esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Cement Paste Analysis
#### 3.1.5 Mercury Intrusion Porosimetry  
The pore structure, i.e., the average pore size, pore size distribution, and total porosity, is the main factor affecting the strength and durability of cement-based materials. Figure 8 depicts the effect of different superplasticizers on the pore structure of the cement paste. Compared with the blank group, although the addition of various water-reducing agents causes the median pore size to shift slightly toward large pore sizes, the cumulative amount of mercury intrusion decreases, demonstrating that the overall porosity is reduced. In fact, since superplasticizer J has the effect of reducing water, it promotes the hydration of cement, improves the compactness of the mortar, and reduces the total number of pores in the hardened mortar. Furthermore, the comparison of macroscopic mechanical properties of the groups containing various superplasticizers reveals that after 28 days the compressive strength of the cement paste of groups B, H, NF, Z, and J is 75.5, 87.5, 90.6, 95.8, and 97.9 MPa respectively, which confirms that superplasticizer J is the most influential superplasticizer in terms of reducing water, promoting cement hydration, and enhancing the compactness of cement mortar.  
Figure 6: Effects of different superplasticizers on cement hydration after 7 days.  
Figure 7: Effects of different superplasticizers on cement hydration after 28 days.
###### Abstract  
Ultrafast compound Doppler imaging (UCDI), which can be used to acquire Doppler information at very high spatial and temporal sampling rates, provides a great improvement to the characterization of the vasculature. The singular value decomposition (SVD) technique takes advantage of the different features of tissue and blood motion in terms of spatiotemporal coherence and strongly outperforms conventional clutter rejection filters in small animals. However, a major challenge of conventional UCDI with SVD clutter filtering for small vessel imaging is that it is not sensitive enough to detect the hemodynamic changes in deep tissue where the majority of the remaining signal is usually noise-saturated. In this study, with the first attempt to apply ultrasonic tissue characterization techniques to UCDI, we propose an H-scan subtraction Doppler imaging method to bypass the limitations associated with the high-order singular value thresholding selection and improve the image quality of fine vessels. The flow phantom experiments with different blood concentrations show that H-Scan is capable of estimating the relative size and spatial distribution of acoustic scattering objects. In the in vivo rabbit brain experiment, the H-Doppler method, together with the global and block-wise local SVD clutter filtering, are proposed to facilitate better power Doppler images with a significant improvement of background noise suppression. These results demonstrate that the contrast-to-noise-ratio (CNR) of the H-scan subtraction Doppler imaging is 15% to 65% higher than that of the conventional UCDI methods. Therefore, this approach can be potentially applied to the clinical applications of the functional ultrasound (fUS) imaging method.  
small vessel imaging; H-scan ultrasound imaging; ultrafast compound Doppler imaging; clutter filtering; singular value thresholding +
Footnote †: journal: Applied Sciences  
article
# Mobile Augmented Reality Application to Evaluate the River Flooding Impact in Coimbra
## 3 State of the Art on Visualisation and Modelling of River Flooding
### Prediction Modelling
They proposed a framework that uses a deep learning neural network to generate reports to predict future flood events by learning from big data collected from various sources such as historic flood events, social media posts and Google and satellite images.
