esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of Pressure Effect on Three-Dimensional Flame Surface Density Estimation
## 5 Results and Discussion
### Three-Dimensional Evaluation at Different Pressures  
Figure 6 displays the processed two-dimensional flame surface density (2D FSD) and a comparison with the evaluation of 3D FSD by M1-M4. It is evident that the 2D FSD increases significantly with pressure due to the significant decrease of small flame front structures, as analyzed in the previous section. Moreover, it is clearly seen that the 3D-evaluated flame surface densities from the above models are larger than that of the 2D values, indicating the underestimation by the 2D measurement, which is also verified by Figure 7. This discrepancy was due to the fact that the 2D PSD calculation ignores the corrugation perpendicular to the measurement plane (the third direction). However, since the flames are three-dimensional in nature, the flame corrugation the third direction plays a very important role determining the true flame surface density (3D FSD). Additionally, it is highly dependent on the wrinkled flame front structure, which is greatly affected by pressure. The influence of pressure on the evaluation of 3D FSD is presented in the coming section.
## 2 Review of Related Works
### Resource Allocation in 5G Networks  
The efficient allocation of network resources is critical in mobile communication networks. Since the network receives many requests at a given time, it should be able to handle these requests without compromising the provided QoS. Therefore, significant efforts are being put into developing techniques for optimal resource allocation through NS. The authors in [17] presented various resource allocation methods for different 5G network scenarios. These include optimization, game theory, auction theory, and machine learning methods. The study in [18] suggests that user assignment, network utility, and throughput play an essential role in formulating resource allocation methods. Furthermore, in [19], the authors emphasized optimal dynamic resource management and aggregation as crucial components for effective resource allocation in 5G networks.
# Comparison of L1 and L5 Bands GNSS Signals Acquisition
## 2 GNSS Signals and Acquisition
### Acquisition of GNSS Signals
It is considered that the accumulations are performed after the FFT-based primary code correlation, and that there is no temporary memory since it is useless for coherent integration only and with non-coherent accumulations the structure would become different than the one of the implementations of the L1 C/A signal. To assess the performance of the different implementations, the processing time and the amount of memory will be evaluated and compared. Therefore, in Figure 1, our focus is on the main memory storing the incoming signal, and on the accumulators after the FFT-based primary code correlation. The primary code correlation is considered traditional, because any method to make it faster such as those proposed in Reference [24; 25; 26] could be applied to any of the following implementations, and thus would not affect the comparison. Likewise for the secondary code correlation, where there are known methods to reduce the complexity by a factor two at least [27; 28; 29]. Thus, these methods are not considered in the evaluation of the memory and processing time in Section 3.
## 2 Spectral Analysis of HY-2B Empirical Accelerations  
The process models and strategies used for the determination of the HY-2B orbit are elaborated in this section. The GNSS Research Center of Wuhan University undertakes routine POD for the associated Haiyang-2 satellites, and the preliminary motivation of this work was to improve the accuracy of the HY-2B satellite POD. The improvements were driven primarily by offset calibration of the GPS antenna, implementation of ambiguity resolution, and a refined PEA model.
# Synergistic Effect of Nonthermal Plasma and ZnO Nanoparticles on Organic Dye Degradation
## 1 Introduction
The degradation efficiency of ZnO nanoparticles alone can sometimes be limited, especially for dyes that are resistant to degradation or have complex molecular structures. The combination of plasma and ZnO nanoparticles is a promising approach for the degradation of organic dyes. Plasma generates reactive species that further oxidize and degrade the organic dye molecules [24]. Further, plasma can activate the ZnO nanoparticles, enhancing their photocatalytic activity and accelerating the degradation process. This innovative technology provides an environmentally friendly solution to increasing dye pollution. However, the exact mechanism of how plasma enhances the performance of ZnO nanoparticles in organic dye degradation is not fully understood and can vary depending on the specific experimental conditions and parameters used. In summary, we investigated the synergistic effects of nonthermal plasma and ZnO on the efficiency of MB dye degradation. We synthesized ZnO NPs via the hydrothermal method and then characterized them using SEM, XRD, XPS, Raman spectroscopy, and UV-visible spectroscopy techniques. The reactive plasma species during the treatment processes were determined using optical emission spectroscopy. The dye degradation performance of the plasma alone and the plasma in combination with different doses of ZnO was evaluated.
## 4 Wireless Body Area Networks (WBANs)
### Requirements for Wireless Medical Sensors in WBANs
#### 4.1.4 Interoperability  
Wireless medical sensors should allow users to easily build a robust WWBAN depending on the user's state of health. Standards governing that interaction of wireless medical sensors will help vendor competition and eventually lead to more accessible systems.
