esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Determination of ABTS Radical Cation Stabilisation Activity of TiO\({}_{2}\)-NT Surfaces  
ABTS assay measures the relative ability of the of TiO\({}_{2}\)-NT surfaces to stabilise ABTS radical cations that are generated in an aqueous phase. The ABTS radical cations are generated from the  
Figure 1: Diagrammatic representation of the experimental set up used for the determination of the redox activity of the titanium dioxide nanotubes (TiO\({}_{2}\)-NT) surfaces.  
reaction between strong water-soluble oxidizing agent potassium persulfate and the ABTS chloride salt. The oxidation of the ABTS salts results in the formation of a blue green ABTS radical cation. The TiO\({}_{2}\)-NT surfaces can reduce back the radical cation through the donation of surface excited electrons, causing the suppression of its characteristic longwave 734 nm absorption maxima, thus enabling the visual monitoring of the reaction. The ABTS\({}^{\bullet+}\) was produced by the reaction of 7 mM stock solution of 2,2'-azino-bis (3-ethylbenzothazoline-6-sulfonic acid) with 2.45 mM potassium persulfate. Before use, the mixture was allowed to stand overnight in the dark and at room temperature. The concentration of the blue green ABTS radical solution was adjusted with methanol to an absorbance of 0.700 (0.020 \(\pm\) mean \(\pm\) 1 SD) at 734 nm. A total of 10 mL of previously adjusted ABTS radical solution was placed over the TiO\({}_{2}\)NT surface in a 5.5 cm \(\mathcal{O}\) petri dish covered with a 5 mm flat quartz glass and placed under a full-spectrum UV light at 10 mWcm\({}^{-2}\). The absorbance was monitored at specific time intervals. The rate at which the ABTS radical cations were stabilized, was determined using first-order rate kinetics.  
Determination of Chemical Degradation Activity of the of TiO\({}_{2}\)-NT Surfaces under Different pH Conditions  
The potential application of TiO\({}_{2}\)-NT surfaces as solid-state wastewater decontamination surfaces was tested against the removal of tannic acid. Tannic acid is a high-molecular-weight, water-soluble hydrolysable tannin found in several different plants. Its presence in industrial wastewaters is a rising challenge because of its toxic influence on natural ecosystems [19; 20]. Furthermore, tannic acid has been found to be very resistant to conventional biological treatments and other methods [21; 22; 23; 24; 25; 26]. In this experiment, a stock solution containing 2 mg/L of tannic acid was prepared and adjusted to pH 2, 3, 6, 7 and 9 using 1M hydrochloric acid and sodium hydroxide. A total of 10 mL of the resultant solutions was placed over the TiO\({}_{2}\)NT surface in a 5.5 cm \(\mathcal{O}\) petri dish covered with a 5 mm flat quartz glass and placed under a full-spectrum UV light at 10 mWcm\({}^{-2}\). The absorbance was monitored at specific time intervals using a UV-Vis spectrophotometer (Shimadzu 1800), a full spectrum was recorded between 190-390 nm in order to check if secondary peaks appear during the oxidation and the absorbance at 276 nm was used as the \(\lambda_{\text{max}}\) as a direct measure for the amount of tannic acid.
_Article_  
**Aerodynamic Force and Comprehensive Mechanical Performance of a Large Wind Turbine during a Typhoon Based on WRF/CFD Nesting**  
**[PERSON] 1,2,*, [PERSON] 1, [PERSON] 2 and [PERSON] 2**  
1 Department of Civil Engineering, Nanjing University of Aeronautics and Astronautics, 29 Yudao Road, Nanjing 210016, China; [EMAIL_ADDRESS]  
2 Jiangsu Key Laboratory of Hi-Tech Research for Wind Turbine Design, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China; [EMAIL_ADDRESS] (J.C.); [EMAIL_ADDRESS] (T.W.)  
