esa-sceva/satcom-chunk-collection huggingface dataset:
# A Compact FEM Implementation for Parabolic Integro-Differential Equations in 2D
## 5 Assembly
### Assembling the Stiffness Matrix  
Since  
\[\phi_{j}(x_{k},y_{k})=\delta_{jk},\ \ \ \ \ j,k=1,2,3, \tag{17}\]  
a simple calculation reveals  
\[\phi_{j}(x,y)=\det\begin{bmatrix}1&x&y\\ 1&x_{j+1}&y_{j+1}\\ 1&x_{j+2}&y_{j+2}\end{bmatrix}\bigg{/}\det\begin{bmatrix}1&x_{j}&y_{j}\\ 1&x_{j+1}&y_{j+1}\\ 1&x_{j+2}&y_{j+2}\end{bmatrix}. \tag{18}\]  
Hence, we obtain  
\[\
abla\phi_{j}(x,y)=\frac{1}{\mathcal{K}}\begin{bmatrix}y_{j+1}-y_{j+2}\\ x_{j+2}-x_{j+1}\end{bmatrix}. \tag{19}\]  
Here, the indices are to be understood modulo 3, and \(\mathcal{K}\) is the area of triangle \(K\). Thus, using (11), the entries of the stiffness matrix can be computed as  
\[A_{jk}=\int_{K}\
abla\phi_{j}(\
abla\phi_{k})^{T}\mathrm{d}x^{\prime}=\frac{1 }{\mathcal{K}}(y_{j+1}-y_{j+2},x_{j+2}-x_{j+1})\begin{bmatrix}y_{k+1}-y_{k+2} \\ x_{k+2}-x_{k+1}\end{bmatrix}, \tag{20}\]  
with indices modulo 3. This can be written as  
\[A=GG^{T},\ \ \ \ G:=\begin{bmatrix}1&1&1\\ x_{1}&x_{2}&x_{3}\\ y_{1}&y_{2}&y_{3}\end{bmatrix}^{-1}\begin{bmatrix}0&0\\ 1&0\\ 0&1\end{bmatrix}. \tag{21}\]  
The pseudocode given in Algorithm 1 computes the local stiffness matrix contributions, based on Equations (17)-(21).  
```
1:procedurestima(\(c4n\))
2:\(d\leftarrow\mathrm{NCOL}(c4n)\)
3:\(M_{1}\leftarrow[\mathrm{ONES}(1,d+1);c4n^{T}]\)
4:\(M_{2}\leftarrow[\mathrm{ZEROS}(1,d);I_{d}]\)
5:\(G\leftarrow\mathrm{INV}(M_{1})*M_{2}\)
6:\(P_{1}\leftarrow[\mathrm{ONES}(1,d+1);c4n^{T}]\)
7:\(S\leftarrow\frac{\mathrm{DET}(P_{1})*G*G^{T}}{\Gamma_{j-1}^{T}}\)
8:return\(S\)
```
9:endprocedure ```
**Algorithm 1** Computation of local stiffness matrix contributions.  
\begin{table}
\begin{tabular}{c c} \hline \hline \(\mathrm{ONES}(m,n)\) & \(m\times n\) matrix with all entries equal to ones \\ \(\mathrm{REPMAT}(A,m,n)\) & Returns \(m\times n\) block matrix with each block equal to \(A\) \\ \(\mathrm{RESHAPE}(A,m,n)\) & Reshape \(A\) into \(m\times n\) matrix provided \(m*n=\mathrm{NCOL}(A)^{*}\mathrm{NROW}(A)\) \\ \(\mathrm{SPARSE}(m,n)\) & \(m\times n\) sparse matrix of zeros \\ \(\mathrm{SUM}(A,k)\) & Sum of elements of \(A\) along the index \(k\) \\ \(\mathrm{UNIQUE}(A)\) & Return entries of \(A\) without repeating \\ \(\mathrm{ZEROS}(m,n)\) & \(m\times n\) matrix with all entries equal to zero \\ \([\cdot]\) & Horizontal concatenation \\ \([\cdot]\) & Vertical concatenation \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._Using the local contributions, one can compute the global contributions using the pseudocode given in Algorithm 2.
