esa-sceva/satcom-chunk-collection huggingface dataset:
# Audlet Filter Banks: A Versatile Analysis/Synthesis Framework Using Auditory Frequency Scales
## 3 The Proposed Approach
### Synthesis Stage  
The synthesis stage consists of a linear operator \(\mathcal{S}((y_{k})_{k})\) mapping the sub-band signals \(y_{k}\) to the output signal \(\bar{\star}\) (see Figure 3) such that the input signal \(x\) is recovered. For uniform analysis FBs,i.e., \(d_{k}=D\forall k\), the operator \(\mathcal{S}\) can be structured as in Figure 2. In that case, _exact_ dual filter \(G_{k}\)'s can be computed [54] with a factorization algorithm that generalizes [23]. The synthesis is then performed by computing \(\widetilde{\mathcal{S}}((y_{k})_{k_{r}}(G_{k},D)_{k})\). For non-uniform analysis FBs, we implement \(\mathcal{S}\) using a _conjugate gradient_ (CG) iteration [49; 55; 56]. This is a very efficient iterative algorithm that is guaranteed to converge when \((H_{k},d_{k})_{k}\) forms a frame, i.e., whenever stable perfect reconstruction is possible. Given the Hermitian operator \(\widetilde{\mathcal{S}}(\mathcal{A}(x,(H_{k},d_{k})_{k}),(H_{k},d_{k})_{k})\), the CG approximates the action of the inverse operator. For Hermitian operators, the CG converges monotonously to 0. In addition, for problems of size \(P\), the CG is guaranteed to converge within \(P\) steps. In practice, convergence speed depends solely on the (potentially unknown) condition number of the linear problem at hand, which, in this case, equals \(\sqrt{B/A}\). Often, it is beneficial to use a preconditioning step to improve the condition number. We propose the operator \(\mathcal{F}^{-1}\operatorname{diag}(1/\mathcal{H}_{0})\mathcal{F}\) as preconditioner (see also [48; 57; 58]). A robust implementation of the appropriate preconditioned CG (PCG) algorithm is conceptually straightforward and was provided in [48]. In the following, we describe a heuristic variant of this PCG algorithm with asymmetric preconditioning, that enables efficient implementation even if the intermediate solutions (denoted by \(x_{j}\) below) are only given in the time domain. Experimentally, Algorithm 1 was observed to converge in the same number of iterations as the robust implementation from [48] (divergence was observed only if the filters \(H_{k}\) were set to uniformly distributed random noise). We denote the analysis of \(x\) with respect to the analysis FB \((H_{k},d_{k})_{k}\) by \((y_{k})_{k}=\mathcal{A}(x,(H_{k},d_{k})_{k})\). We denote the synthesis from \((y_{k})_{k}\) with respect to the synthesis FB \((G_{k},d_{k})_{k}\) by \(\tilde{x}=\widetilde{\mathcal{S}}((y_{k})_{k},(G_{k},d_{k})_{k})\). The composition \(\tilde{x}=\widetilde{\mathcal{S}}(\mathcal{A}(x,(H_{k},d_{k})_{k}),(G_{k},d_{ k})_{k})\) thus represents analysis followed by synthesis.
