esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Simulation Results and Discussions
### Different Numbers of RIS  
Figure 5 shows the SE for different numbers of RISs for one vehicle pass. It can be seen that the SE increases when the number of RIS \(K\) becomes larger. More precisely, when the moving distance is 500 m, the SE gap between \(K=2\) and \(K=8\) is 0.6 bit/s/Hz. Interestingly, however, the growth rate of SE is slowed when \(K\) is large. In fact, the SE cannot keep increasing even if \(K\) becomes infinity, since transceiver HWI exists. Moreover, the analytical expression of SE in (37) matches well with the simulation results. Thus, the figure validates Proposition 2 and we use (37) to generate the rest of figures. It is  
Figure 4: Delay spread for the system using different phase shift sets. \(K=8\).  
Figure 3: Spectral efficiency for the system using different phase shift sets. \(K=8\).  
noteworthy that Figure 5 also reveals that the multi-RIS scheme proposed in this paper is more promising than the single RIS system in [14], i.e., the \(K=1\) case in the figure.  
Figure 6 shows the delay spread for different numbers of RISs for one vehicle pass. In particular, more RISs cause a larger delay spread, although the SE can be improved when \(K\) is not very large. For example, when the moving distance is 500 m, the \(K=2\) case has \(0.7\times 10^{-6}\) s delay spread, but it increases to \(1.2\times 10^{-6}\) s when \(K=8\). It is noteworthy that the maximum cyclic prefix of 5G is \(4.7\times 10^{-6}\) s [43]. Therefore, if \(K\) is large enough, it is possible that the delay spread exceeds the maximum cyclic prefix.  
Figure 5: Spectral efficiency for different numbers of RIS. \(K=1,2,4,6,8\). Note that the analytical results are based on (37).  
Figure 6: Delay spread for different numbers of RIS. \(K=2,4,6,8\).  
Figure 7 validates Proposition 1. In particular, considering the moving distance equal to 500 m, the delay spread for the case when \(K=4\) and \(sp=50\) m is the same (i.e., \(0.8\times 10^{-6}\) s) as in the case when \(K=8\) and \(sp=150/7\) m. In other words, the delay spread depends on the RIS that is the furthest from the BS. Similarly, the case of \(sp=100\) m has the same delay spread (i.e., \(1.1\times 10^{-6}\) s) as that of \(sp=300/7\) m if they have 4 and 8 RISs, respectively. Therefore, the delay spread is not be increased if the \(K\) RIS are deployed properly. Besides, when the BS and the RIS(s) are all far from the vehicle, the delay spread will be static. This can be observed when the moving distance is 0 m to 100 m and from 900 m to 1000 m. Therefore, with regard to mitigating delay spread, the single RIS next to the BS is the better deployment strategy, although the SE cannot be further improved.
## 3 Transient Oscillation Mechanism Analysis of the TVSG
### VSG Dynamic Power Characteristics and Existing Problems  
Based on Equation (7), the APL closed-loop transfer function \(G_{\text{PP}}(s)\) of the APL system in the TVSG is derived as follows [29]:  
\[G_{\text{PP}}(s)=\frac{\Delta P_{\text{out}}}{\Delta P_{\text{ref}}}=\frac{K_{ \text{P}}}{I_{\omega}\omega_{0}s^{2}+(K_{\omega}\omega_{0}+D_{\omega}\omega_{ 0})s+K_{\text{P}}} \tag{9}\]  
As illustrated in Equation (9), \(G_{\text{PP}}(s)\) is a second-order oscillation system, and the dynamic response performance of \(G_{\text{PP}}(s)\) is influenced by the two conjugate poles \(s_{1}\) and \(s_{2}\) in Equation (9). The closed-loop pole positions of \(G_{\text{PP}}(s)\) can be modified by adjusting \(J_{\omega}\) and \(D_{\omega}\) to optimize the dynamic response characteristics of the TVSG. To further elaborate on the influence of \(J_{\omega}\) and \(D_{\omega}\) on the TVSG's dynamic response characteristics, the closed-loop pole distribution trend of \(G_{\text{PP}}(s)\) gradually increasing from 0 to 100 under different J values is shown in Figure 4 and the main circuit parameters of the TVSG are shown in Table 1.  
