esa-sceva/satcom-chunk-collection huggingface dataset:
# Fast Screening Algorithm for Satellites Based on Multi-Constellation System
## 4 Experimental Validation and Result Analysis
### Positioning Accuracy Analysis  
A 24 h static experiment was conducted. The localization results of the traditional GDOP selection algorithm and the optimised GDOP value selection with multi-threshold filtering were compared with the localized true values, setting the selection percentage of the selection algorithm to 70%. The observation frequency was set to 0.2 HZ. The results of the positioning error are shown in Figure 10 and Table 4 (The results are not smoothed in order to verify the validity of the algorithm).  
From Figure 10, it can be seen that the overall positioning accuracy of the proposed star selection algorithm in this paper is maintained better relative to the best GDOP value  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Positioning Error (m)** & **Traditional GDOP** & **Algorithms in This Paper** \\ \hline \(X\)-axis & 0.8315 & 1.1539 \\ \hline \(Y\)-axis & 1.5474 & 1.7790 \\ \hline \(Z\)-axis & 2.2777 & 2.5045 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Positioning error between traditional GDOP and the algorithms in this paper.  
Figure 10: Position errors before and after the satellite screening algorithm for the three-constellation combination system.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Satellite Selection Algorithm**} & \multicolumn{5}{c}{**GDOP Value**} \\ \cline{2-5}  & **Minimum** & **Maximum** & **Average** & **Incremental** & **Standard Deviation** \\ \hline Traditional GDOP & 1.2800 & 1.9985 & 1.5939 & 0 & 0.1586 \\ \hline Algorithms in this paper & 1.3364 & 2.4147 & 1.7303 & 8.56\% & 0.2390 \\ \hline Position error-aided GDOP & 1.2111 & 2.5693 & 1.6847 & 5.70\% & 0.3076 \\ \hline Fast genetic GDOP & 1.6401 & 2.0269 & 1.6401 & 2.89\% & 0.1355 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Statistical analysis of GDOP values under different satellite selection algorithms.  
selection method. In the \(X\)-axis component, the overall error is maintained smoothly, and in the \(Y\)- and \(Z\)-axis components, the number of fluctuations in the positioning error gradually increases after 18 h, but remains within the acceptable range. At the same time, when the satellite configuration changes, the positioning accuracy of the algorithm proposed in this paper is prone to large fluctuations, which is manifested by large jump points. From Table 4, it can be seen that compared with the traditional satellite selection method, the error of the fast satellite selection algorithm proposed in this paper is maintained better, with an increase of 38.77% in the \(X\)-axis direction, 14.97% in the \(Y\)-axis direction, and 9.96% in the \(Z\)-axis direction.
## 3 Results and Discussion
### Metallic Nanoparticle Synthesis by M. magnetotacticum Using Either Fe, Ag, Au or Zn
#### 3.2.1 TEM and EDX Analysis
EDX confirmed the presence of silver with a high signal peak being obtained. Figures S13-S15 in the Supplementary Material show the presence of gold-containing nanoparticles when using MSGM original, MSGM 6 (sodium nitrate) and MSGM 9 (sodium nitrate and Isogro(tm)), respectively. It could however be noted that most of the particles analysed were highly aggregated and undefined thus sizing was not possible. The high degree of agglomeration could be attributed to the magnetic potential of the nanoparticles synthesized and thus the force of attraction to each other. Figure 4: (**A**,**B**) TEM micrographs showing iron-containing magnetosomes formed by _M. magnetotacticum_ after incubation at 30\({}^{\circ}\) for 48 h in MSGM 9 (sodium nitrate and Isogro(tm)) containing iron (10 mM). Size range of nanoparticles is 4–6 nm. (**C**) EDX profile of the observed nanoparticles presented in (**A**). Distinct iron peaks are present confirming presence of iron-containing nanoparticles. Samples were analysed using carbon-coated copper grids and therefore high peaks denoting carbon and copper are also present. The other peaks present are due to medium components and elements present as part of normal bacterial composition. According to [PERSON] et al. (2017), growth conditions greatly influence the final properties of the biosynthesized nanoparticles which is in agreement with the present study due to the addition of different medium components [34]. The TEM and EDX results obtained indicated the presence of iron-containing nanoparticles in MSGM original and MSGM modifications 1-12, the presence of silver in MSGM 9, and the presence of gold in MSGM original, MSGM 6 and MSGM 9, respectively.
