esa-sceva/satcom-chunk-collection huggingface dataset:
# Integrated Module Antenna for Automotive UWB Application
###### Abstract  
In this paper, an integrated module antenna for automotive UWB application is proposed. The target applications of the proposed antenna are for UWB localization and rear passenger detection. The purpose of this work is to design an antenna with a wide and narrow beamwidth that can be attached to the exterior/interior of a vehicle for simultaneous UWB localization and a rear passenger detection sensor with a single-module substrate in a limited space. For UWB localization, the wide beam coverage is required so the antenna receives the signal from any incident angle in horizontal plane. Meanwhile, the rear passenger detection sensor requires a relatively narrower beamwidth than the localization antenna for accurate detection within the vehicle. To integrate two different antennas into a single compact module substrate, modified ground stubs and parasitic radiators are applied. The size of the entire antenna structure is 35 mm \(\times\) 65 mm \(\times\) 1.156 mm. The proposed antenna designed on the multi-layered FR-4 substrate with a dielectric constant of 4.3. The bandwidth of the monopole is 6.14-8.24 GHz, and the patch array is 6.95-8.47 GHz. The isolation between the monopole and the patch array is less than \(-\)23 dBi in the target band. The performance of the proposed antenna is verified with simulation and measurement. In addition, a simulation of the proposed antenna with the real vehicle model is also conducted to verify the feasibility on actual vehicle. Based on this work, the proposed antenna can be applied to multi-function antenna for automotive application with low cost.  
Keywords:integrated antenna; UWB module; modified ground stub; parasitic radiator; UWB localization; rear passenger detection sensor; automotive antenna +
Footnote †: journal: Medical Imaging
## 4 Surrounding Rock Grade Identification Based on Optuna-XGBoost  
### The Process of Constructing a Model  
The selection of input indicators plays a crucial role in ensuring the accuracy of predictions. This study carefully chose six key input features through statistical analysis and feature selection: T, N, F, SE, TPI, and PR. These extracted indicators were then utilized as the input vector for the Optuna-XGBoost model to establish a TBM excavation rock grade identification model capable of accurately predicting surrounding rock grades during the TBM tunneling process.
# Neuro-Fuzzy Model Evaluation for Enhanced Prediction of Mechanical Properties in AM Specimens
## 5 Model Implementation and Performance Analysis
### ANFIS Parameter Tuning and Performance Evaluation
At data point 2, significant overestimation is observed, particularly by the initial and SC models, as the actual strain value is considerably lower. For data points 3 to 9, the models generally produce predictions closer to the actual strain values, with the GP model demonstrating the most consistent performance. In contrast, the initial and SC models exhibit larger deviations, particularly in cases of extreme strain values, such as at data point 1. The initial model for predicting the [PERSON]'s modulus showed an average error of \(\pm\)1.85 GPa and an accuracy of 0.17. After applying the subtractive clustering algorithm, the model improved to an average value error of \(\pm\)1.77 GPa and an accuracy of 0.25. This improvement corresponds to a 5% reduction in error and a 32% increase in accuracy, as depicted in Figure 14. At data point 1, the GP model significantly overestimates the value, whereas the initial and SC models provide predictions closer to the actual value. For data  
Figure 14: Bar plot of predicted values compared to actual values for 9 checking datasets ([PERSON]’s modulus). points 2 to 5, all models perform reasonably well, though the GP model exhibits slight overestimation. At data points 6 to 9, where the actual values approach zero, the initial and SC models deliver more accurate predictions, while the GP model continues to overestimate. Overall, the initial and SC models demonstrate superior performance for lower values, while the GP model shows larger deviations. As demonstrated in this study, applying a common ANFIS model configuration to all three output cases would be unsuitable. The initial value selection and iterative process diagrams clearly show that each prediction model exhibited distinct characteristics, driven by significantly different initial data. The analysis underscores the importance of adapting the ANFIS configuration to each specific output, as a single model cannot adequately capture the unique relationships inherent in varying mechanical properties. The analysis demonstrated that the ANFIS model effectively adjusted printing parameters based on the experimental AM data, resulting in significant improvements in stress, strain, and Young's modulus. The evaluation metrics confirmed the model's reliability in predicting the behavior of printed parts under diverse data. Although the limited sample size posed certain constraints, the results highlight the model's potential for further refinement through larger datasets and more comprehensive testing. Moreover, the experimental data and the obtained results confirm the effectiveness of ANFIS in enhancing the accuracy and efficiency of the printing process, reinforcing its applicability in data-driven manufacturing.
