esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Estimation of the UEHLD's Parameters
### Bayesian Estimators  
Here, we obtain the Bayesian estimator of the UEHLD parameters. The Bayesian estimators of \(\varphi\), and \(\delta\) are regarded under the squared error loss function (SELF), which are, respectively, defined by:  
\[L(\widetilde{\varphi},\varphi)=(\widetilde{\varphi}-\varphi)^{2},L(\widetilde {\delta},\delta)=\Big{(}\widetilde{\delta}-\delta\Big{)}^{2}.\]  
Assume that the prior distribution of \(\varphi,\delta\) denoted by \(\pi(\varphi),\pi(\delta)\) has an independent gamma distribution. The joint gamma prior density of \(\varphi,\delta\) can be written as  
\[\pi(\varphi,\delta)\propto\varphi^{q_{1}-1}e^{-w_{1}\varphi}\delta^{q_{2}-1}e ^{-w_{2}\delta};q_{j},w_{j}>0,j=1,2. \tag{16}\]  
The ML estimator for \(\varphi\) and \(\delta\) is obtained by equating the estimates and their variances with the inverse of the Fisher information matrix of \(\varphi\) and \(\delta\) in order to extract the hyperparameters of the informative priors (see [PERSON] et al. [31] for more information). The joint posterior of the UEHLD with parameters \(\varphi\) and \(\delta\) is obtained using (14) and (16) as:  
\[\pi(\varphi,\delta|\underline{t})\propto\pi(\varphi,\delta)L(\underline{t}| \varphi,\delta).\]  
Then, the joint posterior can be written as:  
\[\pi(\varphi,\delta|\underline{t})\propto\varphi^{q_{1}+n-1}e^{-w_{1}\varphi }\delta^{q_{2}+n-1}e^{-w_{2}\delta}\prod_{i=1}^{n}\frac{t_{i}^{\delta-1}}{ \left(1+t_{i}^{\delta}\right)^{2}}\ \bigg{(}\frac{1-t_{i}^{\delta}}{1+t_{i}^{\delta}}\bigg{)}^{\varphi-1}.\]  
We can employ the Markov Chain Monte Carlo (MCMC) method to acquire the Bayesian estimators. Gibbs sampling and the more general Metropolis within Gibbs samplers are useful subclasses of the MCMC techniques. The two most-well-known MCMC methods are the Gibbs sampling and Metropolis-Hastings (MH) algorithms. We created random samples from conditional posterior densities of \(\varphi,\delta\) using the MH inside the Gibbs sampling steps as follows:  
\[\pi(\varphi|\delta,\underline{t})\propto\varphi^{q_{1}+n-1}e^{-\varphi[w_{1} -\sum\limits_{i=1}^{n}\ln\frac{(1-t_{i}^{\delta})}{(1+t_{i}^{\delta})}]}\sim \text{Gamma}\bigg{(}q_{1}+n,\bigg{[}w_{1}-\sum\limits_{i=1}^{n}\ln\left( \frac{1-t_{i}^{\delta}}{1+t_{i}^{\delta}}\right)\bigg{]}\bigg{)},\]  
and  
\[\pi(\delta|\varphi,\underline{t})\propto\delta^{q_{2}+n-1}e^{-w_{2}\delta} \prod_{i=1}^{n}\frac{t_{i}^{\delta-1}}{\left(1+t_{i}^{\delta}\right)^{2}}\ \bigg{(}\frac{1-t_{i}^{\delta}}{1+t_{i}^{\delta}}\bigg{)}^{\varphi-1}.\]  
The Bayesian estimators were obtained via SELF. The 95% two-sided highest density credible region interval for the unknown parameters or any function of them is given as \([\varphi_{0.025N:N},\,\varphi_{0.975N:N}]\) and \([\delta_{0.025N:N},\,\delta_{0.975N:N}]\) by using the method proposed by [PERSON] and [PERSON] [32].
# Dynamic Stability Finite Difference Time Domain Analysis of Landfill Based on Hypergravity Test
## 3 Establishment of Computational Model
### Material Parameters  
The Mohr-Coulomb constitutive model was taken in the analysis, and parameters for modeling, as shown in Table 1, were determined by referring to previous studies [11; 16; 34; 35]. The shear strength of MSW was selected according to 10% and 20% failure strain.
