esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Geometric Implications for Optimizing PD Arrays for Received Power
### Algorithm  
The arrays are freely moving in the region along and above the center axis of the Tx, as shown in Figure 5. Algorithm 1 shows the pseudocode for determining the array received power, and the abbreviations of the parameters used are explained in the Abbreviations section.  
```
0:Input \(c\), \(\tau(\alpha)\) (\(0^{\circ}\leq\alpha\leq 90^{\circ}\)), \(n\), \(A_{r}\), \(P_{T}\), \(I_{PD}\), \((x_{0},y_{0})\), \(\gamma_{0},x_{min}\), \(x_{max}\), \(\Delta x\), \(y_{min}\), \(y_{max}\), \(\Delta y\), \(\Delta\gamma\)
0:Output:\(P_{r(x,y_{c})}\)
1: Calculate: \(|r_{i}|=\sqrt{x_{0i}^{2}+y_{0i}^{2}}\)
2:\(\beta_{i}=(\gamma_{0i}+\Delta\gamma)\)
3:\(\omega_{i}=-\tan^{-1}\left(\frac{y_{0i}}{y_{0i}}\right)\)
4: Evaluate new relative coordinates of \(i^{th}\) PD to middle PD: \(\left(x_{\Delta\gamma_{i}},y_{\Delta\gamma_{i}}\right)=(-|r_{i}|\text{cos}( \omega_{i}+\Delta\gamma),|r_{i}|\text{sin}(\omega_{i}+\Delta\gamma))\)
5:\((x_{c},y_{c})=(x_{min},y_{min})\)
6:while\(y_{c}<y_{max}\)do
7:while\(x_{c}<x_{max}\)do
8:for each \(i\) PD do
9:\(d_{i}=\sqrt{\left(x_{c}+x_{\Delta\gamma_{i}}\right)^{2}+\left(y_{c}+y_{ \Delta\gamma_{i}}\right)^{2}}\)
10:\(\alpha_{i}=-tan^{-1}\left(\frac{y_{c}+y_{\Delta\gamma_{i}}}{x_{\Delta\gamma_ {i}}+x_{\Delta\gamma_{i}}}\right)\)
11:\(\varphi_{i}=(\alpha_{i}+\beta_{i})/\left(\varphi_{i}\leq 90^{\circ}\right)\)
12:\((x_{c},y_{c})=(x_{c}+\Delta x,y_{c}+\Delta y)\)
13:\(I_{0}=\left(\frac{P_{T}}{2 rd_{i}^{2}\sum_{i=1}^{n}x_{c}(\alpha_{i-1}-\alpha _{i})}\right)\)
14:\(P_{r_{i}}=\tau_{\alpha_{i}}I_{0}e^{-cd_{i}}A_{r}\text{cos}(\varphi_{i})\)
15: Total power: \(P_{r(x,y_{c})}=\sum_{i=1}^{I_{PD}}P_{r_{i}}\)
16:endfor
17:\((x_{c},y_{c})=(x_{c}+\Delta x,y_{c})\)
18:endwhile
19:\((x_{c},y_{c})=(x_{c},y_{c}+\Delta y)\)
20:endwhile
```  
**Algorithm 1** Array Received Power  
\begin{table}
\begin{tabular}{c c c c} \hline
**Jerlov Type** & \(\boldsymbol{c_{\lambda=450 nm}}\) **(m\({}^{-1}\))** & \(\boldsymbol{c_{\lambda=500 nm}}\) **(m\({}^{-1}\))** & \(\boldsymbol{c_{\lambda=550 nm}}\) **(m\({}^{-1}\))** \\ \hline Jerlov I & 0.022 & 0.029 & 0.064 \\ Jerlov IA & 0.028 & 0.033 & 0.066 \\ Jerlov IB & 0.092 & 0.088 & 0.116 \\ Jerlov II & 0.528 & 0.450 & 0.420 \\ Jerlov III & 1.419 & 1.188 & 1.045 \\ Jerlov IC & 0.619 & 0.493 & 0.441 \\ Jerlov 3C & 1.654 & 1.331 & 1.142 \\ Jerlov 5C & 2.167 & 1.711 & 1.447 \\ Jerlov 7C & 3.842 & 3.050 & 2.545 \\ Jerlov 9C & 5.333 & 4.213 & 3.468 \\ \hline \end{tabular}
\end{table}
Table 3: Spectral attenuation coefficients of the different Jerlov water types for 450 nm, 500 nm, and 550 nm wavelengths [25].
