esa-sceva/satcom-chunk-collection huggingface dataset:
# Improvement of the Transglycosylation Efficiency of a
###### Abstract
The combined use of glycosyntase variants and oxazoline substrates has proven particularly useful for the synthesis of LNT2 from \(N\)-acetyl-D-glucosamine 1,2-oxazoline and lactose [17; 23], whereas LNT synthesis suffered from enzymatic as well as spontaneous hydrolysis of LNB-oxazoline substrate when using the LNBase LnbB from _Bifidobacterium bifidum_ JCM 1254 [15]. Obviously, the use of the reaction intermediate--the oxazoline substrate derivative--as donor substrate is not limited to glycosyntase variants, as also shown in the very first report on the use of an oxazoline donor substrate in transglycosylation [24]. Indeed, successful transglycosylation with oxazoline donor substrates has been reported for wild-type GH20 enzymes as well as for variants subjected to other transglycosylation-improving engineering strategies such as loop engineering [15; 16; 17; 18]. In the current work, with the purpose of maximizing transglycosylation efficiency of LnbB (GenBank ABZ78855.1), we designed a set of 12 single amino acid mutations and one deletion mutant of LnbB following several different protein engineering strategies (Figure 2). The 13 LnbB variants were individually expressed heterologously in _Escherichia coli_ BL21 (DE3), and their ability to catalyze LNT formation through transglycosylation was compared in a reaction system of LNB-oxazoline and lactose. For the best-performing variants, the formation of transglycosylation products was studied in more detail in a more classical reaction system comprising pNP-LNB as donor substrate and lactose as acceptor substrate. [PERSON] et al. [9] recently presented a detailed study of transglycosylation by 23 different LnbB variants targeting engineering of substrate-interacting residues. In this work, we present nine additional variants and show further improvement by alternative amino acid substitutions. We also compare LNB-oxazoline and pNP-LNB as donor substrates for the best LnbB variants. Finally, we question the previous statements [9; 15] that LnbB-catalyzed transglycosylation is regioselective.
## 2 Materials and Methods
### Data Fusion
#### 2.4.3 Data Fusion Algorithms  
The main purposes of employing data fusion in different testing environmental conditions are to reduce the probability of error and improve the reliability of defect detection. A wide range of fusion algorithms can be used for the fusion of two-dimensional data, including wavelet-based combinations, Bayesian theory, Kalman filter, Dempster Shafer [39], and artificial neural networks [40]. This piece of work focused on the application of multiple feature-based fusion algorithms, namely averaging, difference, Hadamard, Dempster Shafer rule of combination, and pyramid fusion with saliency detection.  
Simplest of all fusion algorithms, average, is performed at a pixel level--the average of feature matrices obtained from two sources is calculated. On the other hand, difference fusion is estimated based on the feature value difference on each pixel value from different NDT results. Hadamard fusion is calculated with the pixel-wise multiplication of same-size matrices.  
The Dempster-Shafer theory was initially proposed by [PERSON] in the 1960s for reasoning uncertainties and later developed by [PERSON] in the 1970s [41; 42]. The Dempster-Shafer (DS) theory is applicable to instances wherein non-probability uncertainty is evident. The DS evidence theory is based on mass, belief, and plausibility functions. The DS rule of combination is used to calculate the unique evidence mass (\(m\)) for the hypothesis by incorporating the evidence masses (\(m_{1}\), \(m_{2}\)). In this study, the hypothesis was categorized as positive in case of defects and negative in case of non-defects.  
Saliency detection is a hot topic that has recently piqued the interest of researchers from a wide range of disciplines. In our investigation, source images of ultrasonic and radiographic testing were decomposed into approximation base layer coefficients and detail layer coefficients using the multi-scale decomposition algorithm [43]. The salient features of the X-ray and ultrasonic features are extracted using a basic saliency map detection algorithm [44].  
