esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Total Phenolic and Flavonoid Contents  
The total phenolic and flavonoid contents of the methanol extracts prepared from each sample were determined using methods described in our previous study [29]. Fine powders (100 mg) prepared from the different plant parts were extracted with 3 mL of methanol and sonicated for 1 h. The extract was then centrifuged at 20,929\(\times\) g, filtered into a vial through a 0.45-\(\mathrm{\SIUnitSymbolMicro m}\) PTFE hydrophilic syringe filter, and used for further experiments to evaluate the total phenolic and flavonoid contents. For the assay of total phenolic contents, 0.1 mL of sample extract was mixed with 3 mL of ultrapure water, followed by the addition of 0.5 mL of 2N Folin and Ciocalteu phenol reagent (Sigma-Aldrich Co., Yongin, Korea). Having incubated the mixture for 3 min at room temperature, 2 mL of sodium carbonate (20%, _w_/_v_) was added, and following a further 60-min incubation in the dark, the absorbance of each sample was determined at 760 nm. An equivalent calibration curve (standard curve equation: y = 0.0013 x + 0.0313, R\({}^{2}\) = 0.9989) was prepared using different concentrations of gallic acid solution ranging from 12.5 to 250 \(\mathrm{\SIUnitSymbolMicro m}\). The results are presented in terms of milligrams of gallic acid equivalent per gram of dry weight (mg gallic acid equivalent (GAE)/g dry weight (DW)). For the total flavonoid content assay, 0.1 mL of sample extract was mixed with 4 mL of ultrapure water, followed by the addition of 0.3 mL of NaNO\({}_{2}\) (5%, \(w/v\)). After incubating for 5 min at room temperature, 0.3 mL of AlCl\({}_{3}\) (10%, \(w/v\)) was added, followed by incubation for 6 min and the subsequent addition of 2 mL of 1 M NaOH. The final volume of the mixture was brought to 10 mL with ultrapure water. An equivalent calibration curve (standard curve equation: y = 0.0001 x + 0.0380, R\({}^{2}\) = 0.9997) was prepared using different concentrations of rutin solution ranging from 62.5 to 1000 ug/mL. The results are expressed in terms of milligrams of rutin equivalent per gram of dry weight (mg Rutin equivalent (RE)/g dry DW).
# GPSMAP 66 sr: Assessment of Its GNSS Observations and Centimeter-Accurate Positioning
## 3 Assessment of Observation Quality and Antenna Calibration
### Observation Quality
Code noise and multipath levels were estimated using the MP (multipath) linear combination of code observations and dual-frequency carrier-phase observations, which is sometimes also called code-minus-carrier (CMC) linear combination [10]. These observations were collected in a roof-top environment with little multipath disturbances. The results reveal a considerable difference between G1/E1 and G5/E5 (Figure 5). The code noise and multipath levels of the G5/E5 signals are just half as large as those of G1/E1, which can be attributed to the improved signal structure of G5/E5, compared with G1/E1. Only small differences exist between GPS and Galileo. Carrier-phase noise and multipath levels were estimated using a short (few meters long) baseline of two Garmin GPSMAP 66 sr deployed in a roof-top environment with little multipath disturbances. Differences among the four signals are fairly small (Figure 6), with slightly higher RMS values for G5/E5 than those for G1/E1. This difference can be explained by the slightly longer wavelength at the G5/E5 frequency. Figure 4: Carrier-to-noise power density ration C/N\({}_{0}\) of the available GPSMAP 66 sr observations as a function of elevation angle and GNSS signal. Figure 5: Code noise and multipath of the GPSMAP 66 sr, determined from code-minus-carrier linear combination of dual-frequency signals (RMS values). Although the GNSS observations collected by GPSMAP 66 sr are by far not complete, they are of high quality for both main observables: code and carrier phase. Code measurements on G5/E5 reveal a noise and multipath level at a favorable site of around 1 m, only half as high as for G1/E1. Carrier-phase observations reveal noise and multipath variations in the order of several millimeters.
