esa-sceva/satcom-chunk-collection huggingface dataset:
# Bridging Macro-Microscopic Parameters: Correspondence and Calibration Approaches
## 3 Fundamental Parameters in Particle Mechanics  
In the calibration process, performing multiple trials for the same experiment is required. Therefore, selecting simple and feasible experiments can effectively reduce calibration time. Starting from basic soil mechanics methods, we identified two key experiments strongly related to particle normal stiffness and shear stiffness: the uniaxial compression test and the direct shear test. Previous studies have shown that particle normal and shear stiffness, along with their ratio, significantly affect the macroscopic parameters in uniaxial compression tests. Additionally, the uniaxial compression test is characterized by simple instrumentation and relatively fast testing. By comparing the test results with simulations, we can assist in the preliminary calibration of particle parameters [28]. Afterward, direct shear tests are conducted to verify and refine the calibration results, followed by model tests for final validation. This approach allows for the completion of parameter calibration in a relatively short time. The sandy soil sample and its particle size distribution are shown in Figure 1 and Table 1.
Basically, diterpenoids derive from three enzymatic steps: (i) condensation of four units of IPP and formation of the universal precursor GGPP (Geranyl geranyl diphosphate); (ii) the formation of very diversified chemical skeletons, catalyzed by different classes of diterpene synthases (diTPSs) [6], and (iii) the subsequent modification of the diterpene skeletons, catalyzed by cytochrome P450s (P450s or CYPs) [5], yielding a wide variety of complex chemical structures, including acyclic bi-tri- and tetra-cyclic compounds. The _Salvia_ species are rich in tricyclic diterpenoids, and more than 400 diterpenoids with different abietane skeletons have been isolated from _Salvia_ species [7]. Different biological properties for most plant diterpenoids have been reported, including antitumor [8,9], cytotoxic, antibacterial [10], antiplasmodial [11,12], leishmaniasisicidal, gastoprotection, molluscicidal [13], and antifungal activities [14]. Some diterpenoids are currently used in the clinical practice, such as paclitaxel, ginkoglide, ordionin, tanshinones, and triptolide [15]. In this review, we present an integrated overview of the possible avenues for enhancing the biosynthesis of abietane diterpenes in _Salvia sclarea_ hairy roots by either elicitation or metabolic engineering of the MEP pathway from which they derive. Figure 1: **Simplified MEP-derived biosynthetic route of abietane diterpenes and other isorenoids in Lamiaceae species**. The main enzymatic steps, intermediates, and final products are indicated. Abbreviations: MEP, 2-C-methyl-D-erythiol-4P; DXS, deoxyxylulose 5-phosphate synthase; DXR, deoxyxylulose 5-phosphate reductoisomerase; CMS, 4-diphosphocytidyl-methylerythitol synthase; CMK, 4-diphosphocytidyl-methylerythitol kinase; MCS, methylerythitol 2,4-cyclodiphosphate synthase; HDS, hydroxymethylbutenyl 4-diphosphate synthase; HDR, hydroxymethylbutenyl 4 diphosphate reductase; GPPS, GPP synthase; IDI, isopentenyl diphosphate isomerase; CPPS, CPP synthase; IPP, isopentenyl pyrophosphate; DMAPP, dimethylallyl pyrophosphate; GPP, geranyl pyrophosphate; GGPP, geranylgeranyl pyrophosphate; CPP, copalyl diphosphate; entCPP, ent-copalyl diphosphate; GAs, Gibberellins; entCPPS, ent-copalyl diphosphate synthase; MiS, mitriadiene synthase; HFS, hydroxy-fertruginol synthase; C\({}_{20}\)-Ox, C\({}_{20}\)-oxidase. The terpene synthases CPPS and MiS cyclize GGPP to militadiene, which is converted spontaneously by oxidization to abietatriene. A HFS, a cytochrome P450 monooxygenases, oxidizes abietatriene to ferruginol and 11-hydroxyferruginol, through two subsequent oxidation steps. A further oxidation of 11-hydroxyferruginol position at the C\({}_{20}\), by a C\({}_{20}\)-oxidase, produces carmagic acid. A possible biosynthetic route of aetihopinone, 1-oxo-aetihiophenone and salvipisone is also shown. Dotted lines indicated unknown enzymatic steps. In red are indicated the biosynthetic genes whose expression has been modified by metabolic engineering in _S. salarea_ hairy roots.