The data fusion algorithms used, their short descriptions, and mathematical formulas are provided in Table 3.
## 3 Results and Discussion
### Sensory Characteristics
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** &
\begin{tabular}{c} **S seaweed** \\ **Powder** \\ \end{tabular} & **KA** & **SP** & **CL** \\ \hline \multirow{4}{*}{Hardness (N)} & 0\% & 23.24 \(\pm\) 1.49 \({}^{\text{d}}\) & 23.24 \(\pm\) 1.49 \({}^{\text{d}}\) & 23.24 \(\pm\) 1.49 \({}^{\text{d}}\) \\  & 2\% & 39.96 \(\pm\) 1.77 \({}^{\text{c}}\) & 57.41 \(\pm\) 3.95 \({}^{\text{b}}\) & 42.15 \(\pm\) 1.96 \({}^{\text{aB}}\) \\  & 4\% & 56.55 \(\pm\) 1.89 \({}^{\text{b}}\) & 62.37 \(\pm\) 2.68 \({}^{\text{b}}\) & 43.82 \(\pm\) 6.49 \({}^{\text{aC}}\) \\  & 6\% & 67.10 \(\pm\) 1.72 \({}^{\text{aB}}\) & 81.84 \(\pm\) 9.12 \({}^{\text{aA}}\) & 46.04 \(\pm\) 6.90 \({}^{\text{aC}}\) \\ \hline \multirow{4}{*}{Cohesiveness} & 0\% & 0.66 \(\pm\) 0.02 \({}^{\text{a}}\) & 0.66 \(\pm\) 0.02 \({}^{\text{a}}\) & 0.66 \(\pm\) 0.02 \({}^{\text{a}}\) \\  & 2\% & 0.64 \(\pm\) 0.01 \({}^{\text{aA}}\) & 0.65 \(\pm\) 0.00 \({}^{\text{aA}}\) & 0.71 \(\pm\) 0.01 \({}^{\text{aA}}\) \\  & 4\% & 0.67 \(\pm\) 0.02 \({}^{\text{aA}}\) & 0.65 \(\pm\) 0.00 \({}^{\text{aA}}\) & 0.64 \(\pm\) 0.04 \({}^{\text{aA}}\) \\  & 6\% & 0.67 \(\pm\) 0.01 \({}^{\text{aA}}\) & 0.65 \(\pm\) 0.00 \({}^{\text{aA}}\) & 0.67 \(\pm\) 0.11 \({}^{\text{aA}}\) \\ \hline \multirow{4}{*}{Springiness} & 0\% & 0.86 \(\pm\) 0.09 \({}^{\text{a}}\) & 0.86 \(\pm\) 0.09 \({}^{\text{a}}\) & 0.86 \(\pm\) 0.09 \({}^{\text{a}}\) \\  & 2\% & 0.91 \(\pm\) 0.06 \({}^{\text{aA}}\) & 0.86 \(\pm\) 0.02 \({}^{\text{aA}}\) & 0.88 \(\pm\) 0.03 \({}^{\text{aA}}\) \\  & 4\% & 0.91 \(\pm\) 0.01 \({}^{\text{aA}}\) & 0.82 \(\pm\) 0.01 \({}^{\text{aA}}\) & 0.87 \(\pm\) 0.04 \({}^{\text{aA}}\) \\  & 6\% & 0.89 \(\pm\) 0.09 \({}^{\text{aA}}\) & 0.86 \(\pm\) 0.09 \({}^{\text{aA}}\) & 0.87 \(\pm\) 0.08 \({}^{\text{aA}}\) \\ \hline \multirow{4}{*}{Chewiness} & 0\% & 14.78 \(\pm\) 3.14 \({}^{\text{d}}\) & 14.78 \(\pm\) 3.14 \({}^{\text{d}}\) & 14.78 \(\pm\) 3.14 \({}^{\text{d}}\) \\  & 2\% & 21.23 \(\pm\) 1.58 \({}^{\text{cC}}\) & 34.99 \(\pm\) 1.27 \({}^{\text{b}}\) & 26.94 \(\pm\) 2.96 \({}^{\text{aB}}\) \\  & 4\% & 34.33 \(\pm\) 1.53 \({}^{\text{bA}}\) & 32.84 \(\pm\) 2.17 \({}^{\text{bA}}\) & 25.07 \(\pm\) 2.80 \({}^{\text{aB}}\) \\  & 6\% & 39.67 \(\pm\) 0.97 \({}^{\text{aB}}\) & 42.72 \(\pm\) 6.52 \({}^{\text{aA}}\) & 26.10 \(\pm\) 2.11 \({}^{\text{aC}}\) \\ \hline \hline \end{tabular} Mean \(\pm\) standard deviation in the same column with different superscript alphabates (\(p<0.05\)) within the same parameter. Mean \(\pm\) standard deviation in the same row with different superscript alphababets (\({}^{\text{a-C}}\)) indicates there are significant difference (\(p<0.05\)). KA: Sausage with added _Kappapphycus aluruzeli_, SP: sausage with added _Sargassum polycystum_, CL; sausage with added _Culterpa lentilifa_.  
