esa-sceva/satcom-chunk-collection huggingface dataset:
# Sensitivity Analysis of Numerical Coherency Model for Rock Sites
## 2 Numerical Coherency Evaluation Methodology
### Numerical Model  
The numerical simulation was conducted using OpenSees 3.3.0, a finite element analysis code [17]. The Pinyon Flat site is classified as a hard rock site, characterized by very high stiffness; therefore, the elements were modeled with linear behavior. In time history analysis, boundary conditions and element size significantly influence the results. A viscous damper was applied to the lower boundary to simulate elastic bedrock conditions [18; 19]. For the lateral boundaries, the equal degree of freedom (EDOF) method was used to indirectly simulate free-field conditions [20]. To minimize boundary effects, the model width was set to twice the range from which node responses were extracted for coherency analysis.  
The element size was determined according to the criteria proposed by [PERSON] and [PERSON] [21], ensuring accurate simulation of seismic wave propagation up to the maximum frequency of interest, which was set to 50 Hz. The element size applied was 1 m in both width and height. Figure 4 shows the developed computational model with a depth of 150 m. Because the depth of the numerical model may influence of the calculated coherency, it was changed from 50 m up to 200 m, the results of which are presented in Section 3. The range of nodes used for coherency analysis was defined based on the maximum separation distance of the array installed at the Pinyon Flat site [1].  
Figure 4: Developed computational model.  
Figure 3: Example realization of random field generated using the Gaussian model for the Pinyon Flat array.
# The Spotting Distribution of Wildfires
## 4 Discussion  
### Usage of the Spotting Distribution  
One of our primary motives in determining the spotting distribution, is to employ the latter as a redistribution kernel in fire spread models. For example, the model in [35] uses an indicator function approach to describe the interface between burned and unburned regions. They include spotting in the form of a log-normal distribution, but other spotting distributions, such as computed here, could be included as well.  
A somewhat different approach was taken in [29; 30], where an integro-PDE equation approach was used to model the probability of fire at a certain location. The time evolution of the likelihood \(u(t,x)\) to observe a fire a time \(t\) at location \(x\) is given by the integro-PDE  
\[u_{t}=Du_{xx}+\int_{-\infty}^{\infty}\mathbb{S}(x-g)\ u(g,t)\ dg+c(u)u-\delta(u )u, \tag{74}\]  
where \(\mathbb{S}(x)\) is the spotting distribution, \(c(u)\) describes combustion, \(\delta(u)\) denotes the heat loss term and the diffusion \(Du_{xx}\) describes local fire spread. This model was used in [30] to investigate invading fire fronts and their asymptotic invasion speeds. For example, we could there show that spotting is able to increase the fire invasion speed. A model of the above type is quite versatile and it can include various spotting distributions as well as different combustion and fuel dynamics.  
A more standard approach starts from conservation laws for physical quantities like energy of chemical species, leading to coupled reaction-diffusion systems for temperature and mass evolution [31; 52; 53] and knowledge of the spotting distribution might be a useful addition to these models.  
Changing gears from our discussion of the \"spread problem\", knowledge of the spotting distribution is also important for the \"breaching problem\"--of great practical importance for wildfire management, but poorly understood. For example, suppose a crowning forest fire reaches a wide river. On the opposite side is a continuous stretch of dense coniferous forest. The likelihood of a spot fire occurring on the non-burning side of the river equals the integral of the spotting distribution along the opposing side of the river. It gives a direct quantitative measurement for fire risk beyond fuel breaks. A similar problem arises at the wildland-urban interface and the spotting distribution can tell us how far the spotting is likely to reach.