## 7 Evaluation Results
### Comprehensive Evaluation  
Table 5 and Figure 29 compare different routing methods in terms of the bundle arrival rate, the reduction in the number of copies in comparison to \"epidemic routing\", the reduction in the number of hops in comparison to \"location-based routing\" and the reduction in the required buffer size in comparison to \"epidemic routing\". Our proposed routing, whether using the circular covered area model or the vectorial covered area model, maintained a high bundle arrival rate and achieved a high reduction in the number of copies, the number of hops and the required buffer size. In particular, the proposed routing using the vectorial covered area model marked a high score. The proposed method had a high bundle arrival rate, because it determines in advance based on the traveling route information whether the bundle can reach its destinations and relays the bundle only when it is sure that it can reach its destinations. The number of copies was small because the proposed method is such that relaying nodes do not make copies, and thus, only one bundle exists. The number of hops was small because the role of relaying bundles over a long distance is assigned to traveling nodes. The required buffer size was small, because only traveling nodes, which are dedicated to relaying bundles, relay bundles.  
Figure 28: Comparison in terms of the maximum covered area size.  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Routing**} & \multirow{2}{*}{\begin{tabular}{c} **Bundle** \\ **Arrival Rate** \\ \end{tabular} } & \multicolumn{3}{c}{**Reduction in the Number of Copies in**} & \multicolumn{1}{c}{\
\begin{tabular}{c} **Reduction in the** \\ **of Hops in Comparison to** \\ **the Location-Based** \\ \end{tabular} } & \multicolumn{1}{c}{
\begin{tabular}{c} **Reduction in the** \\ **Required Buffer Size in** \\ **Comparison to the** \\ **Epidemic Routing** \\ \end{tabular} } \\ \hline Epidemic routing & 99\% & - & 55\% & - \\ \hline Spray and wait routing & 74\% & 83\% & 67\% & 82\% \\ \hline Location-based routing & 37\% & 97\% & - & 95\% \\ \hline Motion vector routing (MoVe) & 8\% & 98\% & 78\% & 96\% \\ \hline Proposed routing with the circular covered area model & 74\% & 97\% & 80\% & 97\% \\ \hline Proposed routing with the vectorial covered area model & 99\% & 96\% & 80\% & 97\% \\ \hline \end{tabular}
\end{table}
Table 5: Comprehensive evaluation.
## 6 Results
### Computer Simulation-Based Results  
In this analysis, Matlab software is preferred for computer simulation results as it is very attractive and powerful in the field of switching converters. For both operations (non-inverted and inverted) of the voltage step-down capabilities, the input rms voltage is chosen 50 V (70 volts peak). The pulse width or duty ratio of the PWM control is set to 50% to obtain an output rms voltage of 25 V (35 volts peak). For this purpose, the diode is modeled with a 0.8 V internal drop, and the value of internal resistance of a transistor is set to 85 m\(\Omega\). Two capacitors with the values of 1 \(\mathrm{\upmu F}\) and 4.7 \(\mathrm{\upmu F}\) are connected at input and output, respectively, to block the unwanted harmonics or ripples. Additionally, an inductor of 1 mH is used as a filtering and energy storing element. Its presence also helps to avoid the circuit complications resulting from shoot through. The plots of Figure 9a,b show that output voltage (red plot) has the same and opposite phase with respect to input voltage's polarity (blue plot) confirming voltage in-phase and out-phase operation. In the voltage buck process, the maximum voltage value across the PWM control devices cannot exceed the input voltage level, and this point is conformed from Figure 10a,b, where this level is almost 70 V.  
The voltage plots of converter operation in which the required voltage is greater than the input level are demonstrated in Figure 11.  
In this operation, the target is to confirm the voltage boost characteristics. For this purpose, the input 25 V rms voltage (plotted in blue color) is step up to 50 V rms level (plotted in red color) with a duty cycle control of 50%. The plots of Figure 11a,b confirm the voltage boost capabilities for both polarities at the output. Moreover, the plots of Figure 12a,b prove that the voltage of the high switching transistors is not able to exceed the level of the output voltage.  
