esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
A variety of speech recognition applications have been introduced after the commercial success of personal assistant devices. In particular, considerable attempts have been made to apply voice-driven control for moving vehicles, e.g., cars and airplanes (even combat planes). In addition, the convenience of hands-free voice control has extended the range of speech recognition applications to unmanned aerial vehicles (UAV).  
Compared to remote manual control, voice interfaces facilitate rapid and convenient UAV control. Previous studies have proposed several UAV voice-control systems [1; 2; 3]. Most conventional studies focused on a general speech recognition architecture to control a single drone using voice commands. However, for stable voice control of UAVs, it is necessary to carefully investigate and handle various environmental conditions.  
One of the main environmental issues to be addressed for voice-controlled UAVs is the reduction of background noises captured by microphones. Noise signals that interfere with the speech recognition process may deteriorate recognition accuracy. Thus, most systems handle background noise using noise reduction methods [4; 5]. In addition, most conventional methods target a single UAV under the assumption that the speech recognition system is fully assigned to a single UAV. Thus, a new operating scheme is required to direct a target vehicle among multiple UAVs and switch targets without remote control devices when controlling multiple UAVs.  
Standard speech recognition systems comprise several modules, including front-end, recognition and post-processing modules (Figure 1) [6; 7]. The front-end module requires various fundamental processes, e.g., noise reduction, voice triggering, and acoustic feature extraction. The extracted acoustic features are submitted to a recognition module, where speech recognition is performed using general pattern recognition techniques, e.g., deep neural network (DNN) or hidden Markov model (HMM). The recognition process is followed by the post-processing module, which verifies the recognition result and determines whether the result is accepted or rejected.  
The two main issues (i.e., the noise reduction and the operation scheme) to be addressed for voice-control of multiple UAVs can be handled by the front-end module. This study proposes an efficient speech recognition front-end for voice-driven multi-UAV control. In particular, we concentrate on a vehicle-embedded recognition scheme rather than a server-based scheme, which can be affected by network capacity and processes an indirect UAV control.  
This remainder of this paper is organized as follows. In Section 2, we propose an efficient speech recognition scheme for voice-driven multi-UAV control. Section 3 presents the proposed vehicle-embedded speech recognition front-end. In Section 4, several experiments conducted on speech data and their results are reported and discussed. Finally, Section 5 concludes the paper.
## 2 Materials and Methods
### Test Program  
#### 2.3.1 Specim Preparation  
The fatigue test specimen was designed to represent the stress state of the original bridge at vulnerable fatigue details. The effect of the cable force was simulated by pressure added to the anchor plate. For the application of vertical fatigue loading, the test specimen was required to rotate at a certain angle. Due to the limitations of floor space and the loading capacity of the fatigue test machine, a 1:1 scale prototype was impossible. The choice of specimen scale should consider the available floor space (2 m \(\times\) 2 m in plane and 3 m in height) and loading capacity (a maximum capacity of 500 kN and a suggested range of 50-400 kN). The specimen must provide sufficient space for the movement of the loading actuator with a diameter of 0.4 m. The actual supply of plate thickness and instrumentation limits should also be considered. Through a series of model comparisons, shown in Figure 4, the most stressed B17 anchor box was selected as the object of study, and a cable-girder anchorage zone specimen with a scale of 1:3.75 satisfying all laboratory restrictions was obtained. An auxiliary base was added to facilitate the installation of the test specimen, and an extra stiffener was welded to the inner web to improve local stress distribution.  
The fatigue specimen was produced with a fabrication process similar to the original bridge except for a slight difference in the type of steel. Heilongjiang Bridge was made of Q420 qFNH steel, which is the first ultra-low temperature grade F weathering steel for bridges in China, whereas the test specimen was manufactured with the available Q420 qC steel, which is of the same grade but has a lower toughness requirement.  
The cable-girder anchoring structure and base component were transported separately to the structural laboratory at the Beijing University of Technology. The base was fixed with anchor bolts to a strong floor within the full extent of the loading frame of the fatigue test machine. The anchorage structure was then carefully positioned and field-welded to the base. Two flexible cable bracings were installed on the girder webs to restrain the lateral displacement of the test specimen. The test setup is shown in Figure 5.  
Figure 4: Design of fatigue test specimen.  
Figure 3: Anchor box type cable-girder anchorage structure.
