esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Spatiotemporal Changes and Underlying Mechanism  
A significant increasing trend was found in the FPAR time series for terrestrial ecosystems in China from 1980 to 2018. A similar trend was found in NDVI, which is linearly correlated with FPAR [65; 66]. In this study, annual mean air temperature and annual mean carbon dioxide concentrations in the atmosphere were found to have a relatively strong influence on the interannual variability in FPAR over Chinese terrestrial ecosystems.  
The turning point around 2010 in the FPAR temporal trend is of great interest, which has been found previously in the trend of vegetation productivity in China, attributed to both natural and human influences [67]. The warming hiatus which occurred around 2012 could be a key factor contributing to this turning point. During this period, global warming continued, but the rate of surface temperature increase slowed down compared to previous decades [68; 69]. This hiatus is believed to have been caused by natural variability in the climate system [70]. The climate system's response to increased greenhouse gases and other human activities was temporarily masked by these natural factors. As the hiatus ended, the warming trend continued, and this may have contributed to the increasing trend in FPAR after 2010. In high latitudes and high elevations (IM, NE, TP), the effect of temperature is greater, and the annual variability of FPAR was also weakly correlated with nitrogen deposition, which may be related to temperature stress under varying precipitation [71]. Climate anomalies have also been found to affect interannual variability in vegetation growth. For example, the anomaly of an Arctic polar vortex was found to have a negative impact on spring vegetation growth in China [72]. Moreover, changes in land use have been found to contribute 30-45% of annual fluctuations in the terrestrial carbon cycle, which means interannual variability of vegetation growth is influenced by human activities. Future research can explore the causes of the turning point around 2010 in more detail and investigate the relative contributions of natural and human influences to regional differences in FPAR trends. Furthermore, it is imperative to undertake a comprehensive analysis of climate sensitivity based on field observations to enhance the accuracy and comprehensiveness of the findings [73].
# From 5G to 6G--Challenges, Technologies, and Applications
## 2 Fifth-Generation Network's Shortcomings
### C. Link Reliability  
It is equally important to talk about the connection's reliability, which is usually measured by the bit error rate (BER) or by the frame error rate. Many mission-critical applications are in need of ultra-reliable connections to ensure low incident rates in places such as factory automation, vehicle-to-everything (V2X) communication, or railway system control. Specifically, for _Industry 4.0_, it is stated that some applications can require a link reliability of up to \(10^{-9}\) in terms of the frame error rate; however, 5G only promises to support up to \(10^{-5}\)[28]. Thus, to fully implement the concept of smart cities and fully dependable machine operations, such as remote surgery, the connection's reliability has to be improved by several orders of magnitude. Offering higher reliability at different levels in B5G systems will be needed for efficient resource allocation. Synonymous with link reliability, link availability in 5G networks is expected to be five-nines or 99.999% of the time; however, in a given factory setup, control and automation will require service availability to be six-nines or 99.9999% [29]. Moreover, some works went to the extreme by indicating that 6G networks will require service availability to be seven-nines or 99.99999% [19].
# Getting Sugar Coating Right! The Role of the Golgi Trafficking Machinery in Glycosylation
## 2 Glycoslation Enzyme Compartmentalization
Sorting signals--KKXX or RXR on ER residents [37] or \(\varphi\)(K/R)XLX(K/R) on some _cis_-Golgi enzymes--directly interacting with COPI subunits have been identified on the cytoplasmic tails of a subset of enzymes [38; 39; 40]. Vps74 is a COPI interacting protein that was identified as a protein essential for the proper localization of a subset of glycosylation enzymes in yeast [41]. Vps74 recognizes the ([F/L][L/I/V]XX[R/K]) peptide motif on the cytoplasmic tails of a subset of glycosyltransferases [42]. GOLPH3 and GOLPH3L are human homologs of Vps74. They have an affinity for PI4P enriched in the trans-Golgi and recognize membrane-proximal polybasic residues on the cytoplasmic tails of a subset of Golgi enzymes, including ST6 Gal1 and N-acetylglalactosaminyltransferase-2 (GALNT2) [33; 43]. About half the number of enzymes involved in glycosphingolipid synthesis have an LXX[R/K] consensus motif recognized by GOLPH3 on their cytosolic tails [44]. These mechanisms of enzyme compartmentalization within the Golgi are not mutually exclusive.
