esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
(1) The effect of coupled action on methane adsorption is significantly stronger than that of single action. The effect of different actions on CH\({}_{4}\) adsorption: coupling action \(>\) single temperature action \(>\) single water content action. The optimal combination of the effects on methane adsorption was temperature 363 K and water content 8.31%, and the CH\({}_{4}\) adsorption was reduced by 67.61% under the coupling effect compared with that of CH\({}_{4}\) adsorption without water at room temperature.  
(2) Considering the effects of temperature and water content comprehensively, the empirical equations for the effects of the coupling of the two on CH\({}_{4}\) adsorption amount were obtained based on analyzing the correction coefficients of temperature and water content.  
(3) Temperature has a negative effect on the adsorption of CH\({}_{4}\). The adsorption of CH\({}_{4}\) is an exothermic reaction. Temperature will change the average kinetic energy of CH\({}_{4}\) molecules, thereby changing the adsorption of CH\({}_{4}\). The absolute adsorption capacity of CH\({}_{4}\) at low temperatures is significantly greater than that at high temperatures.  
(4) The increase in water content has an inhibitory effect on the adsorption of CH\({}_{4}\). An increase in H\({}_{2}\)O decreases the heat of adsorption. Water molecules can more easily occupy the advantageous adsorption sites due to their small kinetic diameter and structure, which is more favorable for combining with the coal molecule structure and entering the coal molecule. This reduces the adsorption of CH\({}_{4}\) by coal.  
G.H. writing--original draft, data curation, and data analysis. Y.G. and J.C., review and editing, conception, methodology, and software. S.Z. and Z.L., data curation, investigation. All authors have read and agreed to the published version of the manuscript.  
Project 52334007 supported by National Natural Science Foundation of China. Supported by Fundamental Research Program of Shanxi Province (202203021211160). Supported by Postgraduate Teaching Reform Project of Shanxi Province (2022 YJ)[O39]. The Research Project Supported by Shanxi Scholarship Council of China (2022-053, 2023-057).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflicts of interest.  
Figure 10: Mechanism of influence of coupling on CH\({}_{4}\) adsorption.
# Automated Modal Analysis Using Stochastic Subspace Identification and Field Monitoring Data
## 2 Subspace Identification
### Implementation of Stochastic Subspace Identification (SSI)  
SSI begins with the arrangement of the block Hankel matrix, \(\mathbf{H}\), as  
\[\mathbf{H}=\frac{\mathbf{Y}_{p}}{\mathbf{Y}_{f}}=\frac{\left[\mathbf{y}_{1} \,|\,i\quad\mathbf{y}_{2}\,|\,i+1\quad\cdots\quad\mathbf{y}_{j}\,|\,i+j-1 \right]}{\left[\mathbf{y}_{i+1}\,|\,2i\quad\mathbf{y}_{i+2}\,|\,2i+1\quad \cdots\quad\mathbf{y}_{i+j}\,|\,2i+j-1\right]} \tag{3}\]  
where the lagged vectors are arranged as  
\[\mathbf{y}_{1}\,|\,i=\left[\mathbf{y}_{1}^{T}\quad\mathbf{y}_{2}^{T}\quad \cdots\quad\mathbf{y}_{i}^{T}\right]^{T} \tag{4}\]  
It is clear that the block Hankel matrix is split into two partitions; \(\mathbf{Y}_{p}\) and \(\mathbf{Y}_{f}\) are the past and future output Hankel matrix, respectively. From Equation (3), it is obvious that the total number of samples is equal to \(2i+j-1\).  
Then, the state (or system) matrix and output matrix can be inversely computed through the orthogonal projection, which projects the row space of the future outputs onto the row space of the past outputs.  
\[\mathbf{O}_{\text{orth}}=\mathbf{Y}_{f}\mathbf{Y}_{p}^{T}{\left(\mathbf{Y}_{p }\mathbf{Y}_{p}^{T}\right)}^{\dagger}\mathbf{Y}_{p} \tag{5}\]  
This approach is known as SSI-Data. Alternatively, these state-space matrices can be inversely computed through the covariance matrix.  
