esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
If the distance between the vehicle with SLADS and its leading vehicle in the same lane exceeds the maximum car following distance, it will accelerate to its desired speed according to its desired acceleration. Once the distance mentioned above is within the range of the car following state, it will follow the leader with ACC. As the distance between it and its leader is lower than the safety threshold, which is brought about by emergency braking of the leader for instance, it will decelerate with the help of AEB with the safe deceleration or even the maximum deceleration to avoid a rear-end collision. ICV contains many advanced driver assistance functions to help human drivers and even has an automated driving system to fully realize automated driving. Almost all of the current ICVs on Chinese roads only offer driving assistance to human drivers and still require human drivers to complete driving tasks. SLADS has advanced driver assistance functions only available in ICVs. It influences the longitudinal driving behavior directly. Since the research object of this article is SLADS, and the composition of traffic flow represents the different types of traffic participants, it may be agreed that 'ICVs' mentioned in the experimental research parts of this paper represents vehicles equipped with SLADS. The penetration rate of ICV mentioned in the following text is on behalf of the penetration rate of SLADS. In this article, VISSIM, a traffic simulation software, is used to evaluate the impacts of ICVs equipped with SLADS on traffic efficiency, energy consumption and emissions under two typical motorway scenarios in China, which are expressways and secondary motorways. Changes in traffic delay, road capacity and CO\({}_{2}\) emissions are assessed in order to provide suggestions for promoting the development and application of ICVs to maximizing the relevant social benefits. Additionally, SLADS is still a function of driving assistance and the main controller of a vehicle is still the human driver. Therefore, the impacts of driving style and distracted driving behavior are also considered in order to obtain more realistic and accurate evaluation conclusions.
## 4 Discussion
Furthermore, the study focused solely on antibacterial activity without testing other bioactivities, and solvent control was not implemented in the study. However, these limitations should be considered in the context of this being a pilot study, which aimed to provide preliminary insights into the antimicrobial potential of plant extracts. The agar-well diffusion method was selected for this study as it provides a practical and reproducible approach for the preliminary screening of antimicrobial activity of plant extracts. This technique allows for the direct application of a defined volume of extract into wells, facilitating better diffusion into the medium. The study was limited to the well-diffusion method; however, complementary techniques such as MIC, MBC, or other quantitative assays could enhance the evaluation of antimicrobial efficacy in future research. Additionally, the use of reference strains would provide a clearer understanding of the differences in the effects of the extracts on environmental strains. Our study confirmed the research hypothesis, which postulated that the application of extracts from various plant materials is effective against pathogens isolated from rinse water in the agri-food industry. Furthermore, it was established that combining bark extracts with those rich in volatile organic compounds (derived from leaves, shoots, flowers, and fruits) results in an increased effect, enhancing the antibacterial activity against the examined bacterial strains. The presence of various secondary metabolites in the plant species analyzed in this study has been reported in the literature. Although these compounds have been previously identified, their antimicrobial potential against opportunistic pathogens isolated from water has not been extensively investigated.
# Camera Calibration with Phase-Shifting Wedge Grating Array
## 4 Conclusions  
In this study, PWG arrays are used as calibration patterns for accurate camera calibration, which is displayed on an LCD. The 2\(\pi\)-phase points are detected and optimized to obtain sub-pixel accuracy. Then, the PWG centers used as feature points are precisely extracted from the phase by using linear fitting method rather than directly from the intensity. When compared to horizontal/vertical phase-shifting fringe patterns, it is convenient and time-saving with the workload of capturing images reduced by half. Experimental results indicate that the proposed method is accurate and reliable.  
This research was supported by National Natural Science Foundation of China (No. 61775209).  
**Author Contributions:** [PERSON], [PERSON] and [PERSON] conceived and designed the calibration patterns, [PERSON] and [PERSON] performed the experiments and analyzed the data, [PERSON] wrote the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
**Funding:** This research was funded by [National Natural Science Foundation of China] grant number [[61775209].  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Pattern** & **Parameter** & \(f_{\mathbf{x}}\) **(Pixel)** & \(f_{\mathbf{v}}\) **(Pixel)** & \(u_{\mathbf{0}}\) **(Pixel)** & \(v_{\mathbf{0}}\) **(Pixel)** & \(k_{1}\) & \(k_{2}\) & **RMSE (Pixel)** \\ \hline Three-step phase-shifting fringe patterns & 2588.2 & 2588.4 & 1008.7 & 544.4 & \(-\)0.015 & 0.081 & 0.085 \\ Three-step PWG arrays & 2561.6 & 2561.5 & 1010.3 & 546.8 & \(-\)0.005 & 0.077 & **0.046** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Camera intrinsic parameters estimated from different phase-shifting patterns.  
