esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Solution for Reconstructing More Details High Dynamic Range Image
## 2 Related Work
### Patch-Based Method  
During the same period from 2010 to 2015, another group of scholars used the patch-based method to reconstruct HDR images [8; 9]. In 2012, [PERSON] et al. [8] optimized alignment and reconstruction simultaneously by proposing a patch-based energy minimization approach. In 2013, [PERSON] et al. [9] produced aligned images by utilizing a patch-match mechanism. A lot of calculations are required, and practicality is not good when using this method.
# Central Kurdish Text-to-Speech Synthesis with Novel End-to-End Transformer Training
## 1 Introduction
This involves aligning the prior distribution established by the pre-trained encoder with the posterior distribution of the text encoder within latent variables. To augment the expressive capabilities of our method and enable the synthesis of high-fidelity speech waveforms, we employ adversarial training [6] in the waveform reconstruction. By aligning latent distributions and integrating the stochastic duration predictor, our method facilitates the real-time generation of natural Kurdish audio speech. The proposed model is trained directly to maximize the log-likelihood of speech, and this is coupled with the alignment process. One of the crucial steps in developing a TTS system is the creation of a high-quality speech corpus. Developing models that capture the prosodic patterns of Kurdish is essential for creating natural-sounding synthesized speech. Other contributions of this research work can be summarized as follows:  
* A novel end-to-end method for Kurdish text-to-speech conversion based on a VAE framework is introduced. The proposed VAE effectively maps input waveforms to a latent space representation and reconstructs them. * A robust training procedure is developed to align the latent variables of the text encoder with those of the pre-trained waveform encoder of the VAE. This involves ensuring that the prior distribution established by the pre-trained encoder matches with the posterior distribution of the text encoder within latent variables. * The proposed KTTS directly regenerates waveforms from input text by bypassing the intermediate stages required to create mel-spectrograms or linguistic features. * A new dataset comprising aligned pairs of Central Kurdish text sequences and corresponding audio recordings is curated. This dataset serves as a valuable resource for advancing research in Kurdish text-to-speech synthesis. The rest of this article is organized as follows: Section 2 offers a review of the TTS literature, Section 3 explains the methodology for KTTS, and Section 4 details the experimental setup, including the dataset and training parameters. In Section 5, we present the results and a discussion, and finally, Section 6 provides conclusions based on our findings.
## 2 Electrochemical Model of PEM Fuel Cell  
### Operating Principles of PEM Fuel Cell  
The PEM fuel cell consists of three basic elements, _anode_, proton exchange membrane, and _cathode_. The hydrogen gas ionizes producing protons and electrons. The reaction of the anode site is given as follows:  
\[Anode\ (oxidation):\ 2H_{2}\rightleftharpoons 4H^{+}+4e^{-}. \tag{1}\]  
At the _cathode_ site, a reduction process occurs, where oxygen molecules react with electrons from the circuit and protons from the electrolyte (proton exchange membrane) forming water. Equations (2) and (3) describe the reaction at the _cathode_ site and the overall reaction of the cell.  
\[Cathode\ (reduction):\ 4H^{+}+4e^{-}+O_{2}\rightleftharpoons 2H_{2}O \tag{2}\]  
\[Overall\ reaction:\ 2H_{2}+O_{2}\rightleftharpoons 2H_{2}O. \tag{3}\]
## 2 Materials and Methods
### Analytic Signal  
By centering anomalies over the magnetic body and having peaks at the borders of wide bodies [13], the analytic signal transform simplifies the magnetic signal of anomalies [82]. As a result, a straightforward relationship between the converted data and the magnetic bodies' geometry is seen. Analytic signal transform, in contrast to RTP, combines derivative calculations to provide an attribute that is independent of the inclination and magnetization direction of the Earth's main field [83; 84; 85; 86].
