esa-sceva/satcom-chunk-collection huggingface dataset:
# Quad-Band Circular Polarized Antenna for GNSS, 5G and WIFI-6E Applications
## 4 Currents Distribution  
In order to shed more light on the process of generating circular polarization and determining the direction of the circular polarization, the surface current distributions are  
Figure 6: Effect of S on antenna performance: (**a**) S11, (**b**) AR.  
Figure 7: Effect of wf on antenna performance: (**a**) S11, (**b**) AR.  
shown in Figure 8, where Figure 8a shows the current distributions of 0\({}^{\circ}\), 90\({}^{\circ}\), 180\({}^{\circ}\), and 270\({}^{\circ}\) at 1.5 GHz. From the plot, it is seen that currents rotate, in the counterclockwise sense, as the phase increases, which means that the antenna radiates right-handed circularly polarized (RHCP) waves at low frequency bands. In addition, the current is distributed over the frame ground, indicating that the frame also functions as a radiator at 1.5 GHz. It has to be mentioned that most navigation satellite transmitting antennas are right-handed circularly polarized antennas.  
The current distributions from 0\({}^{\circ}\) to 270\({}^{\circ}\), at 4 GHz, are shown in Figure 8b. The current rotates clockwise with the increasing time phase, and the fields radiated LHCP in the +Z direction. For this frequency, however, most of the currents are located in the area of the L-shaped radiator and the patch on the left bottom corner. There are also some currents  
Figure 8: Surface current distributions of the proposed antenna at (**a**) 1.5 GHz, (**b**) 4 GHz, (**c**) 6 GHz, and (**d**) 7.6 GHz.  
on the top part of the frame. Therefore, there are multi-resonance frequencies in this band, which is consistent with Figure 3.  
Figure 8c shows the current distributions from 0\({}^{\circ}\) to 270\({}^{\circ}\) at 6 GHz. The current rotates counterclockwise with the increasing time phase, and the fields radiated RHCP in +Z direction. However, it has to be mentioned that the 6 GHz band also contains several resonances, and the bandwidth is relatively wide. Several areas contribute to the resultant polarization state. In this sense, this band is affected by several parameters, as discussed in the previous section.  
The same observation can be made on the 7.6 GHz band, as seen in Figure 8d. However, the rotational sense is LHCP in the +Z direction. It is interesting to find that the rotational sense changes, alternatingly, with the frequency bands.
## 2 Experiments
### Kinetic Study  
For the kinetic experiments, mixtures of _trans_-2-methyl-2-butenal (T2M2B, henceforth), a reference compound (Ref, cyclohexane or isoprene), and Cl\({}_{2}\) were introduced in the 16-L cell. Ranges of the initial concentrations in the cell were \([\text{T2M2B}]_{0}\) = (3.4-7.1) \(\times\) 10\({}^{14}\) molecule cm\({}^{-3}\), \([\text{cyclohexane}]_{0}\) = (2.9-5.5) \(\times\) 10\({}^{14}\) molecule cm\({}^{-3}\) or \([\text{isoprene}]_{0}\) = (4.6-6.4) \(\times\) 10\({}^{14}\) molecule cm\({}^{-3}\), and \([\text{Cl}_{2}]_{0}\) = (4.7-10.4) \(\times\) 10\({}^{14}\) molecule cm\({}^{-3}\). In the performed experiments, T2M2B and the reference compound mainly react with Cl with rate coefficients \(k\) and \(k_{\text{Ref}}\), respectively, although they can also be lost by heterogeneous reaction onto the walls, photoinduced processes, and/or reaction with the oxidant precursor. The additional loss is described by the first-order rate coefficients \(k_{\text{loss}}\) and \(k_{\text{Ref,loss}}\) that were evaluated in preliminary experiments in which the decay of the compound was monitored without Cl precursor in dark conditions and with the lamps switched on, and in the presence of Cl\({}_{2}\) in the dark, as described in previous studies [19; 20]. Considering the overall losses for T2M2B and Ref, the integrated rate equation is described by the following equation:  
\[ln\left(\frac{[\text{T2M2B}]_{0}}{[\text{T2M2B}]_{t}}\right)-k_{\text{loss}}t =\frac{k}{k_{\text{Ref}}}\left[ln\left(\frac{[\text{Ref}]_{0}}{[\text{Ref}]_{t} }\right)-k_{\text{Ref,loss}}t\right], \tag{1}\]where the subscript 0 indicates the initial concentrations of T2M2B and the reference compound and the subscript \(t\) indicates those concentrations at a reaction time \(t\). These concentrations were monitored by a FTIR spectrometer (Thermo Fisher Scientific, model Nicolet Nexus 870, Madison, WI, USA) coupled to the 16-L cell that has a liquid N\({}_{2}\)-cooled MCT (Mercury Cadmium Telluride) detector. IR spectra were recorded between 650 and 4000 cm\({}^{-1}\) at a resolution of 2 cm\({}^{-1}\), after the accumulation of 32 interferograms, every 2 min. The IR bands selected for monitoring the loss of T2M2B and the reference compounds were 1710-2710 cm\({}^{-1}\) for T2M2B, 2880 cm\({}^{-1}\) for cyclohexane, and 860-950 cm\({}^{-1}\) for isoprene.