\end{table} 
Table 5: Texture profile analysis of all sausage formulations. that of another study, which reported that consumers preferred the control sample more than chicken steak supplemented with the seaweed _Hinanthhalia elongate_.
## 3 Results and Discussion
### Thermal Analysis  
Thermogravimetric analysis (TGA) and DSC have been widely applied as the main methods of evaluating thermal stability of materials for many years. Thermal stability analysis for RBPs was performed by TGA and DSC, and the results are shown in Table 3 and Figure 5. The first mass loss stage near 110 \({}^{\circ}\)C was due to the loss of adsorbed water on the RBP surface [5]. A weight loss of 73-88% after the onset of heat treatment at 280-300 \({}^{\circ}\)C was mainly due to the pyrolysis of RBPs, which was consistent with other polysaccharide studies [11; 25]. Figure 5A shows improvement in the thermal stability of RBPs with increased extraction temperature. According to RBP TGA curves, values related to RBP30, RBP40, RBP50, and RBP60 of around 350-550 \({}^{\circ}\)C are lower compared to RBP70 and RBP80. It is well known that the polar moieties in the network may act as heat barriers and consequently enhance the overall thermal stability of RBP70 and RBP80 [26]. Another factor is their macromolecular  
Figure 4: SEM images of RBPs extracted at different temperatures.  
structure. The thermal behavior and GPC of RBPs are provided in Table 3 and Figure 5. The GPC curves show that the maximum peaks of RBP70 and RBP80 (both laser signals and DRI signals) occur earlier than those of RBP30, RBP40, RBP50, and RBP60. According to Table 3, the molecular weight of RBP70 and RBP80 is higher than that of RBP30, RBP40, RBP50, and RBP60.  
The typical endotherms with peak temperatures of RBPs (98.42-111.88 \({}^{\circ}\)C) appeared in the DSC thermograms in Figure 5B, which was due to a loss of hydroxyl group of RBPs as water molecules.  
The results are in good agreement with previous TGA results. A shoulder exothermic peak was observed by DSC with an onset temperature of \(\sim\)155 \({}^{\circ}\)C, which signifies the second loss of hydroxyl group of RBPs as water molecules. The reaction is as follows: RBP - H\({}_{2}\)O \(\xrightarrow{\Delta}\) RBP + H\({}_{2}\)O. Endothermic and shoulder exothermic peaks revealed that RBP40 and RBP70 had higher heat flow because of different treatment methods. According to Table 3, the upshift of the peak temperature of RBPs prepared under higher temperatures demonstrates that RBPs with higher molecular weights prepared under higher temperatures were enhanced in comparison with the samples treated under lower temperatures, because short-chain RBPs have strong hydrogen bonds within their junction zones that require more energy to decompose the crystalline structure [22].