## 2 Materials and Methods
### Cell Proliferation Assay  
MCF-7 cells were plated in phenol red-free DMEM with DCC-stripped FBS in 96-well plates at a density of 8 \(\times\) 10\({}^{3}\) cells per well. The following day, cells were exposed to test ligands. Proliferation was measured on day 1 (baseline) and day 4 with CellTiterGlo\({}^{\text{\textregistered}}\) Luminescence Cell Viability Assay according to the manufacturer's protocol (Promega). Relative proliferation was determined by normalizing the luminescence output from day 4 to day 1 and presented as % of DMSO-treated cells.
# Ammonia Gas Sensing Characteristic of P3 HT-rGO-MWCNT Composite Films
## 3 Results and Discussion
### UV-Vis Absorption Properties  
Figure 4a shows the optical absorption spectra of the P3 HT film and the PGC-60 composite film fabricated by the drop-casting method. It shows that the P3 HT film strongly absorbs light in the range of wavelength from 400 nm to 600 nm. The strongest absorbance of the P3 HT film (marked '1' peak) is at 558 nm and is characterized by the \(\pi\)-\(\pi\) * bond in the P3 HT series [33]. For PGC-60 composite films, two peaks are appearing in the optical absorption spectrum: the first one (namely peak '2') is at about 514 nm and belongs to a blue shift of P3 HT and the other peak is at 318 nm (peak '3'), which could be due to the presence of rGO with a moderate reduction [34]. It needs to be noted that the bandgap of rGO is about 2.8 eV [35], which is much larger than the bandgap of P3 HT [36] and that the CNT embedded in P3 HT causes no more additional peak. Furthermore, in comparison to the pure P3 HT layer, the absorption of the PGC-60 composite films from 300 nm to 600 nm is much greater. This is the desired factor for a photoactive layer in polymeric composite solar cells, which was observed [27]. These advantages in electrical properties of the PGC composites due to the GQDs/P3 HT heterojunctions and CNTs charge bridges are put into consideration in the preparation of our gas sensors with great sensitivity and selectivity in a humid environment.  
Figure 3: XRD pattern of the PGC-60 film.  
Figure 2: FE-SEM of (**a**) rGO made by the modified Hummers method, (**b**) MWCNTs, and (**c**) P3 HT-rGO-MWCNTs.  
In this work, the UV-Vis spectra were used to estimate the bandgap energy (\(E_{g}\)) using the expression [37]:  
\[\alpha\hbar\
u=A\big{(}\hbar\
u-E_{g}\big{)}^{n} \tag{1}\]  
where \(h\), \(\
u\), and \(A\) are the Planck constant, the frequency of the incident photon, and the constant of the expression, respectively. \(n\) has a value of 1/2 or 2 for a semiconductor with a direct or indirect bandgap, respectively. For both indirect bandgap samples P3 HT and PGC, \(n\) is equal to 2. The dependence of \(\hbar\
u\) on \((\Delta\hbar\
u)^{\frac{1}{2}}\) of the samples were shown in Figure 4b. The calculated results show that the lines segments AB and CD exhibit the linear dependence of \(\hbar\
u\) on \((\Delta\hbar\
u)^{\frac{1}{2}}\). The x intercept of linear extrapolation of AB and CD give bandgap values of 1.92 eV (for P3 HT) and 2.02 eV (for PGC), respectively. The calculated \(E_{g}\) value of P3 HT (1.92 eV) is close to the \(E_{g}\) value reported by [29] where P3 HT was prepared for hybrid solar cells. The increase in values of the bandgap of the synthetic films (from 1.92 eV for the pure P3 HT to 2.02 eV for PGC-60 composite film) can be explained by the presence of both rGO and CNTs, which causes a reduction in the polymer's chain length and leads to an expansion in the bandgap of the polymer [38].
# Helicopter Takeoff and Landing Point Location in Cities for Emergency Services
## 2 Operation Scenario Setting
### Basic Principles  
#### 2.3.1 Surface Flatness  
Slope value expresses the steepness of the raster surface in each image element. The smaller the slope, the flatter the terrain. There are two calculation methods for the slope. The plane method is used in this paper, and the slope data is fitted by a uniform 3 \(\times\) 3 DEM local grid, as shown in Figure 1. The 3 \(\times\) 3 grid is only a specific example for calculating the slope, which is convenient for intuitive feeling and understanding. In the software, the slope can be calculated directly from the elevation data.  