## 3 Results and Discussion
### Assemblies Hygrothermal Assessment: Current Climates
#### 3.2.1 Water Content within Assemblies
While for the uncoated solid brick wall in Lisbon, the water content varies between 5.5 and 136.3 kg/m\({}^{3}\), in Zurich, it varies between 10.3 and 48.3 kg/m\({}^{3}\), which means that the highest values are attained in  
Figure 11: Water content of the solid brick layer for the rendered cases and wind-driven rain (WDR) variation for the adopted conditions in Lisbon (LX) for the first simulation year (**a**) and zoom on January of the same year (**b**). LR—Lime, LCR—Lime-cement, and CR—cement renders. Figure 10: Water content of the solid brick layer for the rendered cases and wind-driven rain (WDR) for the adopted conditions in Zurich (ZR) for the first simulation year. LR—Lime, LCR—Lime-cement, and CR—cement renders. Lisbon. On the other hand, for the cement-rendered cases, Zurich (varies between 9.0 and 14.2 kg/m3) attains higher values than Lisbon (varies between 5.4 and 10.1 kg/m3). This change is due to the lower drying conditions of the cold climates, which means that the wall is less driven to expel moisture and, consequently, has a higher water content value. As the liquid transport coefficients are highly dependent on water content (Figure 3), the moisture transport in the solid brick will be faster; therefore, the exterior surface absorbs a higher amount of moisture than in temperate climates (Table 4). The application of a render layer has a more crucial effect on temperate than on cold climates, since the decrease in water absorbed by the exterior surface is much higher in the former than in the latter climates.
## 1 Introduction
In [29], it shows that BLS has the generalization capability in function approximation and proposes variant structures of original BLS. However, combined with our recent study [30], we find that the original BLS has some limitations in extracting the features' process, as the original BLS cannot extract the potential information of time-frequency features for non-stationary signals. Thus, we point out that the original BLS must be redesigned as a novel variant to accommodate tremor signals. Motivated by the above, in this study, we propose a novel convolutional neural network-based broad learning system filter (CNN-BLSF) combined with broad expansion: incremental learning to predict and compensate for tremor signals. In summary, the work of this study has the following novelties and contributions:  
* For the original BLS, the task of feature extraction is generally achieved by the sparse autoencoder (SAE), which could not reflect the underlying relationship of the adjacent time sequence. To overcome the problem, a convolutional neural network is introduced to extract features, based on which a novel network structure, called the convolutional neural network-based BLS (CNN-BLS), is established. * With our raised convolutional neural network, various feature maps, including feature information in long and short time series regions, can be well obtained. A lateral connection structure, such as the residual network, is employed in the network so that the feature information of the time sequence can be extracted. Our purpose of design is to construct multi-scale feature maps and fuse them. * With the unique construction of BLS, an incremental learning algorithm, which adapts to BLS and can improve the performance of systems without retraining, is raised to remod the network. Combining such an algorithm with our CNN-BLS, a convolutional neural network-based broad learning system with incremental learning (CNN-BLS-IL)is developed. It is worth mentioning that the raised incremental learning algorithm is better than the original incremental learning algorithm and thus could be more effective, as illustrated in Section 4.1.3. The rest of this paper is organized as follows. The problem to be investigated is presented in Section 2. Some control strategies are given in Section 3. The proposed CNN-BLS-based approach is described in Section 4. The simulation and experiment results are shown in Section 5 to demonstrates the effectiveness of our approach. The paper is concluded in Section 6.
