esa-sceva/satcom-chunk-collection huggingface dataset:
# Solar Explosive Evaporation Growth of ZnO Nanostructures
## 2 Materials and Methods  
### Samples Preparation  
ZnO nanostructures were grown on unpolished Si substrates and on polished Si substrates coated by Au and Ag films by MEE using concentrated solar radiation. A 60 nm thick layer of Au film and a 200 nm thick layer of Ag film were deposited on the substrate by radio frequency (RF) magnetron sputtering and thermal evaporation, respectively. Au/Si, Ag/Si, and unpolished Si substrates had the size of 5 \(\times\) 5 mm\({}^{2}\). Before loading substrates into the quartz tube, the last ones were cleaned successively in acetone, ethanol, and deionised water for 10 min for each stage. After that, the substrates were dried in nitrogen flow.  
The growth of ZnO nanostructures was carried out using the solar concentrator of the Geliocenter of the Institute for Problems of Materials Science (Crimea, Ukraine). The solar concentrator based on a converted army floodlight (see Figure 1) is equipped with an automatic sun tracking system and a mounting mechanism for fixing the quartz tube. The mounting mechanism is placed inside the solar concentrator and allows to fix the sealed-on-one-side quartz tube with 10 mm diameter in the focus of a 2-m diameter mirror. The other end of the tube remained open for free access of air. In the middle of the tube was placed the boat filled with precursors of the powder of zinc acetylacetonate hydrate (Zn(AcAc)\({}_{2}\)Zn(C\({}_{2}\)H\({}_{7}\)O\({}_{5}\))\({}_{2}\)-H\({}_{2}\)O) or a mixture of ZnO and graphite powders (with 1:1 mole ratio) with weight of about 1 g. All precursors were from Sigma Aldrich (Saint Louis, MS, USA) with purity no less than 99% [8]. Changing the angle of the louver (please see Figure 1) of the solar concentrator allows the heating temperature in focus to be set up to 3000 \({}^{\circ}\)C. The angle of opening for the louver was changed in the range of 15\({}^{\circ}\)-30\({}^{\circ}\) for adjustment of temperature for precursor evaporation in range from 300 to 900 \({}^{\circ}\)C. The temperature of evaporation was controlled by platinum-rhodium thermocouple. The temperatures of 300 \({}^{\circ}\)C and of 900 \({}^{\circ}\)C were used for the evaporation of precursors of zinc acetylacetonate and of a mixture of ZnO and graphite powders, respectively. Au/Si and Ag/Si substrates together were used in experiment when zinc acetylacetonate was decomposed for obtaining ZnO. The carbothermal reduction of mixtures of ZnO + C powders we used at the deposition of ZnO nanostructures on unpolished Si substrates. In all cases, the substrates were placed at a distance of 20 mm from the heating zone, and the deposition time was about 3 min.
## 4 Conclusions  
A coupled DEM-FDM numerical model simulated EPB shield tunnelling. Its main components are an actual-size spoke-type EPB shield and ground formation. Modeling used large-scale triaxial test analyses for calibration and a two-step ground formulation scheme with DEM-FDM coupling. Subsequent tunnelling simulations adopted varying penetration rate and rotational speed of the screw conveyor, including their automatic control during operation. Torque, thrust force, chamber pressure, discharge, and settlement data were obtained, and their implications were discussed. The conclusions of this numerical study are summarized as follows:  
1. The numerical model successfully simulated EPB shield tunnelling under control of the operational conditions. During an advance of 300 mm, the penetration rate and rotational speed of the screw conveyor were varied four times in response to the monitored TBM data. The adjustments ensured that the measured torque, thrust force, and chamber pressure remained within their specified operational ranges.
2. The simulated torque and thrust force data demonstrated the significance of adopting appropriate penetration rate and rotational speed of the screw conveyor to enhancing the TBM performance. When the torque and thrust force were maintained within their specified operational ranges, the TBM advance was optimal within its specifications.
3. The chamber pressure and discharge data were directly related to the safety of tunnelling. Insufficient or excessive chamber pressure could induce surface settlement or heating. However, controlling the chamber pressure and discharge during operation considerably suppressed surface settlement. Therefore, maintaining appropriate chamber pressure and muck discharge is significant for safe tunnelling with an EPB shield.
4. The provided numerical model could be a useful tool for gathering simulated real-time TBM data and finding the optimal TBM operational conditions for efficient and safe tunnelling. In future, with some modifications, the model can be used in automatic TBM driving, simulating multiple-layer soil tunnelling, and designing cutter heads.  
