esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Parameter Tuning Results
### SVM--Support vector machine accuracy versus sigma  
Multiple values for cost and sigma were examined to determine the ideal values for the SVM model. In particular, the values chosen were sigma = 2'(\(-\)25, \(-\)20, \(-\)15, \(-\)10, \(-\)5, 0) and cost C = 3'(\(-\)7, \(-\)6, \(-\)5, \(-\)4, \(-\)3, \(-\)2, \(-\)1, 1, 2, 3, 4, 5, 6, 7). The resulting cross-validation accuracy values are shown in Figure 9. We observe that the cost value acquires saturation accuracy at approximately 65% for all cases. However, we can break the saturation point and increase the prediction accuracy by an additional 10% to 11% by further increasing the sigma and cost parameter. In our case, a low-cost value results in low accuracy, as a high tolerance level of misclassification causes poor performance as the cost parameter controls the model's flexibility and ability to generalise. Therefore, with a low cost, samples inside the margins are penalized less than with a higher cost. The changes in the sigma parameter in our SVM models also show that this parameter controls the level of nonlinearity introduced in the model. Figure 9 shows that high accuracy is achieved when the sigma value is minimal because a low sigma value results in a highly linear decision boundary. In contrast, the decision boundary tends to be linear as we increase the sigma value, resulting in high inaccuracies in prediction. This result emphasises the importance of carefully determining the sigma and cost values as it may results in high computational time and prediction accuracy.  
Our results indicate that RF consistently generates the most accurate outcomes, followed by KNN, CART and SVM. This result is supported by [45; 46; 47; 48], which indicated that RF generally has the best predictive performance. Although SVM contributes the least to cross-validation accuracy, its difference from the other ML models is not considerable; the average accuracy of these models is approximately 67%. Although SVM can perform well in unbalanced datasets, research in [49] demonstrated that the sample size and imbalanced data of the training samples have a larger effect on the classification accuracy of KNN and RF than on SVM, particularly when small and balanced training datasets are used. Additionally, our research highlights the importance of traditional parametric approaches in ML modelling when the datasets show a considerable degree of variation, which may cause the overfitting of the ML model. Thus, the interpretability, speed and generalisation capabilities of a simple algorithm make it a feasible alternative, particularly when the expected accuracy differences compared to other models are small [50].  
Figure 9: SVM model accuracy for various sigma and cost values.
# Hierarchical Guided-Image-Filtering for Efficient Stereo Matching
## 2 Proposed Method
### Stereo Matching Based on Hierarchical Guided-Image-Filtering (HGIF)  
A major shortage of approaches using the scheme starting from an energy function of Equation (3), including WGIF [21], GDGIF [22], and PGIF [25], is the restriction of the cost metrics to local regions of the support window \(\Omega_{0}(p)\). Even though PGIF [25] exploits the whole image for matching, the effects of patterns far from the pixel under aggregation decay with distance.  
The proposed scheme begins with a down-sampling of the original image pair, \(I_{L,0}\) and \(I_{R,0}\), where the left image is selected as the reference image and the right image as the target image. As the images are down-sampled by a factor of 2, we denote the resultant image pairs as \(I_{L,z}\) and \(I_{R,z}\) for \(z\in\{0,1,\ \dots,K\}\) with \(K\) being the roughest level.  
Firstly, the optimal parameters, \(a_{d,z}^{*}(q)\) and \(b_{d,z}^{*}(q)\), of each image pair, \(I_{L,z}\) and \(I_{R,z}\), are calculated using Equation (6). We define a set of unknown parameters,  
This calculation is conducted using elementwise multiplications and additions in the image plane for each disparity. Finally, the disparity map, \(\hat{D}(p)\), can be obtained using the winner-take-all (WTA) procedure of Equation (2):  
\[\hat{D}(p)=\underset{d}{\mathrm{argmin}}\hat{C}_{d}(p). \tag{18}\]  
The procedure of our proposed algorithm for stereo matching is depicted in Figure 1, taking \(K=2\) as an example, and summarized in the following steps:  
1. Down-sample the image pairs to build a pair of pyramids of images, \(I_{L,z}\) and \(I_{R,z}\) for \(z\in\{0,1,\ \ldots,K\}\), with \(K+1\) levels of resolution;  
2. Calculate the weight matrix \(\omega_{z}(p,q)\) as a multiplication of two weighting factors, \(\overline{W}_{i,x,z}^{H}\) and \(\  
As depicted in Figure 1, the parameters \(\hat{a}_{d,0}(p)\) and \(\hat{b}_{d,0}(p)\) are found by solving the system of linear equations, composed of Equations (11) and (12). As the matrix inverse can be conducted in advance, the calculation can be simplified into matrix multiplication, as demonstrated in Equation (14).  
