esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimal Lyapunov-Based Sliding Mode Control for Slotless-Self Bearing Motor System
## 2 Mathematical Model of the SSBM  
Structurally, the SSBM is considered as 2 main parts: the stationary and the moveable parts. In detail, the configuration and mathematical model of the SSBM was presented in [6; 9]. Which is shown in the Figure 1 below.  
The stator coils are designed according to the hexagonal shape. The six-phases are arranged around the coordinate plane, \(a\)-\(b\)-\(c\) phases symmetry with \(d\)-\(e\)-\(f\) phases, respectively. The phase winding errors, it can affect to the control output, strongly.  
The bearing forces of the symmetrical phase \(a\)-\(b\)-\(c\) is as follows:  
\[\begin{cases}F_{a}=2 Bli_{a}^{f}+\Delta F_{a}\\ F_{b}=2 Bli_{b}^{f}+\Delta F_{b}\\ F_{c}=2 Bli_{c}^{f}+\Delta F_{c}\end{cases} \tag{1}\]  
where \(\Delta F_{a}^{f}\), \(\Delta F_{b}^{f}\), and \(\Delta F_{c}^{f}\) are the fabrication error of each phases, \(l\) is the length of the wire, \(i_{a}^{f}\), \(i_{b}^{f}\), and \(i_{c}^{f}\) are the phase bearing currents, respectively.  
To the balance of the rotor, the total amplitude of the Lorentz force needs to be zero. So, the balance equation Equation (1) is  
\[F_{a}+F_{b}+F_{c}=0 \tag{2}\]  
From Equations (1) and (2) leads to  
\[2 Bli_{a}^{f}+\Delta F_{a}+2 Bli_{b}^{f}+\Delta F_{b}+2 Bli_{c}^{f}+\Delta F_{c}=0 \tag{3}\]  
Therefore,  
\[i_{a}^{f}+i_{b}^{f}+i_{c}^{f}=-\frac{\Delta F_{a}+\Delta F_{b}+\Delta F_{c}} {2 Bl} \tag{4}\]  
**Remark 1**.: _In fact, the right hand side of Equation (4) is not zero. It can be considered as a perturbation value. With \(i_{a}^{f}\), \(i_{b}^{f}\) and \(i_{c}^{f}\) is the phase bearing currents, respectively._  
Figure 1: The SSBM system.  
The torque and bearing forces with considering the disturbance and uncertainty are expressed as follows:  
\[\begin{cases}\tau&=k_{nm}k_{m}A_{m}-(\Delta\tau+d_{\tau})\\ F_{x}&=k_{mb}k_{b}i_{q}-(\Delta F_{x}+f_{F_{x}})\\ F_{y}&=k_{mb}k_{b}i_{d}-(\Delta F_{y}+f_{F_{y}})\end{cases} \tag{5}\]  
The structure of the SSBM was shown as in Figure 1, the torque and forces were not affected by the gravity. We have  
\[\begin{cases}\tau-T_{l}&=J\dot{\omega}\\ F_{x}-F_{lx}&=m\ddot{x}\\ F_{y}-F_{ly}&=m\ddot{y}\end{cases} \tag{6}\]  
where \(\tau\), \(F_{x}\) and \(F_{y}\) are the rotating torque on \(\omega\)-axis and bearing forces on \(x\)- and \(y\)-axis, respectively. \(F_{lx}\), \(F_{ly}\) and \(T_{l}\) are the disturbances on the on \(x\)-axis, \(y\)-axes and \(\omega\)-axis, respectively. Combining the Equations (5) and (6),  
\[\begin{cases}\frac{k_{mb}k_{m}k_{m}A_{m}}{k_{mb}k_{mb}k_{mb}^{\prime}}-\frac{ T_{l}}{\eta}&=\dot{\omega}\\ \frac{k_{mb}k_{b}k_{b}i_{d}}{m}-\frac{F_{ly}}{m}&=\ddot{x}\\ \frac{k_{mb}k_{b}k_{b}i_{d}}{m}-\frac{F_{ly}}{m}&=\ddot{y}\end{cases} \tag{7}\]  
The the perturbations also need to be bounded.
