esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Vehicle State Estimation
### Vehicle Dynamics Model  
The 3 DOF vehicle dynamics model [19] is introduced and used in Figure 1 for state estimation and prediction, including longitudinal, lateral and yaw rate, respectively.  
Based on the 3 DOF dynamics model with motion equations, the formulations are presented as:  
\[\left\{\begin{array}{l}\dot{X}=V_{x}\\ \dot{Y}=V_{y}\\ \dot{\varphi}=\omega\\ I_{z}\dot{\omega}=M_{c,z}\\ ma_{x}=F_{c,x}\\ ma_{y}=F_{c,y}\end{array}\right. \tag{1}\]  
where \(V_{x}\) and \(V_{y}\) are longitudinal and lateral velocities according to the global Cartesian coordinate system in the centroid position of the vehicle; \(m\) and \(\delta\) denote the mass and steering angle of the front tire, respectively; \(F_{c,x}\), \(F_{c,y}\) and \(M_{c,z}\) represent the longitudinal and lateral equivalent forces and torque of the centroid position according to the vehicle coordinate system.  
Figure 1: The 3 DOF vehicle dynamics model.  
Moreover, the state equation is described as:  
\[\left\{\begin{array}{l}F_{c,x}=F_{x}\ cos\ \delta-F_{y,f}\ sin\ \delta-\frac{C_{D}A}{2 1.15}(3.6\varpi_{x})^{2}\\ F_{c,y}=F_{x}\ sin\ \delta+F_{y,f}\ cos\ \delta+F_{y,r}\\ M_{c,z}=F_{y,f}L_{f}\ cos\ \delta+F_{x}L_{f}\ sin\ \delta-F_{y,r}L_{r}\\ a_{x}=\dot{v}_{x}-\omega v_{y}\\ a_{y}=\dot{v}_{y}+\omega v_{x}\\ V_{x}=v_{x}\ cos\ \varphi-v_{y}\ sin\ \varphi\\ V_{y}=v_{x}\ sin\ \varphi+v_{y}\ cos\ \varphi\end{array}\right. \tag{2}\]  
where \(v_{x}\), \(v_{y}\) and \(\omega\) are the longitudinal and lateral velocities, and yaw rate in vehicle coordinate system, respectively; \(X\), \(Y\) and \(\varphi\) denote the longitudinal and lateral positions, and the yaw angle in the global Cartesian coordinate system, \(F_{x}\) represent the longitudinal force of the front-driving tire; \(F_{y,f}\) and \(F_{y,r}\) denote the lateral force of front and rear tires, respectively; \(L_{f}\), \(L_{r}\) and \(L_{z}\) denote the front and rear wheelbases and the vehicle inertia around the vertical axis, respectively.
# Characterizing Micro-Displacements on Active Faults in the Gobi Desert with Time-Series InSAR
## 3 Methodology
### InSAR Technique
Interferogram formation, where K interferograms are formed from K + 1 SAR images. 2. DEM and differential interferograms formation with orbital error removal. This is achieved based on Equation (3), where R is the reference sensor-target distance, \(\theta\) is the local incidence angle with respect to reference ellipsoid, q is elevation, \(\lambda\) is wavelength, and B\({}_{\perp}\) is perpendicular baseline. \[\Phi\)_dem_\(\frac{4\pi}{\lambda\text{R}}\) B\({}_{\perp}\) (\(\chi\)) \(\frac{\text{q(x)}}{\sin\theta}\) (3)
3. Preliminary estimate of the LoS motion, elevation error, and atmospheric contribution, where only scatterers with high coherence are considered. 4. Refinement of step (3) by application of the atmospheric phase screen (APS) through residual phase spatial smoothing, which allows extra scatterers to be identified. During these steps, targets with strong stable signals and subsequently low \(\Phi\)_noise_ are selected. The selected scatterers are expected to be only slightly affected by geometrical and temporal decorrelation and always have coherence greater than a specified threshold value [37]. Commonly, and in this research, this threshold coherence value has been set as 0.75; it is a widely accepted coherence threshold in other PSI-related studies such as [PERSON] [13], [PERSON] [39], [PERSON] [40], and [PERSON] [41]. The contribution of \(\Phi\)_dem_ can be reduced through use of a high-quality DEM and regression to find the relationship between phase value and slight variations in the satellite's position that are indicative of DEM error; \(\Phi\)_orb_ and \(\Phi\)_atm_ can be reduced through spatiotemporal filtering [42]. Therefore, the \(\Phi\)_def_ can finally be measured by minimising the additional phase change contributors on a PS-by-PS basis [36].
