esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive Sensor Array Error Calibration Based Impact Localization on Composite Structure
## 3 Finite Element Simulation  
In order to understand the influence of the array element position parameters on 2D-MUSIC algorithm, a FEA plate model was created in ABAQUS. The model is a 600 mm \(\times\) 600 mm \(\times\) 2 mm aluminum plate with four sides fixed. C3D8I three-dimensional solid elements were used in the simulation. Load a concentrated dynamic load at the center of the plate to simulate the excitation signal source with a center frequency of 50 kHz, as shown in Figure 3. A set of seven nodes with a space of 10 mm from left to right is selected for simulating sensors arranged. The coordinates (x, y) of sensor array are shown in Table 1.  
Figure 2: Process of adaptive piezoelectric sensor array error calibration based on the 2D-MUSIC method.  
The position of the excitation signal source is loaded at the Cartesian coordinates (0, 200) and its polar coordinates is (90\({}^{\circ}\), 200 mm). Lamb wave propagation simulation and response signal of the imprecise sensors array seen in Figure 4. According to the process of adaptive piezoelectric sensor array error calibration based 2D-MUSIC method described in previous section, the sensor array signals simulated by finite element simulation are analyzed.  
Figure 5 shows the comparison of the spatial spectrum estimated by standard 2D-MUSIC and 2D-MUSIC after error calibration, respectively. After considering the position error of sensors array elements, 2D-MUSIC algorithm has a better localization result. The estimation error in distance and direction is 1.2 cm and 0\({}^{\circ}\), respectively. The FEM simulation results show that the proposed adaptive sensor array error calibration based 2D-MUSIC method is effective for signal source localization. In addition, almost all of the side lobes disappear after error calibration the adaptive correction seen from Figure 5, that is the beam directivi  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Sensor** & & & & & & & \\
**Array** & **Sensor \#1** & **Sensor \#2** & **Sensor \#3** & **Sensor \#4** & **Sensor \#5** & **Sensor \#6** & **Sensor \#7** \\ \hline Precisely & (\(-30,100\)) & (\(-20,100\)) & (\(-10,100\)) & (0, 100) & (10, 100) & (20, 100) & (30, 100) \\ Imprecisely & (\(-30,104\)) & (\(-22,98\)) & (\(-10,100\)) & (2, 100) & (10, 100) & (18, 100) & (30, 100) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The coordinates (x, y) of FEM nodes selected for sensor array.  
Figure 4: (**a**) Lamb wave propagation simulation and (**b**) response signal of the imprecise sensors array.  
Figure 3: (**a**) Finite element method (FEM) model with error position of sensor array, (**b**) excitation signal.
## 3 Results and Discussion
### Mechanical and Physical Parameters Analyses of Geogrids
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Tests**} & \multirow{2}{*}{**Parameters**} & \multirow{2}{*}{**Specific Standard**} & \multicolumn{2}{c}{**New Sample 1**} & \multicolumn{2}{c}{**Exhumed Samples**} \\ \cline{5-6}  & & & & **Mean Value** & **Standard deviation** \\ \hline \multirow{4}{*}{**Physical**} & Mass per unit area & PN-EN ISO & \multirow{2}{*}{500} & \multirow{2}{*}{532} & \multirow{2}{*}{-} \\  & (g/m\({}^{2}\)) & 9864 [27] & & & \\ \cline{1-1}  & Aperture Size (mm) & - & 140 & 140.24 & 1.56 \\ \cline{1-1}  & Rib Thickness (mm) & PN-EN ISO & 0.95 & 0.97 & 0.0078 \\ \cline{1-1}  & CMD Bar Thickness & 9863-1 [28] & 2.5 + 2.7 & 2.70 & 0.0640 \\ \cline{1-1}  & (mm) & & & & \\ \cline{1-1}  & Mean Tensile Strength & \multirow{2}{*}{PN-EN ISO 10319 [29]} & \multirow{2}{*}{55} & \multirow{2}{*}{48.92} & \multirow{2}{*}{4.20} \\ \cline{1-1}  & (kN/m) & & & & \\ \cline{1-1}  & Mean Strain at & & & & \\ \cline{1-1}  & Maximum Load (\%) & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Mechanical and physical characteristics of the geogrid determined based on laboratory tests. Figure 8: _Cont._  