## 3 Results
### Materials and Devices Used in the Implementation of Test Stands
#### 3.1.4 Architecture of the Slave Component  
For the practical implementation of the Slave component prototype, several electronic and electrical components were used, which can be seen in Figure 7.  
Figure 5: Front views of the Master component prototype (1—development board with microcontroller; 2—USB connection; 3—accelerometer; 4—display; 5—switch). (**a**) Exterior view. (**b**) Interior view.  
Figure 6: Views of the Master component prototype in operation on the test mannequin—rigid bar equipped with orthoses for fixation.  
The Node MCU Lolin 32 development board manages the operation of the prototype, ensures wireless connectivity through the integrated Bluetooth module, and communicates with the Master component, which is equipped with an identical board, thus enabling real-time data exchange.  
The development board is powered through a dedicated USB connection with a 5 V power source. The Bluetooth connection between the two components is automatically established when the microcontrollers are powered, thereby enabling low-latency interaction between them. Information regarding the connection status is displayed on an OLED screen, which is identical to the one used in the Master component prototype. Additionally, in a manner analogous to the Master component, the Slave component displays real-time information about the position of the mannequin's lower limbs.  
The Slave component is functionally connected to the electric actuator, which is integrated into the extensible prosthetic module (EPM). The connection is facilitated by an L298N module that uses a double \"H\" bridge for controlling the actuator's direction. The module is powered through a dedicated connection with a 12 V supply, and the power can be activated or deactivated via an on/off switch. The main characteristics of the driver are listed in Table 5.  
This electrical configuration allows for precise control of the actuator, ensuring a fast and efficient response to commands received from the main microcontroller (Slave).
# Road Infrastructure Investment Limits Based on Minimal Accidents Using Artificial Neural Network
## 3 Results and Discussion
### MLR, PR and ANN ARA Models
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**MLR**} & \multicolumn{2}{c}{**PR**} \\ \hline Terms & Unit & Coefficient & \(p\)-value & Coefficient & \(p\)-value \\ \hline Constant & & \(-1\),625,191 & 0.0000 & 1.77931 & 0.0000 \\ \hline Freight & MTkm & \(3.56900\times 10^{-01}\) & 0.0000 & \(-1.28461\times 10^{-06}\) & 0.0000 \\ \hline Passenger & Million passenger km & \(-1.07521\times 10^{-01}\) & 0.0000 & \(1.62813\times 10^{-07}\) & 0.0000 \\ \hline CNRV & (\%) & – & – & \(-2.14206\times 10^{-03}\) & 0.0000 \\ \hline Total ERMI & EUR/yr. & – & – & \(-1.50190\times 10^{-10}\) & 0.0000 \\ \hline Total NRII & EUR/yr. & \(1.12648\times 10^{-05}\) & 0.0000 & \(4.42200\times 10^{-11}\) & 0.0000 \\ \hline ERMI Per Capita & EUR/yr./Capital & \(5.40957\times 10^{02}\) & 0.0000 & \(1.08639\times 10^{-02}\) & 0.0000 \\ \hline NRII Per Capita & EUR/yr./Capital & \(-2.65564\times 10^{02}\) & 0.0001 & \(-3.40675\times 10^{-03}\) & 0.0000 \\ \hline Population & Million & \(4.62782\times 10^{04}\) & 0.0000 & \(1.07097\times 10^{-01}\) & 0.0000 \\ \hline Proportion of Work Group & (\%) & \(2.39945\times 10^{04}\) & 0.0000 & \(1.31254\times 10^{-01}\) & 0.0000 \\ \hline Population of Work Group & Million & \(-6.63550\times 10^{04}\) & 0.0000 & \(-1.12287\times 10^{-01}\) & 0.0000 \\ \hline GDP & (USD/capita) & – & – & \(-2.45351\times 10^{-05}\) & 0.0000 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: ARA regression model parameters. various model performances in terms of root mean square error (RMSE) and R\({}^{2}\). Comparing the MLR, PR, and ANN ARA models in terms of RMSE, the ANN model is at least 11 times more accurate than the other models. In accordance with these observations, only the ANN model was considered reliable for the sensitivity analysis of the ARA and the model application example. Table 4 presents the weight and bias matrices of the ANN model's hidden and output layers. These results were used to estimate the relative importance of the predicting variables of the ANN model. Figure 4: Predicted vs. actual annual road accident plot for ANN model. Figure 3: Predicted vs. actual annual road accident plot for MLR and PR models.
