esa-sceva/satcom-chunk-collection huggingface dataset:
# Path Planning for Localization of Radiation Sources Based on Principal Component Analysis
## 2 Proposed Method
### Overview
Because of simple back projection, each projection space pixel has a value that indicates how many times it is overlapped. Details regarding the projection space pixel value are provided in Section 2.3 and Equation (2). When multiple radiation sources are positioned, the k-means algorithm is applied to separate the area of the reconstructed distribution corresponding to each radiation source. Then, to localize each source, the weighted average is calculated relative to each separated area. The details are described in Section 2.5.
# Research on the Performance of an End-to-End Intelligent Receiver with Reduced Transmitter Data
## 2 System Model
### Wireless Channel  
For the Alamouti space-time encoder with two transmitting antennas, assume that the number of antennas at the receiving end is two, while the model is shown in the following Figure 3. When the data sent by the transmitter arrives at the receiving end through the wireless channel, the data received at the first slot receiving end is shown below:  
\[y_{1}=(h_{1}s_{1}+h_{2,j}s_{2})+n(1) \tag{2}\]  
Figure 1: Basic structure of MIMO transmitter.  
Figure 2: Alamouti space-time coding principle.  
The data received in the next time slot is shown below:  
\[y_{2}=(-h_{1}s_{2}^{*}+h_{2}s_{1}^{*})+n(2) \tag{3}\]  
In the above equation, \(y_{1},y_{2}\) denote the data received by the different antennas, \(h_{1},h_{2}\) denote the fading coefficients of the wireless channel, and \(n(1)\), \(n(2)\) denote the complex random variables of interference and noise.
# Optimization of Signal Detection Using Deep CNN in Ultra-Massive MIMO
## 2 Materials and Methods
### Machine Learning Method
#### 2.3.2 Regularized Extreme Learning Machine (RELM)  
While the ELM has proven effective in numerous applications, the implementation of the \(\tilde{N}\) method is recommended to circumvent overfitting and underfitting. As the study in [22] suggests, smaller norm parameters in the RELM designed for SLFNs with a sigmoid function can lead to improved generalization. This equation is versatile and applicable to  
Figure 3: An illustration of an extreme learning machine.  
various activation functions and hidden neuron nodes like kernels, specifically to resolve challenges in the \(l_{2}\) norm of \(\beta\). Consequently, RELM can be described as a method by which  
\[\begin{array}{c}\underset{(\beta_{0},\beta)\in^{\widetilde{\mathrm{N}}x1}}{ \mathrm{minimize}}\frac{C}{2}\|H\beta+\beta_{0}-t\|_{2}^{2}+\frac{(1-a)}{2}\| \beta\|_{2}^{2}+a\|\beta\|_{1}\end{array} \tag{15}\]  
When just the \(l_{2}\) norm penalty (\(\alpha=0\)) with \(\beta_{0}=0\) is considered, the RELM formula is given where \(C\) and \(\beta_{0}\) are regularization parameters.  
\[\hat{\beta}=(H^{H}H+\frac{I}{C})^{-1}H^{H}t \tag{16}\]
## 2 Background
### IoV  
In order to support requests arising from the new era of mobility, such as advanced driving assistants or autonomous driving, systems of intelligent vehicles are moving from communication models in VANETs toward the Internet of Vehicles (IoV) [28].  
An IoV network may use concepts similar to its predecessor--VANETs. This can be observed mainly in communication models such as V2V, V2I, and others. The IoV, however, is not limited only to a few specific models; on the premise of the Internet of Things (IoT), it employs vehicle-to-everything communication (V2X). Due to the V2X communication model, the IoV may use various technologies for connection, including wireless LAN (WLAN) networks and cellular networks such as 5G or Bluetooth [29].  
