esa-sceva/satcom-chunk-collection huggingface dataset:
# The Impact of Polycentric Structure on CO\({}_{2}\) Emissions: Evidence from China
## 3 Results
### Robustness Checks  
#### 3.2.1 Endogeneity  
The results based on the 2 SLS estimator were presented in Columns (4) and (5) of Table 3. The estimation results of the first stage demonstrated that the associations between IVs and polycentricity were significant at a 1% level, and the F-statistic is 12.663, surpassing the threshold for a strong instrument. The K-P LM statistic was significant at a 1% level, indicating the IVs were related to the polycentric spatial structure. And the Hansen J-test indicated that there were no over-identification problems and the IVs were strictly exogenous.  
The results suggested that polycentric spatial structure supports CO\({}_{2}\) emission reduction after considering endogeneity. Compared with the coefficients in Columns (1)-(3), the coefficients estimated by 2 SLS were greater than those estimated by the two-way fixed effect. One plausible reason was that there might be reverse causality between polycentric spatial structure and CO\({}_{2}\) emission because air pollution could also affect the labor market, which would influence the spatial structure of cities [21]. The 2 SLS approach helped mitigate this potential source of bias.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **(1)** & **(2)** & **(3)** & **(4)** & **(5)** \\ \hline  & **FE** & **FE\_DK** & **FE\_BS** & **First Stage** & **Second Stage** \\
**Variables** & **Carbon** & **Carbon** & **Carbon** & **Q** & **Carbon** \\ \hline Q & \(-\)0.050 *** & \(-\)0.050 *** & \(-\)0.050 *** & & \(-\)0.772 *** \\  & (\(-\)2.61) & (\(-\)3.32) & (\(-\)2.48) & & (\(-\)4.06) \\ Hllh & & & & & 5.233 *** & \\  & & & & & (3.57) & \\ Unemploy & & & & & \(-\)0.029 *** & \\  & & & & & (\(-\)4.31) & \\ Control & YES & YES & YES & YES & YES \\ City FE & YES & YES & YES & YES & YES \\ Year FE & YES & YES & YES & YES & YES \\ Observations & 4513 & 4513 & 4513 & 4496 & 4496 \\ R-squared & 0.947 & 0.947 & 0.947 & & 0.882 \\ K-P LM statistic & & & & & 24.219 *** \\ K-P Wald F statistic & & & & & 12.663 \\ Hansen J statistic & & & & & 0.219 \\ \hline \hline \end{tabular} Notes: t/z-values are in parentheses; **, and *** represent the 5%, and 1% significance levels, respectively; the coefficients of the control variables (i.e., the secondary industry as a percentage of GDP, foreign direct investment, population density, the percentage of budget fiscal expenditure and technology expenditure on GDP, and the ratio of the employed population), year and time dummies are not reported to save space; the unit of river/exchange rate is %.  
\end{table}
Table 3: The impact of polycentric spatial structures on CO\({}_{2}\) emissions.
# Solar Radiation Parameters for Assessing Temperature Distributions on Bridge Cross-Sections
## 4 Estimation Model for Solar Radiation in Fujian
### Estimation Model for Daily Global Solar Radiation
#### Influence of Time Scale on Empirical Coefficients  
The daily clearness index and the sunshine duration fraction (both averaged over one month) were chosen by [PERSON] and [PERSON] [10; 11]. The daily solar radiation data measured by 36 meteorological stations in China were compared with the calculated daily solar radiation using different time scales. The accuracy of estimated solar radiation using the monthly time scale is higher than those using daily or yearly time scales [59]. Fuzhou was chosen as an example to analyze the influence of different time scales (daily, monthly and yearly) on the empirical coefficients in the Angstrom-Page equation. The results are listed in Table 1 and illustrated in Figure 4, in which the hollow points denote the data and the solid line denotes the linear regression. The coefficient of correlation (\(\gamma_{\rm xy}\)) for the linear regression analysis using the monthly time scale is the largest among different time scales. Moreover, the root-mean-square error (RMSE) using the monthly time scale is the smallest. As a result, the monthly time scale was chosen to calculate the empirical coefficients in the Angstrom-Page equation.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Time Scale Types** & **Data Points** & \(\mathbf{a^{\prime}}\) & \(\mathbf{b^{\prime}}\) & \(\mathbf{\gamma_{\rm xy}}\) & **RMSE** \\ \hline Daily & 8030 & 0.213 & 0.505 & 0.86 & 0.10 \\ Monthly & 264 & 0.196 & 0.553 & 0.92 & 0.03 \\ Yearly & 22 & 0.242 & 0.422 & 0.79 & 0.01 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Linear regression analysis of clearness index and sunshine duration fraction using different time scales.  