As shown in Figure 4, \(G_{\text{PP}}(s)\) exhibits a pair of conjugate poles, \(s_{1}\) and \(s_{2}\). When \(D_{\omega}\) follows the same trend, a larger value of \(J_{\omega}\) results in the starting positions of \(s_{1}\) and \(s_{2}\) being closer to the imaginary axis. This phenomenon reduces the damping ratio of the system, thereby weakening the stability and adjustment rate of the TVSG. When the virtual inertia \(J_{\omega}\) is constant, as \(D_{\omega}\) increases, \(s_{1}\) and \(s_{2}\) progressively transition into two real poles, following the trajectory indicated by the arrow. This causes the system characteristics of \(G_{\text{PP}}(s)\) to shift from underdamped to overdamped, consequently increasing the adjustment time of the APL system in the TVSG.  
Figure 4: The trend of pole distribution of the APL system in the TVSG.  
According to the above analysis, when the TVSG is in grid-connected operation, the steady-state characteristic is determined by \(D_{\omega}\) and is independent of \(J_{\omega}\), while the dynamic characteristics are related to the values of \(D_{\omega}\) and \(J_{\omega}\). To satisfy the requirements for high damping characteristics and low power overshoot, selecting a large \(D_{\omega}\) may lead to significant steady-state deviation and a reduced system response rate. Conversely, to enhance the adjustment rate of the system, opting for a large \(J_{\omega}\) can aggravate the power overshoot issue and reduce system stability. Therefore, a contradiction arises between the dynamics and the steady-state performance in selecting \(D_{\omega}\) and \(J_{\omega}\) parameters.
# Biogenic Nanoparticles: Synthesis, Characterisation and Applications
## 2 Biogenic Synthesis of Nanoparticles
### Algae  
Algae are part of the Protista kingdom and include autotrophic and aquatic photosynthetic organisms, being single or multicellular. They are eukaryotic organisms without the multicellular structures of the plant. Algae can vary greatly in size, from microscopic microalgae to giant macroalgae. They are known for producing oxygen and form the food source for most aquatic forms of life. seaweed is a typical example of macroalgae, which has been the source of food containing vitamins, minerals and protein. Microalgae have been known to transform metals ions into malleable forms, and are considered green gateways to synthesise nanoparticles [75]. The ability of algae to synthesise nanoparticles is attributed to the inclusion of active compounds in cell walls. Examples of such compounds include alginate and laminarin, which contain reactive groups [76]. Stabilizationand capping of nanoparticles are accomplished by polypeptides by their amino acids or cysteine amides.  
A typical process of algae-assisted nanoparticle synthesis involves (1) preparation of algae extracts in solution at elevated temperature, (2) preparation of reagents and (3) gestation of algae and reagent solutions and then stirring for a specified period of time ([PERSON] et al., 2016). To initiate the reaction, the algal extract is mixed with the metal precursor. A change of colour shows the beginning of a reaction which illustrates nucleations, followed by the growth of NPs where the adjacent nucleonic particles join and form thermodynamically stable NPs of various geometries [77]. The extract biocompounds facilitate the synthesis of NPs, and pH, temperature, concentration and time are the driving factors involved.  
Seaweeds such as _Sargassum wigbitti_ and _Fucus vesicolusus_ have been utilised to synthesise nanoparticles [78; 79]. Numerous studies involving the synthesis of gold and silver using algae have been reported (see Table 3 for examples). _Tetraselmis kochinensis_ has been shown to produce intracellular gold nanoparticles [79]. Besides seaweed, AuNPs and Au-silica bionanocomposites can be synthesised in microalgae like diatoms (_N. atomus_ and _D. gallica_) [80]. Algae is just as essential in nanoparticle synthesis in comparison to other micro-organisms, such as bacteria and the fungus, and thus an ecofriendly approach has recently been studied for nanoparticles' algae-mediated biosynthesis. Although algae offer green synthesis of nanoparticles, they still have limitations, which must be overcome before this method can be considered sustainable in the long run. The process of extract preparation is time-consuming and nanoparticles yield is less compared to physical and chemical methods [81]. Various control factors involved in the synthesis require detailed examination to improve future prospect of algal synthesis.