# Make Patient Consultation Warmer: A Clinical Application for Speech Emotion Recognition
###### Abstract
Therefore, this study employed MFCCs as eigenvalues for voice signals. Voice quality comprises the physical properties of sound, such as jitter, shimmer, and harmonics to noise ratios. MFCCs, which were proposed by [PERSON] and [PERSON] in the 1980s [16], have been universally applied in automatic speech and speaker recognition. Before MFCCs were introduced, linear predictive coefficients and linear prediction cepstral coefficients were the primary eigenvalues employed in automatic speech recognition. Artificial neural networks (ANNs) have been frequently used to solve classification problems by the structure consists of an input layer, an output layer, and multiple hidden layers. These layers are composed of nodes. The number of nodes in the input and output layers depend on the types of data representation marks, and the number of nodes in the hidden layers varies according to user needs. Each layer is connected to the next according to an initial, randomly selected weight. After a sample is selected from the training data set, the sample's value is loaded into the input layer and forwarded to the next layer. In the output layer, the weight is updated through backpropagation. Once the training is complete, new data are classified according to their weights. Emotional voice signals can be identified from a set of retrieved data and employed MFCCs to identify emotions from the voice data [17]. The emotions were categorized as happiness, anger, or sadness. Many algorithms have been designed for feature extraction and speech signal testing. [PERSON] et al. [17], developed function-based MFCCs for the extract of features from voice samples, and the standard deviation of each emotion category was defined according to the means of MFCCs. In their research, SER can attain 80% accuracy based on 60 experiments. [PERSON], [PERSON], [PERSON], and [PERSON] (2015), combined mel-frequency cepstrum eigenvectors with an ANN classifier to achieve an 85.7% classification accuracy [18]. However, voice sample training and test rates should be further improved to overcome erroneous classification. The goal of this study was to develop an SER instrument using ANN and speech processing technology and to integrate other instruments to identify medical physicians' and patients' emotions in clinics.
# Characterization of Carrier Phase-Based Positioning in Real-World Jamming Conditions
## 4 Analysis and Results
### Analysis 3: Effects of Using Multiple Constellations
If a GPS + GLO and a GPS + Galileo solution both have small errors, why would the 3 CS have 3D errors up to 1.8 m? It should be possible to handle the additional constellation in a way that does not increase the error so dramatically. The same is seen in Case 2, where the reintroduction of Galileo at 17:42 makes the 3 CS worse than both 2 CS for a short time. The two issues presented here are basically artifacts of the PPK processing, and not direct consequences of the jamming incident. Disregarding the specific results, however, what is seen in this analysis is that the processing method may actually produce unnecessary errors in the positioning solution, if the data are not handled carefully. _Summary: In general, using more constellations will naturally provide better positioning results during jamming, due to an increased number of satellites available._  
_It is also clear that, while jamming may cause large errors, a solution using several frequencies and constellations will be very robust. Only in the short period at approximately 17:41 in Case 2, will all solutions have a significant error. Otherwise, it is possible at all times to stay within \(RTK\) accuracy despite jamming at the reference station._It was further shown that the processing method matters. If RTK accuracy is to be achieved, then care must be taken when doing carrier phase-based positioning during jamming incidents. For this specific setup, it was noted that, during heavy jamming, any additional constellations included in a GPS solution should be prioritized by satellite availability and C/N\({}_{0}\) values in order to select the least affected constellation first. Further, when introducing jamming-affected constellations to the solution (after a complete loss of integer fix), a weighting scheme or a hold-out/initialization period should be used in order to minimize (or negate) a negative impact on the positioning solution. As a final note, the most robust solution would probably be achieved by using only observations from constellations free of jamming (like GLONASS in jamming Case 1). A similar conclusion is made by [15] using a code-based receiver. For this paper, it is not possible to document the benefits of using only jamming-free constellations, as the capabilities of the current processing software are limited.