* Correspondence: [EMAIL_ADDRESS]  
Received: 22 August 2018; Accepted: 15 October 2018; Published: 19 October 2018  
**Featurered Application: The main research conclusions of this paper could provide references for load prediction of similar wind turbine systems during the occurrence of typhoon, and deepen the understanding on meso/microscale wind field nesting mechanism.**  
**Abstract:** Compared with normal wind, typhoons may change the flow field surrounding wind turbines, thus influencing their wind-induced responses and stability. The existing typhoon theoretical model in the civil engineering field is too simplified. To address this problem, the WRF (Weather Research Forecasting) model was introduced for high-resolution simulation of the Typhoon \"Nuri\" firstly. Secondly, the typhoon field was analyzed, and the wind speed profile of the boundary layer was fitted. Meanwhile, the normal wind speed profile with the same wind speed of the typhoon speed profile at the gradient height of class B landform in the code was set. These two wind speed profiles were integrated into the UDF (User Defined Function). On this basis, a five-MW wind turbine in Shenzhen was chosen as the research object. The action mechanism of speed was streamlined and turbulence energy surrounding the wind turbine was disclosed by microscale CFD (Computational Fluid Dynamics) simulation. The influencing laws of a typhoon and normal wind on wind pressure distribution were compared. Finally, key attention was paid to analyzing the structural response, buckling stability, and ultimate bearing capacity of the wind turbine system. The research results demonstrated that typhoons increased the aerodynamic force and structural responses, and decreased the overall buckling stability and ultimate bearing capacity of the wind turbine.  
**Keywords:** typhoon; wind turbine; meso/microscale; aerodynamic force; mechanical performance
## 3 CFD Model of the Internal Flow Field in the Gearbox
### Grid Convergence Analysis  
Given the robust adaptive capabilities and convenient modification advantages demonstrated by the Hyper Mesh2022 software during the mesh generation process, this paper primarily employs Hyper Mesh for mesh generation to establish the lubricating oil flow space and delineate the fluid region with tetrahedral meshes [37]. To mitigate the influence of the number of mesh elements in the computational domain on simulation results and enhance computational efficiency, it is necessary to perform a mesh convergence test. This study selects five distinct basic cell sizes for mesh elements and identifies a monitoring point 26 mm above the gear engagement region in the model (as shown in Figure 6). Additionally, this paper collects pressure and velocity data at this point under five different mesh quantities, as depicted in Figure 7.  
Figure 5: Time step independence verification.  
The comparative analysis reveals that minimal pressure and velocity variations at the monitoring point are minimal when the basic cell size is 3 mm, 4 mm, and 5 mm. However, a notable shift in pressure and velocity at the monitoring point occurs when the basic size increases to 6 mm, resulting in a significant decline in the accuracy of the results. In light of temporal and cost considerations, this paper selects 5 mm as the basic size for the mesh model. Local grid refinement techniques are employed near the gear surfaces and oil injection ports to construct the flow field computational model, thereby enhancing computational accuracy. The final computational model encompasses 795,487 tetrahedral mesh elements, with grid quality maintained above 0.4. The final mesh model for the inner region of the gearbox is illustrated in Figure 8. To evaluate the effectiveness of grid partitioning, we enlarged and analyzed the grid quality in the near contact area. Figure 8 also shows the grid distribution in the near contact area, where the red box area represents the locally densified grid. By checking the orthogonality and aspect  
Figure 6: Internal monitoring points of the gearbox: (1) middle section and (2) monitoring point.  
Figure 7: Pressure and velocity data under five different grid numbers.  
ratio of the grid, we found that the grid quality in this area is good, with an orthogonality greater than 0.8 and an aspect ratio less than 5.
## 3 Measurement Constraints for BIW Inspection
### Interference Constraints of Station Planning  
An additional critical constraint in station planning is ensuring the laser's unobstructed path to the feature to be measured. For interference verification, the BIW model, exported  
Figure 6: (a) an AAG example; (b) MCSG1 identified from AAG1; (c) an AAG example; (d) MCSG2 identified from AAG2.  
in STL format and represented by a multitude of triangles, is utilized. Interference checking is thus replaced by collision detection between the laser and these triangles.  
