esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Characteristics of Microseismic Events in the Tunnel
### Comparison between Modeling Results and Microseismic Monitoring Results  
A comparative analysis of the spatial evolution of acoustic emissions during the tunnel failure after excavation modeled by RFPA\({}^{\text{3D}}\) and the monitoring data of microseismic events (as shown in Figure 13) shows that the spatial evolution of acoustic emissions based on the twin-shear unified strength theory and the strength reduction method was consistent with the distribution of detected microseismic events. They all prove that the most serious failure of the surrounding rock occurred at the tunnel vault and corners of the side wall, which had high risks of collapse and rock bursts. According to the seismological theory, the ratio of S-wave energy to P-wave energy, \(\text{E}_{\text{S}}/\text{E}_{\text{P}}\), is often used as one of the indicators to reveal the failure mechanism of the surrounding rock. In particular, slip or shear failure often occurs when \(\text{E}_{\text{S}}/\text{E}_{\text{P}}\geq 10\), and tensile failure often occurs when \(\text{E}_{\text{S}}/\text{E}_{\text{P}}\leq 3\)[33]. As can be seen from Figure 10, the majority of microseismic events had \(\text{E}_{\text{S}}/\text{E}_{\text{P}}\leq 3\) in this area and a fraction of microseismic events had \(\text{E}_{\text{S}}/\text{E}_{\text{P}}\geq 10\), so the excavated section of the tunnel was dominated by tensile failure accompanied by shear failure. This conclusion is consistent with the modeling results in Figure 7, which shows that the integrated method based on the combination of field microseismic monitoring and RFPA\({}^{\text{3D}}\) modeling software can be more accurate and effective for stability analysis of the excavation of the #2 diversion tunnel in the Xulong Hydropower Station.  
Figure 12: _Cont._  
Figure 12: Spatial distribution of microseismic events.  
Tensile failure mainly occurs in the excavation of the geologically weak section of the tunnel, and the failure is concentrated at the tunnel vault and corners of the side wall. The existence of joints or faults may worsen the distribution of stress and deformation of surrounding rock. Fractures tend to occur around the locations of joints or faults due to tress concentration and weak mechanical properties of structures. In addition, the energy and magnitude of microseismic events around the structures are relatively large and the failures are dominated by tensile fractures, accompanied with some shear fractures. Therefore, it is necessary to focus on the areas during excavation.
## 3 Methods and Model
### The Q-Learning Algorithm  
RL is a significant subfield of machine learning. An agent can earn rewards and improve its own action strategy; the ultimate goal is to select the best actions in order to maximize cumulative rewards by interacting with the environment. The agent is originally given a state \(s\) by the environment. The agent decides to take action \(a\) in order to maximize the cumulative reward. After completing action \(a\), the environment returns the current state \(s^{\prime}\) and the reward \(r\) to the agent. Figure 1 depicts a schematic of RL.  
[PERSON] et al. [35] first proposed the Q-Learning algorithm. The four components of the algorithm are as follows: (\(S\), \(A\), \(R\), \(Q\)). \(S\) stands for a collection of state spaces, where \(S=\{s_{1},s_{2},\cdots,s_{n}\}\), and \(n\) is the number of states in the state space. The actions that may be performed at a particular state are represented by the action space \(A\). \(R\) stands for the reward attained following the completion of an action, also known as \(r\). The action-value function \(Q(s,a)\) expresses the potential future benefit that can be attained by taking action \(a\) in state \(s\). The total action-value pair is abbreviated as Q. Q can be updated, as shown in (5).  
\[Q(s,a)=Q(s,a)+\eta\cdot[r+\gamma\cdot\max_{a^{\prime}\in A(s^{\prime})}Q(s^{ \prime},a^{\prime})-Q(s,a)] \tag{5}\]  
where the value of \(\eta\), the learning rate, ranges from 0 to 1. The importance of \(r\) is determined by the value of \(\eta\). \(\gamma\in[0,1]\) stands for the reward decay rate, and the significance of the anticipated future reward depends on the size of \(\gamma\). The state that the environment returns to after state \(s\) executes action \(a\) is called \(s^{\prime}\).