## 2 Materials and Methods
### Riser Geometries  
Figure 3a,b respectively show the cross-sections of the steel and composite riser joints in this study. The extracted fluids were transported by production tubings within the riser, which were protected by the riser joints and the riser joints carried all the structural loads. The internal diameters (IDs) of the steel and composite riser joints were fixed at 250 mm while, as the thickness of each joint was determined by the design to accommodate all the loads considered, their outer diameters (ODs) depended on the previous design results [20; 21; 22; 27] (Table 4). From Table 4, the thicknesses for the steel riser (riser 1), AS4-epoxy composites and titanium liner with (liner/\(90/(0/90)_{10}\)) (riser 2) and AS4-epoxy composites and titanium liner with (liner/\(0_{3}/(+53_{5}-53)_{5}/904\)) (riser 3) were 25 mm,  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Material** & **Density** & **Modulus** & **Tangent** & **\(\
u\)** & **Yield Stress** & **Ultimate Stress** & **Elongation at** \\  & **(kg/m\({}^{3}\))** & **(GPa)** & **Modulus (GPa)** & **\(\
u_{12}\)** & **(MPaa)** & **(MPaa)** & **Break (\%)** \\ \hline X80 steel & 7850 & 207 & 1.25 & 0.3 & 555 & 625 & 5.868 \\ Titanium & 4430 & 113.8 & 0.53 & 0.342 & 880 & 950 & 14 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Properties of the materials for the steel riser and liner of composite riser.  
Figure 1: Bilinear kinematic hardening model of **(a)** X80 steel and **(b)** titanium.  
Figure 2: Lamine co-ordinate system.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Material** & **Density** & **E\({}_{1}\)** & **E\({}_{2}\)** = **E\({}_{3}\)** & **G\({}_{12}\)** = **G\({}_{13}\)** & **\(\
u_{12}\)** = **G\({}_{23}\)** & **\(\sigma_{1}^{\mathrm{T}}\)** & **\(\sigma_{1}^{\mathrm{C}}\)** & **\(\sigma_{2}^{\mathrm{T}}\)** & **\(\sigma_{2}^{\mathrm{C}}\)** & **\(\tau_{12}\)** \\  & **(kg/m\({}^{3}\))** & **(GPa)** & **(GPa)** & **(GPa)** & **\(\
u_{12}\)** & **(GPa)** & **(MPaa)** & **(MPaa)** & **(MPaa)** & **(MPaa)** \\ \hline AS4-Epoxy & 1530 & 135.4 & 9.37 & 4.96 & 0.32 & 3.20 & 0.46 & 1732 & 1256 & 49.4 & 167.2 & 71.2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Properties of unidirectional lamina of AS4-Epoxy for composite risers.  
39.5 mm and 30.5 mm, respectively. In other words, riser 2 was a conventional design of FRP composite riser while riser 3 was an optimized design of FRP composite riser. Both FRP composite risers (59.6 kg/m and 45.7 kg/m) achieved significant weight saving compared to the steel riser (169 kg/m) and the optimized FRP composite riser 3 even led to a 23.3% weight reduction to the conventional FRP composite riser 2. For all the risers in this study the length to inner diameter ratios were 100. The fiber angle of 0\({}^{\circ}\) and 90\({}^{\circ}\) were the axial and hoop directions of FRP composite risers, respectively (Figure 4).
## 3 Results and Discussion
### Rotational Viscosity  
The effect of GF-WTB content on the rotational viscosity of a certain content (2, 3, 4, or 5 wt%) SBS-modified binders is shown in Figure 4. Figure 4 is the average of the measurement results. Three specimens of each asphalt were measured to ensure reliable test results. The maximum coefficient of variation (CV) of each asphalt sample group in the rotational viscosity test was 3.5%. As seen in Figure 4, the rotational viscosity values of a certain content SBS-modified binders increase with the GF-WTB content. The values of the 5 wt% SBS-modified binder ranged from 3.5 Pa-s for 0 wt% GF-WTB content to 3.9 Pa-s for 4 wt% content, which are both larger than 3.0 Pa-s, which is the maximum allowable value in SHRP specification considering mixing performance. Therefore, the 5 wt% SBS-modified binder was only used for the trend evaluation in the following analysis. In the figure, the maximum rotational viscosity increasing rate 17% appeared in the 4 wt% SBS-modified binder from 2.3 Pa-s for 1 wt% GF-WTB content to 2.7 Pa-s for 2 wt% content. The rotational viscosity test results will be helpful to further research for GF-WTB/SBS composite-modified asphalt mixture pavement performance.
# A Novel Real-Time Battery State Estimation Using Data-Driven Prognostics and Health Management
## 4 A Novel Battery SoC Estimation Using a Data-Driven PHM Approach  
Modeling battery cells poses challenges that complicate parameter estimation. Among these challenges are intricate transient response dynamics, pronounced voltage hysteresis, and a flat relationship between OCV and SOC [22].  
