battery by 17%, based on the rated power demand considered in this example. If the rated power requirement of the application at hand changes, then this 47% number used for the SOC will have to be changed in the heuristic approach accordingly. Further, the age/health of the battery can also be used to arrive at an appropriate threshold for the SOC to be checked other than 47%-and this is left for future work. Therefore to keep the state-of-charge of the battery greater than or equal to 30%, a lower bound of 47% was chosen for the range. On the occasion that there is no output from the photovoltaic cell, the system will check the remaining sources to meet the demand of the system. Moreover, the heuristic approach could generate a solution in a matter of seconds on Matlab, while the dynamic algorithm used on Lingo sometimes required hours depending on the instance size and model complexity. Please also note that to be able to visualize all the possible switching scenarios in a reasonably short time frame, the battery Ah capacity was reduced the simulation so as not to have to wait for a very long time for energy source switching behavior to be noticed.
# Effects of Drying Process on the Volatile and Non-Volatile Flavor Compounds of _Lentinual edodes_
## 3 Results and Discussion
### Effects of Drying Methods on Free amino Acids of L. edodes  
_Lentinus edodes_ contains a variety of amino acids. They can provide strong umami and pleasant sweet flavors [37]. As shown in Table 3, the contents of total free amino acids in fresh and dried _L. edodes_ ranged from 29.13 to 32.82 mg/g dry weight. The total free amino acids content of the fresh samples was 32.817 mg/g dry weight, which was consistent with the reported value (31.70 mg/g) [38]. The content of total free amino acids in FD samples was the lowest. This may be due to the high temperature promoting protein hydrolysis.  
Free amino acids were classified into umami, sweet, bitter and tasteless based on their taste properties [39]. As shown in Table 3, threonine was the highest sweet amino acid, accounting for 77% to 87%, while glutamic acid was the highest umami taste amino acid. The results are similar to others [36]. The contents of aspartic acid were significantly increased in FD samples compared with fresh, HAD and ND. As revealed in Figure 3, fresh _Lentinus edodes_ contained the sweetest amino acid taste, while the FD sample was the most umami-like and bitter tasting amino acid.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Organic Acids (mg/g)** & **F a** & **FD** & **HAD** & **ND** \\ \hline Tartaric acid & 41.91 \(\pm\) 1.97 A & Nd b & 12.18 \(\pm\) 0.19 B & Nd b \\ Malic acid & 36.22 \(\pm\) 0.70 A & 27.82 \(\pm\) 0.77 B & 23.57 \(\pm\) 2.35 BC & 21.00 \(\pm\) 0.38 C \\ Ascorbic acid & 6.27 \(\pm\) 0.28 A & 5.00 \(\pm\) 0.27 C & 5.66 \(\pm\) 0.11 B & 4.88 \(\pm\) 0.18 C \\ Citric acid & 134.49 \(\pm\) 0.45 C & 107.65 \(\pm\) 2.17 D & 138.13 \(\pm\) 1.90 B & 142.31 \(\pm\) 1.09 A \\ Fumaric acid & 11.68 \(\pm\) 0.03 A & 8.23 \(\pm\) 0.03 B & 6.57 \(\pm\) 0.02 D & 7.61 \(\pm\) 0.02 C \\ Succinic acid & 645.25 \(\pm\) 3.03 A & 472.61 \(\pm\) 3.34 D & 481.71 \(\pm\) 1.72 C & 519.51 \(\pm\) 2.11 B \\ Total & 875.82 \(\pm\) 2.04 A & 621.32 \(\pm\) 3.42 D & 667.82 \(\pm\) 0.48 C & 695.30 \(\pm\) 0.96 B \\ \hline \end{tabular}
\end{table}
Table 2: Organic acid contents of fresh and different dried _L. edodes_.
# Telecommunication Technologies for Smart Grid Projects with Focus on Smart Metering Applications
## 4 Smart Metering Applications--Smart Grid Projects
### Grid Management
#### 4.5.10 Nice Grid  
Grid management and integration of renewable energy sources are issues also examined in the \"Nice Grid\" project that entails participants from France and Belgium. The project indicates the insertion and utilisation of photovoltaic arrays, storage units along with smart meters and smart software components for advanced grid management. For this scope, the \"Linky\" smart meters are selected [107].
