esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results of Finite Element Analysis
### Back-EMF  
From the analysis on back-EMF, its harmonic components and PM flux linkage can be estimated. Hereby, PM flux linkage can be obtained directly from FEA program. Therefore, they are listed in Table 2 for each angle. No-load tests were simulated only at 6000 r/min since other data at different speed are easy to be calculated in linear way; see Equation (18). Evaluated peak values for each model is listed like:  
\[E_{0}=\alpha\lambda_{PM} \tag{18}\]  
Figure 8a,b represents the waveform of back-EMF for a cycle and its magnitudes of 1 st, 5 th, 7 th, 9 th, and 11 th harmonics. In Figure 9, all of values are given in per unit where maximum value among the models at each harmonic order is equal to unity. 1 st order harmonics, a fundamental component, decreases at constant rate as the angle becomes wider. Thus, it is envisaged that PM flux linkage also decreases with respect to decline in the fundamental component. On the other hand, 5 th, 7 th, and 11 th harmonics are, with wider angle, more significantly attenuated than 1 st order. Only 9 th order harmonics tends to increase with the angle in contrary to the other harmonics. However, 9 th and 11 th order harmonics affect relatively a little while 5 th and 7 th order of harmonics are the main contributor of torque ripple.  
To identify the decreasing rate of THD corresponding to the increase in angle, the values of magnet volumes, PM flux linkage, and THD are represented in Table 2. Compared to magnet volumes and 1 st order harmonics, the decreasing rate of THD is apparently higher. Therefore, depending on the requirements, 110-degree model was chosen as the better option.  
Figure 7: Cogging torque evaluated from FEA (**a**) curves of cogging torque and (**b**) peak and RMS values of each model.
## 2 Related Work
In [34], a satellite navigation distributed blanket jamming resource scheduling method based on particle swarm optimization and a genetic algorithm was proposed, in which the objectives of bit error rate, pseudorange measurement error, and jamming cost were optimized. This method effectively scheduled limited resources. Such methods simplify multi-objective optimization into single-objective optimization using linear weighting, but the selection of weighting coefficients directly affects the bias of the final solution. In [35], a taboo search-artificial bee colony algorithm for cognitive cooperative jamming decision-making, which considers multiple objective functions for optimization, was proposed, making it less likely to fall into local optimal solutions. However, such methods have a slow convergence speed during optimization, making it difficult to ensure real-time jamming. Ref. [36] introduced an improved discrete particle swarm optimization algorithm, which can provide the most suitable allocation scheme according to the requirements of different tasks, effectively addressing the static allocation problem of jamming resources. However, the search space of the populations used in such methods is not broad enough, making it easy to fall into using local optimal solutions and difficult to guarantee a certain jamming success rate. The limitations of existing jamming methods, poor flexibility in the deployment of jammers, and subpar decision-making performance in jamming efficiency optimization highlight the shortcomings of the current methods. In response to these deficiencies, this study proposes a LEO satellite downlink distributed jamming optimization method with a non-dominated sorting genetic algorithm, which offers strong flexibility and effectively enhances the real-time jamming performance and success rates.