## 3 Results and Discussion
### Physicochemical Characterization of PAW-N and PAW-P
Concentration profiles of specific reactive species further highlighted differences between PAW-N and PAW-P. DI water had no detectable RONS. PAW-P notably contained \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **DI Water** & **PAW-N** & **PAW-P** \\ \hline pH & 7.70 & 2.15 & 2.54 \\ Conductivity (\(\mu\)S/cm) & 4.223 & 6244 & 1926 \\ ORP (mV) & \(-\)48.9 & 274.0 & 257.4 \\ TDS (mg/L) & 1.704 & 2492.0 & 768.7 \\ Salinity & 0.013 & 3.699 & 0.775 \\ [H\({}_{2}\)O\({}_{2}\)] (mg/L) & 0 & 0 & \(>\)25 \\ [NO\({}_{3}\)\({}^{-}\)] (mg/L) & 0 & \(-\)500 & 0 \\ [NO\({}_{2}\)\({}^{-}\)] (mg/L) & 0 & 10–20 & 10–20 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Physicochemical parameters and RONS concentration of DI water, PAW-N, and PAW-P. Comparison of key physicochemical properties, including pH, conductivity, oxidation-reduction potential (ORP), total dissolved solids (TDS), salinity, and concentrations of hydrogen peroxide (H\({}_{2}\)O\({}_{2}\)), nitrate (NO\({}_{3}\)\({}^{-}\)), and nitrite (NO\({}_{2}\)\({}^{-}\)) for deionized (DI) water, nitrate-rich plasma-activated water (PAW-N), and peroxide-rich plasma-activated water (PAW-P). The distinct profiles highlight the specific reactive environments generated by each plasma system. a substantial concentration of hydrogen peroxide (>25 mg/L), a primary oxidative agent produced by the pin-to-water system. This peroxide concentration can effectively induce oxidative effects on hair proteins without the extensive ionic load observed in PAW-N. Conversely, PAW-N exhibited approximately 500 mg/L nitrate ions, with no detectable hydrogen peroxide, underscoring the distinctive chemical pathway of nitrate generation inherent to the coaxial DBD system. Both PAW-N and PAW-P exhibited similar nitrite (NO\({}_{2}\)\({}^{-}\)) concentrations (10-20 mg/L), suggesting nitrite formation is common across different plasma systems. Though less stable than nitrates, nitrites still contribute significantly to the oxidative modification of hair keratin, potentially altering its structural and surface properties. Overall, Table 2 distinctly illustrates the contrasting physicochemical and reactive species profiles of PAW-N and PAW-P. PAW-N, characterized by high nitrate content, pronounced acidity, and high ionic strength, represents a potent oxidative and ionic environment, potentially conducive to robust interactions with hair fibers. PAW-P, with its dominance of hydrogen peroxide, moderate ionic content, and lower salinity, offers a comparatively less aggressive oxidative milieu, likely minimizing the risk of excessive hair fiber damage. Such differences are critical in guiding the selection of appropriate PAW formulations tailored to specific hair treatment needs, balancing efficacy and fiber integrity.
## 1 Introduction
### Technical Highlights
#### 1.4.2 Construct of Position Map  
In this level, it stores the position map of data blocks. Moreover, the position map of the blocks in this level is static. Therefore, a deterministic order has to be used in a write operation of the blocks in this level. We employ a binary tree in this level to support the deterministic order of write operations. There is a requirement of the static position map that the physical address of each block should be randomly distributed. In this case, the stash size can be bounded well. [PERSON] et al. carry out a pseudo-random permutation (PRP) for each logical block and the result is the physical address of the block. After PRP, the physical addresses of all blocks are randomly distributed.  
The main contributions of our work are as follows.  
* We use the static position map to reduce the communication complexity of the write-only ORAM. The overhead of communication is reduced from \(O(BlogN)\) to \(O(B)\), where \(B\) is the size of data block and \(N\) is the number of data blocks. Moreover, the communication rounds are reduced from \(O(logN)\) to \(O(1)\).
* We carry out a theoretical analysis on the stash in our scheme, in which both the upper bound of stash and the parameter-setting condition are taken into account. The analysis expresses that the probability of the stash size exceeding \(R\) is \((A/Z)^{-R}\) if \(Z(\ln Z/A)-Z+A-\ln 2>0\).  
The rest of the paper is organized as follows. Section 2 introduces the preliminaries, including the concept of write-only ORAM, the random write-only scheme HiVE, and the technique of static position map proposed by [PERSON] et al. Section 3 describes our scheme. Section 4 analyzes and evaluates the efficiency of our scheme. Section 5 gives a conclusion.
