esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Anti-Wrinkle Effects of FBG in HDF Cells  
The cytotoxicity effect of FBG, BG, and WG in HDF cells was determined by MTT assay. For 24 h, the cells were incubated in varying concentrations of all samples. As shown in Figure 3A, FBG and WG had no appreciable toxicity in HDF cells up to 400 \(\upmu\)g/mL, whereas BG reduced the cell viability to 86% at a 200 \(\upmu\)g/mL concentration. Overall, FBG and WG had a lower toxicity compared with BG.  
Figure 2: Effect of FBG, BG, and WG on anti-melanogenic mechanism. The B16F10 cells were treated with 100 \(\upmu\)g/mL of FBG, BG, or WG for 72 h. The expression of tyrosinase, TYRP-1, TYRP-2, and MITF at the mRNA level was determined using qRT-PCR. Arbutin (100 \(\upmu\)M) was used as a positive control. The graph shows mean \(\pm\) SD values of three replicates. # \(p\) < 0.05, #_p_ < 0.01, as compared with control group; * \(p\) < 0.05, **_p_ < 0.01, ***_p_ < 0.001, as compared with the IBMX-induced group.  
Various concentrations (0.5 to 4 mg/mL) were utilized in elastase inhibition (elastase from the porcine pancreas). FBG inhibited elastase to a greater degree than BG and WG in a concentration-dependent manner. At a 2 mg/mL concentration, FBG inhibited elastase by 55.10%, whereas BG and WG inhibited by 48.93% and 39.97%, respectively (Figure 3B).  
Since FBG, BG, and WG modulate elastase activity, we examined the effects of FBG, BG, and WG in HDF cells upon UVB irradiation in vitro. As a positive control, unsolic acid (100 uM) was used. As shown in Figure 3C, elastase activity in UVB-irradiated HDF cells was significantly increased by 3.5-fold (351.8%) compared with unstimulated cells. In comparison with the UVB-irradiated group, FBG at 100 ug/mL reduced elastase activity to a greater degree, by 180.5%, compared with BG (210.7%) and WG (258.7%).  
We clarified the effects of FBG, BG, and WG on the expression of anti-wrinkle-related genes using qRT-PCR analysis. As shown in Figure 4, the mRNA expression of MMP-1 and MMP-9 were increased contrarily, and type 1 pro-collagen (COL-1) was decreased by UVB irradiation. The results showed that FBG suppressed MMP-1 and MMP-9 and conversely induced COL-1, resulting in more significant anti-wrinkle effects than BG and WG.  
Figure 3: Anti-wrinkle effect of FBG, BG, and WG in HDF cells. **(A)** Cytotoxicity effect of FBG, BG, and WG in HDF cells for 24 h. The cells were treated with each sample at the indicated concentrations (50, 100, 200, and 400 ug/mL) for 24 h. The cytotoxicity effect was then determined with MTT assays. **(B)** Inhibition of elastase was determined in response to the indicated concentration of FBG, BG, and WG (0.5, 1, 2, and 4 mg/mL). **(C)** Intracellular elastase activity in UVB-irradiated HDF cells was measured in response to the concentration of 100 ug/mL of FBG, BG, or WG, or unsolic acid (500 uM). The graph shows mean \(\pm\) SD values of at least three replicates. *** \(p<\) 0.001, as compared with the control group; * \(p<\) 0.05, ** \(p<\) 0.01, as compared with the UVB-irradiated group.
## 4 Analysis Program for Liner Corrosion
### Failure Criteria  
It is widely acknowledged that there are two typical failure modes for NCSs subjected to overpressure loads, including functional failure and structural failure [29]. Functional  
Figure 10: Verification of the effectiveness of the gusset sub-model.  