The correction of the grid voltage is one of the key applications of the developed circuit. This circuit can tackle the issue of voltage sag or swell having any voltage depth level as it has the ability to buck or boost the input voltage at the output with dual polarity. A commonly occurring event in a power distribution system is voltage sag and most of the time its depth level is less than 50%. The pulse width or duty cycle to produce the required compensating voltage depends on the level of the correcting voltage. The voltage gain of
# Representing and Inferring Massive Network Traffic Condition: A Case Study in Nashville, Tennessee
## 5 Conclusions  
In conclusion, this research addresses important research gaps in the field of massive road network compression and representation. The utilization of a column-based CUR matrix decomposition technique with a column selection method based on weighted average of L2 norm importance and SVD-based leverage provides a novel approach to efficiently represent the original traffic network with a reduced subset of columns while maintaining acceptable error levels. This approach offers enhanced interpretability and a more refined column selection process compared to previous methods. Moreover, by conducting a comprehensive comparison with random sampling, this study contributes to a better understanding of the advantages of the proposed approach.  
Furthermore, the extension of the evaluation to a wider range of compression ratios adds valuable insights into the scalability and performance of the method. The application of these techniques to radar detector data from Nashville, TN demonstrates their practical utility in real-world scenarios. By drawing inspiration from stock market index computation methodology, the integration of clustering analysis into CUR decomposition is investigated with the anticipation of performance enhancements. Clustering was found to enhance performance, but, notably, this improvement was confined to a limited range of compression ratios. Interestingly, once the compression ratio exceeded a certain threshold, the application of clustering not only ceased to improve performance but also adversely affected it.  
Overall, this research not only contributes to the advancement of data compression and network representation but also highlights the potential of combining CUR decomposition with clustering for various applications in the field of big data analytics and network science.  
Conceptualization, H.Z., Y.G. and L.D.H.; methodology, H.Z., Y.G. and L.D.H.; software, H.Z.; validation, H.Z., Y.G. and L.D.H.; formal analysis, H.Z.; investigation, H.Z.; Y.G. and L.D.H.; data curation, H.Z. and Y.G.; writing--original draft preparation, H.Z.; writing--review and editing, H.Z., Y.G. and L.D.H.; visualization, H.Z.; supervision, L.D.H.; project administration, L.D.H.; funding acquisition, L.D.H. All authors have read and agreed to the published version of the manuscript.  
This research received funding from the Tennessee I-24 Smart Corridor evaluation project (RES2023-18) which is supported by the Tennessee Department of Transportation.  
**Data Availability Statement:** Restrictions apply to the availability of these data. Data were obtained from the Tennessee Department of Transportation and are available from the authors with the permission of the Tennessee Department of Transportation.  
**Acknowledgments:** We acknowledge the financial and data support from the Tennessee Department of Transportation.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Background Theories
### K-Fold Cross-Validation with Time Series Data  
Cross-validation is a data resampling method for estimating the actual prediction performance of models and tuning hyper-parameters. In order to overcome the problem of overfitting, cross-validation is used to check overall model performance to detect this problem. In addition, it is used to adjust appropriate hyper-parameters, such as the appropriate batch size and epoch in ANN model.  
K-fold cross-validation is one of the methods. The procedure begins by randomly splitting the dataset into folds of equal size. The model is trained by using k-1 folds that represent the training set. Then, the trained model is applied to the remaining fold, which represents the testing set and the performance of the model is evaluated. This procedure is repeated until every fold is used as a testing set. The final metrics are the average of the errors obtained in each fold [43].  