## 4 Results and Discussion
### Influence of Shear Key Angle  
In order to study the influence of shear key angle on the shear capacity of S-SWJ, the shear key angle was changed from 40\({}^{\circ}\) to 60\({}^{\circ}\) with an interval of 5\({}^{\circ}\), and the geometric parameters of bridge panels with different shear key angles are illustrated in Figure 13. The other geometric parameters and material properties of S-SWJ in simulations were kept the same as those used in Section 3.  
Figure 14 shows the load-displacement curves of S-SWJ with different shear key angles. It can be seen that the curves show a linear rise, followed by a nonlinear rise, and finally the curves decline after reaching the peak load. The curves exhibit a similar change tendency to those of S-SWJ with different concrete strengths. The ultimate shear capacity of S-SWJ extracted from the curves is summarized in Figure 15. It can be seen from Figure 15 that the ultimate shear capacity first increases quickly and then decreases slowly with the increase of shear key angle. For example, increasing the shear key angle from 40\({}^{\circ}\) to 50\({}^{\circ}\) increases the ultimate shear capacity by 19.6%, while increasing the shear key angle from 50\({}^{\circ}\) to 60\({}^{\circ}\) decreases the ultimate shear capacity only by 4.1%. In the present study, S-SWJ with a shear key angle of 50\({}^{\circ}\) has the highest ultimate shear capacity. The larger the shear key angle, the more difficult to vibrate the concrete in the bottom of shear key. Therefore, to achieve a high ultimate shear capacity for S-SWJ, a shear key angle within the range of 45\({}^{\circ}\) to 55\({}^{\circ}\) is recommended.  
From Figures 12 and 15, it can be seen that the improvement of ultimate shear capacity by changing shear key angle is smaller than that by changing concrete strength. In addition, the displacement corresponding to the ultimate shear capacity for S-SWJ with different shear key angles is also presented in Figure 15. It can be seen that the displacement fluctuates up and down with the increase of shear key angle. It can also be seen that the displacement in Figure 15 is relatively larger than that (Figure 12) of S-SWJ with different concrete strengths.  
Figure 12: Ultimate shear strength and the corresponding displacement of S-SWJ with different concrete strength.  
Figure 13: Bridge panels with different shear key angles (unit: mm).
## 5 Conclusions  
The cyclostationary analysis indeed is an effective tool in the identification and extraction of mechanical faults caused by the modulation of low frequency components based on the cyclostationary level. However, the CMS and FSC based on the STFT have the limitations of low carrier frequency resolution and limited analysis range of cyclic frequency. Therefore, it is difficult to identify the carrier frequency information modulated by fault contents accurately, and it will produce the undesired harmonic components or non-faulty components under low loads effecting the extraction of characteristic frequency for the distinction of mechanical faults. This study proposed an improved CMS analysis method based on CWT which can achieve better demodulation results with higher carrier frequency resolution. Moreover, the proposed CMS-CWT approach can enhance the accuracy of fault identification regardless of whether it is in the cyclic frequency domain or in the carrier frequency domain, due to the fact that the CWT can automatically adjust the window size to achieve a comprehensive analysis of the signal to overcome the shortcomings of STFT. The simulation and experimental analysis results demonstrated the proposed method can achieve better performance than conventional CMS and FSC in reducing the possibility of unexpected downtime of the IMs. Finally, the proposed technique has the potential to be applied to detect other types of faults for rotating machinery fault diagnosis which presents better prospect in the practical applications.  
[PERSON] and [PERSON] investigated and designed the method, [PERSON] and [PERSON] analyzed the data, [PERSON] and [PERSON] were responsible for the subject revision. All the authors have read and approved the final manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 51605133, 51705127 and the Hebei Province Science and Technology Support Program, China, grant number 17394303D.  
The authors would like to thank Centre for Efficiency and Performance Engineering (CEPE) at the University of Huddersfield for providing the materials used for experiments. Meanwhile, the authors would like to appreciate anonymous reviewers and editors for their valuable comments and constructive suggestions.  
The authors declare no conflict of interest.  
Figure 13: Vibration spectrum of the healthy induction motor and damaged motor with one BRB analyzed by CMS-CWT under (**a**) 0% load, (**b**) 20% load, (**c**) 40% load, and (**d**) 80% load.