Figure 9: Temperature distribution in the gusset region under LOCA conditions.  
failure refers to the loss of the leak-tight function of the NCS, typically manifested as the tearing of liners. Structural failure refers to the NCS reaching its ultimate load-carrying capacity, typically characterized by the fracture of reinforcements and prestressed tendons. One of the main objectives of this paper is to assess the impact of liner corrosion on the leak-tightness of the NCS, and thus the functional failure criterion is adopted.  
To predict the functional failure of the NCS in numerical simulations, the Nuclear Regulatory Commission has established a failure criterion for liners that takes into account corrosion effects [14]. In accordance with [PERSON] et al. [12], a modified version of this failure criterion is employed in this paper, which can be expressed as follows:  
\[\epsilon_{\text{p}}=f_{\text{cor}}f_{\text{m}}f_{\text{g}}\epsilon_{\text{pe}} \tag{6}\]  
where \(\epsilon_{\text{p}}\) is the multiplied factor strain; \(f_{\text{cor}}\) is the corrosion factor, which is introduced to quantify the corrosion impact on the ductility of liners, and it can be determined by Equation (7) according to [PERSON] et al. [24]; \(f_{\text{m}}\) and \(f_{\text{g}}\) are the multiaxial stress factor and gauge length factor, which can be determined according to the literature [14]; and \(\epsilon_{\text{pe}}\) is the effective plastic strain, which is calculated by numerical analysis.  
\[f_{\text{cor}}=\frac{1}{1-1.28\eta}\ (0\leq\eta\leq 50\%) \tag{7}\]  
With the above factors, the \(\epsilon_{\text{pe}}\) is adjusted to its uniaxial equivalent value, i.e., \(\epsilon_{\text{p}}\). A tear is assumed to occur when \(\epsilon_{\text{p}}\) exceeds the uniaxial ultimate tensile strain of liners. In this case, the NCS is assumed to undergo a functional failure. Additionally, since the liner at the junction of the gusset and cylinder is typically formed through welding, it is necessary to take into account welding effects. According to the relevant literature [30,31], the ultimate tensile strain of the liner in welded areas is assumed to be reduced by 40%, while the yield strength and ultimate strength are assumed to remain unchanged.
# Responses of Water Temperature and Level to Large Earthquakes in Tibet
## 3 Results
### Co-Seismic Response of Underground Fluid Observation Wells at Lhasa Station  
In the digital water temperature and water level series measured at the fluid well at the Lhasa geomagnetic station, 15 co-seismic (or postearthquake) responses were recorded, of which the water level series recorded 10 responses (Table 2). The statistical results show that the response ratio of the digitized water temperature to the same earthquake (postseismic) was higher than that of the digitized water level; this difference may be related to faulty water level instrumentation or to the segmented record [13]. However, no water level data were recorded alongside the water temperature data following two earthquakes in Tibet. From the perspective of time, this lack of data may have been related to the probe. Both earthquakes were co-seismic events that occurred after the probe was replaced in 2016 (an earthquake of magnitude 6.9 occurred in Minlin County, Linzhi, Tibet on 18 November 2017, and an earthquake of magnitude 6.3 occurred in Metok in 2019).  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Time of** \\ **Earthquake** \\ \end{tabular} } & \multirow{2}{*}{**Lat (\({}^{\circ}\))**} & \multirow{2}{*}{**Lon (\({}^{\circ}\))**} & \multirow{2}{*}{**Lon (\({}^{\circ}\))**} & \multirow{2}{*}{**Magnitude** (\({}\)**Ms)**} & \multirow{2}{*}{**Location**} & \multirow{2}{*}{**Episement** (\({}\)**km)**} & & \multicolumn{2}{c}{**Variation Range**} & \multicolumn{2}{c}{**Co-seismic Response**} \\ \cline{1-1} \cline{6-10}  & & & & & & & **Water** (\({}^{\circ}\)C) & & & **Water** (\({}^{\circ}\)C) & & **Water Temperature** \\ \hline