However, K-fold cross-validation cannot be utilized in the case of time series due to randomly splitting the dataset, because it is irreconcilable in the real world to use values from the future to forecast values from the past. The K-fold Cross-validation with Time Series Data has a different procedure. The idea is that each observation is the first used as a testing set and then added to the training set of the model [44]. The procedure begins by splitting the dataset into k folds of equal size. In the initial iteration, only the first k folds are used as a training set and the next folds are used as a testing set. In the next iteration, the old training set and testing set are merged and used as a training set. This procedure continues until the last fold is tested. The comparable training set only contains observations that occurred before the testing set observation. Hence, no future observations are used to make the prediction [45].
## 2 Experimental Setup  
### Rock Specimens  
The sandstone materials were obtained from a quarry in Sichuan, China. In our experiments, cylindrical specimens were extracted by coring the same rock block, which was of good quality. The rock cores were processed into two specimens with dimensions of \(\Phi\)50 mm \(\times\) 100 mm and \(\Phi\)50 mm \(\times\) 40 mm, as suggested by the International Society for Rock Mechanics (ISRM) [29], which were used to perform the static and dynamic tests, respectively. Two ends of the specimen were ground to be flat to 0.02 mm tolerance and not depart from perpendicularity to its axis by more than 0.001 rad. The side surface of the specimen was smooth, free of abrupt irregularities and straight to within 0.02 mm. The specimens were homogeneous, with the following properties: density \(\rho\) = 2.61 g/cm\({}^{3}\), P-wave speed \(C_{s}\) = 3291 m/s, elastic modulus \(E\) = 25.60 GPa, Poisson's ratio \(v\) = 0.22, uniaxial compressive strength \(\sigma_{c}\) = 76.13 MPa, uniaxial tensile strength \(\sigma_{t}\) = 7.63 MPa.
# Assessment of Internal and External Factors Causing Slow-Moving Landslides
## 3 Results and Discussion
### Analysis of Stratigraphy, Topography, and the Scale of Occurrence  
We examined the stratigraphy and constituent rocks of the 15 sites in the slow-moving landslide areas. The Cretaceous and Precambrian periods were the most common geologic ages, with five (33.3%) and four sites (26.7%), respectively. The Gyeongsang supergroup was the most common at four sites (26.7%) among the supergroups. The Pyeongan group, Sobaeksan gneiss complex, and Okcheon group were each found at two sites (13.3%). These slow-moving landslides were most common in the Gyeongsang supergroup, corresponding to sedimentary rocks. The main constituent rocks of these slow-moving landslide areas were igneous rocks at three (20.0%), metamorphic rocks at six (40.0%), and sedimentary rocks at six sites (40.0%). The grain of these rocks corresponded with the degree and direction of the slope, believed to have facilitated the slow-moving landslide phenomena [9; 11] (Table 4). In these slow-moving landslide area stations, 2 (13.3%) had intrusive rock, and 13 (86.7%) did not have intrusive rock. In terms of the number of joints, slow-moving landslides occurred at eight sites (53.3%) with two joints, seven sites (46.7%) with three or more joints, and all sites with two or more joints. Joint spacing was dense (6-20 mm) in 12 sites (80.0%). It has been analyzed that denser joint spacing is relatively more prone to slow-moving landslides [9; 25]. In the areas in which slow-moving landslides occurred, story slopes existed in 5 sites (33.3%) and did not exist in 10 sites (66.7%). There were 13 sites (86.7%) with weathered rocks, indicating that weathered rocks mainly caused slow-moving landslides. In most areas in which slow-moving landslides occurred, the soil depth was 30-60 cm (8 places, 53.3%). Slow-moving landslides were found to occur frequently in the general soil depths of forest land in South Korea [35]. The soil texture of the slow-moving landslide area was sandy foam at seven sites (46.7%) (Table 4). The average soil in South Korea is 41.7% sand, 41.5% silt, and 16.8% clay. The dominant soil textures are sandy loam (44.5%) and clay loam (34.1%), which account for 78.6% of the total soil [36]. The predominance of sandy loam and clay loam in South Korea's soil texture makes slow-moving landslides possible in any region in the country. These results suggest that it is necessary to identify rocks or soils vulnerable to slow-moving landslides and prepare appropriate countermeasures to minimize the human and material damage caused by slow-moving landslides.
