esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
Numerous researchers have attempted to use VNs to shorten EV arrival times, but the majority of studies have focused on communications technologies and protocols rather than assessing actual VN effectiveness in EMS management. We combined several existing VN applications. The implementation system used in this study had three functions: route guidance, traffic light preemption, and path clearing. Our simulation results indicate that traffic light preemption is the best method for significantly increasing CSRs for acute and critically ill patients (from 32.4% to 90.2%). The second most effective method was route guidance (14.1% and 57.8%), followed by path clearing (0% and 15.5%). Our data also confirm increased travel times and reduced CSRs during peak traffic periods and traffic jams, therefore municipalities must continue to determine the best and most efficient distributions of medical resources.  
Due to space limitations, we narrowed our focus to medical outputs for different approaches designed for emergency services with the assistance of VNs. The effects of radio propagation models, car moving patterns, street topologies, and other parameters were not addressed in this study. Our plans include adding a radio propagation model [42, 43, 44] to our simulations.  
This work was supported in part by the High Speed Intelligent Communication (HSIC) research center, Chang Gung University, Taiwan, and by grants from the Ministry of Science and Technology of Taiwan (NSC-100-2221-E-182-050 and MOST-103-2221-E-182-052).  
All authors worked together to build conception of the research. [PERSON], [PERSON] and [PERSON] conceived and designed the experiments. [PERSON] and [PERSON] performed the experiments. [PERSON], [PERSON] and [PERSON] analyzed the data. [PERSON], [PERSON] and [PERSON] wrote the paper. All authors read and approved the final manuscript.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Measurements of Net Ecosystem Carbon Dioxide Exchange and Ancillary Variables  
The EC instrumentation consisted of a Sonic Anemometer (Windmaster, Gill Instruments, Lymington, UK), which measures wind speed and direction in three dimensions, and an embedded closed path infra-red gas analyzer (LI-7200, LI-COR Lincoln, NE, USA). The flux tower (Clara EC) is located at the center of Clara West (Figure 1a), and the EC tower flux footprint predominantly covers the submarginal (SM) and subcentral (SC) ecotopes, which constitutes of _Narthecium ossifyagum_, _Sphagnum tenellum_, and _Sphagnum magellanicum_. The submarginal and subcentral ecotopes constitute approximately 77% (191.34 ha) of the site, and this study area was used to validate the modelled GPP with EC-derived GPP.  
Figure 1: (**a**) A map of Clara Bog showing the delineation of the bog into the eastern and western sections (the western section boundary highlighted in yellow) and the location of the EC tower. (**b**) Ecotope map of Clara Bog West (EU LIFE Irish Raised Bog Restoration Project (LIFE14 NAT/IE/000032)), with EC tower location shown in yellow.  
The raw EC data were collected at 10 Hz and processed using Eddy Pro version 7.0.6. in advanced mode using site-specific parameters. Additional meteorological and environmental variables were also measured, which included air temperature and humidity (CS215, Campbell Scientific, Shepshed, UK), the volumetric water content of the soil (VWC) (CS616 Water Reflectometer, Campbell Scientific, Shepshed, UK), soil heat flux (HFP01 Hukseflux, Delft, The Netherlands), soil temperature (105E, Campbell Scientific, Shepshed, England), photosynthetically active radiation (PAR) (Skye Instruments, Landrinodod, Wales), and net radiation (NR) (NRLite, Kipp & Zonen, Delft, The Netherlands). The meteorological data were averaged and recorded using a Campbell Scientific micro-logger (model-CR3000) (Campbell Scientific, Shepshed, UK) at thirty minutes intervals. Water table height was recorded at the EC flux tower location using Orpheus Mini vented-pressure level transducers located in phreatic tubes. Precipitation data from the nearby Met Eireann weather station (Horseleap) were used during the study duration.  
The EC processing and quality control procedures included spike removal and double coordinate rotation, while tests on developed turbulence and stationarity were also applied [43; 44; 45]. The flux data were gap-filled using semi-empirical models parameterized using high-quality flux data [46], while fluxes were partitioned through the extrapolation of nighttime respiration [44].