```
1:procedureStiffAssemb(\(n4e,c4n\))
2:\(r\leftarrow\textsc{NROW}(c4n)\)
3:\(A\leftarrow\textsc{SPARSE}(r,r)\)
4:for\(j\gets 1\) to \(r\), do
5:\(A(n4e(j,:),n4e(j,:))\gets A(n4e(j,:),n4e(j,:))+\textsc{stima}(c4n(n4e(j,: ),:))\)
6:endfor
7:return\(A\)
8:endprocedure
```
**Algorithm 2** Computation of the global contributions.
# Deep-Learning-Based Stream-Sensing Method for Detecting Asynchronous Multiple Signals
## 7 Conclusions and Future Work  
This work has proposed a machine-learning-based asynchronous stream-sensing scheme to estimate the number of asynchronously transmitted signals. For the scheme, we propose an asynchronous stream-sensing network (ASensNet) consisting of the AutoEncoder Extractor, the inception layer-based extractor, two linear extractors, and the classifier.  
Through extensive simulation results, the large precision and recall confirm that our proposed ASensNet has notable detection performance with high reliability. Furthermore, the processing time is also fast even on a CPU (31.8 milliseconds for an Intel(R) Core(TM) i7-6900K CPU). Thanks to the fast processing speed and large precision and recall, while moving a disaster area, rescue UAVs with our ASensNet will be able to estimate the number of survivors per zone in real time.  
This paper has demonstrated that it is possible to accurately detect the number of asynchronously transmitted signals, but the current ASensNet actually has a limitation in detecting a limited number of survivors: nine people. In addition, if the current system can be improved to detect the locations of devices, it will be more helpful to rescuers. Hence, in the future, we will provide various ASensNet variants for different numbers of antennas. In addition, based on our ASensNet, we will develop a high-quality survivor discovery protocol with which a rescue UAV not only detects the number of survivors around itself, but also estimates where the survivors are.Moreover, we will develop a high-quality survivor detection system with which a rescue UAV estimates the locations of devices around it by taking into account how the UAV will move through a disaster area to localize devices. Additionally, it can better respond to a situation where the SNR between a device and an UAV changes dynamically. Then, since deep learning could show a better performance that cannot be achieved using mathematical frameworks with assumptions, we will also study a deep-learning-based method that overcomes the theoretical limit and can detect more than \((N_{\mathrm{R}}-1)\) signals even with \(N_{\mathrm{R}}\) antennas.  
Figure 8: Case diagram like a real-time scenario.  
**Author Contributions:** Conceptualization, Y.K. and H.L.; methodology, Y.K.; software, Y.K. and H.L.; writing--original draft preparation, Y.K.; writing--review and editing, H.L.; supervision, H.L.; funding acquisition, H.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2022R1G1A1007058).  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 3 Results Analysis and Optimization
### Regression Equations  
We eliminated the nonsignificant items and ensured that all factors reached the significant or extremely significant level. The regression equation of the maximum cutting force can be expressed as follows:  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Serial** & **Disc Cutter** & **Disc Cutter Rotation** & **Feeding Speed** & **Edge Angle** & **Maximum Cutting** \\
**Number** & **Thickness (mm)** & **Speed (r-min\({}^{-1}\))** & **(m\({}^{-1}\))** & **(r)** & **Force Y1 (N)** \\ \hline \(1\) & \(0\) & \(0\) & \(0\) & \(0\) & \(134.7\  
\[Y_{1}=+127.2-7.38x_{1}-23.52x_{2}-17.82x_{3}-13.03x_{4}+6.26x_{1}x_{2}+12.40x_{1}x_{4 }+5.85x_{3}x_{4}-5.07x_{3}{}^{2} \tag{26}\]  
According to the variance table, the primary terms \(x_{1}\), \(x_{2}\), \(x_{3}\), and \(x_{4}\), interaction term \(x_{1}x_{4}\), and quadratic terms \(x_{3}{}^{2}\) have a significant impact on the maximum cutting force (\(p<0.01\)). The interaction terms \(x_{1}x_{2}\) and \(x_{3}x_{4}\) have significant effects on the maximum cutting force (\(0.01<p<0.05\)). The weights of the factors affecting the maximum cutting force are as follows: disc cutter rotation speed \(x_{2}>\) feeding speed \(x_{3}>\) edge angle \(x_{4}>\) disc cutter thickness \(x_{1}\).
