esa-sceva/satcom-chunk-collection huggingface dataset:
# A Method of Bus Network Optimization Based on Complex Network and Beidou Vehicle Location
## 3 Weight Definition Method of Weighted Bus Network
### Calculation Method of Bus Driving Efficiency  
The physical meaning of bus efficiency refers to the number of passengers served by the unit bus impedance; that is, the bus efficiency between stations is proportional to the bus passenger flow between stations, and is inversely proportional to the impedance between stations, and the impedance can be expressed as a temporal form. Therefore, the calculation method of bus driving efficiency can be put forward:  
\[W_{ij}=\frac{x_{ij}}{t_{ij}} \tag{3}\]  
\(W_{ij}\) represents the bus driving efficiency between station \(i\) and station \(j\); \(x_{ij}\) represents the passenger flow between station \(i\) and station \(j\), which can be obtained by the OD matrix obtained from the IC card swipe data on the bus; \(t_{ij}\) represents the bus travel time between station \(i\) and station \(j\), which can be obtained by BPR function. \(W_{ij}\) is the weight of the edge, the greater the weight of the edge, the higher the efficiency of the bus running on the road [13].  
In Equation (3), the bus driving efficiency is proportional to the passenger flow, and the bus travel efficiency will increase linearly with the increase of passenger flow. However, in real life, when the passenger flow increases beyond a certain extent, it may lead to traffic congestion and reduce bus driving efficiency, so this formula does not accord with common sense and needs to be improved.
## 3 Results
### Real-Time Zone Experiment  
For the real-time part, we compare different approaches using the following two metrics:  
Figure 6: Memory utilization at different memory compression factors. (**a**) Memory compression factor of 5; (**b**) memory compression factor of 9; (**c**) memory compression factor of 22; (**d**) memory compression factor of 27.  
* Maximum response latency: This represents the longest time required for a memory request to receive a response. A lower value indicates higher reliability of the allocation method.
* Average response latency: This represents the average response time for all memory requests. A lower value indicates better real-time performance of the allocation method.  
Using EPmalloc, we process a set of memory requests from a group of real-time tasks with short intervals and compare it with the default allocation method to evaluate its performance. The tests were conducted in a 16 KB memory environment. The results are shown in Figure 7.  
It can be observed that, as the number of real-time tasks increases, EPmalloc is significantly better than the system's default memory allocation method in terms of maximum response speed and average response speed. Specifically, compared to the default method, EPmalloc reduces the maximum response latency by 41.2% and the average latency by 13.7%. Therefore, EPmalloc is superior to the system's default method in terms of real-time performance and reliability. These results are displayed in Table 3.
## 4 Result and Discussion  
### Interpretation of Pearson's Correlation Analysis  
Figure 2 shows the spatiotemporal distribution of PDSI on the study region over time (2015-2019).  
In Pearson's correlation analysis (Figure 3), EVI showed as the most promising indicator in the long-term agreement between vegetation parameters and PDSI. Pearson r values for EVI was the highest negatively correlated parameter, with the PDSI ranging between -0.63 to -0.87 over the years (except 2015) in comparison to NDVI, GPP, LAI, and fAPAR. In 2015, EVI showed a surprisingly higher positive correlation with PDSI (0.50), which could be the result of the massive rainfall situation that hit the state of Arunachal Pradesh during that year [24]. Such a huge amount of rainfall effectively recharged the soil layers, which helped the forest region to overcome the water deficit conditions.  
After EVI, the other vegetation parameters, i.e., LAI, NDVI, GPP, and fAPAR, respectively, were found to have high negative agreement with the PDSI.  
Figure 2: Temporal distribution (2015–2019) of the Palmer Drought Severity Index (PDSI) over Arunachal Pradesh, India. PDSI value –4 represents the dry conditions, and +4 represents the wet conditions.
## 4 Results
### Structural Modeling  
The structural model of this analysis is illustrated in Figure 3. As the calculation was successfully carried out in the CFA test of the measurement model, the validation of the structural model was used to check the goodness of fit indices of the proposed model. The outcome of the SEM reveals (Table 7) that the conceptual framework exhibits an excellent data fit (\(\chi^{2}\)/df = 2.854). The realized value of the Root Mean Square Error Approximation (RMSEA) was 0.073, which justifies the cut-off value of less than 0.08 [114]. Other fit indices like the CFI, GFI, IFI, and TLI met the standard of 0.9 and higher [113].  