## 3 Results
### Reusability of Peptide-Linked Bead  
To evaluate whether the peptide-linked bead is reusable, the amounts of adsorbed/desorbed lead were quantified during six adsorption-desorption cycles. Although the exact mass balance between the adsorbed/desorbed amounts of lead was not observed in Figure 5, small differences in mass balance may be attributed to the incomplete desorbing operation by EDTA. The adsorbed amount of lead, and eventually the desorbed amount, gradually decreased during the successive use of the peptide bead. Compared to the first adsorption capacity (33.4 mg/g bead), the regenerated capacity in the sixth cycle (31.8 mg/g bead) decreased by less than 5%. By optimizing the desorbing process to regenerate the used adsorbent, as has been previously demonstrated by using various chemicals [30; 32], the regeneration efficiency of the constructed peptide bead may improve further. For the reusable gelatin-bentonite adsorbent, 0.1 N nitric acid restored the lead-adsorption capacity to more than 92% of initial capacity after ten adsorption-desorption cycles [32]. For the biochar-based adsorbent used for lead removal, a combination of 0.1 M HCl + 0.1 M CaCl\({}_{2}\) was the best desorbing eluent [30]. The activated carbon is a commonly reported adsorbent for lead removal, and HCl has been shown to be an efficient chemical for desorbing lead from the used adsorbent. However, the repeated use of activated carbon for lead removal seems to not be a good strategy, since the adsorption capacity significantly decreased by 20-27% after the third or the fourth cycle of adsorption-desorption [7; 33].  
Figure 4: Adsorption isotherms of lead onto peptide bead: (**a**) nonlinear fitting by [PERSON] and Freundlich isotherms; (**b**) linear Langmuir isotherm; and (**c**) linear Freundlich isotherm.
# On the Capacity of Optical Backbone Networks
## 3 Channel Capacity
### Optical Reach Evaluation with the Gaussian Model  
In our previous analysis, we determined an upper bound for the optical reach as a function of the number of symbols using the spectral efficiency of a signal with a Gaussian constellation. A more realistic approach for assessing optical reach involves using a metric like BER, specifically the BER evaluated before the FEC operation (pre-FEC BER) that takes place inside the BVTs.  
In this study, we assume that the threshold is \(BER_{pre-FEC}=10^{-3}\). By using the expressions that relate the BER performance with SNR for QPSK [33] and MQAM [34], we arrive at the values given in Table 3.  
It also important to be able to determine the data bit rate associated with each modulation scheme, which is given as [4]  
\[R_{b}=2R_{s}^{\prime}\text{log}_{2}\,M\;, \tag{13}\]  
where \(R_{s}^{\prime}\) is the net payload symbol rate, which is defined as \(R_{s}^{\prime}=R_{s}/(1+OH)\), with \(OH\) being the FEC and mapping overhead within BVTs. For the purpose of this analysis, we assume an overhead of 28% and a symbol rate of 64 Gbaud, resulting in \(R_{s}^{\prime}=50\) Gbaud. By utilizing Equation (6) in conjunction with Equations (5) and (7), along with the parameters given in Table 2 and the optimal channel power values (0.95 dBm for \(L_{s}=80\) km and 2.4 dBm for \(L_{s}=100\) km), we are able to determine the maximum number of spans required to meet the specified SNR values given in Table 3. This calculation, in turn, allows us to assess the reach of the corresponding optical channel, with the obtained results presented in Table 4. Note that the results for the reach at \(L_{s}=100\) km, as presented in Table 4, are close to those in Table I of [35] for \(R_{s}=64\) Gbaud. Similarly, the results for \(L_{s}\)= 80 km match well with the findings illustrated in Figure 8 of [29], once again for \(R_{s}=64\) Gbaud.  
\begin{table}
\begin{tabular}{c c} \hline
**Modulation** & **SNR (dB)** \\ \hline BPSK & 6.77 \\ QPSK & 9.78 \\
8 QAM & 14.38 \\
16 QAM & 16.54 \\
32 QAM & 20.56 \\
64 QAM & 22.55 \\
128 QAM & 26.44 \\ \hline \end{tabular}
\end{table}
Table 3: Values of SNR (\(@BER_{pre-FEC}=10^{-3}\)).