# Structural-Missing Tensor Completion for Robust DOA Estimation with Sensor Failure +
## 5 Simulation
### Comparison of the Estimation Accuracy with Different SNRs and Number of Snapshots  
Furthermore, the estimation accuracy of the above-mentioned methods is compared with 5 impaired sensors in Figure 5. The SNR varies whereas the number of snapshots is set to \(T=100\) in Figure 5a, while the number of snapshots varies whereas the SNRs of sources are set to 15 dB in Figure 5b. The implementation of CPD on an unimpaired URA is presented as reference. In addition, considering the CRB is a common lower bound on the performance of DOA estimators, we use it as the benchmark in the simulation. Specifically, we calculate the weighted sum of CRB for a URA with randomly impaired sensors [30].  
Figure 4: Comparison of accuracy with different number of impaired sensors.  
Figure 5: Comparison of accuracy. (**a**) RMSE vs. SNR. (**b**) RMSE vs. the number of snapshots.  
As shown in Figure 5a, the estimation accuracy of the proposed structural-missing tensor completion algorithm is the highest among the entire tested SNR regime as compared to other methods with array sensor failure. The proposed algorithm outperforms the CPD method with array sensor failure, because it effectively completes the tensor signals with missing fibers or slices. In contrast, the direct CPD on the incomplete tensor statistics suffers from worse capability of parameter retrieval. On the other hand, since the proposed tensor signal model preserves the original 2D angular information, the proposed algorithm also enjoys a significant improvement in accuracy compared to the conventional matrix completion method. Moreover, as the SNR increases, the RMSE curve of the proposed algorithm gradually approaches that of the CPD method for the unimpaired URA, and the two RMSE curves become very close with SNR higher than 10 dB, which also verifies the advantage of the proposed algorithm in overcoming the effect of impaired sensors. Similar result can be found in Figure 5b, where the estimation accuracy of the proposed algorithm is very close to that of the CPD method for the unimpaired URA, and is higher than all other methods.
## 4 Discussion
However, this review did not perform a meta-analysis of the data and, although it was published in 2018, it only evaluated 4 studies compared to the 20 considered by [PERSON] and [PERSON] [1] eleven years earlier. [PERSON] and [PERSON]. [7] carried out a review and meta-analysis on the effectiveness of antibiotic prophylaxis without excluding the maxillary molars. On the other hand, clinical evidence suggests that the post- surgical course is completely different depending on whether the third molar is maxillary or mandibular, with a major onset and severity of complications in the mandibular arch. Furthermore, only studies on amoxicillinlin and amoxicillinlin with clavulanic acid were considered. The Authors reported a significant efficacy of prophylaxis for all modes of administration: pre-, post- surgical and mixed. They also reported a significant increase in complications related to the administration of post- surgical and mixed, but not pre- surgical, antibiotics. [PERSON] and [PERSON]. [9] performed a meta-analysis on exclusively pre- surgical prophylaxis in the case of an operation involving osteotomy. The Authors reported a significant efficacy of prophylaxis both in case of amoxicillinlin and in case of other antibiotics. However, different inclusion criteria led to the exclusion of studies in the present review that were included by [PERSON] and [PERSON] [9]. A meta-analysis by [PERSON] and [PERSON]. [11] evaluated the efficacy of amoxicillinlin prophylaxis including fewer studies and without excluding surgery on maxillary molars. The authors reported an absence of significant efficacy of both pre- and post- surgical prophylaxis with amoxicillinlin. The present meta-analysis illustrates the limitation of not having considered important patient-related factors such as sex, age, tobacco use, co-morbidities and concomitant drug therapies. This limitation is related to the fact that the included RCTs considered these factors as exclusion criteria or did not report the data distinctly. Generally, the presence of risk factors determines greater support to the need for antibiotic prophylaxis, but the judgment must be left to the surgeon. Therefore, considering the foregoing, it seems possible to state that the data present in the literature homogeneously indicate a benefit in the administration of exclusively pre-surgical antibiotic prophylaxis with non-penicillin antibiotics; these data do not support the use of amoxicillinlin in some of the modalities considered and of non-penicillin antibiotics in post- surgical modalities. Pre- and post-surgical administration with non-penicillin antibiotics, although effective, seems clinically less impactful than pre-surgical administration.