The laser beam is from station \(S_{\text{j}}\) to \(F_{\text{i}}\). It can be represented as follows:  
\[L_{\text{i,j}}=P_{\text{j}}+D\cdot t \tag{19}\]  
where  
\[D=\frac{F_{\text{i}}-P_{\text{j}}}{\left|F_{\text{i}}-P_{\text{j}}\right|} \tag{20}\]  
The triangle \(T_{k}\) is represented as follows:  
\[T_{k}=(1-u-v)V_{0}+uV_{1}+uV_{2}\]  
\(V_{0}\), \(V_{1}\), and \(V_{2}\) are the vertex of triangle \(T_{k}\). If \(L_{\text{i,j}}\) intersects with \(T_{k}\), the system yields solutions in \(u\), \(v\), and \(t\) for the following equations (Figure 7):  
\[\left[-D\quad V_{1}-V_{0}\quad V_{2}-V_{0}\right]\begin{bmatrix}t\\ u\\ v\end{bmatrix}=P_{\text{j}}-V_{0} \tag{21}\]  
\[\begin{bmatrix}t\\ u\\ v\end{bmatrix}=\frac{1}{\left[D\times E_{2}\cdot E_{1}\right]}\begin{bmatrix}T \times E_{1}\cdot E_{2}\\ D\times E_{2}\cdot T\\ T\times E_{1}\cdot D\end{bmatrix} \tag{22}\]  
\[E_{1}=V_{1}-V_{0}E_{2}=V_{2}-V_{0},T=P_{\text{j}}-V_{0} \tag{23}\]  
The interference constraint is built up as follows:  
\[t\geq\left|F_{\text{i}}-P_{\text{j}}\right|,u<0\cup v<0\cup u+v>1 \tag{24}\]  
To improve the checking speed, the bounding box and multi-way tree technologies are used to reduce the judgement times.
# Quality Assessment of 2.5D Prints Using 2D Image Quality Metrics
## 4 Results and Discussion
### Are 2D IQMs Responsive to the Quality Variations in 2.5D Prints?
#### 4.1.3. Printer Mode Set  
The differences are expected to be in elevation and in shadows as well as in color. There is a height difference between Alto and Brila modes, where the former has a maximum height of 0.5 mm and the latter 0.25 mm. In addition, the Brila mode introduces a color shift. All selected IQMs have the highest response on the edges in both set-up captures. It is expected that the appearance is different between Alto and Brila modes because the elevation in the Alto mode is opaque while it is relatively transparent in the Brila mode. This difference is captured by the IQMs by being responsive on the edges. The IQMs also showed responsiveness to the differences on the surface of the elevated parts and on the background in both set-up captures. For instance, CIEDE2000 and SCIELAB have the highest response on the edges than on the surface of the elevated parts and on the background in flower and snowflake images. In the packaging image, their response is similar on the background and on the surface of the elevated part. This is because the Brila mode added a yellowish color on the elevated parts of the prints and the content of the packaging image has a somewhat similar color in the background and elevated part. Hence, CIEDE2000 and SCIELAB found similar color differences between Alto and Brila mode prints on both the background and the surface of the elevated part. SSIM, MS-SSIM (both terms, scale 2), and iCID's lightness structure map detected halftone noise in the three images in the MS captures and in the flower image in the SS captures along with being responsive on the edges, on the background, and on the elevated parts' surfaces. The detection of halftone noise by iCID's lightness structure map, SSIM, and MS-SSIM (both terms, scale 2) can be explained with the same reasoning given in the previous set for these metrics. The IQMs better captured differences in the MS captures than in the SS ones in this set.  
To conclude, CIEDE2000, SCIELAB, iCID's lightness difference, lightness contrast, and light structure maps are found to be responsive to the differences between the prints in both set-up captures. The differences detected by these IQMs are more visible in the MS captures than in the SS ones. Based on this, the above-mentioned IQMs are considered in the next subsection. We keep iCID because it shows a capability to be used for perceptual QA of 2.5D prints as it is fairly robust to halftone noise arising from the printing process. We use iCID with an initial lower pixels-per-degree value for further analysis because the location of detected differences is the same regardless of the value of pixels-per-degree (Figure 7). There can be a slight change in the scale which is adequate because at a closer viewing distance we expect to see more differences than at longer distances.
# Development of Inkjet Printable Formulations Based on Polyorganosilazane and Divinylbenzene
## 3 Results and Discussion
### Raman Spectroscopy  
Raman spectra (A = 532 nm) were taken to confirm the presence of free carbon in the pyrolyzed samples (at 1100 \({}^{\circ}\)C). Figure 7 shows the plotting of the spectra of DVB15 to DVB75.  
Figure 7: Raman spectra of SiCN thin film samples pyrolyzed at 1100 \({}^{\circ}\)C with different DVB content with the assignment of the D (disorder) and G (graphitic) bands.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Sample** & **Electrical Conductivity (S/cm)** \\ \hline DVB45 & \(1.1\times 10^{-1}\) \\ DVB60 & \(2.3\times 10^{-1}\) \\ DVB75 & \(1.2\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Electrical conductivities of DVB45, DVB60, and DVB75 pyrolyzed at 1100 \({}^{\circ}\)C.  