# Fourier Transform (FT) Analysis of the Stress as a Tool to Follow the Fatigue Behavior of Metals
## 1 Introduction
### Nonlinear Material Behavior during Fatigue
At the beginning of the test (regime I), a transient period was found with a fast increase (\(dl_{3/1}/dN\
eq\text{const. }\)), followed by a linear increase with the cycle number in regime II (\(dl_{3/1}/dN=\text{const. }\)) leading to failure onset in regime III when \(I_{3/1}\) increases rapidly. The fatigue lifetime (\(N\)/) was found to be inversely proportional to the rate of change of \(I_{3/1}\) (\(dl_{3/1}/dN\)) in regime II as:  
\[N_{f}=A_{I_{3/1}}\,\left(\frac{dI_{3/1}}{dN}\right)^{-1}. \tag{9}\]  
Equation (9) was shown to have a much lower deviation (\(\pm 30\%\)) compared to standard strain-life curves (span of \(N_{f}\) of a factor of 3-4), with the proportionality factor \(A_{I3/1}\) being polymer specific [26]. Furthermore, a nonlinear fatigue criterion was developed based on the cumulative nonlinearity (\(Q\)/) which represents the integral of the nonlinearity (\(Q\)) from the beginning of the test until failure [27]:  
\[Q_{f}=\int\limits_{N=0}^{N=N_{f}}Q\,dN \tag{10}\]  
Similar fatigue criteria exist for lifetime prediction like the cumulative strain energy density [28], or the cumulative dissipated energy density [29]. Above the very low cycle fatigue regime a power-law correlation between \(Q\)/ and \(N_{f}\) was found as:  
\[Q_{f}=A_{Q}\,N_{f}{}^{1.4} \tag{11}\]  
where \(A_{Q}\) is the proportionality constant. Due to the poor reproducibility of mechanical fatigue, the developed concepts are widely applicable from a high-sensitive support tool for material testing in fundamental research to the development of smart surveillance systems (sensors) for mechanical fatigue in construction and transportation applications. Additionally, the detection via \(I_{2/1}\) is not limited to viscoelastic materials, as it changes after the occurrence of a macroscopic crack due to an asymmetry in the stress response [19]. Therefore, its value/trend can be used not only for the detection of cracks in materials or applications, which cannot be detected from the outside, but also represents a non-destructive parameter for the detection of voids or defects from the manufacturing process. From our previous work on polymers, it was concluded that FT analysis of the stress is a powerful tool as the \(I_{2/1}\) intensity can be used to detect the onset of cracks and \(I_{3/1}\) to predict the fatigue lifetime. So, the main objective of this work was to apply the technique of Fourier transform analysis of the stress response under fatigue testing on metallic specimens. By doing so, the idea is to determine if: (I) nonlinear material behavior occurs, (II) the nonlinear material behavior changes due to fatigue and (III) fatigue can be quantified by Fourier transform analysis.
# Effect of an Organic Rectorite on the Properties of a Waste Engine Oil-Modified Asphalt
## 2 Experimental Section  
### Experimental Raw Materials  
This study employed a comprehensive range of materials, including waste engine oil (WEO), organic rectorite (OREC), and SK 70\(\hbar\) base asphalt, each with distinctive technical properties. The OREC was prepared from a natural rectorite and an octadecyl dimethyl benzyl ammonium chloride, and the preparation process has been detailed in reference [17]. These materials were carefully selected to explore their potential applications in sustainable road engineering, as detailed in Tables 1-3.  
oil (WEO) and organic rectorite (OREC) into the asphalt matrix. The process was conducted using a state-of-the-art asphalt high-speed shearing machine, which provided optimal conditions for achieving a homogeneous and stable mix. To begin, the base asphalt was carefully heated to reach a flowing state, precisely at 165 \({}^{\circ}\)C. At this critical temperature, the stirring speed of the machine was set at 2000 rpm to ensure efficient mixing. Gradually, 6% of the waste engine oil (WEO) was methodically introduced into the heated asphalt, and the stirring process continued for a duration of 15 min. This step was crucial in achieving proper dispersion and homogenization of the WEO within the asphalt.  
Subsequently, the organic rectorite (OREC) was incorporated into the system at specific mass fractions, including 0.5%, 1%, 1.5%, and 2% based on base asphalt binder [1; 19]. The addition of OREC was conducted with precision, ensuring a slow and gradual introduction into the asphalt. The stirring process persisted for an additional 30 min, maintaining the same temperature and stirring speed. This carefully controlled blending phase allowed for the full interaction and bonding between the OREC and WEO with the asphalt, leading to the formation of the desired OREC + WEO composite-modified asphalt.