## 3 Results
### Capsules Image Analysis  
The stereoscopic microscope images (Figure 2) of the capsules obtained showed that the hemispherical shapes increase depending on the nucilage concentration. This effect could be attributed to an increase in the viscosity and surface tension of the mix, since they affect the injection flow, which generates pear-shaped or teardrop-shaped matrix capsules (Figure 2), as reported by [PERSON] et al. [55]. As for the alginate, smooth surfaces and spherical shapes are observed when the concentration increased (Figure 2, 0% Mu), which has also been reported by [PERSON] et al. [23] and [PERSON] et al. [56], who found that the dripping mode generates less capsule deformation than those produced by electrohydrodynamic (EHD) atomization when chia seed mucilage is used for glucose oxidase encapsulation.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Mucilage Concentration (\%)**} & \multicolumn{4}{c}{**Herschel–Bulkley Model**} \\ \cline{2-6}  & & **n\({}_{\text{r}}\)** & **Density (g/cm\({}^{3}\))** & **Surface Tension (dyn/cm)** \\ \hline
0.7 & 0.9985 & 0.3997 & 0.386 \(\pm\) 0.0 \({}^{\text{a}}\) & 7.57 \(\pm\) 0.29 \({}^{\text{a}}\) & 0.75 \(\pm\) 0.006 \({}^{\text{a}}\) & 80.33 \(\pm\) 0.58 \({}^{\text{a}}\) \\
0.5 & 0.9948 & 0.1081 & 0.599 \(\pm\) 00 \({}^{\text{b}}\) & 0.39 \(\pm\) 0.01 \({}^{\text{b}}\) & 0.82 \(\pm\) 0.0 \({}^{\text{b}}\) & 62.67 \(\pm\) 0.06 \({}^{\text{b}}\) \\
0.3 & 0.9998 & 0.0896 & 0.595 \(\pm\) 0.01 \({}^{\text{c}}\) & 1.328 \(\pm\) 0.05 \({}^{\text{c}}\) & 0.93 \(\pm\) 0.0 \({}^{\text{c}}\) & 62.67 \(\pm\) 0.58 \({}^{\text{b}}\) \\
0 & 0.9997 & 0.1099 & 0.801 \(\pm\) 0.00 \({}^{\text{d}}\) & 0.44 \(\pm\) 0.01 \({}^{\text{d}}\) & 0.95 \(\pm\) 0.0 \({}^{\text{d}}\) & 58.17 \(\pm\) 0.58 \({}^{\text{c}}\) \\ \hline \hline \end{tabular} Values are presented as mean \(\pm\) standard deviation. Different lowercase letters in the same column are significantly different (\(p\leq 0.05\)).  
\end{table}
Table 2: Rheological and physicochemical parameters of the solutions at different nucilage–alginate concentrations.  
The determination of the shape of the capsules (diameter, major and minor axes, and circularity) is shown in Table 3. Here, the average diameter varies from 0.15 to 0.24 cm; the lowest values correspond to the samples with the highest alginate content, and the largest particle size was obtained when a high concentration of mucilage was used (0.7% Mu). On the other hand, the major and minor axes and the measures of circularity were significantly different between the samples. Likewise, the size and dimension of the droplet depend on the flow's rheology, since as the viscosity of the solutions increases, it is necessary to apply more force to extrude the droplets, producing larger capsules. These results are equivalent to those reported by [PERSON] et al. [57]. It was also observed that the mean circularity values of the capsules improved when the alginate content increased (0% Mu). However, the hemispherical shape and smaller size of the capsule could have a major effect on the release of rhodamine B.
## 1 Introduction
Solid-phase microextraction (SPME) was introduced in the early 1990s and has become the most widely used technique for sampling volatile compounds in various applications, including food analysis, environmental studies, and metabolomics [12; 13; 14; 15]. Several studies have already reported the use of SPME for profiling volatiles of germinated samples [16; 17; 18; 19]. Most of them were performed using headspace (HS) sampling. Quantifying volatiles using HS analysis is challenging due to the physicochemical properties of analytes, such as volatility, polarity, and partition coefficients. The physical state of the matrix (solid or liquid) and matrix effect further complicate the process [20]. Multiple headspace extractions (MHEs) arise as a solution for the aforementioned issue. This technique requires several extractions on the same sample, leading to exhaustive extraction [21]. The analyte concentration decays exponentially, and the total peak area can be obtained as the sum of each extraction [13; 22]. Therefore, the analytes can be quantified using external standard calibration. MHE has not yet been applied for volatile profiling of germinated samples. The overall objective of this study was to investigate, for the first time, the effects of different germination parameters on the FA and volatile profiles of IWG flour. Specifically, this work aimed to (i) investigate key germination parameters such as time (2, 4, and 6 days) and temperature (10, 15, and 20 \({}^{\circ}\)C); (ii) optimize SPME extraction conditions using a central composite rotatable design (CCRD) coupled with response surface methodology; (iii) assess the effects of germination on the IWG volatile profile; (iv) quantify aroma compounds using MHE, calibration curves, and gas chromatography coupled with mass spectrometry (GC-MS); and (v) assess the effects of germination on the FA profile of IWG flour by gas chromatography coupled with flame ionization detection (GC-FID). Correlations between the FA composition and the volatile profile were reported to better understand the changes occurring as a function of germination. This study provides valuable insights into the effects of germination on IWG flour, paving the way for its broader use in food applications.
