esa-sceva/satcom-chunk-collection huggingface dataset:
# Slotted Circular-Patch MIMO Antenna for 5G Applications at Sub-6 GHz
## 2 Evolution of Single Antenna Element
### Performance and Discussion  
Figure 2 illustrates the single proposed circular antenna design's reflection coefficient (S11). The simulation shows that the S11 remains lower than \(-10\) dB across the frequency range of 2.73 GHz to 6.3 GHz, where the minimum simulated S11 is \(-38\) dB. In contrast, the measured S11 for the antenna is lower than \(-10\) dB at (2.967.1 GHz), but achieves a lower minimum measured S11 of \(-42\) dB. This indicates that the fabricated antenna effectively covers all required frequency bands while maintaining good matching and high performance compared to the designed antenna.  
Figure 3 illustrates the changes in gain across the frequency band. The simulated gain starts from 2.1 dBi at 2 GHz and increases gradually, reaching 4.45 dBi at 6.15 GHz as a peak gain. At frequencies of 3.5 GHz and 5.8 GHz, the circular-shaped antenna exhibits simulated gains of 2.7 dBi and 4.3 dBi, respectively. The measured gain starts from 1.7 dBi at 2 GHz and increases gradually, reaching 4.3 dBi at 6.15 GHz as a peak gain. At frequencies of 3.5 GHz and 5.8 GHz, the circular-shaped antenna exhibits measured gains of 2.49 dBi and 3.7 dBi, respectively.  
The radiation pattern of the fabricated antenna was assessed in an anechoic chamber, as illustrated in Figure 4c. Figure 4a,b present a radiation pattern of both simulated and measured electric fields at frequencies of 3.5 GHz and 5.8 GHz, respectively. At 3.5 GHz, the main lobe gain is 2.84 dBi, indicating good radiation intens  
Figure 3: The measured and simulated gain of the single design.  
Figure 2: The measured and simulated S11 of the single design.  
directed at 194\({}^{\circ}\). The angular width measures 92.2\({}^{\circ}\), suggesting a broad main beam. At 5.8 GHz, the main lobe gain increases to 4.23 dBi, indicating strong radiated power. The main lobe is directed at 161\({}^{\circ}\). The angular width is 69.3\({}^{\circ}\), providing a balanced trade-off between directionality and coverage. Simulated and measured radiation patterns nearly resemble each other. The observed slightly asymmetric radiation between back radiation and broadside radiation is attributed to a partial ground, which plays a crucial role in enhancing bandwidth in our antenna design. When a microstrip antenna is constructed on a substrate with a partially grounded plane, surface waves travel along the ground plane until they reach the edges of the antenna. At those edges, the reflection and diffraction of waves lead to somewhat increased back radiation patterns in comparison to broadside radiation.
# Development of a New Pasta Product by the Incorporation of Chestnut Flour and Bee Pollen
## 3 Results and Discussion
### Chemical Composition
The increase of fiber content is attributable to the partial replacement of wheat flour by chestnut flour in pasta formulation, because the chestnut flour contains a much higher amount of total dietary fiber (12.31 g/100 g flour, dm) than wheat flour (2.56 g/100 g flour, dm) [63]. The fat content of enriched pasta (FC4-C50P10) is higher when compared with control (FC4), but it is also with noting that it has a high quality, taking into account the higher percentage of unsaturated fatty acids in chestnut flour as compared with wheat flour. Among fatty acids, the most representative in chestnuts are oleic and linoleic, representing about 78% of the total fatty acid fraction [64]. Enriched pasta (FC4-C50P10) was found to be richer in proteins (14.67 g/100 g dm) than control pasta (10.10 g/100 g dm). However, the increase of this nutrient in FC4-C50P10 can be attributed mainly to pollen powder, since the flour enriched with chestnut flour (FC4-C50) had a protein content of 12.96 g/100 g dm, while the flour enriched with pollen powder (FC4-P10) presented a value of 16.57 g/100 g dm. In fact, bee pollen is one of the most protein-rich products, with content ranging between 10% and 40% and with a proportion of essential amino acids ranging between 34.59% and 48.49%, depending on the botanical origin [65]. However, the protein content of chestnut flour (6.44 g/100 g flour, dm) presents high-quality proteins, with essential amino acids (~5.8%) contributing to the protein quality of FC4-C50P10 pasta [63; 64]. Comparing FC4-C50P10 pasta with control pasta (FC4), the ash content increased three times, reflecting the correspondent rise in mineral content. The increase of mineral content of enriched wheat flour can be attributed either to bee pollen and chestnut flour having as predominant elements potassium, phosphorous and magnesium [64; 66; 67]. Overall, the enriched FC4-C50P10 pasta can be considered a nutritionally balanced product due to its high-quality proteins and high fiber content, besides the high content of polyunsaturated fatty acids, vitamins, minerals and potential bioactive compounds of chestnut flour and pollen powder ingredients [24; 66].