## 4 Omics of _X. dendrorho_: Genomics, Transcriptomics, Proteomics, and Metabolomics
These authors identified two groups of up-regulated proteins: (i) carbohydrate and lipid metabolism proteins, which guarantee acetyl-CoA availability; and (ii) redox-specific proteins (e.g., monooxygenase or cytochrome P450). Both groups presented a scenario that supply the necessary redox potential for the late reactions of the astaxanthin synthesis. These results strongly suggested that astaxanthin production under aerobic conditions is a metabolic tool to scavenge ROS (reactive oxygen species) elements generated as metabolic byproducts in _X. dendrorhous_[93]. The same authors also studied by proteomics and metabolomics the influence of two different carbon sources (glucose (fermentable) or succinate (non-fermentable)) on the metabolism. Lipid and carbohydrate metabolism, carotenogenesis, as well as redox and stress responses proteins, were identified. These data confirmed the connection between the astaxanthin accumulation and oxidative stress in this yeast. Besides, the increase in acetyl-CoA availability when succinate is used as a carbon source was described, which could enhance the cellular respiration rate resulting in ROS elements that induces carotenogenesis [92]. In addition to the carbon source, the carbon to nitrogen ratio is crucial for microbial carotenoids production. This relation was analysed at fixed nitrogen concentrations by [PERSON] and co-workers [94] in _X. dendrorhous_. Intriguingly, cell growth and astaxanthin accumulation were connected to the increasing of the carbon to nitrogen ratio, whereas the astaxanthin amount per cell was inverse. These metabolic adaptations were studied by two-dimensional electrophoresis analysis, where the up-regulation of redox- and stress-associated proteins, as well as carotenogenesis proteins were observed. In contrast, nine proteins involved in the astaxanthin synthesis were down-regulated and the de novo pigments synthesis was inhibited, which justify this peculiar unbalance between growth and cellular pigment accumulation [94]. As a result of nitrosoguanidine treatments, different coloured mutants (red, orange, pink, yellow, and white), which were analysed under the proteomics methodologies, were obtained by [PERSON] and co-workers [91]. The red mutants were total carotenoids (mainly astaxanthin) overproducers, whereas orange and white ones accumulated phytoene as a result of phytoene dehydrogenase mutation. This analysis also demonstrated the close relation among the tricarboxylic acid cycle, stress response, and the carotenogenic process.
# Developing a BIM-Based MUVR Treadmill System for Architectural Design Review and Collaboration
## 4 System Development
### Hardware Requirements  
In this study, two essential VR devices were integrated into the BIM-based MUVR treadmill system: VR headsets and omnidirectional treadmills. A MUVR server was also set up to ensure smooth VR interactions, good visual quality, and stable real-time synchronizations between multiple users, such as resolutions, frame rates, tick rates, and buffering time. Furthermore, two sets of Virtuix Omni VR omnidirectional treadmills were installed, as they can achieve an outstanding balance between speed, comfort level, space demanding, and preciseness [13; 31]. Each treadmill set includes four sub-devices: the low-friction motion platform, harness, overshoes, and tracking pods. The concave motion platform with low-friction features plays a prominent role in enabling smooth and immersive walking motions in the virtual environment. The supportive ring and harness provide maximum safety and versatility during rapid and unconstrained movements, such as walking, running, and turning swiftly.  
After comparing with other popular VR headsets in the market, two sets of HTC VIVE Pro were selected for the system, due to the advantages of precise room-scale tracking, vibrant screen colors with immensely high contrast, and realistic immersion [41]. The high-resolution active-matrix organic light-emitting diode (AMOLED) screens with the 90 Hz refresh rate and 110\({}^{\circ}\) field of view are the primary benefits of offering rich colors, contrasts, and comprehensive views in the VR world. Furthermore, four SteamVR base stations support up to 10 \(\times\) 10 m tracking areas in the physical space. The base station performs its function by flooding the room uniformly with a non-visible light (i.e., infrared pulse) so that the receptors on tracked devices can intercept the light and interpret the user location to the base stations. This feature allows sub-millimeter precise tracking, which is ideal for MUVR interactions in this study.  