# RMCSat: An F10.7 Solar Flux Index CubeSat Mission
## 4 Satellite Architecture and Subsystems
### Payload and Data Generation
The minimum observation time is calculated using,  
\[t_{obs}=\frac{\theta}{360}P, \tag{10}\]  
where \(P\) is the orbit period and \(\theta\) in this case is the antenna beamwidth between first nulls (BWFN). Since 2695 MHz and 2800 MHz channels are monitored concurrently, the larger BWFN determines the observation time required to ensure that the nulls of both frequency patterns are captured, enabling accurate determination of the peak flux values. Table 7 shows the minimum observation time based on BWFN. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Channel** & **Antenna Feed** & **Frequency** & **Bandwidth** \\ \hline
1 & Primary & 2695 MHz & 7 MHz \\
2 & Primary & 2785 MHz & 20 MHz \\
3 & Primary & 2815 MHz & 20 MHz \\
4 & Secondary & 2695 MHz & 7 MHz \\
5 & Secondary & 2785 MHz & 20 MHz \\
6 & Secondary & 2815 MHz & 20 MHz \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Data collection channels for RMCSat. Figure 10: Approximate directivity for a 40-turn helical antenna at 2695 MHz and 2800 MHz using the MATLAB Antenna Designer. The antenna is shown within a box representing the deployment module. At 2800 MHz, a half-power beamwidth (HPBW) of 23\({}^{\circ}\) and directivity of 17 dBi is achieved. At 2695 MHz, the same geometry produces an HPBW of 25\({}^{\circ}\) and directivity of 16.7 dBi.
# An Assessment of Agent-Based Modelling Tools for Community-Based Adaptation to Climate Change
## 3 Results
### Gamma (2d/3d)  
GAMA excels in modelling dynamic interactions within communities, allowing users to efficiently define agents, behaviours, and interactions [74]. This capability is crucial for effective adaptation planning, as the tool supports learning and adaptation processes for community agents, enabling dynamic adjustments based on experiences or environmental changes [75]. This tool integrates socio-economic variables into adaptation strategies by modelling agents with relevant attributes [76]. GAMA also considers demographic factors impacting vulnerability and resilience by allowing the modelling of agents with demographic characteristics. GAMA facilitates participatory modelling, involving community members in development and testing. The platform supports collaborative decision-making and scenario planning, fostering a cooperative approach. Additionally, GAMA handles diverse data types, including climate, geographical, and socio-economic data, and connects to databases and external tools like R [77]. R' refers to the R programming language used for statistical analysis and data handling. The tool offers flexibility in scaling models for different community sizes, addressing scalability challenges in both larger and smaller contexts [78]. While explicit mechanisms for assessing uncertainties and risks are limited, GAMA supports feedback loops, representing the impact of adaptation measures over time. GAMA allows for the monitoring and evaluation of the effectiveness of implemented strategies, enabling users to assess outcomes and contribute to an iterative and adaptive modelling process [79]. In conclusion, GAMA is a powerful and comprehensive tool for community-based adaptation modelling, with strengths in dynamic interaction modelling, learning processes, socio-economic integration, demographic considerations, participatory modelling, collaborative decision-making, data handling, scalability, and feedback mechanisms.
## 3 System Model
### Metrics of Interest  
In our study, we target three metrics: (i) signal-to-noise ratio (SNR), (ii) spectral efficiency, and (iii) attained rate. These metrics are derived as explicit functions of time by accounting for blockage and micro- and macro-mobilities of UE.
# Weak Signal Processing Methods Based on Improved HHT and Filtering Techniques for Steel Wire Rope
## 6 Conclusions  
Owing to the requirement of wire rope weak signal processing, an improved Hilbert-Huang Transform and filtering techniques combined method was proposed in this work. The complexity of wire rope structure and the interference environment make the electromagnetic nondestructive testing signals of wire rope mix with various components, especially for the strand and noise signals, which are featured with similar and overlapped frequency components. By conducting the magnetic flux leakage testing experiments for three typical kinds of wire rope defects under different big liftoff distances, various weak signals mixed with background interferences and noise were obtained and analyzed in the perspective of time-frequency joint domain, while the results manifested that the common single filtering method is incapable of distinguishing these weak defect signals with the noises. Consequently, the basic principles of the Hilbert transform and filtering combined weak signal processing method was introduced. Further comparison of signal processing techniques between the traditional lowpass filtering, wavelet denoising, median filtering methods, and the proposed new method indicates that the weak signal characterizations are difficult to identify by the former methods, while the later proposed new method showed that it was effective in weak wire rope signal processing and defect identification by the obvious defect characterizations of signal peak in time domain, and the signal strength enhancement region through STFT analysis. All in all, the comparison results demonstrated the feasibility and validity of the proposed method which also has a great potential in practical application of wire rope inspection.  