## 4 Discussion
### Future Directions  
Future research should address the limitations identified in this review by prioritizing high-quality longitudinal studies that monitor adolescents during and beyond orthodontic treatment. Such studies are crucial to clarifying causal relationships between orthodontic interventions and TMDs development. The consistent use of standardized diagnostic criteria, such as the DC/TMD adapted for adolescents, is imperative to improve study comparability and ensure reliable outcomes.  
Additionally, research should explore how individual factors--including genetic predispositions, stress, bruxism, and other behavioral habits--interact with orthodontic interventions and influence TMDs progression. These investigations could provide valuable insights into the multifactorial etiology of TMDs in adolescent populations and guide personalized orthodontic treatment strategies.  
To address the challenge posed by the limited number of recent studies, future research should also aim to reflect advancements in orthodontic techniques and diagnostic tools. More diverse and contemporary datasets would enhance the applicability of findings to current clinical practices. Exploring the impact of biological and behavioral changes during adolescence, including hormonal fluctuations and craniofacial growth patterns, on the interaction between orthodontic forces and TMJ health is particularly important.  
Finally, resolving diagnostic variability remains a critical objective. Adopting uniform, evidence-based diagnostic frameworks, such as the DC/TMD criteria, will reduce inconsistencies across studies and support more robust analyses of TMDs prevalence and outcomes. By addressing these research gaps, future studies can refine clinical guidelines, enhance TMJ outcomes, and ensure optimal oral health for adolescents undergoing orthodontic treatment.
## 4 Probability Distribution of Loss Degree Based on Three-Point Estimation Method
### Three-Point Estimation Method  
Supposing the expectation and standard deviation of the random variable \(x_{i}(i=1,2,\ldots,n)\) are represented by \(u_{x_{i}}\) and \(\sigma_{x_{i}}\) respectively, three sampling points are selected to represent the expectation \(u_{x_{i}}\) of \(x_{i}\) and each point in the left and right field of expectation, denoted as \(x_{i,k}(k=1,2,3)\). The expressions are as follows:  
\[x_{i,k}=u_{x_{i}}+\xi_{x_{i},k}\sigma_{x_{i}}\;k=1,2,3 \tag{20}\]  
where \(\xi_{x_{i},k}\) is the position coefficient of the \(k\) sampling point of the \(i\) random variable \(x_{i}\). The position coefficient \(\xi_{x_{i},k}\) and the weight coefficient \(P_{x_{i},k}\) can be expressed as  
\[\left\{\begin{array}{rl}\xi_{x_{i},k}=-\frac{\lambda_{x_{i},3}}{2}+\left(- 1\right)^{3-k}\sqrt{\lambda_{x_{i},4}+\frac{3\lambda_{x_{i},3}}{4}^{2}}\\ \xi_{x_{i},3}=0\\ P_{x_{i},k}=\frac{\left(-1\right)^{3-k}}{\xi_{x_{i},k}\big{(}\xi_{x_{i},1}- \xi_{x_{i},2}\big{)}}\\ P_{x_{i},3}=\frac{1}{m}-\frac{1}{\lambda_{x_{i},4}-\lambda_{x_{i},3}}\end{array} \right.\qquad k=1,2 \tag{21}\]  
where \(\lambda_{x_{i},3}\) is the skewness coefficient of \(x_{i}\)--when \(\left|\lambda_{x_{i},3}\right|\) is the larger, \(x_{i}\) deviates greatly from the standard normal distribution; \(\lambda_{x_{i},4}\) is the kurtosis coefficient--when \(\left|\lambda_{x_{i},4}\right|\) is smaller, the value of \(x_{i}\) is more concentrated near the expected value; and when \(\lambda_{x_{i},4}=0\), the distribution of \(x_{i}\) is consistent with the steepness of the standard normal distribution. Because the standard normal distribution \(\lambda_{x_{i},3}=0\), \(\lambda_{x_{i},4}=3\); \(\xi_{x_{i},1}=\sqrt{3}\), \(\xi_{x_{i},2}=-\sqrt{3}\) can be obtained from Equation (21).  