# Unveiling Accurate Seismic Imaging through the Advanced Target-Oriented Kirchhoff Migration Method
###### Abstract  
A major challenge for the oil and gas industry is expediting the exploration geophysics activity. It is necessary to produce fast and accurate image of the targeted hydrocarbon reservoir, yet it involves extensive dataset due to the target location lies in deeper subsurface. Therefore, target-oriented oil and gas-reservoir imaging is currently seen as more attractive than conventional full-volume migration, in terms of computation efficiency. Through the integration of seismic redatuming with Kirchhoff migration, we presented a target-oriented imaging workflow that can effectively reduce computational cost while simultaneously enhancing the accuracy of the migrated image. Kirchhoff migration is renowned for being one of the simplest and flexible migration techniques, making it straightforward to implement. However, this migration approach is typically avoided in the presence of complex geology, as it tends to introduce artifacts in the images, thereby disrupting the interpretation beneath the complex overburden. Therefore, this paper highlighted the advantages of utilizing seismic redatuming to retrieve data beneath the overburden, followed by the utilization of the redatumed data as input for Kirchhoff migration. With an improved signal to noise ratio, the primary reflection events were amplified, providing clearer representation of the subsurface structure within the hydrocarbon reservoir or targeted area. Additionally, we utilized the benefits of Kirchhoff migration and explored the feasibility of performing patch-based migration in several areas of interest beneath the salt layer. This approach aimed to reduce both the turnaround time and the associated seismic migration costs. The developed workflow then was subsequently put to the test and applied to both a sub-salt SEG Advanced Modeling (SEAM) model and field data situated in East Malaysia. Based on the outcomes of the target-oriented migration, the proposed methodology demonstrated its capability to generate better quality and instant migrated images within the designated region. Furthermore, the feasibility of target-oriented imaging with Kirchhoff migration was substantiated through the integration of seismic redatuming, resulting in significant benefits towards seismic processing, imaging, and interpretation.  
Kirchhoff migration; target-oriented; pre-stack depth migration; exploration geophysics; hydrocarbon reservoir +
Footnote †: journal: Applied Sciences
# Frequency Selective Surfaces: A Review
## 4 Types of FSSs
### FSSs Based on Structure  
Based on the structure, we review four types of FSSs in this survey, including single layer FSSs, multilayer FSSs, antenna-filter-antenna FSSs, and three-dimensional FSSs.
