esa-sceva/satcom-chunk-collection huggingface dataset:
# Influence of Applied Loads on Free Vibrations of Functionally Graded Material Plate-Shell Panels
## 5 Applications
### Nonlinear Analysis of Hinged and Clamped FGM Cylindrical Panels under a Center Point Load  
Two FGM cylindrical shell panels (Figure 6), one with the straight sides simply supported (hinged) and the curved sides free and the other with all sides clamped, are analyzed in this subsection. The geometry is defined by the following: R = 2.54 m, L = 0.508 m, and subtended angle 2\(\theta\) = 0.2 rad, H = 0.0127 m, and it is modelled by 392 triangular finite elements. The constituents are ZrO\({}_{2}\) and Ti-6 Al-4V with the following mechanical properties, respectively: \(E_{c}\) = 168.0 \(\times\) 109 N/m\({}^{2}\), \(\
u_{c}\) = 0.298, \(\rho_{c}\) = 5700 kg/m\({}^{3}\), \(E_{m}\) = 105.7 \(\times\) 109 N/m\({}^{2}\), \(\
u_{m}\) = 0.298, \(\rho_{m}\) =4429 kg/m\({}^{3}\). For this case of geometry and the boundary conditions of the cylindrical panel, the snap-through can occur. The results obtained using the present model for the transverse displacement paths are shown in Figure 7a,b and Figure 8a,b, respectively, for the hinged cylindrical panel and for the clamped cylindrical panel. These analyses have been validated in [PERSON] et al. [11], although for different geometry and material properties. It is observed from the paths obtained for the hinged cylindrical panel that the snap-through does occur, while for the clamped FGM panels, the snap-through does not occur. Additionally, as expected, the displacements increase when the gradient index increases, considering that \(E_{c}\) > \(E_{m}\). Considering the same cylindrical panel with both boundary conditions, but under radial pressure load, the corresponding load-displacement paths are shown in Figures 7b and 8b.  
Figure 6: Cylindrical shell panel geometry and loadings.  
Figure 7: _Cont._Figure 8: (**a**) Load-displacement paths for the clamped cylinder panel under center point load. Inflection point at 140 kN for \(\text{p}=1\). (**b**) Load-displacement paths for the clamped cylinder panel under radial pressure load. Inflection point at 2.25 MPa for \(\text{p}=1\).  
Figure 7: (**a**) Load-displacement paths for the hinged cylindrical panel under point load. (**b**) Load-displacement paths for the hinged cylindrical panel under radial pressure load (\(\text{p}=1\)).
## 1 Introduction  
The concept of mobility has undergone significant transformations in recent years, with driving automation emerging as a pivotal development. This evolution aims to improve road safety, enhance passenger comfort, and achieve energy savings [1]. Autonomous vehicles and advanced driver assistance systems (ADAS) are central to realizing these objectives [2]. Among the various ADAS, adaptive cruise control (ACC) is particularly effective in reducing energy consumption and greenhouse gas emissions by maintaining a target speed while adjusting the vehicle's velocity to follow another vehicle at a safe distance. Even straightforward ACC implementations yield modest fuel savings [3], while further reduction can be obtained through an appropriate optimization framework [4]. Furthermore, full-range ACC, which operates across the entire speed spectrum, from complete stops to highway speeds, can enhance energy savings by intelligently managing stop-and-go situations [5], which typically involve higher and more frequent acceleration and deceleration than highway cruising.  
A driving factor for research in ACC developments stems from the pressing need to reduce the environmental impact of the transportation sector. Internal combustion engine (ICE) vehicles, both conventional and hybrid, which still dominate the global market [6], are significant contributors to greenhouse gas emissions. While electric vehicles (EVs) offer a promising solution, their widespread adoption is currently limited by factors such as high costs, limited driving range, and insufficient charging infrastructure. Consequently, optimizing the fuel efficiency of existing ICE vehicles remains a critical objective. Fuelconsumption and emissions from the transportation sector have a considerable impact on global environmental health [7]. Even minor improvements in fuel efficiency can yield significant environmental benefits [8]. ACC systems optimized for fuel efficiency present a viable contribution to address this challenge.  
This research explores the optimization of full-range ACC using dynamic programming (DP) and two model predictive control (MPC) approaches to balance fuel savings with passenger comfort while maintaining safe driving conditions.