In addition, the MUVR server takes up the role of the authoritative source of events in the multiplayer virtual environment. It is responsible for receiving and processing the clients' input (e.g., spawning objects or lines) and then transmitting and synchronizing accurate data to the connected clients so that the display is stable and reliable [27; 28]. Installing the MUVR server reduces the original workstation's communication workloads and processing burdens if it serves both client and server roles [42]. Furthermore, by entering the correct internet protocol (IP) address under the local area network, the communication between clients can be accomplished without direct access to the internet. As a result, the local server can perform its role smoothly with stable and reliable performance [28; 42; 43]. In other words, the VR software can be operated more steadily with the VR hardware with minimum forced termination, freezing, crashing, and time delay during data synchronizations between multiusers.
# Hyperbolic Asynchronous Method of a Radio Navigation Technique
## 1 Introduction
Next, selected algorithms for determining the position of an object in such a system are characterized in terms of their utility. In turn, the concept of asynchronous hyperbolic system operation and the algorithm of object positioning using such a system are presented in Section 3. Its protocol-system layer is also described. On this basis, a simulator for theoretical research was developed. Its assumptions are presented in Section 4, together with the results and conclusions from this research. On the basis of these conclusions, a demonstrator of asynchronous localization technology was designed and built for measurement tests, whose hardware and software layers are presented in Section 5, while Section 6 describes the results of tests of this demonstrator's operation in real-life conditions at sea. Finally, practical usefulness of the hyperbolic asynchronous system as an alternative navigation solution to GPS service has been summarized.
## 5 Conclusions  
After a complete review of the application of nanofluid as working fluid in PTC, the following advantages and disadvantages can be listed:  
* Among all studied nanoparticles, application of copper nanoparticles contributed to better thermal efficiency enhancement in metallic nanofluids, followed by CuO, TiO\({}_{2}\), and Al\({}_{2}\)O\({}_{3}\).
* For non-metallic nanofluids, MWCNT nanoparticle can lead to the highest thermal efficiency enhancement [40].
* Although, there are some studies [42; 65; 66; 81] reporting no significant enhancement on the thermal efficiency of PTC thermal efficiency even using nanofluids, generally it can be concluded that application of nanofluid can enhance the thermal efficiency of PTC. The maximum reported enhancement was approximately 44% for CuO/water nanofluid by [PERSON] et al. [41].
* Convection heat transfer, also, is enhanced by the nanofluid application in PTC. This increase can be shown in both convection heat transfer coefficient and Nusselt number. The maximum reported convection heat transfer coefficient enhancement was 234% in [PERSON] et al. [58] when using MWCNT/Therminol VP-1 nanofluid as working fluid.
* Exergy efficiency also enhances with the application of nanofluids.
* Based on studies which have done by [PERSON] et al. [58; 59; 60; 61; 66] the entropy generation rate can be reduced by application of nanofluid.  
Disadvantages
* Increasing in pressure drop through PTC receiver tube, due to the presence of nanoparticles is the main design problem in PTC. This effect is mainly discussed by [PERSON] et al. [58; 59; 60; 61] and [PERSON] et al. [37; 80]. Although, it was reported that pressure drop increases by boosting the volumetric concentration, but in the studies conducted by [PERSON] et al. [37; 80] this increment was in the level that did not have too much demand of pumping work raise.
* Capital cost is another major drawback in the application of nanofluid in PTC, since the cost for nanoparticle is much higher than conventional fluids such as water and different oils.  
Challenges  
All in all, it is evident that increasing the number of nanoparticles in nanofluids can lead to higher thermal and exergy efficiency. However, that can lead to higher pressure drop which consequently may contribute to higher pumping power of the system. Considering those two factors emphasizes on finding the optimum concentration value of the nanoparticle for each specific nanofluid used in a PTC by which the acceptable thermal efficiency is achieved as well as avoiding the excessive pressure drop increase in the system future works  
Finally, it is worth noting there were some innovative investigations that could change the use of nanofluid in future a lot. As an example, the application of ferrofluids, a type of nanofluids, which was done by [PERSON] et al. [55] at 0.05 vol. % which is applied in the presence of magnetic brought a significant enhancement in thermal efficiency of 25%.  
All authors contributed equally.  
This research received no external funding.  
The authors declare no conflict of interest.
## 3 Energy-Efficient aDaptive hiEarchical and RobusT Architecture
### EDETA-Enhanced  
EDETA-e is a version of EDETA protocol that enables control over the network formation and over the delays. With EDETA-e, the engineer and not the protocol decides which node becomes a CH. This way, the engineer chooses where to place CH nodes and which type of power supply they will have.  