Figure 8: Two-dimensional (2D) phase-difference pulses of the horizontal/vertical fringe patterns.
# SNR Enhancement of an Electrically Small Antenna Using a Non-Foster Matching Circuit
## 4 Conclusions  
A non-Foster circuit was designed to improve the SNR of an electrically small monopole antenna. The cross-coupled structure of the NFC was optimized using a high frequency circuit simulation software, Keysight ADS, and component values and bias voltages were carefully chosen to meet stability criteria. A two-step stability check was used: 1) An open-circuit stable condition in the frequency domain and 2) The convergence of transient responses in the time domain. The measured  
Figure 11: Signal-to-Noise Ratio enhancement due to the non-Foster circuit.  
Figure 10: The power measurement data with and without the non-Foster circuit: (**a**) received power and (**b**) noise power.  
S\({}_{11}\) < -10 dB bandwidth after applying the NFC was 60-400 MHz, broad enough to cover several VHF and UHF communication bands with an antenna length less than 10 cm. The received power measurement also showed promising results that are more than 17 dB of improvement after applying the NFC. However, the NFC accompanies a highly fluctuating noise power of 7 to 14 dB that adversely affects the SNR.  
Conceptualization, J.-Y.C.; Data curation, Y.-H.L. and S.-y.C.; Formal analysis, Y.-H.L.; Funding acquisition, J.-Y.C.; Methodology, Y.-H.L.; Resources, S.-y.C.; Supervision, J.-Y.C.; Validation, S.-y.C.; Visualization, Y.-H.L.; Writing--original draft, Y.-H.L.; Writing--review & editing, J.-Y.C. All authors have read and agreed to the published version of the manuscript.  
This work has been supported by the Future Combat System Network Technology Research Center program of Defense Acquisition Program Administration and Agency for Defense Development (UD190033 ED). The authors declare no conflicts of interest.
## 3 Results and Discussion
Therefore, it seems that the rigidity of the \(\beta\)-amino alcohol seems to be very convenient for achieving good enantioselectivity in this ATH process. Finally, in agreement with our observations in the ATH of sulfinylamines [38-41], a ligand with a free primary amino group led to higher enantioselectivity than other ligands bearing secondary amino groups: when the ligands with a prolinol backbone **L4** and **L5** were tested, either no reduction product or only traces of it could be detected after working-up the reaction (Table 2, entries 4 and 5). After having established that **L1** was the ligand of choice, we tested two more imines as substrates. The asymmetric transfer hydrogenation of imines **1b** and **1c**, derived from propiophenone and 4-chloroacetophenone respectively, under the conditions of entry 13 in Table 1 gave the expected phosphinamides **2b** and **2c** in high yields and with ee's of 82 and 80%, respectively (Table 2, entries 6 and 7). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**Entry**} & \multicolumn{3}{c}{**Imine**} & \multirow{2}{*}{**Ligand**} & \multirow{2}{*}{**t (h)**} & \multicolumn{3}{c}{**Product 2**} \\ \cline{3-3} \cline{5-8}  & **No.** & & & & **R1** & **R2** & **No.** & **Yield**\({}^{b}\) **(\%)** & _ee_\({}^{c}\) **(\%)** \\ \hline
1 & **1a** & H & Me & **L1** & 20 & **2a** & 87 & 82 \\
2 & **1a** & H & Me & **L2** & 20 & **2a** & 49 & 48 \\
3 & **1a** & H & Me & **L3** & 20 & **2a** & 39 & 53 \\
4 & **1a** & H & Me & **L4** & 22 & **2a** & — \({}^{d}\) & — \\
5 & **1a** & H & Me & **L5** & 25 & **2a** & — \({}^{e}\) & — \\
6 & **1b** & H & Et & **L1** & 20 & **2b** & 90 & 82 \\