Figure 4: \(Cont.\)
## 2 Materials and Methods
### Absorption Coefficients and Refractive Indexes Calculations  
The DOE, as discussed in Section 2.2, did not incorporate the potential existence of a volume fraction of air, \(\
u_{air}\), within the pellets, which could modify the optical parameters. Thus, in order to obtain more precise values of the saccharides' absorption coefficient and refractive index, the value of \(\
u_{air}\) was estimated for all pellets. As already reported in Section 2.3, the mass of the pellets was always lower than the sum of the masses of saccharides and PET used for their preparation. Then, the actual masses of saccharides and PET in the pellets, \(m_{s}^{a}\) and \(m_{PET}^{a}\), respectively, were estimated. The overall lost mass in the preparation is \(m_{p}^{loss}=m_{p}-(m_{s}+m_{PET})\), where \(m_{p}\) is the weighed mass of the pellet. It is then possible to estimate \(m_{s}^{a}\) and \(m_{PET}^{a}\) by assuming that their lost masses, \(m_{x}^{lost}\), are proportional to their weighted mass percentage in the pellet:  
\[m_{x}^{a}=m_{x}-m_{x}^{lost}=m_{x}-\frac{m_{x}}{m_{p}}m_{p}^{loss} \tag{11}\]  
where \(x=s,PET\).  
From the knowledge of the mass densities of the saccharides and PET (values used in the calculations are shown in the Supplementary Materials), it is then possible to calculate their actual volume fraction, \(\
u_{x}^{a}\), as  
\[\
u_{x}^{a}=\frac{m_{x}^{a}}{V_{p}\rho_{x}} \tag{12}\]  
Since \(\
u_{air}+\
u_{PET}+\
u_{s}=1\), it is possible to recover \(\
u_{air}\). The results are shown in Table 1.  
Such values can now be used to calculate the absorption coefficients, \(\alpha_{s}\), and the refractive indexes, \(n_{s}\), of the saccharides. Since the absorption coefficient and the refractive index of the pellets, \(\alpha_{p}\) and \(n_{p}\), respectively, are  
\[\alpha_{p}=\alpha_{s}\
u_{s}+\alpha_{air}\
u_{air}+\alpha_{PET}\
u_{PET}. \tag{13}\]\[n_{p}=n_{s}v_{s}+n_{air}v_{air}+n_{PET}v_{PET}. \tag{14}\]  
by assuming \(\alpha_{air}\) = 0 and \(n_{air}\) = 1 in the in the measured THz range, then  
\[\alpha_{s}=\frac{\alpha_{p}-\alpha_{PET}v_{PET}}{v_{s}}, \tag{15}\]  
and  
\[n_{s}=\frac{n_{p}-v_{air}-v_{PET}n_{PET}}{v_{s}}. \tag{16}\]  
Values of \(\alpha_{PET}\) and \(n_{PET}\) were recovered from measurements of pure PET pellets. The behaviors of \(\alpha_{s}\) as a function of the frequency are shown in Section 3, while those of \(n_{s}\) are shown in Figure S11 of the Supplementary Materials.
## 5 Conclusions  
EIS measurements were conducted for steel embedded in concrete in a chloride environment in various conditions. First, it was confirmed that considerable variations in EIS data were involved that led to complex interpretations. Hence, it is necessary to pay attention to the analysis of the EIS results. It should be noted that this suggestion is not unique to the description of the measured data for corrosion processes. The main observations of this study are as follows:  
1. The impedance response with the contact media was somewhat consistent except for a simulated pore solution. In other words, when corrosion measurements are carried out, the types of contact media are not important because the ohmic resistance included both interfacial effects and concrete resistance.
2. The ohmic and polarization resistance obtained from EIS data can be used to estimate the corrosion rate. However, it is evident that additional studies in the polarized area are still required to evaluate corrosion rate accurately. Depending on the polarized area, the polarization and ohmic resistances are affected considerably.  
Figure 12: Polarization resistance with influencing factors for active state.  
From the simulated values, the influencing factors on the impedance response were ordered as contact pressure > location of counter electrode > contact media. However, it should be noted that the main factor was the estimation of the true polarized area required for the determination of the polarization resistance.  
J.K., conceptualization, methodology, data curation, investigation, formal analysis, writing--original draft preparation, and writing--review and editing. K.-T.P., formal analysis, conceptualization, funding acquisition. T.H.K., methodology, visualization, writing--original draft preparation. All authors have read and agreed to the published version of the manuscript.  