# Evaluation of CYGNSS Observations for Snow Properties, a Case Study in Tibetan Plateau, China
## 2 Method
### Data Description
#### 2.1.2 IGBP Land Cover Classification  
The land surface scattering process is complex. In addition to the influence of surface roughness, the surface scattering signals received directly by GNSS-R receivers are also impacted by other factors such as vegetation layer and topography. There are various types of land cover on the Tibetan plateau, and different vegetation covers have different effects on the received signals. In order to reduce their influence on the received signal power, the consequent analysis is performed in relatively uniform land cover categories. In this way, we can ensure that the features in each zone are comparatively unified, so that the differences in the CYGNSS observations caused by various types of surface coverage can be considered.  
The MODIS Land Cover Climate Modeling Grid Product (MCD12C1) provided are the sub-pixel proportions of each land cover class in each 0.05\({}^{\circ}\) pixel and the aggregated quality assessment information for the IGBP scheme [25]. Here, we have employed the IGBP of 2020 to obtain the land cover information of the Tibetan Plateau region (Figure 2a). The Tibetan Plateau region is dominated by four categories: high-vegetation-covered area; moderate-vegetation-covered area, low-vegetation-covered area, and barren or desert area  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Comment** \\ \hline ddm\_timestamp\_utc & DDM sample time \\ sp\_lat & Specular point latitude, in degrees north \\ sp\_lon & Specular point longitude, in degrees east \\ sp\_inc\_angle & The specular point incidence angle, in degrees \\ sp\_rx\_gain & The receive antenna gain in the direction of the specular point, in dBi \\  & The effective isotropic radiated power (EIRP) of the L1 C/A code \\ gps\_eirp & signal within \(\pm\) 1 MHz of the L1 carrier radiated by space vehicle, \\  & sv\_num, in the direction of the specular point, in Watts \\ rx\_to\_sp\_range & The distance between the CYGNSS spacecraft and the specular point, \\  & in meters \\ tx\_to\_sp\_range & The distance between the GPS spacecraft and the specular point, \\  & in meters \\ power\_analog & 17 \(\times\) 11 array of DDM bin analog power, Watts \\ \hline \hline \end{tabular}
\end{table}
Table 1: The Cyclone Global Navigation Satellite System (CYGNSS) level 1 data variables.  
(Figure 2b). In this paper, we conducted the analysis of snowpack characteristics within the same land cover type, as far as was possible.
# The XTRA Option at the NEUTRA Facility--More Than
## 1 Introduction-Method and Instrumentation
### Properties of the X-ray Setup XTRA  
An X-ray tube, GE ISOVOLT-Titan 320, can be positioned in the beam path of NEUTRA at position 1 close to the SINQ target block wall, thereby providing an almost identical imaging geometry for neutrons and X-rays. The tube features a tungsten target with two focus spot sizes of either 3 mm or 5.5 mm diameter.  
The X-ray tube is mounted on a linear stage, which allows remote controlled positioning of the X-ray source spot in the center of the beam path. For this purpose, one has to remove the first section of the evacuated collimator pipe. A special scintillator-changing box that allows switching between a neutron and X-ray scintillator screen has been built for the detector at position 2 (Figure 3). The corresponding beam collimations for neutrons and X-rays are provided in Table 2 alongside the corresponding geometrical parameters. The initial beam aperture for the neutrons D is about a factor 4 to 7 times larger than the X-ray source spot size P. The collimation length L differs in favor of the neutrons because the neutron aperture is inside the target block close to the moderator tank while the X-ray tube has to be positioned in the flight path downstream of the source shielding (see Figure 2b). Thus, the maximum discrepancy in collimation is about 4, the minimum <2, in all cases in favor of the X-ray collimation due to the small spot size as compared to D. However, in general, the resolution is not limited by the beam divergence but only by the inherent properties of the detector systems used. Therefore, the resulting spatial resolutions of the neutron and X-ray imaging systems in NEUTRA are on par with each other.  