For the first case (Figure 8a), the strength parameters for the geogrid were applied as recommended by the manufacturer: 9.61 kN/m long-term strength [35]. For the latter scenario, the factor of safety was computed based on the actual geogrid tensile strength, determined in a laboratory using samples excavated from the slope after 20 years of its exploitation (Figure 8b). The geotechnical parameters of the filling material were adjusted and increased due to compaction and sand mixing of the waste. In this particular location, an access road to the landfill crest was designed. To increase the bearing capacity of the road, the waste material was mixed with a sand fraction and heavily compacted using appropriate equipment. New parameters were as follows: For the landfill access road sub-base: \(\gamma\) = 17 kN/m\({}^{3}\), \(\varphi\) = 27\({}^{\circ}\), c = 10 kPa; and for waste filling the slope: \(\gamma\) = 14 kN/m\({}^{3}\), \(\varphi\) = 23\({}^{\circ}\), c = 15 kPa. The parameters of the subsoil remained unchanged as there was no deep soil reinforcements applied. The computed factors of safety for the two considered cases were FOS = 1.31 and 1.45, respectively, for the geogrid of nominal tensile strength of 9.61 kN/m and laboratory-tested value of 42.63 kN/m. Such a significant difference is caused by applying different security coefficients in the past, when no standards for design were used. According to the applied safety regulation, FOS = 1.45 is sufficient and meets the \"traffic light\" standards used for the purposes of the present study.
# DeepSOCIAL: Social Distancing Monitoring and Infection Risk Assessment in COVID-19 Pandemic
## 3 Methodology
### People Tracking  
The next step after the detection phase is people tracking and ID assignment for each individual.  
We use the Simple Online and Real-time (SORT) tracking technique [103] as a framework for the Kalman filter [104] along with the Hungarian optimisation technique to track the people. Kalman filter predicts the position of the human at time \(t+1\) based on the current measurement at time \(t\) and the mathematical modelling of the human movement. This is an effective way to keep localising the human in case of occlusion.  
The Hungarian algorithm is a combinatorial optimisation algorithm that helps to assign a unique ID number to identify a given object in a set of image frames, by examining whether a person in the current frame is the same detected person in the previous frames or not.  
Figure 7a shows a sample of the people detection and ID assignment, Figure 7b illustrates the tracking path of each individual, and Figure 7c shows the final position and status of each individual after 100 frames of detection, tracking, and ID assignment. We later use such temporal information for analysing the level of social distancing violations and high-risk zones of the scene. The state of each human in a frame is modelled as:  
\[\mathrm{x}=[u,v,s,r,u^{\prime},v^{\prime},s^{\prime}]^{T} \tag{10}\]  
where \((u,v)\) represent the horizontal and vertical position of the target bounding box (i.e., the centroid); \(s\) denotes the scale (area), and \(r\) is the aspect ratio of the bounding box sides. \(u^{\prime}\), \(v^{\prime}\), and \(s^{\prime}\) are the predicted values by Kalman filter for horizontal position, vertical position, and bounding box centroid, respectively.  
When an identified human associates with a new observation, the current bounding box will be updated with the newly observed state. This will be calculated based on the velocity and acceleration components, estimated by the Kalman filter framework. If the predicted identities of the query individual significantly differ from the new observation, almost the same state that is predicted by the Kalman filter will be used with almost no correction. Otherwise, the corrections weights will be split proportionally between the Kalman filter prediction and the new observation (measurement).  
Figure 6: The network structure of the proposed three-level human detection module.  
Figure 7: People detection, ID assignment, tracking and moving trajectory representation.  
As mentioned earlier, we use the Hungarian algorithm to solve the data association problem, by calculating the IoU (Equation (6)) and the distance (difference) of the actual input values to the predicted values by the Kalman filter.  