# A Review of Progress and Hydrodynamic Design of Integrated Motor Pump-Jet Propulsion
## 3 IMP Bearing and Hydrodynamic Design
### Hydrodynamic Design
#### 3.2.1 Hydrodynamic Performance
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Parameter** & **Year** & **Reference** & **Model** & **Method/Experimental Method** & **Findings** \\ \hline
2019 & [53] & & & 1. & In comparison to conventional propellers, the changes of the hydrodynamic coefficients, particularly the torque coefficient, versus the advance coefficients were substantially reduced. 2. & In all settings, the net imbalance roll moment was very minimal, indicating that the stator was designed correctly. In the behind-hull state, the stator also generated additional thrust, boosting the overall performance by up to 82 percent. \\ \hline
2021 & [54] & & & Computational fluid dynamics (CFD) used to analyze and compare the hydrodynamic performances of a shaftless pump-jet thruster (SPT), a traditional mechanical pump-jet thruster (MPT), and an E779A propeller & 1. & The SPT’s open-water efficiency was 0.662, which was lower than the traditional propeller’s. The SPT’s high-efficiency operating range was somewhat broader than that of the propeller, implying that the SPT could perform well in a wider range of working situations. \\ \hline
**Performance predictions** & 2015 & [55] & The capture of the unsteady rotor-stator interaction & Frozen-rotor method & 2. & The frozen-rotor approach was insufficient to capture the entire unpredictability of the rotor-stator interaction. \\ \hline
2016 & [56] & Mechanical pumping and IMP & Contrastive analysis & 1. & The spray water efficiency of the IMP pump was about 5\% lower than that of the conventional mechanical pump at the same speed. \\ \hline
2016 & [56] & Mechanical pumping and IMP & Contrastive analysis & 2. & When the torque increased, the friction loss increased and the power consumption increased at the same advance speed, compared with the conventional mechanical pumps. \\ \hline \end{tabular}
\end{table} 
Table 3: Cont. \begin{table}
\begin{tabular}{p{10.0 pt} p{108.4 pt} p{108.4 pt} p{108.4 pt} p{108.4 pt} p{108.4 pt} p{108.4 pt}} \hline \hline
**Parameter** & **Year** & **Reference** & **Model** & \multicolumn{2}{p{108.4 pt}}{**Method/Experimental Method**} & \multicolumn{1}{p{108.4 pt}}{**Findings**} \\ \hline
2018 & [57] & \multicolumn{1}{p{108.4 pt}}{Ducted propellers (DPs)} & \multicolumn{1}{p{108.4 pt}}{Panel method} & 1. & An adequate forecast of the propeller and duct loads required an alignment model of the wake shape with the local flow.
## 5 Conclusions  
Drowsy driving is a major issue, and the purpose of this study is to detect drowsiness using computer vision. The proposed method uses NTHU-DDD for training, evaluation,and testing, where the clips are read per frame, and the facial features alongside the head pose are detected. The key metrics of PERCLOS and POM were calculated to provide additional information indicating drowsiness.  
Whereas previous methods used complex deep learning models, this research used simpler machine learning models, such as SVM, Random Forest, and XGBoost, to effectively process detailed data obtained from handcrafted features. These handcrafted features consist of frontalized facial landmarks, head poses, PERCLOS, and POM. The results were promising, showing that it is suitable for use in devices with limited computational resources, such as the Jetson Nano. The experiments conducted showed that XGBoost, although having an overall model size of 5.05 MB, which is considerably small, achieved a high accuracy of 86.848% with increased computational efficiency using less CPU, RAM, and GPU, making it the most suitable classifier for devices with limited computational resources. The evaluation metrics used in this study provided a comprehensive assessment of model performance. Specifically, the XGBoost model achieved an overall precision of 86.25%, recall of 78.5%, and F1-score of 80.75%. Additionally, the use of PERCLOS, POM, and head pose is also shown to have a positive effect, where the detections for each corresponding feature increase notably for PERCLOS and POM, whereas the use of head pose significantly improves the accuracy.  
The findings of this research contribute to the ongoing efforts to enhance road safety by providing an efficient and accurate solution for drowsiness detection. Future studies could explore the integration of additional features, the optimization of model parameters, and the deployment of the system in real-world driving scenarios to further validate its effectiveness and reliability. By advancing the capabilities of drowsiness detection systems, we can take proactive steps towards preventing accidents caused by drowsy driving and ensuring the safety of all road users.  
Experimentation and Research, V.O.; Supervision and Review, N.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Ethical review and approval were waived for this study due to the use of an existing, publicly available dataset. The dataset was collected by other researchers who obtained all necessary ethical approvals and consents for its use in research. This dataset has been de-identified to protect participants' privacy, ensuring compliance with ethical standards for secondary data usage.  
Written informed consent was obtained from the patients to publish this paper.  
Restrictions apply to the availability of these data. Data were obtained from the National Tsing Hua University Computer Vision Lab and are available from Shang-Hong Lai with the permission of the National Tsing Hua University Computer Vision Lab.  
The authors declare no conflicts of interest.
# Photovoltaics and Electrification in Agriculture
## 2 Special Issue Overview: General Topics
### Viability of Photovoltaics Coexisting with Traditional Agriculture
[10], trials were carried out for two different years with the cultivation of celery under solar panels in irrigated cultivation. Although the years were not conducive to the analysis, it can be observed that in any case, the agrovoltaic system does not pose a significant problem for the crop, which would allow concluding that the use of the soil can be shared with the consequent improvement in efficiency of land use.