\[\mathbf{O}_{\text{cov}}=\mathbf{Y}_{f}\mathbf{Y}_{p}^{T} \tag{6}\]  
This approach is known as SSI-Cov. The column space of both approaches yields the extended observability matrix, constituting with \(\mathbf{A}\) and \(\mathbf{C}\). Hence, singular value decomposition (SVD) can be used to decompose the matrix as  
\[\mathbf{O}=\mathbf{U}\mathbf{S}\mathbf{V}^{T}=\begin{bmatrix}\mathbf{U}_{1}& \mathbf{U}_{2}\end{bmatrix}\begin{bmatrix}\mathbf{S}_{1}&\mathbf{0}\\ \mathbf{0}&\mathbf{S}_{2}\end{bmatrix}\begin{bmatrix}\mathbf{V}_{1}^{T}\\ \mathbf{V}_{2}^{T}\end{bmatrix}\approx\mathbf{U}_{1}\mathbf{S}_{1}\mathbf{V}_{ 1}^{T} \tag{7}\]  
where the dimension of \(\mathbf{S}_{1}\) reveals the order of the structural system (\(2n\)), and can be specified, at least theoretically, by considering the non-zero singular values of \(\mathbf{O}\). Understandably, \(\mathbf{S}_{2}\) consists of the zero singular values of \(\mathbf{O}\).  
Finally, SSI ends with its shift invariance property. The state (or system) matrix and output matrix characterizing the LTI structural system can be estimated as  
\[\mathbf{C}=\mathbf{U}_{1}(1:m,:) \tag{8}\]  
\[\mathbf{A}=\mathbf{U}_{1}^{\dagger}(1:im-m,:)\mathbf{U}_{1}(m+1:im,:) \tag{9}\]  
Once the state-space matrices are estimated, the identification of the modal parameters is then straightforward for the structural system.
## 2 Data Challenges and Costs
In both reports, [PERSON] estimated a global cost of US $254 billion for populating the SDG indicators over the 2016-2030 period, taking into account the need for the population census, living standards measurement studies, demographic and health surveys, core welfare indicator questionnaires, and multiple indicator cluster surveys. However, [PERSON] and [PERSON] [34] provided a much lower estimated cost of US $4.5 billion to populate the SDG indicators over the same period. However, these estimates do not consider the use of EO and 'big data', despite the UN \"Data for Development\" report (2015) suggestions that _\"cost savings might result from the use of new technologies\"_ and should result in better quality and efficiency, and that the new technologies _\"may yield lower costs in the long run, but in the near term, they are likely to require new investments\"_ ([6] p. 29). The report estimated a total cost of populating the SDG indicators to be US$902 to US$941 million per annum for all data types across 77 developing countries, where US$787 million would be attributed to traditional national statistical data and US$114 million to EO data (Table 2). EO derived data are increasingly being recognised as a promising resource for tackling the challenges involved in data collection for indicators and the Integrated United Nations Committee of Experts on Global Geospatial Information Management (UN-GGIM) and GEO play key roles in realising this promise. These agencies are working with statistical communities, at both global and national levels, to support them on how they could benefit from the use of EO data to monitor SDGs. As a result, NSOs in countries such as South Africa, the United Kingdom, Nigeria, Philippines, Sweden [27], USA [35], Colombia, Senegal, Sierra Leone, Kenya, Tanzania and Ghana [36] have started exploring the use of EO satellite imagery and geospatial frameworks, especially to address indicators of SDGs 1 (End Poverty), 2 (Zero Hunger), 6 (Clean water and sanitation), 11 (Sustainable cities), 13 (Climate Action), and 15 (Life on Land). EO data can also provide opportunities to measure the indicator at the subnational level [35].
## 2 Materials and Methods
### RTCVC Module  
Although the algorithm previously presented allows for collecting information related to station occupancy percentages, it does not provide a comprehensive perspective on the actual number of passengers in the stations. Therefore, it was decided to implement a complementary algorithm based on object recognition in images using the Azure platform and computer vision AI service. The service utilizes real-time images acquired from bus stations.  
The analysis performed on the station images yielded a list of coordinates corresponding to identified individuals. Each identified person was assigned a probability score, upon which relation (2) was applied. This approach approximates the number of people present in the stations.  
\[n_{pers}=\sum_{i=1}^{m}\partial(c_{i}), \tag{2}\]  
where:  
\(n_{pers}\) is the factor representing the number of identified objects as a person;  
_m_--the total number of objects identified;  
_c_i_--the probability that the identified object is a person;  
_d_--the defined function as follows:  
\[\partial(c_{i})=\begin{cases}1,\ if\ c_{i}\geq 0.5\\ 0,\ if\ c_{i}<0.5\end{cases} \tag{3}\]  
The following illustrates two examples of Azure service usage with images. In Figure 1, one person is waiting at a station. Since the entire body is visible and the light exposure is favorable in the picture, one person is identified after applying relation (2).  
