esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Sensitivity Analysis of Design Parameters
The pitching amplitude in the wing-tip section is one of the most influential parameters related to the rotation motion. The second one is the pitching amplitude at the root of the wing, and the regulating parameter of rotation angle (\(C_{\theta}\)) has minimal impact on both force and power. When \(\theta_{rm,t}\), \(\theta_{rm,r}\), and \(C_{\theta}\) are 0.8 times their respective optimal values, the vertical force is approximately 24%, 7%, and 1% higher than that obtained for the optimized wing. The associated power is increased by 38%, 14%, and 4%, respectively. When the pitching amplitude at the tip remains constant, the increase in the wing-root pitching amplitude causes the angle of attack across all wing sections to decrease. The same is true when the wing-tip pitching amplitude decreases while keeping the amplitude at the root unchanged. When the angle of attack decreases, the vertical force generated by the wing reduces. That is the same situation as the change in lift generated by a non-twisting wing at a low angle of attack (<45 deg). How the spanwise distributions of wing rotation angle outside the currently considered range affect wing performance will be discussed further in the next section. The optimal value of \(C_{\theta}\) determines the profile of the rotation angle approaching the round-trapezoidal shape. As its value gradually increases, the duration of rotation motions decreases. Due to the increase of the angular acceleration, the negative contribution of the inertial component to the total vertical force becomes greater, and thus the resultant force decreases. However, once the value of \(C_{\theta}\) corresponding to the minimum vertical force is exceeded, the contribution of the translational component becomes more dominant. The vertical force gradually increases due to the increased time spent in the translational stage. That also causes a slight increase in the associated power. However, other components, by contrast, are relatively unimportant because the opposite force component is generated during pronation and supination. That makes little contribution to the cycle-averaged value. Further, the power is less sensitive because negative power components are excluded from the mechanical power.
## 7 Evaluating the Robot Performance with and without the Tiling  
Providing access to narrow and difficult spaces, self-reconfiguration is totally reasonable for maximizing the area coverage. However, shape-shifting may sound a waste of energy. In this section, a comparison is illustrated with a fixed-morphology robot with the same size (O reconfiguration in Figure 3) that follows no tiling theory but tries to cover the area like the current commercialized cleaning robots. Figure 14 shows nine such sweeping solutions for the entire experimental environment in which each of the solutions appears with the corresponding numbering and each yellow square is an O morphology, as depicted in Figure 3. In addition, the gray area is the area totally inaccessible and the purple area is the area that is accessible at the expense of re-coverage. The total area coverage percentage and utilized energy amount in each sweeping solution is summarized in Table 11 in which the reported area is the covered area with avoidance of re-coverage, i.e., the whole area subtracted by the gray and purple areas and the area occupied by obstacles.  
Figure 14: Sample sweeping solutions with fixed morphology and no tiling theory.  
Figure 13: A composition of best tiling sets for each sub-area.  
Considering Table 11, it is obvious that the fixed-morphology performance was not as energy-efficient as expected and its energy consumption for sweeping the entire space is almost the same as the best alternative selected by MCDM (5.981 A) or even higher (worse) than that in some cases (sweeping solution 5, 6, 7, 8, 9). However, the shape-shifting morphology evidently results in higher area coverage. The results in Table 11 also justify this fact: In a fixed-morphology scheme with no tiling-path-planning, we have, at most, 92% area coverage, but the approach presented in this paper results in almost 96% area coverage, see Figure 13.  
To summarize, when a higher area coverage is guaranteed by self-reconfiguration, and the energy consumption in a fixed-morphology scheme is at the level of shape-shifting morphology (slightly better: \(5.98-5.23=0.75\) (A) or even worse: \(5.98-6.67=-0.7\) (A)), it sounds reasonable to apply the MCDM-supported tiling path-planning even though it requires a slight compromise in energy consumption.