Two sharp peaks can be seen in all spectra. The peaks are associated to the D (disorder) and G (graphitic) bands of the free carbon phase. The band intensities and the peak center were determined by applying Lorentzian peak fitting on the background subtracted spectra due to the overlapping of the D and G band peaks. Table 6 lists the results. The D band peak shifts between 1320 and 1338 cm\({}^{-1}\) and the G band occurs between 1535 and 1563 cm\({}^{-1}\), depending on the structural order. Both bands are associated to the sp\({}^{2}\) sites [19]. The G band occurs in all sp\({}^{2}\) sites, both in aromatic and olefinic molecules, as it is referred to the in-plane bond stretching mode of pairs of carbon. Whereas the D band is only active in the presence of disorder-induced vibrations and its presence is strictly connected to the presence of sixfold aromatic rings [19], which means that the vinyl groups of DVB were incorporated by radical polymerization into the OPSZ crosslinking network forming free carbon clusters containing aromatic rings after the pyrolysis process. According to the three-stage model of [PERSON] and [PERSON] [19], the carbon present is a mixture between nanocrystalline and amorphous carbon, because the G band is below 1600 cm\({}^{-1}\).  
Table 6 lists the I\({}_{\text{D}}\)/I\({}_{\text{G}}\) ratio and the size of free carbon cluster L\({}_{\text{a}}\) calculated using Equation (1). It is found that the I\({}_{\text{D}}\)/I\({}_{\text{G}}\) ratio increases with the addition of DVB to the system with the highest ratio calculated for DVB45, correspondingly also the size of free carbon clusters. The increase signifies a higher degree of ordering of the aromatic layer in the system, which also explains the increase in electrical conductivity.  
The free carbon cluster size L\({}_{\text{a}}\) increases with the addition of DVB but does not increase linearly with higher content. It is found that above 30% of DVB content, the clusters size remains constant at around 1.7 nm.
## 3 Results and Discussion
### Offset Stability  
The long-term offset stability of the current sensor was measured by observing the outputs over 12 h. The sensor was placed in a shielded environment (cylindrical magnetic shield made of 10 layers of FeNi thin films) with zero applied field. Figure 6 shows the offset of this sensor for a 500-kHz excitation frequency and a 70-mA excitation current. Because the magnitudes of the offset changes were similar, only a 1-h excerpt is shown. After warming up, the offset changes showed a bandwidth of approximately 4.3 nT. In previous studies, [PERSON] et al. [2] reported a printed circuit board (PCB)-based micro-fluxgate sensor with an offset stability of 21 nT bandwidth and [PERSON] et al. [3] reported an RTD technology-based microwire-fluxgate sensor with an improved offset stability of up to approximately 6 nT bandwidth. As compared to similar microsensors reported, the current sensor exhibited superior stability performance.  
Figure 5: (**a**) Linearity analysis of sensor response under the excitation current amplitude of 70 mA at an excitation frequency of 500 kHz; (**b**) The linear range of the sensor with different excitation frequencies; (**c**) The linear range of the sensor with different excitation current amplitudes.  
Figure 6: Offset drift of the sensor over 1 h.
## 3 Results and Discussion
### Prediction of WHC Traits Using Full Spectra  
In this study, three WHC indicators--drip loss, expressible fluid, and salt-induced water gain--were predicted using PLSR models with the full wavelength range (1000-2500 nm), and the predicted results are presented in Table 2. As Table 2 shows, the Rp for the WHC traits of the independent prediction set ranged from 0.52 to 0.73. Drip loss had the best predictive ability  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**WHC Traits**} & \multicolumn{3}{c}{**Calibration Set**} & \multicolumn{3}{c}{**Prediction Set**} \\ \cline{2-7}  & **Min** & **Max** & **Mean \(\pm\) SD** & **Min** & **Max** & **Mean \(\pm\) SD** \\ \hline Drip loss & 0.27 & 5.90 & 1.74 \(\pm\) 1.36 & 0.31 & 8.01 & 1.52 \(\pm\) 1.59 \\ Expressible fluid & 66.65 & 77.74 & 73.29 \(\pm\) 2.51 & 68.11 & 78.15 & 72.98 \(\pm\) 2.58 \\ Salt-induced water gain & 27.91 & 154.00 & 84.19 \(\pm\) 24.63 & 34.11 & 146.33 & 84.78 \(\pm\) 27.82 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Water-holding capacity (WHC) traits of chicken breast fillets.  