Table 8 highlighted the statistic of path model using SEM via AMOS 21.0 software to find the path relationship among constructs as hypothesized. The results (Table 8) indicated that environmental concern influences the subjective norms (\(\beta\) = 0.783; t = 13.069), perceived behavioral control (\(\beta\) = 0.304; t = 5.142), and attitude (\(\beta\) = 0.489; t = 6.029). Likewise, the AMOS output (Table 6) values indicated that the exist relationship between environmental knowledge (\(\beta\) = 0.284; t = 6.043), subjective norms (\(\beta\) = 0.313; t = 3.924), eco-labeling (\(\beta\) = 0.206; t = 5.057) and attitude were significant. In addition to that, environmental knowledge (\(\beta\) = 0.295; t = 5.810), subjective norms (\(\beta\) = 0.172; t = 2.533; \(p<0.05\)), attitude (\(\beta\) = 0.497; t = 6.653), perceived behavioral control (\(\beta\) = 0.324; t = 7.636) and buying intention were also found to be significant. Therefore, we accept hypotheses 1 to 10, all of which are significance at the 1% level except for H\({}_{7}\), which exhibits significance at the 5% level.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Fit Indices**} & **Measurement Values** & **Meas. Values for** & \multirow{2}{*}{**Standards with Sources**} \\  & **for CFA** & **Structural Model** & & \\ \hline \(\chi^{2}\)/df & 2.531 & 2.854 & \(<\)3 & [109] \\ \hline IFI & 0.922 & 0.938 & \(>\)0.900 & [110] \\ \hline NFI & 0.914 & 0.909 & \(>\)0.900 & [110] \\ \hline CFI & 0.942 & 0.938 & \(>\)0.900 & [111] \\ \hline GFI & 0.924 & 0.911 & \(>\)0.900 & [110] \\ \hline AGFI & 0.918 & 0.904 & \(>\)0.900 & [101] \\ \hline TLI & 0.933 & 0.926 & \(\geq\)0.90 & [112] \\ \hline SRMR & 0.055 & 0.061 & \(<\)0.080 & [110] \\ \hline RMSEA & 0.069 & 0.073 & \(<\)0.080 & [112,113] \\ \hline \hline \end{tabular} Source: Selected output of AMOS 21.0.  
\end{table}
Table 7: Results of the CFA and structural model with standards.
# Improvement of the Transglycosylation Efficiency of a
## 3 Results and Discussion
### Screening of LnbB Variants
#### 3.1.2 Conserved Residues: W373F, W373H, W394F, W394H, W465F, W465H, and D467N
While the substitution of Trp to His appears superior in the case of W364 and W465 in LnbB acting on LNB-oxazoline and lactose, more data are required to confirm whether this is a general trend. Indeed, for W373F and W373H, where the improvement of LNT yield was more moderate (839 and 696 uM, respectively) and secondary hydrolysis just as pronounced as for WT LnbB, the substitution of Trp to Phe caused a slight, yet significant, increase in transglycosylation yield compared to substitution to His. Presumably, stacking interactions are important in this position. The less pronounced effect of mutation of W373 compared to that of W394 and W465 is in line with the study of [PERSON] et al. [9]. GH20 \(\beta\)-\(N\)-acetylhexosaminidase and GH84 O-GlcNAcases are known to exhibit tight hydrophobic pockets around subsite \(-1\), where several Trp residues accurately position the substrate's acetamido group near theomeric carbon and possibly also act to stabilize the transition state oxazoline charge development through orbital interactions [45,46]. Indeed, the three conserved Trp residues targeted for engineering line subsite \(-1\) of LnbB (Figure 3). Conserved Trp residues also ensure substrate binding in GH18 chitinases [47,48]. While the number of conserved Trp residues in the active site may be particularly high in GH20s and GH84s due to their role in oxazolinium ion stabilization [45], Trp residues are common at GH binding sites due to their hydrophobic nature and carbohydrate stacking abilities. Hence, targeting substrate-interacting Trp residues appears to be a viable strategy for improving transglycosylation across numerous GH families. In a recent study, the mutation of conserved Trp residues at subsite \(-1\) to His improved transglycosylation yields in a GH2 \(\beta\)-mannosidase, two GH29 \(\alpha\)-L-fucosidases, a GH10 \(\beta\)-endo-xylanase, and in Bbhl of GH20 [26]. The interference with substrate binding invites the use of increased enzyme dosages to compensate for the loss of catalytic efficiency [26]. However, the results on LnbB (Table 1 and [9]) underline that this type of mutants can outcompete the WT and other variants even at equimolar dosages.