# An Empirical Study of a Trustworthy Cloud Common Data Model Using Decentralized Identifiers
## 1 Introduction
This paper describes the application of decentralized identifiers (DID) to prove user identity in the cloud CDM environment. DIDs' transactions are configured using Hyperledger Indy, and CDM subjects are configured as agents based on Hyperledger Aries [14; 15] to evaluate the behavior of CDM use cases. Here, we design and prototype a DID-based user-centric identification system to support the research of registered researchers in the cloud CDM environment involving multiple hospitals and research institutions. The prototype is an extension of the delivery method of encrypted CDM using DID and provides the identification system by limiting the use case of the CDM data of the researcher registered in the cloud CDM. The prototype constructed for agent-based PoC (proof of concept) is utilized for enhanced security of researcher use of ophthalmic CDM data. In this paper, the CDM identity schema and its definition are described by limiting the identity of main entities. This proposed method aspires to provide a unified and efficient data access control policy management framework. It provides strong security and ensures both the integrity and the availability of CDM data. It aims to build upon and improve existing data governance processes between different organizations, translating the information sharing policies they already apply in their current operational interactions into electronically enforceable rules embedded in credentials. The main contributions of our work can be summarized as follows:DID-based user-centric identification is the first to approach supporting researchers autonomously with the identity verification with a verified proof without a third parity having central authority in the cloud CDM. 2. We propose and solve the service model that extends the DID basic model in order to solve the structural problem where it is difficult to participate in external researchers in the hospital situation related to IRB approval. 3. We validate user access control by applying the DID service model in the safe data transfer process between hospitals in Korea. 4. Our service model provides high interoperability by operating the prototype of identity proof using the standard messaging environment using DIDComm.
# Industrial Dust Explosions. A Brief Review
## 2 Dust Explosion Prevention and Mitigation
### Mitigation of Dust Explosions Initiated in Spite of Preventive Measures
#### 2.3.4 Dust Explosion Venting  
_Main principle_  
Dust explosion venting is a method of preventing the pressure being developed by a dust explosion inside a compartment/process unit from exceeding the pressure that the compartment can withstand. This is accomplished by providing a pressure relief opening in the compartment wall. Under normal process conditions, the opening remains closed by a comparatively weak cover that will burst or open in the event of a dust explosion inside the compartment.  
_Parameters influencing the sizing of dust explosions vents_  
* Volume of compartment.
* Length/diameter ratio of compartment.
* Maximum over-pressure \(P_{\text{red}}\) that the compartment wall(s) can withstand.
* Static opening over-pressure \(P_{\text{stat}}\) of vent cover.
* Mass of vent cover.
* Burning rate of dust cloud.  
Explosion venting prevents the rupture of the entire compartment, in which the explosion takes place. However, significant hazards remain. These include:  
* Ejection of strong flame jets and flame balls from the vent opening.
* Emission of blast waves from the vent opening.
* Reaction forces on the equipment caused by the venting process.
* Emission of solid objects (vent panels and other possible objects).
* Emission of toxic combustion products.  
_Vent ducts and quenching tubes_  
The use of vent ducts is one solution to the flame jet/flame ball problem. This implies that a duct of cross-sectional area at least equal to the vent area is mounted between the vent and a place where a strong flame jet/flame ball will not present any hazard. However, vent ducts will generally increase the resistance to the outflow from the vent. Consequently, adding a vent duct increases the maximum explosion pressure in the vented vessel. However, the performance of vent ducts is complex. The pressure may increase with increasing vent duct length, at least up to some length, depending on where in the system ignition of the dust cloud takes place. Sharp duct bends also tend to increase the maximum explosion pressure in the vented system. In some applications where vent ducts are difficult to implement, the quenching tube, invented by [PERSON] [19], may provide a workable solution to in-house dust explosion venting.
## 2 Methods and Principles
### Response Deformation Method
#### 2.1.2 Longitudinal Response Deformation Method  
The longitudinal response displacement method is based on the basic principle that the deformation of a tunnel structure is affected by the displacement of the surrounding soil. In this method, a foundation soil spring is often used to simulate the interaction between the shield tunnel and the surrounding soil. Specifically, normal and tangential springs are used to simulate the action in different directions. The above is demonstrated in Figure 2. In order to calculate the internal force and deformation of the shield tunnel, the maximum deformation of the surrounding soil under the action of an earthquake is added to the constraint end of the foundation soil spring at the response part of the analysis model via applying displacement.  
Figure 1: Calculation diagram of the response displacement method.  