## 2 ESM System Model and Conventional Detectors  
### MIMO System Model  
In MIMO system operating over quasi-static flat fading channel, the received signal is expressed as follows:  
\[\mathbf{y}=\mathbf{H}\mathbf{x}+\mathbf{n}, \tag{1}\]  
where \(\mathbf{H}\in\mathbb{C}^{N_{r}\times N_{t}}\) denotes a channel matrix that follows a complex Gaussian distribution \(\mathcal{CN}(\mathbf{0},\mathbf{1})\) with \(N_{r}\) representing the number of antennas at the receiver and \(N_{t}\) representing the number of antennas at the transmitter. \(\mathbf{x}\in\mathbb{C}^{N_{p}\times 1}\) is the transmitted signal vector. In ESM, when \(N_{p}\) equals to 1, the primary constellation is used, and when \(N_{p}\) equals to 2, the secondary constellation is used. The details of this mechanism are introduced in the next section. And the Gaussian noise \(\mathbf{n}\) follows the distribution of \(\mathcal{CN}(\mathbf{0},\sigma^{2}\mathbf{1})\), where \(\mathbf{I}\) means identity matrix and \(\sigma\) is the variance of the noise and the size of this parameter is \(\mathbb{C}^{N_{t}\times 1}\).
# A Novel SDWSN-Based Testbed for IoT Smart Applications
## 3 System Model
### Physical Network Setup for WSN-Based Clustering
#### 3.2.2 Mobility Scenario Using Transmission Power  
One of the tests involved changing the radio frequency (RF) transmission power output of the TI CC2538 end devices (leaf nodes) based on an indoor scenario (WZ AUT) and an outdoor scenario (around AUT in Auckland) to offer different RSSI values and reflect signal quality. The dynamic alteration of a given node's transmission power output via the C code can be used to evaluate the reliability of communication between the nodes. The hexadecimal value assigned to the selected power mode within the C code can be used to set the intended radio transmission power for the TI CC2538. The power output values used for the indoor and outdoor tests are 22 dBm RF output power with the hexadecimal value 0 xFF set in the power function in C code, and 7.5 dBm output power with the hexadecimal value 0 \(\times\) 42 set in the power function. Figure 15 depicts the power function in C code.  
The RSSI values for the indoor and outdoor scenarios were logged for three rounds based on the selection of the RF transmission power values modified in the TI CC2538, as shown in Table 1. The signal strength was below \(-65\) dBm in both scenarios (indoor and outdoor), as transmission power was increased. This reflects the reliability in communication as well as the impact of altering transmission power to assume the distance between nodes.  
Figure 14: A sample of the RSSI data logged to text file.  
Figure 15: Power function in C code.