# A Rapid and Inexpensive Method for Finding the Basic Parameters of Involute Helical Gears
## 1 Introduction
For mechanisms with parallel axes, the flanks of the spur gears can be described as ruled surfaces, with their generatrixes lying parallel to the wheel axes. The whole gearing process can be studied in a section normal to the axes of the wheels (frontal plane). The intersection between the flanks and the plane normal to the axes of the wheels are named profiles. In this case, the condition of two toothed wheels to maintain a constant transmission ratio is derived from the fundamental law of gearing. This law dictates that for two profiles designed to mesh with a constant transmission ratio, the common normal at their point of contact should always pass through a fixed point [11; 12]. This point known as the \"pitch point \" and lies along the centre line, dividing it in a ratio inversely proportional to the transmission ratio. Therefore, if there are stipulated the centers distance, the transmission ratio and one of the profiles, then, based on the fundamental law of gearing, the profile of the conjugate wheel can be found. From a practical point of view, an extremely pertinent problem is determining a profile curve that, by the gearing fundamental law, conducts to a curve of the same type of the conjugate profile. Therefore, the manufacturing technology of both teethed wheels is unified to a unique one and the costs of the transmission are thus substantially reduced. The single curve that satisfies this condition is the circle involute. This property of the involute makes possible the wide spread of spur gears with involute teeth in engineering applications. The parameters defining the involute teeth are used in all three stages: design, machining, and control. The last stage has a distinct importance since by finding the parameters which define the geometry of the teeth, one can estimate the manner of the fabrication process [13; 14], ensure the accuracy requirements imposed on the gear during the design step, and thus certify the appropriate running of the assembly [15; 16] from which it belongs. Concerning the control methods of the gears, these evolved from classical ones [17; 18], when the over teeth and over pins/over balls dimensions were verified, to modern procedures [19; 20; 21; 22]. One can mention a modern control method of gears which assimilates the helical gears with helical screws [23].
# Resonant Laser Ionization and Fine-Structure Study of Silver in an Ablation Plume
## 2 Theory and Motivation  
The RILIS relies on a non-thermal process which is based on atomic excitations. Ions of a chosen element are obtained via step-wise resonant energy jumps using, mainly, two or three laser beams tuned to the transition frequencies of the element of interest. The method is chemically very selective as each element has a unique electronic shell structure and energy level scheme. RILIS also has the advantage of high efficiency of the order of tens of percent [15]. The ionization of many elements with relatively high ionization potential (IP), including silver, cannot be achieved optimally with other ion sources, such as surface and plasma ion. The plasma ion source should be able to ionize silver but, for typical on-line experiments using a UC\({}_{x}\) production target, the beam produced will be contaminated. A laser ion source is preferred to produce a high purity beam.
# GNSS Real-Time Precise Point Positioning in Arctic Northeast Passage
## 2 Materials and Methods
### GNSS Real-Time PPP Model
#### 2.2.3 RT-PPP Method in NEP  
We processed the RT-PPP method in the NEP in three steps, i.e., data collection, real-time precise orbit/clock calculation, and PPP processing. In the first step, we received data including GNSS observation data, broadcast ephemeris, and real-time orbit/clockcorrections via LEO, such as Iridium. In the second step, we calculated the real-time precise orbit and clock by using the real-time orbit/clock corrections, and the specific calculation process referred to in ([[https://igs.org/wg/real-timel](https://igs.org/wg/real-timel)]([https://igs.org/wg/real-timel](https://igs.org/wg/real-timel)), accessed on 1 February 2022). In the third step, we used the PPP model mentioned above and an extended Kalman filter to obtain the users' positions. The specific RT-PPP method in the NEP is shown in Figure 1.
