esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results  
The chemical and isotopic compositions of the spring waters are summarized in Table S1. Spring temperatures range from 4.7 to 50 \({}^{\circ}\)C. The dominant cations are ordered as Na\({}^{+}\) \(>\) Ca\({}^{2+}\)\(>\) Mg\({}^{2+}\)\(>\) K\({}^{+}\)\(>\) Li\({}^{+}\), with Na\({}^{+}\) concentrations ranging from 10.42 to 1018.80 mol/L, Ca\({}^{2+}\) from 20.93 to 538.95 mg/L, Mg\({}^{2+}\) from 6.95 to 116.15 mg/L, K\({}^{+}\) from 1.18 to 39.49 mg/L, and Li\({}^{+}\) from 0 to 3.16 mg/L. The primary anions follow the order Cl\({}^{-}\) \(>\) HCO\({}_{3}\)\({}^{-}\)\(>\) SO\({}_{4}\)\({}^{-}\)\(>\) NO\({}_{3}\)\({}^{-}\)\(>\) F\({}^{-}\)\(>\) Br\({}^{-}\). Cl\({}^{-}\) concentrations range from 9.50 to 2195.86 mg/L, HCO\({}_{3}\)\({}^{-}\) from 118.58 to 1959.57 mg/L, SO\({}_{4}\)\({}^{2-}\) from 76.03 to 797.38 mg/L, NO\({}_{3}\)\({}^{-}\) from 0 to 34.04 mg/L, F\({}^{-}\) from 0.64 to 4.89 mg/L, and Br\({}^{-}\) from 0 to 4.89 mg/L. The measured pH values range from 6.39 to 8.17, indicating neutral to slightly alkaline conditions. Conductivity ranges from 539 to 3635 \(\mu\)s/cm. \(\delta\)\({}^{18}\)O and 8D values ranged from \(-\)13.22% to \(-\)6.16% and \(-\)93.34% to \(-\)29.81%, respectively. SiO\({}_{2}\) concentrations range from 3.19 to 47.12 mg/L, reflecting varying degrees of water-rock interaction and geothermal input.  
Hydrochemical anomaly thresholds were established using time-series monitoring data from the S13 spring. Here, the background concentrations were defined by the mean values, while anomalous levels were determined using \(\pm\)2\(\sigma\) (standard deviation) thresholds. The mean concentration of Na\({}^{+}\) is 466.73 mg/L, Cl\({}^{-}\) is 137.67 mg/L and SO\({}_{4}\)\({}^{2-}\) is 155.42 mg/L. The mean value of \(\delta\)\({}^{18}\)O is \(-\)10.20%, 8D is \(-\)64.70%, and \(\delta\)\({}^{13}\)C DIC is \(-\)3.60%. The mean concentrations of 13 trace elements (Li, B, Al, Ba, Ge, Rb, Cs, V, Be, Mo, Cr, Sr, and Sb) in spring water is 688.57, 4987.94, 32.47, 91.29, 19.21, 26.37, 45.60, 0.07, 0.03, 0.17, 0.18, 480.28, and 0.05 \(\mu\)g/L. More detailed information can be seen in Tables S2 and S3.  
The O\({}_{2}\) concentrations of the gas samples from S1 and S13 are 0.31% and 0.02%, respectively, indicating minimal atmospheric contamination. The helium isotope ratios further confirm negligible atmospheric contamination during sampling and analysis. The \({}^{4}\)He/\({}^{20}\)Ne ratios (46 and 737) significantly exceeded the atmospheric ratio (0.32) (Table 1). The gas composition is CO\({}_{2}\)-dominated at S1 and N\({}_{2}\)-dominated at S13. The \(\delta\)\({}^{13}\)C values of CO\({}_{2}\) are \(-\)4.182% at S1 and \(-\)10.3% at S13.