## 2 Materials and Methods
### Spatial Dynamic Panel Lag Instrumental Variables Fixed-Effects Model (SDPD IV)
Most studies have found that meteorological factors often have uncertain or varying effects across different geographic areas. Therefore, this study uses these variables as instrumental variables that indirectly influence economic growth, labor productivity, and population settlement. Incorporating them in this way helps improve the accuracy of the SDPD IV model estimates and reduces the volatility associated with meteorological conditions. This approach also contributes to the academic literature by offering conclusions from a more diverse range of perspectives. In the first stage our main explanatory variables are estimated as follows:  
\[\hat{X}_{j,i,t}=\delta_{1}LnLandtemp\_night_{i,t}+\delta_{2}LnRainfall_{i,t}+ \delta_{3}LnWindspeed_{i,t}+\varnothing_{j}X_{j,i,t-1}+\epsilon_{i,t} \tag{7}\]  
where \(X_{j,i,t}\) are the logarithms of GPP per capita (_LnGPPpc_), GPP per capita squared (_LnGPPpc_2), population density (_LnPop_dens), and road density (_LnRoaddens_) where \(j\) is variable lists, \(i\) is individual unit and \(t\) is time period. After completing the estimation in the first-stage equation (stage 1), the estimated coefficients are further analyzed in the second-stage equation (stage 2) to obtain the most accurate estimated equation. The statistical test results of the first-stage regression can be found in Appendix F, Table 10.  
The specific form of the second-stage Spatial Dynamic Panel Lag IV Fixed-Effects Model for the whole region, BKK&VIC, CE, EA, WE, NE, NO, and SO is as follows:  
\[ \begin{split} LnEnergycons\_pc_{i,t}=a+\varphi\sum\limits_{j=1}^{P}w _{ij,}LnEnergycons\_pc_{j,t}+\rho LnEnergycons\_pc_{i,t-1}+\sum\limits_{j=0}^{q} \beta_{1j}LnG\dot{P}pc_{i,t-j}\\ +\sum\limits_{j=0}^{q}\beta_{2j}LnG\dot{P}ppc^{2}_{i,t-j}+\sum \limits_{j=0}^{q}\beta_{3j}LnP\dot{p}\_dens_{i,t-j}+\sum\limits_{j=0}^{q} \beta_{4j}LnRo\dot{a}ddens_{i,t-j}+\gamma_{y,i}f_{y,t}+\epsilon_{i,t}\end{split}  \tag{8}\]
# A Space-Interconnection Algorithm for Satellite Constellation Based on Spatial Grid Model
## 2 Methods
### GeoSOT-3D and Aerospace Grid BigTable
#### 2.1.2 Aerospace Grid Indexing BigTable  
The grid coordinate code is globally unique and contains spatial information. It can be associated with the spatial information of aerospace and multi-sourced data (Figure 3a). Subsequently, the one-dimensional grid code is used as the primary key in the aerospace grid indexing BigTable, and the k-dimensional search is then converted to a one-dimensional search to reduce the complexity of the algorithm. Considering that the code is continuous, organizing spatial data according to the coding order can ensure efficient access to its spatial neighborhood. Generally speaking, spatial data that are adjacent in space are more likely to be accessed simultaneously. The structure of the aerospace grid indexing BigTable is summarized in Table 2.  
In the designed BigTable (Table 2), id is an integer or string type, supporting 2-D or 3-D grid code and various spatial operations based on code algebra. Visible is stored in an array format, that is, the visible grids of _id_ can be stored as an array here.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Id** & **Name** & **Type** & **Description** \\ \hline
1 & id & STRING/INT & GeoSOT-3D grid code \\
2 & visible & STRING/INT & Visible grid codes \\
3 & DataExist & INT & Whether there are data in the grid (true or false) \\  & Data & JSON & Data description \\
4 & Time & TIME & Time, including the time to enter the grid, the time to leave the grid \\  & DataSource & JSON & Data source description \\  & Metadata & JSON & Metadata information \\ \hline \hline \end{tabular}
\end{table}
Table 2: Structure of Aerospace grid indexing BigTable.  