The limitations of VANETs in computation and storage are becoming overcome thanks to the employment of remote computation in the form of cloud, edge, and fog computing. This enables the IoV to process big data collected from the environment and consecutively improve the system's performance. The IoV networks can face security issues similar to those observed in the VANET; however, there are attempts to alleviate the constraints through the employment of novel concepts such as blockchain [30] or deep learning algorithms [31], wherein each is enabled by a higher communication throughput and generally better computation and storage resources. The four main kinds of IoV applications for intelligent transportation are safety-based, efficiency-based, comfort-based, and information/entertainment-based applications. By identifying potential collision scenarios within the transportation system, safety-based IoV applications seek to avoid or reduce the number of accidents. When collision avoidance system (CAS) information is shared with nearby vehicles in the IoV, these are known as the cooperative collision avoidance system (CCAS). IoV applications that focus on comfort try to inform drivers in a way that will make the journey enjoyable and comfortable. This could contain data on the weather, route navigation, parking lot information, and the locations of information kiosks for tourists, restaurants, gas stations, and other facilities. Applications that focus on efficiency try to increase the mobility of vehicle items inside the IoV network. The timing of traffic signals at intersections based on the volume of traffic to minimize waiting times is an example application. Applications based on information and entertainment are intended to provide drivers and passengers with information about amusement. Access to the internet and other file-sharing services would fall under this category. Due to the dynamic and evolving nature of the vehicle objects in the IoV network, it is currently difficult to provide vehicles with access to the global internet and maintain the information [32].
# Central Kurdish Text-to-Speech Synthesis with Novel End-to-End Transformer Training
## 3 Related Work for the Kurdish Language
### Model Architecture
#### 4.6.3 Wave Decoder  
Our wave decoder architecture is modeled after WaveNet [2], as illustrated in Figure 6. It comprises a transposed 1D convolution with a filter size of 64, along with 30 dilated residual convolution blocks. The skip channel size and kernel size of the 1D convolution are configured to 64 and 3, respectively. The wave decoder receives a sliced hidden representation \(z\) with a channel size of 256 generated by the wave or text encoder, corresponding to a brief audio clip, as its input. It then utilizes transposed 1D convolution to upsample the slice, aligning it with the length of the corresponding audio clip.  
Similar to prior works [2; 13; 36], we incorporate adversarial training into the wave decoder. The discriminator \(D\) in the adversarial training adopts the same structure and configurations as those of Parallel WaveGAN [36]. \(D\) distinguishes between the waveform \(\hat{x}\) generated by the wave decoder and the ground-truth waveform \(x\). We optimize the wave decoder by incorporating the multi-resolution short-time Fourier transform (STFT) loss in conjunction with the discriminator loss from the least squares generative adversarial network (LSGAN), aligning with the methodology of Parallel WaveGAN [36].
# Translational Echocardiography: The Dog as a Clinical Research Model of Cardiac Dysfunction
## 8 Conclusions
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Technique** & \\ \hline Echocardiographic positioning & **Dogs** are usually imaged in right and left lateral position. The **human** patient should be supine or left lateral decubitus. This will bring the heart away from the sternum. \\ \hline Normal Basic Echocardiographic Views & **Human:** Parasternal long axis, parasternal short axis, apical four chamber, apical four-chamber view, subxiphoid (subcostal), suprasternal view, and IVC views. **Canines:** Four-chamber right-sided parasternal long-axis view, five-chamber right-sided barternal long-axis view, right-sided short-axis view of the left ventricle at the level of the papillary muscles, right-sided short-axis view at the level of the left atrium and aorta. \\ \hline Quantitative assessment of mitral regurgitation & Commonly used in human patients, but seldom utilized in canine patients and practical application is limited (defining EROA and flow convergence shape can be challenging) [60]. \\ \hline Flow convergence area measurement by PISA & A standard gold method in **humans**, not routinely performed in cardiologic evaluation in **dogs** (PISA quantification showed a wide range of RF in a clinical study) [61; 62]. \\ \hline Color flow imaging of the mitral regurgitation jet area & The most commonly used technique for assessing severity in **dogs**. The former method is not used in **humans** as it is not considered reliable for determining the severity of mitral insufficiency. \\ \hline Measurement of Left Ventricular Volume & M-mode echocardiography is widely used in **canine** cardiology, but its utility is debated. American Society of Echocardiography recommends against using linear measurements in the **human** patient. SMOD is recommended [100; 102; 103]. \\ \hline Quantification of the severity of left ventricular remodeling & **Canine:** AI: Ao ratio, AI diameter indexed to weight, VI: Ao ratio, the normalized internal diameter of the VI at the end of normal diastole. Widely used. **Human:** Cardiovascular magnetic resonance is the gold standard and more accurate than echocardiography [118; 119]. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of Transthoracic Echocardiographic Techniques in Humans and Canines. **Author Contributions:** Conceptualization, C.A.F.D. and I.A.C.Y. ; writing--original draft preparation, C.A.F.D., I.A.C.Y., R.M.G., J.C.H.R., M.F.M.G., S.M.G.C. and I.C.G.R. ; writing--review and editing, C.A.F.D. and I.A.C.Y. ; supervision, C.A.F.D. ; project administration, C.A.F.D. All authors have read and agreed to the published version of the manuscript. This research received no external funding. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Not applicable. **Acknowledgments:** The authors gratefully acknowledge [PERSON], [PERSON], [PERSON], and the faculty and administrative personnel of the Veterinary Sciences Research Institute, Autonomous University of Baja California. The authors declare no conflict of interest.