## 3 Results and Discussion
### Adsorbent Characterization
The evaluation of the typical textural parameters such as surface area and pore size distribution will confirm the formation of silica in the galleries of the interlayer region. Textural parameters obtained from the corresponding Nitrogen adsorption-desorption isotherms of N\({}_{2}\) at \(-\)196 \({}^{\circ}\)C correspond to type IV (IUPAC) [36; 37], characteristic of mesoporous materials (Figure 2B, Table 2). The surface area values (S\({}_{\text{BET}}\)) obtained are quite high (above 500 m\({}^{2}\)/g), indicating a large accessible surface within the material due to the presence of silica galleries in the interlayer region being lower than the pure heterostructures (620 m\({}^{2}\)/g) [33], showing that the incorporation of phenyl groups decreases the surface area of the material. Regarding the volume of pores (V\({}_{\text{P}}\)), these materials have very high values, even more than the pure silica heterostructures (0.543 cm\({}^{3}\)/g). This may be due to the presence of larger pore diameters as shown in the distribution of pores. The surface chemical composition of the adsorbent \(\Phi_{5}\)-PPH before and after contact with AB113 (sample \(\Phi_{5}\)-PPH + AB113) is shown in Table 3. Upon contact with the dye, the percentages of C and N increase and S are now detected. However, Na was not detected indicating that the dye anion was taken up. The empiric formula of the dye anion is C\({}_{32}\)H\({}_{21}\)N\({}_{5}\)O\({}_{6}\)S\({}_{2}\) and the theoretical N/S atomic ratio is 2.5. The observed N/S atomic ratio after incorporation of the dye is 2.6, a value very near the theoretical one indicating the incorporation of the dye. Sulfur is the only element of the dye that is not present in the adsorbent \(\Phi_{5}\)-PPH and the S 2\(p\) core level spectrum for sample \(\Phi_{5}\)-PPH + AB113 shows a S 2\(p_{3/2}\) contribution at 168.2 eV assigned to the presence of S(VI) as the sulfonic group of the dye [43].
## 5 Experimental Investigation
### Spatial Dependence of the Returning Signal  
Returning signal as a function of depth is shown in Figure 5a: the signal returned by the sensor is clearly visible for all tested depths from 103 to 35 cm. The shallowest sensor position yields a poor signal due to the strong directivity of the GPR radiation pattern, with the dipole antenna preferentially radiating towards the high permittivity soil. Since the shallowest sensor position is located in front of the antenna rather than below, the antenna gain is poor in this direction, accounting for the lower signal level.  
To address the antenna directivity, the GPR antenna is moved on the surface in a direction orthogonal to the tubes: the resulting radargram is exhibited in Figure 5b, with the fast time axis in the vertical range and the GPR antenna position as the abscissa. During this experiment, the sensor is kept at a fixed location and depth of 61 cm: a usable signal is only recorded as the GPR antennas are located above the sensor, with the signal attenuated below noise level at position 0 (left and right part of the chart) and abscissa between 100 and 150 cm. The distance along which the sensor signal is usable is about 1.5-times its depth, so an angular field of view of \(2\times\arctan(0.75)\simeq 70^{\circ}\) is estimated. Indeed, the high permittivity soil (with respect to air) directs the electromagnetic wave preferentially towards the higher capacitor and, hence, shapes the radiation pattern of the dipole located over the ground towards the sensor [48].  
Similarly, the sensor position offset along the antenna dipole axis impacts the returned signal level. Figure 6 exhibits the results of two measurements while moving the sensor inside the pipes, parallel to the longest direction of the dipole of the GPR antenna. The length along which the sensor signal is usable is about equal to its depth, suggesting an angular field of view of \(2\times\arctan(0.5)\simeq 50^{\circ}\) of the dipole antenna.