After the detection and tracking process, for every input frame \(I_{u\times h}\) at time \(t\), we define the matrix \(D_{t}\) that includes the location of \(n\) detected human in the image carrier grid:  
\[D_{t}=\{P_{(x_{u}y_{n})}^{t}\mid x_{n}\in w,\,y_{n}\in h\} \tag{11}\]
## 3 Case Studies
### Carbon Dioxide and Dinitrogen
#### 3.3.1 CO\({}_{2}\) Adsorption in K-FER  
Figure 9 shows representative VITR spectra in the v\({}_{3}\) region (antisymmetric stretching) of CO\({}_{2}\) adsorbed in a K-FER sample that had a Si:Al ratio of 27.5:1, details can be found elsewhere [99]. The main IR absorption bands, peaking at 2355 and 2346 cm\({}^{-1}\), should clearly correspond to two different types of CO\({}_{2}\) adsorption complexes. Further evidence comes from an isothermal (room temperature) series of IR spectra recorded at increasing CO\({}_{2}\) dosage on a K-FER zeolite that had a Si:Al ratio of 8.6:1, which is shown in the inset to Figure 9). When compared to the corresponding gas-phase value of CO\({}_{2}\) (v\({}_{3}\) = 2349 cm\({}^{-1}\)) the 2346 cm\({}^{-1}\) band is red-shifted (by \(-\)3 cm\({}^{-1}\)) while the other one is blue-shifted (+6 cm\({}^{-1}\)). Note however that CO\({}_{2}\) confined inside the pores of silicaite (a purely siliceous zeolite) was reported to show the v\({}_{3}\) mode at 2341 cm\({}^{-1}\)[100]. Taking this value as the reference, both of the CO\({}_{2}\) adsorbed species in K-FER result to have their v\({}_{3}\) mode blue-shifted, by 5 and 14 cm\({}^{-1}\), respectively.  
After computer resolution and determination of the integrated absorbance of the bands 2346 and 2355 cm\({}^{-1}\) in Figure 9 (main body), the van't Hoff plots shown in Figure 10 were obtained. From these linear plots, \(\Delta H^{0}\) values of \(-\)40 and \(-\)43 kJ mol\({}^{-1}\) for the formation of the adsorbed CO\({}_{2}\) species giving rise to the bands at 2346 and 2355 cm\({}^{-1}\) respectively. DFT calculations [99] gave values of \(\Delta H^{0}\) in the range of \(-\)36 to \(-\)41 kJ mol\({}^{-1}\) for CO\({}_{2}\) adsorbed on single cation sites (K\({}^{+}\cdots\)OCO species), and \(-\)43.7 kJ mol\({}^{-1}\) for adsorption on dual cation sites (K\({}^{+}\cdots\)OCO\(\cdots\)K\({}^{+}\) species), which are very close to the experimentally found values for the adsorbed species giving rise to the IR absorption bands at 2346 and 2355 cm\({}^{-1}\), respectively. Note that, as expected, the relative amount of dual sites increases when the Si:Al ratio of the zeolite is decreased, i.e., when the concentration of K\({}^{+}\) ions increases, as shown in the inset to Figure 9. It is also noteworthy that, at a difference with CO, the higher wavenumber band is now that one corresponding to CO\({}_{2}\) species adsorbed on dual sites, because of the different symmetry of the adsorbed molecule.  
Figure 9: Representative VITR spectra (v\({}_{3}\) region) of CO\({}_{2}\) adsorbed on K-FER [(Si:Al) = (27.5:1)]. From top to bottom, temperature goes from 268 to 301 K; and equilibrium pressure from 0.21 to 0.50 mbar. The zeolite blank spectrum was subtracted. The inset shows IR spectra of increasing doses of CO\({}_{2}\) adsorbed on K-FER [(Si:Al) = (8.6:1)] at room temperature (adapted from [99]).
## 3 Results and Discussions
### XRF Soil Analysis
#### 3.2.5 The Enrichment Coefficient (EC)  
The EC values presented in Table 10, calculated using Equation (3) from Table 1, are mostly greater than one, indicating that the source of heavy metals is primarily external, originating from traffic or industrial activities.  
For Ag and Hg, the highest values of EC were calculated, indicating a very high level of pollution: 16.31, 11.12 and 2.25. An average of the values of EC indicated that the order of locations is \(6>1=5>10>7>4>3>8>2>9\). For locations 1, 4, 7, 8 and 9, the values of EC from the edge of the park are higher than the values from the sample collected from the playground [17; 27].  