# Damage Identification of Stay Cables Based on a Small Amount of Deflection Monitoring Data
## 2 Theory Related to the Reduction in Measurement Points
### Damage Degree Identification
\[P_{obj}=min\sum_{i=1}^{N}(\overline{w}_{i}(z)-w_{i}(z))^{2}\] (16) where \(\overline{w}_{i}(z)\) is the \(i\)-th component of the deflection difference vector corresponding to point z estimated using [PERSON]'s proxy model; \(w_{i}(z)\) is the \(i\)-th component of the deflection difference vector actually measured; \(z\) is the vector formed by the damage index variable; \(N\) is the vector length. Figure 14: Kriging model between the deflection difference of M\({}_{16}\) and damage index (\(\tau_{k16}\) and \(\tau_{l\mathcal{I}}\)). Figure 13: Kriging model between the deflection difference of M\({}_{7}\) and damage index (\(\tau_{k16}\) and \(\tau_{l\mathcal{I}}\)). It can be seen from Table 3 that the identification value of the damage index obtained through the reduction in the measurement points is essentially the same as the actual value, and the error is small (the maximum is only 1.14%). It can be seen that the method of identifying the cable damage degree by using a small number of measurement points and based on the [PERSON] proxy model is more accurate.
## 5 Numerical Results
### Case 1--Effect after Introduction of Profit Maximization without Incentive  
Figure 11 illustrates the coordination between the PV and the BESS after the introduction of profit maximization. Compared to the former operation without the maximization algorithm, the BESS coordinated discharge after the opportunities of the profit maximization, regardless of the interval set previously.  
Figure 10: Operation of the BESS and the PV in coordination without profit maximization: (**a**) intraday operation of the BESS with WPG without profit maximization; (**b**) intraday operation of the BESS with the PV without profit maximization.  
Figure 9: Solar power metered with hourly granularity and Pattern of the SMP on 11 March 2020.  
Compared to the prior scheduling methods and considering the fixed windows as illustrated in Figure 11a,b, the BESS coordinated with the renewables via the profit maximization was scheduled, taking into account the gross profit from the resource portfolio in the demonstration site. As illustrated in Figure 11c, the BESS was scheduled to be charged within the tolerance of the PCS, therefore minimizing the operation cost and maximizing the SMP revenue from the coordinated renewable. Likewise, the BESS with the PV also operated and avoiding unnecessary actions, causing greater operating cost with consideration of the SMP patterns, as illustrated in Figure 11d.  
Tables 9 and 10 compare the revenue generated from the portfolio in the subject complex. The total revenue from the portfolio with the PV and the BESS increased by 14.25%, simply by introducing the control algorithm. Furthermore, the SMP revenue and the total revenue from the portfolio with the PPG and the BESS also increased by 25.57% and 0.62%, respectively, because the scheduler also considered the settlement with the multipliers, whereas the previous scheduling method was used to pursue revenue from the SMP only.  
Figure 11: Operation between the BESS and the PV in coordination without profit maximization: (**a**) intraday operation of the BESS with WPG without profit maximization; (**b**) intraday operation of the BESS with the PV without profit maximization; (**c**) profit-maximized intraday operation of the BESS with the WPG; (**d**) profit-maximized intraday operation of the BESS with the PV.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Resource**} & **Revenue from** & **Revenue from** & **Revenue from** & **Total Revenue** \\  & **SMP** & **REC1** & **REC5** & **(KRW/USD)** \\  & **(KRW/USD)** & **(KRW/USD)** & **(KRW/USD)** \\ \hline \multirow{2}{*}{PV-BESS} & KRW 368,129 & KRW 384,979 & KRW 534,676 & KRW 1,287,784 \\  & USD 309.02 & USD 323.16 & USD 448.82 & USD 1081.00 \\ \hline \multirow{2}{*}{WPG–BESS} & KRW 2,552,486 & KRW 3,203,885 & KRW 0 & KRW 5,756,371 \\  & USD 2689.43 & USD 2142.63 & USD 0.00 & USD 4832.05 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Intraday revenue generated from the prior operating method.