# Approximating the Temporal Neighbourhood Function of Large Temporal Graphs
## 3 Computing Temporal Neighborhood Functions through Reverse Temporal Cones
### Approximating the Size of the Reverse Temporal Cones
#### 3.1.2 Bottom-\(k\) Sketches  
One of the most popular sketch techniques is the _bottom-\(k\)_ technique, which works as follows. Given a mapping \(r:U\rightarrow\{1/n,2/n,\ldots,n/n\}\) and a subset \(A\) of \(U\), we denote as \(H_{k}(A)\) the first \(k\) elements of \(A\) according to \(r\) (or \(A\) itself if \(|A|<k\)). A _bottom-\(k\) sketch_ for \(A\) is \(H_{k}(A)\).  
\(\mathtt{INIT}\ (S(A))\)**:**: \(S(A)=\emptyset\).  
\(\mathtt{UPDATE}\ (S(A),u)\)**:**: return \(H_{k}(S(A)\cup\{u\})\).  
\(\mathtt{UNION}\ (S(A),S(B))\)**:**: return \(H_{k}(S(A)\cup S(B))\).  
\(\mathtt{SIZE}\ (S(A))\)**:**: If \(|S(A)|<k\) return \(|S(A)|\). Otherwise, return \(\frac{k-1}{\max_{u\in
## 5 Experiments
### Experiment of Interference Monitoring
#### 5.2.2 Post-Correlation  
For the post-correlation interference monitoring, GPS signals with the broadband and narrowband interferences whose ISRs are 16 dB-24 dB are analyzed. The parameter settings are the same as pre-correlation, and the experimental results are shown in Table 3.  
Table 3 shows the comparison of classification accuracy and speed between LSTWSVM and the standard SVM under the broadband and narrowband interferences whose ISRs are both 16-24 dB, and the results indicate that both the LSTWSVM and the standard SVM are of high classification accuracy, but the training speed of LSTWSVM is about 55 times faster than standard SVM. The monitoring time of the two algorithms are the same (both equal to 15 us), which satisfies the requirement of the real-time interference monitoring for GNSS signals
# Comparison of Abrasive Wear Resistance of Hardox Steel and Hadfield Cast Steel
## 2 Materials and Methods

Table 1: Chemical composition of the analyzed high-manganese steels and Hardox steels with results of hardness measurements. Technologies--National Research Institute in Radom. The difference between the tester T-07 and the tribotester described in the international standard ASTM G65 [39] consists in the way that locating the examined material is performed. In T-07, the specimen is placed horizontally, and in the tribotester described in ASTM, it is placed vertically. During examination, specimens sized 30 \(\times\) 30 \(\times\) 3 mm, made up of the research and the reference materials, were subjected to wear with abrasive particles introduced to the friction contact zone in identical working conditions, i.e., speed and load. As abrasive material, aloxite no. 90 acc. to ISO 8486-2:2007 [40] was used, and the reference specimen was made of steel C45 in an as-normalized condition. The duration of the test was selected in correspondence with the material hardness and was equal to 30 min (1800 revolutions of the roll). The coefficient of relative abrasion resistance \(k_{b}\), calculated according to Formula (1), was used as a measure of abrasion resistance. The view and layout of the tester is shown in Figure 3.  
\[k_{b}=\frac{Z_{ww}\rho_{b}N_{b}}{Z_{wb}\rho_{w}N_{w}} \tag{1}\]  
where:  
\(k_{b}\)--coefficient of relative abrasion wear resistance [dimensionless],  
\(Z_{ww}\)--mass consumption of standard sample [g],  
\(Z_{wb}\)--mass consumption of the tested sample [g],  
\(N_{w}\)--the number of rotations of the rubber-rimmed steel wheel during the test of the standard sample,  
\(N_{b}\)--the number of rotations of the rubber-rimmed steel wheel during the test of the tested sample,  
\(\rho_{w}\), \(\rho_{b}\)--material density of the standard sample and tested sample [g/cm\({}^{3}\)].
