esa-sceva/satcom-chunk-collection huggingface dataset:
# An Emotion Speech Synthesis Method Based on VITS
## 1 Introduction
On the other hand, the training of the two-stage model is separate, so the connection relies on pre-defined intermediate features, which makes it difficult to learn the hidden representation directly from the previous stage to further improve performance [18]. A complete end-to-end model, on the other hand, does not require separate training, so it avoids these problems to some extent. Second, if the control features of synthesized speech have only one granularity, the result is likely to be boring. In expressive TTS, variation information can be modeled at different granularity. It can be roughly divided into coarse-grained levels of information that reflect global features and fine-grained levels of information that reflect local features. For example, speaker level, paragraph level, and utterance level information are coarse-grained information, while word level, phoneme level, and frame level are fine-grained information [19]. The emotions of human speech are notoriously complex. When an intense emotion is extracted and applied to a new text, the veracity of the voice is often reduced because of the sharp intonation. Selecting a single emotion feature to control will make the emotion expression of synthesized speech decline. In this paper, to solve the above problems, we put forward the Emo-VITS system. Emo-VITS is based on the Variational Inference with adversarial learning for end-to-end Text-to-Speech (VITS) emotional speech synthesis method. VITS is a completely parallel end-to-end TTS method proposed by [PERSON] et al. [18], which connects two modules of the TTS system through latent variables and generates sound audio that is more natural than that generated by the two-stage model. VITS has wider application prospects in the field of emotion speech synthesis because the structure, which is based on VAE, is more controllable than other structures, such as Tacotron. In this paper, Emo-VITS is proposed based on VITS. Emo-VITS has two emotion encoders to encode the extracted reference speech emotion features and transform the synthesized speech emotion features from both global and local perspectives. At the same time, an emotion feature fusion layer was used to integrate the extracted global and local emotion features, following the example of Attention Feature Fusion (AFF) [20]. In the prediction process, when given a reference audio with a given emotion, the Emo-VITS model is able to extract its features and synthesize a speech with that emotional feature, while achieving intensity control of that emotional feature. At the same time, we also cluster the emotional feature vectors of the training data so that the desired type of features can be synthesized without reference to the audio. Emo-VITS also supports the fusion of the global features of one reference voice with the local features of the other reference voice, and then generates the emotional voice with two speech features.
## 4 Tests and Results
### Parameters of the Bio-Inspired Model
#### 4.1.1 OCR Results  
As an example, Figure 10 shows the license plate to be reviewed with and without the application of a bio-inspired retina model. In this image, it is apparent that the bio-inspired retina model adjusts the luminosity and reinforces the contours of the features of interest.  
The results show an improvement in the reading of the samples and present superior performance in the presence of screws or rivets that make reading difficult, and in photographs with a certain inclination. An example of the above can be seen in Figure 11, where the OCR algorithm, using detection without a retina, failed to obtain any digit or letter, whereas in the case with a retina, a license plate reading \"JD GC 57\" was obtained with 83% accuracy. On the other hand, for the same license plate captured from a frontal view, both approaches achieve an accuracy of 83%.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{1}{c}{**Light Regulator**} & \multicolumn{2}{c}{**Filter \(F_{ph}\)**} & \multicolumn{2}{c}{**Filter \(F_{h}\)**} & \multicolumn{2}{c}{**Logarithmic**} \\  & & & & & & **Compressor** \\ \hline \(V_{0}\) & 0.7 & \(\beta_{ph}\) & 0 & \(\beta_{h}\) & 0.2 & \(c_{f}\) & 1 \\ \(V_{max}\) & 255 & \(\alpha_{ph}\) & 1 & \(\alpha_{h}\) & 100 & & \\  & & \(\tau_{ph}\) & 1 & \(\tau_{h}\) & 1 & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters used in the bio-inspired retinal model.  
Figure 9: Representation of the architecture of an SNN. The network consists of an input layer and a processing layer that represents the 8 layers of the CNN architecture. An example image of the digit 8 is taken from the dataset.  