# Multifrequency Impedance Method Based on Neural Network for Root Canal Length Measurement
###### Abstract  
Root canal therapy is the most fundamental and effective approach for treating endodontics and periapicalitis. The length of the root canal must be accurately measured to clean the pathogenic substances in it. This study aims to present a multifrequency impedance method based on a neural network for root canal length measurement. A circuit system was designed which generates a current of frequencies from 100 Hz to 20 kHz in order to augment the data of impedance ratios with different combinations of frequencies. Several impedance ratios and other quantified characteristics, such as the type of tooth and file, were selected as features to train a neural network model that could predict the distance between the file and apical foramen. The model uses leave-one-out cross-validation, adopts the Adam optimizer and regularization, and has two hidden layers with nine and five nodes, respectively. The neural network-based multifrequency impedance method exhibits nearly 95% accuracy, compared with the dual-frequency impedance ratio method (which demonstrated no more than 85% accuracy in some situations). This method may eliminate the influence of human and environmental factors on measurement of the root canal length, thereby increasing measurement robustness.  
root canal measurement; multifrequency impedance; data augmentation; neural network +
Footnote †: journal: Applied sciences  
_Article_  
Multifrequency Impedance Method Based on Neural Network for Root Canal Length Measurement
# Early Gapping and Platoon Merging Strategies for Autonomous Vehicles using Local Controllers
## 3 Results
### Effect of Inlet Density  
Figure 4 shows the effect of inlet density (\(T_{g}(x=0)\)) on the merging success and traffic flow (for an example case with \(x_{merge}=1.5\) km, \(\%d_{stb}=5\%\) and \(v_{sapping}=90\) km/h). In the graph, the \(x\)-axis corresponds to inlet density (with \(T_{g}(x=0)\) between 1-2.2 s). From the graph, it is clear that, for high inlet densities (with \(T_{g}(x=0)=1\)-1.5 s), the merging success does not exceed 30% while maintaining a high flow rate of 2700 veh/h. As inlet density reduces (with \(T_{g}(x=0)=1.5\)-2.2 s), the merging success increases to up to 82%, and the  
Figure 3: Two example of position–time space vehicle dynamics along the road section of the main lane (blue lines). Red lines indicate merging vehicles. (**a**): vehicles 1–100 for a case with zipper with 10% merging; (**b**): vehicles 1–800 for the case of 30:2 platoon size ratio.  
flow rate remains in the range of 2500-2700 veh/h. In all cases, the algorithm allowed for the controlling of a high flow rate for all the examined densities.  
For high inlet density (e.g., rush hour, \(T_{g}(x=0)=1\) s), although the resulting flow is high, the merging success is relatively low (23%). To explore the gapping conditions that allow high flow rates at low merging percentages, we focused on the specific case of (\(T_{g}(x=0)=1\) s) while changing the gap profile parameters.
# Intent to Adopt Location Sharing for Logging Safety Applications
## 5 Conclusions  
LS technology may increase team situational awareness on logging operations by providing loggers with crucial system information about the positions and status of other crew members. Based on surveys of Idaho loggers at three training programs, we recommend further development of LS for logging safety applications. Devices, such as two-way radios with LS capabilities or smartphone-based receivers, would ideally possess multiple functions. For instance, they should be able to indicate when isolated workers, like hand fallers, need help and may be incapacitated (e.g., automatic position updates) and expedite search and rescue efforts with shared GPS coordinates of injured workers. They could also alert loggers, specifically machine operators, of potential hazards for ground workers in low visibility conditions. These applications may be most relevant to cable yarding or partially-mechanized systems, so further research is necessary to evaluate the perceived need for LS on fully mechanized operations. Research on use of LS for logging safety could also benefit from additional sampling efforts and analysis to characterize hand fallers' perspectives, as they were underrepresented in this study but would be highly impacted by LS practices. Furthermore, although LS has potentially life-saving implications for loggers, the technology has limitations, such as positioning and signal transmission errors associated with GNSS-RF devices in certain environments. Therefore, before LS is widely implemented, we recommend development of safety guidelines outlining appropriate and inappropriate operation of LS, accounting for device capabilities and preferred uses of the technology. Lastly, the TPB has been employed to understand adoption of technology and occupational safety behaviors separately; our study supports applying the TPB to a novel combination of these two themes (i.e., technologically-based occupational safety), demonstrating that instrumental attitude, descriptive norms, and perceived behavioral control correlate with intent to adopt LS safety practices.  