## 5 Conclusions  
The results demonstrated that the chlorophyll content, leaf temperature, green-peak reflectance, and Fv/Fm at 10 days were significantly lower than those at 40 days, regardless of the sampling date or SO\({}_{2}\) concentration. Based on this, the resistance of the three green tree species to SO\({}_{2}\) at different ontogenies was comprehensively evaluated. The SO\({}_{2}\) resistance for the 10-day leaves was consistently lower than that for the 40 days leaves. On 9 September, the 10-day leaf resistance performance of the three tree species under different SO\({}_{2}\) stress concentrations was as follows: _P. crasifera > S. oblate > U. pumila_. Finally, the spatial products of FPAR, GPP, LAI, and NDVI were combined to explore the resistance mechanisms of broadleaf trees to different SO\({}_{2}\) concentration classes during varied seasons on a large scale. Resistance decreased with the increasing \(\rho(\text{SO}_{2})\) class in different seasons. The resistance of different tree species to SO\({}_{2}\) was affected by season. In addition, the GPP change was consistent with the SO\({}_{2}\) concentration change in the previous four days, indicating a lag in GPP change in SO\({}_{2}\) in the previous four days. We conclude that mature leaves are more resistant to SO\({}_{2}\) stress than young leaves are. The results of this study on changes in the functional traits of urban greening trees and environmental factors, as  
Figure 10: Relationship between GPP and urban air SO\({}_{2}\) concentration for the different vegetation types in 2019.  
well as the response of functional traits to environmental changes, will provide a basis for the scientific guidance of artificial plant community construction and the prevention of vegetation degradation in the future.  
All authors contributed meaningfully to this study. A.H.: conceptualization, writing--original draft. Y.B. ([PERSON]): investigation, data curation. X.L.: conceptualization, methodology. Z.T.: writing--review and editing. S.Q.: visualization, supervision. Y.B. ([PERSON]): conceptualization, resources. J.Z.: conceptualization, writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This study was financially supported by the National Natural Science Foundation of China (31770770); The International (Regional) cooperation and Exchange Programs of National Natural Science Foundation of China (41961144019); The Major Scientific and Technological Program of Jilin Province (20200503002 SF); The Science and Technology Development Planning of Jilin Province (20190303081 SF).  
Not applicable.  
The authors are grateful to the anonymous reviewers for their in insightful and helpful comments to improve the manuscript. Furthermore, thanks to [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] of the Disaster Institute group for their help in the field experiments of this study.  
The authors declare no conflict of interest.
## 2 Materials and Methods  
### Data Collection Site  
The data collection was conducted using two CSGs of the same size (60 m \(\times\) 8 m), which were oriented lengthwise in the east-west direction at Shenyang Agriculture University, Liaoning, China (41\({}^{\circ}\)49\({}^{\prime}\)46\({}^{\prime\prime}\) N, 123\({}^{\circ}\)33\({}^{\circ}\)51\({}^{\prime\prime}\) E). This study was conducted under two different scenarios: a greenhouse with crops and a greenhouse without crops. Figure 1 shows the cross-section of the structure of the two CSGs, which featured a sturdy north-facing wall with a partial roof on the north side and a cover over the southern side. The north wall (0.45 m thick) was a layered structure of brick with Styrofoam insulation and an air layer. The north roof (0.2 m thick) was made of several layers of wood, Styrofoam, and other structural materials. The cover placed on the south roof (0.00012 m thick) during the daytime was made of polyvinyl chloride (PVC) film, and a cotton blanket (0.50 m thick) was laid over the roof each night. During the daytime, the south roof of the greenhouse was covered with a thin plastic film to allow entry of the sunlight; at night, it was covered with a cotton blanket to provide insulation. The front and rear slope elevation angles of the greenhouse roof were 32\({}^{\circ}\) and 41.20\({}^{\circ}\), respectively, and the rear slope horizontal projection was 0.16 m. The vent was opened at 11:00 h and closed at 13:30 h in the daytime during the winter season, except on snowy days. Sunrise and sunset were between 6:40 h to 7:05 h and 16:05 h to 16:25 h, respectively.  
Strawberry (_Fragaria ananassa_; variety: Yanli) was planted on 10 September 2018 in one of the CSGs, and the soil under the plants was covered by a black plastic film. The plant-to-plant and row-to-row distances were 15 cm and 20 cm, respectively. The number of rows was two per ridge, and the ridge length was 6.90 m. The interval between the ridges was 1.0 m, and the total number of ridges was 56.
# Assessment of Bioactive Surfactant Levels in Selected Cereal Products
## 3 Results  
### Validation and Quality Control  
The PFAS determination method was tested in terms of its recovery, repeatability, linearity, sensitivity, the limit of detection (LOD), and limit of quantification (LOQ), according to the general requirements set out in ISO standard 17025.  