## 3 Test Program and Procedures
### Sampling and Geochemical Analysis  
Grade VI to III granite samples at every test site were continuously retrieved from the borehole before performing PMTs using a triple-core barrel to minimize sample disturbance (Figure 4), and XRF analysis was conducted for the retrieved samples to evaluate the degree of weathering. The fused glass beads of the fine powder samples were prepared, and a wavelength dispersive (WD) X-ray fluorescence spectrometer (XRF-1700; Shimadzu, Japan) at The National Center for Inter-University Research Facilities (NCIRF) was used under the conditions of 40 kV and 95 mA; the XRF-1700 supports 20-60 kV of accelerating voltage and 5-140 mA of X-ray current, respectively, and it can detect a material content with a range from 10 ppm. The weight percentage of major oxides in the samples was obtained as the result of XRF analysis and was transferred to the molecular ratio to calculate the chemical weathering indices, which indicate the extent to which the samples were  
Figure 3: Interpretation of pressuremeter test (PMT) curve: (**a**) pressuremeter modulus (\(E_{m}\)); (**b**) limit pressure (\(P_{L}\)).  
weathered. Several previous studies for estimating the degree of weathering based on chemical weathering indices have shown that a particular chemical weathering index composed of alkali and alkaline oxides appropriately indicates the weathering intensity of granite because the oxides decrease with weathering [12; 13; 16; 20]. In addition, among various chemical weathering indices, the indices based on a group of mobile oxides and immobile oxides are more appropriate for characterizing the weathering [21; 22]. Therefore, in this study, the four chemical weathering indices were selected. The indices and their ideal tendency with the degree of weathering are presented in Table 1.
## 3 Improved SIFT Algorithm
### Simplified Feature Descriptor  
In the original SIFT algorithm, there were 128 high-dimensional feature descriptors and redundant data. The computational complexity of computing the feature descriptor is  
\[O(keypoints.size\times d1\times d2\times(n^{2}+scale^{2})) \tag{20}\]  
where \(O()\) represents the time complexity of the corresponding algorithm; \(keypoints.size\) represents the number of feature points; \(d_{1}\) and \(d_{2}\) represent the dimension of the feature descriptor; \(n\) represents the number of iterations of the algorithm; and \(scale\) represents the Gaussian scale. The time complexity of the 128 dimensions is  
\[O(keypoints.size\times 4\times 4\times(n^{2}+8^{2})). \tag{21}\]  
The higher the dimension of the feature descriptor, the higher the time complexity. Therefore, this article simplified the descriptor. The specific division of the simplified descriptor is shown in Figure 3, the red dots in Figure 3 represent feature points. Compared with the square area divided by the SIFT algorithm, the \(4\times 4\) grid pixels with 4 corners were discarded. The basis for this was that the smaller the distance between the pixel and the feature point, the smaller the contribution to the matching. The the weight distribution of the descriptor data was in line with the Gaussian kernel, so the pixel information that was far away and had little influence on the matching effect was discarded. Therefore, obtaining a \(3\times 4\times 8\) (96)-dimensional feature descriptor reduced the descriptor dimension of the original SIFT algorithm by 25%. The time complexity of 96 dimensions is  
\[O(keypoints.size\times 3\times 4\times(n^{2}+8^{2})), \tag{22}\]  
so the consumption time was shortened, and the computational complexity and amount of computation of the algorithm was reduced. Although a small amount of feature information in the corners was discarded, the uniqueness of the descriptor improved, and the matching of feature points with the same name was faster and more stable.  
If \(T=(t_{1},t_{2},\ldots,t_{96})\) is a 96-dimensional feature descriptor of a feature point, it needs to be normalized to remove the influence of illumination changes.  
**Definition 2**.: _Normalized feature vector According to Formula (8) and the 96-dimensional feature descriptor model, the improved normalized feature vector is shown in Formula (21):_  
\[f_{i}=\frac{t_{i}}{\sqrt{\sum\limits_{j=1}^{96}t_{j}}} \tag{23}\]  
_Therefore, the feature descriptor \(\hat{T}=(f_{1},f_{2},\ldots,f_{9}6)\) was obtained._
# Path Loss Model for 3.5 GHz and 5.6 GHz Bands in Cascaded Tunnel Environments
## 4 Determination of ELC by RT Simulation
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{\(f\) (GHz)} & \multirow{2}{*}{\(R\) (m)} & \multicolumn{4}{c}{ELC (dB/100 m)} & \multirow{2}{*}{ \begin{tabular}{c} **Average** \\ **RMSE (dB)** \\ \end{tabular}  } \\ \cline{3-5}  & & \(d_{1}\) (m) & \(d_{2}\) (m) & \(d_{3}\) (m) & RMSE (dB) \\ \hline
3.5 & 300 & 7.18 & 7.03 & 7.17 & 1.66 \\
3.5 & 600 & 4.35 & 4.28 & 4.66 &  
In addition, the fitting parameters in Equation (19) and the root mean square error (RMSE) between the value of ELC and fitted values are shown in Table 6. The fitting function can be used as an empirical formula to calculate ELC at 3.5 GHz and 5.6 GHz in the cascaded tunnel.