## 1 Introduction  
With the growing concern for the environment due to greenhouse gas emissions resulting from the use of fossil fuels to generate electricity, the concept of the smart grid has emerged. The smart grid allows renewable energy to be integrated into the power grid, reducing dependence on power plants that use fossil fuels to generate electricity. A smart grid, also referred to as an intelligent grid [1], uses network and information technology to modernize the power infrastructure and provide a better power supply to customers. The two-way communication mechanism is used by a smart grid to transmit electricity between centralized generation plants and end users and from distributed energy sources (DES) to other customers. In delivering electricity to consumers, the smart grid is much more autonomous and has higher reliability and efficiency. Utilities will use existing power grids in smart grids, which will reduce the cost of building new power plants and substations. The smart grid has evolved from a vision to a mission that is increasingly being implemented around the world. Clear energy policies and audit management are helping to support global smart grid initiatives [1; 2; 3]. The authors of [4] presented the importance and effects of big data analytics in smart grid performance to achieve sustainable development goals (SDG). Many developed countries have already implemented smart grid technology in their power grids. However, some other countries are lagging behind in implementingsmart grid technology. The introduction of smart grids required the traditional utility grids to be updated [5; 6; 7]. The control system applied in smart grids and microgrids with a focus on techniques related to Petri nets is reviewed in Reference [8].  
This paper discusses and explains the smart grid architecture, its operation, and implementation issues. This paper aims to show the importance of smart grid technology in designing the hybrid electricity system of the traditional grid and achieve higher efficiency in power transmission and distribution than the traditional grid. It addresses the definition of a smart grid, the growth of smart grids, technology for smart grids, innovation for smart grids, cybersecurity for smart grids, and functionality of smart grids. This paper also aims to explain the research work on smart grids, its implementation challenges and problems, and possible future research areas. More productive knowledge and experience in smart grid operation will allow researchers and policymakers to show greater interest in building the capabilities and implementation of the smart grid. A large number of research papers have been analyzed to find the most up-to-date details on the fundamentals, innovations, characteristics, threats, and future potential of smart grids. Smart grid technologies such as smart electricity meters and their applications in smart grids are also discussed. Finally, the role of smart grid metering and communication technology for real-time measurement, monitoring, and the issue of data protection are studied.
## 3 Numerical Results
### Bayesian Update
#### 3.4.2 gPCE-MDA  
The ES-MDA approach is based on a multiple assimilation process to provide a better model constraint especially on strongly non-linear models. Despite the potential improvement with respect to the standard ES method, the MDA approach is rarely used in practice mainly because of the large computational cost associated to the generation of several ensembles. The introduction of an inexpensive surrogate model in place of the full forward model can make also the MDA technique more affordable in real-world problems.  
We consider two test cases differing in the selection of the inflation factors \(\alpha_{MDA}\): (i) constant \(\alpha_{MDA}=n_{MDA}\), and (ii) decreasing \(\alpha_{MDA}=(18.66,14,8,4,2)\). The number of successive assimilations is fixed a-priori and equal to \(n_{MDA}=5\). Each assimilation step is performed by using ensembles with \(n_{MC}=3000\) realizations. This ensemble size allows to achieve the convergence also with the ES technique, as shown in Figure 7. Figure 8 shows the mean and the standard deviation of the updated parameter ensembles in the five successive assimilation steps considering the \"true\" parameter set  
Figure 6: The same as Figure 5 with the forecast ensembles, in grey, obtained through the gPC expansion.
## 3 Results and Discussion  
### Structural and Composition Characterization  
The SEM images of the HABDD surface and its inner structure are shown in Figure 2a. Each hole had a diameter of approximately 0.5 mm (500 \(\upmu\)m) with a depth of 1.5 mm, which is the same as the HABDD thickness. All the substrate surfaces, including the planar surface on both sides and along the hole depth, were covered with diamond phase. The HABDD had a uniformly rough surface compacted with diamond microcrystals, as shown in Figure 2b. Polycrystalline film and diamond crystallites sized at approximately 5 \(\upmu\)m can be observed with good reproducibility. Raman spectrum was used to examine the crystalline quality of the HABDD (Figure 2c). The sharp peak at 1332 cm\({}^{-1}\) indicated the presence of sp\({}^{3}\) carbon as the main component in the HABDD. Besides, there was a small amount of sp\({}^{2}\) carbon in the film, characterized by the peak at 1550 cm\({}^{-1}\). The boron concentration in the HABDD was estimated as 1000 ppm according to the resistivity at 300 \(\Omega\) cm with 10 \(\upmu\)m of thickness, which was measured by the four-probe method with a conductivity meter (DDSJ-318T, Shanghai, China).  