## 2 Theory of Numerical Simulation  
### LES Method  
The flow field of the model is numerically calculated by LES, and turbulent fluctuation pressure is extracted from the simulation. In the theory of LES, turbulent vortices are divided into two parts: large-scale vortex and small-scale vortex. The large-scale vortex provides the major part of the turbulent energy. However, the small-scale vortex only dissipates the turbulent energy. More specifically, a filter function is established in the method, which can filter out the small-scale vortex. The large-scale vortex is introduced into the Navier-Stokes equation (NS) and solved. The sub-grid stress terms are added to the NS equation to show the effect of the small-scale vortex on the flow field. The filtered NS equation is as follows:  
\[\frac{\partial}{\partial t}(\rho\overline{u_{i}})+\frac{\partial}{\partial x _{i}}\left(\rho\overline{u_{i}u_{j}}\right)=-\frac{\partial\overline{p}}{ \partial x_{i}}+\frac{\partial}{\partial x_{j}}\left(\mu\frac{\partial \overline{u_{i}}}{\partial x_{j}}\right)-\frac{\partial\tau_{ij}}{\partial x_{ i}} \tag{1}\]  
\[\frac{\partial p}{\partial t}+\frac{\partial}{\partial x_{i}}(\rho\overline{u _{i}})=0, \tag{2}\]  
where \(\tau_{ij}=\rho\overline{u_{i}u_{j}}-\rho\overline{u_{i}}\overline{u_{j}}\) is the term of sub-grid stress, and \(\tau_{ij}\) is also called the filtered stress tensor. The dynamic sub-grid model proposed by [PERSON] is added to make Equation (1) enclosed [25], which can be suitably adapted into the local turbulent structure near the wall.  
\[\tau_{ij}-\frac{1}{3}\tau_{kk}\delta_{ij}=-2\mu_{t}\overline{S}_{ij}, \tag{3}\]  
where \(\mu_{t}\) is the coefficient of sub-grid eddy viscosity, \(\delta_{ij}\) is the sub-grid scale Reynolds stress, and \(\overline{S}_{ij}\) is the rate of strain tensor, which can be written as:  
\[\mu_{t}=(C_{s}\Delta)^{2}|\overline{S}|, \tag{4}\]  
\[|\overline{S}|=\sqrt{2\overline{S}_{ij}\overline{S}_{ij}},\ \overline{S}_{ij}=\frac{1}{2} \left(\frac{\partial\overline{u}_{i}}{\partial x_{j}}+\frac{\partial\overline {u}_{j}}{\partial x_{i}}\right),\Delta=\left(\Delta_{x}\Delta_{y}\Delta_{z} \right)^{1/3} \tag{5}\]  
where \(\Delta\) is the scale of the filter, and \(C_{s}\Delta\) is equivalent to the mix length. The dynamic sub-grid model needs to be continuously adjusted to suit different computational processes. The convection field needs to be filtered many times, and the results are as follows:  
\[C_{s}^{2}=\frac{1}{2\Delta^{2}}\frac{\left\langle L_{ij}M_{ij}\right\rangle}{ \left\langle M_{ij}M_{ij}\right\rangle}, \tag{6}\]  
\[L_{ij}=\overline{\overline{u}_{i}}\overline{\overline{u}_{j}}-\overline{ \overline{u}_{i}\overline{u_{j}}} \tag{7}\]  
\[M_{ij}=2\Delta^{2}\left(\alpha^{2}-1\right)\left|\overline{S}\right| \overline{\overline{S}}_{ij} \tag{8}\]  
where \(L_{ij}\) is the resolved stress, and \(M_{ij}\) is the tensor.
## 3 Results  
Fifty-three participants had complete assessments, with twenty-two subjects being excluded from analysis for incomplete evaluations from study assessments. Characteristics of the final study sample are reported in Table 1. Most of the automated measurements had mean values higher that those taken manually: +0.59 cm (neck), +1.14 cm (right armpit), +1.16 cm (left armpit), +0.21 cm (right bicep), +0.24 cm (left bicep), +0.05 cm (right elbow), +0.06 cm (left elbow), +0.19 cm (right wrist), +0.21 cm (left wrist), +2.31 cm (chest), +0.70 cm (breast), +1.20 cm (waist), +1.20 cm (belly), +0.13 cm (hip), +0.07 cm (right thigh), +0.02 cm (left thigh), +0.05 cm (right knee), +0.06 cm (left knee), \(-0.09\) cm (right calf), \(-0.15\) cm (left calf), \(-0.13\) cm (right ankle), and \(-0.21\) cm (left ankle). ICC results indicated strong overall consistency between methods. The best-performing measurements with optimal reliability were neck, biceps, elbows, breast, waist, hip, thighs, knees, calves, and ankles. The worst-performing were measurements of armpits and chest, which showed also wider confidence intervals. Based on the clinically acceptable threshold of \(\pm\)1 cm difference between manual and automatic measurements, agreement of over 90% of values was found for biceps, elbows, wrists, thighs, knees, calves, and ankles. Moderate agreement was found for hip (88.7%), belly (75.5%), breast (77.4%), waist (62.3%), and neck (60.4%) measurements, while low agreement (<60%) was found for chest and armpits. Biceps, elbows, wrists, thighs, knees, calves, and ankles showed low random error (<0.5 cm). Neck, breast, and hip showed moderate variability, and the highest SEMs (>1 cm) were found for armpits, chest, waist, and belly. Concerning the CV, the highest percentages were of belly (6.22%), armpts (3.97% and 5%), and chest (3.62%). Bland-Altman agreement analysis showed that the average difference between manual and avatar-derived measurements indicated the highest positive biases for chest (\(-\)2.44 cm), waist and belly (around \(-\)1.2 cm), and armpts (around \(-\)1.1 cm). These same regions had also wider limits of agreements, showing greater individual variability. Conversely, limbs (e.g., biceps, thighs, knees), showed narrower limits. The complete results are reported in Table 2. A significant proportional bias was found for right (slope = \(-\)0.102, R\({}^{2}\) = 0.136, \(p\) = 0.007) and left wrist (slope = \(-\)0.097, R\({}^{2}\) = 0.105, \(p\) = 0.018), and chest (slope = \(-\)0.220, R\({}^{2}\) = 0.425, \(p\) < 0.001). Other regions had consistent error across body sizes.