# Deep Transfer Learning-Based Fault Diagnosis Using Wavelet Transform for Limited Data
## 5 Performance Evaluation
### Diagnosis Performance Analysis
Another interesting observation is that adding more training samples \(r=0.6\) does not considerably improve the classification accuracy of SVM, while it still improves the performance of the CNN. The classification accuracy of the CNN is closer to that of SVM for \(l=1\) when the training data is small \(r=0.2\). On the other hand, CNN-TL achieves a high accuracy of greater than \(0.97\) for \(l\geq 3\), even with the strictly limited samples \(r=0.2\). One of the fundamental issues of the deep learning-based fault diagnosis is the heavy demand of the training data to meet certain classification accuracy, since the amount of training data substantially impact the classification performance. Figure 10 presents the required number of training data with the different accuracy demands \(\theta=0.8,\ldots,0.95\) of the three classification models as a function of various severity levels \(l=1,\ldots,9\). Given the severity level, we compute the required number of training data based on all of the considered ratios of the training data. We set low accuracy demands for SVM since the high classification accuracy \(\theta\leq 0.9\) is not achievable even with sufficient training data. Overall, the required training samples decrease as the fault condition becomes severe for the three classification models. As a key insight, the proposed CNN-TL requires fewer training samples, and therefore less training time, to meet a certain accuracy demand compared to other models. In particular, the required number of training samples of the  
Figure 10: Required number of training samples of SVM, the CNN, and CNN–TL with different severity labels, \(l=1,\ldots,9\). Figure 9: Classification accuracy of SVM, the CNN, and CNN–TL with different severity labels, \(l=1,\ldots,9\). CNN is considerably large as the accuracy demand becomes more strict. CNN-TL requires numerous training samples for the minor fault condition \(l=1\), while the CNN does not even meet the accuracy demands of \(\theta=0.85,0.95\), even when using all available training samples \(r=0.8\). SVM fails to meet the demands of \(\theta=0.8,0.85\) for \(l\leq 2\). CNN-TL provides early fault detection at the cost of more training data. However, the number of training samples is still less than that of the CNN model.
## 3 Results and Discussion  
### XRD Analysis  
The crystal structure analysis through XRD powder diffraction was performed for all three compounds, and as observed in Figure 1, the samples present a very good crystallinity, proven by sharp and well-defined peaks. The perovskite-type structure is characteristic for the undoped LaMnO\({}_{3}\) sample indexed in the R-3c space group (corresponding to JCPDS card file no. 01-089-0678). This structure has also been reported for other LaMnO\({}_{3}\) materials obtained by the sol-gel method [17; 18]. The 1% \(\chi^{3+}\)- and Ga\({}^{3+}\)-doped LaMnO\({}_{3}\) structures induced changes in the crystal structure, as some of the peaks on the observed diffractograms are not split compared to the pristine compound (marked with symbols in Figure 1). Therefore, the 1% Ga- and Y-doped LaMnO\({}_{3}\) samples are indexed in the Pm-3m space group (JCPDS card file no. 01-075-0440). The refined unit cell parameters extracted for the perovskite samples using the R-3c space group (undoped LaMnO\({}_{3}\) sample) and the Pm-3m space group (Ga-/Y-doped LaMnO\({}_{3}\) samples) are indicated in Table 1.
# Angle-Only Cooperative Orbit Determination Considering Attitude Uncertainty
## 3 Attitude and Orbit Determination Method with Angle-Only Measurements
### Results and Discussion  
During the observability test, the state transfer matrix is obtained using MATLAB function ode45, the rank of OM \(\mathrm{rank}(\mathbf{N})\) is calculated using function rank and the CN of the system \(\mathrm{cond}(\mathbf{N})\) is obtained by function cond. All the following operations are executed on MATLAB R2018b [40]. In addition, the particular situations for the autonomous attitude and orbit determination with two spacecraft are simulated to verify the observability analysis. The nominal orbit elements are given in Table 1. The estimation problems are solved using the traditional fifth-order CKF. For convenience, the quaternion of spacecraft \(S_{1}\) is transferred into the form of Euler angles using the MATLAB function quat2 angle. The results are given as follows.  