7 & **1c** & Cl & Me & **L1** & 20 & **2c** & 85 & 80 \\ \hline \end{tabular} \({}^{a}\) The solution of the imine **1** (0.5 mmol) in _i_-PrOH (9 mL) was added to a solution of the ruthenium catalyst [prepared by refluxing a mixture of [RuCl\({}_{2}\)(_p_-_)_]\({}_{2}\) (0.025 mmol) and amino alcohol **L** (0.05 mmol) in _i_-PrOH (2 mL) at -20 \({}^{\circ}\)C. Then, _t_-BuOK (0.25 mmol, as a 0.1 M solution in _i_-PrOH) was added and the reaction mixture was stirred at the same temperature for 1 h and then it was allowed to gradually reach room temperature. \({}^{b}\) Isolated yield after purification by column chromatography (silica gel, hexane/acetone: 9/1) based on the starting imine **1**. \({}^{c}\) Determined by HPLC using a ChiralCel OD-H column or a Chiralpak AS-H column. The _R_-enantiomer was the major one in all cases. \({}^{d}\) Only traces of the product were detected in the crude reaction mixture. \({}^{e}\) No reduction product was detected in the crude reaction mixture.  
\end{table} 
Table 2: Test of different amino alcohols as chiral ligands for the ruthenium-catalyzed transfer hydrogenation of \(N\)-phosphinylamines **1**\({}^{a}\).
## 4 Results and Discussion
### Predictive Machine Learning Models for Airport Recommendation  
Multiple machine learning models, including LR, RF, SVM, KNN, GB, and NN models, were developed to predict airport recommendations based on Skytrax's online reviews. Their classification performance was validated using five-fold cross-validation, with the results presented in Table 5.  
\begin{table}
\begin{tabular}{c c} \hline
**Feature** & **Coefficient** \\ \hline Terminal Cleanliness & 0.968 \\ \hline Terminal Seating & 0.034 \\ \hline Terminal Signs & 0.051 \\ \hline Food and Beverages & \(-\)0.180 \\ \hline Airport Shopping & 0.527 \\ \hline WiFi Connectivity & \(-\)0.024 \\ \hline Airport Staff & 0.606 \\ \hline \end{tabular}
\end{table}
Table 4: Logistic regression coefficients.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Mean Accuracy** & **Std. Deviation** \\ \hline Logistic Regression & 85.41\% & 2.25\% \\ \hline Random Forest & 87.73\% & 1.80\% \\ \hline SVM & 84.66\% & 2.52\% \\ \hline KNN & 84.19\% & 2.58\% \\ \hline Gradient Boosting & 88.15\% & 1.55\% \\ \hline Neural Network & 87.37\% & 1.93\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Cross-validation results.  
The results reveal that Gradient Boosting achieved the highest mean accuracy (88.15%) with a low standard deviation (1.55%), indicating excellent performance and stability. This makes Gradient Boosting the most reliable model among those tested. Random Forest was close behind Gradient Boosting, and also performed well with a mean accuracy of 87.73% and a standard deviation of 1.80%. The Neural Network model achieved a high mean accuracy (87.37%) but with a slightly higher standard deviation (1.93%) compared to Gradient Boosting and Random Forest. Logistic regression showed good performance with a mean accuracy of 85.41% and a standard deviation of 2.25%. The SVM had a mean accuracy of 84.66% and a standard deviation of 2.52%, indicating stable performance but slightly lower accuracy compared to the top models. The K-Nearest Neighbours achieved the lowest mean accuracy (84.19%) and the highest standard deviation (2.58%), suggesting that it is the least reliable model among those tested.  
The analysis indicates that Gradient Boosting and Random Forest are the top performers for predicting whether an airport will be recommended. Both methods show high accuracy and stability, making them suitable for deployment in practical scenarios. The Neural Network model also performs well and could be considered, especially when exploring more complex relationships in the data. Logistic regression and SVM are reasonable alternatives but may not perform as well as the ensemble methods. KNN, while useful in certain contexts, showed the least favourable performance in this analysis. This comprehensive and comparative evaluation approach ensures robust and reliable method selection, enhances the predictive capability and guides improvement in airport management.