# An Efficient Relayed Broadcasting Based on the Duplication Estimation Model for IoT Applications
## 4 Simulation Study
### The Effect of the Duplication Ratio Approximation  
In this section, we evaluated the effectiveness of the duplication ratio approximation by comparing the performance deviations between DRBF and ADRBF and between DRBF\({}_{\text{RQ}}\) and ADRBF\({}_{\text{RQ}}\). For this purpose, we introduced two performance indices: \(G(\overline{F}_{val})\) and \(G(\overline{T}_{dis})\), defined as,  
\[G(\overline{F}_{val})=\frac{\overline{F}_{val}\text{ of }\texttt{ ADRBF}}{\overline{F}_{val}\text{ of }\texttt{ DRBF}},\ \ G(\overline{T}_{dis})=\frac{\overline{T}_{dis}\text{ of }\texttt{ ADRBF}}{\overline{T}_{dis}\text{ of }\texttt{ DRBF}}.\]  
Similar to \(G(\overline{F}_{val})\) and \(G(\overline{T}_{dis})\), we defined \(G_{RQ}(\overline{F}_{val})\) and \(G_{RQ}(\overline{T}_{dis})\) by considering the performance of DRBF\({}_{\text{RQ}}\) and ADRBF\({}_{\text{RQ}}\). Figure 14 shows these four performance indices measured when \(N_{node}\) ranged from 60 to 140. As \(N_{node}\) was increased, the values of \(G(\overline{F}_{val})\) and \(G_{RQ}(\overline{F}_{val})\) (represented as bars in Figure 14) increased from 0.955 and 0.981 to 0.980 and 0.998, respectively; \(G_{RQ}(\overline{F}_{val})\) was slightly higher and closer to 1 than \(G(\overline{F}_{val})\). Meanwhile, for the whole range of \(N_{node}\), the values of \(G(\overline{T}_{dis})\) and \(G_{RQ}(\overline{T}_{dis})\) (represented as lines in Figure 14) were between 0.948 and 0.992 and between 0.866 to 1.046, respectively. From these observations, we can derive the following conclusions:  
Figure 13: Performance comparisons of (a) DRBF and DRBF\({}_{\text{RQ}}\) (b) ADRBF and ADRBF\({}_{\text{RQ}}\).  
* The average values of \(G(\overline{F}_{val})\) and \(G_{RQ}(\overline{F}_{val})\) were 0.971 and 0.993, respectively, confirming that the reliability of flooding hardly deteriorated due to the approximation error of the duplication ratio. Moreover, its influence diminished as the node density increased or the re-queuing scheme was applied, i.e., \(G(\overline{F}_{val})<G_{RQ}(\overline{F}_{val})\approx 1\).
* The average value of \(G(\overline{T}_{dis})\) was 0.977, meaning that the average traffic dissemination time of ADRBF is comparable to that of DRBF, i.e., the approximation of the duplication ratio has a negligible effect on the traffic dissemination time. Even with the approximation error, \(\overline{T}_{dis}\) of ADRBF\({}_{\text{RQ}}\) was usually smaller or slightly higher than DRBF\({}_{\text{RQ}}\); i.e., the average value of \(G_{RQ}(\overline{T}_{dis})\) was 0.938.
* The duplication ratio \(DR(\tau)\) in (2) can be effectively approximated as \(ADR(\tau)\) in (3) without severely degrading the performance in terms of \(\overline{F}_{val}\) and \(\overline{T}_{dis}\).
## 2 Model and Materials
### Materials  
The parameters used in the model at zero electric field and room temperature (300 K) are shown in Table 1[11].  
\begin{table}
\begin{tabular}{c c c c} \hline  & \(\rho_{elec}\) (\(\Omega\) m) & \(\kappa\) (W/m K) & \(C\) (J/Kg K) \\ \hline SiO\({}_{2}\) & \(10^{14}\) & 1.3 & 1050 \\ TiN & \(10^{-6}\) & 14 & 784 \\ W & \(5.4\times 10^{-8}\) & 175 & 132 \\ \hline \end{tabular}
\end{table}
Table 1: Numerical values for electrical conductivity \(\rho\), thermal conductivity \(\kappa\), and specific heat \(C\) of SiO\({}_{2}\), TiN, and W.  
Figure 1: Cross-sectional view of the simulated mushroom phase change memory (PCM) cell.  
Temperature dependent electrical resistivity of crystalline (fcc) and amorphous GST close to room temperature is extrapolated up to \(T_{melt}\) (Figure 2a) to model the metastable \(\rho(T)\) functions [14]. A constant value of \(\rho_{liquid}=5\) u\(\Omega\cdot\)m is used for liquid GST [15].  
The thermal conductivity (\(\kappa\)) of solid GST [Figure 2b] is approximately the sum of a phonon contribution (\(\kappa_{ph}\)), which is assumed to be independent on the temperature [16], and an electronic contribution (\(\kappa_{el}\)), which is calculated using the Wiedemann-Franz law [17]. Thermal conductivity of liquid GST is assumed to be dominated by \(\kappa_{el}\).  
