esa-sceva/satcom-chunk-collection huggingface dataset:
# Fractal Metasurfaces and Antennas: An Overview for Advanced Applications in Wireless Communications
## 4 Fractal Metasurfaces
### Fractal Metamaterial Polarizers  
Metamaterials are often used in the design of polarizers to manipulate the polarization state of electromagnetic waves [70]. A polarizer is a device that selectively transmits or absorbs EM waves based on their polarization. Metamaterials can be designed to manipulate the polarization of electromagnetic waves in novel ways. This includes controlling the orientation or ellipticity and even switching between different polarization states. Such capabilities are valuable in applications ranging from telecommunications to imaging. Traditional polarizers can be limited in their ability to operate over a wide range of frequencies. Metamaterial-based polarizers can be designed to provide broadband capabilities, allowing for effective polarization control over a wide frequency range [70]. In addition, metamaterials provide a platform for designing multifunctional polarizers that can perform additional functions beyond polarization control. For example, they can be integrated into devices that combine polarizing capabilities with filtering or beam-steering functions. Metamaterial-based polarizers have applications in antennas, imaging systems, and optical devices [70]. They can be integrated into these systems to increase performance, improve signal quality, or enable new functionalities.  
Recently, some fractal-based metamaterial polarizers have been proposed in the literature. In particular, a compact dual-band circular polarizer using a twisted Hilbert chiral metamaterial was proposed in [71]. The paper demonstrated how chiral behavior is enhanced by fractal perturbation. The layout of the proposed meta-atom is shown in Figure 22. The structure can emit circularly polarized waves from a linearly polarized  
Figure 21: Triple-band metamaterial TL absorber MA [59]: (**a**) measured absorption as a function of frequency at different angles of incidence; (**b**) top view of the MA element. (Reproduced with the permission from [PERSON], Physical Review B; published by American Physical Society, 2012).  
Figure 20: Minkowski-inspired circular fractal MA: (**a**) fractal construction, (**b**) simulated absorptivity (Adapted with permission from [58], Springer Nature, 2023).  
incident wave with high transformation coefficients over two microwave bands. In ref. [72], a fractal-based metasurface was investigated as a dual-polarization converter operating in the terahertz (THz) band. Figure 22b shows the layout of the structure, for which the top metasurface is patterned as an array of H-shaped tree-like fractal resonators. The converter has been numerically tested, demonstrating its ability to perform both linear-to-cross (LX) and linear-to-circular (LC) polarization conversions over three operating bands, namely 1.2-1.83 THz, 2.52-3.1 THz, and 3.78-3.9 THz.
# A Comprehensive Review of GNSS/INS Integration Techniques for Land and Air Vehicle Applications
## 1 Introduction
Integrity of GNSS measurements can be broken by various factors, including environmental factors, atmospheric effects and interference. To provide reliability and integrity of measurements, one can use augmentation systems, receiver autonomous integrity monitoring (RAIM), redundancy, etc. [58; 59]. Thus, the main problems faced by developers of integrated systems are as follows:  
* unavailability of GNSS signals, leading to the accumulation of errors in determining the position and velocity of the object due to the functioning of the integrated system in the INS mode;
* the presence of outliers and violation of integrity of GNSS measurements (for example, pseudorange) due to multipath effect, ionospheric and tropospheric effects, interference, environmental factors, which can lead to instability of SNS measurements;
* the presence of errors and effects in INS measurements due to the characteristics of the sensors, such as static and dynamic errors (bias) of the INS, the lever arm effect, crosslinks, etc. This paper considers the analysis of the current state in the field of available approaches for INS/GNSS integration for accurate determination of the position and velocity of moving objects. The focus will be on integrated systems designed for mobile land and aerial vehicles, on the main factors affecting the accuracy and functionality of the integrated system and on the issues for solving of related problems. The Section 2 discusses various approaches to INS/GNSS integration based on filters and measurements. The Section 3 discusses the functionality of GNSS and INS to determine the position and velocity of an object and the features of development of an INS measurement error model that are necessary for integration. The Section 4 considers the main problems of GNSS/INS integration, a comparative description of the solutions for these problems proposed by the authors and their analysis. The Section 5 contains conclusions and recommendations.