Figure 2: _Cont._
## 2 Production of Lipases
### Production of Recombinant Lipases
#### 2.2.2 Eukaryotic Expression Systems
phaffii_ was performed by using well-characterized integration targets for both strains - the _leu2_ locus in _Y. lipolytica_ and the _his4_ locus in _K. phaffii_. CALB was expressed in _Y. lipolytica_ under the control of erythroid inducible promoter pEYK3 AB and in _K. phaffii_ under the methanol inducible pAOX1. The results showed that, in _Y. lipolytica_, growth rate and detected CALB activity were higher than those achieved in _K. phaffii_. The difference in growth rate has been suggested to be due to the loss of some of the carbon source methanol, which can be converted directly to CO\({}_{2}\) instead of biomass production in _K. phaffii_. Interestingly, even though enzyme activity was higher in _Y. lipolytica_, the mRNA expression levels of CALB encoding gene in _K. phaffii_ were higher than in _Y. lipolytica_. Higher CALB stability with suitable secretion levels may explain the better performance of _Y. lipolytica_[89]. The filamentous fungus _A. niger_ represents an interesting alternative host because of its high production yield of extracellular enzymes, non-toxicity, robustness and GRAS status [101]. However, there are indications that lipase might not be secreted properly by _A. niger_. As an example, the production of a lipase from _T. languinosus_ (accession no. AF054513) resulted in only 1% of _A. niger_ total cellular protein because most of the lipase produced was attached to the mycelium [90]. Another promising fungal host for lipase production is Trichoderma reesei, with potential for high protein secretion and a well-established fermentation platform. It also has a well-characterized genetic toolbox and transformation can be achieved using selection markers and by stable integration into the genome [102]. A drawback encountered with T. reesei as a lipase production host is that it produces a high level of cellobiohydrolase I (CBHI) that represents approximately 50% of all proteins secreted. The reduction in the unnecessary production of CBHI could result in improved levels of heterologous expression. The RNAi mediated gene silencing of cbhI improved the production of the lipase lip from A. niger in T. reesei, with an increase up to 3.2 fold. The reduction in CBHI enhanced the availability of the protein folding and secretion machinery. Moreover, this strategy allowed the host to still have CBHI present, thereby keeping the strain in a more natural state [91].
# Novel Scoring for Energy-Efficient Routing in Multi-Sensored Networks
## 3 The Proposed Mechanism
### Preliminary Formation for the Proposed Work
#### 3.1.4 Identical CNs  
It is probable that at some particular locations in the sensor field, some nodes reside at a very near distance to some other nodes. Such closely located sensor nodes generally sense data in parallel and get RREQs from the same source nodes. Two or more closely deployed nodes with the same connections to other nodes can be denoted as identical to each other. Such nodes will have relatively similar sensed data and similar RREQ from other nodes. It is feasible to utilize such nodes by assigning them more load compared to other nodes. For closely related nodes, an appropriate distance must be configured. A higher distance leads to a larger number of identical nodes, which may cause a negative impact. Contrarily, a lower value reduces the sets of identical nodes, making the proposed work ineffective. In our experiments, we assumed that the nodes with a distance of a quarter of the maximum transmission range of the nodes were identical CNs.  
In Figure 1, nodes \(a\) and \(b\) are CNs of each other and also have similar connections to other nodes, while node \(c\) is alone and has a similar hop level. In case one of the identical nodes exhausts its energy completely, the effect is limited compared to a stand-alone node. Therefore, it is wise to utilize nodes \(a\) and \(b\) more frequently than node \(c\).  