## 1 _Editorial_
Incremental Dynamic Analysis (IDA), which is commonly used for seismic analysis, requires a large number of nonlinear time-history analyses and may be influenced by the scaling of ground motion records. To overcome these possible drawbacks, [PERSON] et al. [5] proposed a pushover-based procedure called Incremental Modal Pushover Analysis (IMPA\(\beta\)). An irregular girder bridge of 200 m with four equal spans of 50 m was selected as a case study, and the results obtained through the IMPA\(\beta\) were compared with those obtained by IDA. In [6], [PERSON] et al. investigated the seismic response of bridges with various aspect ratios subjected to both near-fault and far-fault ground motions. The seismic capacity of the bridge columns was evaluated by nonlinear static analysis. Moreover, the effects of the aspect ratio and ground motion on the displacement and force response and the change in the natural frequency of the bridge were studied using modal and dynamic analyses. The dynamic and seismic behavior of rigid block structures has recently attracted the attention of several researchers worldwide. [PERSON] et al. [7] proposed an energy-based methodology to assess the seismic stability of rocking rigid blocks under NFEs. The developed theoretical model was verified based on numerical analyses performed using the Discrete Element Method. Recently, more and more curved bridges have been built throughout the world; thus, they require a deep study of their seismic response. A separated foundation model was proposed by [PERSON] et al. [8] to study the seismic behavior of curved bridges considering the soil-structure interaction. The authors outlined the importance of considering the spatial variation of ground motion to avoid underestimating the structural response. The seismic risk of nuclear power plants should be carefully analyzed, including the influence of the permanent ground displacement/deformation hazards. In [9], [PERSON] proposed a procedure, based on the disaggregation of seismic hazards, for defining the permanent ground displacement that can be adopted for the design of new plants as well as for the safety assessment of existing ones. The displacement to be used for the marginal evaluation was also discussed. The seismic behavior of dams is an important issue as several dams have been built in high seismic zones. In the last paper, [PERSON] et al. [10] analyzed the nonlinear seismic response of conventional roller-compacted concrete (CC-RCC) under NFEs considering the soil-structure interaction. The effects of near-fault pulse records and non-pulse records, including obliquely incident P waves, were investigated. This research received no external funding. We thank all the authors, reviewers and readers of this Special Issue, \"Effects of Near-Fault Ground Motions on Civil Infrastructure\". Finally, we welcome discussion on any of these papers. The authors declare no conflict of interest.
## 4 Results and Discussion
### Discussion
What research has been conducted on rural adoption of technologies is mostly isolated to Western rural areas, with a relative dearth of research on the adoption of technologies in rural areas of the developing world. Additionally, \"rural areas\" compose a broad spectrum of attitudes and cultures, even within the same country, making it difficult to generalize any one response across every rural community [39]. Given the aforementioned difficulties in explaining how BILSTM-LFSOA achieves its results, a careful approach will be necessitated to ensure cooperation from rural communities. The advantages of the model would need to be thoroughly explained to key local stakeholders, and a robust and holistic strategy for implementation of the model that caters to the specific features of the community would need to be devised. Local expertise will be a key element of successful adoption, as local experts can provide information on local perspectives and needs. Finally, we conclude this discussion by noting the potential opportunities that smart farming affords to local communities. The integration of data-driven forecasting into traditional agriculture will be essential in the coming years as worldwide arable land shrinks while populations continue to grow [40]. Machine learning and data-driven techniques can help reduce the uncertainties that farmers face in decision-making and reduce the need for costly and time-consuming methods [40; 41]. Successful adoption of IoT-based sensors can allow for real-time monitoring of crop conditions, while the usage of drones and unarmed vehicles enables better modeling of crop conditions. Robotics in agriculture have also increased productivity by automating the watering and spraying processes [40]. All these elements contribute to increasing the financial security of farmers and their incomes, resulting in greater integration into the agricultural value chain [40].