2.1 Case I: Both Orbits of \(S_{1}\) and \(S_{2}\) Are Known, and the Attitude of \(S_{1}\) Is Unchanged  
Figure 3 displays the observability results of case I, using the two-body dynamics (state model as Equation (3)). For case I, 60 measurements (equivalent to 60 s) are executed during the navigation process. As shown in Figure 3, the upper stacked subplot illustrates the rank of the observability matrix (OM), where the red line represents the unobservable period (i.e., \(\mathrm{rank}(\mathbf{N})<4\)) and the blue line represents the observable period (i.e., \(\mathrm{rank}(\mathbf{N})\geq 4\)). Moreover, the lower stacked subplot demonstrates the condition number (CN) of OM (note that in Figure 3, the y-label represents the reciprocal of CN). In addition, the logarithmic value of the reciprocal of CN is also given in the lower stacked subplot, making the change curve of CN more obvious. Figure 3 shows that the CN deceases with respect to the observation time, which means that the observability of the system continuously improves as the number of measurements increases.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Spacecraft** & _h/_**km** & \(\boldsymbol{e}\) & _i/_**deg** & \(\Omega\)/_**deg** & \(\omega\)/_**deg** & _n/_**deg** \\ \hline \(S_{1}\) & 500 & 0 & 45.05 & 29.93 & 132.9 & \(-107.74\) \\ \(S_{2}\) & 1000 & 0 & 45 & 94.8 & 199.0 & \(-54.13\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Nominal orbit elements of the circle orbits.  
The detailed value of the corresponding index in Figure 3 is selectively illustrated in Table 2. For case I, the system is completely observable after only two measurements (at epochs \(t_{0}\) and \(t_{k}\), respectively).  
Figure 4 depicts the estimation errors of the attitude of spacecraft \(S_{1}\). It is shown that the errors converge to zero at approximately 50 s when the initial Euler angle errors are set to be \(2.5^{\circ}\).
## 3 Results and Discussion
### Docking Studies
Remarkably, domain I has been reported to bind hemin and fatty acids, and thus the site has been represented as a major drug binding pocket. The major residues involved in binding are Tyr138, Tyr161, Arg114, His146, Lys190, Ile142, Arg117 and Leu182. Among these Tyr138 and Tyr161 are the crucial residues involved in drug recognition [50,51]. Our results are consistent with these earlier reports wherein the identified ligands were found to have interactions with these major residues. Thus, from our docking studies it is envisaged that the presence of more potential ingredients in the 'Bidan' cultivar might have led to multiple binding pockets in HSA and thereby it might have enhanced the binding affinity towards HSA. Kiwifruit has also been reported for preventing platelet aggregation and has shown an effect on Angiotensin converting enzyme (ACE) [52]. Quercetin identified as one among the top hits has been reported with effective antiviral activity against herpes viruses and Dengue virus [53]. Similarly, kaempfer, quercetin and hesperidin were reported as promising drug candidates to prevent enterovirus 71 replication [54]. Thus, bioactive flavonoids identified and investigated in the present study with potential binding ability with HSA have several health benefits to the human system both as an immune booster as well as in terms of antibacterial and antiviral activities [55].
# A Study of LoRa: Long Range & Low Power Networks for the Internet of Things
## 5 LoRaWAN Analysis
### Total Capacity and Channel Load  
The total capacity of the network is not only related to payload size. As two transmissions on the same frequency, but at different spreading factors, can be decoded simultaneously, in what follows, a logical channel is defined by a pair (frequency band, spreading factor).  
Figure 9: Maximum throughput attained by a single device using LoRaWAN.  
The total transmission capacity of a LoRaWAN network is the sum of the capacities of all of the logical channels. In a 125-kHz frequency band, there are six possible spreading factors (from 7 to 12), which brings the total capacity of a 125-kHz channel to 12,025 bps.  
In the EU frequency band, the set of mandatory channels contains three 125-kHz channels [23], which make the minimum total capacity of the network 36 kbps. Networks operators are free to add more channels (sent to the devices using _NewChannelReq_ commands), thus increasing the capacity of the network.  
As the transmission bit rate is dependent on the spreading factor, not all logical channels have the same capacity. In what follows, the load for a logical channel is defined by the time average of the numbers of LoRa devices trying to send data. This coincides with the natural definition of the load: in optimal conditions, i.e., with a perfect synchronization of the devices, a load of one can be reached, saturating the channel.