## 5 Conclusions  
In this paper, we implemented and evaluated multiple hardware QAM demodulation architectures by performing an extensive DSE in terms of demodulation algorithms, arithmetic, and approximation techniques. The examined algorithms involve soft- and hard- decision decodings of different complexity, whereas, regarding the approximation techniques, we employ approximation-configurable adders and multipliers on floating- and fixed-point arithmetic. Our experimental analysis shows that the proposed approximate 64-QAM demodulators provide BER ranging from \(10^{-1}\) to \(10^{-4}\) for SNR 0-14 dB, while they deliver significant resource gains in the Xilinx ZC106 FPGA. From the DSE and experimental results, we conclude the following: (i) The high-order QAM circuits for the Exact LLR algorithm demand an increased amount of resources, even when adopting fixed-point representation and approximation techniques, while its BER performance is matched by the approximate LLR algorithm. (ii) By introducing approximations in the datapath of the approximate LLR algorithm, we significantly reduce the circuit complexity in exchange for negligible accuracy loss, and thus, it can be implemented on the FPGA without utilizing all the resources. (iii) The piecewise LLR and approximate maximum likelihood algorithms utilize a limited amount of resources and deliver increased throughput; thus, very high-order QAMs are facilitated. However, the piecewise LLR provides slightly worse BER compared to approximate LLR algorithm, while the approximate maximum likelihood gives inefficient BER performance. (iv) A single QAM circuit solution that fits in all circumstances/systems cannot easily be obtained without considering additional constraints (e.g., accurate modeling of transmission channel, arithmetic errors from other DSP functions, and resources required for other digital functions).  
Conceptualization, V.L. and I.S.; investigation, G.A. and I.S.; methodology, D.S.; implementation, I.S. and G.A.; simulation, G.A.; validation, I.S. and G.A.; visualization, V.L. and I.S.; writing--original draft, I.S. and V.L.; writing--review and editing, G.L. and D.S.; supervision, G.L. and D.S. All authors have read and agreed to the published version of the manuscript.  
The work was partially funded by the European Union's Horizon 2020 research and innovation program, under project SDK4 ED, grant agreement No. 780572.  
The authors declare no conflict of interest.
# Numerical and Experimental Characterization of a Coanda-Type Industrial Air Amplifier
## 4 Numerical Methodology
### Grid Convergence  
A mesh sensitivity analysis was performed to avoid dependence of the observables (i.e., the amplification factor) with respect to spatial resolution. For this particular study, the compressed air inlet pressure was set at \(p_{c}=0.4\) MPa and the clearance parameter was set to \(\delta=0.23\) mm. Four different hexahedral meshes (named \(h_{i}\quad i=1,\ldots 4\)) were generated, all of them satisfying enough wall normal resolution to comply with a \(y^{+}\approx 1\) criterion to ensure a proper solution of the full boundary layer. An overview of the spatial discretization, together with a close view of the most conflictive regions, is depicted in Figure 14. The relative error between the different grids is computed with the following expression:  
\[\epsilon=\frac{Q_{o/c}^{h_{i}}-Q_{o/c}^{h_{i+1}}}{Q_{o/c}^{h_{i+1}}}\cdot 100. \tag{11}\]  
The results of the three-dimensional analyzes are gathered in Table 2. For the two finest meshes, \(h_{3}\) and \(h_{4}\), the calculated amplification ratio varies below a relative error of 2%. Therefore, to balance the computational load and accuracy demand, grid \(h_{3}\) was used during the simulations.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Grid** & **Reference Grid Size [mm]** & **Number of Elements** & \(Q_{o}/Q_{c}\) & \(\epsilon\) [\%] \\ \hline \(h_{1}\) & 5 & 622,536 & 21.22 & - \\ \(h_{2}\) & 2.5 & 2,784,133 & 24.06 & 11.8 \\ \(h_{3}\) & 1.25 & 9,025,234 & 23.62 & 1.9 \\ \(h_{4}\) & 0.625 & 15,103,121 & 23.90 & 1.2 \\ \hline \end{tabular}
\end{table}
Table 2: Grid convergence process and volumetric flow ratio relative error.  
Figure 14: Three different levels of detail of the mesh \(h_{2}\) at section \(Z=0\) m.