# Semantic Attention Guided Hierarchical Decision Network for Age Estimation
## 4 Experimental Results and Analysis
### Comparisions with State-of-the-Arts
#### 4.4.2 Experiments on MegaAge-Asian Dataset  
To verify the performance of our framework on other races, we also conduct experiments on the MegaAge-Asian dataset. Facial images in Morph II are usually from Whites and Blacks, but MegaAge-Asian dataset only contains Asians. In Table 2, we report the comparison results in terms of MAE, CS(3) and CS(5).  
As reported in Table 2, it can be seen that our model achieves an MAE of 3.09, with CS(3) at 63.11% and CS(5) at 82.30%. The performance is competitive when compared with other state-of-the-arts. Among non-hierarchical methods, this translates into about 15-40% relative improvement in CS(3) and 11-36% in CS(5) over classical CNN baselines  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{1}{c}{**Method**} & \multicolumn{1}{c}{**Year**} & **Pretrained** & **MAE** & **CS(3)** & **CS(5)** \\ \hline Non-Hierarchical & DEX [32] & 2018 & ImageNet & 3.25 & - & 66.90 \\  & DEX [32] & 2018 & IMDB-WIKI & 2.68 & 64.50 & 78.10 \\  & MV [37] & 2018 & IMDB-WIKI & 2.16 & 75.00 & 89.90 \\  & DAA [9] & 2023 & IMDB-Wiki & 2.06 & - & - \\  & MSDNN [16] & 2024 & ImageNet & 2.59 & 67.00 & 86.00 \\ Hierarchical & MTWGP [31] & 2017 & - & 6.28 & - & - \\  & Posterior [34] & 2017 & - & 2.87 & - & - \\  & Posterior [34] & 2017 & IMDB-WIKI & 2.52 & - & 80.40 \\  & Ranking-CNN [39] & 2017 & IMDB-WIKI & 2.96 & - & 83.70 \\  & SSR-Net [15] & 2018 & IMDB-WIKI & 3.16 & - & 76.30 \\  & C3A2 [18] & 2019 & IMDB-WIKI & 3.13 & - & 65.00 \\  & BridgeNet [19] & 2019 & IMDB-WIKI & 2.38 & 72.00 & 88.00 \\  & LSTM [27] & 2020 & IMDB-WIKI & 2.36 & - & - \\  & ADPF [28] & 2022 & - & 2.71 & 73.00 & 87.00 \\  & ALD-Net [38] & 2023 & - & 2.65 & - & - \\  & J-MIMO [40] & 2023 & ImageNet & 2.28 & - & - \\  & DDIL [41] & 2023 & ImageNet & 4.96 & - & - \\  & ODDL [42] & 2023 & ImageNet & 2.45 & - & - \\  & GroupFace [20] & 2024 & IMDB-WIKI & 2.01 & - & - \\  & OURS & 2025 & - & 2.29 & 74.70 & 88.45 \\  & OURS & 2025 & ImageNet & 2.18 & 79.93 & 90.96 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison results for age estimation on Morph II.  
such as MobileNet [15] and DenseNet [15]. Compared with recent label-distribution and hierarchical methods such as SSR-Net [15] and ALD-Net [38], our model also exhibits noticeable gains on both CS(3) and CS(5). Although DAA [9] achieves the best CS(3) 68.82% and CS(5) 84.89% by combining extra IMDB-WIKI pre-training, binary age-code supervision and multi-stage incremental training, these additional mechanisms substantially increase model complexity and training overhead.