Since CHs are fixed at design time, EDETA-e only considers one initialization phase. After that, all nodes will always remain in the normal operation phase.  
In underwater acoustic networks, where current state-of-the-art transceivers have more power consumption compared with their radio-frequency counterpart and the propagation speed might vary over time, having a planed and fixed infrastructure can help the deployment of underwater networks. Moreover, since GPS signals are heavily attenuated underwater, the localization of sensor nodes and AUVs (Autonomous Underwater Vehicles) is not trivial and the fixed nodes can be used for localization proposes.
## 3 Results
### Evaluation of Annual MODIS-Derived WUE  
Annual WUE derived from eddy flux data varied with ecosystems, which ranged from \(0.37\pm 0.08\)-\(4.32\pm 0.63\) g\(\cdot\)C\(\cdot\)kg\({}^{-1}\)\(\mathrm{H}\)O across the eight flux sites (Figure 5c). Generally, forest ecosystems had higher annual WUE than grassland ecosystems. Compared to the observed WUE, MODIS WUE had a smaller variation across the eight ecosystems, with a range of \(0.34\pm 0.05\)-\(1.98\pm 0.07\) g\(\cdot\)C\(\cdot\)kg\({}^{-1}\)\(\mathrm{H}\)O. Mean annual WUE was underestimated by 14%-54% at four forest sites, 45% at one cropland site and 7% at DX alpine grassland site, but overestimated by 66% and 9% at the NM temperate grassland site and the HBGC alpine grassland site, respectively. The underestimation of annual WUE in CBS, QYZ and XSBN forests was caused by low annual GPP and high annual ET (Figure 5a,b). In the DHS subtropical forest, annual WUE was undervalued, because there was a larger overestimation in annual ET (72%) than annual GPP (48%). In contrast, the low annual WUE at the YC cropland site mainly resulted from the underestimation of annual GPP (54%).  
Figure 4: Comparison of MODIS and observed leaf area index (LAI) at the Yucheng (YC) cropland site.  
Figure 5: Comparison of MODIS- and eddy covariance-derived annual (**a**) Gross Primary Production (GPP), (**b**) Evapotranspiration (ET) and (**c**) Water Use Efficiency (WUE) at eight ChinaFLUX sites.  
MODIS WUE also had a smaller interannual variation during 2003-2008 across the eight ecosystems, in comparison to the observed WUE. WUE derived from eddy flux data had a small interannual variability at the seven ecosystems with coefficients of variation (CVs) of 6%-22%, except for the NM temperate grassland ecosystem with a CV of 46%. In contrast, the CVs of annual WUE from MODIS data at the eight ecosystems varied from 3%-14%.
## 2 Materials and Methods
### Research Methods
#### 2.3.3 Estimation of Glacier Mass Balance  
There are several steps in the glacier mass balance estimation process: (1) First, the glacier elevation change data were post-processed. Off-glacier areas were masked using a glacier boundary dataset (Second Glacier Inventory Dataset of China). We also removed SRTM DEM voids, singular values of elevation change, low coherence area, and large slopein the glacier area, to obtain reliable results of glacier surface elevation change. (2) Based on the maximum and minimum values of the SRTM-C DEM, the glacier surface elevation changes were segmented at 100 m elevation intervals, and the number of segments \(Num\) was calculated. The number of pixels in each elevation segment was counted as \(M_{i}\), as well as the mean value of elevation change dh_single(i) and the standard deviation STDEV(i) [5]; pixels with greater than 3 STDEV(i) distance from the mean elevation change dh_single(i) in each of the elevation segments were eliminated. Statistics were then re-calculated for each of the segments. Finally, the overall elevation change of the entire image pair was calculated using an area-weighted method (Equation (3)), i.e., the weight \(P(i)\) was determined according to the area or the number of pixels in each segment (Equation (4)).  
\[dh=\sum\limits_{i=1}^{Num}dh_{i}\cdot P_{i} \tag{3}\]  
\[P_{i}=\frac{M_{i}}{\sum\limits_{i=1}^{Num}M_{i}} \tag{4}\]  
(3) We combined the glacier surface elevation change (\(dh\)) with ice density (\(\rho\) = 900 \(\pm\) 17 kg m\({}^{-3}\)) to estimate the glacier mass balance (\(MB\)) (Equation (5)) for single pair of bi-static InSAR images or the entire SETP region [29].  