The sample point no. 6, initially selected using different criteria and considered a control sample, was actually one of the most polluted sites based on the results. It showed maximum values for As and Ni and values close to the average for the elements Rb, Ni, Mn, Zr, Hg, V, Sb and Ag. Additionally, according to the IPI classification, with an average value of 2.19 (classified as a high level of pollution), it was identified as the most polluted site. Similarly, for I\({}_{\text{geo}}\) and EC, location 6 ranked as the most polluted site, with average values of 1.25 (classified as a moderately polluted) and 3.08 (indicating enrichment from various sources), respectively.
## 3 Results and Discussion
### Effect of FNGB and FGB Ethanol Extracts on Melanogenesis in Zebrafish Embryos  
In this study, phenylthiourea (PTU) was used as the positive control group. Results showed that the melanin content of zebrafish embryos treated with PTU was significantly reduced by approximately 57.88-67.67% compared with that in untreated zebrafish embryos (\(p<0.05\); Figure 6), while after treatment with FNGB ethanol extract at 10, 25, 50, and 100 ug/mL, the melanin content in zebrafish embryos significantly decreased by 19.35-25.19% (\(p<0.05\); Figure 6A) compared with that in the control group. In addition, after the zebrafish embryos were incubated with 50 or 100 ug/mL of FGB ethanol extract, the melanin content significantly decreased by 15.50% and 32.89% compared with that in control group, respectively (\(p<0.05\); Figure 6B). In summary, when zebrafish embryos were incubated with ethanol extracts of FNGB and FGB at below 100 ug/mL, melanogenesis was inhibited without causing malformations, death, or decreased heart rate in zebrafish embryos.
# In-Situ Nanoparticles: A New Strengthening Method for Metallic Structural Material
## 5 Optimizing Microstructure
### Optimizing Inclusions  
It is well known that the inclusions usually form during solidification in steel. Generally, cracks can be generated by large-sized brittle inclusions and lead to failure. The shape, size and distribution of inclusions in steel have a great influence on the microstructure and mechanical performance [89; 90]. To date, \"oxides metallurgy technology\" has been proposed to render oxide at micro/sub-micron scale in steel melt to be heterogeneous nucleus of sulfide, carbide or nitride, thus the inclusions can be well controlled [91]. Differently, in our investigation the in-situ nanoparticles give rise to distinct heterogeneous nucleation effect and significantly optimize inclusions. As shown in Figure 15a, with appropriate addition of Ti, the amount and the size of inclusions remarkable decrease due to the effect of Ti\({}_{3}\)O\({}_{5}\) oxide [56]. In addition, many cubic inclusions (100-200 nm) originally located in the matrix change to circular ones (about 50 nm) and distribute in the intersections of grain boundary, indicating less possibility of crack generation. Notably, initial harmful inclusions can turn to serve as beneficial strengthening nano-scale phases. Furthermore, heterogeneous nucleation behavior can be proved by the composite precipitated particles existing in nanoparticle strengthened HSLA steel, as seen in Figure 15b. By TEM and EDS characterization, NbC and Ti\({}_{3}\)O\({}_{5}\) are clearly identified, respectively [58]. In general, this brand new method is disparate from the traditional idea of removing large-size inclusions in the melt. Instead, it focuses on transforming inclusions to be favorable strengthening phases with refined size and optimized distribution, which can greatly improve the strength and toughness of steels simultaneously.
# Getting Young People to Farm: How Effective Is Thailand's Young Smart Farmer Programme?
## 3 Data and Methods
### Sampling, Data Collection and Questionnaire  
Data collection comprised two phases. First, young farmers (aged 17-45 years) participating in the Young Smart Farmer programme were interviewed in Thai between September and mid-October, 2018. We received a list of names, addresses, and contact numbers of all current participants from the Prachin Buri Provincial Agricultural Extension Office (2018) and the Farmer Development Division (2018), in the Department of Agricultural Extension. A total of 123 participants were listed, all of whom were contacted. About 50% (61 YSF programme participants) agreed to be interviewed.  