## 3 Uncertainty Modeling Techniques
### Possibilistic Methods
#### 3.2.2 Fuzzy Set Theory  
Fuzzy set theory, introduced by [PERSON], forms the basis of many possibilistic methods by allowing elements to have varying degrees of membership in a set, rather than being restricted to binary membership as in traditional set theory. This approach is widely used in systems that need to handle ambiguous or continuous data.  
Recent advancements in fuzzy set theory and its applications in uncertainty management are highlighted in two pivotal studies. In [38], the research shifts from conventional type-1 fuzzy sets to type-2 fuzzy sets to better accommodate high levels of linguistic and numerical uncertainties, which are prevalent in practical applications such as the reliability evaluation of phasor measurement units. The study emphasizes the effectiveness of type-2 fuzzy sets, which incorporate secondary membership functions to handle these uncertainties, proving to be a superior method especially in contexts with insufficient field data. In [39], climate change mitigation by nuclear energy is discussed. This reference has used the nonlinear fuzzy set theory for the assessment of climate change.  
A fuzzy set \(\overset{\sim}{A}\) in the universe of information \(U\) can be defined as a collection of ordered pairs, mathematically expressed as follows:  
\[\overset{\sim}{A}=\left\{\left(y,\mu_{\overset{\sim}{A}}(y)\right)\mid y\in U\right\} \tag{44}\]  
Figure 5: Fuzzy trapezoidal membership function.  
Here, \(\mu_{-}(y)=\) degree of membership of \(y\) in \(\overset{\sim}{A}\), assuming values in the range from 0 to 1, i.e., \(\mu_{-}(y)\in[0,1]\).
## 3 Results and Discussion
### Microscopic Dust Analysis  
The particles of tropical dust samples (Figure 7) have the size of \(<\)100 \(\upmu\)m. From the safety and occupational hygiene perspective, particles below 100 \(\upmu\)m are the most dangerous in the working environment [2]. The scans clearly show that the fibers of the dust particles maintain their anatomical structure with the fibrous character of particles [66]. The two-dimensional pictures of particles can be used for determining the smallest particle sizes.  
Figure 6: Marblewood dust particles and their shape.  
Picture of wood samples, after sanding, taken by an electron microscope, show different and complex shapes of particles of wood dust (Figure 7). As described in the Particle Atlas [67], the diverse geometric expression could be observed, such as cylinders, cones, rectangular prisms, and spheres [60].  
[PERSON] et al. [60] provided microscope analyses of dusts from two hardwoods (sessile oak, oak-tree), two tropical hardwoods (padouk, iroko), and three softwoods (pine, spruce, and larch). These were obtained using a grinding machine with 360-grit sanding paper.  
[PERSON] and [PERSON] [61] analyzed the particle characteristics in Quindio (size distributions, aerodynamic equivalent diameter (_Da_), elemental composition, and shape factors), and particles were then characterized via scanning electron microscopy (_SEM_) in conjunction with energy dispersive _X-ray_ analysis. Results from their analysis of particulate matter showed that the cone-shaped particle ranged from 2.09 to 48.79 \(\upmu\)_Da_; the rectangular prism-shaped particle from 2.47 to 72.9 \(\upmu\)_Da_; the cylindrically-shaped particle from 2.5 to 48.79 \(\upmu\)_Da_; and the spherically-shaped particle from 2.61 to 51.93 \(\upmu\)_Da_.  
Oak dust provides a similar comparison with marblewood dust, as oak is the hardest Slovak wood. In all investigated oak fractions, the isometric shape of particles with sharp edges and rounded corners, which is more typical for fraction \(<\)100 \(\upmu\)m [3].  
Figure 7: Light microscopic images of tropical dust fibers with 100\(\times\), 200\(\times\), and 400\(\times\) zoom. Legend: Blue line presents a size of 100 microns (\(\upmu\)m) in a 2D layout.
# Leveraging Hybrid RF-VLP for High-Accuracy Indoor Localization with Sparse Anchors
## 4 Results and Discussions
### Impact of Receiver Spacing on Localization Accuracy  
The impact of receiver spacing on localization performance is further analyzed. The experimental setup is depicted in Figure 10. The square receiver arrangement, which achieves the highest accuracy in previous experiments, is employed for this analysis. Reducing the square side length from 60 cm to 30 cm significantly increases the mean positioning error to 64 cm, highlighting a drop in localization accuracy. The closer receiver spacing results in a higher geometric dilution of precision (GDOP) [29] when applying Equation (10) for positioning, leading to greater errors in LED coordinate estimation.  