# Atmospheric Density Inversion Based on Swarm-C Satellite Accelerometer
## 4 Discussion and Conclusions
Therefore, the correction scheme for the accelerometer still needs to be improved, and we also expect a higher precision correction algorithm to be developed and published by the ESA. ESA regularly publishes corrected accelerometer data from the Swarm satellites. However, the data update frequency is not high, and the time delay is relatively large. Therefore, we expect the ESA to provide higher precision corrections. This method can release the corrected accelerometer data results timelier and provide convenience for scientific researchers. 2. The shadow model used in this paper to calculate satellite radiation pressure only considers the projection of the Earth to the satellite. However, the Moon or other large artificial space objects can cause satellites to be projected during the operation of the satellite. When approaching the Moon and other nearby objects, the drag coefficient in that region increases as the density of surrounding particles increases due to gravitational pull. The radiation pressure of the satellite during the flight changes, so the modeling accuracy still needs to be improved. For other space objects, whether they are celestial bodies or artificial flying objects, the projection of satellites can essentially use the solar radiation model and shadow area judgment model mentioned in this paper to simulate radiation pressure. In the follow-up research, we will collect more motion trajectories of occluded objects before modeling. Suppose the trajectories of enough other space objects can be known. In that case, the accuracy of the illumination radiation pressure model used in this study will be significantly improved. 3. Only accelerometer data from the Swarm satellites are used in this study. The Swarm satellite's strength lies in its ability to detect the Earth's magnetic field changes. Therefore, the follow-up research can consider using the magnetic field data of the Swarm satellite combined with the accelerometer data for research. For example,based on magnetic field data to explore the Earth's magnetic length anomaly and magnetic pole reversal. The influence of geomagnetic anomalies on atmospheric density is further explored based on accelerometer data and magnetic field data. Conceptualization, L.W., W.Z. and M.L. ; methodology, L.Y. and J.T. ; software, L.Y. and J.T. ; formal analysis, Z.Y. and J.T. ; data curation, J.T. and Z.Y. ; writing--original draft preparation, L.Y. and W.Z. ; writing--review and editing, L.Y., M.L. and W.Z. ; funding acquisition, W.Z. All authors have read and agreed to the published version of the manuscript. Supported by the Sichuan Science and Technology Program (2021 YQ0003). Not applicable. The data used in this paper are all open source and available for access from European Space Agency at [[https://earth.esa.int/eogateway/missions/swarm/data](https://earth.esa.int/eogateway/missions/swarm/data)]([https://earth.esa.int/eogateway/missions/swarm/data](https://earth.esa.int/eogateway/missions/swarm/data)) (accessed on 18 November 2022). The authors declare no conflict of interest.
## 1 Introduction
Therefore, we need two separate processes: one to discover the server node that provides the service and a second one to obtain a route to that node. The effect of combining these two separate processes is known as \"cross-layer service discovery\". Integration of routing and service discovery is an idea that was first introduced by [PERSON] _et al._[4]. Now, there are several different proposals that integrate service discovery in reactive, as well as in proactive routing MANETs. Cross-layer service discovery optimizes control overhead and reduces service acquisition latency [5]. For any node seeking a service, the route to the server node is known at the same time as the service provider. The main focus of this article is to present a service discovery mechanism integrated into the OLSRv2 [6] protocol that supports service advertisements in _ad hoc_ networks. This mechanism was previously defined for OLSRv1 [7] in [8]. In [9], we gave new results that demonstrate that the protocol could be a serious candidate for discovering services in proactive _ad hoc_ networks. Since then, the mechanism has suffered some changes to integrate it into OLSRv2. We will explain these changes, and we will also give new results about the performance of the service mechanism for different _ad hoc_ network scenarios. We compare the results obtained with the solution described for OLSRv1. We also compare our new design to a service discovery solution over the _Ad hoc_ On-demand Distance Vector (AODV) [10] reactive routing protocol. We evaluate the approaches thoroughly using simulations, and we conclude that the new design over OLSRv2 is superior to the one over OLSRv1. Moreover, in large and high mobility networks, the OLSRv2 service discovery solution behaves better than the AODV service discovery protocol, regarding service availability, service latency and network overhead. The improvements of the OLSRv2 protocol and the characteristics of proactive networks make the new design a serious candidate for service discovery in large and high mobility _ad hoc_ networks. The remainder of this paper is organized as follows: In Section 2, we present an overview of MANET routing protocols, and we briefly review OLSRv1 and OLSRv2 protocols. In Section 3, we discuss the need for a cross-layer approach for service discovery in _ad hoc_ networks and the implications of integrating service discovery with routing. Section 4 gives a short overview of the research efforts made in service discovery in MANETs. Section 5 describes our solution for integrating service discovery information into the OLSRv2 routing protocol. Section 6 details the results from our simulation experiments. Finally, Section 7 concludes the paper and refers to our future research directions.