Figure 2 illustrates a scenario involving seven individuals. Of these seven individuals, only five are correctly identified by the Computer Vision service, as two blend into the background because of low lighting conditions.  
To assess the accuracy of the person detection service using Azure Computer Vision, tests were conducted on 100 real-world images captured from various bus stations and different angles. The goal was to evaluate the service's confidence level in identifying individuals within different scenarios.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Google Maps Service Status** & **Congestion Factor** \\ \hline Light & 1 \\ Moderate & 2 \\ Heavier-than-usual & 3 \\ Heavy & 4 \\ Peak hours & 5 \\ Standstill & 6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Correlation between the congestion factor and Google Maps Service status.
# Symmetric-Key-Based Security for Multicast Communication in Wireless Sensor Networks +
## 2 Related Work
To the best of our knowledge, no symmetric-key-based method for group key management in distributed WSNs has been described in literature.
## 3 Results Analysis
### Displacement of Breakwater and Overtopping  
Displacement is a direct criterion to assess the stability of revetment breakwater. The horizontal and vertical displacements of structure B recorded in Test 3 are shown in Figure 6. As illustrated in Figure 6, the horizontal displacement of structure B is very small and can be ignored. However, the vertical displacement of structure B increases rapidly in the first hour. After that, the increment of vertical displacement gradually decreases in the following three hours, and it basically reaches the final stable state at the end of test. The final settlement of structure B is about 6 mm. It can be observed that the revetment breakwater built on the loose reclaimed coral sand foundation in the South China Sea will have a settlement of 6 cm under the long-term impacting of waves according to the geometrical scale. In fact, this magnitude of displacement will not cause irreparable damage to the revetment breakwater. Therefore, the stability of the revetment breakwater can be basically guaranteed.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Test No.** & **Dry Density * (kg/m\({}^{3}\))** & **SWL (m)** & **Wave Height (m)** & **Wave Period (s)** & **Armor Blocks** & **Time (h)** \\ \hline
1 & 1320 & 0.48 & 0.19 & 3.0 & No & 2 \\
2 & 1320 & 0.71 & 0.30 & 2.2 & No & 2 \\
3 & 1320 & 0.71 & 0.30 & 3.0 & No & 4 \\
4 & 1320 & 0.71 & 0.30 & 3.0 & Accropodes & 2 \\
5 & 1320 & 0.71 & 0.30 & 3.0 & Rubbles & 2 \\
6 & 1320 & 0.71 & 0.30 & 3.0 & Accropodes + Rubbles & 2 \\
7 & 1500 & 0.71 & 0.30 & 3.0 & No & 2 \\ \hline \hline \end{tabular}
* Note: Dry density of the reclaimed coral calcareous sand foundation.  
\end{table}
Table 3: Test conditions considering the wave parameters and the wave energy dissipation measure.  
seawater passes over the revetment breakwater, it cannot be discharged quickly back to sea due to the blocking of structure B. This seawater would be accumulated on the surface of the reclaimed land. It will return to the sea slowly through the underground seepage and drainage system. As a result, the overtopped sea water will cause some fatal ecological disasters, such as the death of vegetation behind the revetment breakwater and pollution of the desalinated underground water. The results of this physical model test suggest that the revetment breakwater has a poor resistance to overtopping under the impacting of extreme storm.  
Figure 7: Revetment breakwater is being impacted by regular wave and water overtopping is occurring, observed in Test 3.
## 3 Results and Discussion
### Resin Hydrophilicity  
Contact angle measurements are arguably the most widely used surface analysis technique directly related to polymer surface hydrophilicity, coating abilities, and is strongly representative of surface adhesion properties [22; 23]. Static contact angle measurements along with their standard deviations for resins prepared with tung oil, tung oil fatty acids, and tung oil methyl esters are listed in Table 1. As anticipated, the tung oil fatty acid resin exhibited the most favorable interactions with water, reflected in the lowest contact angles measured. These results indicate an increase in the hydrophilicity of the polymer when tung oil is replaced with a more polar molecule, \(\alpha\)-eleostearic acid. Not surprisingly, resins prepared from tung oil and tung oil methyl esters exhibited comparable results, associated with the similar chemical structure and polarity of their main bio-based components.  