Besides, the cost volume \(\hat{C}_{d}(p)\) is calculated using the linear model of Equation (17). In contrary to the current multi-scale approaches in the literature, such as References [27; 28; 29], where the cross-scale aggregation is based on costs themselves, the parameter-based cross-resolution aggregation of the proposed procedure is unique and efficient.
## 3 Results and Discussion
### Antioxidant Activity of Major Phenolic Components in Commercial Croatian Wines  
The polyphenols present in wines determine many of their sensory characteristics, such as appearance, color, astringency, bitterness, and taste, as well as their stability to subsequent oxidative processes that lead to browning in white wines and oxidation in red wines. Apart from sensory properties and wine quality, polyphenols have also been shown to be highly beneficial to health, including antioxidant and cardioprotective effects [4; 67]. The antioxidant capacity of a wine depends largely on its phenolic profile, since different compounds have different levels of activity that are closely related to their chemical structure [68]. Therefore, our aim was to compare for the first time the antioxidant activity of the main polyphenolic components of Croatian wines and to predict their possible importance in the antioxidant properties of wine. In our study, most of the selected polyphenols also proved to be important distinguishing features between Croatian wines. The obtained results are presented in Table 6 and Suppl. Tables S3-S5. The most representative phenolic components in the studied Croatian wines showed a strong antioxidant effect, mostly exceeding the effect of Trolox as a reference antioxidant. Gallic acid proved to be the most potent DPPH and NO radical scavenger, reaching IC\({}_{50}\) values of 0.33 \(\upmu\)g/mL and 12.36 \(\upmu\)g/mL, respectively. Of the seven polyphenols tested, resveratrol had the weakest potential to inhibit the production of free DPPH (IC\({}_{50}\) = 7.56 \(\upmu\)g/mL) and NO radicals (IC\({}_{50}\) = 35.08 \(\upmu\)g/mL) but showed a much better ability to inhibit lipid peroxidation (IC\({}_{50}\) = 5.02 \(\upmu\)g/mL). The strongest inhibitor of lipid peroxidation was myricetin (IC\({}_{50}\) = 1.68 \(\upmu\)g/mL), followed by quercetin (IC\({}_{50}\) = 2.06 \(\upmu\)g/mL), while caffe acid showed the weakest effect, achieving 50% inhibition of lipid peroxidation at a concentration of 141.76 \(\upmu\)g/mL. Our study showed that the selected polyphenols from Croatian wines inhibit lipid peroxidation at very low concentrations (except caffeic acid), which confirms their strong ability to protect against this type of cellular damage as an indicator of oxidative stress in cells and tissues. The high lipid content of cell membranes makes them one of the most important targets for reactive oxygen and nitrogen species. NO is a biologically relevant free radical that, when formed in excessive amounts, reacts with other free radicals, such as superoxide anion, to form a highly reactive nitrogen species [69]. The selected polyphenols were found to be potent NO radical scavengers, indicating their excellent antioxidant properties and related significant contribution to the beneficial health effects of wine.  
Figure 3: Principal component analysis of the clustering of Croatian white wines based on phenolic composition (**A**) and biplot with active variables (**B**). The abbreviations corresponding to the wine samples are shown in Table 2.