# A Perspective on Ethernet in Automotive Communications--Current Status and Future Trends
## 4 TSN Standards for Automotive Communications  
The TSN family is a set of standards, a flexible \"tool-set\" developed by the IEEE 802.1 Working Group that adds a number of novel features to IEEE 802.1Q switches. Each TSN standard introduces protocols and mechanisms that provide a given property, such as ultra-low latency, reliability, clock synchronization robustness, etc. Depending on the application context, the single TSN standards can be flexibly combined with each other to build a solution that fulfills all the requirements of a given application. Figure 2 shows the main TSN standards that are relevant for in-car communications.
## 4 Results and Discussions
### Magnetic Resonance Images  
The pore water distributed space in the thawed soils is discerned with NMRI scans visually according to the \(T_{2}\)-weighted images. In this study, the transverse cross-sectional images of the saturated soils were captured to investigate the variations of pore water signal intensities of thawed soils after the artificial freezing process. Three \(T_{2}\) weighted cross-sectional images at different heights (namely the upper, middle and bottom of the tested sample) as well as the profile of the saturated sample were observed respectively, as shown in Figure 8. The pore structures reflected in pseudo-color images show a rising trend in areas of bright spots, revealing an enhancement of water-retainment capacity as the freezing temperature drops. It can be seen from Figure 8a that after the freezing treatment at \(-5\)\({}^{\circ}\)C, the \(T_{2}\)-weighted image is comparatively dark. Besides, the fringe areas of the image are apparently brighter than the central areas, indicating that the pores at the soil brims contain much free water. Figure 8b shows that after the freezing treatment at \(-10\)\({}^{\circ}\)C, a few bright spots appear at the central areas of the image, which reflects the formation of secondary pores within the thawed soils. The changes in pore structures of soils after frozen at \(-10\)\({}^{\circ}\)C are presented in the Figure 8c, which shows that the freezing and thawing effect at \(-20\)\({}^{\circ}\)C has reinforced the expansion of pores within soils and consistently damages pore structures, especially for the pores in the centre of samples. When the freezing temperature of soils dropped to \(-30\)\({}^{\circ}\)C, the bright areas of pore water gradually covered the entire cross-sections at different heights and the profile of the tested sample (Figure 8d). The heterogeneous features of pore water distributions in the image decreased distinctly, which proved that the pore structure in thawed soil became more homogeneous at an extremely low temperature. Thus, the results indicated that the structural damage of thawed soils was aggravated as continuously dropping the freezing temperature. Thus, the developing trend of the pore structure was verified based on NMRI measurements from a visual perspective.
# Technology Acceptance in Healthcare: A Systematic Review
## 2 Literature Review
Based on the theory of planned behavior (TPB), the results revealed that attitudes and perceived behavioral control factors significantly influence the intention to use the e-CME solution. The study included only one user group (i.e., GPs), with a limited sample compared to the optimum sample for factor analysis. Further, [PERSON] et al. [29] have discussed the acceptance of interactive documentation systems by therapists and patients in an addiction therapy center in Germany. The study relied on extending the UTAUT model with the attitude construct. The key results indicated that attitude is the strongest predictor of intention to use. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Source**} & **Multiple** & **Multiple** & **Databases** & **Coverage** & **Aim** \\  & **Acceptance** & **Models** & **Technologies** & & & \\ \hline \multirow{2}{*}{[30]} & \multirow{2}{*}{-} & \multirow{2}{*}{✓} & 16 datasets (names & Before July 2008 & Literature review of 20 articles to study \\  & & & not reported) & (not clearly) & the application of TAM in the \\  & & & & reported) & healthcare domain. \\ \hline \multirow{4}{*}{[40]} & \multirow{4}{*}{-} & \multirow{4}{*}{-} & PubMed, EMBASE, CINAHL, & \multirow{4}{*}{199–2009} & Systematic review for 60 studies to explore the barriers and facilitators to implementation. \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\ \hline \multirow{4}{*}{[41]} & \multirow{4}{*}{-} & \multirow{4}{*}{✓} & MEDLINE, EMBASE, CINAHL, & \multirow{4}{*}{19–0–2007} & Systematic review for 101 studies to explore the factors that facilitate or limit the implementation of ICTs in clinical settings. \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\ \hline \multirow{4}{*}{[42]} & \multirow{4}{*}{-} & \multirow{4}{*}{✓} & MEDLINE, EMBASE, CINAHL, & \multirow{4}{*}{19–5–2009} & Systematic review for 37 review studies to identify the barriers and facilitators to be-health implementation and outstanding gaps in the literature. \\  & & & & & \\  & & & & & \\ \hline \multirow{4}{*}{[43]} & \multirow{4}{*}{-} & \multirow{4}{*}{✓} & Science Direct, Springer, & \multirow{4}{*}{19–9–2010} & Qualitative review to analyze 50 articles to study the possible predictors of TAM. \\  & & & & & \\  & & & & & \\ \hline \multirow{4}{*}{[33]} & \multirow{4}{*}{✓} & \multirow{4}{*}{✓} & ACM Digital Library, CINAHL, & \multirow{4}{*}{19–5–2009} & Systematic review for 16 studies provides an overview of factors that influence the acceptance of electronic technologies that support older adults. \\  & & & & & \\ \cline{1-1}  & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Previous review studies on technology acceptance in healthcare.