## 4 Selected Use Cases and Opportunities
### Underwater  
The information can be transmitted from the onshore base station to an Autonomous Underwater Vehicle (AUV) via a floating buoy equipped with DF relay architecture, as depicted in Figure 4. The signal from the onshore base station to the floating buoy will utilize THz communication, while the retransmission from the floating buoy to the AUV will utilize laser-based VLC. Lasers are preferred for deep-sea communication over LEDs due to their ability to extend the link distance. However, the use of lasers introduces pointing errors to the system due to the high turbulence experienced underwater. Appropriate mitigation techniques should be employed to maximize the system's reliability in this setting.
## 3 The Clustering Protocol
### CH Selection  
In the traditional protocols, while selecting the CHs, the residual energy and position of the node are often neglected, resulting in an uneven final distribution of the selected CHs, among which the CHs with lower residual energy tend to die prematurely.  
In Figure 7, the green dot is the CH, and the red dot is the BS. It can be determined that in the process of CH selection, the LEACH protocol will generate a serious phenomenon of an uneven CH distribution, resulting in a relatively large distance between the CH and its member node, which inevitably has an unfavorable effect on the extension of the  
Figure 5: The time slot allocation of the clustering protocol. CH—cluster head.  
network lifetime.  
Figure 6: Flowchart of the clustering protocol, including node deployment, CH selection, cluster setup, and data transmission.  
Figure 7: The three rounds of uneven CH distributions generated by the LEACH (Low-Energy Adaptive Cluster Hierarchical) protocol.  
Based on this phenomenon, this paper uses the BS as the center and divides the area into four small cells. Then, based on the idea of CH selection in [31], combining the residual energy of a node with the distance from the node to the BS, we can construct a CH selection factor and select the top 25% of the CH selection factors as the CHs in each of the small cells. The formula for the CH selection factor is as follows:  
\[S_{CH}\Big{(}E(i),\ d_{t0 BS}^{i}\Big{)}=\alpha\cdot Nor(E(i))+\beta\cdot Nor \Big{(}d_{t0 BS}^{i}\Big{)}, \tag{9}\]  
In the above formula, \(\alpha\) and \(\beta\) represent weight factors, where \(\alpha+\beta=1\);\(E(i)\) represents the residual energy of node \(i\); \(d_{t0 BS}^{i}\) represents the distance from node \(i\) to the BS. \(Nor()\) represents the normalization. The larger the value of \(S_{CH}\) of node \(i\), the more likely it is to be selected as the CH.  
Through the mechanism, an appropriate amount of nodes with larger residual energy and shorter distances from the BS are selected as CHs in each small area, which can better ensure that there is a close transmission distance between each CM and the CH of the joined cluster. It avoids the loss of too much residual energy of some nodes due to its large transmission distance. Figure 8 shows a CH distribution diagram of the protocol in this paper. It can be determined that the distribution of the CHs is relatively uniform and can meet the low power consumption of the network.
# Static Wind Reliability Analysis of Long-Span Highway Cable-Stayed Bridge in Service
## 3 Functional Function and Parameter Distribution for Aerostatic Instability Analysis
### Probability Distribution of Parameters in Functional Function  
The correction coefficient \(C_{w}\) is affected by many uncertain factors in the practice, it has no significant change rule by the analysis of the results from wind tunnel tests and field experimental data. To be conservative, the average value of \(C_{w}\) is taken as 1.0, the variation coefficient is taken as \(\delta[C_{w}]=\sigma[C_{w}]/E[C_{w}]=0.1\), and it obeys normal distribution.  
The gust coefficient \(G_{v}\) is defined by the ratio of the maximum gust wind speed to the 10 min average wind speed, its average value refers to Wind-resistant Design Specification for Highway Bridges (JTG/T 3360-01-2018) in China [22], and the mean value of \(G_{v}\) is taken as 1.39, the variation coefficient is taken as \(\delta[G_{v}]=\sigma[G_{v}]/E[G_{v}]=0.1\), and it also obeys normal distribution.  
The critical aerostatic instability wind speed \(U_{s}\) can be given by finite element simulation analysis such as ANSYS software. Generally, aerostatic instability wind speed follows lognormal distribution, therefore, it needs to be normalized by the equivalent before reliability calculation.