## 6 Experimental Results  
Table 4 presents the computational results obtained on randomly generated sets of three-player polymatrix games with different sizes and density. The size of the polymatrix games generated goes from \((2\times 2\times 2)\) to \((5\times 5\times 5)\). The density of the polymatrix games generated goes from 12.5% to 100 %. The entries of the polymatrix games generated are integers. The optimization of the master and secondary linear programs \(P\) and \(Q\) uses a C++ implementation of the Simplex algorithm in exact arithmetics.  
In Table 4, column (Size) lists the different numbers of strategies for each player before elimination of strategies strongly dominated. The column (\(d\)) lists the different densities of the partial payoff matrices generated. The column (\(\epsilon\)) lists the values of the parameter \(\epsilon\) used. The column (\(ne\)) indicates the average number of \(\epsilon\)-proper equilibria encountered over 10 polymatrix games randomly generated. Finally, the value (Time) indicates the average time in seconds required to find an \(\epsilon\)-proper equilibrium with a maximum value of \(\sigma\). Finally, the experimental results are obtained under MS Windows on workstations equipped with 3.3 GHz Intel Core i5 vPro processors and 3.2 GB RAM.  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \(\epsilon=10^{-4}\) & \(d=0.125\) & \multicolumn{2}{c}{\(d=0.25\)} & \multicolumn{2}{c}{\(d=0.5\)} & \multicolumn{2}{c}{\(d=0.75\)} & \multicolumn{2}{c}{\(d=1.0\)} \\
**Size** & **ne** & **Time** & **ne** & **Time** & **ne** & **Time** & **ne** & **Time** & **ne** & **Time** \\ \hline \(2\times 2\times 2\) & 5.1 & 11.7 & 2.5 & 9.3 & 1.8 & 7.9 & 1.5 & 7.8 & 1.0 & 6.7 \\ \(3\times 2\times 2\) & 7.3 & 22.3 & 4.9 & 20.3 & 3.5 & 18.5 & 1.6 & 8.7 & 1.2 & 8.1 \\ \(3\times 3\times 2\) & 8.6 & 35.2 & 5.3 & 24.3 & 2.5 & 21.2 & 1.8 & 20.8 & 1.5 & 19.5 \\ \(3\times 3\times 3\) & 11.0 & 47.8 & 7.6 & 35.6 & 2.9 & 27.4 & 2.0 & 27.0 & 1.6 & 26.7 \\ \(4\times 4\times 4\) & 13.7 & 72.7 & 8.5 & 61.8 & 3.9 & 56.4 & 3.7 & 45.2 & 2.6 & 41.5 \\ \(5\times 4\times 3\) & 15.2 & 87.7 & 9.4 & 86.5 & 4.7 & 82.4 & 4.2 & 79.9 & 2.8 & 74.6 \\ \(5\times 4\times 4\) & 16.8 & 109.4 & 10.0 & 97.1 & 6.2 & 93.6 & 5.3 & 81.4 & 3.2 & 77.1 \\ \(5\times 5\times 5\) & 18.2 & 123.6 & 10.3 & 115.4 & 6.7 & 106.9 & 5.9 & 91.1 & 4.7 & 84.5 \\ \hline \end{tabular}
\end{table}
Table 4: \(\epsilon\)-proper experimental results.  
In particular, one can observe that reducing the partial payoff matrices density increases the computing time required to find an \(\epsilon\)-proper equilibrium as it also increases the total number of \(\epsilon\)-proper equilibria detected for the polymatrix game.
###### Abstract  
The current research on confined submerged waterjets mainly focuses on the flow field of the impinging jet and wall jet. The double-sided wall vertically confined waterjet, which is widely used in many fields such as mining, cleaning and surface strengthening, has rarely been studied so far. In order to explore the influence of a double-sided wall confined width on the velocity field of submerged waterjet, an experiment was conducted with the application of 2D particle image velocimetry (PIV) technology. The distribution of mean velocity and turbulent velocity in both horizontal and vertical planes was used to characterize the flow field under various confined widths. The results show that the vertical confinement has an obvious effect on the decay rate of the mean centerline velocity. When the confined width changes from 15 to 5, the velocity is reduced by 20%. In addition, with the decrease of the confined width, the jet has a tendency to spread horizontally. The vertically confined region induces a space hysteresis effect which changes the location of the transition region moving downstream. There are local negative pressure zones separating the fluid and the wall. This study of a double-walled confined jet provides some valuable information with respect to its mechanism and industrial application.  