# Cyclic Detectors in the Fraction-of-Time Probability Framework
## 2 Materials and Methods
### Single Cycle (SC) Detectors
#### 2.6.3 Qf Detector Structure
In addition, FSK radars are used for a highly accurate range detection. The disturbance is constituted by additive colored circular Gaussian noise and narrow-band interference. The colored circular Gaussian noise has power spectral density (PSD) proportional to \(\mathrm{sinc}^{2}(f/B_{n})\), with \(B_{n}=3/(2T_{p})\). The noise power fluctuates from one realization to another. The average (over all realizations) signal-to-noise ratio (SNR) is \(-20\) dB. Thus, the noise PSD completely overlaps the PSD of the SOI. The interference is constituted by three tones. The amplitudes, frequencies, and phases of the tones change randomly from one realization to the other. The signal-to-interference ratio (SIR) for each realization is \(-20\) dB. \(N_{\mathrm{MC}}=10^{4}\) Monte Carlo trials are made to compute the sample statistics for data-record lengths \(T=2^{k}T_{s}\) with \(k=15,\ldots,19\). For cyclic spectra estimates, the width of the frequency-smoothing window is \(\Delta f_{T}=T^{-2/3}\). Such a rate assures the asymptotic complex normality of the frequency-smoothed cyclic periodogram (see [45] (Theorem 4.7.11) applied to the special case of ACS signals). Single-cycle detectors and quadratic-form detectors based on cyclic spectrum estimates have a performance similar to those of the corresponding detectors based on cyclic autocorrelation estimates. Thus, in order to avoid crowded figures, the results for detectors based on cyclic spectrum estimates are not reported in the following sections. Cyclic statistical functions are estimated at cycle frequency \(\alpha=1/T_{p}\). For the quadratic-form detectors, only one value of the parameter \(\theta\) is considered (\(K=1\) in (47)). The chosen value is the one that maximizes the magnitude of the considered cyclic statistic. The cyclic CDF and pdf are evaluated for the real part of the complex-valued received signal.
# A Realistic Approach of the Maximum Work Extraction from Solar Thermal Collectors
## 3 Suggested Model
### Mathematical Description
#### 3.2.1 Energy Balance in the Receiver  
The available solar energy delivered to the collector is given by Equation (8). The symbol (\(G\)) is the solar intensity that is able to be utilized by the collector. For a flat plate collector, it includes the diffuse radiation, while for an imaging concentrating collector it includes only the beam radiation. Moreover, it is essential to say that this solar intensity is assumed to be vertical to the collector aperture, so the incident angle has to be taken into consideration.  
\[Q_{solar}=A_{a}\cdot G \tag{8}\]  
The energy balance in the receiver is presented in Equation (9), while the radiation heat losses are given by Equation (10).  
\[Q_{solar}\cdot\eta_{opt}=Q_{u}+Q_{loss}, \tag{9}\]  
\[Q_{loss}=A_{r}\cdot\varepsilon_{r}\cdot\sigma\cdot\left(T_{r}^{4}-T_{amb}^{4 }\right) \tag{10}\]  
At this point, it is important to state that an assumption of the present model is that the collector exchanges irradiation with the ambient temperature and not the sky temperature. This assumption is  
Figure 1: Description of the suggested model.  
totally valid for some collectors (for instance the linear Fresnel reflector and the solar dish) or partially for others (for instance the low part of the parabolic trough collector absorber).  
Equation (11) gives a general equation for optical efficiency calculation. More specifically, all the possible reasons for the optical loss are taken into consideration. The reflector reflectivity (\(r\)), the intercept factor (\(\gamma\)), the cover transmittance (\(\tau\)), and the absorber absorbance (\(\alpha\)) are the four products of the optical efficiency. The impact of the incident angle can be included in the intercept factor. So, the incident angle modifier impact can be modeled in the intercept factor.  
\[\eta_{opt}=\rho\cdot\gamma\cdot\tau\cdot\alpha \tag{11}\]  
The collector thermal efficiency is calculated as the ratio of the useful energy to the available solar energy to the collector aperture. Equation (12) shows this determination and simultaneously includes extra calculation steps.  
\[\eta_{coll}=\frac{Q_{u}}{Q_{solar}}=\eta_{opt}-\frac{Q_{loss}}{Q_{solar}}=\eta _{opt}\cdot\left\{1-\frac{\varepsilon_{r}\cdot\sigma\cdot T_{amb}^{4}}{C\cdot G \cdot\eta_{opt}}\cdot\left[\left(\frac{T_{r}}{T_{amb}}\right)^{4}-1\right]\right\} \tag{12}\]  
The concentration ratio (\(C\)) is calculated according to Equation (13):  
\[C=\frac{A_{a}}{A_{r}} \tag{13}\]
# First Retrieval of Sea Surface Currents Using L-Band SAR in Satellite Formation
## 5 Results and Discussion
### Quantitative Validation  
Figure 12b shows the current reference data from the CMEMS. The data were transformed to align with the radial direction of LuTan-1.  