The center of each grid is an elevation point, and the slope of the center point e is calculated [19]. See Formula (3):  
\[\begin{array}{l} Slope=arctan\sqrt{\left(\frac{dz}{dx}\right)^{2}+\left(\frac{dz}{dy }\right)^{2}}\\ \frac{dz}{dx}=\frac{4(\varphi+2\theta+\epsilon)/\sqrt[4]{11-4(\sigma+2\epsilon+ \epsilon)/\sqrt[4]{2}}}{8G}\\ \frac{dz}{dy}=\frac{4(\epsilon+1+2\sigma+2\epsilon)/\sqrt[4]{3-4(\sigma+2 \theta+\epsilon+\epsilon)/\sqrt[4]{4}}}{8G}\end{array} \tag{3}\]  
In the formula: \(\epsilon\)1-\(\epsilon\)9 are the grid point elevations; \(\overline{w1}-\overline{w4}\) is the weighted count of the area where the effective pixels are located; \(G\) is the resolution of the DEM grid.  
The undulation is a quantitative indicator used to describe the landform, reflecting the maximum relative elevation difference of all grids in the selected area. It is widely used in land utility evaluation and ecological environment evaluation. Topographic undulation can be used as an important reference indicator for the division of topographic forms and is suitable for macro-topographic analysis. This paper intends to use the height difference method to calculate the undulation. See Formula (4):  
\[F=H_{\text{max}}-H_{\text{min}} \tag{4}\]  
where \(F\) is the height difference; \(H_{\text{max}}\) and \(H_{\text{min}}\) represent the elevations of the highest and lowest points in the selected area, respectively.
## 4 Engineering Applications
### Energy Characteristic Analysis  
Adaptive optimal kernel (AOK) time-frequency technology is suited to analyze the time-frequency characteristics of blasting vibration signals, which can effectively obtain the detailed characteristics of vibration signals [22; 23]. In order to verify the noise reduction effect of the WOA-VMD algorithm, AOK time-frequency technology is used to obtain the  
Figure 4: blasting vibration signal after denoised.  
Figure 5: Time-frequency spectrum of signals.  
signal time spectrum before and after noise reduction, as shown in Figure 5, where \(X\) is the energy and \(Y\) is the dominant frequency.  
As Figure 5 shows, the dominant frequency of the signal before and after noise reduction is 26.4 Hz, the peak energy is 361.3 J and 358.9 J, the vibration duration is about 0.5 s, and the vibration energy is mainly distributed in the range of 0-250 Hz. By comparing Figure 5a,b, we can see the signal spectrum before noise reduction has significant noise points, as shown in the red box in the figures. These noise points are noise components with a frequency above 250 Hz, which are the main cause of signal distortion. After signal noise reduction, the original noise components are effectively eliminated. The dominant signal frequency before and after noise reduction is the same, and the peak energy is only reduced by 2.4 J. It shows that the noise reduction method can eliminate the high frequency noise effectively and has little influence on the low frequency vibration.
## 2 Related Works
\begin{table}
\begin{tabular}{|p{42.7 pt}|p{113.8 pt}|p{113.8 pt}|} \hline \multicolumn{3}{|c|}{Stability and Robustness} \\ \hline + & Numerical Stability & ask the explanation HPD matrices have all positive eigenvalues \\ \hline + & Robust Signal Processing & HPD matrices often lead to more stable and reliable algorithms due to their well-behaved nature \\ \hline \multicolumn{3}{|c|}{Optimality in Estimation} \\ \hline - & Minimum Variance & previous methods [19; 20] \\ \hline - & Positive Definiteness in Metrics \\ \hline - & Distance Measures & previous methods [19; 20] \\ \hline \multicolumn{3}{|c|}{Flexibility in Handling Ill-posed Problems} \\ \hline + & Weight Adjustment & Application of the proposed method which is based on weighted Moore–Penrose Inverses \\ \hline \multicolumn{3}{|c|}{Improved Solutions for Underdetermined Systems} \\ \hline - & HandlingUnder-determined Systems & previous methods [19; 22] \\ \hline \multicolumn{3}{|c|}{Robustness Against Perturbations} \\ \hline + & Reduced Sensitivity & Weighted Moore–Penrose inverses can help mitigate the effects of noise and perturbations in the data \\ \hline \end{tabular}
\end{table} 
Table 1: The advantages of the proposed method compared to the previous method. + where our method is better; - where the previous ones gave a better result. that the solutions remain within a feasible and stable region and by using weights to adjust for any issues related to boundary conditions. Using HPD matrices in conjunction with weighted Moore-Penrose inverses can enhance the stability and robustness of solutions, particularly in the presence of noise and perturbations. HPD matrices provide a solid foundation for stability, while the weighted inverse offers flexibility and robustness. The use of Hermitian positive definite matrices may allow for more efficient data representation, potentially increasing the amount of information that can be embedded within the cover medium compared to traditional methods. The incorporation of weighted Moore-Penrose inverses could facilitate the allocation of bits in a way that optimizes capacity while maintaining the integrity of the host data. These two advantages can be related to embedding capacity. Techniques like LSB (Least Significant Bit) embedding might offer high capacity but are often easily detectable and less robust against attacks. The proposed method could mitigate these issues. Compared to methods like spread spectrum or DCT (Discrete Cosine Transform) based approaches, the new method may balance capacity and robustness more effectively by using a mathematical foundation that inherently supports data integrity. By leveraging advanced mathematical frameworks, this method could be more resistant to common steganalysis techniques, making it harder for adversaries to detect embedded data. Also, the mathematical properties of Hermitian matrices might help in maintaining the quality of the cover medium against modifications like compression or noise, which are challenges for existing techniques. All this has a positive effect on robustness.