# Intelligent Identification of Trend Components in Singular Spectrum Analysis
## 4 Combination of Automatic Identification and Separability Improvement  
### Automatic Identification of Trend  
By trend, we mean a slowly varying component of a time series. We will use the Fourier decomposition. For \(\mathsf{F}=(f_{1},\ldots,f_{N})\),  
\[f_{n}=c_{0}+\sum_{k=1}^{\lfloor\frac{N-1}{2}\rfloor}\sqrt{c_{k}^{2}+s_{k}^{2}} \cos(2\pi nk/N+\phi_{k})+c_{N/2}(-1)^{n};\]  
the last summand is zero if \(N\) is odd.  
The periodogram of the time series \(\mathsf{F}\) is defined as [4] (Equation (1.17)):  
\[\Pi_{N}^{f}\bigg{(}\frac{k}{N}\bigg{)}=\frac{N}{2}\left\{\begin{array}{ll}2c _{0}^{2}&\quad\text{if}\quad k=0,\\ c_{k}^{2}+s_{k}^{2}&\quad\text{if}\quad 0<k<N/2,\\ 2c_{N/2}^{2}&\quad\text{if}\quad k=N/2\ (N\ \text{is even}).\end{array}\right.\]  
Introduce the measure  
\[P_{N,\omega_{0}}(\mathsf{F})=\sum_{0\leq\frac{k}{N}<\omega_{0}}\Pi_{N}^{f}( \frac{k}{N})/\|\mathsf{F}\|^{2}. \tag{2}\]  
We call \(P_{N,\omega_{0}}(\mathsf{F})\) the contribution of low frequencies, where \(\omega_{0}\) is the upper bound of low frequencies, which can be different for different kinds of time series and different problems.  
**Algorithm of trend identification**[14; 15]:  
* Input: time series \(\mathsf{F}\) of length \(M\), parameters: \(\omega_{0}:0<\omega_{0}<\frac{1}{2}\) and the threshold \(T_{0}:0<T_{0}<1\).
* The time series \(\mathsf{F}\) is considered as a part of trend, if \(P_{M,\omega_{0}}(\mathsf{F})>T_{0}\). If there are several time series at the input, the result of trend identification is the set of numbers of time series for which \(P_{M,\omega_{0}}(\mathsf{F})>T_{0}\). We will apply this algorithm to elementary time series and call it TAI(\(T_{0},\omega_{0}\)).
# Miniaturized Arrow-Shaped Flexible Filter-Embedded Antenna for Industrial and Medical Applications
## 3 Experimental Results
### Gain and Efficiency  
Figure 11 represents the gain and radiation efficiency of the proposed flexible filtenna. It can be seen from the displayed figure that the antenna offers gain >2.6 dBi throughout the operational bandwidth of 2.2-2.8 GHz with a peak value of 2.9 dBi at a resonance frequency of 2.45 GHz. It is also observed that at filtering spectrum 3-11 GHz, the antenna gain is below \(-3\) dBi. The measured value provides strong support in favor of simulated results. Furthermore, the radiation efficiency plot in Figure 10 demonstrates a value >90% over the entire operational spectrum, whereas for filtered bands the value of efficiency is less than 40%. The farfield performance of the proposed flexible filtenna endorses it as a suitable contender for imminent electronic devices operating over the ISM band.  
Figure 10: Radiation pattern of the proposed filtenna at 2.45 GHz.
# A Method to Produce vsiRNAs in Plants with Cross-Kingdom Gene Silencing Capacity
###### Abstract  
Plants have evolved defense mechanisms to suppress viral transcription and replication by transcriptional and post-transcriptional gene silencing mediated by virus-derived small interfering RNAs (vsiRNAs). Based on this response, virus-induced gene silencing (VIGS)-based technology has been developed to silence target genes on either host plants or insect pests. This mechanism could also be used for the silencing of genes of interest in the medical field. We used the VIGS vector PEuMV-YP:Krt18, which was obtained by inserting the _Mus musculus_ (_M. musculus_) Krt18 sequence into peuMV-YP:\(\Delta\)AVI. The objective was to evaluate the capacity of peuMV-YP:Krt18 to induce _Nicotiana benthamiana_ (_N. benthamiana_) production of visRNAs of a specific sequence that belongs to neither the plant genome nor the wild virus genome, which were used to induce cross-kingdom gene silencing between plants and mammals. The percentage of visRNA for each viral gene was calculated from an sRNA library of _N. benthamiana_ plants infected by pEuMV-YP: Krt18. When the visRNAs were characterized, it was found that they corresponded to all the genes of the pEuMV-YP:Krt18 vector. These visRNAs induced the silencing of the Krt18 gene in _M. musculus_ macrophages, supporting the ability to use VIGS vectors in plants as biofactories for the production of sRNAs that induce gene silencing in mammals.  