**Author Contributions:** Conceptualization, A.M.W. and R.F.K.; Methodology, A.M.W., R.F.K., S.M.N. and T.B.P.; Formal Analysis, A.M.W. and R.F.K.; Investigation, A.M.W. and R.F.K.; Writing-Original Draft Preparation, A.M.W.; Writing-Review and Editing, A.M.W., R.F.K, S.M.N. and T.B.P.; Visualization, A.M.W.; Funding Acquisition, R.F.K. and S.M.N.  
**Funding:** This research was funded by the U.S. Centers for Disease Control and Prevention (CDC) National Institute for Occupational Safety and Health (NIOSH) grant number 5 U01 OH010841.  
**Acknowledgments:** The authors would like to thank the loggers who participated in this study, as well as the University of Idaho Extension Office's Logger Education to Advance Professionalism (LEAP) program for permitting to us to present and administer the survey at 2018 LEAP update meetings. The authors would also like to thank [PERSON], [PERSON], and [PERSON] for pre-testing survey questions.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Optimal Design of Transmit Waveform and UAV Altitude  
### Optimal Solution to Our Design Problem  
Problem \(\mathbf{P}_{1}\) is non-convex because the objective function is non-convex with respect to \(h\). More specifically, \(\mathbf{G}_{u}(h)\) for all \(u\in\mathcal{U}\) in the objective function is a very complex, non-linear function with respect to \(h\). Thus, it is extremely hard to find an optimal solution in general. To tackle this problem, we first mathematically investigated it and then developed an effective method to solve it, as presented in the next subsection.  
For a given \(h\in[H_{\text{min}},H_{\text{max}}]\), \(L_{\text{WPT}}(\mathbf{X},h)\) in (16) is a convex function, so the objective function of problem \(\mathbf{P}_{1}\) is also a convex function. Hence, we can find an optimal waveform (\(\mathbf{X}\)) and an optimal scaling factor (\(\gamma\)) by solving the following semidefinite programming (SDP) problem:  
\[\mathbf{P}_{2}^{\forall h}:\min_{\mathbf{X},\gamma} \rho L_{\text{Rad}}(\mathbf{X},\gamma)+(1-\rho)L_{\text{WPT}}( \mathbf{X},h)\] s.t. \[[\mathbf{X}]_{n,\tau}=\frac{p_{T}}{N},\ \forall n\in\mathcal{N}, \tag{20}\] \[\mathbf{X}\succeq\mathbf{0}. \tag{21}\]  
We can numerically obtain the optimal solutions to problem \(\mathbf{P}_{2}^{\forall h}\) with a well-known SDP solver, such as CVX [1; 29; 30], and denote the solutions by \(\mathbf{X}^{*}(h)\) and \(\gamma^{*}(h)\) with a given \(h\).  
On the other hand, for a given \(\mathbf{X}\) and \(\gamma\), \(L_{\text{Rad}}(\mathbf{X},\gamma)\) in (16) is a constant term; thus, an optimal altitude (\(h\)) can be obtained by solving the following problem:  
\[\mathbf{P}_{3}^{\forall\mathbf{X},\gamma}:\min_{h} L_{\text{WPT}}(\mathbf{X},h)\] s.t. \[H_{\text{min}}\leq h\leq H_{\text{max}}.\]In this case, the expected harvested power of the \(u\)-th EHD can be expressed as  
\[\text{Tr}\left(\mathbf{G}_{u}(h)\mathbf{X}\right) = \tag{22}\] \[= \tag{23}\]  
where (23) is obtained by substituting (10)-(13) into (22), and \(\Psi_{r_{u}}(h)\) and \(\Omega_{r_{u}}(h)\) are functions with respect to the optimization variable (\(h\)) as well as the \(u\)-th EHD's distance (\(r_{u}\)), which are defined as follows:  
\[\Psi_{r_{u}}(h) = a_{1}\exp\biggl{(}-b_{1}\biggl{[}\frac{180}{\pi}\arctan\left( \frac{h}{r_{u}}\right)-a_{1}\biggr{]}\biggr{)}, \tag{24}\] \[\Omega_{r_{u}}(h) = 10 \tag{25}\]  
In (24) and (25), \(\Psi_{r_{u}}(h)\) and \(\Omega_{r_{u}}(h)\) result from \(P_{\text{LoS}}(\psi_{u})\triangleq\frac{1}{1+\Psi_{r_{u}}(h)}\) and \(10^{0.1K_{u}^{\text{dB}}}\triangleq\Omega_{r_{u}}(h)\), respectively, for notation simplicity.  