double-side wall confined; submerged waterjet; PIV; velocity field +
Footnote †: journal: Applied Sciences
_Article_  
**Early Age Carbonation of Fiber-Cement Composites**  
**under Real Processing Conditions:**  
**A Parametric Investigation**  
**[PERSON] 1,2, [PERSON] 2,*, [PERSON] 2, [PERSON] 3 and**  
**[PERSON] 1,***  
1 Department of Chemical Engineering, Kasetsart University, Bangkok 10900, Thailand; [EMAIL_ADDRESS]  
2 Institute for Environment, Health, and Safety, Belgian Nuclear Research Centre (SCK\(\bullet\)CEN), 2400 Mol, Belgium; [EMAIL_ADDRESS]  
3 Mahapant Fibre-Cement Public Company Limited, Loppuit 15220, Thailand; [EMAIL_ADDRESS]
* Correspondence: [EMAIL_ADDRESS] (Q.T.P.); [EMAIL_ADDRESS] (T.S.); Tel.: +32-1433-3240 (Q.T.P.); +66-(2)-797-0999 (T.S.)  
Received: 14 December 2017; Accepted: 24 January 2018; Published: 26 January 2018  
This paper presents the outcome of a comprehensive experimental program undertaken to study the performance of cellulose pulp and synthetic PVA (polyvinyl alcohol) based fiber-cement composite under both carbonated and non-carbonated curing conditions at early age. The composites were produced at different rolling pressures (2.5 to 9.0 bar) and subjected to various curing conditions in which the effects of CO2 pressure (1 to 3 bar) and curing time (3 to 9 h) were studied. The mechanical properties (modulus of elasticity (MOE), modulus of rupture (MOR), and toughness), as well as the physical properties (porosity, bulk density, and water absorption), were measured for all samples. Scanning electron microscopy (SEM) was used to investigate the effect of carbonation on porosity change and adhesion of fiber-matrix. A parametric investigation of the effects of the carbonation curing period, CO2 pressure, and rolling pressure on the improvement of the physical and mechanical properties during carbonation curing was performed. Results showed that fiber-cement composites cured with an elevated CO2 pressure of 3 bar, rolling pressure of 3 bar, and 5 h of curing time provided optimal curing conditions resulting in the most desirable mechanical and physical properties. However, toughness was greatly reduced with the increase of the CO2 pressure, curing time, and rolling pressure. Additionally, the carbonation curing improved the bonding between the fiber and the cement matrix because of the precipitation of calcite particularly in the pores of the interfacial transition zone (ITZ) between the cement matrix and the fibers.  
fiber-cement composite; mechanical properties; carbonation; parametric study
## 3 Results and Discussion  
### Correlation Between Needle Penetration and the Air-Dry Density  
For each of the 68 samples, a pair of data was obtained: average of the penetration resistance tests; air dry density. A scatter plot was constructed using these two sets of data, showing their relationship on a two-dimensional plane (Figure 6). In this graph, each blue dot represents the data for one sample.. The calculation of the correlation resulted in a coefficient of determination (R\({}^{2}\)) value of 0.76, which can be considered a reasonable or good correlation. A linear regression analysis between the needle penetration depth and wood density resulted in an R\({}^{2}\) of 0.76 (r = 0.872, \(n\) = 68). The relationship was statistically significant, t(66) = 14.5, \(p\) < 0.0001, indicating that penetration depth is a strong predictor of the density.  
Figure 6: Results of the needle penetration test versus density along with upper and lower standard deviations (SDVs).  