## 3 Results and Discussion
### Coatings Nanostructure
In order to obtain further information on the dispersion of the MMT in the SB coating, XRD results were coupled with SEM-EDX analyses. These were carried out on the same polymeric films used in the XRD. SEM-EDX images gave different results for sample SB-MMT and CSB-MMT. In both cases, the elemental analyses confirm the presence of atoms typical for clay composition, such as Mg, Al, and Si. However, Figure 5 shows that these elements do not present the same distribution in the two polymeric matrices. As it can be observed  
Figure 3: XRD diffractograms of (**a**) MMT powder, (**b**) film obtained from the MMTD, (**c**) SB-MMT film, (**d**) CSB-MMT film. from the elemental distribution of Si, the nanoclay in SB forms a kind of oriented strips of about 100 \(\upmu\)m length and 5 to 50 \(\upmu\)m width uniformly dispersed in the matrix. On the contrary, CSB-MMT forms agglomerates of more than 100 \(\upmu\)m width, indicating that the nanoclay presents a worse distribution in the CSB polymer than in the SB. The main element detected in both polymers is carbon, which is the principal component of the SB coating. In the CSB-MMT sample, nitrogen atoms could also be detected, which can be attribute to the methylol melamine molecules of the cross-linking agent. It can be observed in Figure 5 that the distribution of C and N atoms does not perfectly match. N seems to be more concentrated in the nanoclay agglomerate, rather than homogeneously dispersed in the polymeric matrix. This suggests a correlation between the formation of nanoclay agglomerates and the addition of the cross-linking agent. This can be due to the addition of a new chemical component in the dispersion, which increases the number of possible interactions that can occur between polymer and nanoclay, influencing the development of the coating structure [66]. Moreover, the marked difference in the distribution of the C and N atoms in the CSB-MMT matrix indicates that the reaction between the carboxylated styrene-butadiene chains and the methylol melamine cross-linking agents did not uniformly occur. To the authors' best knowledge, no studies are available about the effect of nanoclay on the cross-linking process of carboxylated styrene-butadiene with methylol melamin. However, some studies have reported that nanoclays can influence the curing process of polymers [67; 68]. This is attributed, on one hand, to the reduction of the molecular mobility, caused by an increase in the viscosity of the polymer dispersion,  
Figure 4: Film of (**a**) MMT, (**b**) SB-MMT, (**c**) CSB-MMT used for RXD and SEM analyses. Figure 5: Elements distribution in polymer films of SB-MMT (**a**) and CSB-MMT (**b–d**). Magnifications 100\(\times\). because of the incorporation of nanoclay particles [69]. On the other hand, nanoclays can also influence the reaction mechanism that governs the curing process [68]. Therefore, it can be supposed that, in presence of nanoclay, the methylol melanin cross-linking agent has not fully reacted with the carboxylated styrene-butadiene copolymer, but, rather, it has concentrated around the nanoclay agglomerates.
## 3 Results and Discussion
### Six-DOF Chain Model with Heavy Damping  
To further validate the reliability of this method, this paper uses the Group 2 numerical model which is also a six-DOF chain model, as shown in Figure 8. It is characterized by a set of heavy damping and similar modes. The mass matrix \(M\), stiffness matrix \(K\), and damping matrix \(C\) of the system are expressed as follows:  
\[M=\left[\begin{array}{cccccc}1&0&0&0&0&0\\ 0&1&0&0&0&0\\ 0&0&2&0&0&0\\ 0&0&0&2&0&0\\ 0&0&0&0&1&0\\ 0&0&0&0&1\end{array}\right]kg\;K=\left[\begin{array}{cccccc}1200&-600&0&0&0 &0\\ -600&1200&-600&0&0&0\\ 0&-600&1200&-600&0\\ 0&0&-600&1200&-600\\ 0&0&0&-600&1200&-600\\ 0&0&0&0&-600&1200\end{array}\right]N/m\]  
\[C=0.05M+0.01K\frac{N\text{-}sec}{m}\]  
Figure 8: Six-DOF chain model with heavy damping.  
Figure 7: Mode shapes of six-DOF chain model of a cantilever beam (the solid line is an analytic solution, the dotted line is ERA/DC identification result).  
The damping matrix \(C\) of this system also uses proportional damping, and the responses of six degrees of freedom are individually excited by nonstationary white noise using the Newmark method. The initial state is stationary to obtain the displacement response of the time domain for numerical simulation. The time-domain sampling interval is 0.01 s, the number of sampling points is 131,072.  
Figure 9 is the stabilization diagram of the system, roughly showing five frequencies of about 8, 19, 27, 31, and 43 rad/s. It is observed that the identification state of the mode of 31 rad/s is relatively unstable before order 10, so the identification order \(\alpha\) is 12. The modal parameter identification can be performed effectively in this order, and the results are shown in Table 2 and Figure 10.  