2011-03-11 & 38.10 & 142.60 & 20 & 9.0 & Japan & 4790 & 0.101 & 0.005 & Yes & Yes \\
2015-04-25 & 28.20 & 84.70 & 20 & 8.1 & Nepal & 638 & 0.046 & 0.011 & Yes & Yes \\ T14:11:26.0 & 2008-05-12 & 31.00 & 103.4 & 14 & 8.0 &
\begin{tabular}{c} Wenchuan, \\ Sichuan \\ \end{tabular} & 1200 & 0.075 & 0.0079 & Yes & Yes \\ \hline
2016-03-02 & -4.90 & 94.21 & 20 & 7.8 & Sumatra & 3870 & 0.009 & 0.0005 & Yes & Yes \\
2015-05-12 & 27.80 & 86.10 & 10 & 7.5 & Nepal & 522 & 0.005 & 0.0050 & Yes & Yes \\ T15:05-18.0 & 2015-12-07 & 38.20 & 72.90 & 30 & 7.4 & Tadzhikistan & 1920 & 0.017 & 0.0008 & Yes & Yes \\
2014-02-12 & 1717:19.50.0 & 36.10 & 82.50 & 12 & 7.3 &
\begin{tabular}{c} Yutian, \\ Xinjiang \\ \end{tabular} & 1070 & 0.017 & 0.0020 & Yes & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistical table of the responses of the digitized water level and water temperature to large earthquakes in the Lhasa geomagnetic platform well.  
Figure 5: Annual dynamic characteristics of water temperature (unit: \({}^{\circ}\)C) over the period from 2010 to 2021.
# Opportunities and Challenges for Error Control Schemes for Wireless Sensor Networks: A Review
## 4 Review on Energy Consumption and Efficiency Model
### Single Hop Network
This tool is available as an online resource that can be used to estimate instruction energy consumption and overall energy consumption for programs evaluated on the StrongARM SAA-1100 and Hitachi SH-4 microprocessors. The key disadvantage of using this tool is that the tool is not updated according to the changes in sensor technologies. [PERSON] et al. estimate power consumption in Field Programmable Gate Array (FPGA) using Xilinx Integrated Software Environment along with Xilinx Power using Xilinx Virtex-EXCV200E6 CS144 and in Application Specific Integrated Circuit (ASIC) is based on Synopsys's Design Compiler and Design Vision [69]. The power analysis is performed using VHDL design using technology independent components. The power report is generated after compilation using power constrained scenarios. Finally, a report is generated to estimate power consumption in both technologies, and it is found that the power consumption in ASIC implementation is much less than FPGA implementation [69]. The outcomes from this paper is experimentally based and good at the time of research. However, those results need to be updated according to recent changes in ASIC and FPGA technologies. In [70], the authors compare the performance of decoders without considering transmission, electronics and encoding cost. The paper introduces a measure called Decoding Energy per Bit (DEB) and savings on transmit energy per bit metrics. DEB is evaluated using decoder power consumption, decoding throughput and power supply voltage. The paper presents DEB and savings on transmit energy per bit for RS and convolutional code. The paper claims that the use of FEC is not energy efficient for a short distance due to encoding and decoding overhead. The paper further claims that RS code with efficient implementation can reduce energy consumption of sensor node. However, these claims are superficial as the results are based on DEB, which does not consider transmission cost. In the SHAS network (as discussed in Section 3.2), it is assumed that a central base station has access to power supply and possesses no computational and power constraints. Thus, the decoding cost and any other energy cost at the base station do not need to be considered in the SHAS network. Therefore, the energy consumption model or energy analysis only need to be considered only at the sensor node but not at the base station. Therefore, the energy efficiency equation (i.e., Equation (19)) reduces significantly for a SHAS network. The energy efficiency analysis and model used for a multi-hop network is entirely different and need to be handled differently.