In Figure 12, an example is shown with the presence of two rivets that negatively affect the reading of the photo without a retina (0% success rate). When applying the retina, the license plate reading \"JF FP 14\" was obtained (another case with 83% success).  
However, in Figure 13, the opposite case occurs, where the result obtained without a bio-inspired retina is superior, achieving 100% success, while the retina-based approach yields 0% success. This could be attributed to the fact that the retinal model highlights edges or contours. Therefore, it injects more noise into the image to be analyzed by the OCR algorithm.  
The processing time for each detection is around 50 to 60 milliseconds in the absence of the retinal model, while, when incorporating the bio-inspired retinal model, the processing time is between 55 to 63 milliseconds.
Researchers have focused on understanding how plasma sheath turbulence affects the bit error rate in FSO communication systems, deriving expressions for the wave structure function and scintillation index of light waves propagating through plasma sheaths [38]. This research provides an effective method for analyzing the performance of free-space optical communication systems under the influence of plasma sheath turbulence [39]. Moreover, studies have explored the propagation and polarization characteristics of radially polarized partially coherent vortex beams within anisotropic plasma turbulence [40]. These investigations have determined the beam intensity distribution as it traverses space and analyzed the impact of anisotropic plasma turbulence parameters on the beam's polarization features [41]. Recent examinations have delved into the propagation attributes of partially coherent flat-topped beams and Laguerre-Gaussian beams as they pass through atmospheric and plasma turbulence. The results indicate that partially coherent beams experience less disturbance to their propagation properties compared to fully coherent beams during bidirectional transmission under such conditions [42; 43]. Based on the existing body of research, it has been demonstrated that array beams exhibit significant advantages in disturbance resistance compared to single beams [28; 44; 45; 46]. Building on our previous research, we further investigate the excellent characteristics of partially coherent flat-topped array beams during transmission through turbulent atmospheres and plasmas. In this paper, we explore the propagation characteristics of partially coherent flat-topped beam rectangular arrays in the presence of atmospheric turbulence and plasma. Our findings reveal that beam parameters significantly influence the intensity distribution and M\({}^{2}\)-factor of these beams as they traverse through turbulent atmospheres and plasma environments. This study suggests that by optimizing the beam parameters, it is possible to mitigate the adverse effects caused by turbulence and plasma, thereby enhancing transmission performance. The research has promising applications for long-distance FSO communications, offering insights that could lead to more robust and reliable communication systems under challenging conditions.
# Enhanced QoS Routing Protocol for an Unmanned Ground Vehicle, Based on the ACO Approach
## 2 Method
### Route Optimization Establishment  
In this part, we describe the method of determining the best path from ST to DT. It should be noted that we consider the optimum route problem to be the optimization problem given in Equation (1). To address this obstacle, we provide an ACO-based technique capable of dealing with such nonlinear combinatorial optimization challenges in dynamic and complex systems. Algorithm 1 illustrates the firefighting UAV routing establishment.
## 4 Discussion
### Clinical Tessification  
The optimal preset sonography imaging scan was then clinically tested in the Taichung Armed Forces General Hospital (TAFGH), Taiwan. As shown in Figure 5, two cases were analyzed in this study. Case 1 concerned a female aged 65 who had a diabetic history of 20 years. Her right leg underwent skin flap surgery after a car accident, but the wound did not heal. Computed tomography angiography revealed multiple arterial occlusions in both lower limbs. The CT scanned area (A) was 47.9 mm\({}^{2}\), whereas the conventional (C) and optimal (E) settings of sonography scans were 36.8 and 42.5 mm\({}^{2}\), respectively. Case 2 included a female aged 88 with a diabetic history of 23 years. She accidentally had an open fracture of the right lower limb. The wound did not heal, and several toes were blackened after emergency orthopedic surgery. A computed tomography angiogram showed multiple chronic arterial occlusions in the patient's right lower limb. The CT scanned area (B) was 17.4 mm\({}^{2}\), whereas the conventional (D) and optimal (F) settings of the sonography scans were 24.1 and 20.7 mm\({}^{2}\), respectively. In both cases, a better sonography image quality and more accurate derivation were obtained in the optimal scan.  