\[MB=\rho\cdot dh \tag{5}\]
## 4 Parameter-Identification-Based Configuration Design
### Effectiveness in Algorithm
In Figure 9b, it can be seen  
\begin{table}
\begin{tabular}{l c l c c} \hline \hline \multicolumn{2}{c}{**Structure**} & \multicolumn{3}{c}{**Solution**} \\ \hline
**Parameter** & **Value** & **Parameter** & \multicolumn{3}{c}{**Value**} \\ \hline \multirow{4}{*}{Initial aperture \(D_{a}\) (mm)} & \multicolumn{3}{c}{Theoretical and simulated} & \multicolumn{3}{c}{(2190.726, 2190.726)} \\  & 2000 & maximum aperture \((D_{a}^{*},D_{a}^{*})\) (mm) & \(\left[\begin{array}{ccc}-0.460911&-0.742475&0.486099&516.74\\ 0.0482902&-0.567922&-0.821664&289.684\\ 0.886132&-0.35524&0.297616&-158.249\\ \hline \multirow{2}{*}{Number of panels \(n\)} & \multirow{2}{*}{18} & Compensation parameters \((k_{1}^{*},k_{2}^{*})\) & \(0\) & \(0\) & \(1\) \\  & 5 & Compensation parameter \(\beta^{*}\) (deg) & & \((0.118094,0.861829)\) \\ \cline{1-1} \cline{3-3} \  
that point \(C_{1}\) rises in distance after folding, and the displacement is \(10\,\mathrm{mm}\) after measurement, by which GC 3 is therefore satisfied. From the top view in Figure 9c, line segment \(A_{1}B_{1}\) is in the radial direction, and points \(A_{1}\) and \(D_{1}\) colored in green overlap entirely. Hence, the \(Z\)-axis is coplanar with points \(A_{1},B_{1}\) and \(D_{1}\), which suggests that GC 4 is satisfied. In addition, it is found in _Mathematica_ that the distance between points \(B_{1}\) and \(B_{2}\) is exactly \(5\,\mathrm{mm}\), and all the panels form the shape of a perfect paraboloid after deploying by an angle of \(\theta_{\mathrm{d}}=149.952^{\circ}\), as shown in Figure 10c. No interference was found during the deployment when simulating the deployment process in _Mathematica_. We also discovered in Table 2 that the theoretical and simulated RSE are identical, and that the residual \(E^{*}\) approximates to 0. All the facts above verify the effectiveness in antenna configuration, proving the effectiveness of the parameter identification algorithm for this specific antenna. Figure 10: Structure design of Cassegrain antenna. **(a)** Folded state. **(b)** Intermediate state. **(c)** Deployed state. (1) The antenna structure primarily consists of honeycomb panels, trusses, hub, subreflector, deployment mechanisms, axis-adjusting device and base. (2) The structure composed of a panel and framework in subfigure **(a)** is called ‘petal’. Figure 9: Verification of configuration. **(a)** Bimetric view. **(b)** Front view. **(c)** Top view. Extensive simulations further suggest that the results are similar to those in Table 2 and Figure 9 if the structural parameters of the antenna are assigned with other values, thereby verifying the effectiveness of the configuration parameter identification algorithm for the antenna configuration presented in Algorithm 1.
# On the Energy Performance of Iridium Satellite IoT Technology
## 2 Related Work
### Satellite IoT  
A few published works have surveyed satellite IoT systems [3; 4; 5; 6; 7]. [PERSON] et al. provided a comprehensive survey of the state-of-the-art in the area of satellite IoT, which focuses on current solutions, research, and industrial/commercial initiatives, and future research opportunities, among others [6]. [PERSON] et al. explored the use cases for satellite IoT, and pointed out open issues, especially in terms of connectivity and interoperability between sensors, satellites, and the Internet [7]. The authors created a taxonomy of satellite IoT systems, whereby end devices (e.g., sensors) use satellite connectivity directly or indirectly (in the latter, a terrestrial gateway covering a sensor network uses a satellite link for Internet connectivity). [PERSON] et al. analyzed the characteristics and open issues of direct-to-satellite systems [3]. [PERSON] et al. focused on the benefits of LEO satellite systems for IoT and provided a holistic LEO satellite architecture overview [5]. [PERSON] et al. reviewed the panoply of medium access control (MAC) protocols for satellite IoT systems, with a focus on low-cost nanosatellite systems [4].