Research for this paper was carried out under the KICT Research Program (project no. 20220217-001, Development of DNA-based smart maintenance platform and application technologies for aging bridges) funded by the Ministry of Science and ICT.  
Not applicable.  
The authors declare no conflict of interest.
# [PERSON]: Extreme Plastic Pollution Pathways in Riparian Waterways
## 4 Findings
### Directional Behavior (Turning Angles, Bearings)  
The directional behavior of the tracker was analyzed using turning angles and bearings, providing insight into how the tracker's movement interacted with the river's environmental conditions.  
The tracker's turning angles, calculated as the change in direction between consecutive bearings, ranged widely from 0\({}^{\circ}\) to 360\({}^{\circ}\). The mean turning angle was 174.77\({}^{\circ}\), indicating frequent sharp or near-complete directional changes, which were likely caused by currents, obstacles, or eddies in the river. This variability, with a standard deviation of 79.81\({}^{\circ}\), highlights the influence of environmental forces on the tracker's path. Periods where the tracker was stationary (resting states) were excluded from the analysis, as turning angles were undefined in these instances.  
The bearings, representing the direction of movement relative to true north, revealed a predominantly south-southwest trajectory with an average heading of 187.66\({}^{\circ}\). The bearing between consecutive GPS coordinates was calculated to determine the direction of movement relative to true north. This directional pattern aligns with the general flow of the Saigon River, suggesting that the tracker consistently followed the river's natural course. Variability in bearings reflects localized deviations due to currents or interactions with obstacles.  
Figure 6: Pathway of tracker from launch point until end point (image).
## 4 Simulation Results
### Reliability and Effectiveness Experiments  
An MMC grid-connected system bottom on the VSG emulation model was created in MATLAB/Simulink to validate the dependability and efficacy of the proposed prototype for optimizing specific parameters by integrating the simulation results to examine the dynamics and reaction times of the system.  
The simulation time of each episode was set to 10 s before the training, and the sampling time was set to 0.0005 s. Additionally, the random disturbance must be added to the environment to aid the model in determining the best action decision-making mechanism and assessing the model's robustness. The parameters for the upcoming experiment are listed in Table 4.  
The MMC has different structural properties from other inverters. It comprises several submodules in each arm. The dispersion and volatility of the submodule capacitor voltage are the main determinants of whether or not the MMC can run steadily. The output voltage is displayed in Figure 12 when the MMC is off grid. The MMC has 17 output levels, which complies with the 2N + 1 rule.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Simulation Parameters** & **Symbol** & **Value** \\ \hline Simulation Time & \(T_{s}\) & 10 s \\ Sampling Time & \(T_{f}\) & 0.0005 \\ Fixed VSG Inertial & \(J_{M\text{MC}}\) & 0.4 \\ Fixed VSG Damping & \(D_{\text{MMC}}\) & 8.4 \\ DC Side Voltage & \(U_{dc}\) & 800 V \\ Grid Line-Line Voltage & \(E_{ab}\) & 380 V \\ System Rated Frequency & \(f\) & 50 Hz \\ Sub-Modules of MMC Bridge & / & 17 \\ Sub-Module Capacitance & \(C_{SM}\) & 60 uf \\ PIT Parameter for Current & \(K_{p}/K_{i}\) & 180/3200 \\ Inner Loop & \(L_{0}\) & 1.5 mH \\ Arm Resister & \(R_{0}\) & 0.03 \(\Omega\) \\ AC Side Inductance & \(L_{i}\) & 2.5 mH \\ AC Side Resister & \(R_{i}\) & 0.05 \(\Omega\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: The comparison experiment parameters.  
Figure 11: Episode reward for TD3 agent training.  
Each phase of the MMC has a lower and an upper bridge arm with eight identical submodules. The bridge arm of each phase is shown by their actual measured capacitor voltage values in Figure 13. It can be concluded that the typical capacitor voltage sequencing mechanism in NLM is used for the submodule capacitor voltage balancing. As a priority, the MMC will charge the capacitor submodule with the lowest voltage level and discharge that with the highest voltage level. The capacitor voltage swings from 95 to 110 V can be seen in the waveform, which satisfies the criteria for steady operation of the MMC. Simulations in this part also demonstrate the success of the modulation method and the functionality of TD3-based VSG-MMC.