After determining the value of each sampling point and its corresponding weight coefficient, and because the expected value of the sampling point of each random variable has been included, \(n-1\) points are repeated, whereas the three-point estimation method has 3n sampling points and only 2n + 1 deterministic power flow calculation is required. Therefore, the system cutting load \(P_{Loss}\) can be obtained by 2n + 1 deterministic DC power flow calculation and \(Loss=\theta\times P_{Loss}\), and further obtain the probability distribution of loss degree.
# Robust Control with Uncertain Disturbances for Vehicle Drift Motions
## 2 Vehicle Drift Motion Mechanism Analysis
### Vehicle Dynamics Model
#### 2.1.1 Vehicle Dynamics Model Equations  
In order to ensure driving safety, vehicle wheels remain in contact with the ground under drift conditions. A 4-DOF nonlinear vehicle dynamics model is established to describe drift motion in Figure 1. The \(o\)-\(x\)-\(y\)-\(z\) coordinate system is used to describe the chassis coordinate system, where the \(o\)-point is the origin of the coordinate system, the horizontal  
Figure 1: Vehicle dynamics model.  
\(x\)-axis is in vehicle longitudinal motion direction, the horizontal \(y\)-axis is perpendicular to the \(x\)-axis and the vertical \(z\)-axis is positive in the upward direction. The \(x_{tf}\)-\(y_{tf}\) and the \(x_{tr}\)-\(y_{tr}\) coordinate systems are used to describe the horizontal coordinate systems of the front and rear tires, where the \(x_{tf}\)-axis and the \(x_{tr}\)-axis are in tire revolution directions. Considering vehicle dynamics characteristics, the model is established with longitudinal, lateral, roll, and yaw motions, and the equations of motion are as shown in Equation (1).  
\[\left\{\begin{array}{c}m\dot{v}_{x}-mv_{y}\gamma+m_{b}h_{b}\dot{\phi}\gamma=F _{xf}\cos\delta_{f}-F_{yf}\sin\delta_{f}+F_{xr}-F_{d}\\ m\dot{v}_{y}+mv_{x}\gamma-m_{b}h_{b}\dot{\phi}=F_{xf}\sin\delta_{f}+F_{yf}\cos \delta_{f}+F_{yr}\\ I_{x}\dot{\phi}-I_{xz}\dot{\gamma}-m_{b}h_{b}\left(\dot{v}_{y}+v_{x}\gamma \right)=m_{b}gh_{b}\sin\phi-K_{\phi}\phi-C_{\phi}\dot{\phi}\\ I_{z}\dot{\gamma}-I_{xz}\dot{\phi}=I_{f}\left(F_{xf}\sin\delta_{f}+F_{yf}\cos \delta_{f}\right)-I_{r}F_{yr}\end{array}\right. \tag{1}\]  
where \(v_{x}=v\cos\beta\), \(v_{y}=v\sin\beta\), \(F_{d}=0.5\rho_{a}C_{d}A_{f}{v_{x}}^{2}\).  
Considering load transfer caused by lateral force, the equilibrium of forces in the vertical direction and the equilibrium of moments are used to find front and rear axle normal loads:  
\[\left\{\begin{array}{c}F_{zf}+F_{zr}-mg=0\\ I_{f}F_{zf}-I_{r}F_{zr}+\sum F_{x}h_{g}=0\end{array}\right. \tag{2}\]  
where \(\sum F_{x}=m\dot{v}_{x}-mv_{y}\gamma+m_{b}h_{b}\dot{\phi}\gamma=F_{xf}\cos \delta_{f}-F_{yf}\sin\delta_{f}+F_{xr}-F_{d}\).