# Seismic Resilience in Critical Infrastructures: A Power Station Preparedness Case Study
## 2 Background  
### Seismological Risks  
In seismic regions worldwide, seismological risks significantly threaten communities, critical infrastructures, and natural landscapes. This study specifically focuses on the Israeli region, highlighting the Great Rift Valley as a notable area of interest due to its significant seismic activity [21]. The Great Rift Valley is an expansive geological rift system from the Beqaa Valley in Lebanon to Mozambique in the south. It forms part of a larger system that encompasses both the Eastern and Western Rift Valleys, shaped by the gradual separation of the Earth's crust, which leads to the creation of fault lines, escarments, and profound valleys. Beyond its geological significance, the valley is notable for its rich biodiversity and crucial archaeological sites that provide insight into human evolution. This rift system presents a notable earthquake hazard to regions it traverses, including Israel. It influences a distinct movement pattern where the land east of the Jordan River shifts northward while the terrain to the west moves southward. These tectonic activities have progressed slowly, 5 to 10 mm per year over the last 10 to 20 million years. Although Israel is geographically situated in a region with low seismic activity, its history reveals several earthquakes that have resulted in considerable loss of life and damage to structures. The pattern of earthquake occurrence rates along a portion of the Great Rift Valley indicatesthat earthquakes with a magnitude of 6 and above, happening in and around Israel, align with a trajectory stretching from the Lebanon Valley to the southern Dead Sea. Figure 1, on the top left, displays a map to determine the epicenters of these earthquakes since 1900, categorized by their intensity. Furthermore, the Carmel Rift is another zone of notable seismic activity from the Carmel region to the Sea of Galilee.  
Seismological centers primarily assess seismic risk, estimating the potential ground motions seismic waves trigger. These potential ground motions are commonly quantified using Peak Ground Acceleration (PGA), which serves as an indicator of the intensity of ground shaking at a site during an earthquake [22]. Such assessments commonly incorporate uncertainty, leading to the inherently probabilistic nature of seismic risk evaluations. The Israeli standard (IS 413) [23; 24] accounts for the likelihood of buildings being subjected to significant ground motion (which does not necessarily stem from a major earthquake). It suggests that, if a building is expected to serve its occupants for 50 years, there is a 90% probability that it will not experience ground motions more severe than those prescribed by the standard, given a PGA with a cumulative probability of 10%.  
Figure 1: Earthquake hazards analysis.
## EMD_CS_ST Denoising Method
### EMD_CS_ST Denoising
#### 2.4.2 Improvement of CS Denoising  
In view of the problems in Section 2.4.1, the MS signal is decomposed by EMD, and combined with the advantage of CS theory with certain denoising and soft-thresholding denoising, a method of denoising based on EMD_CS_ST is proposed. The specific steps of the denoising method are shown below.  
Step 1: The EMD method is used to decompose the MS signal \(x(t)\) into a series of high-to-low frequency components, containing n IMF components and a residual component, denoted as \(I=\{inf_{1}\), \(imf_{2}\),..., \(imf_{k}\),..., \(inf_{n}\), \(r(t)\}\);  
Step 2: Calculated the correlation coefficient of \(n\) IMF components by Equation (2), denoted as \(C\) = \(\{cof_{1}\), \(cof_{2}\),..., \(cof_{k}\),..., \(cof_{n}\}\), and found the first local minimum value of \(C\), and the next IMF component corresponding to it is the boundary between noise and signal, recorded as \(imf_{k}\);  
Step 3: Performed discrete wavelet transform sparse processing on the \(imf_{k}\) component and the components before it, and calculated the coefficient \(\theta\) to be reconstructed of \(k\) components, which are denoted as \(\theta=\{\theta_{1}\), \(\theta_{2}\),..., \(\theta_{k}\}\);  
Figure 3: Ricker waveform after compressed sensing threshold denoising. (**a**) The original signal, (**b**) original sparse coefficients, (**c**) reconstructed sparse coefficients, and (**d**) recovered signal.  