# Frequency-Domain Characterization of Finite Sample Linear Systems with Uniform Window Inputs
## 1 Introduction
As the marginally stable system is an artifact due to the zero-value assumption for unavailable samples, the methods discussed here apply to all FS-LTI systems, whether the corresponding IS-LTI systems are FIR or IIR, stable or unstable. Although our focus is on the theoretical equivalence of \(H_{0}[k]\) and \(h[n]\) for FS-LTI systems, our approach to finding the IRF offers the advantages of frequency domain filtering in practical signal processing. The literature on FS-LTI systems is largely concerned with error estimations in system identifications. The textbook [4] lays the background for system identifications and reviews the theories and techniques up to the publication time. Reference [5] discusses the quality of system identification for general linear models using the standard quadratic prediction error criterion and bounds the difference between the expected value of the identification criterion evaluated at the estimated and optimal parameters. An extension to [PERSON]'s sampling theorem for the finite-time case is found in [6], which demonstrates that a higher sample rate is necessary to recover finite-duration signals. When an observed signal is modeled as the output over a finite interval of an LTI operator having a short-duration impulse response, ref. [7] shows that complex exponentials over the observation interval are pseudomodes of LTI operators, corresponding to pseudo-eigenvalues that are samples from the operator's frequency response. Finite-time bounds on the identification error of least-squares estimates for a broad range of heavy-tailed noise distributions and transition matrices are provided in [8]. A data-driven method for order selection and the achievement of the precise estimation of system parameters using an approach closely related to the Ho-Kalman subspace algorithm is presented in [9]. Assuming mild marginal mean square stability, ref. [10] identifies how much data are needed to estimate an unknown bilinear system up to a desired accuracy with high probability. The limits and optimization algorithms in finite-time linear system identifications for the so-called fixed budget and fixed confidence settings are discussed in [11]. The end-to-end sample complexity for evaluating the robust linear observer's performance under coprime factor uncertainty is examined by [12]. A review of the non-asymptotic theory on system identification can be found in the tutorial [13]. Most recently, a distribution-free, finite-sample data perturbation method for finite-sample identification of linear regression models with residual-permuted sums was discussed in [14]. In the following, we outline the background and introduce additional notations in Section 2. We show how to find \(h[n]\) from \(H_{0}[k]\) for the cases of \(L\) and \(N\) being coprime in Section 3 and for general cases in Section 4. In demonstrating the equivalence of \(h[n]\) and \(H_{0}[k]\), we introduce two DFT pairs. Applications are discussed in Section 5. The main points are summarized in Section 6.
# Signal Detection Based on Separable CNN for OTFS Communication Systems
## 4 Proposed SeCNN-OTFS Based Signal Detection Method  
### Separable Convolutional Neural Network  
Depthwise separable convolution is an efficient convolutional operation that decomposes a standard convolution into two separate steps: depthwise convolution and pointwise convolution. This decomposition significantly reduces computational complexity and the number of trainable parameters.  
Depthwise convolution operates independently on each input channel. For an input feature map of size \(H_{\text{in}}\times W_{\text{in}}\times C_{\text{in}}\), depthwise convolution applies \(C_{\text{in}}\) kernels of size \(K\times K\times 1\), with each kernel convolving over a single channel. The output feature map has a size of \(H_{\text{out}}\times W_{\text{out}}\times C_{\text{in}}\). This step extracts spatial features, but does not allow information exchange between channels.  