# A New Rotor Position Measurement Method for Permanent Magnet Spherical Motors
## 3 Rotor Position Measurement for a PMSM
### Analysis of Rotor Motion in a PMSM
#### 3.3.4 Calculation of the Spinning angle_\(\omega\)  
While the rotor shaft moved within the maximum angle, the rotor was spinning around \(z\) axis in the RCF as shown in Figure 11 and the spinning angle \(\omega\) is needed to determine the rotor position.  
Figure 12 shows the images of the three OFPs taken by the high-speed camera when the rotor shaft was spinning.  
Figure 11: Spinning motion of the rotor.  
The angle \(\omega\prime\) can be computed by  
\[\omega\prime=\arctan\frac{O_{v}}{O_{u}} \tag{15}\]  
where \(O_{v}\) is the distance between \(L_{l}^{*}\) and \(L_{s}^{*}\) in the direction of \(v\) axis, and \(O_{u}\) is the distance between \(L_{l}^{*}\) and \(L_{s}^{*}\) in the direction of \(u\) axis.  
With the rotation angle \(\omega\prime\), the spinning angle_\(\omega\) in the range of 0-2\(\pi\) can be expressed in the four quadrants as:  
\[\left\{\begin{array}{ll}\omega=\omega\prime&\text{The first quadrant}\\ \omega=\pi-\omega\prime&\text{The second quadrant}\\ \omega=\pi+\omega\prime&\text{The third quadrant}\\ \omega=2\pi-\omega\prime&\text{The fourth quadrant}\end{array}\right. \tag{16}\]
# A Comprehensive Worst Case Bounds Analysis of IEEE 802.15.7
## 4 Worst Case Bound Analysis Using Network Calculus
### Maximum Delay Evaluation  
The accurate delay bound in this worst case can be calculated by calculating the maximum horizontal deviation \(h(\alpha,\beta_{stair})\) between the arrival curve \(\alpha(t)=b+r\cdot t\) and \(\beta(t)\). The maximum horizontal deviation is reached at the angular point defined by the intersection between the slope of \(\alpha(t)\) and the burst size b.  
Figure 6: Service curve for Visible Light Communication (VLC) Medium Access Control (MAC)—star topology.  
Now considering the burst size \(b<C\cdot T_{data}\), the delay is the maximum horizontal deviation between the angular point and the first stair. In such a case, based on Equation (2) the maximum delay can be given as:  
\[D_{max}=\ \frac{b}{R}\ +(BI-T_{s}) \tag{10}\]  
Considering all the guaranteed timeslots in the superframes, for \(n\) timeslots, the delay can be given as:  
\[D_{max_{n}}=\ \frac{b}{R}\ +(BI-n(T_{s})) \tag{11}\]  
The value of \(R\) can be expressed as the rate of the number bits sent in each beacon interval, in such a case it is represented as:  
\[R=\ \frac{T_{data}\cdot C}{BI} \tag{12}\]  
Using Equation (12) in Equation (10) the \(D_{max}\) can be represented as:  
\[D_{max_{n}}=\ \frac{b\cdot BI}{C\cdot T_{data}}\ +(BI-n(T_{s})) \tag{13}\]
## 4 Multidisciplinary Design, Analysis, and Optimization of an Adaptive Wing
### Airfoil Shape Optimization
The airfoil shape was optimized for angles of attack between 0 and 5 degrees. The parameters used in these algorithms are the size of the colony, the maximum number of iterations, and the number of variables, with the number of iterations fixed at 500. Figure 11: The adaptive airfoil shape optimization methodology. Figure 12: Pressure distribution (Cp) of the optimized airfoil PSO for AOA = 3 for a flow speed of 30.1 m/s. The aerodynamics conditions are fixed for each airfoil optimization at the cruise altitude of the UAV S45 (2000 ft) and a flight speed of 30.10 m/s. The objective of the optimization is to maximize the fitness function \(f=C_{L}/C_{D}\), expressed as the lift/drag coefficients ratio, which can be used as an efficiency or a performance factor of the airfoil shapes. Once the optimization has been performed by each algorithm for each angle of attack between 0 and 5 degrees, it is possible to compare their results. The \(C_{L}/C_{D}\) are analyzed and compared in Figure 14, where we can observe that the optimized airfoils have improved efficiency for the angle of attack (AOA) between \(2^{\circ}\) and \(4^{\circ}\). The \(C_{L}/C_{D}\) values for the AoA = \(3^{\circ}\) were improved by 21% and 26% with the ABC (long dash line) and the PSO (short dash line) algorithms, respectively, when compared to the reference airfoil shape (solid line). The aerodynamic shape optimization method described in Section 5, coupled with the aero-structural analysis method presented in Section 5.2, together with the airfoil parameterization in Section 5.4, allows for aero-structural optimized airfoil shapes with increased performance.