###### Abstract  
Coherent optical orthogonal frequency division multiplexing (CO-OFDM) has attracted a lot of interest in optical fiber communications due to its simplified digital signal processing (DSP) units, high spectral-efficiency, flexibility, and tolerance to linear impairments. However, CO-OFDM's high peak-to-average power ratio imposes high vulnerability to fiber-induced non-linearities. DSP-based machine learning has been considered as a promising approach for fiber non-linearity compensation without sacrificing computational complexity. In this paper, we review the existing machine learning approaches for CO-OFDM in a common framework and review the progress in this area with a focus on practical aspects and comparison with benchmark DSP solutions.  
fiber optics communications; machine learning; artificial neural network; support vector machine; clustering; nonlinear equalization; coherent optical OFDM +
Footnote †: journal:  
_Future Internet_**2019**, _11_, 2; doi:10.3390/f11010002
# Using AI to Improve MIMO Antennas with SRR for 26 GHz by Analyzing Data
## 1 Introduction
### Contributions  
This work introduces a structured and innovative methodology for the intelligent design and optimization of MIMO antennas operating at 26 GHz, leveraging machine learning techniques, particularly random forest, to enhance performance and reduce design time. The contribution begins with the analytical design of a single rectangular microstrip patch antenna, which establishes a baseline geometry optimized for impedance matching and gain. This traditional method is then extended by training ML models on a large dataset of geometric configurations to predict optimal antenna dimensions, offering improved S11 performance and radiation characteristics.  
A second key contribution lies in the integration of split ring resonators (SRRs) to reduce mutual coupling in the two-element MIMO system. A dedicated dataset (>5000 samples) is constructed to vary SRR parameters, such as outer and inner radii, gap size, and trace width. This database is used to train ML models that efficiently predict the best SRR configurations to maximize isolation (minimize S21) while preserving return loss. Furthermore, another dataset explores the spatial placement and number of SRR units, allowing for a trade-off study between isolation and reflection. This multi-database strategy not only improves isolation performance, but also provides flexibility in tuning the physical layout of the antenna.  
Additionally, this work emphasizes the generalization of the design methodology across the 20-34 GHz frequency range, enabling the determination of optimal dimensions for both antennas and SRRs for various frequencies within this band. This generalization ensures that the proposed methodology can be applied to a wide range of 5G and future 6G applications, providing scalable solutions for efficient antenna design.  
Also, this study highlights the necessity of experimental validation to ensure that the machine learning-predicted antenna parameters and SRR configurations accurately translate into real-world performance, reinforcing the reliability and practical relevance of the proposed design methodology.  
Finally, this work evaluates the optimized MIMO system across a wide range of performance indicators, including the total active reflection coefficient (TARC), channel capacity loss (CCL), diversity gain (DG), envelope correlation coefficient (ECC), gain, and efficiency. These comprehensive evaluations confirm the benefits of combining electromagnetic simulation with machine learning prediction, offering a fast, reliable, and scalable solution for high-performance MIMO antenna design in 5G and future 6G applications.