Table 2 shows the result of modal parameter identification using ERA/DC for a six-DOF chain model with heavy damping. The sixth mode has a higher error in identification of natural frequency, which is 14.6%, the others are lower than 2%, and the damping ratio errors are less than 30%. Figure 10 shows the corresponding exact and identified mode shapes of this structure; the fourth measurement DOF in the fifth mode shows phase contrary sign. The computed MAC value of the fifth mode is lower than 0.70, and the rest are approximately coincident with exact mode shapes.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Mode**} & \multicolumn{3}{c}{**Natural Frequency (rad/s)**} & \multicolumn{3}{c}{**Damping Ratio (\%)**} & \multirow{2}{*}{**MAC**} \\ \cline{2-2} \cline{5-8}  & **Exact** & & & & & & \\ \hline
1 & 8.72 & 8.72 & 0.00 & 4.65 & 5.67 & 22.03 & 1.00 \\
2 & 19.89 & 19.82 & 0.37 & 10.07 & 9.59 & 4.81 & 0.99 \\
3 & 27.63 & 27.39 & 0.87 & 13.91 & 13.39 & 3.69 & 0.98 \\
4 & 31.74 & 32.35 & 1.94 & 15
## 2 System Configuration  
The thermal system used for data reconciliation calculation in this paper adopts the waste heat recovery system of a gas turbine proposed by [PERSON] et al. [35], which uses an S-CO\({}_{2}\) cycle with two S-CO\({}_{2}\) turbines. The system layout diagram is shown in Figure 1. The S-CO\({}_{2}\) bottom cycle consists of three heat exchangers, two S-CO\({}_{2}\) turbines, one S-CO\({}_{2}\) compressor, and one cooler. The technical data is listed in Table 1.  
In the gas turbine cycle, after the air enters the cycle through stream 1, it first enters the air compressor and compressed. The compressed air enters the cold side of the air preheater to be heated by the gas turbine exhaust, then it enters the combustion chamber. In the combustion chamber, the fuel entering the circulation from stream 8 and preheated air are mixed for combustion, and the generated high-temperature gas makes the gas turbine generate power. After the work is done, the gas turbine exhaust enters the hot side of the air preheater for heating, then enters the heat exchanger to continue heating CO\({}_{2}\). The heated exhaust of gas turbine is stream 7.  
Figure 1: Diagram of coupled system layout.  
In the S-CO\({}_{2}\) bottom cycle, the CO\({}_{2}\) which is heated by the gas turbine exhaust enters the S-CO\({}_{2}\) turbine 1 from stream 9 to do work. After the work is done, the CO\({}_{2}\) successively enters a high-temperature recuperator and a low-temperature recuperator to heat another stream of CO\({}_{2}\) from stream 17 to stream 19, then enters the cooler for cooling and enters the S-CO\({}_{2}\) compressor for recompression. After being compressed by the S-CO\({}_{2}\) compressor, the CO\({}_{2}\) from stream 15 is divided into two streams. Stream 16 enters the heat exchanger to exchange heat with the gas turbine exhaust for continuous circulation. Stream 17 is heated by the two recureptors respectively, then enters the S-CO\({}_{2}\) turbine 2 to generate power. After the work is done, it is merged with another stream of CO\({}_{2}\) at stream 12 between the two recureptors. In this simple coupled circulation system, two S-CO\({}_{2}\) turbines use heat exchangers to exchange heat with the gas turbine exhaust to generate electricity, which solves the recovery of gas turbine waste heat well.  
In this paper, a gas turbine with exhaust temperature of 527.60 \({}^{\circ}\)C and exhaust mass flow of 92.90 kg/s was used for system construction and data reconciliation calculation. The following assumptions were made to simplify the calculation conditions:  
1. The waste heat recovery system is in a stable state, and the CO\({}_{2}\) is always in a supercritical state;
2. The external environment and the cooling water used by the cooler are both at 25 \({}^{\circ}\)C, 101 kPa;
3. The pressure loss and heat loss of circulating equipment and pipelines can be ignored;
4. The isentropic efficiencies of the air compressor and the S-CO\({}_{2}\) compressor are constants, which are 80% and 85%, respectively;
5. The isentropic efficiency of the gas turbine and the two S-CO\({}_{2}\) turbines are also constants, which are all 90%.