## 4 Conclusions  
Experiments and kinetics of NO reductions of rice husk char were investigated in a fixed-bed reactor. The effects of CO concentration, reaction temperature, initial NO concentration, and char  
Figure 8: Arrhenius plots for NO reduction with rice husk char.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Char-NO** & **Char-NO–CO** \\ \hline n & 0.71 & 0.82 \\ E\({}_{\alpha}\) & 60.21 & 34.60 \\ (k\textgreater{}mol\({}^{-1}\)) & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Reaction orders and activation energies for NO reduction of rice husk char.  
loading on the heterogeneous NO reduction were analyzed. Based on this study, the conclusions can be drawn as follows.  
In the absence of CO, the NO reduction reaction could be represented by the direct reaction of NO with char. An enhanced NO reduction by CO over char and a decreased carbon consumption were found with increasing CO concentration. At higher levels of CO (CO/NO = 10), the consumption of carbon was nearly zero and the char-NO-CO reaction dominated the overall reaction.  
NO reduction was increased with increasing temperature for a given CO concentration. The influence of CO concentration on NO reduction was strengthened with decreasing temperature, and this could be due to a low reactivity of char at low temperature. Char just provided a surface to catalyze the NO-CO reaction with CO/NO = 10, which was independent of temperature (750-900 \({}^{\circ}\)C).  
The enhancement of NO reduction caused by increasing the initial NO concentration was increasingly significant with increasing CO/NO ratio. Increasing the char loading led to a gradually decreased influence on NO reduction with increasing CO/NO ratio. It is not suggested that NO reduction can be enhanced simply by increasing the amount of char at excess CO/NO ratio.  
Reaction orders in the NO concentration were found to be 0.71 and 0.82 for the char-NO reaction and char-NO-CO reaction, respectively. The activation energy was 60.21 kJ-mol\({}^{-1}\) for NO reduction by char, against 34.60 kJ-mol\({}^{-1}\) for NO reduction by CO over char.  
Conceptualization, N.L. and S.C.; methodology, S.C.; formal analysis, N.L., Y.W., and X.J.; investigation, N.L., Y.W., and S.C.; writing--original draft preparation, N.L.; writing--review and editing, S.C.; supervision, X.J.; and funding acquisition, Y.W. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the National Natural Science Foundation of China (No. 51702007) and Foundation for Innovative Research Groups of the National Natural Science Foundation of China (No. 51621003).  
The authors would be very grateful to the anonymous reviewers for their suggestions and comments on improving the quality of the paper.  
The authors declare no conflict of interest.
# Study on Lubrication Performance of Journal Bearing with Multiple Texture Distributions
## 4 Lubrication of the Concave/Convex Textured Bearing
### Effect of Texture Depth  
With a fixed area density \(\lambda\) of 0.5, simulations with different texture depths \(h_{p}\) from \(5\times 10^{-7}\) to \(3\times 10^{-6}\) m are performed. The load capacity \(W\) and the friction coefficient \(\mu\) of the plain bearing, the concave textured bearing, and the convex textured bearing are plotted in Figure 4. For \(h_{p}=5\times 10^{-7}\)m, the load capacity of the bearing with convex texture is the highest, that of the plain bearing is in the middle, and that of the concave textured bearing is the lowest. The load capacity of the bearing with convex spherical texture increases with increasing texture depth \(h_{p}\) from \(5\times 10^{-7}\) m to \(3\times 10^{-6}\) m, while that of the concave textured bearing decreases in the same case. Taking the load capacity of the plain bearing (1014 N) as the baseline value, when the texture depth \(h_{p}\) is increased to \(3\times 10^{-6}\) m, the load capacity of the convex textured bearing (1171 N) greatly improves by 7.93%, while that of the concave textured bearing decreases by 6.54%. The friction coefficient, seen in Figure 4b, is not affected by the texture depth as much as the loading capacity. The friction coefficient of the convex textured bearing reduces with increasing area density, but the decrement is slight, and that of the concave textured bearing increases a little bit. The lowest friction coefficient of the convex textured bearing can be achieved (0.072) at \(h_{p}=3\times 10^{-6}\) m, the improvement is only 0.96% comparing with the plain bearing (0.0727), and that of the concave (0.0728) gets even worse, but also to a trivial degree of 0.14%.  