Figure 4: The fish-bone plot according to the diameter difference between the theoretical preset and practical measurement. A significant difference between the area- and diameter-based results shows a fluctuating outcome according to two difference scenarios.
## 5 Experimental Results and Analysis
### Case Studies  
To further illustrate the performance of the TDEDA model for the multimodal rumor detection task, we used actual text and image multimodal tweets as examples and correctly analyzed identified posts and misidentified tweets separately.
## 3 Results
### Evaluation of Effectiveness  
By means of the Kruskal-Wallis model, the significance of the study variables was tested. At the baseline, the number of lesions was not significant compared with groups I and II (\(p\) \(>\) 0.05), but significant at V2 and V3 (\(p\) = 0.013). There were no significant differences between groups I and II in the size of the lesions measured at the baseline (\(p\) \(>\) 0.05), but statistically significant differences at V2 and V3 (\(p\) = 0.0407). There were no significant differences between groups I and II in the measurement of pain measured with the VAS scale (\(p\) \(>\) 0.05), whereas a statistically significant difference was detected at experimental times V2 and V3 (\(p\) = 0.0094). There were no significant differences between groups A and B in temperature measurement (\(p\) \(>\) 0.05) and at the experimental times of V2 and V3 (\(p\) \(>\) 0.05) (Figure 1). No deviations, side effects, or adverse reactions to treatment were recorded in groups I and II. No drop out was recorded in the study population.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **USS Score** & **Number** & **Size** & **Duration [**]** & **Free Period [**]** & **Pain** & **Site** & **Temperature** \\ \hline
**Mean** & 20.42 & 1.200 & 2.433 & 1.900 & 9.850 & 2.733 & 2.300 & 36.86 \\ \hline
**Std. deviation** & 5.893 & 1.031 & 1.924 & 0.2034 & 0.2980 & 1.680 & 1.878 & 0.7614 \\ \hline
**Std. error of mean** & 1.076 & 0.1882 & 0.3513 & 0.03714 & 0.05440 & 0.3068 & 0.3429 & 0.1390 \\ \hline
**Lower 95\% CI of mean** & 18.22 & 0.8152 & 1.715 & 1.824 & 9.739 & 2.106 & 1.599 & 36.57 \\ \hline
**Upper 95\% CI of mean** & 22.62 & 1.585 & 3.152 & 1.976 & 9.961 & 3.361 & 3.001 & 37.14 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Summary of the descriptive statistics of the outcome parameters or group II evaluated at the V2 timepoint [** from the protocol inclusion].  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **USS Score** & **Number** & **Size** & **Duration [**]** & **Free Period [**]** & **Pain** & **Site** & **Temperature** \\ \hline
**Mean** & 14.72 & 0.2667 & 0.4000 & 3.383 & 9.367 & 0.7667 & 0.5333 & 36.44 \\ \hline
**Std. deviation** & 4.068 & 0.4498 & 0.8137 & 0.6390 & 0.6940 & 1.331 & 0.8996 & 0.7398 \\ \hline
**Std. error of mean** & 0.7427 & 0.08212 & 0.1486 & 0.1167 & 0.1267 & 0.2430 & 0.1642 & 0.1351 \\ \hline
**Lower 95\% CI of mean** & 13.20 & 0.09872 & 0.09617 & 3.145 & 9.108 & 0.2697 & 0.1974 & 36.17 \\ \hline
**Upper 95\% CI of mean** & 16.24 & 0.4346 & 0.7038 & 3.622 & 9.626 & 1.264 & 0.8692 & 36.72 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Summary of the descriptive statistics of the outcome parameters or group II evaluated at the V3 timepoint [** from the protocol inclusion].