(Rp = 0.73 and RMSEp = 0.93) of all the traits; these results were in accordance with previous studies [8]. The predictive ability of salt-induced water gain in intact chicken breast meat was Rp = 0.70 and RMSEp = 17.64. The result was also in agreement with those reported in a previous study by the author, working with pork meat samples [30]. Finally, expressible fluid had the lowest predictive ability (Rp = 0.52 and RMSEp = 2.19). The raw spectral curves of all 90 fillets are shown in Figure 3.
## 1 Introduction
Hence, the research of a multi-agent system with switching interaction topologies will be more meaningful. In some previous studies, some preliminary results have been acquired for first-order consensus under switching topologies [24; 25]. In [26], it was shown that if there is a spanning tree frequently in the union of each directed subgraph for first-order agents, then the consensus will be achieved asymptotically. In [27], [PERSON] studied the first-order leader-following consensus under an undirected switching communication graph. From the perspective of the formation problem, predecessors have also carried out some research. [PERSON] proposed a distributed LQR consensus protocol for the quadrotors and two-wheeled mobile robots under a switching directed graph [28]. Compared with the previous relevant results, the contributions of this paper are as follows: First, the follower agents are not only required to achieve the predesigned time-varying formation; meanwhile, they should track the states of the leader agent. Compared with the time-invariant formation, the time-varying formation will bring the derivative of the formation information; this brings difficulty to design and analysis. The results for time-invariant formation tracking control cannot be directly applied to the time-varying formation problem. Second, the communication topology graph among the agents could be switching. The methods to deal with the fixed topology situations cannot be extended to the switching topologies situations. Third, in this paper, a distributed adaptive formation controller is proposed. The adaptive coupling weights could be updated by the information from the neighbors. This makes the strategy of control more flexible and precise. The rest of this paper is organized as follows. In Section 2, some basic math concepts and problem formulation are introduced. The analysis for multi-agent fixed and switching systems is presented in Section 3. In Section 4, two numerical simulations are shown to verify the obtained results. Conclusions are drawn in Section 5.
# A LiDAR-Based Backfill Monitoring System
## 2 Materials
### LiDAR-Based Backfill Monitor  
The LiDAR-based backfill monitor consists of two primary components, the LiDAR and the main body case, as shown in Figure 2. The LiDAR is connected to the main body casing via a swivel component, which is used to fine-tune the positioning angle in complex installation environments. The main body casing features an explosion-proof design, comprising a power port, a power switch, and a Type-C port. Additionally, a mainboard removed from the NUC is fixed at the center of the casing. A single external power source can supply power to both the LiDAR and the NUC mainboard simultaneously by simply pressing the power switch. The LiDAR collects point cloud data, which are then transmitted via an Ethernet cable to the NUC mainboard for processing. To minimize costs while maximizing operational efficiency and quality, an Intel NUC-11 (manufactured by Intel Corporation, Santa Clara, CA, USA) and Livox Mid-40 LiDAR (manufactured  
Figure 1: Coordinate of one point scanned by LiDAR.  
by Livox Technology Company, Shenzhen, China) were selected. Table 1 shows the basic parameters of the Mid-40 LiDAR.
###### Abstract  
This experimental investigation focused on the mechanical and damping properties of raw and alkali treated flax fibers reinforced cementitious composites. The objective of this study was to research the influence of flax fiber for the damping properties of cementitious composites. The mechanical strengths were obtained using compressive and flexural tests and damping properties were measured using a dynamic mechanical analysis. Structural modifications to the treated flax fibers were analyzed using scanning electronic microscopy and mercury intrusion porosimetry. Results showed that cement compressive strength was decreased, and flexural strength was increased by the addition of untreated flax fibers. Alkali treatment improved the mechanical strength of flax reinforced cement. The addition of flax fibers improved cement damping properties, and the damping effect of untreated flax fibers was greater than that of alkali treated fibers. The loss tangent of the cementitious composites reinforced with raw and alkali treated flax fibers at an amount of 1 wt % of cement was improved by 45.83% and 37.5% at 20 Hz, respectively. The damping properties of flax fiber was attributed to friction between elementary fibers and between adjacent fiber cell walls and slipping between the fiber and matrix. Callouses, pectin, and lignocellulosic in untreated flax fiber contributed to damping and their removal by alkali treatment was the main reason why untreated flax fibers had a greater damping effect as compared to alkali-modified flax fibers.  
Keywords:flax fibers; cementitious composites; mechanical strength; damping properties +
Footnote †: journal: Applied Sciences