## 3 Related Works
### MQTT Security  
Due to its lightweight nature, the MQTT standard does not include any default security and privacy features. In the literature, several works highlighted that this is currently an open problem [23; 24; 25]. In this section, we briefly survey some of the security issues.  
The first issue regards confidentiality. The basic approach consists of using TLS to establish secure channels [26]. However, it negatively impacts performance and energy consumption [27]. Therefore, more advanced solutions have been proposed in the literature to achieve confidentiality [28; 29; 30; 31; 32; 33; 34; 35]. Another aspect to consider is authorization [36; 37]. A lot of works aimed at achieving both authorization and confidentiality are based on variants CP-ABE or KP-ABE tailored to constrained devices [38; 39; 40; 41]. Authorization is also integrated with authentication in MQTT [42; 43; 44].  
Privacy aspects should also be considered in MQTT. Actually, there are a few proposals aimed at achieving privacy. Among these, the study in [45] has the goal of obfuscating the topics with respect to public brokers. Another way to achieve privacy is by guaranteeing the network anonymity of publishers and subscribers by employing known anonymity protocols proposed in the field of anonymous communication networks (ACN) [46; 47; 48; 49]. Among these proposals, we mention [8; 50]. Ref. [8] proposes a crowds-based protocol in which brokers form a P2P network to relay clients' messages. In [50], the authors propose a solution based on the Tor protocol, in which messages, encrypted in an onion fashion, are routed by brokers.
# Using AI to Improve MIMO Antennas with SRR for 26 GHz by Analyzing Data
## 5 Conclusions  
This study presents a transformative machine learning framework for designing high-performance 26 GHz MIMO antenna systems, achieving breakthrough results through the intelligent optimization of both individual antenna elements and SRR-based decoupling structures. By developing hybrid datasets encompassing geometric parameters and metamaterial configurations, we trained ML models to simultaneously optimize impedance matching (S11 \(<-\)45 dB), gain (10.22 dB), and inter-element isolation (S21 \(<-\)40 dB)--parameters that typically require trade-offs in conventional designs. The final system demonstrates exceptional metrics, including near-ideal ECC (<0.005), DG (\(\approx\)9.99 dB), and CCL (<0.3 bits/s/Hz), while maintaining >80% radiation efficiency. Beyond its immediate application to 5G MIMO arrays, this work establishes a scalable ML methodology that can accelerate the development of metamaterial-enhanced wireless systems. Future extensions could incorporate active learning for autonomous dataset refinement, extend to reconfigurable intelligent surfaces, or adapt the framework for THz-band applications, potentially revolutionizing how we design next-generation communication hardware. The success of this approach underscores machine learning's growing role in solving complex electromagnetic optimization challenges that defy traditional analytical methods.  
Conceptualization, L.C. and C.E.; methodology, L.C.; software, L.C.; validation, L.C., C.E. and B.B.-S.; formal analysis, L.C.; investigation, L.C.; resources, L.C.; data curation, L.C.; writing--original draft preparation, L.C.; writing--review and editing, L.C., M.B.M. and H.S.; visualization, L.C.; supervision, C.E. and H.S.; project administration, M.B.M. funding acquisition, H.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research work was funded by Umm Al-Qura University, Saudi Arabia under grant number: 25 UQU 4331171 GSSR02.  
**Data Availability Statement:** The datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request. Due to the customized nature of the databases created for antenna parameter prediction and SRR optimization, the data are not publicly archived. However, all relevant data supporting the findings of this study can be made available upon request, respecting applicable privacy and intellectual property considerations.  