As shown in (23), the objective function of problem \(\mathbf{P}_{3}^{\
u\mathbf{X},\gamma}\) is a non-convex, very complex, non-linear function of \(h\); thus, finding an optimal \(h\) by analytically solving it is extremely difficult. Therefore, we devised a Particle Swarm Optimization (PSO)-based method to solve our design problem, as presented in the next subsection.
## 1 Introduction
[14] presented methods for the online calculation of the sprung mass inertial properties by a combination of measurements of sprung mass responses to road inputs and simplified vehicle models. These methods require a certain level of vehicle-inbuilt sensor technology, which is usually available in conventional vehicles. Since these sensors are missing in the vehicles considered in this research, an application of these methods was not possible. Studies of the center of gravity locations of human-powered vehicles can be found in the research area of wheelchairs and similar vehicles. [PERSON] [15] presented a steering system for the wheelchair that is based on shifting the center of gravity. Extensive studies were carried out by [PERSON] et al. [16; 17; 18; 19; 20]. Based on anthropometric models and photographic analysis of movement, [PERSON] et al. [16] studied the influence of rider movement on the center of gravity of a wheelchair-rider system. In sources [17; 18], a test  
Figure 1: (**a**) Trike; (**b**) Velombile. bench was presented that allowed the determination of the center of gravity of a wheelchair-rider system, taking into account dynamic rider movements and the tilt of the wheelchair. The methodology described therein thus also allowed the determination of range values for the change in the center of gravity depending on the driver's movement. This method was used in source [19] to investigate the deviation of the driver center of gravity from the wheelchair trajectory considering different driving maneuvers. In source [20], the influence of center of gravity motion on rolling resistance and the required propulsive force for a wheelchair were discussed. In addition to measurements, mass data were often determined from CAD models or approximated based on simple basic bodies, as in sources [21; 22; 23]. As has been shown, there is already a large number of studies on the mass properties of conventional vehicles as well as of some human-powered vehicles. This is not yet the case for a large number of recumbent bicycles and especially for velombiles. The aim of this research work was, therefore, to describe the range of mass properties for the considered class of human-powered vehicles as a basis for vehicle dynamics simulations and the development of assistance systems. For this purpose, wheel-load measurements were used to determine the center of gravity positions of the vehicles considering the static driver influence. Measurements of the moment of inertia were performed on a rotational inertia test bench. Finally, the results of the inertia measurements were used to determine approximation models for yaw inertia. In summary, this research work yielded ranges of parameters relevant to vehicle dynamics for the vehicle class under investigation as input variables for future studies on vehicle dynamic models, sensitivity analyses, and controller development.
## 4 RADC: Resource Allocation Based Data Classification
### Content Classification  
As mentioned above, most of the proposed methods in the NDN do not provide a robust model for content classification, as they typically use simple parameters to classify their content (e.g., request frequency). One of the most important keys introduced by NDN that can be exploited to classify contents efficiently is the data naming, which adopts the same structure as URL naming. The names in NDN are significant sources of information and knowledge; however, processing names is a difficult task regarding their unstructured format. We use a Naive Bayes (NB) classifier [17], which has been widely used to analyze and solve many scientific problems due to its simplicity, efficiency, and effectiveness [18; 19]. Naive Bayes is computationally inexpensive and also needs a very low amount of memory [20]. The role of the NB in this study is to classify the received content into two classes, where the first class represents the consumed data that belong to the server or domain of the addressed network, and the second class is the consumed data that do not bring any benefit to the addressed network. Before starting to use the BN model, a step concerning the preparation of the dataset for the training and testing of the model should be performed. The dataset used should contain a sufficient number of names of the requested contents in each selected class. Once the preparation of the dataset is complete, the conversion of the dataset into a word count matrix is performed, with each row representing a data name, and the columns representing the features that represent the entire vocabulary existing in the dataset. The model uses the n-gram model [21] to extract more features, and also uses the Term-Frequency Inverse Document Frequency (TF-IDF) model [22] to weigh the selected features.