Based on the longitudinal response displacement method, the beam-spring combination model is used to simulate the lining segments of the shield tunnel and the surrounding soil mass to study the joint-stretching value of every joint of the integrated shield tunnel. The beam unit is adopted as a model to simulate the main body of the sinking tube tunnel. In the model, the foundation soil springs along the axial and lateral directions of the tunnel are set at each node to simulate the effect of the subsoil layer on the integrated sinking tube tunnel. An analytical model of the shield tunnel-joint-foundation system is formed, as shown in Figure 3.
# Research on Key Technology of Wind Turbine Drive Train Fault Diagnosis System Based on Digital Twin
###### Abstract  
Fault diagnosis of wind turbines has always been a challenging problem due to their complexity and harsh working conditions. Although data-mining-based fault diagnosis methods can accurately and efficiently diagnose potential faults, the visibility is extremely poor. In this paper, digital twin technology is introduced into the fault diagnosis of wind turbine drive train systems, and a wind turbine drive train fault diagnosis method based on digital twin technology is proposed, which monitors and simulates the actual operating condition in real-time by establishing a digital twin model of the wind turbine drive train. In addition, an improved variational modal decomposition combined with particle swarm optimization least squares support vector machine (IVMD-PSO-LSSVM) fault diagnosis method is proposed, which not only improves the accuracy of fault diagnosis but also effectively shortens the diagnosis time and strengthens the response speed of the system. Finally, a digital twin system for condition monitoring and fault diagnosis of wind turbine drive trains is developed based on the Unity 3D platform. Experiments show that the proposed IVMD-PSO-LSSVM can accurately identify fault types with an accuracy rate of 99.1%, which is an improvement of 3.4% compared to before. The proposed digital twin model can be used for real-time monitoring of wind turbine vibration data and provide a more intuitive real-time simulation of the wind turbine's operating status. This facilitates quick fault location and enables more accurate and efficient maintenance.  
digital twin; fault diagnosis; wind turbine; least squares support vector machine; variational modal decomposition
## Appendix A  
The main symbols in this paper are shown in Table A1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbol** & **Meaning** \\ \hline \(N_{e1}\) & The number of error points in target area 1 \\ \hline \(N_{e2}\) & The number of error points in target area 2 \\ \hline \(N_{ob}\) & The number of far-field observing points \\ \hline \(N_{c}\) & The number of constraint points \\ \hline \(NS\) & The number of candidate secondary loudspeakers \\ \hline \(NL\) & The number of the selected secondary loudspeakers \\ \hline \(\mathbf{Z}_{pel\_sca}\) & The matrix (dimension \(N_{e1}\times 1\)) of scattered acoustic transfer functions of target area 1 when one plane wave is incident \\ \hline \(\mathbf{Z}_{pel\_sca}\) & The matrix (dimension \(N_{e2}\times 1\)) of scattered acoustic transfer functions of target area 2 when one plane wave is incident \\ \hline \(\mathbf{q}_{p}\) & The strength vector(dimension \(1\times 1\)) of one incident plane wave \\ \hline \(\mathbf{q}_{s}\) & The strength vector (dimension \(NL\times 1\)) of the selected secondary loudspeakers. \\ \hline \(P_{\text{max}}\) & The maximum allowable power of the secondary loudspeakers \\ \hline \(\mathbf{Z}_{sel\_uni}\) & The matrix (dimension \(N_{e1}\times NL\)) of transfer functions from the \(NL\) uniformly placed secondary loudspeakers to target area 1 \\ \hline \(\mathbf{Z}_{sel\_uni}\) & The matrix (dimension \(N_{e2}\times NL\)) of transfer functions from the \(NL\) uniformly placed secondary loudspeakers to target area 2 \\ \hline \(\lambda\) & The regularization parameter \\ \hline \(\mathbf{Z}_{sel1}\) & The matrix (dimension \(N_{e1}\times NS\)) of transfer functions from the \(NS\) candidate secondary loudspeakers to target area 1 \\ \hline \(\mathbf{Z}_{sel2}\) & The matrix (dimension \(N_{e2}\times NS\)) of transfer functions from the \(NS\) candidate secondary loudspeakers to target area 2 \\ \hline \(\gamma_{CLASSO}\) & The sparse penalty parameter in CLASSO-RLS method \\ \hline \(\gamma_{LASSO}\) & The sparse penalty parameter in LASSO method \\ \hline \(NR_{\gamma_{n}}\) & The noise reduction of the entire error points when the sparse penalty is \\ \(\gamma_{CLASSO}=\gamma_{n}\) in CLASSO-RLS method \\ \hline \(NR_{c\_7\_n}\) & The noise reduction of the constraint points when the sparse penalty is \\ \(\gamma_{CLASSO}=\gamma_{n}\) in CLASSO-RLS method \\ \hline \(NR_{\text{c0}}\) & The maximum noise reduction of the constraint points under a certain condition in CLASSO-RLS method \\ \hline \(NR_{\text{max}}\) & The maximum noise reduction of the entire error points in CLASSO-RLS method \\ \hline \(\Delta_{NR}\) & A small value of noise reduction in CLASSO-RLS method \\ \hline \(NR_{ob}\) & The noise reduction of one observing point in criteria of the control performance \\ \hline \(NR_{\text{e}}\) & The noise reduction of the entire \(N_{e1}+N_{e2}\) error points in criteria of the control performance \\ \hline \hline \end{tabular}
\end{table}
Table A1: Main symbols in the simulation.