Figure 1: Identical CNs (\(a\) and \(b\)).
## 3 Flexible Merging Decision Methods
### Incomplete Information Static Game Models
#### 3.1.3 Merging Timing Decision Based on Pure-Strategy Nash Equilibrium  
The preceding paragraphs describe the calculation of the expected payoffs of each strategy, considering the safety, timeliness, comfort, and environmental impact of each. The resulting matrix of expected payoffs is presented in Table 2. This study employs the pure-strategy Nash equilibrium as the basis for road allocation. If a strategy combination allows any player to maintain their maximum payoffs while changing the strategies of other players, it is considered a Nash equilibrium. The most widely used equilibria are pure-strategy Nash equilibrium and mixed-strategy Nash equilibrium. Due to space limitations, a detailed description of Nash equilibrium is omitted. However, it is necessary to explain why the mixed-strategy Nash equilibrium is not suitable for this study. This is because the output of mixed-strategy Nash equilibrium is probability. In the practical engineering application related to safety, the output in the form of probability is extremely terrible because even if the probability is small, it may occur (causing accidents). Therefore, the Bool variable of pure-strategy Nash equilibrium output is more suitable for this study. In static games, multiple pure-strategy Nash equilibria may exist or may not exist. This creates ambiguity in determining whether the current MDV driver is willing to cooperate. Consequently, the CAV is unable to determine whether it should insert itself into the main lane. Therefore, when the equilibrium is only \"Merge and Allow\", the CAV can take the action of merging, and in this study, a smooth and continuous micro-Sigmoid curve is used to plan the merging trajectory for the CAVs. Alternatively, when the equilibrium is only \"Not Merge and Not Allow\", CAVs should give up inserting MDV fronts. In the rest of the cases, CAVs are at a loss for what to do. Therefore, this study introduces the DRL algorithm to guide CAVs to take appropriate actions to achieve equilibrium as soon as possible.
## 3 Results and Discussion
### Acetaldehyde Removal Performance of Functionalized Amine
The acetaldehyde removal capabilities of aniline-treated MSP-20 and K-C are, remarkably, increased by approximately 350 and 220 times, respectively. Although the N-functionalized QW showed almost no improvement due to its very low surface area, we found that the amine functional group from aniline enhanced the acetaldehyde removal for all other porous carbons. However, the A@CACF-H was still the best absorbent for acetaldehyde removal, which was confirmed by the removal capability trends of the non-treated carbons. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \begin{tabular}{c} **Total** \\ **Removed** \\ **Amount** \\ **[mg/gl]** \\ \end{tabular} & \begin{tabular}{c} **Breakthrough** \\ **Time [h]** \\ \end{tabular} & \begin{tabular}{c} **Supported** \\ **Aniline** \\ **Amount [\%]** \\ \end{tabular} & \begin{tabular}{c} **Supported** \\ **Aniline** \\ **Amount** \\ **[mmol]** \\ \end{tabular} & \
\begin{tabular}{c} **Total Removed** \\ **Amount** \\ **[mmol]** \\ \end{tabular} &
\begin{tabular}{c} **Acetaldehyde** \\ **Uptake Amount** \\ **Ratio by 1 mol of** \\ **Aniline** \\ \end{tabular} \\ \hline
1\% A@CACF-H & 60.5 & 142 & 4.3 & 0.023 & 0.076 & 3.30 \\
2\% A@CACF-H & 70.7 & 165 & 7.8 & 0.042 & 0.088 & 2.09 \\
3\% A@CACF-H & 40.9 & 95 & 8.7 & 0.047 & 0.051 & 1.08 \\
5\% A@CACF-H & 27.0 & 63 & 9.9 & 0.053 & 0.033 & 0.62 \\
10\% A@CACF-H & 6.5 & 15 & 10.7 & 0.057 & 0.008 & 0.14 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Elucidation of total removed amount and removal ratio of acetaldehyde by aniline supported CACF-H.  