Figure 2: (**a**) Original standard configuration of NEUTRA using the thermal beam from the SINQ D\({}_{2}\)O moderator; (**b**) NEUTRA layout with X-ray tube at Position 1 in operating position; (shielding removed in images to enable visibility); the beams come from the right-hand side in this drawing. The length of the shown outer collimator is on the order of 10 m.
# Elemental Abundances of Moon Samples Based on Statistical Distributions of Analytical Data
## 4 Discussion
### Geochemical Signatures of Elemental Abundances
#### 4.1.4 Other Trace Elements  
Except for the aforementioned major oxides, REEs, and trace elements, there are only eight remaining elements of Sc, V, Co, Ni, Cu, Zn, Be, and B, which are reported abundances both of moon samples and the MORB. Here we supplement Ti, Cr, Mn, and Fe to the eight elements to form a series of the first transition elements plus Be and B. Therefore, 12 elements were used to form the spider diagrams of the moon samples which are illustrated in Figure 6.  
Figure 5: Spider diagrams of moon samples normalized to the MORB. Abundances of the MORB are from [PERSON] and Klein [14].  
Figure 6: Spider diagrams of moon samples on first transition elements plus Be and B normalized to the MORB. Abundances of the MORB are from [PERSON] and Klein [14].  
With respect to the MORB in terms of their concentrations, the moon samples (including the Moon, low-Ca, and high-Ca) are all clearly rich in Cr, Ni, and Be and poor in V, Cu, and Zn (Figure 6).  
With respect to the MORB, the spider diagrams of moon samples (including the Moon, low-Ca, and high-Ca) are nearly flat (variations are limited to one order of magnitude), except for the clear enrichment in Cr and depletion in V, Cu, and Zn (Figure 6). In addition, the diagram for the high-Ca moon sample is also clearly enriched in Ni and B (Figure 6).  
In summary, the geochemical signatures of major oxides, REEs, and trace elements in moon samples (including the Moon, low-Ca, and high-Ca) are rich in Cr, REEs, Th, U, Pb, Zr, Hf, Cs, Ba, W, and Be and poor in Na, V, Cu, and Zn in terms of their concentrations, and are enriched in Cr and depleted in Na, K, Rb, P, V, Cu, and Zn in the spider diagrams, relative to the MORB. Among the total moon samples, low-Ca samples, and high-Ca samples, the low-Ca samples are closer to the total moon samples in concentration or patterns (or diagrams), which is not only illustrated in Figure 3 to Figure 6 but also consistent with the counts of analytical data with equal weighting used in this study. Except the 46 elements discussed in this paper, the other 26 elemental abundances in moon samples are not discussed here because of the data lack on the MORB.
## 6 Open Issues and Future Research Directions
### Simulation Tools  
Different network simulation tools and mobility models are utilized to assess the existing and proposed routing protocols for FANETs. Partially, OMNET++, OPNET, MATLAB, NS-2, and NS-3 are used to evaluate the performance of the majority of current routing systems. Nevertheless, most simulators, including NS-3, do not enable 3D mobility models or mimic any specific channels for UAV communication. Only 2D random mobility models are supported throughout most simulators, not preset control-based mobility. Consequently, the vast majority of them fail to provide realistic or reasonable outcomes. To develop topology-based routing protocols in FANETs, significant improvements for a new simulator that supports more realistic mobility models and satisfies multi-UAV requirements are crucial to gain realistic and reasonable output results. Recently, several researchers developed a new FANETs simulator AVENS, a novel FANETs simulator with code generation for UAVs [115]. The Opportunistic Network Environment (ONE) Simulator enables real-time simulation and emulation. Further, it can be used to assess the performance of FANETwith DTN while accounting for a range of characteristics such as buffer storage, mobility patterns, and routing algorithms [116].