Keywords:vsiRNA; cross-kingdom; VIGS; gene silencing +
Footnote †: journal: _Sci._**2022**, 12, 5329. [[https://doi.org/10.3390/app12115329](https://doi.org/10.3390/app12115329)]([https://doi.org/10.3390/app12115329](https://doi.org/10.3390/app12115329))
# Intra-Mode Decision Based on Lagrange Optimization Regarding Chroma Coding
## 4 Proposed Lagrange Optimization Scheme
### Distortion Analysis  
Owing to the assistance of luma coded blocks, chroma intra prediction utilizes the reference pixels for further compression in the modes of LM, LM_T, and LM_L. This can result in distortion drift from the luma to chroma, and the propagation distortion chain can grow as subsequent intra coding progresses. Here, the pixel distortion \(d\) located at (\(i\),\(j\)) can be expressed as (9) based on the principle of the chroma intra prediction.  
\[d(i,j)=b(i,j)-b^{\prime}(i,j). \tag{9}\]  
Here, the variable \(b\) is the original pixel value in current chroma block \(B_{n}\), and \(b^{\prime}\) denotes the reconstructed pixel value in \(B^{\prime}{}_{n}\) based on the reconstructed reference pixel. This can be rewritten as (10) by using the reconstructed pixel value \(b_{o}\), which is based on the original reference pixel.  
\[d(i,j)=[b(i,j)-b_{o}(i,j)]+[b_{o}(i,j)-b^{\prime}(i,j)]. \tag{10}\]  
Here, the distortion \(d\) can be divided into two segments: the difference between the original pixel in the current block and the reconstructed one obtained by intra prediction using the original reference pixel, and the difference in the reconstructed pixel using the original reference pixel \(b_{r}\) with respect to the reconstructed reference pixel \(b_{r}\). The former represents the pixel distortion in the current chroma block, and the latter reflects the luma propagation distortion related to the reference chroma block. Generally, intra prediction coding can be described using the Gauss-Markov model for pixels, as shown in [28], and it holds for the following:  
\[b_{o}(i,j)\approx\theta b_{r}(i,j)+\eta, \tag{11}\]  
\[b^{\prime}(i,j)\approx\theta b^{\prime}_{r}(i,j)+\eta, \tag{12}\]  
Figure 5: Ratio of each optimal mode in BasketballDrive.  
Figure 6: Distribution of each optimal mode in BasketballDrive.  
where \(\theta\) and \(\eta\) are model parameters, respectively. Then, by substituting them into (10), it can be derived as follows:  
\[\begin{split} d(i,j)=[b(i,j)-b_{0}(i,j)]+[\theta b_{r}(i,j)+\eta- \theta b_{r}^{\prime}(i,j)-\eta)]\\ \approx d_{c}(i,j)+\theta d_{r}(i,j),\end{split} \tag{13}\]  
where \(d_{c}\) indicates the distortion between the original chroma pixel and the reconstructed chroma pixel based on the original luma reference pixel, and \(d_{r}\) represents the distortion between the original luma reference pixel and the reconstructed luma reference pixel. Thus, for the \(k^{\text{th}}\) chroma block, the total distortion \(D_{k}\) can be obtained as follows:  
\[D_{k}\approx D_{kc}+\theta D_{kr}. \tag{14}\]  
In conformity with the study above, it can be discovered that the total coding distortion for LM, LM_T, and LM_L is related to the distortions \(D_{kc}\) of the current chroma block and the distortion \(D_{kr}\) of the reference luma block. This indicates the characteristic of distortion propagation from the luma to chroma.