## 2 Dielectric Resonator Antenna
### Single-Feed CP DRA
The proposed antenna is fed with a microstrip-coupled cross-slot at the bottom, as shown in Figure 9. However, the position of the metallic plates (\(dx\), \(dy\)) has an effect on the AR. The AR value will be degraded if the value of \(dx\) is 0 or negative. Thus, we need to maintain the positive value of \(dx\) for a good AR value. Figure 8: Proposed rectangular DRA: (**a**) 3D view; (**b**) top view of the microstrip feedline [49]. Figure 7: Fabricated DRA: (**a**) 3D view; (**b**) top view [46]. In [52], a dual-band dual-CP DRA is proposed for unmanned aerial vehicle (UAV) applications. It consists of a metal strip with a rotated angle, a stacked rectangular dielectric resonator, and a substrate integrated waveguide cavity with a slot for feeding, as shown in Figure 10. The shared-aperture merged-structure design of DRA and a slot-dipole antenna are applied to realize circular polarization features. The CP waves are controlled by a phase compensation method with tuning processes. It is easier to obtain impedance matching by using this feeding technique than a contacting feed. However, the manufacturing process of stacked DRAs is complicated and needs to be carefully optimized in order to avoid any errors. A summary of some previous works on various single-feed circularly polarized DRAs are presented in Figure 5:0 It can be concluded that a single feed has a simple geometry because no hybrid coupler is required to generate a 90\({}^{\circ}\) phase shift between the two orthogonal modes. CP waves can be realized by modifying the structure of the dielectric resonator to excite two orthogonal modes and the required phase shift. In addition, the dielectric permittivity \(\varepsilon_{r}\) of the dielectric resonator and its shape will affect the performance of an antenna. Thus, by choosing a dielectric resonator with a high dielectric permittivity, the size of the DRA will be reduced. Moreover, each mode of a DRA has a unique internal and external field distribution. Therefore, different radiation characteristics can be realized by exciting different modes of a DRA. Figure 5:1 Overall configuration of a dual-band dual-CP DRA [52]. Figure 5:2 Proposed cross-slot-fed CP DRA: (**a**) perspective view; (**b**) top view [51].
# Bayesian Optimal Experimental Design for Race Tracking in Resin Transfer Moulding
## 2 Computational Model  
### Constitutive Equations  
This paper is limited to considering the 2D case, which is sufficient to model the vast majority of net-shaped parts for which RTM is used. However, the computational model and all Bayesian formulations in this model can be applied to higher-dimensional formulations.  
For the slow-moving thermoset resins that are typically used in RTM, [PERSON]'s law adequately describes the 2D flow through a porous medium [1; 5; 13; 20; 22; 23; 24]:  
\[\mathbf{q}=-\frac{\mathbf{K}}{\mu}\
abla p, \tag{1}\]  
where \(\mathbf{q}\in\mathbb{R}^{2}\) is the flux vector of resin, \(p\) is the pressure, \(\mu\) is the fluid viscosity, and \(\mathbf{K}\in\mathbb{R}^{2\times 2}\) is the permeability tensor, which is symmetric and positive definite. Combining [PERSON]'s law with conservation of mass gives:  
\[-\
abla\cdot\left(\frac{\mathbf{K}}{\mu}\
abla p\right)=0. \tag{2}\]  
We base the following notation on the notation used in [19] to describe this problem. The domain of the mould \(D\) can be partitioned at any point in time into \(F(t)\) and \(U(t)\), which represent the filled and unfilled regions of the domain, respectively. The boundary of \(D\) is \(\delta D=\delta D_{I}\cup\delta D_{N}\cup D_{O}\), where \(\delta D_{I}\) represents the inlet, \(\delta D_{N}\) represents the impermeable walls of the mould, and \(\delta D_{O}\) is the outlet vent. We consider the case where resin is injected from a gate at a fixed constant pressure (\(p_{\text{in}}\)) and exits from a vent. At this vent, resin pressure is fixed at atmospheric pressure, taken to be 0 without loss of generality. The walls of the mould have zero normal flow. All unfilled regions are also fixed at zero pressure, neglecting the scenario in which air is sealed and compressed in the mould, which we are trying to prevent. In summary, the problem has the following boundary conditions, where \(\mathbf{n}(\mathbf{x})\) is the normal vector to the domain boundary at \(\mathbf{x}\):  