Figure 6: (**A**) Raman spectra of (a) tung oil fatty acid crude resin, and (b) tung oil fatty acid resin cured at 130 \({}^{\circ}\)C for 18 h. (**B**) DSC curves of (a) tung oil resin cured at 130 \({}^{\circ}\)C for 18 h, (b) tung oil fatty acid resin cured at 130 \({}^{\circ}\)C for 18 h, (c) tung oil methyl ester resin cured at 130\({}^{\circ}\)C for 18 h, and (d) tung oil methyl ester resin cured at 130 \({}^{\circ}\)C for 24 h.  
In order to limit the interference of factors, such as surface roughness and sample heterogeneity, a dynamic analysis was also conducted (Table 1), revealing a similar trend to that of the static contact angles. The overall higher dynamic contact angles in comparison to static angles measured is most likely the result of water absorption during static measurements. Water absorption is also believed to be the reason for the high hysteresis value observed for the fatty acid resin in comparison to the other resins evaluated (Table 1). This is further supported by water uptake/absorption studies (Table 1).  
Water uptake experiments were used to determine the polymer's ability to absorb water, which, consequently, can be used to make inferences about its ability to biodegrade. The ratio reflects the percentage of water absorbed/uptake. The sample prepared from fatty acids displayed 90% water uptake (Table 1), indicating favorable interaction with water and a high potential for biodegradability. The sample prepared from methyl esters also exhibited a significant amount of water uptake (43%) despite its hydrophobic nature, displaying good potential for biodegradability. The considerably smaller water uptake observed for the sample prepared from tung oil (7%) can be related to its more cross-linked structure, preventing swelling and water absorption, which can significantly impact its biodegradability and recycling potential.
# Computational Analysis of PDE-Based Shape Analysis Models by Exploring the Damped Wave Equation
## 2 Theoretical Background
### PDE-Based Models
#### 2.3.1 Wave Equation and Damped Wave Equation  
Let us first recall the _wave equation_ introduced for the shape matching scenario in [6],  
\[\partial_{tt}u(x,t)=\Delta_{\mathcal{M}}u(x,t),\ x\in\mathcal{M},\ t\in \mathcal{I} \tag{6}\]  
on a surface \(\mathcal{M}\) and the interval \(\mathcal{I}=[0,T]\). As has been shown in [6], this PDE may be considered the PDE-based model that gives the best results among the PDEs from classical physics in the numerical shape-matching setting. As a key model for our investigations, we employ the _damped wave equation_  
\[\partial_{tt}u(x,t)+k\partial_{t}u(x,t)=\Delta_{\mathcal{M}}u(x,t) \tag{7}\]  
with \(x\in\mathcal{M}\) and \(t\in\mathcal{I}\). Choosing \(k=0\) will lead back to the standard wave Equation (6). Making use of both equations, we study the expansion of an initial wave over the surface of a given shape.  
To distinguish clearly between the corresponding feature descriptors, we employ the following notions. We use the _damped feature descriptor_ to denote the feature descriptor obtained from the solution of the damped wave Equation (7) and just _feature descriptor_ to denote the one obtained from the wave equation without damping (6).
## 3 Multidisciplinary Data and Methods
### Earthquake Data Analyses
#### 3.1.2 Revised Acceleration Seismic Release (R-AMR)  
Although not always present in seismic sequences, several studies (e.g., [54]) indicate that accelerating seismicity is another characteristic phenomenon which, when the right conditions are respected [55], may be observed as a sequence of moderate-sized earthquakes preceding the large earthquake. The critical point theory is one of the theoretical frameworks used to explain and model this conduct. As the stress drives the seismogenic region to a \"self-organised critical\" (SOC) state\" [55], the acceleration appears as a cascade of small earthquakes leading to much larger events, and, lastly, to the mainshock (i.e., the phase transition) at a certain \"failure time\", \(t_{f}\). This behaviour is usually termed the \"accelerating moment release\" (AMR) and its mathematical model is a simple power-law time-to-failure equation [56] as the following:  
\[s(t)=A+B\Big{(}t_{f}-t\Big{)}^{m}, \tag{1}\]  
where \(t_{f}\) is the time of the mainshock, \(A\) and \(B\) are constants, and \(m\) is a measure of how fast acceleration grows towards \(t_{f}\), being generally about 0.3 [57]. The quantity \(s(t)=\sum_{i}\sqrt{E_{i}}\) is called _cumulative Benioff strain_: the \(i\)-th earthquake of magnitude \(M_{i}\) releases an amount of strain \(s_{i}\) proportional to the square root of its energy \(E_{i}=10^{1.5M_{i}+4.8}\). To detect acceleration, the AMR, and, particularly, its revised version--henceforth called R-AMR--is computed by analysing seismicity around the region under study [39]. R-AMR differs from classic AMR for the weight \(G(r_{i})\) modulating through the inverse of the distance (i.e., \(r_{i}{}^{-1}\)), the amount of deformation released on the mainshock fault by the \(i\)-th earthquake that occurs within the considered seismogenic area: \(\delta_{i}=s_{i}\cdot G(r_{i})\). In this way, given the same magnitude, distant earthquakes are weighed less than nearby ones.  