## 4 Conclusions  
In this study, the influence of stress-strain curves for C45 material at different strain rates on the flange-forming force during the flange forming of C45 billet tubes was analyzed. Additionally, the optimization of the flange-forming process for C45 billet tubes was performed through simulation and experimental verification. The following key conclusions are highlighted:  
The stress-strain curve model for C45 material significantly affects the simulation results. The best comparison between simulated and experimental flange-forming force and deformed shape was achieved by using the flow curve equation of materials considering both elevated temperature and strain rates, with a small deviation of 4.19% in the flange-forming force of the product.  
\(S0/D0\) has the most significant influence on the flange-forming force of C45 billet tubes at 65%, while \(H0/D0\) and \(\alpha\) have lesser effects with impact percentages of 21.1% and 13.9%, respectively.  
The optimal parameter set for minimizing the flange-forming force is \(H0/D0=1.0\), \(S0/D0=0.2\), and \(\alpha=5^{\circ}\). This parameter set was simulated and experimentally verified, yielding results with a deviation of 3.92%.  
A mathematical model representing the relationship between the input parameters (\(H0/D0\), \(S0/D0\), \(\alpha\)) and flange-forming force was developed and evaluated for reliability using the Fisher quality coefficient. The values of the flange-forming force determined from the regression equation were validated against corresponding experimental values, resulting in a maximum deviation of 4.26% in all three cases. Thus, the mathematical model demonstrates high reliability.  
Conceptualization, T.-H.-L.P. and T.-T.L.; Methodology, T.-H.-L.P., T.-T.L. and D.-T.N.; Software, T.-H.-L.P.; Validation, T.-T.L.; Formal analysis, T.-H.-L.P.; Investigation, D.-T.N.; Resources, T.-T.L.; Data curation, T.-T.L.; Writing--original draft, T.-H.-L.P. and T.-T.L.; Writing--review & editing, D.-T.N.; Visualization, D.-T.N.; Proper administration, T.-H.-L.P.; Funding acquisition, T.-H.-L.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Hung Yen University of Technology and Education, Vietnam, under grant number UTEHYL.2021.15.  
The authors declare no conflict of interest.  
Figure 10: Comparison between the flange-forming force obtained from regression and experimental functions for different cases.
## 4 Research on Critical Technologies
### Atmospheric Channel Modeling
#### 4.5.1 Log-Normal Distribution  
A log-normal distribution describes the scintillation and decay statistics in a weakly turbulent region, and its probability density function is expressed follows [29]:  
[PERSON]f_{l,L}(I)=\frac{1}{\sqrt{2\pi}\sigma_{I}^{2}I}\exp\Biggl{\{}-\frac{\ln(I/I_{0 })-E(I)^{2}}{2\sigma_{I}^{2}}\Biggr{\}} \tag{1}\]  
where \(\sigma_{I}^{2}\) is the logarithmic light intensity variance, [PERSON]I_{0}\) is the value of the light intensity at the transmitter end, and \(I\) is the normalized light intensity value arriving at each receiver.
## 1 Introduction
* We presented an analytical framework for the performance analysis of the proposed state model, from the aspect of energy consumption, cumulative average delay, cost and low-power radio signaling load. * We analyzed the effect of the the energy consumption of femto BSs when they are entering into the sleep state and waking up from the sleep state. * The proposed analytical framework was validated by simulations. The remainder of this paper is organized as follows: In Section 2, states for femto BSs are defined, and state transitions between them are modeled. Furthermore, the steady-state probability of each state is derived using a semi-Markov approach. Then, the performance of the energy-efficient femto BSs is analyzed in detail. In Section 3, numerical examples are provided, from the aspects of energy consumption, cumulative average delay, cost and low-power radio signaling load. Finally, conclusions and further works are drawn in Section 4.