esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Simulations Parameters  
The proposed DNNGRU framework has been evaluated on a comprehensive performance matrix including accuracy, precision, recall, ad f-Score as simulation parameters. The accuracy of a system is calculated by the accumulative summation of the True Positives (TP), the True Negatives (TN), the False Positives (FP), and the False Negatives (FN). The recall is considered an essential element in ascertaining the system's performance. It denotes the average number of correct analyses released by an algorithm. In some cases, the term precision swapped places with recall because it affirms the accumulative projected by a framework.
# Dynamic Stall Characteristics of the Bionic Airfoil with Different Waviness Ratios
## 4 Results and Analysis
### Lift and Drag Coefficients of Different Airfoils  
Figure 12 shows the lift and drag characteristic curves of airfoils with different values of R, as well as the baseline airfoil in dynamic stall for a Reynolds number of 3 \(\times\) 10\({}^{5}\).  
When R = 0.2, the peak lift coefficient of the bionic airfoil was almost the same as that of the baseline airfoil. When R = 0.4, the peak lift coefficient of the bionic airfoil decreased slightly. With increasing R, the bionic wavy leading edge gradually weakened the hysteresis effect of the airfoil lift coefficient.  
When R = 0.6, 0.8, and 1.0, the bionic leading edge reduced the peak value of the lift coefficient and reduced the delay in the flow reattachment. The peak value of the drag coefficient during pitch-up was significantly reduced for all the bionic airfoils compared with the baseline airfoil, whereas the drag coefficient of each bionic airfoil was the same as that of the baseline airfoil during pitch-down.  
Table 2 shows the calculated results for the maximum lift coefficient \(C_{\text{lmax}}\), the maximum drag coefficient \(C_{\text{dmax}}\), and the \(\Delta C_{\text{lmax}}\) and \(\Delta C_{\text{dmax}}\) compared with the baseline airfoil. The five bionic leading edges significantly reduced the peak drag, although with degrees of loss of lift. There were significant improvements in the drag characteristics at a small cost in the loss of lift for cases of R = 0.2 and R = 0.4.
# A Survey on 5G Coverage Improvement Techniques: Issues and Future Challenges
## 5 6G and Its Challenges  
Beyond 5G and 6G network applications, users need more network capacity, throughput, data rates, spectral efficiency, low latency, and an extended coverage area compared to 5G networks. It will transform the nation into automation, i.e., smart production, a smart society, and smart lives, by 2030 [193].  
In the literature, several studies have focused on 6G networks [194; 195; 196; 197; 198; 199; 200; 201]. In Ref. [194], a thorough analysis of 6G is provided based on a review of 5G advances, covering technology trends and issues with the goal of addressing the problems with coverage, capacity, user data rates, and mobility of mobile communication systems. In Ref. [195], authors provided a detailed overview of the 6G network and highlighted the use cases and applications of the 6G network in various dimensions. The key parameters and future research challenges of 6G are discussed. In Ref. [196], the authors focused on the use cases and technologies of 6G networks. Sixth-generation enabling technologies and their applicable use cases are compared. In Ref. [197], the authors provided the major issues and challenges relating to the security, privacy, and trust problems in 6G networks. In Ref. [198], the authors discussed the security and privacy challenges that may emerge with the 6G requirements, novel network architecture, applications and enabling technologies including physical layer security, distributed AI/ML, Visible Light Communication, THz bands, and quantum communication. In Ref. [199], the authors discussed 6G security and privacy issues in the four key aspects such as distributed AI, real time edge computing, 3D intercoms, and intelligent radio.  
It is necessary for 6G capabilities to be realised at scale for extremely immersive applications such as 3D communication, virtual reality, and digital twins [200]. The advantages of the upcoming 6G technology for processing real-time applications on edge networks have been discussed [201]. The challenging issues and future directions of 6G based edge computing have also been discussed. The future challenges of 6G networks that need to be addressed are quantum machine learning for 6G communications, ultra-reliable communications for edge computing, energy harvesting for extended battery life, and so on.
# Design of a Modified Compact Coupler with Unwanted Harmonics Suppression for L-Band Applications
###### Abstract
In this paper, the proposed BLC is designed, simulated and measured. The fabrication is realized on a RT-Duroid substrate with a thickness of 0.508 mm and dielectric constant of 2.2. Several different resonators are exploited to design the presented BLC with high miniaturization and suppressing ability.