Figure 2: GeoSOT-3D model: (**a**) Global grid model; (**b**) Z-order filling curve.  
The most important aspect of BigTable is to build an index implementation mechanism based on grid code to improve the query efficiency in applications. In our study, a spatio-temporal secondary indexing model is established; that is, the spatial code is first sorted and then the time code in each grid is sorted (Figure 3b).  
In the indexing model (Figure 3b), \(Code_{i}\) is the \(i\)th grid code in the aerospace, \(Tcode_{i}\) is the \(i\)th time code (using the coding method in [37]), and \(Data_{i}\) is the spatial data or entity.
## 6 Conclusions and Future Work  
In this work, firstly, we have developed the 3 GPP's 5G NR V2X network simulation model using the ns-3 discrete event simulator and then studied the V2X performance under various network settings. We found that, in 5G NR V2X, a higher sub-carrier spacing (\(\mu=2\)) has an overall better performance than a lower sub-carrier spacing (\(\mu=0\) or \(\mu=1\)) in terms of achieving a higher throughput and packet reception ratio and achieving a lower inter-packet reception delay. From the analysis, we also observed that, in the highway setting, until the 1000 m communication range, there were no noticeable packet drops in NR V2X communication. Additionally, we found that increasing the transmitting power is positively associated with improving the performance. However, although increasing the packet size boosts throughput, it is negatively associated with achieving a lower inter-packet reception delay. We have also found that increasing the modulation and coding scheme (MCS) value increases the network performance for different sub-carrier spacing. However, with a communication range setting of 600 m, for \(\mu=2\), we obtain the best result at an MCS value of 10, whereas, for \(\mu=0\) and \(\mu=1\), we need to increase the MCS value up to 15 to obtain the same or better performance. The result is consistent with similar studies that have been conducted [1; 53; 54].  
In our future research work, we want to extend our simulation by introducing communication from infrastructure to vehicle (I2V) and vehicle to pedestrian (V2P). Another interesting future research direction might be leveraging the developed NR V2X communication model to achieve enhanced and extended cooperative perception for improved vehicle motion planning.  
This work was completed with contributions from all the authors. Conceptualization, G.G.M.N.A., M.N.S, M.S.M. and S.A.S.; methodology, G.G.M.N.A., M.S.M. and S.A.S.; software, G.G.M.N.A., M.N.S. and S.A.S.; validation, G.G.M.N.A., M.N.S., M.S.M. and S.A.S.; formal analysis, G.G.M.N.A. and M.S.M.; data curation, G.G.M.N.A., M.N.S. and S.A.S.; writing--original draft preparation, G.G.M.N.A., M.N.S., M.S.M., S.A.S. and Y.W.; writing--review and editing, G.G.M.N.A., M.N.S., M.S.M., S.A.S. and Y.W.; visualization, G.G.M.N.A., M.S.M. and S.A.S.; funding acquisition, G.G.M.N.A. and Y. W. All authors edited, reviewed, and improved the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Faculty Scholarship Award (FSA) for the academic year of 2022-2023 from Bradley University under grant no. 1326841.  
The data can be shared upon request. The data are not publicly available due to legal issue.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
3 GPP 3 rd Generation Partnership Project C-V2X  Cellular Vehicle-to-everything, also known as (LTE-V2X) LTE-V2X  Long Term Evolution Vehicle-to-everything DSRC  Dedicated Short Range Communication 5G NR 5 th Generation New Radio 5 GAA 5 th Generation Automotive Association
## 2 Methodology
### Formal Definition
#### 2.5.2 States  
The state space for the DQN is both the visual comfort and discomfort of the occupants. The visual comfort/discomfort is defined based on the daylight availability and discomfort glare. The state-space here is continuous based on these two parameters. The comfortable daylight availability in the workplace considered here is between 300 and 3000 lux. Any light level above 3000 lux increases the discomfort as it becomes too bright in a workspace. The imperceptible glare scenario is considered based on the DGP values between 0.2 and 0.36. Any value above 0.36 increases the chance of glare.  