**Acknowledgments:** The authors extend their appreciation to Umm Al-Qura University, Saudi Arabia for funding this research work through grant number: 25 UQU 4331171 GSSR02.  
**Conflicts of Interest:** Author [PERSON] was employed by the company EITA Consulting. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 3 Results and Discussion
### Velocity Distributions
Here, the transverse gradient of the streamwise velocity for \(y/W<0.3\) near the outer bank increases more sharply with the SST \(k-\omega\) model. The vertical profile of the spanwise velocity obtained from the SST \(k-\omega\) model also shows the secondary flow to be stronger than that of other turbulence models. These results indicate that the SST \(k-\omega\) model produces the stronger shear flows in the sharply curved meander bend compared to the \(k-\varepsilon\) and \(k-\omega\) models. Therefore, the performance of the turbulence models may directly impact solute transport mechanisms related to the bulk shear dispersion as well as the turbulent diffusion.
## 3 Materials and Methods
### Rnn
results confirmed that the RNN model using Adam converged faster than conventional optimization techniques and demonstrated enhanced predictive performance. Various regularization techniques are applied to prevent overfitting and improve training stability. Dropout randomly deactivates specific neurons in the hidden layers, enhancing generalization performance, while batch normalization normalizes the distribution of hidden states, accelerating training. RNN is trained over a predefined number of epochs, iterating through the dataset multiple times. Validation data is used to monitor performance and detect potential overfitting. Once training is complete, the final model is saved as a file for predictions or real-time applications. The performance of RNN can be optimized by tuning hyperparameters such as hidden state size, number of hidden layers, and learning rate. With these structural improvements, RNN effectively learns complex patterns in sequential data, providing strong predictive performance. RNN Model and Methodology in This Paper  
The second RNN layer consists of 64 neurons and applies the same ReLU activation function, batch normalization, and dropout as the first layer. It also maintains \"return sequences = True\" to preserve sequential information. The third RNN layer comprises 32 neurons and applies the same activation and regularization techniques to extract high-dimensional features from the input data, where \"return sequences = False\" is set to output only the final hidden state. Following the RNN layers, a dense layer with 16 neurons and the ReLU activation function is added. This layer incorporates L2 regularization, batch normalization, and 20% dropout to enhance model generalization. The final output layer consists of 3 neurons with the softmax activation function, returning class probabilities for multi-class classification. The model is trained using the Adam optimization algorithm, with the learning rate set to 0.0005. The sparse categorical cross-entropy loss function optimizes predictions for integer-encoded class labels. Accuracy is the performance evaluation metric used to monitor model performance during training. The model is trained for 300 epochs with a batch size of 128, ensuring efficient learning. A validation set evaluates performance at each epoch, allowing continuous monitoring of the gap between training and validation performance. The model's training configuration is summarized in Table 2. Ultimately, the trained RNN model effectively learns the temporal patterns of sequential data, making it well suited for multi-class classification tasks. The model's performance can be further optimized by adjusting hyperparameters such as the number of neurons and learning rate.
# Unified Performance Analysis of MIMO Mixed RF/FSO Relaying System
## 7 Diversity-Multiplexing Tradeoff Analysis  
The diversity-multiplexing tradeoff is an important performance metric for comparing and choosing different MIMO schemes. For example, when the channel conditions are poor, we can maximize the diversity gain to combat fading. On the other hand, we can maximize the multiplexing gain to increase the data rate when the channel states are good.  
Outage diversity order for MIMO RF under a Rayleigh channel can be obtained as follows ([24] [(4)])  
\[d_{RF}(r)=(N_{t}-r)(N_{r}-r),0\leq r<\min(N_{t},N_{r}). \tag{31}\]  
For the MIMO FSO link, the outage diversity order under the \(\Gamma\Gamma\) channel is given as ([25] [Equation (47)])  
\[d_{FSO}(r)=\left\{\begin{array}{ll}\frac{MN(1-r)}{2}\min(\alpha,\beta)+\frac {(M-1)(N-1)r}{2},&0\leq r<1\\ \frac{(M-r)(N-r)}{2},&1\leq r\leq N.\end{array}\right. \tag{32}\]  
Refer to ([26] [Equation (6)]), the diversity-multiplexing tradeoff of the considered asymmetric dual-hop MIMO mixed RF/FSO DF single-relaying system can be derived as follows  
\[d(r)=\min(d_{RF}(r)d_{FSO}(r)),0\leq r<\min(N_{t},N_{r},M,N). \tag{33}\]