Conceptualization, S.L. and Y.S.; methodology, S.L.; software, S.L.; validation, S.L. and Y.S.; investigation, S.L. and Y.S.; resources, S.L. and Y.S.; writing--original draft preparation, S.L.; writing--review and editing, S.L. and Y.S.; visualization, S.L.; supervision, L.H. and Y.K.; project administration, S.L. and Y.S.; funding acquisition, S.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (NNSFC), grant number 52005203, the Fundamental Research Funds for the Central Universities grant number 2662022 GXQD002, and China Postdoctoral Science Foundation, grant number 2019M662600.  
Not applicable.  
The data supporting reported results are contained within this paper.  
We appreciate the financial support from the National Natural Science Foundation of China, the Fundamental Research Funds for the Central Universities, and China Postdoctoral Science Foundation. We would also like to thank the valuable comments from the reviewers.  
The authors declare no conflict of interest.
## 4 Displacement Analysis of Bowden Cable-Fabric Transmission
### Stiffness Model of Bowden Cable-Fabric System  
When the auxiliary force is generated by the Bowden cable-fabric force transmission system, it is nonlinear because of the friction between the wire rope and the wire sheath, the friction between the fabric and the knee joint, the inelastic deformation of the fabric, and other uncertain factors which exist in the force transmission system. Therefore, it is necessary to establish the stiffness model of the Bowden cable-fabric force transfer system by testing the internal flexible link, so that the controller can compensate the nonlinear deformation in the Bowden cable-fabric force transfer system.  
Figure 8: Element force analysis of fabric contact section. (**a**) Force and torque analysis of fabric elements; (**b**) fabric contact with cylinder diagram.  
Using the desired reference force as input, the required displacement of the system is tested, and then, the stiffness model of the force-transmitting system is obtained. In order to obtain the stiffness model, the method used was to capture the relationship between the tension and the encoder displacement during the power-assisted experiment. Because we used an in vitro experiment, the lower leg model was moved to the position at 13% of the gait cycle for the test [23]. In each test, the control motor generated a force of 20 N to 60 N at a constant speed of about 0.06 m/s and released the wire rope at the same speed. The displacement change from the motor encoder during five wire rope pull-and-release cycles under this experimental condition was recorded.  
The output force of the motor was taken as the horizontal coordinate, and the displacement of the motor encoder was taken as the vertical coordinate. By drawing the scatter plot and analyzing and fitting the data, it was found that the quadratic function had the highest degree of fitting. Therefore, the quadratic function was used to fit the force-displacement curve, and the fitting results are shown in Figure 9, where the dotted line is the original data from the five experiments, and the solid line is the fitting model. The function expression is shown in Formula (10).  
\[x_{1}=a_{k}F_{k}^{2}+b_{k}F_{k}+c_{k} \tag{10}\]  
where \(F_{k}\) is the output force of the motor, \(x_{1}\) is the displacement of the motor encoder, \(a_{k}\) is the quadratic coefficient, \(b_{k}\) is the primary coefficient, and \(c_{k}\) is a constant.  
After data fitting, it was determined that the value of \(a_{k}\) is \(-0.0017\) mm/N\({}^{2}\), the value of \(b_{k}\) is 0.3 mm/N, and the value of \(c_{k}\) is 9.7 mm. After calculation, we obtained the regression judgment coefficient R\({}^{2}=0.9868\), which indicates that the regression model has a good fitting ability.
## 5 Implications  
### Theoretical Implications  
First, this study applied the extended UTAUT to the field of a robotic golf caddy. The results showed that the five dimensions of the extended UTAUT, which include performance expectancy, effort expectancy, social influence, facilitating conditions, and perceived risk, significantly affect attitude, which confirms Hypotheses 1, 2, 3, 4 and 5. These results coincide with earlier studies that employed this model in the context of technology-based products/services [40; 41; 46; 44; 47]. They argued that the five dimensions of the extended UTAUT are crucial predictors of attitude. This study confirmed the high reliability and validity of the extended UTAUT. Furthermore, this study found that the five dimensions of the extended UTAUT significantly affect attitude. This study simulated and broadened the previous literature by being the first study to affirm the impact of the five dimensions of the extended UTAUT on attitude from the perspective of a robotic golf caddy.  
An additional key finding of this study is its clear demonstration of the role of attitude within the context of robotic golf caddies. The SEM results showed that a positive attitude enhances both intentions to use and word-of-mouth intentions, supporting H6 and H7. It implies that golfers with a favorable attitude toward robotic golf caddies are more likely to use them on the course and recommend them to others. It also aligns with prior works on technology acceptance behavior [58; 59; 31], reinforcing that attitude is a predictor of behavioral intentions (i.e., intentions to use and word-of-mouth intentions). Thus, validating the predictive influence of attitude in the context of robotic golf caddies serves as a significant theoretical contribution of this research.  