This section proposes a data-driven alternative for the model-driven methodology presented above by learning the battery characteristics and behavior from past input data and then estimating the battery SOC with minimum knowledge about the system and its underlying conditions. The data-driven method used herein is a bidirectional LSTM, a special type of RNN, where it takes the inputs and the outputs, as in the model-driven method, but creates a network of hidden states from a back-propagation through time (BPTT) RNN algorithm [23]. A stepwise approach is proposed herein, which comprises two stages: (1) an offline processing which derives the best model for the system-of-interest and (2) a real-time inference which deploys the model on real-time data to determine the adequate health indicator, the battery SOC.  
This proposed approach is a machine learning-based methodology for the PHM of embedded systems, aiming at generalization and real-time performance. It is intended to be applicable to other similar systems with little to no modification, beyond the specific case study and its dataset, as demonstrated in [14; 24].  
Our approach focuses on four main steps during offline processing. Detailed steps processes are depicted in Figure 3.  
1. Measurement noise handling
2. Additive features attribution
3. Automated model's hyperparameter optimization
4. Model cross-validation
## 2 System Model and Problem Formulation
### Problem Formulation  
In this paper, we focus on selecting optimal handover satellites for TTGs. Since each handover of TTG \(k\) to satellite \(m\) introduces a signaling overhead and increases the risk of transmission interruptions, we propose a cost function to quantify the handover cost defined as  
\[\Lambda_{k}^{t}=\begin{cases}-\mu,&\text{if }\mathbf{s}_{k}^{t}\
eq\mathbf{s}_{k}^{t-1 },\\ 0,&\text{if }\mathbf{s}_{k}^{t}=\mathbf{s}_{k}^{t-1},\end{cases} \tag{17}\]  
where \(\mu\) is a positive constant.  
Additionally, to enhance the QoS for TTGs and encourage connections to satellites with a better link quality, we consider the SNR between TTGs and satellites. Furthermore, to achieve load balancing between satellites while comprehensively considering the current and future traffic demands of terminals, we define a load cost as  
\[\Psi_{k,m}^{t}=-\Big{(}l_{m}^{t}+(1-w_{p})\cdot d_{k,cur}^{t}+w_{p}\cdot d_{k, pre}^{t}\Big{)}, \tag{18}\]  
where \(d_{k,cur}^{t}\) represents the current load of the TTG, \(d_{k,pre}^{t}\) denotes the predicted load of the TTG, and \(w_{p}\in[0,1]\) is the prediction weight.  
By integrating these factors, the problem of handover selection can be formulated as  
\[\begin{split}\max_{\mathcal{A}}&\sum_{t=0}^{T}\sum _{k=1}^{K}\Big{(}\Lambda_{k}^{t}+\beta_{p}\cdot\text{SNR}_{k,m}^{t}+\gamma_{p} \cdot\Psi_{k,m}^{t}\Big{)},\\ \text{s.t.}&\\ \text{C1:}& s_{k,m}^{t}\in\{0,1\},\quad\forall k\in \mathcal{K},\forall m\in\mathcal{M}_{k}^{t},\\ \text{C2:}& a_{k}^{t}\in\{1,\ldots,M_{k}^{t}\},\quad \forall k\in\mathcal{K},\forall t\in\mathcal{T},\\ \text{C3:}&\theta_{k,m}^{t}\geq\theta_{min},\end{split} \tag{19}\]  
where \(\mathcal{A}=\{a_{1}^{1},a_{2}^{1},\cdots,a_{K}^{1},\cdots,a_{1}^{T},a_{2}^{T}, \cdots a_{K}^{T}\}\) denotes the set of handover strategies across all TTGs and slots, and \(\beta_{p}\) and \(\gamma_{p}\) are weighting coefficients that balance the contributions of the SNR and load cost, respectively.
# Central Non-Linear Model-Based Predictive Vehicle Dynamics Control
## 3 Central Predictive Control
### Prediction
#### 3.1.1 Roll Behavior  
To build the prediction model for the roll behavior, the vehicle body is cut free in the \(y-z\) plane. Subsequently, the principle of angular momentum is set up around the vehicle's roll center. The resulting equation can be transformed according to the roll acceleration \(\bar{\varphi}(k)\) at a certain time step \(k\):  
\[\begin{array}{l}\frac{1}{T_{xx}}\left|h_{\text{GR}}ma_{y}\cos\varphi(k)+h_{ \text{GR}}mg\sin\varphi(k)-u_{1}(k)-u_{2}(k)\\ -2\left(s_{\text{S},t}^{2}c_{\text{S},t}+s_{\text{S},t}^{2}c_{\text{S},r}\right) \sin\varphi(k)\\ -\left((u_{3}(k)+u_{4}(k))s_{\text{D},t}^{2}\right)\dot{\varphi}(k)\cos\varphi (k)\\ -\left((u_{5}(k)+u_{6}(k))s_{\text{D},t}^{2}\right)\dot{\varphi}(k)\cos\varphi (k)\\ =\dot{\varphi}(k).\end{array} \tag{1}\]  
Here, \(J_{xx}\) represents the moment of inertia about the \(x\)-axis, \(h_{\text{GR}}\) the distance between the center of gravity and the roll center and \(m\) the mass of the vehicle body. The external input variables are the lateral acceleration \(a_{y}\) and the gravitational acceleration \(g\). In addition to the external input variables, the chassis elements also have an effect on the roll motion. Apart from the active stabilizers and the semi-active dampers, the vehicle is equipped with passive springs. These passive springs are characterized by the spring stiffnesses \(c_{\text{S},i}\). Furthermore, \(s_{\text{S},i}\) and \(s_{\text{D},i}\) indicate the distances of the springs and dampers from the vehicle's center plane. The index \(i\) indicates which vehicle axle is concerned.  