Figure 4: Effect of the texture depth on (**a**) the load capacity and (**b**) the friction coefficient.
## 2 Methodology
* VT setting * Image capture: definition of physical spots to capture interior and exterior images of the building, combining aerial and ground photographs using a 360\({}^{\circ}\) camera [65] and a drone [66]. This sequence is recurrent over time, allowing to capture spatial arrangement changes. * Image processing: captured images are processed to develop the website VT using the website PhotoSphereViewer library [71] and the JSON configuration files to establish a chronological framework for various historical periods in a specific location and to identify associated named locations. * VT link to IoT: the creation of identifiable objects in the VT to establish a request to the real-time IoT database. This allows the identification of certain elements corresponding to controllers or actuators in the real space. As a case study, a temperature reader has been used to display the historical values of temperature in a specific room in relation to the global external temperature. * VT link to BIMs: the upload of BIMs into a Common Data Environment (CDE) and establishment of a connection between the photo location in the VT--_'the room'_--and the related room object in the BIMs (i.e., IfcSpace). All information associated with the room should be accessible from the VT. * Accreditation: the implementation of a module to manage user accreditation to access certain spaces and data. Accreditation takes place at four levels: (a) public (can visit only non-confidential spaces); (b) building users (+ access to certain confidential spaces and data); (c) group leaders (+ access to sensible confidential data); and (d) managers (access to the entire system). * Evaluation: testing connectivity and evaluating system implementation and replicability. * Scalability: the development is implemented in the building currently housing the Smart Living Lab, which includes 34 offices and 3 meeting rooms and large communal spaces [68]. The use of this ongoing process allows us to evaluate the tasks, effort, and operability problems encountered in the transition from the test bed to a real-life building.
## 3 Results
### ITS Results from UAV-LiDAR Points under Leaf-On Conditions  
The three seed points based ITS methods (i.e., the seed points based Korean method: Seeds + Kmeans, the seed points based Dalponte2016 method: Seeds + Dalponte2016, and the seed points based Silva2016 method: Seeds + Silva2016) using seed points extracted during the leaf-off season are more effective than the four ITS methods (Dalponte2016, Li2012, PTrees, LayerStacking) for ITS from UAV-LiDAR data during the leaf-on season (Figure 9 and Table 5). The \(F\) score for the three seed points based ITS methods are all stable (i.e., exceeding 0.9; Figure 9). The results of these three seed points based ITS  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Plot**} & \multirow{2}{*}{**Reference**} & \multicolumn{6}{c}{**Local Maximum-CHM**} & \multicolumn{6}{c}{**Local Minimum-PCD**} & \multicolumn{6}{c}{**Mean Shift**} \\ \cline{3-14}  & & **Trees** & **TP** & \(r\) & \(p\) & \(F\) & **TP** & \(r\) & \(p\) & \(F\) & **TP** & \(r\) & \(p\) & \(F\) \\ \hline Y8-1 & 57 & 57 & 1.00 & 1.00 & 1.00 & 57 & 1.00 & 1.00 & 1.00 & 57 & 1.00 & 1.00 & 1.00 \\ Y11-1 & 42 & 42 & 1.00 & 0.91 & 0.95 & 42 & 1.00 & 0.98 &methods are significantly different for the Y16-2 sample plot (\(F\) = 1.00, 0.97, and 0.93 for Seeds + Silva2016, Seeds + Dalponte2016, and Seeds + Kmeans, respectively). All three seed points based ITS methods have the same F score in the sample plots of Y8-1, Y12-1, Y12-2, Y14-2, Y14-4, and Y17-2 (Figure 9). In contrast, the F score for the four ITS methods without seed points varied widely among sample plots with significantly low accuracy in the sample plot of Y12-2 (Figure 9). Overall, Seeds + Silva2016 performed best for ITS (418 trees are correctly segmented of a total of 421 trees from the UAV-LiDAR data during leaf-on conditions; Table 5). The Seeds + Silva2016 method also had the highest overall \(F\) score (0.99, Table 5), compared to the Seeds + Kmeans and the Seeds + Dalponte2016 methods (0.98, Table 5). For the four ITS methods without seed points, the Dalponte2016 and Li2012 methods performed better (i.e., with the overall \(F\) score of 0.91; Table 5) than the other two ITS methods (Table 5).