\[p(\mathbf{x},t) =p_{\text{in}}, \mathbf{x}\in\delta D_{I}, \tag{3}\] \[p(\mathbf{x},t) =0, \mathbf{x}\in\delta D_{O},\] (4) \[\
abla p(\mathbf{x},t)\cdot\mathbf{n}(\mathbf{x}) =0, \mathbf{x}\in\delta D_{N},\] (5) \[p(\mathbf{x},t) =0, \mathbf{x}\in U, \tag{6}\]  
and the following initial conditions:  
\[U(0)=D-\delta D_{I},\quad(F(0)=\delta D_{I}). \tag{7}\]
# SimEx: A Tool for the Rapid Evaluation of the Effects of Explosions
## 2 SimEx Capabilities
### Single-Degree-of-Freedom System Analysis
#### 2.1.4 Post-Processing  
After integration, three plots appear in a pop-up window and a summary table is provided at the bottom left corner of the main window. The left plot shows the instantaneous displacement (solid line) and the permanent displacement, or deformation (dashed line). The central plot shows the temporal variation of the forcing term (i.e., the blast pressure wave, solid line) together with the resistance strength of the SDOF system (dashed line). The right plot shows the displacement-resistance graph, in which it is possible to determine more clearly whether permanent deformations occur or not. Finally, the table of results shows the maximum displacement obtained, \(x_{\text{max}}\), along with two damage indicators: the ductility ratio, \(\mu=x_{\text{max}}/x_{\text{u}}\), defined as the ratio of the peak deflection to the ultimate elastic deflection, and the maximum support rotation, \(\theta\), whose calculation depends on the type of structure under study.  
By integrating different combinations of charge weights and standoff distances for the same structural element, damage level diagrams can be rapidly obtained in the CW-S distance space. SimEx has a function for it located in the central part of the integrator module. One can select the range of charge weights and standoff distances, the number of intermediate values and the type of damage in terms of the quantitative indicators \(\mu\) and \(\theta\)[15]. From the two quantitative indicators, the structural damage level can be classified qualitatively into: superficial, moderate, heavy, hazardous failure, and blowout, with response limit boundaries between these levels denoted respectively by B1 (superficial to moderate), B2 (moderate to heavy), B3 (heavy to hazardous failure), and B4 (hazardous failure to blowout). Convenient limits for the boundaries of component damage levels for common structural components in terms of \(\mu\) and \(\theta\) are provided in [15]. An example of adamage level diagram for the facade of a conventional building subject to blast loading computed with SimEx will be presented in Section 3.3.
# Congestion Control and Traffic Differentiation for Heterogeneous 6 TiSCH Networks in IIoT
## 6 Conclusions and Future Work  
In this paper, we proposed a congestion control and service differentiation strategy to support heterogeneous 6 TiSCH networks in IIoT applications. The congestion was monitored through sharing the backlog information among neighbor nodes, which was implicitly embedded in the DIO message. The preferred parent of each node was selected based on its queue occupancy to achieve a fair load distribution in the network. A modified Trickle timer strategy was also introduced to detect and act on congestion in timely manner while keeping overhead to a minimum. Moreover, we proposed a multi-queue model to support prioritized and low delay transmission of critical traffic. In addition, we provided a mathematical analysis of the average waiting time of each priority queue. Performance evaluations were carried out, and the results proved the effectiveness of our method to achieve  
Figure 17: PDR comparison of T\({}_{3}\) for different traffic rates.  
Figure 18: Average number of DODAG Information Object (DIO) messages for different traffic rates of T\({}_{3}\).  
improved packet delivery and low queue losses under heavy load scenarios, as well as the improved delay performance of critical traffic with an insignificant increase in the overhead.  
As future work, the performance of the proposed framework can be further investigated in terms of power consumption and network lifetime metrics, as well as providing mathematical models for the throughput and worst-case delay of each traffic category. Moreover, we plan to have a full implementation of the proposed framework to evaluate its performance in real scenarios. Optimizing the TSCH schedule to further improve the channel access and transmission delay of critical data is also left as a future investigation.  