Figure 2: Ricker waveform reconstructed by compressed sensing. (**a**) Original signal, (**b**) original sparse coefficients, (**c**) reconstructed sparse coefficients, and (**d**) recovered signal.  
Step 4: Performed soft-thresholding filtering on the \(\theta\) to obtain \(k\) reconstruction coefficients \(\theta_{new}\), which are denoted by \(\theta_{new}\) = {\(\theta_{1,new}\), \(\theta_{2,new}\),..., \(\theta_{k,new}\) };  
Step 5: Reconstructed the reconstruction coefficient \(\theta_{new}\) by CS to obtain \(k\) IMF\({}_{new}\), denoted as IMF\({}_{new}\) = {\(\textit{imf}_{1,new}\), \(\textit{imf}_{2,new}\),..., \(\textit{imf}_{k,new}\) };  
Step 6: Recombined the \(k\) IMF\({}_{new}\) components with the Remaining Components (RCs) to obtain the denoised signal, which is the effective MS signal \(x(t)_{new}\) after denoising.  
The EMD_CS_ST algorithm flow chart is shown in Figure 4.
# Phase Space Insights: Wigner Functions for Qubits and Beyond
###### Abstract  
Phase space methods, particularly Wigner functions, provide intuitive tools for representing and analyzing quantum states. We focus on systems with SU(2) dynamical symmetry, which naturally describes spin and a wide range of two-mode quantum models. We present a unified phase space framework tailored to these systems, highlighting its broad applicability in quantum optics, metrology, and information. After reviewing the core SU(2) phase-space formalism, we apply it to states designed for optimal quantum sensing, where their nonclassical features are clearly revealed in the Wigner representation. We then extend the approach to systems with an indefinite number of excitations, introducing a generalized framework that captures correlations across multiple SU(2)-invariant subspaces. These results offer practical tools for understanding both theoretical and experimental developments in quantum science.  
Keywords:quantum information; phase space; Wigner functions +
Footnote †: journal: Applied sciences
## 5 Results
### Impact of Number of Turns on Coupling Coefficient for Different Structures of the Cores  
The simulations were performed for the case of the air-gap length of 17 cm. The other parameters of the coils (Figure 16) are presented in Table 1. The results are presented in Figure 17.  
The results show that the higher number of turns determines the higher coupling coefficient values just in the lower range of the number of turns. From a particular point, the increase in the number of turns does not produce growth of the coupling coefficient. The highest sensitivity here shows the coil structures with ferrite bars for both circular and square-type geometry cases.  
Impact of Width of the Ferrite Bars on Coupling Coefficient of Coil Systems with Bar-Plate Core Structure  
The simulations were performed for the case of the air-gap length of 17 cm. The other parameters of the coils (Figure 18) are presented in Table 1. The results are presented in Figure 19.  
Figure 16: Geometries of the circular coils: (**a**) with 10 turns; (**b**) with 20 turns.  
Figure 17: Coupling coefficient vs. number of turns: (**a**) circular coils; (**b**) square coils.  
The results show that the dependencies of the coupling coefficient vs. width of the bars have a nearly linear relationship in the considered range. It is valid for both circular and square-type geometries. Predictably, the higher coupling coefficient values are produced by the coil structures which have backings with higher ferrite volume.
### Contributions  
In real life, urban environmental sounds include background noise with low SNR [24]. Therefore, minimizing the impact of noise on classification models and improving the robustness of these models to noise is a crucial issue in ESC tasks. Conventional classification models cannot fully extract audio features, and introducing transfer learning can enhance the specificity of the training process. However, this approach objectively brings the risk of overfitting during training [25]. Therefore, based on existing research schemes, this paper proposes new solutions and improvements.  
Acknowledging these limitations, grounded in the current research landscape, we dedicate ourselves to present novel solutions and advancements. We endeavor to address the aforementioned challenges by introducing innovative strategies that not only lessen the influence of noise but promote the generalizability and robustness of ESC models as well, thereby advancing the state-of-the-art in our manuscript. Our main contributions are divided into the following three parts:  
1. A novel Patch-Mix feature fusion scheme is proposed. Unlike traditional methods that perform feature fusion by simply stacking multiple dimensions, the Patch-Mix strategy randomly assembles patches within samples instead of merely merging multiple dimensions. This provides the model with more comprehensive feature information without increasing the feature dimensions. Additionally, introducing patches from noisy samples for replacement can enhance the robustness of the feature learning process in noisy environments, helping the model better adapt to real-world scenarios during training.