Pointwise convolution then uses \(C_{\text{out}}\) kernels of size \(1\times 1\times C_{\text{in}}\) to linearly combine the depthwise outputs across channels, projecting them to the desired number of output channels. The size of output feature map becomes \(H_{\text{out}}\times W_{\text{out}}\times C_{\text{out}}\). This step enables inter-channel information fusion and dimensionality transformation.  
The computational cost of standard convolution is given by the following:  
\[\mathcal{O}(K^{2}\cdot C_{\text{in}}\cdot C_{\text{out}}\cdot H_{\text{out}} \cdot W_{\text{out}}). \tag{8}\]  
The computational cost of depthwise separable convolution includes both depthwise and pointwise parts:  
\[\mathcal{O}_{\text{depthwise}}=K^{2}\cdot C_{\text{in}}\cdot H_{\text{out}} \cdot W_{\text{out}},\quad\mathcal{O}_{\text{pointwise}}=C_{\text{in}}\cdot C _{\text{out}}\cdot H_{\text{out}}\cdot W_{\text{out}}. \tag{9}\]  
Therefore, the total computational cost of depthwise separable convolution compared to standard convolution is reduced by the ratio of \(\frac{1}{C_{\text{out}}}+\frac{1}{K^{2}}\). When \(K=3\) and \(C_{\text{out}}=256\), the computational cost is reduced to approximately \(\frac{1}{8}\) to \(\frac{1}{9}\) of the standard convolution. Similarly, the number of parameters is reduced from \(K^{2}\cdot C_{\text{in}}\cdot C_{\text{out}}\) to \(C_{\text{in}}\cdot(K^{2}+C_{\text{out}})\).
## 1 Introduction  
There are many established techniques used for clinical assessment of skin pathology of skin tissues, such as photo-acoustic imaging [1], optical coherence tomography [2], confocal reflectance microscopy [3], and high frequency ultrasound [4]. However, no imaging modality has been established for studying skin tissue depths beyond 1.5 mm from the surface. For inherently 3D imaging techniques, the assessment of skin lesions has proved to be difficult with conventional Magnetic Resonance Imaging (MRI), computerized tomography (CT) and positron emission tomography (PET) scanning [5].  
Melanoma is the 3 rd most commonly diagnosed cancer in Australia, with an annual incidence of 64.4 per 100,000 [6]. The current death rate is about 10% if treated and nearly 100% if left untreated [6; 7]. Out of an abundance of caution in ensuring compete removal, \(\sim\)60% of cases have more tissue than is necessary excised. Larger excisions, however produce greater morbidity leading to a 20% increase in non-melanoma post-surgical mortality [8]. The precise depth of lesions is clinically important to assess the prognosis and also to plan surgical treatment [8]. The current staging of melanomas includes estimation of thickness, with stage T\({}_{1}\) is \(<\) 1.0 mm, T\({}_{2}\) is 1.0-2.0 mm, and T\({}_{3}\) 2.0-4.0 mm with melanomas greater than 4.0 mm thickness classified as stage T\({}_{4}\)[8; 9; 10]. An accurate thickness measurement would reduce the chances of residual melanoma remaining and would help to avoid unnecessarily large surgical resection. The more extensive the resection, the greater the morbidity [8], thus, the problem is not merely the curing of melanoma, but also minimal post-surgical morbidity and deformity. Characterisation and imaging of up to several millimeters of skin is of paramount importance and can be only achieved by combination of different optical techniques over the UV-vis-IR-THz spectral ranges. The permittivity (complex refractive index) at this wide spectral range should be known for different materials encountered at the surface (0-4 mm) of skin.