## 3 Stability Analysis
### Floquet Multipliers
 \tag{14}\]  
The matrix \(\mathcal{S}\) is the so-called saltation matrix, which accounts for the non-smoothness at the switching time \(t^{*}\) and has the form:  
\[\mathcal{S}=I+\frac{\left(\mathbf{f}_{OFF}(\mathbf{x}^{*})-\mathbf{f}_{ON}( \mathbf{x}^{*})\right)\cdot\mathbf{n}^{T}}{\mathbf{n}^{T}\cdot\mathbf{f}_{ON} (\mathbf{x}^{*})+\frac{\partial h_{r}(\mathbf{x},t)}{\partial t}}, \tag{15}\]  
where \(\mathbf{n}=\
abla h_{r}(\mathbf{x},t)\) is the normal vector to the switching surface, which in our case can be written as  
\[\mathbf{n}=[-1,\;0,\;0,\;k_{V},\;k_{int}]^{T}, \tag{16}\]  
and, similarly,  
\[\frac{\partial h_{r}(\mathbf{x},t)}{\partial t}=-\frac{A}{T}. \tag{17}\]  
With this, we can summarize the steps to establish the stability of a Period 1 orbit in the following algorithm:  
1. Find the state at the beginning of the periodic orbit \(\mathbf{x}(0)\) and the duty cycle \(d\) that fulfill the following conditions \[\mathbf{x}(0) = \mathbf{x}(T)\] \[h(\mathbf{x}(dT),dT) = 0\] (18)
2. With the solution \(d\) and \(\mathbf{x}(0)\), calculate the state transition matrices (11) and (12) and the saltation matrix (15), to obtain the monodromy matrix (10). 3. Obtain the Floquet multipliers as the eigenvalues of \(M\).
## 4 Case II: Active Eavesdropper
### Bit-Error-Rate (BER) Performance
#### 4.4.2 BER at the Eavesdropper User E  
At the active eavesdropper user E, the BER performances versus \(SNR_{E}\) are also calculated to evaluate the amount of the message that user E can decode. As shown in Figure 20, it can be seen that the combination of both the jamming signal from user A and the self-interference component at user E themself has a major impact on the estimating and decoding process of user E. This is because the combined power of these two signals is larger than the power of the intended signal, and user E only uses the blind or semi-blind scheme without feedback for channel estimation and decoding, regardless of the knowledge of channel coding used for decoding and the four pilot symbols. The best BER that user E can obtain is about \(BER_{E}=10^{-2}\) at \(SNR_{E}=30\) dB, corresponding to the lowest level of the self-jamming-to-noise ratio from user A, \(\rho_{AE}=0\) dB. Consequently, when the power of the self-jamming signal from user A increases, user E needs a very large \(SNR_{E}\) to decode the intended message from user B.  
Furthermore, Figure 21 shows the BER of user E versus the \(SNR_{E}\) for various values of the self-interference-to-noise ratio of itself, \(\rho_{EE}\), while the self-jamming-to-noise ratio of user A, \(\rho_{AE}\) is fixed at 30 dB. It shows that if user E tries to increase the power of the jamming signal that is sent to user A, it leads to an increase in their BER because of the increase in the  
Figure 19: \(BER_{A}\) versus \(SNR_{A}\), \(\rho_{AA}=30\) dB in case of an active eavesdropper: (**a**) Blind feedback; (**b**) Semi-blind feedback.  
self-interference-to-noise ratio \(\rho_{EE}\). Although SI can be suppressed by the knowledge of the SI signal by the classical DSIC process, the interference from the self-jamming signal from user A still significantly impacts the blind scheme without feedback and the semi-blind scheme without feedback. It looks like the case of a passive eavesdropper when user E cannot suppress the interference from the jamming signal of user A well. However, the BER of user A is less sensitive to the increased power of user E, especially for the semi-blind feedback scheme, as shown in Figure 19.  
Therefore, it can be concluded that user E cannot decode the message well, regardless of using the blind scheme without feedback or the semi-blind scheme without feedback.