2. The CL-Transformer model is proposed to address the overfitting weaknesses of pre-trained Transformer models during training, especially on small-scale datasets, by introducing a contrastive learning strategy. This approach incorporates contrastive learning loss, providing an effective regularization mechanism for the Transformer model during training, thereby alleviating overfitting issues. Additionally, the contrastive learning mechanism enhances the model's robustness in noisy environments. This strategy aims to improve the generalization ability and performance of the model, and to mitigate the overfitting problems that often occur in downstream tasks after transfer learning. As a result, our novel CL-Transformer model in our manuscript emerges as a more versatile and robust solution, better suited for diverse downstream applications and conditions.  
3. An improved data augmentation technique has been developed to help the model better adapt to different input variations and noise by augmenting the original audio waveforms and spectrograms. This enhances the model's robustness and alleviates overfitting issues in downstream tasks with limited datasets.  
In total, our state-of-the-art methods presented in this manuscript bolster the performance and applicability across a broader spectrum of real-world scenarios.
## 4 Effect of Viscoelastic Damping on Underwater Acoustic Radiation
### The Acoustic Field Model and Viscoelastic Damping Material  
After obtaining the vibration response and modes of the submerged shell structure, the acoustic radiation field induced by the structural surface can be calculated. The acoustic fields are established in xoy and yoz planes with 20,000 \(\times\) 20,000 mm sizes. The intersection of the two fields coincides with the central axis of the conical shell, as shown in Figure 8.  
As is known, applying damping on the structure can effectively reduce the amplitude of structural vibration and reduce the propagation of noise. However, as for the typical  
Figure 8: Acoustic model of the ring-stiffened conical shell.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Mode**} & \multicolumn{2}{c}{**The Present Method**} & \multicolumn{2}{c}{**Ref. [37]**} \\ \cline{2-5}  & \(f_{\mathbf{n}}\) **(Hz)** & \(\mathbf{\eta}\) & \(f_{\mathbf{n}}\) **(Hz)** & \(\mathbf{\eta}\) \\ \hline
1 st & 81.71 & \(1.28\times 10^{-3}\) & 81.79 & \(1.37\times 10^{-3}\) \\
2 nd & 494.14 & \(6.20\times 10^{-3}\) & 504.16 & \(5.43\times 10^{-3}\) \\
3 rd & 1323.4 & \(8.06\times 10^{-3}\) & 1380.34 & \(9.38\times 10^{-3}\) \\
4 th & 2449.8 & \(1.19\times 10^{-2}\) & 2627.87 & \(1.36\times 10^{-2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of the results of the sandwich beam.  
Figure 7: Finite element model of the viscoelastic sandwich beam.  
ring-stiffened conical shell with bases, the effect of geometric parameters and damping location on underwater acoustic radiation remains unclear. Therefore, the acoustic-structure coupling characteristics considering the viscoelastic damping effect are analyzed in this section, which is the key point in this work. The sinusoidal exciting force is imposed normally on the plate of the base with an amplitude of 100 Pa. Thirdly, the BEM is applied for acoustic-structure coupling analysis. The computation frequency is limited to 1500 Hz to avoid the high computational cost.