## 4 Simulation Results
### Results  
Simulations are run in three scenarios, including \"fatal\", \"serious\" and \"normal\", and the simulation results of cooperative rescue plan would be compared with non-cooperative rescue plan. In non-cooperative rescue plan, rescue teams only rescue the victims which are detected by themselves. Each simulation is repeated 200 times. The simulation results are analyzed through three output indicators, i.e., relative survival probability \(\overline{p}_{rs}\) (the initial relative survival probability is 1), ratio of survivors getting rescued \(\overline{p}_{r}\) and average elapsed time for one site getting rescued \(\overline{t}_{a}\), shown in Table 4. The comparison shows that the auction-based cooperative rescue plan outperforms non-cooperative rescue plan in three scenarios at 1% significance level, which can be seen from the independent-samples \(t\) test in Table 4.  
Figure 4: The deterioration rate of survival probabilities of victims with different injury severity.  
In Figures 5-7, \(x\)-axis indicates the simulation step, which is also the time in the process of disaster relief. The primary \(y\)-axis indicates relative survival probability, and the secondary \(y\)-axis indicates ratio of rescued victims. As can be seen from the three figures, relative survival probability under auction-based cooperative rescue plan remains higher than non-cooperative rescue plan at every step in the simulation. Relative survival probability decreases rapidly in the beginning, because of the existence of victims who are heavily injured, and their survival probability decrease extremely rapidly, as shown in Figure 4. When rescue lasts for about 300 min, the decrease of relative survival probability would slow down. Because the victims who are heavily injured would die if not rescued within 300 min, there exist only victims who are slightly injured or not injured, whose survival probability decrease slowly.  
The simulation results indicate that the cooperative rescue plan would improve the rescue result a lot, compared with the non-cooperative rescue plan. In the above three scenarios, it increases victims' relative survival probability by 7-15%, increases the ratio of survivors getting rescued by 5.3-12.9%, and decreases the average elapsed time for one site getting rescued by 16.6-21.6%. Moreover, as the scenario worsens, the improvement would be more significant generally.
## 3 Robust Helmert Variance Component Estimation (RVCE)
The relationship between the redefined weight matrix and the estimated components is as follows:  
\[\hat{P}_{k}=\frac{c_{0}}{\sigma_{0,k}^{2}}P_{k} \tag{13}\]  
where \(\sigma_{0,k}^{2}\) is the estimated variance component for the three GNSSs, \(c_{0}\) is constant and usually selected from one of the estimated variance components (i.e., \(\sigma_{0,k}^{2}\)), and \(P_{k}\) is the initial weight for the three GNSSs. With this new weight matrix, the residuals of the three GNSSs are recalculated until the unit weight variance components of the three systems are equal or have ratios approximately equal to one. The Helmert VCE is able to obtain an appropriate posterior weight, however, it is not robust to the influence of outliers. In the domains of time and frequency transfer, outliers are inevitably encountered due to code observation noise, equipment failures, and external interference factors [27; 28], which can all result in failure of the estimated parameters to reasonably represent the characteristics of the atomic clocks. Therefore, robust estimation [29; 30] is introduced to reduce the influence of outliers when combining three GNSSs for time and frequency transfer, this has also been widely applied in geodesy [31; 32]. On the basis of the robust estimation theory, the LSQ estimation can be written as follows:  
\[\overline{X}=\left(A^{T}\ \overline{P}A\right)^{-1}\ A^{T}\overline{P}L \tag{14}\]  
where, \(\overline{P}\) denotes an equivalent weight matrix, which is derived from the distribution of individual GNSS residuals and their equivalent weight function. In this study, the equivalent weight function can be defined by the Institute of Geodesy and Geophysics 3 (IGG3) scheme [33; 34], which is constructed with three segments [32; 35] based on the following formula:  
\[\overline{P_{i}}=\left\{ \begin{array}{ccc}P_{i}&,&\left|\widetilde{V}_{ki} \right|\leq k_{0}\\ P_{i}\frac{k_{0}}{|\widetilde{V}_{ki}|}\left(\frac{k_{1}-|\widetilde{V}_{ki}| }{k_{1}-k_{0}}\right)^{2}&,k_{0}<\left|\widetilde{V}_{ki}\right|\leq k_{1}\\ 0&,&\left|\widetilde{V}_{ki}\right|>k_{1}\end{array} \right. \tag{15}\]  
where \(k_{0}\) and \(k_{1}\) denote threshold constants depending on the actual distribution, which are typically chosen as \(k_{0}\in[1.0,\ 1.5]\) and \(k_{1}\in[2.0,\ 8.0]\), respectively.