Figure 8: Localization result under different receiver arrangement.  
Figure 9: Localization error under different receiver arrangement. **(a)** RMSE under different receiver arrangement. **(b)** CDF under different receiver arrangement.  
Additionally, excessively close receiver spacing causes BLE ranging errors to contribute disproportionately to the overall ranging results, further degrading the computational accuracy of the MDS algorithm. When BLE ranging is not significantly affected by distance-related interference, larger receiver spacing improves the localization accuracy. In practical localization scenarios, where receivers are typically randomly distributed, this condition is often naturally satisfied.
## 5 Implementation Details
### EMQ Cluster  
MQTT is a lightweight publish/subscribe message protocol, and its implementation is useful for the collection of data coming from devices with limited resources. In this work, the message broker EMQ v4.0.0 was used to build a cluster of EMQ brokers. EMQ broker is a distributed, massively scalable, highly extensible MQTT message broker [46]. The EMQ cluster is used as the first point of entry for data that come from the homes of elderly people.  
Figure 10: The microservices of the e-health system are designed with an MVC design pattern. The graphical user interfaces provide access to core services and diet management services.  
For the architecture implementation, we deployed the EMQ cluster with three nodes in e-Health-System-K8s-cluster nodepool-2 (see Section 4.2.5) with images from Docker Hub.
## 1 Introduction
In the following two years, five more classification works were published using the same database, proposing a variety of feature extraction techniques and classification algorithms: (i) a set of eleven statistical features from the estimated instantaneous frequency of non-segmented PCG signals, for the binary classification and multiclassification comparing both random forest and KNN classifiers [10]; (ii) time-frequency features from the segmented cycles of the PCG signal through the wavelet synchrosqueezing transform, for a multiclass classifier for four of the five classes (MR, MS, AS, N) using a random forest classifier [11]; (iii) centroid frequency as the main feature to compare the performance of KNN and SVM, on both binary and multiclass problems [12]; (iv) local energy and local entropy features of the Chirplet transform from the PCG signal for a multiclass classifier for four of the five classes (MR, MS, AS, N) using a composite classifier [13]; (v) raw data and a WaveNet neural network for a multiclass classification model was proposed, to classify the PCG into the five classes [14]. All the above-mentioned works are briefly summarized in Table 3.  
The present work stands out by using a deep learning algorithm that exploits frequency dynamics present throughout the cardiac cycle to differe  
Figure 1: Heart sounds (S1, S2) and noises (\(nX\)) present in different phonocardiographic recordings, concatenated for graphical purposes. The complete cardiac cycle has an approximate period of 800 ms, where the average duration of normal heart sounds is 70–150 ms for S1 and 60–120 ms for S2. abnormal cardiac conditions. In particular, there are three properties that place this work as a new methodological proposal:  
1. The use of Convolutional Neural Networks (CNN), together with the Continuous Wavelet Transform (CWT) and MFCCs, turning the time series classification problem into image classification, which has not been reported before for HVD classification, since all the related works use a vector-like feature arrangement or the raw time series. 2. A transfer learning approach, which allowed us to migrate the deep learning algorithm previously trained for multiclass classification into a binary classification, permitting us to test the pre-trained model in a new classification problem, ensuring that the learning process generalizes and does not memorize, seeming quite flexible when compared. 3. The complete model of the deep learning algorithm consists of two main stages. The first is formed by three parallel neural networks, each processing one of the three different features for individual cardiac cycles. The second is constituted by a perceptron that integrates the outputs of the previous stage, finding new patterns between them, improving the efficiency and effectiveness compared with the related works.