# LED Current Balance Using a Variable Voltage Regulator with Low Dropout \(v_{DS}\) Control
## 2 Aspect of Linear Current Regulator for LEDs
### Dimming with a Constant Voltage Supply to LCR  
When using a LCR loaded with one string LED, with a constant supply of \(V_{dc}\), for example, in the dimming process, the operation point of the MOSFET under different drives \(v_{GS}\)'s will change dramatically, since the voltage drop \(v_{R}\) on the source resistor \(R_{s}\) will vary far more than the voltage \(Nv_{D}\). In this description, three kinds of gate voltages \(v_{GS}\)'s are explored, to dim the string \(N\)-LED. As mentioned previously, the operation of LCR is initially placed at point \(q_{2}\) under normal temperature \(T_{0}\), as shown in Figure 4, in which the \(N\)-LED current \(i_{D}=I_{D2}\) under the MOSFET is driven by \(v_{GS2}\). If the gate voltage changes from \(v_{GS2}\) to \(v_{GS1}\) while \(v_{GS1}<v_{GS2}\), the operation point \(q_{2}\) will move to point \(q_{1}\) on the flat \(v_{GS1}\)-characteristic curve, since the voltage \(Nv_{D}\) will decrease due to the decrease of \(i_{D}\), lowering the LED luminous output. On the other hand, if the gate voltage varies from \(v_{GS2}\) to \(v_{GS3}\) for example, as well as \(v_{GS3}>v_{GS2}\), the voltage \(v_{DS}=V_{DS3}\) will move left, to point \(q_{3}\) on the triode region of the \(v_{GS3}\)-characteristic curve. In this instance, the drain current \(i_{DS}\) will increase to \(l_{D3}\) since the voltage drop on \(R_{s}\) and \(Nv_{D}\) will increase, leading to the decrease of \(v_{DS}\). Therefore, although an increase of the \(N\)-LED current \(i_{D}=I_{D3}\) will raise the luminance, the luminance change may not be proportional to the dimming level, disturbing its uniform distribution variation.
# Estimating Degradation Costs for Non-Cyclic Usage of Lithium-Ion Batteries
## 2 Materials and Methods
### Data Used in the Simulation  
All simulations were performed applying the models described in the previous sections.  
For NMC cells, models and data are extracted from literature. For Sanyo UR18650E cells, the cited [PERSON] et al. [19] degradation model was used. In the present work, \(OCV\) is modeled as the fourth-grade polynomial described in Equation (52) fitted with data extracted from [PERSON] et al. [20]. The cells used by [PERSON] et al. [20] and [PERSON] et al. [19] are the same. The Thevenin values used came from [PERSON] et al. [38], which uses INR18650-20R cells. Thevenin values are considered constants. The nominal voltages of UR18650E and INR18650-20R cells are the same at 3.6 V; thus, it is expected that they are compatible.  
\[OCV_{NMC}\ =\ -3.0208\cdot SoC^{4}+7.3282\cdot SoC^{3}-5.4919\cdot SoC^{2}+2.0406 \cdot SoC+3.3339 \tag{52}\]  
Table 1 shows the main characteristics of the NMC cell used in the simulations.  
For LFP cells, the data and models are also extracted from literature. The degradation model used was the [PERSON] et al. [11] model fitted for 26,650 cells, and also [PERSON] et al. [33], which is based on the [PERSON] et al. model [11]. Thevenin values and \(OCV\) came from [PERSON] et al. [35], and are fitted for APR18650M1A cells. The models provide functions dependent on \(SoC\) and \(T\), and also take into account the hysteresis of LFP cells.  
Table 2 shows the main characteristics of the LFP cell used in the simulations.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Rated Capacity**} & **Nominal** & \multirow{2}{*}{**Maximum Continuous Current**} & \multirow{2}{*}{**Electrodes**} \\ \cline{3-3} \cline{5-5}
**at 20 \({}^{\circ}\)C** & & & **Charge** & **Discharge** \\ \hline
2050 mAh & 3.6 V & 1C & 3C & Graphite/Li(NiMnCo)O\({}_{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of the NMC cell used in the simulations (UR18650E).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Rated Capacity**} & \multirow{2}{*}{**Nominal Voltage**} & \multicolumn{2}{c}{**Maximum Continuous Current**} & \multirow{2}{*}{**Electrodes**} \\ \cline{3-3} \cline{5-5}
**at 20 \({}^{\circ}\)C** & & & **Charge** & **Discharge** \\ \hline
2050 mAh & 3.3 V & 1C & 3C & Graphite/Li(NiMnCo)O\({}_{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Characteristics of the LFP cell used in the simulations (26650).  
Figure 8: Voltage and temperature values. Simulation for NMC and LFP cells for 30 min periods of rest, charge, rest, discharge, rest, charge, and rest. Charging and discharging at CC, \(I\) 1.5 A, \(\delta_{t}\) 15 s., and \(T_{a}\) 22 \({}^{\circ}\)C.  
Parameters of the thermal model were obtained from [PERSON] et al. [39] and [PERSON] et al. [24].  
The same thermal coefficients were used for both cell technologies.