Conceptualization, H.C. and C.L.; methodology, H.C. and C.L.; software, H.L.; investigation, H.L., H.C., S.-W.C., S.-H.C. and T.-H.K.; data curation, T.-H.K.; writing--original draft preparation, H.L.; writing--review and editing, C.L.; visualization, H.L., S.-W.C. and T.-H.K.; supervision, C.L.; project administration, S.-H.C.; funding acquisition, S.-W.C. and S.-H.C. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Korean Agency for Infrastructure Technology Advancement under the Ministry of Land, Infrastructure and Transport of the Korean government (No. 21 SCIP-C129646-05).  
Figure 16: Comparison of surface settlement after the EPB shield has advanced 300 mm: (**a**) transverse settlement trough; (**b**) longitudinal settlement trough.
# Joint QoS and Congestion Control Based on Traffic Prediction in SDN
## 7 Simulation Results
### Computational Complexity and Maximum Link Utilization  
In this subsection, the execution time of the QRTP algorithm and RQRTP are compared. Section 6 presents the configuration of the system used to execute the algorithms.  
The results of computational complexity analysis are stated in Table 4. The optimization time of QRTP increases exponentially by increasing the number of active flows; however, since RQRTP decreases the number of active flows by aggregating the number of inputs, it has a dramatically lower optimization time. Additionally, QRTP has an absolute value in its objective function, which increases the optimization time of QRTP compared with RQRTP. As shown, the execution time of RQRTP is very low; hence, it can be used as a solution to reconfigure the network for resource reallocation.  
Note that we ignore problem transformation time, i.e., all time comparisons are based on the optimization time. With the purpose of exploring the effect of relaxation on the accuracy of the solution, the maximum link utilization is calculated. In Figure 9, the maximum link utilization of the QRTP and Relaxed QRTP (RQRTP) is compared. As can be seen, the results of these schemes are the same while the optimization time of the relaxed version is sufficiently lower in a large number of flows.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Flow** & **QRTP (s)** & **RQRTP (s)** \\ \hline
10 & 2.16 & 2.12 \\
50 & 2.32 & 2.31 \\
100 & 3.16 & 2.66 \\
200 & 27.89 & 2.98 \\
300 & 40.89 & 4.22 \\
400 & 106.70 & 27.53 \\
500 & 178.13 & 41.45 \\
1000 & 1227.26 & 46.07 \\
2000 & \textgreater{}1 h & 88.77 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Computational complexity.  
Figure 8: Comparison of QRTP (QoS-aware resource reallocation traffic prediction) and relaxed QRTP (RQRTP) based on the number of routing matrix element. Increasing the number of active flows in the network makes the superiority of RQRTP more clear. This happens because RQRTP exploits flow aggregation technique to reduce the number of routing matrix elements.
# Development on Solid Polymer Electrolytes for Electrochemical Devices
## 3 Basic Solid Polymer Electrolytes
### Strategies to Improve Ionic Conductivity of SPEs
#### 3.2.6 Composite Solid Polymer Electrolytes
From the vibrational studies, [PERSON] and co-workers observed that the presence of LiTNT in SPE helped in the increment of lithium perchlorate dissociation, whereas the impedance spectroscopy showed two semicircles that are related to lithium-ion conductivity [161]. The semicircle in the higher frequency region is accredited to the bulk resistance of lithium transport across the polymer matrix, whereas the second is ascribed to lithium transport through the interaction with the LiTNT [161]. Some authors have proposed that lithium transport can take place via three possible routes, i.e., polymer--salt matrix, polymer--filler interface and inside filler grain [162; 163]. [PERSON] et al. [163] showed that Li-ion movement took place mainly via ceramic filler LLZO grain for PEO-LiClO\({}_{4}\)-LLZO system, using NMR results. On the other hand, for PEO-LiClO\({}_{4}\)-TEGDME-LLZO electrolyte, it was found that Li-ion motion via TEGDME-related phase was preferred over filler or polymer matrix or polymer--filler interface [164]. In our opinion, a complete understanding on the conduction mechanism of such SPEs remain challenging as there are many influential factors behind it. Other than the abovementionedc ceramic fillers, Li6.4 La3 Zr2 Al0.2O12 and LiSnZr(PO4)3 have also been incorporated into PEO-LiTFSI [165] and PVDF-LiTFSI [166] electrolytes, respectively. Another category of active fillers in CSPEs are the metal organic frameworks (MOFs) which display excellent properties such as microporous structures with high surface area, good mechanical and thermal properties, and controllable pore structure. MOFs have been credited to an increased lithium-ion transference number, ionic conductivity and improve the cycling stability of batteries [167]. Several examples of MOFs as active fillers in CSPEs for battery application include aluminum(III)-1,3,5-benzenetricarboxylate (Al-BTC) [168], D-TiO-66-NH2 [169], copper-1,4-benzenedicarboxylate (Cu-BDC) [170], HKUST-1(Cu) [171] and magnesium-benzene tricarboxylate (Mg-BTC) [172] to name a few. Overall, MOFs having 3D framework and controllable pore structures improve the interfacial contact between electrode and electrolyte. This is reflected with the addition of Zn4O(1,4-benzenedicarboxylate) in PEO-LiTFSI electrolyte which stabilized the interfacial resistance and improved the cyclability up to 100 cycles [173]. As usual, its ionic conductivity was enhanced too [173]. CSPE having Cu-BDC showed enhanced thermal stability which led to safer battery at high temperatures as compared to filler-free SPE [170]. More detailed reviews on CSPEs are available elsewhere [174; 175; 151; 176]. Some other CSPEs systems that have been reported by many researchers are tabulated in Table 7.