[PERSON] conceived the proposed protocol framework, produced the mathematical model and the simulation results, and wrote the paper. [PERSON] and [PERSON] supervised the overall work and revised the manuscript. All authors read and agreed to the published version of the manuscript.  
This work was supported by the Swedish Knowledge Foundation (KKS) (Grant No. 2018170) and the Swedish Foundation for Strategic Research (SSF) (Grant No. SM18-0044).  
The authors declare no conflict of interest.
# Terahertz Frequency-Scaled Differential Imaging for Sub-6 GHz Vehicular Antenna Signature Analysis
## 5 Conclusions  
In this work, an application of the approach in the near-field exploration of metallic objects explained in [3] is used to perform the focusing of a metallic car with and without a monopole antenna in order to estimate the induced currents on the vehicle surface using a differential methodology. This method makes it possible to estimate the distribution of the currents induced by the antenna on the vehicle's surface using a very simple method, which is also combined with the frequency-dimension scale translation.  
The technique presented in [3] has been extended to a different measurement scenario in a 3D scan in the terahertz band and by using the differential current concept as both a novel form and a major contribution.  
The findings verify that, while distance discrimination increased with the bandwidth, the transverse resolution (\(XY\) plane) did not change significantly.  
The results show promising performance from this technique using differential currents, as depicted in the 300 GHz band. Gaining knowledge of these vehicle-antenna-created currents may offer hints regarding the impact produced by the shapes (borders and corners), discontinuities (slots and windows), or materials (composition or roughness) introduced into the vehicular platform.  
Conceptualization, J.A.S.-P., J.-M.M.-G.-P., J.R., and L.J.-R., Methodology, J.A.S.-P. and J.-M.M.-G.-P.; Software, J.A.S.-P. and J.-M.M.-G.-P.; Validation, J.A.S.-P., J.-M.M.-G.-P., J.R., and L.J.-R., Resources, J.-M.M.-G.-P. and M.-T.M.-I.; Data curation, M.-T.M.-I.; Writing--Original Draft Preparation, J.A.S.-P., Writing--Review and Editing, J.A.S.-[PERSON], [PERSON]-[PERSON], [PERSON], [PERSON], [PERSON], and [PERSON]; Supervision, [PERSON]-[PERSON]-[PERSON]-P., [PERSON], and [PERSON], [PERSON], [PERSON], and [PERSON]; Project Administration, [PERSON]-[PERSON]-[PERSON]P. and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Education and Science, Spain (PID2019-107885 GB-C33/AEI/10.13039/501100011033, TEC2016-78028-C3-1-P, TEC2016-78028-C3-2-P and TEC2016-78028-C3-P, MDM2016-O6 OO), Catalan Research Group 2017 SGR 219, and the European FEDER funds.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# Earthquake Risk Probability Evaluation for Najin Lhasa in Southern Tibet
## 4 Discussion
### Analysis of the Applicability of the Attenuation Relationship  
In this study, the PSHA method and the attenuation relationships in Tables 4 and 5 have been applied in the seismic hazard assessment of several engineering projects in China (e.g., [49; 50; 51; 52]). The actual calculation results showed that it is suitable for the area near Najin Lhasa, Tibet. According to the obtained planning data, the maximum period of the decay relationship used in this work is 6 s, which meets the actual work requirements. As shown in Figure 5, setting M = 5-8 and the damping ratio to 5%, we have concluded the attenuation relationship between the long and short axes of the peak acceleration of the horizontal bedrock in the region. Figures 6 and 7 show that at epicentral distances R = 50 km and 100 km, the attenuation relationship curve of the response spectrum of the horizontal bedrock acceleration differs. The response spectrum of different magnitudes gradually decreases with increasing distance; when the distance is the same, the response spectrum increases with increasing magnitude (Figures 5-7).