Figure 3: The comparison of CH\({}_{3}\)CHO adsorption performances of 2% aniline supported samples of CACF-H, MSP-20, K-C, and quartz wool (QW).
## 2 Materials and Methods
### Overall Methodology  
#### 2.5.1 Extraction of SbPPs and GbGPPs  
The nine satellite precipitation products used in this study were extracted through different methods. PERSIANN group of products were directly obtained from the Center for Hydrometeorology and Remote Sensing (CHRS) in CSV file format. IMERG and TRMM products were obtained as NetCDF files from National Aeronautics and Space Administration, U.S.A. (NASA) GESDISC portal. Afterwards, IMERG was extracted through the process of merging the files in Climate Data Operator (CDO) followed by the extraction using R coding in RStudio. Finally, the TRMM products were merged together, using a similar approach to IMERG. However, the extraction of the point rainfall data was carried out using MATLAB. Furthermore, GPCC, APHODITE_V1801 and APHRODITE_V1901 were extracted as NetCDF file format.
## 3 Results
### The Structural Prediction and the Catalytic Region Analysis
All these residues were so oriented that their side chain groups could form hydrogen bonds with a polysaccharide, thus exercising certain functions in the binding of Man26E with mannan, and as a result, affecting the catalytic function. On the basis of the binding mode of CjMan26 C, Gal1 Man4 was docked into the Man26E modeled structure (Figure 4). Figure 3: The active apparatus of Man26E. (**a**) Schematic representation of surface electrostatic potential distribution for Man26E. The catalytic cleft on the upper part of the molecule was indicated by an arrow. The figure was made using the software PyMol 2.3.2; (**b**) sketch map of surface electrostatic potential distribution for Man26E docking Gal1 Man4; (**c**) sketch map of surface electrostatic potential distribution for CjMan26C docking Gal1 Man4. Gal1 Man4 represented in red; (**d**) the structural elements participating in the Man26E active center. Residues were shown in ball-and-stick model. The conserved catalytic residues (Glu183, Glu282) and other hydrogen-bonding polar residues that surrounded the exposed tryptophan residues (containing Trp79, Trp188, Trp314 and Trp318) jointly constituted the active center of Man26E. The catalytic residues were shown in green, the hydrophilic and charged residues in light blue, and the exposed tryptophan residues in yellow. The catalytic device, which was located at the \(-1\) subsite, consisted of the catalytic acid/base Glu183, and nucleophile Glu282. The mannose at the \(-2\) subsite made several interactions with Man26E. O2 produced a polar connection with the side chains of Trp314. The exocyclic oxygen made polar interactions with Trp314 and Tyr43, O3 and O6 also formed hydrogen bonds with Tyr43. The amino acid residues interacting with the substrate were less conserved a at the \(-2\) subsite of _Cj_Man26C and Man26E, and only the residue Trp314 in Man26E was structurally equivalent to Trp373 in _Cj_Man26C. The catalytic nucleophile was likely to stabilize its position by interacting with Arg179 and Tyr258. His182 and Glu282 made hydrogen bonds with O2 of the \(-1\) subsite sugar in its distorted conformation. Other two polar interactions between the \(-1\) subsite sugar and Man26E occurred at O3 which formed polar links with the imidazole ring of His114 and carbonyl oxygen in the side chain of Glu183. Unusually, no more polar interactions were found between the \(-1\) subgar and Man26E. The sugar at the \(+1\) subsite made limited polar interactions with Man26E. O3 hydrogen bonded to carbonyl oxygen in the side chain of Glu183, whereas O6 produced a polar link to the side chain of Asp234 (Figure 3d). Man26E had a hydrophobic platform extending from the-1 subsite, in which Trp314 stacked against the sugar ring, while Trp188 took on the same planar orientation as the pyranose sugar at the \(+1\) subsite (Figure 3d). At the \(+2\) subsite, Asp234 interacted with O2 and O3, whereas Arg237 made polar connections with O1, O2 and the endocyclic oxygen at almost equal distances.