## 2 System Model
### Uniform Distribution in the Spherical Space
For \(\sigma_{X^{\prime}}^{2}=1\), the situation for various dimensions of \(\mathbf{X}^{\prime}_{j}\) is summarized in Figure 3.  
As we have mentioned, the multidimensional approaches are limited in the dimension, specifically, \(d=8\) in [9; 10; 11]. In this case, the Gaussian random vectors form the so-called octonions [20]. In the level of Gaussian random raw data, an octonion \(\text{O}_{j}\in\mathbb{R}^{8}\) is built up from eight units \(X_{j0\ldots j,7}\in\mathbb{N}\big{(}0,\sigma_{X}^{2}\big{)}\), as:  
\[f(g^{*})=\frac{1}{\sqrt{2\pi\sigma^{2}}}e^{\frac{-(g^{*}-\mu)^{2}}{2\sigma^{2 }}}. \tag{13}\]0where \(\mathrm{Re}\in\mathbb{R}\) stands for the real part, while \(\mathrm{Im}_{i}\in\mathbb{C}\), for \(i=1,\,i\leq 7\) identifies the \(i\)-th imaginary units, respectively. [PERSON]'s noisy \(\mathrm{O}^{\prime}_{j}\) is \(\mathrm{O}^{\prime}_{j}=X^{\prime}_{j,0}\mathrm{Re}+X^{\prime}_{j,1}\mathrm{ Im}_{1}+\ldots+X^{\prime}_{j,7}\mathrm{Im}_{7}\), where \(X^{\prime}_{j,0\ldots 7}\in\mathbb{N}\big{(}0,\sigma_{X^{\prime}}^{2}\big{)}\). In the multidimensional case the uniformity of the \(d\)-dimensional Gaussian random raw data vectors \(\mathbf{X}_{j}\in\mathbb{R}^{d}\), \(d\leq 8\), can be achieved only in the multidimensional spherical space, over the unit sphere \(\Gamma^{d-1}\). The process requires complex operations and transformations [9; 10; 11] that are so undesirable in a practical CVQKD scenario. In comparison to these approaches, our proposed scalar reconciliation uses only simple scalar operations on the raw data, which makes it possible to eliminate the spherical calculations from the reconciliation phase.