## 3 Materials and Methods
### Sample Pre-Treatment  
The first step in sample preparation consisted of milling the fish's GITs. Next, the frozen samples (\(-\)20 \({}^{\circ}\)C) were chopped with an agate mortar with liquid nitrogen and finely ground in a cryogenic grinder (6875\({}^{\text{\textregistered}}\) Freezer/Mill) in 3 cycles of 1 min each. Then,a digestion process was used to eliminate the organic matter according to the protocol developed by [PERSON] et al. [PERSON] et al. (2018). Very briefly, the samples were weighted in glass vials, filled with a brine solution and mixed with a vortex. The mixtures were then transferred to a glass beaker with 100 mL of NaCl (120 g/L), homogenized and left to rest for an hour. Then, the supernatant was filtered through 0.45 \(\upmu\)m filters using a vacuum pump and the filters were submerged in 50 mL KOH 10% overnight. After this time, the filters were recovered and used to filter the KOH solution using a vacuum pump. The filters were treated with 40 mL HNO\({}_{3}\) 20% to remove biological material. The final filters were kept in glass Petri dishes and dried in the extraction hood overnight. The dry filters were observed with an optical microscope (\(\times\)100) to identify fibres and bigger particles, and the different shapes and colours were registered. Finally, each filter was weighted and divided into one half and two quarters.  
The first half was used to analyse polymers of MNPLs using APC-(\(\pm\))APPI-HRMS, another quarter was used for the analysis of plastic additives in the MNPLs with LC-(\(\pm\))ESI-HRMS, and the other quarter for the analysis of MPLs bigger than 5 \(\upmu\)m with FTIR.  
_Extraction of polymers for APC-(\(\pm\))APPI-HRMS analysis._ Half of the filter sample was introduced in a glass beaker and extracted with 10 mL toluene for 10 min using USAE. Then, the supernatant was collected and transferred to an amber 50 mL vial. This process was repeated twice, and the final supernatant (30 mL) evaporated to approximately 1 mL under a gentle nitrogen stream and a temperature of 40 \({}^{\circ}\)C. Then, the extract was transferred to an LC vial, evaporated under a nitrogen stream and reconstituted to 0.5 mL with toluene. The final extracts were kept at \(-20\) \({}^{\circ}\)C until analysis.  
_Extraction of plastic additives for LC-(\(\pm\))ESI-HRMS analysis._ A quarter of each filter was extracted using USAE with 10 mL of methanol per 10 min. The same process was repeated twice, and the extracts were combined. Then, the solvent was evaporated to approximately 1 mL under a gentle nitrogen stream and transferred to an LC vial. Finally, the samples were centrifuged (10 min at 4000 rpm, 20 \({}^{\circ}\)C), and the supernatants were transferred into new LC vials, evaporated nearly to dryness, reconstituted in 0.5 mL (methanol: water, 1:9) and kept until analysis at \(-20\) \({}^{\circ}\)C.
## 5 Finite Element Analysis
### Finite Element Analysis and Test Result Comparison
#### 5.2.2 Failure Mode  
Figure 26 shows the general behavior of the specimens corresponding to the typical failure modes, which matched well with the observed failure modes in terms of overall deformation, yielding distribution and failure type. The weakest component deformation corresponding to the typical failure mode is illustrated in Figure 27, which shows that the end-plates, the tube face, the weak beam and the bolts in the FEA models and in the test underwent similar deformation evolutions. The FEA model predicted that the stress concentrations were observed around the bolt holes under high loads, which showed the yield-lines pattern with four-point loads, and they spread towards the corners of the steel tubes. High stresses were noted in the end-plate around the tensile flange of the beam. For the tensile blind bolts, part of the shank between the head and threaded cone had reached the ultimate stress, but most of the embedded shank and the anchor nut in the concrete remained in elasticity. The stresses in the beam compressive and tensile flanges exceeded the yield strength of steel, and the buckling deformation of the beam compressive flange was observed. Whereas the other strong steel beams were basically in elastic stage. Figures 26 and 27 show that a generally good agreement was achieved between the simulated and experimental results.
## 4 Discussion
### Caveats and Limitations  
While this study provides new insights into the physiological and morphological responses of a _Spirulina_ strain under varying salinity levels, several limitations should be noted that may affect the generalizability and interpretation of the results.  
First, the taxonomic status of the _Spirulina_ strain employed in this study was deduced from morphology alone. The genus _Spirulina_ is polyphyletic, and many morphologically similar strains have been reclassified into _Arthrospina_, _Limmospira_, and _Glaucospira_[40], and we did not use molecular identification (e.g., 16S rRNA gene sequencing) to identify the strain to a specific taxon. We recommend that future studies use genotypic validation to make accurate comparisons between studies and to obtain a better ecological understanding of strain-specific responses.  
Second, optical density at 450 nm was used as a surrogate marker for biomass in the experiments. OD is a widely used indicator of growth in microalgal cultures, but may not be as reliable in filamentous cyanobacteria as _Spirulina_ due to differences in filament orientation, coiling, and aggregation. The filaments were mostly linear rather than spiral in this study, which may reduce such variability, but OD may not be a direct measure of biomass in all treatments. Future work should include dry weight measurements, cell volume estimates, or chlorophyll content measurements in addition to OD measurements.  