## 6 Evaluation and Demonstrator Use Cases  
In order to evaluate the usefulness and applicability of the SBT framework, two demonstrator scenarios were implemented. A smart home demonstrator was set up using only SBT components. Furthermore, an industrial use case was considered. Here, an existing model factory was extended with additional sensors for Condition Monitoring (CM) and obtaining process information.
## 4 Pre-Evaluation Based on the Spatio-Temporal Model
### Passenger Path Selection Model  
After calculating the OD accessible path set, the next step is to assign passenger travel demand to the most 'optimal' path. The path selection of passengers is influenced by various factors, which need to be comprehensively analyzed based on available data.  
These factors typically include the following: (1) passenger attributes: age and occasional or rigid travel; (2) path attributes: distance, whether or not transfers are necessary, the number of stops, the waiting time, and the walking time; (3) passenger travel habits: whether a travel route is habitual and the frequency of trips taken; (4) the relationship with other paths: the number of replaceable paths and the ratio to the optimal path.  
Figure 4: Bus route model.
## 3 Results and Discussion
### Load-Displacement Curves for Different Connectors  
Figure 7 shows the load-displacement curves for honeycomb sandwich structures of different thicknesses, connected by various methods. It can be seen that each connection method has its peculiar performance and failure modes. At the initial stage of loading, all connectors have almost the same rigidity and load increases for larger end surface displacement. As a high-strength bolt material has superior shear strength [36; 37], the ultimate bearing capability of the corresponding connector is significantly higher than the ultimate bearing capability of the other two connection methods; it also has larger ultimate displacement. The ordinary bolt connection and self-tapping bolt connector have similar ultimate bearing capacities. When the self-tapping bolt connector approaches the limit, its bearing capacity fluctuates and the connection performance is unstable.  
Figure 8 compares the load-displacement curves measured from experiments and the corresponding finite element simulations for the three bolt joints designs. It can be seen that numerical results agree well with experimental data: the largest deviation is less than 3.6%.  
Figure 8: Comparison of experimental tests and FE simulations for the load-displacement curves relative to different bolt connections and sandwich wall thicknesses. (**a–c**) High strength bolts and self-tapping bolts; (**d–f**) Ordinary bolts.  
Figure 7: Comparison of load-displacement curves obtained experimentally for the different types of connectors for (**a**) 10 mm, (**b**) 15 mm, and (**c**) 20 mm wall thickness.  
Since finite element analysis implemented simplified constitutive models, the simulated load-displacement curves show a distinct yielding point and strengthening phase. However, experimental and numerical load-displacement curves have identical growth trends.  
When a high-strength bolt connector is subject to shear loading, severe stress concentration develops in the bolt causing bending deformations. The connection plate opening is substantially damaged and the bolt hole is oxidized. Because of the relatively low shear strength of an ordinary bolt connector, the connection plate and pad under shear force will not develop significant deformation. A significant stress concentration occurs at the bolt shear surface, and significant shear deformation causes bolt failure. As the self-tapping bolt connector's screw thread has relatively weak mechanical bite force, the bolt under load develops significant deflection and bending deformation, which causes conspicuous warp deformation between a connection plate and honeycomb sandwich structure (Figure 5f,g).