## 3 The Effects of the Second Phases on Properties
### \(\delta\)-Ferrite Phase  
The \(\delta\)-ferrite phase exerts a notable influence on the high-temperature mechanical properties and corrosion resistance of AISI 321 stainless steel. As outlined in Section 2.2, variations in solidification mode and cooling rate can impact the hot deformation behavior of AISI 321 stainless steel, predominantly in the as-cast state. However, the majority of materials utilized in everyday applications are in the rolled form, which leads to differences in their performance compared to the as-cast materials.  
[PERSON], [PERSON] doubled the tensile strength of vermicular \(\delta\)-ferrite grains by thinning them into small particles through ultrasonic vibration [29]. [PERSON], [PERSON] argued that \(\delta\)-ferrite facilitated crack branching, preventing the unidirectional propagation of cracks, and enhanced the steel resistance to crack propagation and stress corrosion (Figure 6) [7]. [PERSON], [PERSON] noted that low \(\delta\)-ferrite content may exacerbate stress corrosion issues [20].  
While a modest ferrite presence (ranging from 3% to 8%) can significantly ameliorate stress corrosion resistance, effectively stymying crack growth [30], the impact of \(\delta\)-ferrite on the mechanical properties of AISI 321 stainless steel remains a subject of debate [31; 32]. [PERSON], [PERSON] performed a tensile test on austenite specimens with ferrite growing in the vertical direction, and the strength of the samples taken horizontally was significantly higher than that of the samples in the vertical direction, and in the vertical direction, the tensile strength of the ferrite structure located between the layers decreased, and the growth of ferrite in the vertical direction was the main reason for the difference in tensile properties between the horizontal and vertical directions. The horizontal stress mainly resists the grain boundary forces, while the vertical stresses mainly resist the intragranular forces and the bonding forces between the depositary layers (Figure 7) [33].  
Figure 5: AISI 321 stainless steel creep fatigue fracture surface, **(a)** TiC; **(b)** Cr\({}_{23}\)C\({}_{6}\)[27].  
Figure 6: \(\delta\)-ferrite morphology of 321 stainless steel [7].
# Peak Energy Reduction in Flow Shop including Switch-Off Policies and Battery Storage
## 6 Conclusions and Future Development  
The research proposed in this paper extends the use of energy storage systems integrated with switch-off policies to reduce the energy costs in manufacturing systems. In response, our first research question asked: _what is the impact of the energy storage system characteristics to reduce the energy use over the peak power constraint?_ The simulation results have demonstrated how the major impact is due to the charge rate and power fluctuations of the tasks, whereas the battery capacity and the particular models with energy storage systems have a lower impact.  
However, in response to our second research question: _Can the switch-off policies significantly improve the use of the energy storage system to reduce the energy use over the peak power constraint?_ The switch-off policies can improve further the energy costs reduction. In particular, the switch-off policies have a greater impact when the speed of charging is lower. This means that the introduction of switch-off policies allows using batteries at lower costs because the charge rate can be lower. The results of the model proposed can support, at the managerial level, the choice of the combination of battery characteristics (charging speed and capacity) related to the power fluctuation of the manufacturing tasks to reduce the peak power penalty.  
Future development paths can be the following: the investment cost analysis of the battery related to the characteristics (capacity, charging speed, etc.) should be studied to evaluate the energy-saving costs globally. Also, the degradation of battery and thus the performance losses, the economic and technical benefits of energy storage replacement will be analyzed. The comparison of different battery technologies and their performances will be the focus of future work.  
Conceptualization, [PERSON] and [PERSON]; [PERSON] designed and performed the experiments; [PERSON] and [PERSON] wrote and revised the paper. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** We would like to thank the anonymous reviewers for their careful reading of our manuscript and their many insightful comments and suggestions.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Exp. No.** & **Battery Capacity** & **Charge Rate** & **Power Variability** & **Switch-Off Policy** & **Energy Over Peak** & **Average Grid Utilization** & **Peak Grid Power** & **Battery Utilization** \\ \hline  
\hline \end{tabular}
\end{table}
Table 1: Cont.
# Emergency Communication System Based on Wireless LPWAN and SD-WAN Technologies: A Hybrid Approach
## 4 A Hybrid Approach to Emergency Communication Systems  
An ECS is a communication mechanism that deals with emergencies where communication facilities are damaged or the area is out of the communication grid. Its purpose is to use unconventional and hybrid methods for establishing communication between a specific site and the outside world [44]. Therefore, a smooth communication system should be ready to report disasters and implement rescues or to connect off-grid areas and critical infrastructures with a communication network.