The CMEMS dataset provides average information regarding large-scale spatial features of the sea surface. Based on these reference values, the InSAR measurements offer significantly higher resolution and accuracy in capturing detailed sea surface characteristics. Therefore, this validation focuses on comparing the ATI-derived surface current measurements with the relative deviations from the CMEMS reference data. Owing to the significantly lower spatial resolution of CMEMS reference data (0.083\({}^{\circ}\) lon/lat) compared to LuTan-1, quantitative comparisons were conducted exclusively at the sampling points of CMEMS. A statistical comparison of the ATI-derived ground range direction current velocity array \(\mathbf{a}=\left(a_{ij}\right)\) and CMEMS current velocity array \(\mathbf{b}=\left(b_{ij}\right)\) was conducted, considering the mean differences as follows:  
\[\text{MAE}=\left\langle\sum\sum\lvert a_{ij}-b_{ij}\rvert\right\rangle \tag{23}\]  
The root-mean-square (RMS) difference can be calculated as follows:  
\[\text{RMSE}=\sqrt{\left\langle\sum\sum\left(a_{ij}-b_{ij}\right)^{2}\right\rangle} \tag{24}\]  
The correlation coefficient can be determined as follows:  
\[R=\frac{\sum\left(a_{ij}-\bar{a}\right)\left(b_{ij}-\bar{b}\right)}{\sqrt{ \sum\left(a_{ij}-\bar{a}\right)^{2}\sum\left(b_{ij}-\bar{b}\right)^{2}}} \tag{25}\]  
Figure 13 shows a scatter diagram illustrating the relationship between the inverse current and the CMEMS data within an identical coverage region. Notably, the CMEMS data reflect the average results within 6 hours, whereas SAR captures the transient results during imaging. Therefore, it is unrealistic to expect perfect alignment between the inversion results and CMEMS data. The mean differences between the currents derived from LuTan-1 and CMEMS were 0.033 m/s, with an RMS value of 0.041. The correlation coefficient was 0.610. The statistical comparison results demonstrated relatively good consistency between the inverse current and the CMEMS data.
## 1 Introduction
In addition, the current control strategy based on the kinematics model ignores the tire sideslip, resulting in low tracking accuracy at medium and high speeds. At the same time, the end-to-end control strategy based on the dynamics model is too complex, which is not conducive to the real-time deployment of the controller [32]. In addition, the VTT has multiple types of structure, and the current control strategy is not universal. Under these challenges, the main contribution as well as the core goal of this paper is to develop a reconfigurable control strategy that could apply to any configurations and drive modes of VTTs for dynamics path-tracking control, meanwhile reducing the hinge forces and tire sideslip. A simple and elementary control strategy has been reported in [33]; nevertheless, it just referred to the all-wheel-drive vehicles with two axles per module and used a hypothetical theoretical model without specified vehicle parameters. Differentiating from the previous work, this paper introduces an extended modeling process, a newly designed and completed controller, and a very different vehicle case study. Considering the different number of modules, each module may have a different number of axles, and only some axles have driving capacity, for example, the four-module six-axle SRT; leveraging this methodology, one could just set the defined \"Boolean matrix\" based on the vehicle configuration and perform minor tuning, and a new dynamics model and controller would be obtained. Moreover, for the structural specificity SRT, in addition to the improved MPC and hierarchical architecture, we proposed CG generalized force redistribution and the \"virtual axle\" method to avoid significant control errors caused by insufficient actuators in the middle two modules. The vehicle dynamics model of the SRT is established based on the authentic parameters, and the proposed control strategy is verified through a hardware-in-the-loop (HIL) real-time simulation. It is hoped that this approach will provide a new perspective for VTT dynamics control. The second part of this paper introduces the structure of the SRT and establishes the vehicle dynamics model. The third part proposes and implements a path-tracking control strategy based on an improved MPC algorithm and hierarchical architecture. The effectiveness of the control strategy is verified through an HIL real-time simulation in Section 4. The conclusion is drawn in Section 5.