Seebeck coefficients of GST (Figure 2c) are measured up to 740 K, and modeled to be constant between 800 K and 873 K, and assumed to have a constant value of 1 uV/K in the liquid phase [18]. Seebeck coefficients for TiN (Figure 2d) are measured in the 300-800 K range and extrapolated to 1000 K [8].  
The specific heat (C\({}_{P}\)) of the GST layer is assumed as a constant value of 202 J/(kg\(\cdot\)K), which is independent on the temperature and the phase state. The latent heat of the amorphous-to-crystalline transformation \(\Delta H_{v}\) is taken from Ref. [19]. This work has some limit due to the fact that the thermal boundary resistances (TBR) is not included in the focus on thermoelectric effects, and the effect of TBR requires further study.
# Breakthrough and Challenging Application: Mixed Reality-Assisted Intracardiac Surgery
## 2 Materials and Methods
### Aortic Valve Segmentation  
Materialise Mimics provides the capability to visualize intracardiac areas and consequently segment the aortic valve affected by stenosis. Also for the segmentation of the valve, the procedure is primarily manual, although in the literature, deep learning algorithms have been proposed [31; 32].  
In relation to the segmentation of the aortic valve, the initial step involves identifying the stenotic valve across the three planes. Subsequently, the multiplanar reconstruction (MPR) technique was employed to optimize the visualization and segmentation of the valve. The MPR technique is the most straightforward image processing technique and has been readily available for several years. This technique allows to rotate each plane differently from the standard axial, coronal, and sagittal sections. So, MPR enables the most effective visualization of anatomical structures that extend across planes different from the standard ones, aiding in accurate diagnostic analysis.  
The final step involved utilizing the automatic threshold algorithm to identify the calcifications to be added to the 3D model. The density range of 389 \(\div\) 868 [hnsf'U] was set to extract the calcifications, considering that the range of adjacent tissues was 148 \(\div\) 195 [hnsf'U].
# Efficient Multi-Objective Simulation Metamodeling for Researchers
## 4 Results
### Continuous News Vendor Simulation Case Study
To further analyze the potential effect this causes in solution quality, we consider the Pareto fronts from experiments with a time limit of 150 s. The best-performing Pareto front from a 150 s time limit experiment is plotted in Figure 14 (and is from a GR-GA pair) and can be compared with the best Pareto front from a 150 s time limit experiment specifically from a NN-GA pair, shown plotted in Figure 15. In these figures, the Pareto front of interest and the optimal Pareto front from the FE records are plotted, with the dominated area for each shaded in the appropriate color. The main area of difference between the two Pareto front solutions is the area corresponding to high quantities sold and low profits. To follow up on this, consider the best NN-GA Pareto front from an experiment with a higher time limit of 250 s plotted in Figure 16. Although the Pareto front from this experiment captures the corner corresponding to high quantities sold and low profits, it does not capture part of the medium quantities sold and medium profits area. These findings can indicate an issue faced by the genetic algorithm optimizer relative to the Basic optimizer when parent populations are relatively small due to Pareto fronts having only a few points in them. It is harder to identify ex ante which metamodel would under-perform in this situation. However, the hypervolume evaluation shows that the GR-B pair performs the best and the NN-GA pair performs the worst for this problem. Figure 14: Continuous News Vendor experiments—optimal and best Pareto front. Figure 15: Continuous News Vendor experiments—optimal and best NN-GA Pareto front. The crowding distance evaluation plotted in Figure 17 confirms that the NN-GA pair has the most widely spaced Pareto fronts of the four pairs, while the GR-B has the closest spacing. Additionally, at a time limit of 250 s, there are GR-B Pareto fronts with normalized crowding distances below 1.
## 3 Results
### Downtime Prediction Modeling Using Descriptive Time Series Analysis  
We decomposed the time series for the LINAC downtime dataset into several components representing trends, seasonality, and residuals, as shown in Figure 4. First, Figure 4A shows that downtime increased over the years. The time series has seasonality, which is unaffected by the specific seasonal factor in Figure 4B. For the residuals, a downtime surge occurred due to major LINAC part replacement from 2006 to 2007, as shown in Figure 4C.  
Figure 5 shows the prediction of downtime from 2022 to 2030 from the downtime dataset between 2001 and 2021. The prediction boundary indicated by the dark gray band (mean square error = 0.25) is shown along with the predicted data from 2022. As time moves past the first forecast point, 2022, the uncertainty increases, thereby increasing the amplitude of the forecast boundary. The ARIMA model predicts that the downtime will increase by 95 h/year after 2022 and 100 h/year after 2023. However, the forecast error is also expected to accumulate at an annual rate of 25%. Moreover, the cumulative downtime in 2029 is expected to be about 2820 h.  