## 2 Materials and Methods
### Image Analysis
The normalized flow displacement and jet angle were determined by first constructing a cross-section perpendicular to the centerline at every millimeter. Next, at each cross-section, the normalized flow displacement (distance between the centerline point and center of velocity normalized for the vessel diameter times 100%) and flow jet angle (angle between the centerline and mean velocity vector) were calculated as previously described [15]. The maximal normalized flow displacement and flow jet angle within each segment were assessed and used for statistical analysis. For the calculation of vorticity and helicity of the velocity vector fields (\(\underset{V}{\rightarrow}\)), a spatial Gaussian differential operator (\(\underset{V}{\rightarrow}\)) with a standard deviation equal to the reconstructed in-plane voxel size was used. The vorticity (\(\underset{\omega}{\rightarrow}=\underset{V}{\rightarrow}\times\underset{V}{ \rightarrow}\)) norm and absolute helicity (\(H=\mid\underset{V}{\rightarrow}\cdot\underset{\omega}{\rightarrow}\)) were quantified via methods previously described by [PERSON] et al. [9]. The vorticity norm and absolute helicity were both normalized for the reconstructed voxel size, and the mean values within each segment were used for statistical analysis. Since a previous study demonstrated that a threshold of >0.6 for absolute LNH (\(LNH=\frac{\overset{\overset{\rightarrow}{V}\cdot\omega}}{\left|\overset{ \rightarrow}{V}\right|\mid\overset{\rightarrow}{\omega}}\)) is the optimal criterion for detecting differences between patients and healthy volunteers [13], this threshold was used in our study to select volumes with increased absolute LNH. The volumes with increased absolute LNH were assessed and used for the statistical analysis. The in-house developed software can be made available for scientific collaboration per reasonable request. To derive 3D WSS maps for each of the five systolic phases, CAAS MR Solutions v5.2 was used. At each point of the 3D WSS map, WSS\({}_{\text{mag}}\) was calculated as well as the WSS\({}_{\text{ax}}\) and WSS\({}_{\text{cir}}\) components. The WSS\({}_{\text{angle}}\) between the WSS\({}_{\text{cir}}\) and WSS\({}_{\text{ax}}\) was quantified for each point as previously described [19]. For each anatomical segment, the mean WSS\({}_{\text{mag}}\), WSS\({}_{\text{ax}}\), WSS\({}_{\text{cir}}\), and WSS\({}_{\text{angle}}\) were used for statistical analysis.
## 6 Conclusions  
In this paper, a robust broadside frequency-invariant beamforming method is proposed for a differential loudspeaker line array. We present a method to calculate the ideal broadside beampattern combining various criteria, namely, null information, maximizing the directivity factor, and achieving the beampattern with equal sidelobes. We derive the modal domain target beampattern of a given ideal broadside differential beampattern. We propose a robust modal matching method with Tikhonov regularization to design the broadside beampformer in the modal domain. Simulations and experimental results show that the proposed method outperforms the null-constrained and MM-JAD methods, achieving frequency-invariant broadside beamforming over the range of 250 Hz-4 kHz. It is worth noting that the above analysis and results are obtained under free-field conditions. The proposed method was only validated at a distance of 3 m in an anechoic chamber. In a regular room, the directional performance of the proposed method may degrade due to the influence of room reflections. Future work will verify the proposed method in more complex and real-world environments.  