## 4 Si Transporters
### Brief Introduction of Si Transporters in Some Other Plants
#### 4.2.3 Tomato  
Using the whole-genome sequence of tomato, a NIP subfamily gene was identified and isolated. This gene is highly homologous to the reported rice OsLsi1 amino acid sequence, so it was named _Sllsi1_[28]. The full-length _Sllsi1_ gene is 1109 bp, with an ORF of 852 bp, encoding a 284-aa protein. The amino acid sequence of _Sllsi1_ is predicted to contain six transmembrane regions and two Asn-Pro-Ala motifs. Sllsi1 exhibits Si transport activity in both the rice _lsi1_ mutant and _X. laevis_ oocytes. The expression of _Sllsi1_ in the roots is constitutive. Using immunostaining, Sllsi1 was located on the plasma membrane of the root tip and basal region and showed no polar distribution. In addition, the overexpression of Sllsi1 in tomato increased the Si concentrations in the root and root cell sap but had no effect on the Si concentration in the shoot, suggesting that Sllsi1 is a functional Si import transporter. However, after expressing the cucumber functional gene _Cslsi2_ in tomato, the uptake of Si in tomato was significantly increased, resulting in increased Si accumulation in leaves and enhanced leaf tolerance to water shortage and high temperature. The findings suggest that the low Si accumulation in tomato is attributable to the lack of a functional Si efflux transporter Lsi2 for active uptake of Si, despite the functionality of Sllsi1 (Figure 4) [27].
# A Comparative Study of 3D UE Positioning in 5G New Radio with a Single Station
## 3 3D Positioning Algorithms
### Statistics-Based Approach  
Another method used in this work is space-alternating generalized expectation-maximization (SAGE) [22; 23], which is based on the EM [24] algorithm. The EM algorithm is used to estimate latent states or parameters when parts of the observations are missing or censored. In general, it is an integration process that contains an expectation step (E-step) and a maximization step (M-step). In the context of the statistics approach, we can define the vector \(\eta_{k}=[\theta_{k},\phi_{k},\tau_{k}]\) to indicate the position of the \(k_{th}\) source. In the E-step, the estimate of the \(k_{th}\) source \(\hat{x}_{k}(t;\hat{\eta})\) of the current iteration can be written as:  
\[\hat{x}_{k}(t;\hat{\eta})=\zeta_{k}(t;\hat{\eta})+\beta_{k}\mathbf{N}_{k}(t) \tag{14}\]  
where \(\zeta_{k}(t;\hat{\eta})=\mathbf{a}(\theta_{k},\phi_{k})\mathbf{s}_{k}\) is the assumed receiving signal from the \(k_{th}\) source on the URA without noise, \(\beta_{k}\) are non-negative parameters, \(\sum_{k=1}^{K}\beta_{k}=1\) holds, and \(\mathbf{N}_{k}(t)=\mathbf{Y}(t)-\sum_{k=1}^{K}\zeta_{k}(t;\hat{\eta})\) is intermediate noise. In the M-step, the updated value of \(\eta_{k}=[\theta_{k},\phi_{k},\tau_{k}]\) can be obtained:  
\[\hat{\eta}^{\prime}(\hat{x}_{k})=\arg\max_{[\theta_{k},\phi_{k},\tau_{k}]} \int_{0}^{T}\mathbf{a}^{*}(\theta,\phi)\hat{x}_{k}(t;\hat{\eta})\mathbf{s}^{* }(t-\tau)dt \tag{15}\]  
where \(\mathbf{a}(\theta,\phi)\in\mathbb{C}^{1\times MN}\) is the URA manifold and \(T\) is the observing duration, which is selected to cover the sequence length and maximum propagation delay. The value of \(T\) in this work equals one slot time. \(\mathbf{s}(t)\) is the ideal original SRS. The E-step and M-step are iteratively implemented until the algorithm reaches the convergence point. In this paper, we use the value of intermediate noise \(\mathbf{N}_{k}(t)\) as the convergence condition. The value of \(\mathbf{N}_{k}(t)\) keeps reducing in each EM iteration and reaches its extreme limit point when the estimated parameters are approximately fully recovered. The extreme limit point of \(\mathbf{N}_{k}(t)\) is reached when the power difference of \(\mathbf{N}_{k}^{step(n)}(t)\) and \(\mathbf{N}_{k}^{step(n-1)}(t)\) approaches zero. The flowchart of the algorithm is shown in Figure 3:  
Figure 3: The signal flow of the EM algorithm. This processing flow was also used in [23].