The acceleration is found when the comparison between the linear fit (due to the background seismicity) and the fit with the power-law is such that the latter is better. A measure of the goodness of the comparison is the C-value [54]. C is defined as the ratio of the sum of the squares of the residuals of the power-law fit \(\left(RMSE_{(pow-law)}\right)\) to the same quantity for the linear fit \(\left(RMSE_{(linear)}\right)\) : the smaller the C-value (at least less than 0.6), the greater the predominance of the acceleration compared to the linear trend:  
\[C=\frac{RMSE_{(pow-law)}}{RMSE_{(linear)}}. \tag{2}\]  
Thus, continuing the characterisation of the seismicity, we proceeded in the search of possible acceleration before the beginning of the sequence. For this purpose, we applied the R-AMR [39; 58] algorithm to the complete seismic catalogue.
# Physics of Within-Tissue Wave Propagation Generated by Pulse Propagation in the Carotid Artery
## 4 Results
### Depth of the Carotid Artery  
Another parameter that might affect the wave propagation from the arterial lumen to the skin surface is the depth of the artery. To investigate this effect, two additional depths of 5 and 15 mm were considered while simulating a traveling pressure with velocities of 4, 7 and 10 m/s. Figure 7 shows the spatio-temporal displacement (y-direction) plots for the skin, revealing a general decrease in the maximum displacement amplitude of the main wave front (indicated with speed V\({}_{1}\)) with increasing depth of the artery. Indeed, the maximum amplitude of the main wave front varied from 0.12, 0.11 and 0.08 mm in the model with d = 5 mm for a PWV of 4, 7 and 10 m/s, respectively, to 0.03, 0.06 mm and 0.05 mm in the models with d = 15 mm for a PWV of 4, 7 and 10 m/s, respectively. Nevertheless, the variation of arterial depth did not affect the wave speed estimation of the main wave front at the skin surface for V\({}_{\rm z}\) of 7 and 10 m/s, where the agreement was optimal with the imposed conditions (maximal deviation of -5% between estimated wave speed at skin surface and imposed PWV). However, for an imposed V\({}_{\rm z}\) of 4 m/s, estimated V\({}_{1}\) at the skin surface deviated 4% and 33.5% from imposed velocity for 5 and 15 mm arterial depth, respectively. The secondary wave fronts propagated with a variable velocity (V\({}_{2}\)) which is indicated on the top of the plots in Figure 7. This figure also clearly demonstrates a different arrival time of the main wave front at the skin surface for both arterial depths: \(\pm\)4.5 ms for d = 15 mm vs. \(\pm\)2 ms for d = 5 mm due to the difference in traveling distance for the main wave front to reach the skin. Furthermore, the spatial lag between the main wave front location at lumen and skin surface increased with increasing arterial depth and increasing V\({}_{\rm z}\): the spatial lag is 0.15, 5.50 and 9.36 mm in the models with d = 5 mm for imposed V\({}_{\rm z}\) of 4, 7 and 10 m/s respectively and increased to 4.28, 15 and 22.50 mm in the models with d = 15 mm for imposed V\({}_{\rm z}\) of 4, 7 and 10 m/s respectively.  
Figure 6: TOF plots at the skin surface for a traveling impulse propagating with a velocity V\({}_{\rm z}\) of 4 and 10 m/s. The text above each plot indicates the propagation velocity of the main wave front (V\({}_{1}\)), marked by the red dashed line, and the speed of the secondary wave front (V\({}_{2}\)), indicated by the solid line.