Conceptualization, Y.Z.; methodology, Y.Z.; software, Y.Z.; validation, Y.Z. and H.W.; formal analysis, Y.Z. and Q.Z.; investigation, Y.Z.; resources, Y.Z.; data curation, Y.Z. and H.W.; writing--original draft preparation, Y.Z.; writing--review and editing, Q.Z.; visualization, Y.Z.; supervision, Y.Z.; project administration, Y.Z. and H.W.; funding acquisition, Y.Z. and H.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Research Project of Anhui Educational Committee (Grant No. 2023 AH050422), Open Project of Anhui Digital Intelligent Engineering Research Center for Agricultural Products Quality Safety under the Peak Cultivation Discipline of Electronic Information (Grant No. APDI202304), and Open Project of Anhui Province Photovoltaic Industry Common Technology Research Center under the Peak Cultivation Discipline of Electronic Information (Grant No. AHPV202303).  
Not applicable.  
Data Availability Statement: Data are available upon request.  
The authors declare no conflicts of interest.
# Stochastic and Higher-Order Effects on Exploding Pulses
## 3 Exploding Dissipative Solitons and Higher-Order Effects
### Results  
Equation (8) without considering higher-order effects is the optical version for the CQGLE studied in Section 2. Coefficients can easily be converted from Equations (1)-(8): \(\delta=\mu\); \(\epsilon=\beta_{r}\); \(1=\beta_{i}\); \(\mu=\gamma_{r}\), \(\
u=-\gamma_{i}\); \(\beta=D_{r}\); and \(\frac{D}{2}=D_{i}\). In optics, to use \(\epsilon\) as a control parameter is meaningful because \(\epsilon\) is related to the pumping power.  
As in Section 2, to solve Equation (8), we used a pseudo-spectral split-step numerical method along to \(N=8192\) Fourier modes, \(dt=0.01\) and \(dz=0.004\). Our parameters are: \(\delta=-0.1\), \(\beta=0.125\), \(\mu=-0.1\), \(\
u=0.6\) and \(\epsilon\sim 1.0\).  
For \(\epsilon\) around \(1.02\) (and \(\delta=s=\tau_{R}=0\)), the energy \(Q(z)=\int_{0}^{T}|\psi|^{2}d\tau\) exhibits different maxima \(Q_{max}\), which can be plotted in a logistic map (see Figure 4a), giving the aspect of a complex picture, natural consequence of explosive chaotic behavior. While in Figure 5a, one can observe the evolution of the amplitude \(|A|\) in a \(\tau-z\) plot for \(\epsilon=1.02\). There, one can notice the random distribution of locations in \(z\) between explosions (the analogue of the random distribution of times between explosions in Equation (1)). The distribution of locations in \(z\) obeys a narrow distribution centered around a mean value.  
Figure 4: (**a**) Logistic map for \(Q_{max}\) without including higher-order effects. Complex picture, natural consequence of explosive chaotic behavior; (**b**) Logistic map for \(Q_{max}\) including higher-order effects. One can observe a window without chaos around \(\epsilon=1.022\).  
Considering \(T_{R}\sim 3\) fs [37] and \(T_{0}\sim 0.1\) ps, we estimate \(\tau\sim 0.03\), and noticing that \(\omega_{0}T_{0}\sim N\sim 100\) (number of cycles tangled by \(|A|\)), \(s\) can also be estimated as \(s\sim 0.01\). Now, following the same above procedure for the CQGLE, but including higher-order nonlinear and dispersive effects (\(\delta_{3}=0.016\), \(s=0.009\), \(\tau_{R}=0.032\)), we notice the appearance of windows corresponding to non-chaotic behaviors (see Figure 4b). Around \(\epsilon=1.022\), one can observe period-halving bifurcations leading to order, and period-doubling bifurcations leading to chaos. Around \(\epsilon=1.019\) and \(\epsilon=1.024\), the limit of the ratio of distances between consecutive bifurcation intervals tends to \(4.6\) (close to the Feigenbaum constant).  
Figure 5b shows the evolution of the amplitude \(|A|\) in a \(\tau-z\) plot, for \(\epsilon=1.02\), considering higher-order nonlinear and dispersive effects. In contrast to Figure 5a, we see that there is a fixed distance in \(z\) between explosions and explosions repeat exactly after a period. We are in the presence of periodic explosions.