The DGP values are obtained from the fisheye images generated from the radiance simulations. Section 2.3.1 explains how the DGP values are generated. Similarly, Section 2.3.2 explains how daylight at a workspace is calculated using radiance simulations.  
The DGP values are calculated for each workspace separately, which means the state of each occupant can be different. This results in high dimensional state space. At the same time, the daylight is calculated for 3 points for each workspace. This adds another 12 dimensions to it. Therefore the illuminance value is averaged for workspace 1 and 4 (both near the facade) and workspace 2 and 3 (both away from the facade). This reduces the dimensions of state space to only 6. This can be represented as follows.  
\[\langle\langle d_{1},d_{2},d_{3},d_{4}\rangle,\langle l_{1,A},l_{2,3}\rangle\rangle\]  
where \(d_{1},d_{2},d_{3},d_{4}\) are DGP values at respective workspaces and \(l_{1,A},l_{2,3}\) are averaged workplace illuminances.
# Influence of Carbon: Metal Ratio on Tribological Behavior of Mo-W-C Coating
## 4 Discussion
These findings indicate that Mo-W-C coatings have outstanding tribological behavior (very low \(\mu\)-0.033-0.052 and negligible wear of both the coatings and the counterparts) at ambient temperature, and the formed tribolayer contains WS\({}_{2}\) and graphitic carbon debris particles. No significant influence of the C/(Mo+W) ratio is observed at ambient temperature. However, low C/(Mo+W) ratios are particularly beneficial to achieve excellent tribological properties at 200 \({}^{\circ}\)C. The in situ formation of well-known solid lubricants MoS\({}_{2}\) and WS\({}_{2}\), along with the presence of amorphous carbon debris particles in the protectivetribolayer, are the keys to achieve significantly low \(\mu\) (\(\sim\)0.042-0.072) during lubricated sliding at 200 \({}^{\circ}\)C. This protective tribolayer continuously reduces the \(\mu\) with an increase in sliding distance (Figure 8) and simultaneously guards the coating from being worn out (Figure 13). It adheres to the counterpart surface (Figure 9) and protects it from high wear. On the other hand, a high C/(Mo+W) ratio does not possess these specific qualities to a similar extent and eventually leads to severe wear of the counterpart surface.
## 3 Experimental Setup
### Implementation
#### 3.2.2 Method B: Image Rotation and 1D Gradient Evaluation
The correction of the natural transition line \((Y)_{\text{tr, nat}}\) in the image by again applying the robust linear regression algorithm RANSAC on the selected \(\Delta_{\text{nat}}\) after the previous step. The second step is the calculation of the final actual transition line. In order to close potentially existing gaps at the tip of turbulence wedges a morphological dilation followed by an erosion is conducted. Afterwards, the first pixel upstream in each column contained in \(\Delta_{\text{tr}}\) is saved as \(y_{\text{tr,i}}\). All \(y_{\text{tr,i}}\) for all image columns \(i=1,...,I\) are saved as the final actual flow transition line \((Y)_{\text{tr, act}}\). In a last step, the transition line is smoothed with a \(\sigma=1\) Gaussian filter in \(x\)-direction to reduce the influence of remaining outliers. 5. Calculation of the Laminar Flow Reduction (LFR)  
After acquiring the flow transition lines, the natural and actual flow transition as well as the leading edge position over the image columns \(i=1,...,I\) is used to calculate the laminar flow reduction with Equation (2) based on the definition in Section 2.3.