## 4 Results and Discussions
### Influence of Distance to Track Center on Aerodynamic Pressure  
Figure 13 illustrates the fluctuation curve of aerodynamic pressure on the sound barrier at various distances from the center of the rail. It is evident that when a high-speed train travels at 360 km/h through a sound barrier, the peak aerodynamic pressure on the surface of the sound barrier diminishes as the barrier moves farther from the railway line's center. As the length of the train nose extends, ranging from 3.0 m to 9.0 m, and with an increase in the distance between the sound barrier and the center of the rail from 2.0 m to 3.0 m, the aerodynamic pressure peak decreases gradually. When the distance from the center of the rail increases from 2.0 m to 2.5 m, the aerodynamic pressure peak diminishes by 36%, 35%, 35%, 33%, and 31%, respectively. With an increase in the distance from the center of the rail from 2.5 m to 3.0 m, the aerodynamic pressure peak experiences a decrease of 28%, 26%, 26%, 23%, and 23%, respectively. These findings highlight a non-linear trend in the reduction of aerodynamic pressure peaks on the sound barrier as the distance between the sound barrier and the center of the rail increases. This phenomenon is attributed to the expanding distance between the sound barrier and the train, leading to a more open airflow field and diffusion in the airflow.  
Figure 12: Change curve of peak aerodynamic pressure under different working conditions: (**a**) Distance to track center is 2 m; (**b**) Distance to track center is 2.5 m; (**c**) Distance to track center is 3 m.  
Figure 13: Variation curves of peak aerodynamic pressure for different sound barrier–train center distances.
## 3 Results and Discussions
### Effect of Contact Time  
One of the most significant factors affecting the adsorption equilibrium is the contact time between adsorbent and pollutant. Pb\({}^{2+}\) and NR dye's adsorption capacities over MnFe\({}_{2}\)O\({}_{4}\)/GO nanocomposite were investigated at contact time ranging from 0 to 120 min and 0 to 30 min, respectively. The adsorption capacity and removal efficiencies of Pb\({}^{2+}\) ions and NR dye over MnFe\({}_{2}\)O\({}_{4}\)/GO nanocomposite at different time intervals were shown in Figure 7.  
During the contact time effect study, we hold other parameters constant including adsorbent dose, volume, and concentration. The dose, volume, and concentration of Pb\({}^{2+}\) ions and NR dye were (8 mg, 50 mL, and 50 mg/L) and (12 mg, 6 mL, and 50 mg/L), respectively. According to Figure 7, the removal efficiency and adsorption capacity of Pb\({}^{2+}\) ions and NR dye increased quickly at the first stage then gradually became slower. The first stage was observed for Pb\({}^{2+}\) ion and NR dye adsorption at the first 15 and 20 min, respectively. The tremendous available numbers of adsorption sites over MnFe\({}_{2}\)O\({}_{4}\)/GO nanocomposite are responsible for this rapid increase of adsorption capacity at the first  
Figure 6: The effect of pH on Pb\({}^{2+}\) ions (**a**) and NR dye (**b**) adsorption over MnFe\({}_{2}\)O\({}_{4}\)/GO nanocomposite.  
stage. After this stage, the surface saturation was reached, and hence the equilibrium reached with no further significant increase in the adsorption capacity. As indicated from the results, the adsorption equilibrium was not significantly dependent on the adsorbate concentration. The Pb\({}^{2+}\) ions and NR dye maximum removal were 98% and 94%, respectively. Subsequently, the optimum contact times for Pb\({}^{2+}\) ions and NR dye were 120 and 30 min, respectively at which the equilibrium reached.
## 3 Using ML for Water Activities
### Water Resource Management and Quality Prediction
#### 3.1.3 Water Demand Prediction and Climate Adaptation  
* **Precise water demand predictions[248]**: Advanced ML techniques were utilized for accurate predictions of water demand in urban areas. These models underscored the significance of temporal dynamics in water usage patterns.
* **Vapor pressure deficit in Egypt[249]**: ML algorithms, including random forest (RF), were used to predict vapor pressure deficit (VPD) in different regions of Egypt. RF emerged as the top-performing model, supporting climate adaptation efforts.
# Prioritization of Drugs for Fungal Keratitis Eye Infections: An In-Silico Analysis
## 2 Result and Discussion
### Binding Site Prediction  
The quality of the modelled structures was assessed through online quality evaluation tools such as PROCHECK, ERRAT, and Verify 3D, provided through Structural Analysis and Verification Server (SAVES) of the UCLA-DOE Lab. The phi and psi angles that explore the stereo-chemical parameters of the energy-minimized models were determined by using PROCHECK; the 1D-3D structure compatibility of the best models were verified by Verify 3D, and the regions of the modelled structure that can be rejected at the 95% and 99% confidence intervals were predicted through the ERRAT programs. In general, \(>\)85% of residues in the modelled structures that fall under the most favored regions of the RC plot are considered suitable for further study. Verify 3D calculates the amino acid propensity of each residue of the model compared to the PDB structures and reveals the final score as a sum of the propensities of the individual residues in the built model [27; 28]. Usually, the ERRAT scores are expressed as a percentage. The ERRAT scores of \(>\)95% were graded for high resolution structures, while low-resolution structures were graded at approximately 91%.  