# FFT-Based Scan-Matching for SLAM Applications with Low-Cost Laser Range Finders
## 3 FFT-Based Scan-Matching
### Rotation Parameters  
Let \(A(x,y)\) be the transformed scan image of \(B(x,y)\), with rotation angle \(\alpha\), translation \((x_{0},y_{0})\), and scale factor \(\lambda\):  
\[A(x,y)=B[\lambda(x\cos\alpha+y\sin\alpha)-x_{0}\,(-x\sin\alpha+y\cos\alpha)-y_{ 0}] \tag{13}\]  
In this paper, there is no scaling relation between two scan images and \(\lambda=1\). We can write (13) as follows:  
\[A(x,y)=B[(x\cos\alpha+y\sin\alpha)-x_{0}\,(-x\sin\alpha+y\cos\alpha)-y_{0}] \tag{14}\]Taking the FFT of both sides of (14) gives  
\[|A(u,v)|=|B(u\cos\alpha+v\sin\alpha,-u\sin\alpha+v\cos\alpha)| \tag{15}\]  
Performing polar coordinate transformation gives  
\[u\cos\alpha+v\sin\alpha=\rho\cos(\theta-\alpha) \tag{16}\]  
\[-u\sin\alpha+v\cos\alpha=\rho\sin(\theta-\alpha) \tag{17}\]  
We can write (17) as follows:  
\[|A(\rho\cos\theta,\rho\sin\theta)|=|B(\rho\cos(\theta-\alpha),\rho\sin(\theta -\alpha))| \tag{18}\]  
Let  
\[A(\rho,\theta)=|A(\rho\cos\theta,\rho\sin\theta)| \tag{19}\]  
\[B(\rho,\theta)=|B(\rho\cos\theta,\rho\sin\theta)| \tag{20}\]  
which gives  
\[A(\rho,\theta)=B(\rho,\theta-\alpha) \tag{21}\]  
Equation (21) is similar to the 1D FFT. Using the 1D FFT theorem, rotation angle \(\alpha\) can be solved. In contrast to traditional image registration, there is no scaling relationship between scan images. Therefore, the rotation angle can be obtained by performing polar coordinate transformation without further conversion of the logarithmic coordinate.
# Ultrawideband Cross-Polarization Converter Using Anisotropic Reflective Metasurface
## 8 Conclusions  
In summary, this paper proposed an ultra\(-\)wideband, highly efficient, wide\(-\)angle cross\(-\)polarization converter reflective metasurface. A broadband cross\(-\)polarization conversion was achieved for a wide frequency range of 13-26 GHz. Numerical simulations were performed to validate the performance and justify the claim of a large operational bandwidth. To demonstrate the physical mechanism behind the polarization conversion, the reflected amplitude and phase difference between the \(u-\) and \(v-\)axes and surface current distribution plots were presented for different resonance frequencies at the top and bottom metallic layers. In addition, the polarization converter showed a stable performance over a wide range of oblique incidence angles. Resultantly, the proposed metasurface behaved like a high impedance surface and reflected the incidence wave in its orthogonal counterpart. Furthermore, by tuning the geometric parameters of the unit cell, the operating spectrum of the suggested metasurface was also shifted to lower frequencies, i.e., 9-18 GHz. In the low\(-\)frequency spectrum, the conversion efficiency was also above 90%. The proposed converter has many potential applications, including satellite communication, radar cross\(-\)section reduction, and navigation systems.  
Conceptualization, T.A. and R.M.H.B.; methodology, T.A.; software, T.A.; validation, R.M.H.B., A.A.R. and M.A.N.; formal analysis, A.N. and H.M.; investigation, T.A., A.M. and M.A.S.; resources, A.A.R. and H.M.; data curation, T.A.; writing\(-\)original draft preparation, T.A.; writing\(-\)review and editing, M.A.N.; R.M.H.B. and A.M.; visualization, M.M.A. and A.A.R.; supervision, A.A.R., R.M.H.B. and A.N.; project administration, M.A.S. and M.M.A.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.  
Figure 7: (**a**) Co\(-\) and cross\(-\)polarization component \(r_{yy}\) and \(r_{xy}\); (**b**) Polarization conversion ratio (\(PCR\)); (**c**) Magnitude of orthogonal components \(r_{u}\) and \(r_{v}\); (**d**) Phase difference.