# Mobile Robot Path Planning with a Non-Dominated Sorting Genetic Algorithm
## 2 Related Work  
Since the advent of the genetic algorithm, it has been favored by many scholars due to its simple operation process and powerful search ability [39; 40]. Genetic algorithms demonstrate robust optimization performance in various areas [41; 42; 43]. In path planning, genetic algorithms have also received extensive attention. In [44], the genetic algorithm was designed to shorten the path length in the grid space. In [45], the efficiency of probabilistic roadmap (PRM) and genetic algorithm (GA) for attaining a viable route was studied. NSGA-II was employed to optimize path length and clearance in the grid environments [46]. In [47], multiple techniques of representing a path in the grid environments were presented. Moreover, NSGA-II was used to improve length, smoothness and safety. A new selection operator was designed to avoid falling into a local trap and premature convergence [48]. Then, the adaptive method based on GA was designed to optimize path length. In [49], control points of the Bezier curve were enhanced by NSGA-II to reach the Pareto-optimal solutions. In [50], an efficient initialization technique based on directed acyclic graphs was proposed. This technique can provide multiple feasible minimum paths for the genetic algorithm, which enhances the computational efficiency of the entire genetic algorithm. In [51], a viable route was given with the genetic algorithm and then smoothed by the piecewise cubic Hermite interpolation polynomial. In [52], the environment was converted with a matrix-binary code-based genetic algorithm (MGA). Then, the navigation time and the path length were optimized. Control points of the Bezier curve were optimized with an improved genetic algorithm in [53]. Then, the optimum smooth path could be selected by choosing these control points. In [54], an improved crossover operator was designed in static environments. In [55], NSGA-II was used to improve the clearance and smoothness of the path by optimizing the three parameters obtained by the potential field method. Moreover, the position of the virtual obstacle was redefined for the case where the end point of the path was farther away from the obstacle. This can help the robot safely drive away from obstacles. Finally, a method for identifying obstacles that affect the robot in cluttered environments was presented. As far as we know, genetic algorithms show great vitality in path planning problems. However, due to the existence of obstacles, after using the traditional operators (crossover, mutation), the newly-generated individuals are generally no longer feasible paths. More practical evolutionary operators are needed to further enhance evolution efficiency.  
In this study, the path planning problem is resolved with the improved NSGA-II in static environments. Multiple objectives are considered. More practical evolutionary operators are presented. In the remainder of this article, the improved algorithm is proposed in detail.
## 3 Design Concept and Characteristics
### Simulation of the System
#### 4.2.2 Time History Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Frequency** & **U-D** & **PTMD-D** & **ATMID-D** & **U-A** & **PTMD-A** & **ATMID-A** & **V.R.E** & **V.R.E** & **V.R.E** & **V.R.E** \\
**(rad/s)** & **(m)** & **(m)** & **(m)** & **(m)s** & **(m/s)** & **(m/s)** & **PTMD-D** & **ATMID-D** & **PTMD-A** & **ATMID-A** \\ \hline
10 & 3.2936 & 0.0368 & 0.019 & 329.35 & 3.677 & 1.8960 & 98\% & 99\% & 98\% & 99\% \\ \hline
9.22 & 0.0527 & 0.0279 & 0.0195 & 4.4316 & 2.3375 & 1.6374 & 47\% & 62\% & 47\% & 63\% \\ \hline
7.9 & 0.0168 & 0.0227 & 0.0112 & 1.0588 & 1.4161 & 0.6959 & \(-\)35\% & 33\% & \(-\)34\% & 33\% \\ \hline
7.31 & 0.0111 & 0.0197 & 0.0085 & 0.5892 & 1.0313 & 0.4442 & \(-\)76\% & 23\% & \(-\)75\% & 24\% \\ \hline
7.13 & 0.0105 & 0.0188 & 0.0082 & 0.537 & 0.9564 & 0.4152 & \(-\)80\% & 21\% & \(-\)78\% & 22\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Response structure in uncontrolled (U), PTMD-controlled and ATMID-controlled. Figure 11: Structural maximum acceleration-maximum displacement in time-domain analysis at frequencies of 10 and 7.13 (rad/s).