# Quality Assessment of Organic Kefirs Made with Kefir Grains and Freeze-Dried Starter Cultures
## 3 Results and Discussion
### Analysis of Organic Kefirs During Refrigerated Storage
#### 3.3.5 Microbiological Analysis  
Common LAB species found in traditional kefir and kefir grains include species from the lactobacilli group: _Lactobacillus kefiranofaciens_, _Lentilactobacillus kefir_ (previously known as _Lactobacillus kefir_), _Lactobacillus acidophilus_, _Levilactobacillus brevis_ (previously _Lactobacillus brevis_), and _Lentilactobacillus parakefir_ (previously _Lactobacillus parakefir_), as well as lactococci: _Lactococcus lactis_ and _Leuconstoc mesenteroides_([PERSON] et al., 2019; 2020). Samples of organic kefirs were analyzed for their microbiological features immediately after inoculation (time 0) and during the fermentation process (Table 6). The counts of microorganisms were also determined during the 3-week refrigerated storage (Table 7). The counts of lactobacilli, lactococci, and yeasts were higher in organic milk inoculated with KGs than in milk inoculated with the freeze-dried culture (Table 6). The counts of the mentioned microorganisms increased throughout the fermentation process in milk with the freeze-dried starter culture, while in milk with KGs, the counts were more stable. The initial counts of lactobacilli, lactococci, and yeasts were distinctly higher in samples inoculated with KGs. However, the results showed that these groups of microorganisms did not proliferate in milk with KG. Only microorganisms originating from the freeze-dried culture were able to multiply in milk. This observation may result from the fact that, in the case of milk with kefir grains, proliferation occurs inside the structure of the grains, causing the KGs' weight gain. In the microbiological assessment, milk was tested after the separation of the grains; thus, an increase in microorganisms was not observed. However,
# Time-Delay Vibration Reduction Control of 3-DOF Vehicle Model with Vehicle Seat
## 5 Dynamic Performance Simulation and Analysis
### Suspension Dynamic Performance Analysis under Harmonic Excitation Input
By comparing the values in Table 3, it can be seen that the active suspension control strategy with time-delay control designed in this paper can optimize the passenger vertical acceleration by \(90.42\%\), the body vertical acceleration by \(84.08\%\), the suspension dynamic deflection by \(32.62\%\), and the tire dynamic load by \(85.57\%\) compared with the passive suspension performance. Compared with the active suspension based on inverse control, the active suspension control strategy with time-delay control designed in this paper can optimize the passenger vertical acceleration by \(82.71\%\), the body vertical acceleration by \(83.13\%\), the suspension dynamic deflection by \(13.50\%\), and the tire dynamic load by \(80.29\%\). Active suspension with time-delay control improves the performance of suspension significantly. This shows that the method proposed in this paper achieves the purpose of improving suspension comfort. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sinusoidal** & **Passive** & **Active Suspension** & **Active Suspension with Time-Delay** & **Optimized Percentage** & **Optimized Percentage** \\
**Excitation** & **Suspension** & **Control** & **with** **Suspension** & **Compared to Passive** & **Compared to** \\ \hline Passenger & & & & **Suspension** & **Backstepping Control** \\ acceleration & 3.1906 & 1.7677 & 0.3057 & 90.42\% & 82.71\% \\ (m/s\({}^{2}\)) & & & & & \\ Body acceleration & 2.4710 & 2.3319 & 0.3935 & 84.08\% & 83.13\% \\ suspension dynamic displacement & 0.0561 & 0.0437 & 0.0378 & 32.62\% & 13.50\% \\ (m) & & & & & \\ Tire dynamic load & 1093.1 & 792.6011 & 156.1892 & 85.57\% & 80.29\% \\ (N) & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Suspension performance root mean square value comparison table. Figure 6: _Cont._
# Thermal Modelling and Experimental Validation in the Perspective of Tool Steel Laser Polishing
## 3 Results and Discussion
### Moving Experiment: High-Speed Imaging
#### 3.2.2 Simulation Results  
Simulations were performed with the same main parameters as those used for the fixed laser case. The velocities of the laser were set to 20, 30, 50 and finally 100 mm/s. The corresponding time steps are: 0.5, 0.28, 0.2, 0.1 ms. The laser powers are identical to those in the experiments. For each case, the whole simulation corresponds to a laser polishing treatment length of approximatively 10 mm. The absorption coefficients used were \(a_{s}=35\%\) and \(a_{l}=30\%\).  
To confirm the predictability of the model, the size, shape and position of the melt pool relative to the laser spot were also simulated for different velocities and power. The results of the evolution of the size and shape for the simulated melt pools at the different velocities and adjusted power are shown in Figure 14. The laser powers were chosen to give approximately the same maximum temperature and this is confirmed by the color coded scale. In these conditions, the melt pool is becoming smaller with an increased velocity and also trails further away from the laser spot. The shape of the melt pool also differs from the observations with the high-speed camera. Indeed, the shape of the melt pool is almost perfectly round at low speed and has an increasing tail as the velocity increases, but maintains a round shape at the front of the melt pool (front on top of each simulation in Figure 14). This is not exactly what was observed in Figure 13, as the shape at low speed is mostly round with a flat tail and almost elliptical at high speed. The reason for these differences is not yet known, and could be due once again to the effect of convection in the liquid.