# Noncoherent Channel Combining for GNSS Signal Tracking with an Adaptive Antenna Array
## 3 Proposed Method
### Noncoherent Channel Combining
The two-quadrant arctangent discriminator is used in the carrier loop. The mathematical model can be expressed as follows:  
\[\delta\tau=\left(1-\frac{d_{\mathrm{s}}}{2}\right)\frac{|\tilde{r}_{\mathrm{ E}}|-|\tilde{r}_{\mathrm{L}}|}{|\tilde{r}_{\mathrm{E}}|+|\tilde{r}_{\mathrm{L}}|}T_{\mathrm{c}} \tag{22}\]  
\[\delta\tilde{\varphi}=\arctan\left(\frac{\mathrm{Im}(\tau_{\mathrm{P},1})}{ \mathrm{Re}(\tau_{\mathrm{P},1})}\right) \tag{23}\]where \(\delta\tilde{\tau}\) and \(\delta\tilde{\phi}\) are the code and carrier discriminator outputs, and \(\text{Re}(\cdot)\) and \(\text{Im}(\cdot)\) denote the real and imaginary parts of a complex scalar. The discriminator outputs in Equations (22) and (23) are then fed into the loop filters, which act as low-pass filters, to produce smoother error signal estimates. These estimates are sent to numerically controlled oscillators (NCOs) to adjust the local carrier and code replicas to close the processing loop [19]. The above closed-loop architecture is actually the well-known delay-locked loop (DLL) and the phase-locked loop (PLL). More details are beyond the scope of this paper and can be found in [20]. Based on Equations (22) and (23), it is possible to conclude that the proposed noncoherent channel combining has the following characteristics:  
1. It accumulates the correlation envelopes from different channels to increase the SNR at the input of the code discriminator. But its performance cannot reach that of the coherent channel combining due to the squaring loss. 2. It differs from the non-coherent integration in a single-antenna receiver. It exploits the useful signal power from different antenna channels without an increased integration time, which can reduce tracking robustness against signal dynamics. 3. It cannot significantly improve the carrier tracking performance compared to the PI algorithm. It also suffers from the carrier phase distortion induced by the adaptive array. This problem can be alleviated by using phase compensation [21].
## 3 Results
### Temperature Loss along the Exhaust Pipe  
Sometimes, because of limitations in available space or ease of installation, the selected place for energy recovery devices moves away from the location with the maximum temperature available (immediately before the after-treatment systems). Average temperature of the exhaust gas in the first 50 cm downstream of the DOC was obtained from the validated model to quantify the loss in temperature when moving the energy recovery device away from the DOC.  
Figure 12: Temperature distribution for the 2400 rpm–110 Nm mode in (**a**) monolith, outlet cone and exhaust pipe and (**b**) cross-section of DOC outlet (before exhaust pipe).  
Figure 13: Plot for coefficient of variation in temperature distribution at DOC outlet. White dots represent engine modes in which chemical processes were not active.  
Test-bench results with natural convection were compared with external forced convection (as in a moving vehicle with velocity \(v_{\infty}\)). Forced convection was simulated as boundary condition in pipe walls. Two different modes (lowest and highest engine power within the test matrix) were studied for each external condition. Forced convection coefficients were calculated with external parallel flow correlations [38] taking into account velocities of the vehicle at those engine conditions. Notice that natural convection conditions can also be relevant in moving vehicles since the part of the exhaust pipe adjacent to the DOC could not be in contact with car underbody air.  
Average temperature decreases (\(\Delta T/L\)) for external natural convection (test-bench) range from 0.9 \({}^{\circ}\)C/dm in the lower power mode to 1.2 \({}^{\circ}\)C/dm in the higher power mode. For forced convection, decrease in temperature ranges from 1.2 \({}^{\circ}\)C/dm to 2.6 \({}^{\circ}\)C/dm (see Table 11).
## 4 Proposed Architecture
### Application Layer  
The final layer is the Application Layer, where healthcare professionals access patient data through a secure, web-based dashboard. The dashboard acts as an MQTT subscriber, receiving real-time updates from the cloud broker through WebSocket connections. The dashboard presents encrypted health metrics with live updates and supports role-based access control to restrict user permissions according to clinical responsibilities. Communication between the web application and the cloud is conducted over HTTPS, and all user actions are logged to ensure transparency and accountability.