The accuracy of the method was verified by recovery studies by spiking the test sample of wheat flour with the standard solution mixture. PFAS native standard mixture (100 ng/mL) was inserted into the sample at a volume of 62.5 \(\mu\)L to obtain a fortification level of 5 ng/g. The recovery value was determined based on the following equation (Equation (1)):  
\[\mathrm{R}=(\mathrm{c}/\mathrm{c}_{0})\times 100\% \tag{1}\]  
where:  
\(\mathrm{c}\)--mean analyte concentration in the reference material;  
\(\mathrm{c}_{0}\)--nominal concentration of the analyte in the reference material [45].  
The recovery values were in good agreement with Commission Recommendation 2010/161/EU on the monitoring of perfluoroxyl substances in food (of 70-120%); it varied from 87% for PFBA to 102% for PFOA. The repeatability was assessed to be below 10% for all analytes. The linearity of the method was tested by designating the equations of the calibration curves (y = ax + b) and computing the corresponding coefficient of determination (R\({}^{2}\)) for all analyzed PFAS in the concentration range from 0.01 to 20 ng/mL. The calibration slope coefficient (a) was a determinant of the sensitivity of the method. The highest sensitivity was calculated for PFHS (7.786); the lowest was for PFDA (0.965).  
The limit of detection (LOD) was calculated as three times higher than the level of noise using the qualifier ion, and the limit of quantification (LOQ) was equal to ten times the noise level. The level of noise was measured from the chromatograms obtained for the standard solutions with the lowest concentration, i.e., 0.01 ng/mL. The LOQ for the used method was calculated as the signal-to-noise ratio, which ranged from 0.043 ng/g for PFHS to 0.072 ng/g for PFHxA. The results correspond well with the value specified in Commission Recommendation 2010/161/EU (1 \(\mathrm{\SIUnitSymbolMicro g}\)/kg).  
The calibration curves were constructed by plotting the ratio of the peak areas of particular analytes, divided by the peak area of the L-PFOA (for PFCA) and L-PFOS (for PFSA) against the concentrations of the analytes. The analyte signal from the blank matrix was subtracted from the corresponding analyte signal from the fortified samples.  
Table 1 summarizes the obtained results and contains the recovery values, limits of detection (LODs), limits of quantification (LOQs), coefficients of determination (R\({}^{2}\)), and calibration slopes (a) determined for PFAS.
## 3 Results and Discussion
### Case Study
#### 3.2.2 Taiwan Earthquake  
An earthquake with a magnitude of 5.2 happened in Taiwan on 16 December 2018, 05:21:05 (LT). The location of the epicenter was 23.71\({}^{\circ}\) N, 121.80\({}^{\circ}\) E, and the focal depth was 26 km. On the same day, an earthquake with a magnitude of 5.7 broke out at 12:46:07 (LT) in Sichuan. The location of the epicenter was 28.24\({}^{\circ}\) N, 104.95\({}^{\circ}\) E, and the focal depth was 12 km.  
As shown in Table 4, the IR3 channel appeared abnormal at first, and anomalies in other channels appeared simultaneously and disappeared simultaneously, which were a little later than that in the IR3 channel. In images from the IR3 channel, a high-value region arose on 6 December 2018, shown in Figure 12. In the IR3 channel image, there was a weak anomaly between the two epicenters. Relatively, there were more obvious anomalies in the high latitude region, but they were far from the two epicenters, which is not significant for the prediction of this earthquake. It became an isolated region on 8 December 2018, while the anomalies in the other three channels just started, as shown in Figure 13. The anomalous positions of the two long-wave channels and the medium-wave channel were close, but the anomaly area of the medium-wave channel was smaller. The anomaly area of the IR3 channel was the largest, and it has obvious location deviation from the other three channels. On 9 December 2018, the anomalies in IR1 and IR3 obtained their maximums, as shown in Figure 14. In the abnormal region of the IR3 channel image, the southern part is closer to the epicenter of Taiwan, in which anomaly intensity was significantly higher  
Figure 11: The cover area of the anomalies before and after the Sichuan earthquake.  
Figure 10: Power spectrum images on 20 July 2017.  
than that in the northern part. Similar to the first case, the anomaly occurred between the epicenters of the two earthquakes. The abnormal region was mainly on the land and distributed along the coastline. The epicenter of the Taiwan earthquake was at the edge of the anomaly. The anomaly in the IR3 channel was closest to the epicenter. There was only 1.7 km between the anomaly and the epicenter. The anomalies in four channels all disappeared completely on 12 December 2018. According to Figure 15, the anomalies went through appearance, increase, decrease and disappearance before the earthquake. The anomaly in the IR4 channel intersected with that in other channels, but the distance from the epicenter was far beyond the radius of the earthquake preparation region calculated theoretically.  
Figure 12: Power spectrum images on 6 December 2018.  
Figure 13: Power spectrum images on 8 December 2018.