# Recovering Microscopic Images in Material Science Documents by Image Inpainting
## 2 Related Works and Backgrounds
### Image Inpainting Methods
In Figure 2a, CNN-based methods basically follow an encoder-decoder network architecture [34; 35; 36]. Here, the encoder transforms inputs into a state within a latent space, whereby the decoder reconstructs the compressed outputs from the encoder [37]. For instance, [PERSON] et al. proposed a context encoder network that utilizes both L2 reconstruction loss and adversarial loss in order to improve visual quality [37]; however, this method is not suitable for high-resolution images. [PERSON] et al. proposed a shift-net algorithm, which preserves the features of an image by introducing shift connections in a specific layer, thereby allowing the generation of high-resolution images from low-resolution inputs [23]. However, they still encountered difficulties in terms of not preserving details that were too sophisticated and thus only producing visually convincing results. In recent years, a GAN technique has appeared due to the advancement of machine learning algorithms. In Figure 2b, GAN-based architectures consist of generator and discriminator networks. In detail, the generator generates virtual images by using a feature map in the latent space, whereby the discriminator distinguishes the filled images from the real images [38]. For example, [PERSON] et al. proposed the GAN-based method that uses a three-step process, which involved a completion network, a global context discriminator, and a local context discriminator in order to generate high-quality results for large areas of missing parts [24]. Additionally, [PERSON] et al. proposed a generative network that uses a context attention module in order to maintain consistency with the surrounding context [25]. However, this method can still produce unrealistic results, such as a distorted structure when using free-form masks. In order to overcome the challenge, [PERSON] et al. proposed a gated convolution to improve the color consistency and inpainting quality of free-form masks [26]. This method applied a gated convolution to each spatial location in all layers in order to solve the color mismatch and blurriness issues of vanilla convolutions. In this study, we focused on deep-learning-based methods because they are superior to the other methods due to their lower computing costs and higher prediction performance  
Figure 2: Architectures of deep learning methods (In the figures, input images are the example of the microscopic images in the reprinted with permission from Ref. [14]. Copyright (2019) Elsevier. ): (**a**) represents the CNN-based method and (**b**) represents the GAN-based method. up to now. In particular, we employed one CNN-based method and three GAN-based methods, which are shift-net, global and local, contextual attention, and gated convolution. We used the same architecture in all of the models proposed in the references [23; 24; 25; 26].
# Investigation of Data Quality Assurance across IoT Protocol Stack for V2I Interactions
## 3 Overview of Network Protocols
### Transport Layer
#### 3.2.1 Datagram Congestion Control Protocol  
In 2006, DCCP [68] was standardised by IETF to support applications that require timely delivery of data packets, an inherent feature of UDP, also benefiting from the congestion control mechanisms of TCP. DCCP aims to reduce network congestion while enabling speedy data transfer between nodes. It achieves this by offering an unreliable flow of datagrams with congestion control mechanisms in place. DCCP is specifically designed to cater to applications like streaming media, online multiplayer games, and Voice over IP (VoIP) services. These applications can tolerate certain data loss but are sensitive to delays, making them suitable for application in IoT communication.
## 3 Permeability Test Results and Discussion
### Effect of FIR  
Figure 7a shows the test results of the permeability coefficients at four different injection rates (FIR = 25%, 50%, 75%, and 100). Compared with FIR = 25%, the FIR = 50% permeability coefficient and its rate of change were small. When the FIR was greater than 75%, the permeability coefficient was less than \(5\times 10^{-5}\) cm/s within 60 min, which had little effect on the change in the permeability coefficient of the conditioned sand. The experimental results showed that the permeability coefficient first increased and then stabilized with increasing FIR.  
During the permeability experiments, the injected foam volume was determined by the FIR when the FER was constant. As shown by Figure 7b, when the volume of the foam was larger than the  
Figure 6: Effect of FER on conditioned sand permeability. (**a**) Conditioned sand permeability coefficient under different FERs, (**b**) flowability of foam under different FERs, and (**c**) foam defoaming between sand particles when FER = 20.  