# A Review on Rainfall Measurement Based on Commercial Microwave Links in Wireless Cellular Networks
## 8 Challenges and Future Directions
### Extending Machine Learning Capabilities  
Machine learning has a great application potential in the field of earth environment systems [121]. In particular, DL can extract the spatio-temporal structure and characteristics of data, and it is a good solution to the problem of strong time dependence such as rainfall simulation and prediction. However, machine learning also has some challenges, such as the cost of big data and interpretability [122; 123]. The cost of a big data platform is high, and only when the cost of data collation and cleaning is low can the advantages of big data be maximized. Machine learning lacks behavioral interpretation. Therefore, in the future, we should try to give machine learning the necessary causal reasoning ability to get rid of the traditional \"black box\" operation [124].
# Design and SAR Analysis of a Dual Band Wearable Antenna for WLAN Applications
## 2 Design Methodology
### Metamaterial Based Approach
#### 2.2.1 Dual Band EBG Unit Cells  
This section summarizes the design and analysis of two different unit cells of the two different EBG structures for the desired frequency bands (2.5 GHz and 5.196 GHz). Polyethylene with a thickness of 3 mm was utilized as a substrate material in the design of these two EBG structures. For the unit cell, the resonant frequency depends on the effective capacitance (C) and inductance (\(L\)) of the unit cell, i.e.,  
\[f_{r}=\frac{1}{2\pi\sqrt{LC}} \tag{2}\]  
\[C=\frac{we_{o}(1+\epsilon_{r})}{\pi}\cosh^{-1}\frac{a}{g} \tag{3}\]  
\[L=\mu_{o}h \tag{4}\]  
where \(\mu_{0}\) and \(\epsilon_{0}\) are the free space permeability and permittivity, respectively. Figure 2 presents the design of the two dual band EBG unit cells. The first one is the conventional mushroom-like EBG unit cell. It is a square shape with overall dimensions of 32.5 mm \(\times\) 32.5 mm. The radius of the via is 1.5 mm. The second unit cell is different from the conventional mushroom-like EBG unit cell. A plus-shaped slot is created in the unit cell. The creation of slots increases the capacitance and thus makes the proposed unit cell more compact in size as compared to the conventional mushroom-like EBG unit cell [36]. The second EBG unit cell with a plus-shaped slot has a total size of 26.5 mm \(\times\) 26.5 mm. The second EBG unit cell is about 18.46% more compact in size as compared to the conventional mushroom-like EBG unit cell. The width and length of the plus-shaped slot in the second EBG unit cell are 3 mm and 13 mm, respectively.  
The in-phase reflection of the proposed dual-band EBG unit cells are shown in Figure 3. It is observed that in the case of each EBG unit cell, the in-phase response is shown on the desired resonances (2.5 GHz and 5.2 GHz).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & **Description** & **Value (mm)** & **Parameter** & **Description** & **Value (mm)** \\ \hline
**ls** & Substrate or ground & & & & \\ plane length & 68 & **g** & Gap at sides of the feed line & 5 \\
**ws** & Substrate or ground & 73 & **st** & Depth of the gap with the feed line & 3.5 \\  & plane width & 44.75 & **c** & Right side cut length & 10 \\
**wp** & Patch width & 47 & **w1** & or centered or bottom strip & 5 \\
**h** & Substrate thickness & 3 & **w2** & Width of the right & 3.5 \\  & Patch or ground & & & or left side strip & \\
**mt** & plane thickness & 0.03 & **w3** & Width of the p shaped cut & 9 \\
**lf** & Microstrip feed line length & 15.125 & **w4** & Width of the I shaped cut & 12 \\
**wf** & Microstrip feed line width & 5 & **l1** & Length of the I shaped cut & 26 \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of simulation parameters along with their optimized values.  
Table 2 presents the values of the parameters of the unit cells given in Figure 2.  
Table 2 shows that the EBG unit cell with the plus-shaped slot is more compact than the mushroom-like EBG unit cell.