## 4 Results and Discussion
### Nonlinear Vibration Results of Different Models
Some of the vibration energy is consumed by friction, so the amplitude change is no longer linear. It also shows that the nonlinear vibration characteristics of stator blades are simulated by this method. The vibration responses of stator blades under various normal loads and excitation forces are solved. A very important phenomenon needs to be briefly discussed here. The sensitivity of the model studied in this paper to input parameters is critical. It can be seen from the above analysis that when the airflow force load is defined as a constant, the stator blade response curve is less sensitive to the larger normal load of the contact surface. When the normal load is in the lower parameter range, the response curve has strong sensitivity to the change of normal load. Of course, the normal load cannot be defined as too small, otherwise the computational model will not converge. Similarly, if the normal load is defined as a constant, then the change in the flow force directly affects the change in the response curve. In other words, the vibration response of the stator blade is very sensitive to the airflow force parameters. Figure 13: Response normalization amplitude curves under different exciting force with 30 N normal load. (**a**) Circumferential amplitude curve. (**b**) Axial amplitude curve. In order to visualize this change, the variation curve of the equivalent stiffness coefficient is given in Figure 14. The logarithmic coordinates are also applied in the figure. The curve change here is just the opposite of the tendency of the constant excitation force to change the normal load. When the normal load of the contact surface is fixed at a certain level, the friction motion state of the contact surface also changes with the increase of the airflow excitation force to a certain value. It changes from viscous or fretting slip to macroscopic slip; therefore, the equivalent stiffness coefficient also takes a step. This phenomenon gives an important hint that the level of exciting force in this operation should be particularly considered during the installation of stator blades, because once the stator ring is assembled and running, its contact surface is kept tight by the normal pressure provided by the fixing bolt and other structures. However, if a wide range of conditions, such as variable operating conditions, occur during operation, blade vibration under some exciting forces under design conditions cannot be considered alone. Because of the high parameters of some operating conditions, the larger vibration force is likely to cause a blade contact surface friction state change. Figure 14: The equivalent stiffness coefficients of different exciting force at the resonant frequency of 30 N normal load.
## 3 Results and Discussion
### Analysis and Discussion of Release Results
The fact that the solid dissolution rate constant is the same with the one found before for the pure drug supports the physical validity of the model. The total fraction of released drug is computed as \(C_{\mathrm{F}}V/\mathrm{m}\). The rest of the drug is in a crystallized insoluble form (fraction \(\varphi\)). The value of \(\varphi\) is 0.11, 0.18, and 0.214 for drug content 5%, 10%, and 20% without clay, respectively; 0.121, 0.242, and 0.325 for drug content 5 wt %, 10 wt %, and 20 wt % with 10 wt % clay, respectively; 0.02, 0.103, and 0.156 for drug content 5 wt %, 10 wt %, and 20 wt % with 5 wt % clay, respectively. The insoluble fraction increases with drug content, but shows a nonmonotonic behavior with clay content, indicating the complexity of drug-clay interaction.
## 1 Introduction
### Relevance of Carbon Nanotubes for Property Improvement
[30] proposed a method to evaluate the structural effects of CNTs on polymer-modified binders (PMBs), focusing on the behavior of asphaltene-resin complexes (ARCs), the primary structure-forming components of PMBs. Their results showed a 30% increase in ARC dispersity and up to a 10% increase in binder viscosity within the operational temperature range. This improved viscosity reduces delamination and enhances the stability of PMB properties, thereby improving the performance and durability of asphalt concrete mixtures. The combined research demonstrates that CNTs offer significant advantages in improving the structural and functional properties of bitumen-based materials. By enhancing durability, viscosity, and fatigue resistance, CNTs contribute to longer-lasting asphalt pavements. However, the broader industrial application of these findings is contingent upon addressing challenges such as the uniform dispersion and stable integration of nanoparticles within the binder matrix. Furthermore, additional studies are needed to refine methods for evaluating nanomodified systems and to optimize nanoparticle concentrations for maximum performance and cost efficiency.
## 4 Conclusions  
In conclusion, we analyzed THz band-pass filters fabricated based on two different additive manufacturing technologies. It can be noted that both technologies allow the efficient manipulation of THz waves, i.e., band-pass filtering through the printing of FSSs. It can be seen that 3D-printed THz BPF offers a narrow pass-band i.e., a higher quality factor as compared to the PE-based analyzed THz BPF. Additionally, the implementation of the Moire interference technique based on the printed FSSs can potentially open up new possibilities for the mass production of reconfigurable THz FSS at lower cost. Remarkably, the maximum modulation depth obtained experimentally at 0.22 THz is 41% for a 20\({}^{\circ}\) rotation, which is relatively adjacent to the expected value obtained by simulation, which was 58% at 0.25 THz in the case of PE-based Moire FSS. By improving the alignment and limiting the air gap between the two samples, the discrepancy between the simulation and the experimental results could be minimized and the modulation depth could be improved. This validates the usefulness of the proposed printing technology and of the Moire THz reconfigurable FSS.  