# Quantum LDPC Codes Based on Cocyclic Block Matrices
## 3 Quantum Codes Based on Cocyclic Quasi-Jacket Block Matrices
### Cocyclic Matrix Derived from Jacket Block Matrices
#### 3.2.1 Quasi-Jacket Block Matrices with Size \(4^{m}\) by \(4^{m}\)
Similarly, we can form another group \(\mathcal{B}\) based on \(P_{4}\), and take the element \(B^{i}_{4^{m}}\) (same structure to \(A^{i}_{4^{m}}\)) as the generator \(G^{z}\). As a result, a large-length generator matrix with size \(4^{m}\) by \(4^{m}\) is gained.
## 4 Performance Comparison of Observability Indices
### The Process of Numerical Simulation of Kinematic Calibration
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Algorithm** & \multicolumn{4}{c}{**Improved DETMAX**} & \multicolumn{4}{c}{**Traditional DETMAX**} \\ \hline Number of poses & 20 & 30 & 40 & 20 & 30 & 40 \\ \(O_{1}\) & 0.0357 & 0.0361 & 0.0362 & 0.0352 & 0.0355 \\  & \(O_{2}\) & \(2.459\times 10^{-4}\) & \(2.457\times 10^{-4}\) & \(2.463\times 10^{-4}\) & \(2.432\times 10^{-4}\) & \(2.443\times 10^{-4}\) & \(2.441\times 10^{-4}\) \\ Results & \(O_{3}\) & \(1.395\times 10^{-3}\) & \(1.709\times 10^{-3}\) & \(1.973\times 10^{-3}\) & \(1.393\times 10^{-3}\) & \(1.707\times 10^{-3}\) & \(1.970\times 10^{-3}\) \\  & \(O_{4}\) & \(3.413\times 10^{-7}\) & \(4.165\times 10^{-7}\) & \(4.784\times 10^{-7}\) & \(3.337\times 10^{-7}\) & \(4.104\times 10^{-7}\) & \(4.738\times 10^{-7}\) \\  & \(O_{5}\) & \(4.361\times 10^{-4}\) & \(5.399\times 10^{-4}\) & \(6.255\times 10^{-4}\) & \(4.314\times 10^{-4}\) & \(5.379\times 10^{-4}\) & \(6.247\times 10^{-4}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance comparison between improved DETMAX algorithm and traditional DETMAX algorithm. Let \(\mathrm{d}q_{\mathrm{B}}=\mathrm{d}q_{\mathrm{B}}\)\", \(q_{\mathrm{B}}=q_{\mathrm{B}}\)\", \(\mathrm{C}=\mathrm{C}\)\", and \(\mathrm{k}=\mathrm{k}+1\). Then, return to step 3 and continue to execute, until the condition in step 5 is met, then end the iteration. After the iteration, calculate the difference between \(\mathrm{C}\)\" and \(\mathrm{C}\), then the result \(\Delta\)C represents the identification result of the kinematic parameter error of the six-hardpoint positioning mechanism. \(\mathrm{C}\)\" is the kinematic parameter after calibration and the kinematic model K\" based on \(\mathrm{C}\)\" is the kinematic model after calibration.