Previously, several authors reported varying correlations between wood density and NPR tests using Pilodyn 6]. The range of results is not surprising given the variability of wood density between different wood species as well as the variability of density in the same wood species. Various timber species were tested and correlation coefficients between the Pilodyn results and density ranged from 0.74 to 0.92, depending on the number of measurements and the species [38; 39]. The authors of [34] investigated the correlation between penetration resistance using Pilodyn and the density of structural wood elements of the Scots pine (_Pinus sylvestris_ L.) species obtained from an 18 th century building, achieving a coefficient of determination of 0.59. The authors of [40] found good correlation coefficients (\(-\)0.73 and \(-\)0.68) between the wood density and pin penetration depth for _Pinus pinaster_ standing trees. Also, the author of [41] obtained a stronger correlation (R\({}^{2}\) = 0.91) between the Pilodyn penetration depth and density of chestnut (_Castanea sativa_ Mill.) wood. The authors of [39] tested in situ two king-post timber structures with a moisture content different from that of the laboratory conditions and found a moderate coefficient of determination (R\({}^{2}\)) of 0.50, and the authors of [6] tested _Pinus sylvestris_ and _Pinus pinaster_ samples with 195 readings, finding a coefficient of determination (R\({}^{2}\)) value of 0.80.
## 3 Routing
### Routing in CRSN: Rising Issues
#### 3.2.1 Control Signaling  
Efficient control signaling is critical to any routing protocol, and its implementation depends on the routing approach from source to sink. In a traditional WSN, control signaling design is not an important issue because the channel is usually pre-assigned before field deployment. However, in the case of CRSNs, the dynamic nature of the available channel makes the task of designing a control channel quite challenging. By implication, CRSNs incur more overhead than WSNs in terms of energy and communication to negotiate a control channel. Efficient schemes in this regard should be characterized by minimizing this additional overhead. Again, most applicable algorithms designed fortraditional ad-hoc networks evidently do not consider minimizing this overhead as an issue because there is an abundance of resources. Because CRSN topology can be either _ad-hoc_, clustered, hierarchical or mobile, it should also be noted that topology dictates the most effective algorithm to adopt. Below are the three design methods:  
1. Dedicated common control channel (DCCC): In the DCCC method, a dedicated channel in the ISM band is usually assumed to be available across the width of the network and is strictly assigned to all nodes for signaling. This idea is very simple and can easily support any of the four topologies of a CRSN. Notwithstanding, the practicability of this idea has always been doubtful, especially for large networks. Another setback of this scheme is its high susceptibility to entire network failure from a simple jamming attack to the DCCC.
2. Group-based control channel: In this case, a control channel is assigned by a cluster head in a strict cluster association or in a distributed manner [20] in virtual clusters. The strength of this scheme lies in its support for spatial frequency reuse in large networks. However, in the case of strict cluster association, channels are assigned randomly or in a dedicated fashion to separate clusters based on the decision of a fusion center, which is usually assumed not to be constrained in any capacity. This assignment makes the former more suited to data mining applications. In the case of virtual clusters, a control channel is decided upon in a distributed manner, which makes it a better option for both _ad-hoc_ and clustered topologies and real-time applications.
3. Sequence-based control channel negotiation: This scheme takes inspiration from Bluetooth communication in that unsynchronized nodes run channel-hopping algorithms until the nodes meet at the same channel, after which they exchange synchronization packets and hop over a sequence for data exchange. In this case, the efficiency of an algorithm depends on the time required for nodes to meet at the control channel. The energy expended on communication in this scheme is greater than that of the two methods described above. Again, the algorithm is bested suited to peer-to-peer communication and will be highly demanding in terms of latency when extended to multi-hop communication.
# Performance and Scalability Analysis of SDN-Based Large-Scale Wi-Fi Networks
## 5 Experiment, Results and Discussion
### Experiments and Results
#### 5.1.2 UDP Parameters Graph
The performance and scalability analysis of SDN-based large-scale Wi-Fi networks is a novel approach to manage and control large-scale Wi-Fi networks using SDN technologies. Using various performance metrics, this study looks at how well the proposed SDN-based architecture works and how well it can grow. The proposed method can dynamically distribute network resources based on how the network is working in real time. This  
Figure 11:0 Graph showing flow setup latency. Figure 11:1 Graph showing flow setup latency with three topologies. lets the network make the best use of its resources, reduces congestion, and improves the network's performance. Using SDN technology makes it possible to control and manage the network from one place. This makes setting up and managing the network easier. However, the proposed study does not focus on mobility because it assumes that the Wi-Fi network is set up in a fixed location, such as the smart campus described in the proposed study. Mobility brings new problems, such as switching between access points and network congestion caused by clients moving around. These problems need more network resources and ways to manage them and can be addressed in future work. Therefore, by focusing on a fixed location deployment, the study can isolate the effects of scalability and performance on the network and provide a more accurate analysis of the proposed approach's effectiveness.