Conceptualization, R.A., M.Z. and F.B.; experimental validation, R.A., M.Z. and X.R.; simulation investigation, R.A.; resources, F.B.; supervision, F.B.; writing--review and editing, R.A., M.Z., X.R., N.D.T., C.B., J.S. and F.B. The manuscript was written through contributions by all the authors. All authors have read and agreed to the published version of the manuscript.  
This work was financially supported by NSERC (2016-05020), the Canada Research Chair (CRC-2019-127). Furthermore, [PERSON] is a recipient of the PBEEE doctoral research fellowships provided by the Fonds de Recherche du Quebec-Nature et Technologies (FRQNT).  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.  
Figure 6: Moiré pattern image taken using 2f imaging system for (**a**) 10\({}^{\circ}\) rotation; (**b**) 20\({}^{\circ}\) rotation; (**c**) 30\({}^{\circ}\) rotation. (**d**) Experimental normalized transmission as a function of frequency for different rotation angles. (**e**) Obtained modulation depth in the case of simulation and measured findings as a function of rotation angles.  
**Acknowledgments:** The authors would like to thank the Printability and Graphic Communications Institute, Montreal, QC, Canada for providing a fabrication facility for printed electronics samples and [PERSON] from Nanogrande for the 3D printed samples.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Related Work
In [42], an improved ant colony optimization (IACO) technique was given to construct the rule of pheromone update and transfer function in each sensor node as well as to adaptively choose a data path by taking use of the network's dynamic state, which can facilitate node mobility. With surrounding nodes, when the given protocol exchange the broadcast messages and the hops information throughout the whole network, the connection forms and renew the pheromone value in the gradient field and accordingly the route. The corresponding probability is computed by the obtained values of surrounding nodes as delay packet loss rate, data transmission, and residual energy of the node. The quickest path and the level of leftover energy are taken into account while choosing a routing strategy. Data are exchanged during the route maintenance procedure to raise the pheromone concentration. Since data packets are transported via the shortest pathways, the network as a whole uses less energy, closer to the average. A VC++ simulator is used to simulate IACO, which is then compared against CO, the Dijkstra algorithm, and data equilibrium-based algorithms (DEBA). In [43], the communication and data compression approach for the decentralized estimation scenario is provided. It is shown the frameworks for centralized and decentralized estimates that may be seen as one unified optimization model. The authors offer a cyclic optimization approach that can manage altering the gains and phase-shifts of the sensor nodes even in challenging situations like discrete phase-shifts or sensor selection by over-parametrizing the original design problem. The developed framework has a low computing cost since each iteration combines the Gram-Schmidt process with iterations that resemble those in the power approach. creating the design challenge for a fusion center, too. In [44], the routing protocol Ant Colony Inspired Energy efficient OLSR (AC-OLSR) was presented. Pheromone concentration was computed using a combined link quality technique that could enhance wireless network performance. Link quality and neighbor link quality, two parameters of the general ACO algorithm, were used. In order to evaluate AC-OLSR, it was simulated and compared against OLSR, EE-OLSR, and AntNet. The summary and comparison of the related work is presented in Table 1 that includes the protocol based or enhanced version of which algorithm, the parameters used for coming up with the route decision, the simulation results comparison, and the limitations of the research works.
## 6 Enabling Technologies in Healthcare
### Healthcare Cloud, Fog, and Edge Computing  
Healthcare cloud computing utilizes cloud computing technologies in the healthcare industry. Cloud computing is a model that enables ubiquitous, convenient, on-demand access to a shared pool of configurable resources, such as computer networks, servers, storage, applications, and services. These resources are rapidly provisioned and released with minimal management effort or service provider interaction. It is a technology that facilitates the storage, processing, and sharing of medical data in the cloud [56; 57]. Cloud computing in healthcare involves the utilization of remote computing resources for data storage and processing, offering healthcare organizations a secure and cost-effective method to store and manage large volumes of data. Additionally, it provides them with access to data from anywhere at any time, facilitating easier collaboration on projects. The adoption of cloud computing also helps reduce \(\Pi\) costs by eliminating the need for additional hardware and software, while allowing scalability as required. Furthermore, it fosters seamless collaboration between healthcare organizations, leading to improved patient care.  
Figure 8: Enabling Technologies in Healthcare Domain.  
By adopting cloud computing, healthcare organizations can securely store and access vast amounts of data in an efficient and cost-effective manner.  
Fog computing is a decentralized computing architecture that incorporates cloud computing into the IoT. It enables data processing close to the source of data collection rather than in a centralized cloud. This approach allows healthcare organizations to process data closer to where it is created, resulting in improved response times and reduced latency. Additionally, it helps alleviate the strain on data centers, leading to cost reductions. Moreover, fog computing enables quick and secure data sharing with other organizations, ultimately enhancing patient care. Furthermore, fog computing provides healthcare organizations with the flexibility to scale their data processing operations as needed, facilitating rapid adaptation to changing demands [58; 59; 60; 61].  