## 3 Post-Layout Simulation Results  
The proposed wideband reconfigurable integrated LPF was designed and verified using a 65-nm CMOS process. Layout of the designed filter in a quadrature baseband stage implementation is shown in Figure 8. The total occupied area for a single quadrature path is 0.429 mm\({}^{2}\), where LP and HP path occupy 0.251 mm\({}^{2}\) and 0.178 mm\({}^{2}\), respectively. A single FDOA without Miller compensation elements occupies 0.0035 mm\({}^{2}\) and 0.0058 mm\({}^{2}\) in LP and HP path, respectively.  
Figure 8: Layout of the wideband reconfigurable integrated low-pass filter in quadrature baseband configuration.
## 4 Discussion
### Model Accuracy and Current Distribution  
The MaxEnt algorithm is considered as one of the best due to its performance and precision [27,45], since it provides AUC values based on the assessment locations used in each model execution [46]. In this study, the average values of AUC calculated from 30 model repetitions were used to measure the model performance, since generally the AUC is the measure accepted as the best to evaluate the model performance [47]; values between 0.8-0.9 indicated a very good model quality [48].  
The habitat suitability models (Figure 2) are a congruent reflection of the abundancy and species capture zones, since the digitized points correspond to abundancy maps and fishing boats; therefore, it is assumed that in the upper Gulf of California only blue and brown shrimp are found, and in the zone exist two activities of special attention: the resources exploitation and the ecosystem conservation [49]. The space occupied by fishing in the UGC is large and it includes approximately 75% of the aquatic surface; the core zone and the vaquita refuge area are zones not highly exploited, and they must function as recruitment areas and for population recovery of sea species affected by fishing [50,51].  
In the coast of Sonora, Guaymas is one of the most important zones in terms of riparian shrimp fishing due to its habitats that include shallow waters, sandy beaches, rocky areas, islands, coastal dunes, mangrove vegetation, and seaweed beds; likewise, it is a place for reproduction and the breeding of brown, blue, and white shrimp [52], which is congruent with the habitability models because these species have a preference for these types of oceanographic conditions. In the state of Sinola, the habitability models indicated that the three shrimps are distributed in most of the state, because the coast has a series of estuaries, bays, coastal Lagoons, and wetlands, which generate protected environments from wave activity and predators that are perfect for reproduction development [53].  
Finally, in the state of Baja California Sur (BCS), the lagoon complex of Magdalena-Almejas Bay is the main zone for brown shrimp extraction, which is the most conspicuous species in the captures, significantly partaking in seasons both with high and low total production [5]. The lagoon complex is divided into three zones: the northwest zone, the Magdalena Bay zone, and the Almejas Bay zone. Each of them is irregular in shape and is composed of a large number of estuaries, small Lagoons, and by waterways with an average depth of 3.6 m [5].
# Digital twins: The New Frontier for Personalized Medicine?
## 10 Limitations and Challenges  
Different limitations and challenges need to be considered when implementing DHTs in healthcare.  
First, DHTs rely on accurate and comprehensive data from various sources, including electronic health records, medical devices, and wearables. Ensuring the quality, integrity, and interoperability of these diverse data sets can be a significant challenge, as Inconsistent or incomplete data can lead to inaccurate or unreliable digital twin models [61; 62].  
The lack of standardized models and protocols can represent scalability and interoperability challenges. Each healthcare system may have unique requirements and data formats, making it difficult to exchange or combine digital twin models between different organizations.  
Moreover, the development of accurate and reliable models for complex physiological systems or diseases can be challenging, as related to a deep understanding of the underlying biological processes and the ability to represent them effectively in the virtual model.  
The validation and calibration of these models with real-world data are crucial but can be time-consuming and resource-intensive [63].  
Building and maintaining digital twin infrastructure can be costly, requiring significant investments in computational resources, storage, and expertise. Smaller healthcare providers or resource-constrained settings may face challenges in implementing and sustaining digital twin s due to financial and resource limitations.  
Addressing these limitations and challenges requires collaboration between healthcare professionals, data scientists, technologists, policymakers, and regulatory bodies. It is crucial to invest in research, standardization efforts, and robust governance frameworks to unlock the full potential of digital twins in healthcare while addressing these concerns.