# An Energy-Efficient Coverage Algorithm for Macrocell--Small Cell Network Systems
## 5 Results and Discussions
### Effects of the Number of SBSs on the Various Performance Aspects
For instance, when the number of SBSs is 310, power dissipation decreases by about 33%. The effect of the number of SBSs on the average transmission rates for the proposed algorithm, SBS-Cell-Enlarged, and SBS-Random, is demonstrated in Figure 20. When the number of SBSs is low (e.g., 40), all the three methods fail to reach the average required transmission rate (i.e., 2 Mbps). As the number of SBSs increases, the average transmission rates of the three methods (particularly the proposed algorithm and SBS-Cell-Enlarged) increase as well. For example, when the number of SBSs increases to 100, the average transmission rates of the proposed algorithm and SBS-Cell-Enlarged are higher than the required value, while SBS-Random's average transmission rate is still lower than the required value. As the number of SBSs increases,  
Figure 19: Effect of the number of SBSs on the total power consumption levels of transceivers at all the BSs for the proposed algorithm, SBS-Cell-Enlarged, and SBS-Random. Figure 20: Effect of the number of SBSs on the average transmission rates for the proposed algorithm, SBS-Cell-Enlarged, and SBS-Random. the proposed algorithm gradually outperforms the other two methods. When the number of SBSs reaches 160, the curve of the proposed algorithm approaches a horizontal line. As shown in Figure 18, when the number of SBSs is 160, the outage ratio of the proposed algorithm approaches zero. This suggests that, at this quantity, the SBSs could handle the current traffic volume, meaning that each user could be provided with services through the MBS or SBSs. Moreover, the main effect of the increased number of SBSs on the proposed algorithm is the gradual transfer of MBS users to SBSs (the outage ratio is maintained at zero). As a result, changes in average transmission rate are small. When the number of SBSs reaches about 280, the average transmission rate of SBS-Cell-Enlarged catches up again with that of the proposed algorithm. Due to the fact that all SBSs are in the cell-enlarged state in SBS-Cell-Enlarged (higher power output provides higher transmission rate) and that the outage ratio of SBS-Cell-Enlarged continuously decreases (as shown in Figure 18), a transmission rate that is higher than that of the proposed algorithm is thus attained. Moreover, the average transmission rate of SBS-Cell-Enlarged is substantially higher than that of SBS-Random when the number of SBSs is high. Since the larger number of available SBSs as compared to SBS-Random allows SBS-Cell-Enlarged to provide users with more channels, this results in a lower outage ratio, and therefore, a higher average transmission rate.
# Path Planning for Conformal Antenna Surface Detection Based on Improved Genetic Algorithm
## 3 Improved Genetic Algorithm Combining the Historical Optimal Population
### Local Optimization Operation  
Local optimization operations generally include 2-opt operation, 3-opt operation, neighbor-node exchange operation, etc. Combining local optimization operations with intelligent algorithms can generally achieve better optimization results. In this work, 3-opt and neighbor-node exchange operations were used to locally optimize the merged population, i.e., to optimize the parent, the offspring, and the historical optimal populations. In this case, the principle of the 3-opt operation is shown in Figure 9. The method traverses all points and inserts the point into all feasible locations. If \(d_{i,j+2}+d_{i,j+1}+d_{i+1,j+1}<d_{i,j+1}+d_{i+1,j+2}+d_{i,j+1}\), the individual is updated, otherwise, the individual is kept unchanged. The time complexity of the operation is O(N\({}^{2}\)) and N is the city size. To reduce the computation, the probability \(P_{3}\) is set to 0.1. When the random number \(P<P_{3}\), the 3-opt operation is executed for this individual; otherwise, it is not executed. The neighbor-node exchange operation exchanges every two neighboring nodes. If \(d_{i,j+2}+d_{i+2,i+1}+d_{i+1,j+3}<d_{i,j+1}+d_{i+1,j+2}+d_{i+2,j+3}\), the individual is updated; otherwise, the individual stays unchanged, as shown in Figure 10. Since the time complexity of this  
Figure 8: Comparison of iteration curves (Whether or not historical optimal population is used).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Use of the Historical Optimal Population** & **Number of Tests** & **Average Length** & **Error Rate** &
\begin{tabular}{c} **Average Number** \\ **of Iterations** \\ \end{tabular} \\ \hline No & & 7616.64 & 0.957\% & 196.1 \\ Yes & 10 & 7561.63 & 0.228\% & 177.6 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of results before and after the introduction of the historical optimal population.  
operation is only O(N), it is performed for all individuals. The genetic algorithm with the addition of the local optimization operation sped up the convergence of the preliminaries and hence the iterations compared to when no local optimization was introduced, as shown in Figure 11.
# Current Trends in Fully Automated On-Line Analytical Techniques for Beverage Analysis
## 3 Solid-Phase Microextraction (SPME)
### Automated SPME  
SPME can easily be coupled with gas chromatography. The thermal desorption of the analytes from the SPME fiber is carried out easily in the chromatograph injector, working at a suitable temperature. Therefore, optimization of desorption time is one of the first steps when developing a new SPME method. Desorption of the analytes can take from a few seconds to several minutes, depending on the class of compounds being analyzed. The thermal desorption in the GC injector demands that the compounds present certain thermal stability when using the SPME technique. In the case of thermally labile analytes, a derivatization stage can be done in situ, in the medium, in which extraction is carried out so that they can also be determined by SPME-GC [51].