# Energy Efficiency Analysis of MIMO Wideband RF Front-End Receivers
## 5 Conclusions  
Energy efficiency behavior modeling has been presented in this paper. The proposed model sought to place different front-end receivers in relation to different communication schemes. More precisely, two different receiver architectures composed by LNA and LNTA, respectively, were employed with SISO, AS, and SVD schemes, for different ranges.Nakagami-\(m\) fading distribution is used to characterize the wireless channel, in order to be better compliant with IoT scenarios, since the parameter \(m\) can be used to model scenarios where nodes have line-of-sight conditions. After an analysis of the energy efficiency between receivers based on the state of the art, the best candidate for each front-end architecture was selected. A comparative performance study of the different communication schemes was carried out, showing which distances and conditions stand out for the selected front-end designs. The results show that, for short-range scenarios, LNA presents increased EE performance, particularly due to its very low power consumption. On the other hand, when the communication distance increases the very low noise figure provided by N-path LNTA-based architectures outperforms the low power consumption of the LNA-based designs, yielding higher EE for SISO and AS transmission schemes. For SVD, the LNA always presents the better performance in terms of EE, exposing that IoT applications are strongly dependent on energy consumption of the RF circuits. Finally, our analysis also shows that the selected front-end architecture depends on the number of active antennas at the receiver,  
Figure 6: Energy efficiency of the SVD scheme as a function of the number of antennas (\(N_{\rm t}=N_{\rm r}\)), for \(d=\{50,400\}\) m.  
Figure 7: Energy efficiency of the AS and SVD schemes as a function of the number of antennas for the LNTA designs in [17; 18], for \(d=400\) m.  
so that low noise figure is more important with a few active antennas at the receiver, while low power consumption becomes more important when the number of active RF chains at the receiver increases.  
Conceptualization, E.N.J., A.A.M., G.B., R.D.S. and T.T.; methodology, E.N.J., A.A.M. and G.B.; software, E.N.J. and G.B.; validation, E.N.J., A.A.M. and G.B.; investigation, E.N.J., E.L.d.S. and G.T.; resources, E.N.J., E.L.d.S. and G.T.; data curation, E.N.J. and G.B.; writing--original draft preparation, E.N.J.; writing--review and editing, A.A.M., G.B., R.D.S. and T.T. All authors have read and agreed to the published version of the manuscript.  
This work has been partially supported by CAPES, Finance Code 001, and CNPq.  
The authors declare no conflict of interest.
## 4 Results and Analysis
### Predictive Modelling of Material Removal Using ANFIS
#### 4.2.3 Training the Network and Prediction Performance  
Prediction of the depth of cut of the process by ANFIS consists of two main parts; training data and testing data. Hence, among data sets, 70% data is selected stochastically for the training of the ANFIS network and 30% for testing the developed model.  
During the training in ANFIS, 27 sets of data were used to conduct learning, and this ceased after 250 iterations, as shown in Figure 15. The step size for parameter adaptation had an initial value of 0.1. The proposed ANFIS training parameters are given in Table 4. Adaptive neuro-fuzzy inference system (ANFIS) learning in this work uses the hybrid method for updating membership function parameters, which are comprised of back-propagation for the parameters linked with the input membership and least-squares estimation for the parameters related to the output membership functions. The results projected from the fuzzy model of belt grinding have been assessed with the experimental data for validation. Figure 16 gives the comparison of the predicted depth of cut using the established fuzzy model and the data taken from the belt grinding experiments.  
Figure 14: Change in shape of the sigmoidal membership function for each input before and after Training.  
The developed ANFIS system gave an overall 93.5% accuracy, as illustrated in Figure 17. Comparing outputs generated by the fuzzy model with the experimental data from 27 test conditions based on Taguchi DoE (Design of Experiments), the variations are found to be minimum. Thus, it can be inferred that there is a co-relation between the simulated results and the experimental results obtained at the same machining conditions.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Neuron level & 5 \\ Size of input data set & 243 \\ Training Set & 70\% \\ Testing Set & 30\% \\ andMethod & Prod \\ orMethod & Max \\ defuzzMethod & Wtaver \\ impMethod & Prod \\ aggMethod & Max \\ Number of output & 1 \\ Membership function & Sigmoidal membership \\ Learning rules & Least square estimation-Gradient descent algorithm \\ Number of epoch & 250 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Adaptive Neuro-Fuzzy Inference System (ANFIS) training parameters.  
Figure 16: Comparison of predicted with actual experimental values for depth of cut.  
Figure 15: Plot of error versus epochs for modelling material removal.