Replacing the previously failed parts makes the LINAC operation more stable. In other words, the lower limit of the gray error region in Figure 4 can be interpreted as the period to maintain the stable performance of the LINAC due to the replacement of the previously failed parts. Conversely, an upper bound in the gray area indicates that critical part replacement or subsequent QA procedures can cause more downtime than predicted.  
Figure 3: Annual downtime analysis from 2001 to 2021. In this period, excluding 2009 and 2016, the average downtime was 63 h \(\pm\) 20.98. The downtime increased for years when replaced parts took longer to supply and needed complex QA procedures, e.g., 2009 IQR [(median:14, Q1:6.5, Q3:19.88) (5–25.5)].  
Figure 4: Time series decomposition for the LINAC downtime dataset. (**A**) The downtime increased over the years. (**B**) The time series has seasons uninfluenced by the specific seasonal factor. (**C**) The downtime surge occurred due to major LINAC part replacement from 2006 to 2007.  
Figure 5: Downtime prediction with the ARIMA model (mean square error = 0.25, represented by dark gray bands).
## 3 Results
### TerraSAR-X/TanDEM-X Dataset
Unfortunately, the coherence bias is space-varying and cannot be predicted or compensated for. There is another bias effect other than correlation due to the limited sample size [42], which introduces a slight underestimation noticeable only in highly structured areas, where the bias due to correlation is negligible. Figure 7: Unfiltered modulus of interferogram of TSX-TDX SLC pair of the Euskirchen test site. The maps of the interferometric phase calculated from the original and whitened data using NL-InSAR are compared in Figure 11. The high coherence of the scene results in regular and clean phase maps. The benefits of whitening, however, are noticeable in terms of a lower noise level and reduced fragmentation of the map. A figure of merit of the quality of the phase map, in terms of its suitability for unwrapping, is provided by the residues, which are singularities in the 2D phase field that may hamper the phase-unwrapping process and compromise the accuracy of the topographic reconstruction. The residues of the phase field are overlaid on the coherence maps and shown in Figure 12 for the original and whitened data. Although the residues are mostly concentrated in heterogeneous areas, there is a 24% reduction in the number of residues for the whitened case. This supports the conclusion that the proposed whitening is beneficial not only for achieving unbiased coherence estimation but also for improving the accuracy of the phase map, regardless of the method used to process the interferogram. Figure 8: Modulus of interferogram of Euskirchen filtered using NL-INSAR: (**a**) from non-whitened SLC pair; (**b**) from whitened SLC pair. Figure 10: Difference in coherence calculated from whitened and original data of Euskirchen: the overestimation due to correlation reaches 16% in homogeneous areas. Figure 4:0 Coherence maps of Euskirchen estimated using NL-InSAR: (**a**) from non-whitened SLC pair; (**b**) from whitened SLC pair.
## 3 Results and Discussion
### Precision  
Table 5 gives the results of the precision measurements (two repetitions) for each of the eight days. Entering the data in Table 5 into the MINITAB statistical package and applying ANOVA gives Table 6, which is the analysis of variance table.  
Table 6 shows in red font the values of the variance for the factor that varies (day) and for the error, which represents the repeatability. Therefore, the following results are obtained:  
* Standard deviation: \(\rm{s_{r}=\sqrt{s_{error}{}^{2}}=\sqrt{0.00061}=0.02470\;mg/L}\).
* Relative standard deviation, from equation \(\rm{RSD_{r}=\frac{\kappa}{\chi}100=2.3\%}\), as \(\rm{\overline{x}=1.07\;mg/L}\) the average of the measurements in Table 7.  
Figure 8: Correlation plot and regression analysis table, to check the accuracy of the method.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c} \hline \hline
**Day:** & **1** & & **2** & & **3** & & **4** & & **5** & & **6** & & **7** & & **8** \\ \hline
**DEHP mg/L:** & 1.11 & 1.05 & 1.09 & 1.06 & 1.04 & 1.01 & 1.07 & 1.09 & 1.05 & 1.03 & 1.10 & 1.06 & 1.10 & 1.14 & 1.04 & 1.06 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Precision test results.  
* Limit of repeatability, from equation \(\mathrm{r}=2.8\times\mathrm{s_{f}}\): \(\mathrm{r_{s}}=0.069\) mg/L, for two repeated measurements under repeatability conditions and 95% significance level.