Lastly, health consciousness was found to moderate the relationship between performance expectancy and attitude. It can be interpreted that highly health-conscious golfers have a more positive attitude regarding robotic golf caddies when they perceive that using them improves the efficiency of a round of golf, compared to golfers with low health consciousness. The concept of health consciousness is a significant issue in diverse fields, and prior research consistently argued that people with a high level of health consciousness tend to care more about staying healthy [60; 66]. The results of this study resemble prior studies. Golfers with a high level of health consciousness have a favorable attitude towards a robotic golf caddy if the performance aspect of a robotic golf caddy is satisfactory, because walking during golfing has a great effect on health [70]. This study confirmed and expanded the knowledge in this regard by analytically establishing health consciousness as a moderator in the framework of a robotic golf caddy.
## 4 Results and Discussion
### Enclosure Deformation Prediction  
To assess the selected optimal model's generalization ability in a real-world context, which allowed for an evaluation of its predictive capability and effectiveness, the test set was applied to the RF model to obtain an accuracy assessment.  
The results of the accuracy assessment of the test set's predictions are shown in Table 5. The results show that R\({}^{2}\) is 0.9905, MAE is 0.8572 mm, and RMSE is 1.9119 mm; the accuracy of the prediction results of the test set with unknown data is in close agreement with the prediction accuracy of the training data. The scatterplot of true versus predicted values is illustrated in Figure 7; the dots are the mapping of the true values in the dataset to the corresponding predicted values. The plot reveals that the prediction results are concentrated in the vicinity of a straight line representing a satisfied fitting. These outcomes indicate that the model's predictive performance is not only acceptable but also satisfactory for the specific project. Based on the statistical findings and graphical representations, the model generated by the RF prediction model can be effectively employed for predicting the horizontal displacement of the enclosure structure.  
Figure 6: Scatterplot of CV true-predicted values for each machine-learning model. (**a**) extra tree; (**b**) CatBoost; (**c**) multilayer perceptron; (**d**) random forest; (**e**) decision tree; and (**f**) gradient boost.  
Figure 7: Scatter plot of the RF true value-predicted value on test data.
## 5 Handling Stability Simulation and Comparison
### Simulation and Comparison of the Drift Tests  
The drift test is an important method to study the transient responses of vehicles under the limit operating condition. In the drift simulation, the truck reaches a steady-state condition in the first 10.0 s. A steady-state condition is one in which the truck has the desired steer angle 360\({}^{\circ}\), the initial throttle 20.0, and the initial velocity 40 km/h. In 1.0-4.0 s, [PERSON] ramps the steering angle from an initial value to the desired value. It then ramps the throttle from 0.0 to the full throttle value 100.0 from 5.0 s to  
Figure 8: A comparison of the curves of the body roll angle versus the lateral acceleration for the truck with: (**a**) the multi-leaf spring, and (**b**) the taper-leaf spring.  
Figure 9: A comparison of the curves of \(\alpha_{1}-\alpha_{2}\) versus the lateral acceleration for the truck with: (**a**) The multi-leaf spring, and (**b**) the taper-leaf spring.  
10.0 s. Finally, it keeps the throttle fully open from 10.0 s to 15.0 s. The same simulation parameters were set for the two truck models with the multi-leaf spring and the taper-leaf spring, respectively. A comparison of the simulated dynamic responses is shown in Figure 10.  
From Figure 10a, it can be seen that for the same input, when the steering angle reaches the set value, the steering wheel torque of the truck with the multi-leaf spring suspension is significantly larger than that of the truck with the taper-leaf spring suspension. The result shows that the truck with  
Figure 10: A comparison of the simulated dynamic responses obtained from the drift tests: (**a**) The steering wheel torque, (**b**) the yaw rate, and (**c**) the truck sideslip angle.  
the taper-leaf spring suspension has better steering portability than the truck with the multi-leaf spring suspension. From Figure 10b, it can be seen that the two truck models have almost the same yaw rate. The result shows that replacing the multi-leaf spring with the taper-leaf spring has almost no effect on the yaw rate. From Figure 10c, it can be seen that when the steering wheel angle is increased to the set value, the sideslip angles on the basis of the two truck models are almost the same. With the increase of the throttle opening, the sideslip angles on the basis of the two truck models begin to deviate. The sideslip angle of the truck with the taper-leaf spring suspension is obviously larger than that of the truck with the multi-leaf spring suspension. The result shows that the handling of the truck with the taper-leaf spring suspension is poorer than that of the truck with the multi-leaf spring suspension. The main reason should be related to that the torsional stiffness of the taper-leaf spring is less 18.8% than that of the multi-leaf spring.