## 3 GNSS Data and Results
### Corfu-Paxoi Islands  
At the North Ionian Islands, three GNSS stations were processed (Table 1): two in Corfu, KASI and KERK; and the PAXO station located at Paxoi Island. The linear type of motion in all three components for the whole-time span is evident in these stations. There was no static deformation due to the seismic activity that occurred in the south, i.e., related to the 2015 Lefkada or the 2014 Cephalonia events (Figure S1). The calculated velocity vectors are similar to the ones that are presented in [68; 89]. For the two Corfu \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Site** & **Latitude** & **Longitude** & **Period** & \(\mathbf{V_{East}}\) & \(\mathbf{V_{North}}\) & \(\mathbf{V_{Up}}\) \\  & **(°)** & **(°)** & & **(mm/Year)** & **(mm/Year)** & **(mm/Year)** \\ \hline  & & & 2 December 2013–25 & \multirow{4}{*}{12.83 \(\pm\) 0.03} & \multirow{4}{*}{\(-\)2.5 \(\pm\) 0.03} & \multirow{4}{*}{6.40 \(\pm\) 1.24} \\  & & & October 2018–5 July & \multirow{4}{*}{2.37 \(\pm\) 0.42} & \multirow{4}{*}{6.40 \(\pm\) 1.24} \\ ZAKY & 37.7792 & 20.8850 & & & & \\  & & & 0.69 years & & & \\  & & & 6 July 2019–31 October & \multirow{4}{*}{11.21 \(\pm\) 0.10} & \multirow{4}{*}{\(-\)2.68 \(\pm\) 0.09} & \multirow{4}{*}{\(-\)0.88 \(\pm\) 0.32} \\  & & & 2.32 years & & & \\ \hline  & & & 23 November 2018–31 & \multirow{4}{*}{\(-\)8.30 \(\pm\) 0.83} & \multirow{4}{*}{20.61 \(\pm\) 0.51} & \multirow{4}{*}{\(-\)3.39 \(\pm\) 2.18} \\ ZNTE & 37.7176 & 20.7308 & & 0.52 years & & \\  & & & 1 June 2019–31 October & \multirow{4}{*}{10.17 \(\pm\) 0.08} & \multirow{4}{*}{\(-\)0.48 \(\pm\) 0.08} & \multirow{4}{*}{0.10 \(\pm\) 0.025} \\  & & & 2021 & & & \\  & & & 2.42 years & & & \\ \hline  & & & 6 October 2016–25 & \multirow{4}{*}{3.87 \(\pm\) 0.08} & \multirow{4}{*}{\(-\)5.31 \(\pm\) 0.09} & \multirow{4}{*}{\(-\)0.46 \(\pm\) 0.24} \\ STRF & 37.2454 & 21.0156 & & October 2018 & & \\  & & & 2.05 year & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Velocity components for the Zakynthos–Strofades GNSS stations (ITRF 2014).
## 2 System Model of the New Wheel Structure  
### Mathematical Model  
The schematic diagram of a new vibration-absorbing wheel structure proposed in this paper is shown in Figure 1a. To facilitate the establishment of a mathematical model, this new structure can be simplified to the model diagram shown in Figure 1b. The actuator provides the time-delay feedback control force. The excitation of the road surface causes the vertical vibration of the vehicle body, mass block and the wheel, and the sensors detect the signals of them. The signal conditioner transmits the displacement signal of the vehicle body, wheel and mass block to the controller. According to the signal from the sensor, the controller uses time-delay feedback control to make comprehensive judgments and input the best feedback control parameters \(g\) and \(\tau\). The actuator controls the applied force by changing the voltage value.  