# Satellite Measured Ionospheric Magnetic Field Variations over Natural Hazards Sites
## 5 Results
### Explosions
#### 5.3.2 Talhuelilpan Pipeline Explosion  
Another man-made catastrophe was the gasoline pipeline explosion in the town of Tlahuelilpan in the Mexican state of Hidalgo on 18 January 2019. According to eyewitness reports a fuel spill ignited into a fireball with tragically loss of life for many people in the immediate vicinity. Such a shock wave at the ground has the ability to release energy into far distance. For CSES HPM the variations are shown in Figure 8. For most of the frequency bands CDSM has an enhancement, i.e., higher values after the explosion. The pre- and post-variations for FGM1 are similar, the Y-component values are larger, all three components  
Figure 7: Distribution of _Swarm_ A VFM magnetic field fluctuations grouped in individual frequency bands for the explosion (2017-12-12T07:45 UTC) in Baumgarten, Austria. The colour coding is the same as in Figure 2, the geomagnetic activity is \(|\)Dst\(|\)\(\leq\) 29 nT for the selected time span.  
have a slightly wavelike feature (redistribution between scales) along their particular frequency band axis.
## 2 Materials and Method
### Simulation
#### 2.1.6 Image Detector  
In the modeled system, the selenium image detector (Z = 34, A = 78.96 g/mol, and \(\rho\) = 4.79 g/cm\({}^{3}\)) had the shape of a cuboid with a rectangular base of 24 cm \(\times\) 29 cm and a height of 250 \(\upmu\)m. Two areas in the detector served as logical detectors. Their arrangement is shown in Figure 3. The detector marked with the symbol O was located in the \"shadow\" of the tumor simulated in the virtual breast model. Detector B was the reference detector. In the simulations, all energy deposits in these logical detectors were recorded. The sum  
Figure 2: Sample spectrum used in simulations, generated for a molybdenum anode at a voltage of 28 kV. The K\({}_{\alpha 1}\) and K\({}_{\beta 1}\) emission lines are visible. \(\Phi\), photon fluence; E, energy of photons.  
of the energy deposits was a measure of the signal at the logical detector, and the mean square error was a measure of noise.  
The \(Q\) parameter was used as a measure of the image quality-to-dose ratio and defined as follows:  
\[Q=\frac{SDNR^{2}}{D_{f}}, \tag{2}\]  
where \(D_{f}\) is the absorbed dose in the inner part of the breast phantom (see Figure 4), and the \(SDNR\) parameter is the signal-to-noise ratio, determined according to the following formula:  
\[SDNR=\frac{\varepsilon_{B}-\varepsilon_{O}}{\sqrt{\frac{\sigma_{O}{}^{2}+ \sigma_{O}{}^{2}}{2}}}, \tag{3}\]  
where \(\varepsilon_{B}\) and \(\varepsilon_{O}\) indicate the energy deposited in the logical detector in the background image and the object image, respectively; \(\sigma_{b}\) and \(\sigma_{O}\) indicate the standard deviations of energy deposits in the logical detector in the background image and the object image, respectively.  
Optimization was performed so that high voltage values were searched for a given breast phantom thickness, at which the \(Q\) parameter reached its maximum.  
Figure 3: Image detector model, top view. The logical detector under the tumor (O) is marked in red. The logical detector, which was the reference one and located in the background radiation area (B) during the measurement, is marked in green.