Second, as a control group, non-participants within the same age range were interviewed between mid-October and late-December, 2018. We used a purposive sampling technique to select non-participants who lived in the same or neighbouring villages as the interviewed participants, with the intention to interview three times the number of non-participants than participants (183 people), as suggested by [PERSON] and [PERSON] (2018). Upon request, the village headmen provided assistance in contacting non-participants for  
Figure 2: Study area in Prachin Buri province, Thailand.  
the interviews. Although 183 people were contacted, about 81% (149 non-participants) agreed to be interviewed.  
We used semi-structured questionnaires, which were slightly modified after a pilot test with seven participants in early-September, 2018. The questionnaire for the programme participants and non-participants consisted of three similar parts: (1) demographic, family, and social characteristics, (2) farming and other occupational experiences, and (3) receipt of other support from the government. The participant questionnaire included an additional section regarding the programme participation.
# Benefits of Probiotics--Biodetoxification
## 2. Probiotics--Biodetoxification of Food
### The Role of Probiotics in Food Biodetoxification
#### 2.2.5 Acrylamide  
In various foods (fries, cereals, bakery products, cookies, coffee, meat) submitted to heat processing (e.g., frying, roasting, and baking), harmful acrylamide can be formed in the Strecker or acrolein pathway [48]. Acrylamide content in food is also related to storage conditions and duration as well as to domestic food cooking procedures. The main precursor is free asparagine, especially in foods rich in reducing sugars (fried potatoes and bakery products) [49; 50]. Toxicological consequences of acrylamide include carcinogenesis, mutagenic, neurotoxicity, and reproductive toxicity [51]. After ingestion, it is easily absorbed and distributed in many organs (thymus, liver, heart, brain, kidneys) [50]. Due to the presence of peptidoglycan in cell walls, LAB strains can bind acrylamide, as confirmed in the in vitro study by [PERSON] et al. (2017) [52]. One of the strategies that have been tested on a lab scale to reduce the acrylamide content of food is the addition of asparaginase. In relation to the high cost of this enzyme, the study results showing that the bacterial L-asparaginase can inhibit acrylamide generation are of great importance. In the study by [PERSON] et al., L-asparaginase produced by _Bacillus subtilis_ reduced the acrylamide content in fried potato chips by 20% [53; 54; 55].
## 5 Conclusions  
The pressure-relieving and roof cutting technology improves the stress environment of the rock surrounding the road by pre-splitting the roof. This technique improves the pace of coal resource recovery in addition to preserving the roadway's stability. Hence, this method exhibits considerable social value. The Guqiao Coal Mine's 1127 (1) working face was chosen as the research object for this study. Through field research, the mechanical properties of the surrounding rock of the roof were ascertained, as were the geological conditions of Guqiao Coal Mine. Through theoretical calculation, the cutting height was determined to be 13.5 m and the cutting angle should be more than 13.6\({}^{\circ}\). The numerical model was established using FLAC3D software. The effects of roof cutting height and angle on the vertical stress fluctuations and subsidence of the roadway roof were studied through simulations. The results indicate that when the roof is cut at a height of 13.5 m and an angle of 20\({}^{\circ}\), the peak vertical stress on the right side of the roadway roof decreases from 22.01 MPa to 13.63 MPa. Consequently, the stress and subsidence of the roadway roof are effectively managed, and the effects of the roof cutting and pressure-relief technology are good, exhibiting certain guiding significance for similar engineering conditions.  
Conceptualization, H.Y. and Z.L.; methodology, H.Y. and Z.L.; software, H.Y.; validation, H.Y. and Z.L.; formal analysis, H.Y.; investigation, H.Y.; resources, H.Y. and Z.L.; data curation, H.Y.; writing--original draft preparation, H.Y. and Z.L.; writing--review and editing, H.Y. and Z.L. All authors have read and agreed to the published version of the manuscript.  
The research was financially supported by the National Natural Science Foundation of China (No. 52074013).  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
Conflicts of Interest: The authors declare no conflicts of interest.  
Figure 9: Vertical displacement distribution map of the roadway with different roof cutting heights: (a) uncut roof; (b) 9 m; (c) 13.5 m; (d) 18 m.