# Polyhydroxyalkanoates (PHAs): Biopolymers for Biofuel and Biorefineies
## 2 Sources of PHAs  
PHAs are long polymeric molecules that exhibit ester linkages in their structures. PHAs are synthesized by various types of prokaryotic microorganisms as intracellular storage materials comprising significant amounts of carbon and energy [11]. A PHA molecule is typically made up of 600 to 35,000 fatty acid monomer units. Each monomer unit consists of a side chain (R) group that is usually a saturated alkyl group but can also be unsaturated, branched, or substituted alkyl group [12; 13; 14]. Depending on the number of carbon atoms in the PHA monomer, these biopolymers can be classified into short-chain length (scl) PHAs (generally 3-5 carbon atoms), medium-chain length (mcl) PHAs (comprising 6-14 carbon atoms), and long-chain length (lcl) PHAs (contains 15 or more carbon atoms). More than 150 PHA monomers have been recorded, where the chemical or physical modification of existing PHAs led to the production of new types of PHAs. These features of PHAs gave rise to a variety of PHA properties suitable for a number of applications, such as biodegradable packaging materials and various medical products, as shown in Figure 1 (Lignocellulose (biomass-derived) PHAs and their applications) [2; 15; 16].  
The synthesis of PHAs is a simple biosynthetic process consisting of only three genes and three enzymes. Figure 2 shows the PHAs biosynthetic pathway in bacteria. The Halobacteraceae, a family of bacteria, contains more than 300 bacteria sources for PHA synthesis. _Pseudomonas oleovorans_ and _Pseudomonas fragi_ are bacteria that can produce mcl PHAs through the beta (\(\beta\)) oxidation of alkanoic acids by producing hydroxylkanoyl CoA as a substrate [17; 18]. Furthermore, PHAs containing 2-hydroxypropionate (2 HP), 3-hydroxypropionate (3 HP), 4-hydroxybutyrate (4 HB), 3-hydroxyvalerate (3 HV), and mcl 3-hydroxyalkanoate (3-HA) monomer units can all be produced by _Escherichia coli_ via genes or exogenous and endogenous processes [19; 20; 21]. In addition, the synthesis of PHAs was also studied in other eukaryotes, namely microalgae (both recombinant and even wild type). However, the bacterial synthesis of PHAs is not co  
Figure 1: Applications of polyhydroxyalkanoate (PHAs) in various fields. Reproduced with permission from [17]. Copyright 2020 Elsevier Ltd.  
to petroleum-based plastics; therefore; there is great interest in discovering a synthetic pathway for PHA production in eukaryotic cells, especially in crops [22; 23; 24].
# Experimental Simulation of Lightning Current Discharge on Rocks
## 3 Results  
### Mineralogy of Target Rocks  
Prior to the experiment, the testing sample A0, samples A1-A4, and samples B1-B2 were observed using optical microscopy. Samples A0-A4 showed a dark-colored, fine-grained texture, while samples B1 and B2 showed light-colored, coarse-grained texture.  
X-ray powder diffraction (XRPD) was used to identify the mineralogical composition of the rock samples. Testing sample A0 was composed mainly of pyroxene, plagioclase, and magnetite, whereas the main constituents of samples A1-A4 were pyroxene, plagioclase, magnetite, and quartz (Figure 4).  
Accessory and alteration minerals include micas, talc, and amphiboles in sample A0; and micas and amphiboles in samples A1-A4. The XRPD data on samples B1 and B2 revealed the presence of quartz, plagioclase, K-feldspar, and micas (Figure 5).  
According to the XRPD results and optical microscopy observations, the testing sample A0 and samples A1-A4 represent a basalt rock type, whereas the samples B1 and B2 represent a granite rock type. All samples used for the experiments were homogeneous and isotropic. Quantitative mineralogical  
Figure 4: X-ray diffraction pattern of samples A1–A4. Px = pyroxene; Plg = plagioclase; Qtz = quartz; Mag = magnetite; Bio = biotite; Act = actinolite. Observed spectra (red line), fitted spectra (blue solid line), difference plot (below spectra), and Bragg peak positions (tick marks above difference plot) are shown. The weighted R-factor, Rwp, was calculated using the observed and calculated intensities in the powder diffraction patterns.  
analyses and determination of the content of amorphous material were performed on the test sample A0, samples A1-A4, and samples B1 and B2. Results are shown in Table 1.  
[PERSON] and [PERSON] [16] determined the energy at one bar of pressure required to promote the melting of quartz to form sand fulgurite. Following [PERSON] and [PERSON] [16], considering the density of basalt is 3 g/cm\({}^{3}\)[26], the enthalpy of basalt at 1 bar is 9.145 J/mol [27], the molecular weight of basalt is 125 g/mol [28], and the internal diameter of fulgurite observed via optical microscopy is 0.08 cm (this work), the energy per unit length (E) required to vaporize basalt to make the fulgurite glass is 17.238 J/cm\({}^{3}\).