# Morphometric Analysis and Three-Dimensional Computed Tomography Reconstruction of Thai Distal Femur
## 2 Materials and Methods
### CT Scans and 3D Reconstruction  
All participants were set up in the supine position and the foot was in a neutral position with the use of a 64-slice spiral computed tomography (CT) scanner (Optima CT660, GE Healthcare, Chicago, IL, USA). The CT protocol was defined with the following parameters: 120 kVp with automatic value in the range of 50 to 320 mA, the dose reduction of 20%, and the slice thickness of 2.5 mm with the reconstruction of 0.625 mm in the scan length of 90 to 30 cm. Next, the CT images were imported into the medical imaging processing software (MIMICS, Materialise N.V., Leuven, Belgium) for the reconstruction of 3D surface models. CT image data were segmented for selecting the required tissue type by MIMICS software as shown in Figure 1. The Hounsfield scale threshold was used to control and correct the segmentation of the CT image; the Hounsfield value depended on density. The higher Hounsfield value indicates high-density objects such as cortical bone, while the lower Hounsfield value represents low-density objects such as muscles and soft tissue. This study selected a lower limit of 226 Hounsfield units (HU) and an upper limit of 3071 HU to separate bone from soft tissue. When the segmentation had finished, there is an integrated function to calculate the 3D model from these CT images that were exported in stereolithography (STL) format. The surface of the reconstructed 3D model was meshed by a triangle. For measurement based on 3D models, the distance was measured between the triangular points. The adaptive remeshing function in Materialise 3-Matic (Materialise N.V., Belgium) was used to improve the quality of the triangular element. Since the triangle size influenced the accuracy of measurement on the 3D models, the maximum edge length of 0.2 mm (the edge length in the software ranges from 0.10 to 3.00 mm) was used in this study.  
The femoral 3D models as shown in Figure 2 were adjusted for alignment before measurement by Materialise 3-Matic [1; 20; 30]. Firstly, the mechanical axis, a line from the center of the femoral head to the apex point of the intercondylar notch, was aligned in a sagittal plane. Secondly, the line between the most medial posterior points and the most lateral posterior points of femoral condyles were aligned in a coronal plane. Finally,the transverse plane was defined as the line connecting the most inferior points of both femoral condyles.
## 2 Materials and Methods
### Data Correction and Calibration  
To accurately obtain particulate VSF and evaluate the performance of the VSFlab, a series of work including correction and calibration were carried out. These processes were implemented in the laboratory based on samples of nearly monodisperse standard polystyrene spherical beads (Duke STANDARDSTM) from Thermo Fisher Scientific Inc.,  
Figure 2: Schematic of different prisms and angle design of new prism. (**a**) P2. An air gap, as shown in the red box, is created inside the prism to prevent the light reflected onto the top surface of the prism from going back to the basin. However, diffused light, represented by the short green arrows, easily occurs in practice at the surface of the air gap when the surface is not smooth. (**b**) Designed prism structure. The solid green line and dashed green line represent the transmitted light and reflected light, respectively. (**c**) Angle design of new prism. These angles (\(\theta_{1},\theta_{2}\), and \(\theta_{3}\)) make the transmitted light vertical to the surface of the prism. (**d**) The manufactured new prism.  
Waltham, MA, USA. As the standard particles are nearly perfectly spherical and their size distribution and refractive index have been correctly characterized by the manufacturers, their VSFs can be predicted with high accuracy by using Mie theory and thus provide a means to calibrate the functioning of the instrument. Mie theory considers: (1) wavelength of light; (2) particle size (mean size and size distribution); (3) size parameter, defined as \(x=2\pi\tau/\lambda\), where \(r\) and \(\lambda\) are the sphere radius and wavelength of light (in the medium surrounding the particle); (4) the complex index of refraction of the spheres (\(n_{p}=n_{r}+n_{i}\)) relative to the surrounding medium (\(n_{w}\)), \(n=n_{p}/n_{w}\). The polystyrene beads used in this study are listed in Table 1. The real index of refraction for polystyrene was calculated based on the results of [PERSON] et al. and we used a value of 0.00035 \(\pm\) 0.00015 for the imaginary part of the refractive index [28]. The index of refraction of pure water at room temperature is 1.3368 at 520 nm and 1.3363 at 532 nm.