## 2 Materials and Methods
### Sample Measurements  
#### 2.3.1 Structural and Morphological Analysis  
The crystal structures of the perovskite films were analyzed using high-resolution X-ray diffraction (XRD; SmartLab, Rigaku, Tokyo, Japan) with Cu-K\(\alpha\) radiation (\(\lambda\) = 1.542 A). The surface morphologies and grain structures were examined using field-emission scanning electron microscopy (FE-SEM; Hitachi S-4700, Tokyo, Japan).
## 4 Results and Discussions  
### Measurements of the BSRM Parameters in Normal Conditions  
The BSRM drive has started randomly with an initialised rotor position, but the sliding mode observer measures the rotor position nearly after 150 microsec. Figures 7-10 show the estimated and actual phase currents and current errors of both Phase-A and Phase-B windings, respectively. The differences between the measured and actual currents of both the phases are almost less than 0.05 amp, which means the measured currents are almost the same as the actual currents.  
Figure 8: Error between the estimated and actual currents of phase-B.  
Figure 7: Estimated and actual phase-B currents.  
Figures 11-14 show the actual and estimated suspension forces of both the X and Y directional windings, respectively. The actual and estimated suspension forces reached their steady states quickly, in less than 0.05 s only. Similarly, from Figures 13 and 14, it can be observed that the observer tracks the rotor positions in less than 0.0001 s for both phases. In Figure 15, the estimated and actual net torques are presented. From Figure 15, it is observed that the actual torque consists of both positive and negative ripples, because there is no technique that is employed to decrease the torque ripples in the actual system, but the observer's torque consists only of positive ripples and no more negative ripples due to the sensorless observer operation.  
Figure 10: Error between the estimated and actual currents of phase-A.  
Figure 9: Estimated and actual phase-A currents.  
Figure 11: Actual and estimated X-directional suspension forces.  
Figure 12: Estimated and actual Y-directional suspension forces.  
Figure 13: Estimated and actual rotor positions under phase-A.
## 5 Conclusions  
This study pioneers the integration of ABSA with advanced deep-learning models for predicting flight ticket fares. By introducing a novel feature fusion approach that combines historical price data with customer review data, our research offers a unique perspective on fare prediction. This methodology utilizes timeseries patterns of air ticket prices and incorporates the often-overlooked sentiment trends from passenger evaluations, providing a more comprehensive analysis.  
Our findings underscore that integrating ABSA with the GRU model significantly enhances its performance compared to conventional models, emphasizing its practical applicability in aviation pricing strategies. This synergy between sentiment analysis and deep learning showcases a novel approach to fare prediction, contributing to the body of knowledge in this domain. The study also highlights the importance of considering external information sources, such as customer sentiments, in fare prediction models. Traditional models often overlook these aspects, yet our research demonstrates their significant impact on the accuracy of fare predictions.  
Acknowledging the challenges in long-term fare prediction, our model opens avenues for future research, especially in enhancing efficiency over extended periods. Understanding the influence of external factors, such as global events and promotional activities, is crucial in improving long-term forecasting.  
Figure 14: Comparison of actual vs. predicted flight ticket fares using the ABSA_GRU model in different prediction timeframes.  
\begin{table}
\begin{tabular}{l l l} \hline
**Time Interval** & **RMSE** & **R2** \\ \hline
7 days & 0.0072 & 0.9769 \\ \hline
14 days & 0.0078 & 0.9181 \\ \hline
21 days & 0.0671 & 0.8246 \\ \hline
30 days & 0.0881 & 0.8143 \\ \hline \end{tabular}
\end{table}
Table 8: Performance of ABSA_GRU model over various timeframes.  
In summary, this research marks a significant step forward in integrating sentiment analysis and deep learning for predicting flight ticket fares. The insights gained from this study can inform airlines' pricing strategies, potentially leading to increased profitability and enhanced passenger satisfaction. The research's ability to incorporate a wide range of service aspects, as evidenced in our comprehensive assessment, underscores its potential as a transformative factor in the aviation industry.  
W.A.D., conceptualization, methodology, software, validation, formal analysis, investigation, visualization, and writing--original draft; B.-S.L., methodology, conceptualization, validation, supervision, resources, and writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data are not publicly available due to privacy concerns regarding the use of flight fare transaction data.  
The authors declare no conflicts of interest.