## 6 Case Study Analysis
### Simulation Setup  
In this case study, the tropical cyclone event is validated by simulating the path of Typhoon Maria, the eighth typhoon of 2018 in China. The typhoon's path data are mappedonto the grid topology, yielding the modified IEEE 14 system topology, with the typhoon path annotated as shown in Figure 5. In this case study, the maximum wind speed of the wind radius (with radius \(R_{\text{max}}\)) is defined as the risk wind zone based on the critical wind speed for plastic fatigue damage. The transmission towers are constructed using Q345 material, with a unit distance of 5 km between two towers. All wind farms use the same model of wind turbine, the American NREL 5 MW wind turbine. The cut-in, rated, and cut-out wind speeds of the turbine are as follows: \(v_{\text{in}}=3\) m/s, \(v_{\text{rate}}=11.4\) m/s, and \(v_{\text{out}}=25\) m/s. The specific parameters of the wind turbines, towers, and transmission lines are provided in Table 1.  
The IEEE 14 system represents a portion of the U.S. power system (located in the Midwest), including 14 buses and 20 transmission lines. In this system, the generators at nodes 2, 3, and 8 are replaced with wind farms, each with an installed capacity of 40 MW and equipped with 8 NREL 5 MW wind turbines. The load data at various time points during the passage of the typhoon are shown in Figure 6.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{**(a) Parameters of Towers**} & \multicolumn{3}{c}{**(b) Parameters of Transmission Lines**} \\ \hline \(a\) & \(b\) & \(v_{\text{m}}\) & \(v_{\text{cr}}\) & \(W_{\text{max}}^{\text{norml}}\) & \(W_{\text{max}}\) \\ \hline \(1.9249\times 10^{-7}\) & 0.0055 & 53 m/s & 20 m/s & 0.7 kN/m\({}^{2}\) & \(2.1\) kN/m\({}^{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The parameters for the towers and transmission lines.  
Figure 5: Typhoon path and IEEE 14 system topology.
## 2 Basic Concepts of Optical Thermography NDT
### Passive Thermography  
In passive thermography, no external energy is used to excite thermal gradients on the surface of the structural component under investigation. The features of interest are naturally at a lower or higher temperature than the background. In general, it is a qualitative approach, since the main objective is to pinpoint discontinuities [21]. This approach operates well in the SWIR range of 3-5 \(\upmu\)m and LWIR range of 8-12 \(\upmu\)m and has many applications such as process monitoring, condition monitoring, predictive maintenance, medical imaging, building thermal efficiency, forest fire detection, monitoring of road traffic, monitoring of power stations, night vision and surveillance, agriculture, and biology [2; 22]. Unfortunately, this technique is limited in NDT applications as a sufficient, natural thermal contrast should be present.
# Graph-Based Taxonomic Semantic Class Labeling
## 3 Graph-Based Taxonomic Semantic Associative Community Labeling
### Graph-Theoretical Foundations
#### 3.1.2 ConGraCNet Method  
The ConGraCNet method [5] relies on a set of syntactic relations used to construct dependency-based multilayer lexical networks. Each layer is constructed from lexemes collocated in a syntactic dependency that can be harnessed for their semantic potential and function [45; 50]. The basic coordination network layer is constructed from collocated lexemes in the syntactic dependency _and/or_, which typically associates two ontologically related entities, attributes, or processes. The highest-ranked co-occurrences of the seed lexeme in the coordinated second-order construction [lexemeA and/or lexemeB] are used to build and analyze a graph of syntactically collocated lexemes. They form conceptually associated local clusters, or a second-order friend-of-a-friend, lexical graph with subgraph communities of conceptually associated lexemes, FoF\([and|or]_{s}\), representing the conceptual domains related to the source lexeme \(s\). The semantically coherent lexical clusters form the basis for dependency-based labeling. More details on the method, including the relevant theoretical grounding, can be found in [5].  
For an example of FoF\([and|or]_{ethics}\) graph see Figure 1. Note the various identified subgraphs exhibiting various conceptual domains closely related to the meaning of the lexeme _ethics_ as expressed in the enTenTen13 corpus. In particular, the community of famous philosophers is clustered around the lexemes _[PERSON]_ and _aristotle_.  
For a selected polysemous source lexeme \(s\), the ConGraCNet method first constructs a weighted undirected clustered graph FoF\([and|or]_{s}\) and computes the centrality of its lexeme nodes. Once the algorithm provides the graph structure with subgraph communities, the ground has been set for the task of assigning labels to each subgraph community.  