# A Novel 3D Analytical Scattering Model
## 5 Second Order Fading Statistics
The results for \(D_{c}\) against \(\lambda\) observed from AS and GS for different values of \(\tau_{\max}\) are plotted in Figure 18a,b, respectively. The \(D_{c}\) increases linearly with the increase in \(\lambda\), but it decreases by increasing \(\tau_{\max}\), because the shadow of AS on the \(x\)-\(y\) plane moves inside the TESR for a given height of AS. The rate of decrease changes rapidly with the increase \(\tau_{\max}\). When observed from GS, \(D_{c}\) is showing the same behaviour as observed from AS. The proposed model is applicable for all of the altitudes of AS as long as \(h_{\mathrm{AS}}>h_{\mathrm{GS}}\) and \(h_{\mathrm{AS}}>\sigma_{c}\). The proposed analysis can be helpful in designing error control codes and diversity schemes required in future digital communication systems. LCR and AFD can also be used for the guidance of the selection of the packet length to minimize the packet error rate. The proposed analysis can be extended to devise AS's velocity estimation algorithms by exploiting the relationship of LCR in (42), where it is evident that the maximum Doppler frequency is directly related to LCR, the spatial spread and other physical channel parameters. The proposed analysis on the coherence distance can be useful in studying the stability of the communication channels. The proposed analysis on the quantification of the spatial spread can be helpful in calculating the precise antenna correlations for designing efficient multi-antenna beamforming systems for future aeronautical communication networks.
## 3 Numerical Visualization
### Time Skew with Differential Driven Mode  
Figure 4a,b illustrates the overall transfer function in the presence of positive and negative differential time skews with differential driven mode, respectively. The bias was still at \(\pi/4\). Note that 0 ps skew (black curve) equaled the case of conventional IM-DD, in which power fading at 14.0 GHz can be observed. As the differential time skew grew from 0 ps to 16 ps (green curve), the frequency response gradually became smoothed. However, when we continuously increased the time skew larger than 20 ps, a new frequency notch appeared around 19.8 GHz. Nevertheless, time skew was much more powerful than the previous two imbalances, since it could mostly compensate for the power fading, rather than moving, the nulls. In addition, by comparing Figure 2a with Figure 2b, it can be concluded that the same transfer function can be realized with either positive or negative skew values. Therefore, it would be convenient to just focus on the abosulte values of time skew in practical implementations.  
Figure 3: (**a**) Calculated transfer function of differential driven mode with various amplitude ratios k. (**b**) Calculated transfer function versus different bias phases and amplitude ratios.  
Figure 4c,d shows the transfer function at BTB with positive and negative skew values. Unlike bias deviation and amplitude mismatch, time skew results in a frequency-dependent tranfer function, even at BTB. Due to the partial response-like inter-symbol interference (ISI) in the time domain, the bandwidth tended to decrease as the skew increased.
# Evaluating Swarm Robotics for Mining Environments: Insights into Model Performance and Application
## 2 Case Study: Applications to Pilbara Iron Ore Mine
### Simulation Setup Using Pilbara Geological Data  
To investigate possible improvements in operational aspects of the Pilbara iron ore mine, a comprehensive simulation model of the mine has been created by using precise geological data from the region. The model is well suited to Pilbara open pit mining techniques as the simulation accurately depicts the topography and levels of the mine site, which are of relatively even terrains and flat surfaces [30; 31]. For the large-scale mining of the low-grade goethite and high-grade hematite, this approach involves removing the overburden to reach the shallow ore bodies. Similar approaches are presented in Figure 3, where (a) is a top view of the South Flank Pilbara mine and (b) is open-pit mining operations operated by BHP Billiton Mining Group [32].  