As shown in Figure 1, \(m_{s}\) is the body mass, \(m_{t}\) is the wheel mass, and \(m_{a}\) is the mass block. \(k_{t}\) is the stiffness of the tire. \(k_{s}\) and \(c_{s}\) are the stiffness and damping coefficients of the suspension; \(k_{a}\) and \(c_{a}\) are the stiffness and damping coefficients of this new structure; \(u\) is the time-delay feedback control force; and \(x_{s}\), \(x_{t}\), \(x_{a}\) and \(x_{r}\) are the body displacement, tire displacement, mass block displacement and external excitation displacement, respectively. The model is used to investigate the vibration reduction performance of the vehicle with the new vibration-absorbing wheel structure, and the vehicle system model parameters are given in Table 1.  
According to D'Alembert's principle, the dynamic equation of the model can be expressed as  
\[\left\{\begin{array}{l}m_{s}\ddot{x}_{s}+c_{s}(\dot{x}_{s}-\dot{x}_{t})+k_{s} (x_{s}-x_{t})=0\\ m_{t}\ddot{x}_{t}+c_{s}(\dot{x}_{t}-\dot{x}_{s})+c_{a}(\dot{x}_{t}-\dot{x}_{a}) +k_{t}(x_{t}-x_{r})+k_{s}(x_{t}-x_{s})+k_{a}(x_{t}-x_{a})+gx_{a}(t-\tau)=0\\ m_{a}\ddot{x}_{a}+c_{a}(\dot{x}_{a}-\dot{x}_{t})+k_{a}(x_{a}-x_{t})-gx_{a}(t- \tau)=0\end{array}\right., \tag{1}\]  
where \(g\) is the time-delay feedback gain coefficients of the active control force; \(\tau\) is the time-delay; and \(t\) is the time.
## 2 Methodology
### Numerical Method  
For spatial discretization, the control equations are discretized using the finite volume method. Considering computational resources, we choose to use a segregated flow solver to solve the discretized algebraic equation system. Additionally, we employ the semi-implicit method for pressure-linked equations (SIMPLE) to address the coupling between velocity and pressure in the control equations. For turbulence modeling, the Improved Delayed Detached Eddy Simulation (IDDES) method is used, specifically, the Shear Stress Transport  
Figure 5: The visualization of the mesh at a specific moment during the process of a train entering the tunnel: (**a**) the mesh refinement distribution of the longitudinal center section of the train; (**b**) a locally magnified view near the head car; (**c**) a locally magnified view near the tail car; (**d**) the prism layer mesh of the tunnel wall; (**e**) the prism layer mesh of the train wall.  
(SST) k-\(\omega\) model [50] for turbulence closure. Detached Eddy Simulation (DES) is a hybrid modeling approach that employs the Reynolds-Averaged Navier-Stokes (RANS) model to solve boundary layers and irrotational flow regions while using Large Eddy Simulation (LES) for other regions. IIDES is an improved form of the DES model that provides the DES model with Wall-Modeled LES (WMLES) capabilities, achieving a balance between computational accuracy and time to some extent. This method has been widely used in the study of aerodynamic characteristics of high-speed trains [51; 52].  
The convective flux terms are discretized using a second-order upwind/bounded central differencing hybrid method with a blending factor of 0.15, meaning 15% second-order upwind interpolation and 85% bounded central differencing. The temporal term is discretized using a second-order implicit scheme. The time step for the computational model is set to 4.4 \(\times\) 10\({}^{-4}\) s, ensuring that the maximum Courant number does not exceed 1.0 and meets the time step requirements for the overset mesh method. The maximum number of inner iterations per time step is 10. As the grid size increases, the convergence speed of traditional iterative solving algorithms such as Jacobi, Gauss-Seidel, and ILU significantly decreases, leading to increased computation time. To improve the robustness and convergence speed of the linear systems generated by the solver, the Algebraic Multigrid (AMG) method is used. The simulations were conducted on a supercomputer, utilizing 4 nodes. Each node was configured with 2 \(\times\) X86 7285H processors, 256 GB of memory, and 32 cores. The total simulation time was approximately 32 h.