Figure 7: Effect of FIR on conditioned sand permeability. (**a**) Conditioned sand permeability coefficient under different FIRs, and (**b**) penetration of foam between sand particles.  
voids in the sand, the foam completely filled in the voids in the sand. The fine foam, together with the void pressure of the sand particles, resisted the external water pressure and uniformly penetrated along the voids of the sand. At this time, the permeability of the sand was mainly determined by the adhesiveness of the foam (related to the \(T_{\rm{hl}}\)). The loss and dissipation of foam caused the foam volume in the sand decreasing over time. When the volume of the foam was smaller than the void volume of the sand, water mainly penetrated along the voids of the sand filled with no foam, and the permeability coefficient of the sand started to increase. Therefore, the FIR primarily affected the rate of change of the permeability coefficient, but had a smaller effect on permeability. The smaller the FIR was, the faster the permeability coefficient changed, and the larger FIR led to slower permeability coefficient changes.
## 2 Applications of ANNs to Renewable Energies
### Applications of ANNs for Wind Power and Speed Prediction  
Within the renewable energy mix, wind energy is currently considered the most economical way to generate electricity. Recently, there has been new research into methods capable of predicting wind speed. This is of great importance due to the continuous growth of wind power generation worldwide [60]. For proper operation of wind farms, a constant stream of data about wind speed and wind direction is required. Artificial neural networks are an excellent method for short-, medium-, and long-term wind speed forecasting. The following Table 1 summarizes the main research pieces found when performing the review. The studies have been classified according to the ANN structure, journal and region, input and outputs for the network, and the activation function employed. The applications have different characteristics in several aspects, such as the ANN structure, the input data, the activation function used, and the training algorithm. As can be seen from the literature, various studies on wind speed prediction have been carried out for more than twenty years in different parts of the world, most of them located in Turkey, India, China, or Iran. The main characteristics of the networks studied are detailed below. * ANN type: from the 23 references analyzed, the MLP network has been used in 17 of them, followed by the RBF in two of them. Five of them did not specify the type of ANN used. * Structure of the ANN: the predominant type is simple with one hidden layer (70%) and the rest with two hidden layers (26%) with the exception of the investigation of [41], which uses three hidden layers. The number of neurons in the hidden layer is usually around 15, while in other cases more than 63 are selected [44]. * Amount of data: the percentage of research that makes use of data for validation is 8.69%. * I/O configuration: the inputs to the models usually take in situ measured features such as past wind speeds, temperature, relative humidity, altitude, month, or pressure. * Activation function: only 13 of the 23 cases detail the activation function used. In the hidden layer, linear functions are used, with tansig and logsig being the most commonly used, while in the output layer, linear functions of the purelin type are adopted. Figure 3 details the most common inputs and outputs used by ANNs in wind power and wind speed prediction and the operating scheme.
###### Abstract  
During this period, as distributed energy resources (DERs) are crucial for meeting energy needs and renewable technology advances rapidly, photovoltaic (PV)-powered energy islands (EIs) requiring a constant energy supply have emerged. EIs represent a significant milestone in the global energy transformation towards clean and sustainable energy production. They play a vital role in the future energy infrastructure, offering both environmental and economic benefits. In this context, reliance on information and communication technologies for system management raises concerns regarding the cybersecurity vulnerabilities of PV-supported EIs. In other words, since EIs transmit power through power converters--integral cyber-physical components of these systems--they are uniquely susceptible to cyber-attacks. To tackle this vulnerability, a cyber-attack detection scheme using a supervised machine learning (SML) model is proposed. The initial goal is to ensure the transfer and maintenance of energy demands without power loss for critical loads by detecting cyber-attacks to establish a defense mechanism. Two distinct artificial neural network (ANN) structures are implemented to identify cyber threats and support subsequent power demand, resulting in a complementary approach. The findings reveal the model's effectiveness, demonstrating high accuracy (e.g., a cross-entropy loss of 12.842 \(\times\) 10\({}^{-4}\) for ANN-I with a 99.98% F1 score and an MSE of 1.0934 \(\times\) 10\({}^{-7}\) for ANN-II). Therefore, this work aims to open the fundamental way for addressing this issue, particularly concerning hijacking attacks and false data injection (FDI) cyber-attacks on PV-powered EIs. The success of this model and its outcomes confirm the effectiveness of the proposed approach method.  
photovoltaic (PV) systems; energy islands; power electronic converters; cyber-attacks; supervised machine learning; artificial neural networks (ANNs) +
Footnote †: journal: Applied sciences