Using the scheme of the semi-implicit Euler method [23], the roll rate \(\dot{\varphi}(k+1)\) and the roll angle \(\varphi(k+1)\) can be predicted as a function of the manipulated variables, starting from the roll acceleration \(\ddot{\varphi}(k)\):  
\[\dot{\varphi}(k+1)=\dot{\varphi}(k)+\ddot{\varphi}(k)t_{\text{S}}, \tag{2}\]  
\[\varphi(k+1)=\varphi(k)+\dot{\varphi}(k+1)t_{\text{S}}. \tag{3}\]  
Here, \(t_{\text{S}}\) denotes the fixed step size.
## 6 Conclusions  
To assess the degree of deformation in the oilfields of Karamay, Xinjiang, China, the D-InSAR technique was applied to ALOS-PALSAR data and the PS-InSAR and SBAS-InSAR to the ENVISAT-ASAR dataset. The results showed that all the three methods could provide useful information for identifying the boundaries of deformation and revealed two areas of land uplift near the oilfield. The maximum deformation velocity was estimated to be 33 mm/year. As previous research suggests, the subsurface water injection used to enhance oil recovery is the likely cause of this land uplift. The comparison of results from each of the three methods indicates that the correlation of the SBAS-InSAR and D-InSAR results is higher than that of the PS-InSAR and D-InSAR results. For this study area, the SBAS-InSAR method was found to be more robust than the PS-InSAR method. Future investigation can improve the findings of this study by utilizing GPS base stations in the high deformation areas and monitoring long-time land deformation in combination with more accurate and improved satellite data.  
We would like to thank the JAXA for providing the ALOS-PALSAR data and the ESA for providing the ENVISAT-ASAR data. This research was supported by the National Natural Science Foundation of China (Grant No.41361043 and 41661037). We would also like to thank the editor and the two anonymous reviewers for their critical and insightful comments that greatly helped to improve the quality of this manuscript.  
[PERSON] conceived the work, processed the SAR data and wrote the paper, [PERSON] provided auxiliary data sources and revised the manuscript, [PERSON] and [PERSON] guided during the data analyzes and revised the manuscript, and [PERSON] supervised the research.  
The authors declare no conflict of interest.
## 6 Conclusions  
In this paper, the proposed method significantly mitigates the DME interference in LDACS signal reception, as verified by comprehensive simulation experiments. By employing the Cyclic-MUSIC algorithm for precise DOA estimation and using the INCM reconstruction method for beamforming, our approach effectively suppresses the impact of DME signals on OFDM receiver. Quantitative analysis reveals the algorithm's superior performance in forming a high-gain main lobe towards the OFDM signal and deep nulls at interference points, which is evident in the improved output SINR across various scenarios. Compared to existing algorithms based on INCM reconstruction, the proposed method not  
Figure 11: BER versus SNR curve.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithm** & **Running Time/Second** \\ \hline Proposed & 0.063519 \\ INCM-volume & 0.690478 \\ INCM-linear & 0.263194 \\ INCM-projection1 & 0.022785 \\ INCM-projection2 & 0.528757 \\ INCM-subspace & 0.018252 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average running time for a single Monte Carlo simulation.  
only demonstrates lower computational complexity and higher beamforming robustness but also achieves a lower BER, making it a promising solution for advanced interference mitigation in aeronautical communications.  
Conceptualization, L.W. and X.H.; methodology, L.W.; software, X.H.; validation, H.L.; formal analysis, L.W.; writing--original draft preparation, X.H.; writing--review and editing, L.W.; supervision, H.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, U2233216; Science and Technology Innovation Projects for Postgraduates at Civil Aviation University of China, 2023 YJSKC02005.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.