## 3 Results
### VSV Kernel for Lung Tissue  
The lung and ST VSV kernel used for convolution calculations are shown in Figure 3 as a plot of the \(AD\) to the target voxel per unit decay versus the target-source voxel distance. The lung VSV kernel is also provided as supplemental material for this work (S-values kernel for the lung tissue of 0.296 g/cm\({}^{3}\) over 2.21 mm voxel size, available in csv formatted file as position indices (\(i,j,k\)) followed by the absorbed dose values expressed in mGy/MBq-s). For the lung \(AD\) data, a long and widespread tail is observed starting at approximately 38 mm. This is due to the greater predominance of the bremsstrahlung to the \(AD\) for target voxels farthest from the radiation source, with an \(AD\) ranging from approximately \(\left(10^{-8}\text{--}10^{-7}\right)\frac{\text{mGy}}{\text{MBq}+\text{s}}\) (Supplementary Material). The VSV kernel for the lungs, calculated in this work, was validated by comparing the lungs' \(\overline{AD}\) obtained using the SVOX method with that obtained from \(MC\) simulations. This comparison was performed with the reference phantom, which had activity only in the lungs, a \(LS\) of 10%, and a uniform lung density of 0.296 g/cm\({}^{3}\). Before performing SVOX calculations with the lung VSV kernel, the liver activity was cropped from the activity map. This is because the presence of  
\begin{table}
\begin{tabular}{c c c c} \hline \hline _Lung Shunt_ & \(\textbf{U}_{ROI}\) (\%) & \(\textbf{u}_{min}\) (\%) & \(\textbf{u}_{max}\) (\%) \\ \hline
10\% & 2.5 & 1.1 & 5.6 \\
20\% & 1.8 & 0.8 & 3.9 \\
30\% & 1.5 & 0.6 & 3.4 \\
40\% & 1.3 & 0.6 & 2.7 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Uncertainty (CL = 95%) of the \(MC\) simulation for each \(LS\) case. \(U_{ROI}\) corresponds to the \(\overline{AD}\) uncertainty (Equation (5)), while \(u_{min}\) and \(u_{max}\) represent the minimum and maximum values of the relative uncertainty of the absorbed dose at the voxel level. An example of the \(AD\) distribution and relative uncertainty map is shown in Figure 2.  
Figure 2: \(AD\) distribution (values expressed in Gy/GBq) within lungs (**A**) and the associated relative uncertainty map (**B**) for \(LS\) = 40%.  
liver activity produces a cross-irradiation from the liver to the lungs that is significantly higher than that obtained by the MC simulation. With these assumptions, \(\overline{AD}\) to lungs was 4.29 Gy/GBq with the SVOX approach, which is in good agreement with 4.23 Gy/GBq obtained from the MC simulation (\(RD\) = 1.5%). When using the SVOX calculation with the lung VSV kernel and considering only the liver activity, the cross-irradiation from the liver to the lungs was approximately 0.60 Gy/GBq. This value is considerably higher than the 0.18 Gy/GBq obtained by the MC simulation.
## 1 Introduction
The structure of the weighting coefficients of the HDQM is also studied [25]. Comparative study shows that the HDQM can yield more accurate higher mode frequencies [26]. The HDQM has successfully analyzed the bending of circular plate [27] and geometrically non-linear behavior of doubly curved shell [28]. [PERSON] combined the HDQM with the DQM or with the finite difference method (FDM) to study the buckling and dynamic behavior of rectangular plates [29; 30]. Recently, the HDQM is used to study the buckling and vibration of a pressurized CNT (carbon nano tube) reinforced functionally graded truncated conical shell under an axial compression [31]. Compared with the DQM, however, the HDQM has not been widely used, since its solution accuracy is slightly lower than the DQM when the number of grid points is small and the same when the number of grid points is large. Obtaining the static behavior of sandwich panels under locally distributed load including the concentrated load is regarded as a challenging problem for point discrete methods such as the DQM and HDQM. Although the problem can be solved using the differential quadrature element method (DQEM) [15; 16], difficulty may be also encountered when the load is distributed in a very small area, since the aspect ratio of the element where the load is applied is very large. Besides, the simplicity and computational efficiency may be lost as comparing to the DQM and HDQM, especially when more locally distributed loads are applied on the sandwich panel. In such a case, more differential quadrature elements are required. Therefore, it is desirable to use the DQM or HDQM in the analysis for simplicity and computational efficiency considerations. A general and rigorous way is proposed to obtain the work equivalent load to circumvent the difficulties in dealing with the locally distributed loads by using the HDQM. The major objective of the present investigation is to use the HDQM for analyzing the static behavior of sandwich panels under locally distributed load. The soft-core is directly modeled by two-dimensional (2D) elasticity theory without a priori assumption. The numerical results are compared with finite element data for verifications. The effects of locally distributed loadson the displacement and stress distributions of the soft-core sandwich panels are investigated. Some conclusions are drawn based on the results reported herein.