As high-grade ores become increasingly scarce, the simulation takes into consideration the growing need for beneficiation processes to increase the grade of the mined ore [27; 28; 29]. The integration of automated haulage systems that are critical to operations was modelled using a comprehensive simulation. These systems reduce the need for human intervention, increase productivity, and speed up the transportation of ore at large-scale mining sites. The simulation includes these logistical elements in addition to haulage operations. The main themes of the simulation are geological topography, ore-grade variations, and the contribution of the automated haulage to improving operational efficiency, selectivity, and reliability. Environmental factors such as temperature, dust, and the remote location of the mine site [26] are outside the scope of this research, and hence, are not included in the model. The aim is achieving improvements in the efficiency and sustainability of Pilbara iron ore mine's operations by testing and fine-tuning the mining strategies in this virtual environment.
## Results and Discussion  
### FT-IR Spectroscopy  
FT-IR spectra of TiO\({}_{2}\), GO and nano-PDA@GO-TiO\({}_{2}\) composites are shown in Figure 3. As for GO, many clear strong absorption peaks at 3415, 1727, 1617, 1405, and 1060 cm\({}^{-1}\) are ascribed to the stretching vibrations of water O-H stretching, carboxylates or ketones C=O stretching, water O-H bending and C=C stretching, epoxide C-O-C or phenolic C-O-H stretching, and C-O stretching, respectively [29; 30]. As to the spectrum of TiO\({}_{2}\), the absorption around 575 cm\({}^{-1}\) and 1115 cm\({}^{-1}\) are assigned to the vibration of Ti-O-Ti and Ti-O bonds in TiO\({}_{2}\)[31; 32]. Compared with pure GO, after combination with PDA and TiO\({}_{2}\), the peak of C=O stretching vibrations at 1727 cm\({}^{-1}\), almost disappeared in nano-PDA@GO-TiO\({}_{2}\) composites, there are mainly attributed to the removal of oxygenfunctionalities from GO as a result of the reduction effect of PDA. In addition, the new peaks that appear at 1055 cm\({}^{-1}\) and 1504 cm\({}^{-1}\) are corresponding to the of C-N stretching and aromatic C=C stretching/N-H bending that originated from PDA, respectively [30]. In the spectrum of PDA@GO-TiO\({}_{2}\), the broad band between 400-1000 cm\({}^{-1}\) stretching in a TiO\({}_{2}\)-based composites [31]. In addition, after the addition of dopamine, the color of the GO suspension changed from yellowish brown to black, that also implying the reduction of GO to grapheme [33; 34]. These results support that the preparation of nano-PDA@GO-TiO\({}_{2}\) composites with our process is successful.
## 5 Experimental Results
### Influence of Model Parameters on Classification Performance  
#### 5.5.1 Influence of the Ensemble Size  
The results presented so far were about the \"final\" bagging made of 100 trees. In order to study the influence of ensemble size on bagging construction, we present in Figure 5 the evaluation of the _average accuracy, F-measure_ and _minimum accuracy per class_, which are average values through all the datasets, with respect to ensemble size throughout the bagging induction processes, i.e., from 1 up to 150 trees for all the bagging methods. We can observe that a larger ensemble size is beneficial to the classification improvement of the multi-class imbalance data. However, it could lead to increased computational complexity. In particular applications, the balance between the computational complexity and the performance should be considered. One of the main objectives with the design of our algorithm is to obtain a performance improvement while ensemble less trees, faster and in a more straightforward way than with traditional bagging, UnderBagging and SMOTEBagging. Although, the curves of Figure 5 have similar trends for those imbalance learning algorithms. The margin based bagging curves have a faster increase from 1 to about 30 trees. This has a practical interest since it means that designing a stopping criterion based on performance will be possible for the margin based bagging induction to achieve good performance with low time complexity. This stopping criterion has not yet been included in the process of our margin based algorithm, but it is an important mechanism to design in future work.