# Earthquake Risk Probability Evaluation for Najin Lhasa in Southern Tibet
###### Abstract  
The probabilistic seismic hazard analysis (PSHA) method is effectively used in an earthquake risk probability evaluation in seismogenic regions with active faults. In this study, by focusing on the potential seismic source area in Najin Lhasa, southern Tibet, and by incorporating the PSHA method, we determined the seismic activity parameters and discussed the relationship of ground motion attenuation, the seismic hazard probability, and the horizontal bedrock ground motion acceleration peak value under different transcendence probabilities in this area. The calculation results show that the PSHA method divides the potential source area via specific tectonic scales and detailed tectonic markers, which reduces the scale of the potential source area and better reflects the uneven spatial distribution of seismic activity in the vicinity of Najin. The corrected attenuation relationship is also in line with the actual work requirements and is suitable for earthquake risk analysis. In addition, the major influences on the peak acceleration of ground motion in the study area are mainly in the potential source areas of Qushui (M7.5), Dangxiong (M8.5), and Kangma (M7.5). The peak horizontal ground motion acceleration (PGA) with a transcendence probability of 10% in 50 years is 185.9 cm/s\({}^{2}\), and that with a transcendence probability of 2% in 50 years is 265.9 cm/s\({}^{2}\).  
Tibet; PSHA; source area; ground motion acceleration; transcendence probability +
Footnote †: journal: Applied Sciences
# Exploiting Domain Knowledge to Address Class Imbalance in Meteorological Data Mining
## 3 Datasets
### Predictor Variables  
In the pre-processing phase we applied data reduction using the Principal Component Analysis (PCA) extraction method to remove highly correlated variables from the ERA-40 dataset. We used the SPSS statistical software package to process the entire ERA-40 dataset and to produce a new one that consisted of a reduced number of uncorrelated variables.  
After applying PCA and examining the component matrix of loadings and the variable communalities, we deleted a total of 36 variables from our initial dataset that consisted of 71 variables. The component model was re-specified six times with a final outcome of 35 variables and 9 components with eigenvalues greater than 1. This is exactly the same methodology we used in a previous work of ours [18]. The analysis revealed the findings of Table 3.  
Table 4 displays the variance explained by the rotated components and additionally the corresponding nine most highly correlated variables. The Total column gives the eigenvalue, or amount of variance in the original variables accounted for by each component. The % of Variance column gives the ratio of the variance accounted for by each component to the total variance in all of the variables (expressed as a percentage). The % Cumulative column gives the percentage of variance accounted for by the first 9 components.  
The first nine rotated components explain nearly 85.2% of the variability in the original variables and it is possible to considerably reduce the complexity of the data set by using these components, with a 14.8% loss of information. As a result, we can reduce the size of the ERA-40 dataset by selecting the 9 most highly correlated variables with the 9 principal components [18; 19]. These meteorological parameters could express the state of the troposphere where precipitation is created and reaches the ground. The reduced ERA-40 dataset with the 9 chosen variables, as predictors, and the precipitation, as class variable, comprised our experimental dataset with 61,364 examples. The size of the dataset is explained by the fact that we have four daily examples (one every 6 h) for a period of 42 years (42 \(\times\) 365 \(\times\) 4 = 61,320 examples plus 11 \(\times\) 4 = 44 examples for the 11 extra leap year days of that period).  
\begin{table}
\begin{tabular}{c c c c} \hline
**Rain/Drizzle** & **Snow/Sleet** & **Thunder** & **Total “Yes’** \\ \hline
7154 & 547 & 2181 & 9882 \\
11.66\% & 0.89\% & 3.55\% & 16.1\% \\ \hline \end{tabular}
\end{table}
Table 1: Natural distribution of values within the minority class variable (precipitation ‘yes’).  
\begin{table}
\begin{tabular}{c c c} \hline
**Fog** & **Fair/Cloudy** & **Total “No’** \\ \hline
1395 & 50,087 & 51,482 \\
2.27\% & 81.62\% & 83.9\% \\ \hline \end{tabular}
\end{table}
Table 2: Natural distribution of values within the majority class variable (precipitation ‘no’).