# Experimental Study on Slender CFRP-Confined Circular RC Columns under Axial Compression
###### Abstract  
The test results on the performance of carbon fiber-reinforced polymer (CFRP)-confined reinforced concrete (RC) columns under axial compression load are presented in this study. Twelve slender CFRP-confined circular RC columns with a diameter of 200 mm were divided into two groups. Six specimens with different slenderness ratios of 12, 20, 32, 40, 48, and 56 were contained in each group. The experimental results demonstrated the circumferential CFRP wrap was effective in enhancing the ultimate axial load of slender CFRP-confined circular RC columns compared with unwrapped RC columns. The experimental investigation also showed that the slenderness of the specimens had important influences on the axial compressive behavior, and the axial bearing capacity of slender CFRP-confined circular RC columns decreased as the slenderness ratio increased. In order to predict the load-carrying capacities of slender CFRP-confined circular RC columns, a formula was proposed and the prediction agreed with the experiments. The slenderness of slender CFRP-confined circular RC columns was recommended to be less than 26.5 in practical engineering.  
CFRP; circular RC columns; slenderness ratio; axial load-carrying capacity +
Footnote †: journal: Applied Sciences
# Reversible Data Hiding in Encryppted Images with Extended Parametric Binary Tree Labeling
## 3 Proposed EPBTL-RDHEI
### Pixel Labeling and Data Hiding
#### 3.2.3 Labeling of Non-Embeddable Group  
Our labeling of the non-embeddable pixels is completely different from PBTL or IPBTL. By extending the representation range of prediction error, we further divide the non-embeddable group into the self-recordable group \(P_{ns}\) and the non-recordable group \(P_{m}\). When the prediction error satisfies Equation (6), it can be represented with \((8-\beta)\) bits and recorded in its current position and classified as a self-recordable pixel.  
Suppose \(\alpha=3\), \(\beta=2\), and the embeddable range of prediction error is \(-e_{min}\) to \(+e_{max}\). Then, the \(8-\beta=6\) bits are used to represent prediction error of a self-recordable pixel. The leading bit is used as sign bit and the remaining five bits are used to represent \(e-e_{min}\) for \(e<0\) or \(e-e_{max}\) for \(e>0\). For example, a non-embeddable pixel with \(e=3\) is represented as '10000100', where the leading code is '1' for positive, '00001' represents the absolute error \(e-e_{max}\) and '00' is the group label. Thus, no auxiliary information is required to record a self-recordable pixel.  
For a non-recordable pixel in the \(P_{nn}\) group, in addition to the group label '00', the remaining six bits are marked with a stream of zeros '000000' and the whole eight bits are recorded as a part of the auxiliary information.
# A New Hybrid Dynamic FMECA with Decision-Making Methodology: A Case Study in an Agri-Food Company
## 2 Methodology
### Criticality Analysis (CA)  
The CA consists of a qualitative, quantitative, or semi-quantitative analysis used to identify the critical causes of a system failure or those where it is convenient to intervene most urgently. There are several methods to perform this analysis. One of these is to use the Hazard Score Matrix, in which only S and O are considered to assess the various risks of hazards (and incidents), and whose product is compared either with threshold values or through the Pareto principle analysis [33]. according to which 20% of the causes of faults cause 80% of the total faults.  
The Pareto principle of 80-20 is applied to the EDAS method's ranking to identify the most important critical issues.  
In traditional FMECA, once the causes of critical failures have been identified, they are analysed individually and in a static way, i.e., without seeing how they evolve. Furthermore, in doing so, the interdependencies between the different failure modes are not considered [12; 31]. This is a weakness strongly discussed by scholars--the lack of dynamism. In doing so, one never looks at the machine's totality and how its conditions change over time.  
A criticality analysis with a System Dynamics model is carried out to overcome this problem.  
System Dynamics is an approach to considering a system not as a set of single independent components but as a single complex system in which causal relationships feed over time. Usually, simulation software captures the system's relational aspects and studiesits behaviour over time. The concept of time is essential because, in classic FMECA, the causes of failure are considered one at a time, without considering how one influences the other and how the sum of their contributions accumulates over time, increasing the system's criticality.  
In order to use System Dynamics correctly, it is first necessary to understand and be able to adequately represent the system's behaviour, finding and highlighting how the elements are reciprocally connected.  
Then, the criticality analysis is conducted in a dynamic way to examine how the simultaneity of the various causes of failure and their interactions influence the total probability of failure.