Edge computing minimizes network latency by reducing the amount of data sent over the network. Data processing occurs locally instead of being transmitted to a cloud-based server, resulting in faster processing times. This approach enables healthcare providers to reduce costs associated with data processing and enhance the security of patient data. Additionally, it allows for the storage of large data volumes closer to the network edge, enabling faster and more secure data processing [62; 63]. Healthcare cloud computing, fog computing, and edge computing assist healthcare providers in processing large volumes of data quickly, securely, and cost-effectively. These technologies play a significant role in Healthcare 4.0, positively impacting healthcare research and service improvement. They enhance quality, increase affordability for a larger population, and improve patient outcomes.
## 4 Discussion
### Performance Results for the Real Salt Structure Inversion  
For the real data-based inversion of the considered salt body, it is confirmed that the final configuration obtained from the joint inversion of the tensor gradiometric data is more compact with respect to the configurations obtained by gravimetric/magnetic data, defining its limits in a better way. This is likely due to the fact that the variation of densities corresponding to the smoothing factor \(\tau\) is less considerable in the gradiometric inverted model, concentrating these variations in the borders of the salt body to a greater extent.  
As can be observed, the results are analogous to those obtained for the synthetics models. Again, as a search-based parallel algorithm is not deterministic, execution times can vary slightly among multiple executions of the codes, so results in Figure 34 are statistical between 10 different executions of the code under the same conditions. Nevertheless, the general behavior is always the same.  
With respect to the computing time, it is notable from Figure 34a that the execution time for the magnetometric inversion improves 12.34% for 24 threads (11.75\(\times\)) with respect to 12 threads (10.30\(\times\)), while the improvement for the same threads of gradiometric and gravimetric inversions is only of 2.94\(\times\). With respect to the speed-up results (see Figure 34b), it is noticeable that the gravimetric case follows an almost perfect speed-up in the 1 to 12 threads range, while the results of magnetometric and gradiometric inversions are still close to the perfect speed-up in the same range of threads. Once the logical cores are reached (after thread 13), performance drops and HT offers no more than a small increase in performance which is only important for 24 threads. Finally, in the workstation in which the tests were performed, it must be emphasized that in the most resources-demanding case (gradiometric inversion) for the real model involving a salt structure, the parallelization scheme reduced the execution time from 16 h:56 m:15 s (in serial mode) to 01 h:30 m:41 s (for 24 threads), which means that the design and implementation of the parallel algorithm based on OpenMP is satisfactory.
## 3 Results and Discussion
### Core-Flooding Effluent Analysis
#### 3.3.3 Extensional Measurements  
The results of the e-VROC measurements are described in Figure 12. The highest (and apparently linear) extensional viscosity can be clearly seen for Flopaam 6035 S with deionized water. The presence of a higher amount of divalent cations (reservoir brine) exhibited the negative impact to lower the polymer extensional viscosity, as compared to solutions prepared with a lower amount of divalent cations (synthetic seawater). Minimum extensional viscosity can be seen for the core flood effluent with (reservoir brine) at 55 \({}^{\circ}\)C, which also confirms a significant reduction in extensional properties around the well bore within a radius of a few inches. As previously mentioned, in the results of SAOS measurements (presented in Figure 11, on the right), solutions did not depict any viscoelastic behavior, but elongational measurements showed otherwise.  
We have observed from Figures 7 and 8 (coupling core flood data and elongational, e-VROC(r)/core-flood) that solutions depicted a viscoelastic response. These observations support the hypothesis that oscillatory measurements fail to predict the solutions' behavior in porous media, which is in agreement with data reported by [PERSON] et al. [84; 85].
# An Application of Instantaneous Spectral Entropy for the Condition Monitoring of Wind Turbines
###### Abstract  
For economic and environmental reasons, the use of renewable energy sources is a key aspect of the ongoing transition to a sustainable industrialised society. Wind energy represents a major player among these natural, carbon-neutral sources. Nevertheless, wind turbines are often subject to mechanical faults, especially due to ageing. To alleviate Operation and Maintenance costs, Vibration-Based Inspection and Condition Monitoring have been proposed in recent times. This research proposes Instantaneous Spectral Entropy and Continuous Wavelet Transform for anomaly detection and fault diagnosis, departing from gearbox vibration time histories. The approach is validated on experimental data recorded from a turbine suffering bearing failure and total gearbox replacement. From a computational point of view, the proposed algorithm was found to be efficient and therefore even potentially applicable for real-time monitoring.