###### Abstract  
Improving the accuracy of parking space recognition is crucial in the fields for Automated Valet Parking (AVP) of autonomous driving. In AVP, accurate free space recognition significantly impacts the safety and comfort of both the vehicles and drivers. To enhance parking space recognition and annotation in unknown environments, this paper proposes an automatic parking space annotation approach with tight coupling of Lidar and Inertial Measurement Unit (IMU). First, the pose of the Lidar frame was tightly coupled with high-frequency IMU data to compensate for vehicle motion, reducing its impact on the pose transformation of the Lidar point cloud. Next, simultaneous localization and mapping (SLAM) were performed using the compensated Lidar frame. By extracting two-dimensional polarized edge features and planar features from the three-dimensional Lidar point cloud, a polarized Lidar odometry was constructed. The polarized Lidar odometry factor and loop closure factor were jointly optimized in the iSAM2. Finally, the pitch angle of the constructed local map was evaluated to filter out ground points, and the regions of interest (ROI) were projected onto a grid map. The free space between adjacent vehicle point clouds was assessed on the grid map using convex hull detection and straight-line fitting. The experiments were conducted on both local and open datasets. The proposed method achieved an average precision and recall of 98.89% and 98.79% on the local dataset, respectively; it also achieved 97.08% and 99.40% on the nuScenes dataset. And it reduced storage usage by 48.38% while ensuring running time. Comparative experiments on open datasets show that the proposed method can adapt to various scenarios and exhibits strong robustness.  
Keywords:autonomous vehicles; free space recognition; tightly coupled SLAM; polarized Lidar odometry; parking space annotation +
Footnote †: journal: _Applied_
###### Abstract
In addition, sport technical classifiers perform the technical evaluation through tests to assess basic physical skills (coordination, balance, agility, strength, power, speed) and football-related skills from the individual point of view (ball kicks, passes, and shots, dribbles and controls, and the goalkeeper's technique) and collective (in reduced games) to assign a sport class [1]. Over the last decade, there has been a considerable increase in the number of football players with CP, as well as the number of events organized at the regional, national, and international levels. Currently, 88 countries worldwide actively participate in CP football. Additionally, there are 93 male teams (compared to 84 in 2020) and 94 female teams (compared to 28 in 2020), according to data Worldwide Participation of IFCPF. The rise of this para-sport has also been reflected in the scientific context, with a large increase in publications on football with CP [8; 9; 10]. Research into CP football has extensively explored various aspects, including specifying the anthropometric and performance characteristics of the players [11], assessing physical performance through functional tests [12; 10; 13; 14; 15], and assessing the validity and reliability of different tests [6; 16; 17]. Specifically, it has been shown that football players with CP exhibit lower distances covered (total, high intensity, and sprint distance) than non-disability players [18] in match-play. However, they covered a greater total distance (4%), more distance walking (38%), and more distance in medium-intensity running (5%), and maintained a similar maximum velocity (0.4%) to conventional F7 players [19]. Additionally, significant differences have been found between match demands and small-sided game demands (2 vs2+Goalkeeper, pitch size 27 \(\times\) 19 m) in football players with CP [20]. Understanding both the profiles of para-footballers with CP and the specifics of relevant tests is essential. Additionally, knowledge of their responses during match-play is crucial for effective training planning. The availability of this information would not only aid classifiers in making informed decisions during the classification process but also assist coaches in optimizing training for competitions. Based on the aforementioned information, the main aim of this systematic review was to summarize the physical and physiological responses encountered by para-footballers with CP during the competition according to sport classes.
## 3 Gene Discovery and Gene Expression Analysis in the Pre-NGS Era
### Other Plant Metabolites Produced by Endophytic Fungi
#### 4.6.2 Rotenoids: Rotenolone and Tephrosin  
The two rotenoids rotenolone and tephrosin (Figure 1), previously reported in plants belonging to the genera _Derris_ and _Tephrosia_, were found to be produced by the endophytic fungus _Pseudofisicocum stromaticum_ isolated from the timber tree _Myracordruon urundeuva_[79]. In plants, rotenoids are formed from flavonoids in a multi-step pathway where chalcone isomerase (CHI) and CHS play a role as key enzymes [98]. During the investigation of the _P. stromaticum_ genome, the gene encoding for a CHI-like protein was identified, elucidating the ability of this fungus to produce plant-related rotenoids, while no CHS homologue could be found. Protein alignment analysis and maximum likelihood phylogenetic reconstruction analysis have revealed that fungal CHI-like enzymes have more than one substrate specificities since they recognize chalonoids other than (2S)-naringenin [79].