The Ramachandran plot (PROCHECK) of the energy-minimized models of RNAP from _A. flavus_ (Figure 1b) and _A. fumigatus_ (Figure 2b) exhibited most of the residues in the most favorable region and 1.3% and 1.7% were in the disallowed region. whereas the Ramachandran plot (RC plot) of the template (3h0g_B chain) exhibited 4.6% residues in the disallowed regions and 59.2% in the most favored regions. Significantly, this revealed that the generated models of the RNAP are best, as they exhibited a greater number of residues (76.1% and 84.3%) in the most favorable regions and a low number of residues in the disallowed region of the RC plot. Further, the ERRAT plot values of the modelled RNAP from _A. flavus_ (Figure 1c) and _A. fumigatus_ (Figure 2c), and the Verify 3D measured quality factor values, suggested that the built models are relevant, reliable, and of good quality (Table 2).
## 1 Introduction
### Our Contribution  
This paper presents an experimental implementation of a bidirectional V2V communication based on VLC technology for the low latency exchange of messages. Experimental measurements have been carried out in a real urban scenario. Characterization of the transmission latency observed using a recently developed VLC system implementing a bidirectional link realized via modulation of light intensity emitted by motorbike head- and tail-lights [29] is reported. In such system, a bidirectional (round-trip) connection is established and performance is measured for relative distances between the two motorbikes up to 27 m and with data rates up to 57 kBaud. In addition, the performance of VLC as a technology for the assisted breaking system in modern cars has been evaluated and compared to RF-based technologies. A mobility model was used to evaluate the breaking distance of a modern car, based on the latency of the alert message arriving at the car.  
The main contributions of this paper can be summarized as follows:  
* First characterization of latency in a bidirectional VLC-based communication system for ITS application;
* Experimental measurements campaign have been carried out in a real urban scenario with real head- and tail- motorbike LED-based lamps;
* The results demonstrate that our low-cost system guarantees good latency performance, compared with the URLL 5G communication threshold [7];
* The simulation results using the experiments-based mobility model can highlight the benefit of VLC systems for future vehicular applications, as the communication interface currently planned by 6G.  
The rest of the paper is organized as follows. Section 2 shows the measurements campaign setup, including the hardware equipment. Section 3 comments the experimental results and Section 4 shows the performance comparison between RF and VLC technologies and discusses the benefit of VLC in the urban platooning scenario. Finally, Section 5 concludes the paper.
## 3 Results and Discussion
### Optimization  
The optimization was carried out to achieve the maximum protein yield according to the mathematical model. The optimal levels for temperature, alkaline solution, and time are given in Table 6. An additional experiment was conducted to validate the model, a protein yield of 48.30% was achieved with 70 \({}^{\circ}\)C, 44.7 min, and pH 12.68. This result was higher than that reported by [PERSON] et al. (2015), who obtained a maximum protein extractability of 33.82% for defatted canola flour using alkaline solutions generated by electrolysis [22]. Furthermore, our result was slightly higher than that of 47.0% obtained by [PERSON] et al. (2020) when optimizing soy protein extraction by using enzyme treatment for 1 h and alkaline extraction for 2 h [46]. Even though the protein yield was not higher than traditional alkaline extraction reports (~70%) [24], a ~50% of protein yield using by-products is significant, and the use of this industrial waste that is generally discarded may be scalable and form an industrial symbiosis.  
The mathematical model we obtained is valid since the response was within the confidence interval of 46.66-52.93 (Table 6), and it is important to highlight that the protein content in the SPI obtained was 91.85 \(\pm\) 0.30%, which matched the protein content for commercial products [48; 49].  
Figure 3: Contour plots of the interactions of temperature vs. time (**A**), temperature vs. alkaline solution (**B**), and time vs. alkaline solution (**C**).