## 2 Materials and Methods
### Extraction of Spectral Data from Cucumber Leaves and Statistical Analysis  
For the extraction of the spectral information of the cucumber leaves, a hyperspectral camera (Noor Iman Tajhiz Co., Kashan, Isfahan, Iran) at the range of 400-1100 nm was used. More specifically, 327 spectral images uniformly distributed in this range were obtained for each leaf, with a wavelength increment of 3.37 nm. To block out ambient light, the camera was located in an illumination chamber and exposed to two 10-watt tungsten halogen lamps (SLI-CAL, StellarNet Inc., Tampa, FL, USA). The spectral information in the Vis-NIR region was extracted and stored in a laptop. This laptop was a model DELL (DELL Co., Round Rock, TX, USA) with Intel Core i5, 2430 M at 2.40 GHz, 4 GB of RAM, and Windows 10. The original wavelengths were corrected by two methods: multiplicative scatter correction (MSC) in ParLeS software ([PERSON], Curtin University, Bentley, Australia), shown in Figure 3; and the smoothing operation by the Savitzky-Golay (SG) filtering algorithm [22].  
Before creating the regression models, we checked whether the spectral information of the treatments are statistically significantly different or not. The spectral data of the four treatments (control, N\({}_{30\%}\), N\({}_{60\%}\) and N\({}_{90\%}\)) were examined with two statistical tests: ANOVA test [23] and Duncan test [24]. The null hypothesis is that the mean values of the spectral wavelengths are the same for all the categories, and the alternative hypothesis is that they are different. That is, the input to the statistical tests are the 4000 samples, each of them a tuple of 327 values.  
As it is well known, ANOVA test analyzes the null hypothesis for all the classes, while the Duncan test is a multiple comparison procedure between the pairs of classes. The results obtained for the ANOVA test are presented in Table 1, and those corresponding to Duncan test are contained in Table 2.  
The results of these tests indicate that the differences between the classes are statistically significant, both in the ANOVA test (Table 1) and in the Duncan test (Table 2), with a high level of significance. That is, the spectral wavelengths that reflect the presence of  
Figure 3: Sample of the application of spectral preprocessing using ParLeS software.  
nitrogen are affected by the amount of nitrogen contained in the leaves. Thus, this justifies the development of different regression models for each class, since each of them produces distinct spectral features.  
These tests do not prove any evidence about the linearity or non-linearity between the spectral information and the nitrogen content of the leaves. They only indicate that each class produces different spectral data. This is the reason for creating separate regression models. Although we could create a single regression model for all the classes, the statistical tests involve that, since the spectral data are different, more precise results can be obtained with individual models for each category.
# Impact of Silicon Substrate with Low Resistivity on Vertical Leakage Current in AlGaN/GaN HEMTs
## 3 Results and Discussion
Figure 4: Band diagrams of the GaN-on-Si structure when the top surface is forward biased. (**a**) p-type, (**b**) n-type for 0–400 V and (**c**) p-type, (**d**) n-type for 400–600 V.