## 3 Methodology
### Grid Independence Study  
In order to get the mesh independent solution, a grid sensitivity analysis was conducted using four different meshes--M1, M2, M3, and M4--by varying the grid resolution from 3.6 million to 8 million. The details of the mesh independence study are described in Table 4. The nodes along the upward direction (\(N_{U}\)), nodes along the downward direction (\(N_{D}\)), and nodes along the airfoil length (\(N_{CL}\)) were kept constant and equal in number for all four mesh independent studies. The nodes along the trailing edge (\(N_{TE}\)) and leading-edge (\(N_{LE}\)) were increased by a count of 50, whereas the nodes along the radial direction (\(F_{D}\) ) and in front of the leading edge were increased by a count of 25 from the initial counts. Hence, every time mesh was loaded and generated to run the simulation on ANSYS 16 [36]. The steady-state simulation for all four meshes (M1-M4) was carried out with a wind speed of 7 m/s. After each mesh independence study, the \(C_{l}\), \(C_{d}\), allocated memory, and CPU times for 10 iterations were recorded by the solver and compared with the other one. On the basis of \(C_{l}\) and \(C_{d}\), the transition from M3 to M4 resulted in an error recovery of less than 1%. Therefore, by considering the grid resolution and computational accuracy, M3 was selected to perform all the subsequent simulations in the present study. The distribution of the mesh topology with a specific name in the computational domain for the mesh independence study is shown below in Figure 8. The mesh distribution in the computational domain is listed in Table 4 with other details.  
Figure 8: Mesh topology structure and its name.
# Evaluation of Measurement Strategy for Track Side Monitoring of Railway Wheels
## 1 Introduction
This paper presents a study of the representability of a one-position or one-slice measurement strategy with respect to the wheel parameters from measurements carried out during in-workshop inspection and utilizing wayside monitoring systems to validate the same. The aim of this study is also to improve the possibility of classifying wheels that are in operation with respect to the standard wheel parameters. An improved classification could result in better ride comfort, less wheel and rail wear, and lower maintenance cost for both infrastructure managers and rolling stock owners/operators. The main aspect investigated in this study is the possibility of using single-wheel profile measurements from wayside measurement stations giving one-slice measures of the wheel profile for calculating the standard wheel parameters. The remainder of this paper is structured as follows. Section 2 introduces the technical background of wheel profiles and standard wheel parameters. Section 3 elaborates the methodology followed for this study. The results and analysis are explained in Section 4, and the conclusions and future work are discussed in Section 5.
# The Use of Machine Learning Methods in Road Safety Research in Poland
## 2 Materials and Methods
### RPart Classification with Cross-Validation Algorithm  
Recursive Partitioning and Regression Trees (RPart) is an algorithm that belongs to the class of decision-making algorithms and is based on the classification and regression trees (CART) method [64; 65]. It is used for classification and regression by dividing data into smaller, more homogeneous groups (partitions) with respect to the target variable (class for classification). RPart, like other decision trees, splits the dataset into subsets based on the input features to achieve the highest possible homogeneity of explanatory variables in relation to the dependent variable within each resulting group (tree nodes) [66]. In this study, cross-validation was also applied to the RPart algorithm to improve the accuracy and reliability of the model's performance, following a similar methodology as with Random Forest [67].