* Intermediate precision:
* Standard deviation: \(\mathrm{s_{I}}=\sqrt{\mathrm{s_{day}}^{2}+\mathrm{s_{error}}^{2}}=\sqrt{0.00057+0.0 0061}=0.0344\) mg/L.
* Relative standard deviation, from equation \(\mathrm{RSD_{I}}=\frac{8}{3}100\) = 3.2%, as \(\overline{\mathrm{x}}\) = 1.07 mg/L the average of the measurements in Table 7.
* Limit of repeatability, from equation \(\mathrm{r}=2.8\times\mathrm{s_{I}}\): \(\mathrm{r_{s}}=0.096\) mg/L, for two repeated measurements under repeatability conditions and 95% significance level [7; 11; 21].  
The \(\mathrm{RSD_{r}}\) and \(\mathrm{RSD_{I}}\) values satisfy the acceptable values predicted based on the concentration level of DEHP \(\left(\overline{\mathrm{x}}=1.07\frac{\mathrm{mg}}{\mathrm{L}}\right)\)[16]. Additionally, it is worth noting that there does not seem to be any variation in the measurements between the different analysis days. This is proven statistically, for a significance level of 95%, by the \(p\)-value = 0.082 \(>\) 0.05, which is shown in Table 6.
# Fast Deployment of a UWB-Based IPS for Emergency Response Operations
## 5 Performance Evaluation
### Anchor Self-Location
In the IPS presented here, two processes can be run in parallel: the deployment and calibration of the GNSS-RTK self-location system (with a duration of \(T_{\text{GNSS-RTK}}\)), and the synchronization of the UWB network (with a duration of \(T_{\text{UWB}}\)). Generally, \(T_{\text{UWB}}\ll T_{\text{GNSS-RTK}}\), so that \(T_{\text{IPS}}\equiv T_{\text{GNSS-RTK}}\). Equation (1) details the different elements of \(T_{\text{GNSS-RTK}}\), where \(T_{start}\) corresponds to the GNSS-RTK initial startup time, \(k\) to the number of anchors to deploy, \(T_{mov}\) to the time devoted to move the GNSS-RTK module from one anchor to another, and \(T_{pos}\) to the \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Anchor ID** & **\#1A40** & **\#2 CDB** & **\#8828** & **\#5 BA0** & **\#3 EB** & **\#1A25** \\ \hline
**\#1A40** & - & 7 & 29 & 2 & 9 & 39 \\
**\#2 CDB** & 7 & - & 21 & 0 & 19 & 47 \\
**\#8828** & 29 & 21 & - & 44 & 24 & 33 \\
**\#5 BA0** & 2 & 0 & 44 & - & 26 & 8 \\
**\#3 EB** & 9 & 19 & 24 & 26 & - & 16 \\
**\#1A25** & 39 & 47 & 33 & 8 & 16 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Absolute distance error between anchors according to the GNSS-RTK self-location system (cm). Figure 9: Dispersion of GNSS-RTK samples. All the subfigures plot an area of 2 \(\times\) 2 m.  
GNSS-RTK positioning time. In the presented testbed with \(k=6\) anchors these timings correspond to \(T_{start}=300\) s, \(T_{mov}=30\) s, and \(T_{pos}=60\) s. In consequence, \(T_{\text{IPS}}\geq 840\) s (i.e., \(\geq 14\) min). \[T_{\text{IPS}}\equiv T_{\text{GNSS-RTK}}\geq T_{start}+k\cdot\left(T_{mov}+T_{ pos}\right) \tag{1}\]  
An anchor can be deployed by just one person. However, if all the anchors had their own embedded GNSS-RTK module and the deployment could be performed by a team of \(n\) FRs (being \(1\leq n\leq k\)), \(T_{\text{IPS}}\) could be greatly reduced (see Equation (2)). Specifically, for \(n=2\), the testbed presented herein could have been deployed in 450 s (i.e., 7.5 min). \[T_{\text{IPS}}\equiv T_{\text{GNSS-RTK}}\geq T_{start}+\left\lceil\frac{k}{n} \right\rceil\cdot T_{mov}+T_{pos} \tag{2}\]  
Furthermore, the potential use of a UAV constellation, where each UAV would act as an anchor supporting GNSS-RTK self-positioning capabilities, would not only reduce \(T_{\text{IPS}}\) (see Equation (3)), but also simplify the whole IPS infrastructure-deployment process. Moreover, this option would improve the flexibility and dynamism of the infrastructure, since it could be redeployed and adapted to a changing environment in a simple manner. \[T_{\text{IPS}}\equiv T_{\text{GNSS-RTK}}\geq T_{start}+T_{mov\left(\text{ UAV}\right)}+T_{pos} \tag{3}\]