An important step in the labeling process is to identify the most important nodes of each subnetwork FoF\([and|or]_{i}^{s}\), as these nodes carry the most representative community features. Since the \(SLI\) measure detects the level of node integration in the network, it plays an important role in the subsequent task of weighting semantic class labels, both for syntactic _is_a_ dependency labeling and hypernym labeling.
# CT-RPL: Cluster Tree Based Routing Protocol to Maximize the Lifetime of Internet of Things
## 3 Cluster Tree-Based Routing Protocol
### System Model  
#### 3.1.1 Network Model  
The network area comprises of \"N\" nodes, which are placed randomly. The DODAG root is deployed on the top of the network. Initially, all the nodes have the same energy. Figure 1 shows the CT-RPL network model.  
Figure 1: Cluster tree-based routing protocol (CT-RPL) network model. DODAG: destination-oriented directed acyclic graph.  
The following assumptions were made for CT-RPL.  
* All the nodes are same types of nodes with equal initial energy.
* The nodes are stationary and located randomly in the network.
* The CT-RPL follows the multi-hop routing from the CH node to the DODAG root.
* All the cluster members in the cluster communicate with the DODAG root through the CH node.
# Potential Use of Paper Mill Sludge in Improving Soil Quality for Plant Growth
## 2 Materials and Methods  
### Raw Materials and Composting  
To prepare a soil modifier from paper mill sludge (hereafter referred to as PMS), dehydrated PMS was provided by Moorim P&P Co., Ltd. in Jinju, Korea. It has been reported that the PMS does not contain harmful heavy metals in the soil [1]. The auxiliary raw materials used to make the soil modifier were oilseed cake (hereafter referred to as CO) from camellia (_Camellia japonica_), oilseed cake (hereafter referred to as TO) from the toothache tree (_Sapium japonicum_), and peat moss (P, NordTorf's Miniballen, Aknistes novads, Latvia) with an organic nitrogen content of 0.3% and an organic carbon content of 52%. CO was provided by the Korea Camellia Research Institute in Tongyeong, Korea, and TO was supplied by Jirisan Sancho Co. in Hadapo, Korea. All the raw materials were ground to a particle size that was passed through a 40-mesh wire before mixing. Table 1 shows the types of raw materials used in the study.  
The mixtures of PMS with camellia oilseed cake (CO) and peat moss (P), as well as with oilseed cake of toothache tree (TO) and peat moss (P), were mixed with water in a ratio of 1:1 and then composted in a sealed container at room temperature for 30 days. Every 15 days, these mixtures were turned upside down to mix, aerate, and accelerate the degradation process.
# Effects of Bilberry Extract on Hepatic Cholesterol Metabolism in HepGG2 Cells
## 3 Results
### BE on Genes for Fatty Acid Metabolism  
The mRNA levels of genes for hepatic lipogenesis and fatty acid oxidation were measured to examine whether BE can alter fatty acid metabolism in HepG2 cells. The lipogenic transcription SREBP1c can activate genes for de novo fatty acid biosynthesis such as acyl-CoA carboxylase (ACC), fatty acid synthase (FAS) and stearoyl-CoA desaturase-1 (SCD-1). In 100 \(\upmu\)g/mL of BE-treated HepG2 cells, the expression of SREBP1c was significantly attenuated, with concomitant decreases in the mRNA level of ACC and FAS. There was no significant alteration of SCD-1 by BE treatment. The expression of CPT1, the key regulatory enzyme for mitochondrial fatty acid oxidation, was significantly and dose-dependently increased in BE-treated HepG2 cells. Furthermore, BE significantly increased the mRNA abundance of ACOX, the essential enzyme for peroxisomal fatty acid oxidation (Figure 5).  
Figure 4: The effect of BE on the genes regulating biliary cholesterol efflux in HepG2 cells. The cells were treated with either 50 or 100 \(\upmu\)g/mL of BE for 24 h. (**a**) The mRNA abundance was determined by qRT-PCR analysis. Bars with different letters are significantly different (\(p<0.05\)). Values are means \(\pm\) S.E.M. \(n=6\). (**b**) The representative image of the Western blot is shown. \(\upbeta\)-actin served as a loading control.  
Figure 5: The expression of genes involved in fatty acid synthesis and oxidation in BE-treated HepG2 cells. Cells were treated with either 50 or 100 \(\upmu\)g/mL of BE for 24 h. The mRNA abundance was determined by qRT-PCR analysis. Bars with different letters are significantly different (\(p<0.05\)). Values are means \(\pm\) S.E.M. \(n=6\).