Third, the current study examined growth dynamics, productivity, and filament size structure but did not include direct assessments of cell viability, especially under the extreme salinity treatments (i.e., 40-45 ppt). The assessment of viability (e.g., via live/deadstaining or metabolic activity assays) is crucial for understanding whether the observed reductions in growth at extreme salinity levels reflect metabolic slowdown or actual cell mortality. Future work should incorporate such assays to more accurately define the physiological limits and stress tolerance thresholds of _Spirulina_ under hypersaline conditions.  
Fourth, important trends in growth, productivity, and filament size under salinity stress were observed, but the molecular mechanisms of adaptation were not investigated. Osmolyte synthesis, changes in protein expression, and metabolic pathway regulation in hypersaline conditions are likely to have a significant effect on the observed physiological and morphological traits. We have acknowledged this limitation and recommend that transcriptomic and proteomic analyses should be integrated into future studies to reveal the regulatory networks of salinity adaptation in _Spirulina_.  
Lastly, salinity tolerance and associated traits may vary significantly among strains. Thus, comparative studies involving multiple, well-characterized strains are required to distinguish generalizable responses from strain-specific adaptations.  
Our study provides valuable empirical evidence on the effects of salinity on key biological properties of _Spirulina_, which can be useful in developing optimized cultivation strategies, especially in salinizing environments.
# Unmanned Aerial Vehicle Depth Inversion to Monitor River-Mouth Bar Dynamics
## 2 Video Acquisition and Rectification  
### Field Site: Suruga Coast (Japan)  
We conducted field tests on Suruga Coast in Shizuoka Prefecture, Japan. The coast is located on the west side of Suruga Bay, which opens to the Pacific Ocean (Figure 1a,b). Swells from the Pacific Ocean propagate over a deep trough running along the bay axis and significantly influence the bay's inner coasts. The Suruga Coast consists of steep beaches made of coarse sand and gravel supplied from the Oi River. A long stretch of the coastline around the river mouth suffers from coastal erosion due to active sand mining and dam construction upstream in the past. Additionally, the coastline has changed significantly due to harbor construction on both sides of the river mouth, which has interfered with the longshore sediment transport. The highly altered coast is one of 12 sites in Japan under direct control of the national government for severe coastal issues.  
Figure 1c shows the coastal area around the mouth of the Oi River. We set three test areas with different bathymetric features, A1, A2 and A3, as indicated by the red rectangles. A1 has nearly uniform bathymetry alongshore, and wave propagation usually occurs in a one-dimensional way. A2 has a series of detached breakwaters for shore protection, and therefore, two-dimensional wave transformation due to dissipation, reflection, and diffraction by the structures creates non-uniform bathymetry. The last site, A3, is the main target of the present study, which covers the area around the mouth of the Oi River. Fluvial sediment usually forms a submarine delta out of the deflected river mouth, and incoming waves refract over the convex depth contours. Figure 2 shows annual changes of the significant wave height (SWH), the significant wave period (SWP), and the tidal water level (TWL) during our test period (from October 2018 to September 2019) at Omaezaki Station located at the Suruga Bay entrance (see Figure 1b). The superposition of swells propagating from the Pacific Ocean and local wind waves from the northeast direction characterise the incident wave conditions on this coast. From late summer to autumn, when typhoons take northward tracks in the north-western Pacific, long-period swells tend to dominate local wind waves. The tides are semidiurnal with mean spring and heap tidal ranges of approximately 0.5 m and 1.5 m. We obtained aerial footage of the sea surface in the three areas under significant swell conditions.
## 3 Results and Discussion
### Microstructural Investigations
#### 3.2.2 EDX Energy-Dispersive X-Ray Analysis  
These investigations provide quantitative data on the atomic and mass concentrations of the elements in the chemical composition. They were carried out in the central area of the surfaces of specimens tested for cavitation erosion for 165 min (Figure 8).  
Figure 7: SEM images of transverse sections through cavitated surfaces at various exposure durations: (**a**) 15 min; (**b**) 60 min; (**c**,**d**) 165 min.  
Figure 9 and Table 4 show, respectively, the dispersion spectrum and the concentrations of the alloying elements detected in the chemical composition of the surface after 165 min of cavitation erosion.  
Figure 8: SEM image of the cavitated surface with the examined area marked.  
Figure 9: Energy-dispersive X-ray (EDX) spectrum.  
Key observations drawn from the analysis of these data are as follows:  
* A decrease in the chromium concentration from approximately 22% to approximately 19%, attributed to the removal of carbide particles containing this alloying element.
* No change in the iron and titanium concentrations, elements that dissolve in the Ni-based solid solution matrix but also form carbides.
* A reduction in the molybdenum concentration from approximately 9% to approximately 7%, with molybdenum being a constituent of MC- and M\({}_{6}\)C-type carbides.