# Atmospheric GNSS RO 1D-Var in Use at UCAR: Description and Validation
## 2 Materials and Methods
### The UCAR 1D-Var
#### 2.2.4 Error Covariance Matrices
(**a**) Diagonal blocks (lower left to upper right) show self-correlations of temperature (denoted \(T\)), relative humidity (\(q\)), and pressure (\(p\)) errors, while off-diagonal blocks indicate cross-variable correlations. (**b**) Error correlations among the elements at 10 km and those at other heights. (**c**) Same as in (**b**) but for 5 km. Although the 1D-Var does not require any simplification or heuristic approximation for **B**, there still are some practical difficulties to overcome. When the 1D-Var uses the entire pressure profile (instead of the lowest-level pressure) as the pressure control variable, **B** can be as large as 2580 \(\times\) 2580. Computing \(\textbf{B}^{1/2}\) of that size at the runtime for each RO event is impractical, especially for real-time data processing. On that account, instead of computing \(\textbf{B}_{\
u}^{1/2}\) on the fly, the 1D-Var ingests a precomputed \(\textbf{B}_{\
u}^{1/2}\), the cost of which is trifling. The preparation of \(\textbf{B}_{\
u}^{1/2}\) is through the following procedure. We evaluate **C** on the a priori model grid, interpolate it to the computational grid, and compute and store \(\textbf{C}^{1/2}\). Because **C** did not strongly depend on the longitude and its temporal variation appear slow, we compute and store \(\textbf{C}^{1/2}\) for each season and in 20\({}^{\circ}\) latitude bins. In addition, we prepare \(\textbf{C}^{1/2}\) for four possible bottom heights of the computational grid (0, 5, 10, and 15 km), among which the 1D-Var picks up the one closest to the actual bottom height. What allows the 1D-Var to use precomputed \(\textbf{C}^{1/2}\) is that the computational grid has a fixed number of vertical levels, regardless of the bottom height, which is indeed the reason for using \(\eta\) coordinates in the 1D-Var. The bottom of the precomputed \(\textbf{C}^{1/2}\) can differ by up to 2.5 km from the actual bottom of the computational grid. This difference causes a discrepancy in the grid spacing between the computational grid and \(\textbf{C}^{1/2}\), but only slightly because the grid's total vertical extent is far greater than the bottom-height difference. We consider this discrepancy a worthy trade-off for the efficiency. That is to say, the inaccuracy of specifying **C** due to this discrepancy is insignificant compared to the uncertainty of **C** itself. We stored the diagonal elements of **D** on a \(5^{\circ}\times 5^{\circ}\) grid for each month of the year, the expense of which is negligible. The 1D-Var interpolates these diagonal elements available on NWP model levels to the exact heights (rather than to \(\eta\) levels) of the computational grid at the runtime. The 1D-Var constructs \(\textbf{B}_{\
u}^{1/2}\) using **D** and \(\textbf{C}^{1/2}\) based on Equation (18) and acquires \(\textbf{B}^{1/2}\) through Equation (19).
# The Impact of Hollow Wear on the Stability of High-Speed Railway Vehicles
## 3 Impact of Hollow Worn Wheel on Vehicle Stability  
### Evaluation of Vehicle Stability Using Velocity-Reducing Method  
In order to determine the critical speed of the vehicle under various hollow wear conditions, the five different profile wheels in Table 1 were further studied, using SIMPACK software to conduct simulation analyses on the vehicle system. Initially, the vehicle's initial speed is set to a relatively high value. Excitations are then applied to the track, followed by the application of longitudinal forces to decelerate the vehicle. Through integration calculations, the lateral displacement of the wheelset is recorded over time. Based on the relationship curve between the lateral displacement of the wheelset and the vehicle's traveling speed, the vehicle speed corresponding to the point where the lateral displacement of the wheelset gradually decays to the equilibrium position is identified. This is a process to obtain the critical speed of a vehicle using the velocity-reducing method.  
The findings from the simulation demonstrate that there is a decrease in the vehicle's critical speed as the hollow wear increases, as depicted in Figure 8. With the operational speed range for EMUs being 200-250 km/h, the allowable upper limit is established at 275 km/h, incorporating a 10% safety buffer. Nonetheless, the critical speeds for the vehicle associated with wheels W4 and W5 fall below this designated maximum speed. Consequently, when the vehicle reaches speeds above 265 km/h, the vibration amplitude of the wheelset stops converging and begins to escalate instead. This occurrence is referred to as hunting instability.  
Figure 7: Equivalent conicity curves of each wheel tread.
## 5 Experimental Results and Discussion
### Ablation
#### 5.1.1 Prefix-Length  
From Table 3, it is evident that FAP-Prefix with prefix-tuning based on three pre-trained models reaches a local peak when the prefix sequence length is set to three tokens.  
\begin{table}
\begin{tabular}{c c} \hline
**Item** & **Content** \\ \hline transformer layers & 12 \\ attention heads & 8 \\ optimizer & Adam optimizer \\ learning rate & \(10^{-3}\) \\ \(\beta_{1}\) & 0.9 \\ \(\beta_{2}\) & 0.999 \\ epochs & 12 \\ steps in each epoch & 100 \\ batch size & 8 \\ \hline \end{tabular}
\end{table}
Table 2: Experimental environmental parameters.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Prefix-Length**} & \multicolumn{3}{c}{**Auc**} & \multicolumn{3}{c}{**Acc**} & \multicolumn{3}{c}{**F1 Score**} \\ \cline{2-10}  & **BERT** & **UniLM** & **ALBERT** & **BERT** & **UniLM** & **ALBERT** & **BERT** & **UniLM** & **ALBERT** \\ \hline
2 & 0.841 & 0.833 & 0.856 & 0.838 & 0.873 & 0.851 & 0.840 & 0.854 & 0.856 \\
3 & 0.845 & 0.926 & 0.878 & 0.863 & 0.943 & 0.939 & 0.8547 & 0.9345 & 0.9085 \\
4  
As the prefix sequence length increases to four and five tokens, the performance of the BERT and UniLM experimental groups declines significantly. This is because an excessively long prefix sequence may cause unnecessary information, which can interfere with the model's attention mechanism. The model needs to extract hidden information from a longer sequence, placing higher demands on its contextual understanding abilities. However, the ALBERT experimental group shows a relatively minor decrease in model performance when the length is extended to four or five tokens. ALBERT replaces BERT's NSP (next-sentence prediction) task with the SOP (sentence order prediction) task. This task focuses more on sentence coherence rather than on topic prediction, making ALBERT more effective when dealing with different prefix lengths. Interestingly, when the prefix sequence length is set to six, seven, or eight tokens, the predictive performance of the BERT experimental group continues to decrease significantly due to the increased difficulty in extracting hidden information. While the performance of the ALBERT and UniLM experimental groups shows some recovery. Because they can better capture contextual information.
## 4 Performance and Simulation Results
### Actual Simulation Environment  
To evaluate the performance of MBA protocol, simulation area dimensions of 3.5 m \(\times\) 10 m is taken in which 3.5 m \(\times\) 3.5 m area is the reader identification zone, as shown in Figure 3. Within this area, the tags are moving at a specific velocity (\(v\)) from border to border so that some tags will enter the reader zone as arriving tags (which did not exist in the past frame but are executed in the current frame), and other tags leaving reader zone as leaving tags (which are executed in the current frame but will not be executed in the next frame). On the other hand, staying tags are those tags that do not leave reader zone after the duration of one frame (executed in the past and current frame). To compare the proposed protocol with the other protocols, the parameters of the simulation are illustrated in Table 1. So, there are a number of tags (\(n\)) moving in a simulated area of 3.5 m \(\times\) 10 m with a stationary probability; the probability of whether the tag will move or not during the period of one frame; (\(ps\)) equal to zero (moving constantly) and the tags velocity (\(v\)); the distance that each tag moves during a period of one frame when it moves; of 1 m/frame blocking velocity and 3.5 m/frame non-blocking velocity. Finally, the reader weight factor (\(z\)), which is responsible for estimating the number of the tags in the reader zone, was taken as 0.5. Each simulation has been conducted for 1000 frames by Matlab software to simulate the proposed protocol and the other older protocols, and to represent the area in which the tags move for the purpose of comparing and showing our big improvements in dealing with RFID problems.