esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
The concept of the metaverse, where individuals use their avatars to enjoy leisure and work in a virtual world, has become deeply ingrained in our society. The creation of avatars using personal photos is no longer a novel technology, and virtual showcases have become commonplace. Consequently, it has become important for objects in a virtual environment to react as if they were real, minimizing the disparity between virtual and real-world environments. Although emotion research based on voice and facial expressions has been ongoing even before the metaverse, the emotional states being assessed are often fragmented. While the development of eye-tracking, hand-tracking, and other technologies that transfer physical movements into virtual environments is ongoing, research on emotions remains fragmented due to the complexity of the environment. Accurate analysis of emotions is crucial for ensuring that objects in the metaverse interact with real-world entities in a more natural manner. Therefore, this study proposes the \"Face and Voice Recognition-based Emotion Analysis System (EAS) to Minimize Heterogeneity in the Metaverse.\" The system analyzes the user's emotional state by simultaneously recognizing their voice and facial expressions. The EAS uses a microphone and camera to analyze the user's voice and expressions and combines these inputs to determine the user's overall emotional state. This paper aims to study three neural networks to develop the EAS. First, the Facial Emotion Analysis Model (FEAM) is designed to classify human expressions into seven emotions using facial landmarks, and a training dataset is generated to verify whether emotions can be accurately assessed from facial expressions. Second, the Voice Sentiment Analysis Model (VSAM) is designed to recognize emotions from the human voice. To improve accuracy, a model called MCycleGAN is proposed to extract voice data in noisy environments. Finally, the Metaverse Emotion Recognition Model (MERM) is designed to infer the overall emotional state of the speaker by combining the results of FEAM and VSAM. This paper is structured as follows: Section 2 discusses previous research on emotion analysis using voice and video recognition. Section 3 explains the structure and logical design of the proposed EAS. Section 4 presents the training and testing of the neural networks used in each module of EAS using public datasets to verify the system's ability to recognize emotions at a level comparable to that of humans. Section 5 provides a summary of the proposed EAS and discusses future directions based on simulation results.
## 3 Experimental Results
### Results of Dynamic Uniaxial Compression Tests on Soft Ice  
#### 3.2.1 Uniaxial Compressive Strength of Soft Ice  
Testing conditions of soft ice, which is collected during the early melting season, are the same as that of hard ice. The significant difference between soft ice and hard ice is the air porosity: the former contains obvious air bubbles and has a higher air porosity than the latter. Typical stress versus nominal strain profiles of soft ice specimens are shown in Figure 14. Under the employed loading rates, stress profiles increase uniformly before reaching the peaks and they do not drop rapidly after the peaks. Similar to profiles of hard ice specimens, peak stress derived from the upper load cell is greater than that derived from the lower load cell and the difference between them increases with increasing loading rate. However, the differences are less than 10%, so ice specimens are deemed to achieve balanced force in the loading direction at these loading rates, and the inertial effect is negligible.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Temperature** & **Sample** & \begin{tabular}{c} **Nominal** \\ **Strain-Rate** \\ **(\({}^{\circ}\)C)** \\ \end{tabular} & \begin{tabular}{c} **Sustaining** \\ **Name** \\ **Name** \\ \end{tabular} & \begin{tabular}{c} **Average True** \\ **Strain-Rate** \\ **(s\({}^{-1}\))** \\ \end{tabular} & \
\begin{tabular}{c} **Nominal** \\ **Strain-Rate** \\ **(s\({}^{-1}\))** \\ \end{tabular} &
\begin{tabular}{c} **True** \\ **Ultimate** \\ **Strain (\%)** \\ \end{tabular} \\ \hline \multirow{7}{*}{\(-10\)} & HV01 mps3 & 2 & 4.84 & 0.43 & 0.97 & 2.08 \\  & HV02 mps2 & 4 & 2.36 & 0.97 & 0.94 & 2.29 \\  & HV05 mps2 & 10 & 1.18 & 2.11 & 1.18 & 2.49 \\  & HV1 mps3 & 20 & 0.20 & 4.93 & 0.40 & 0.99 \\  & HV2 mps3 & 40 & 0.11 & 9.01 & 0.44 & 0.99 \\  & HH01 mps2 & 2 & 5.20 & 0.49 & 1.04 & 2.55 \\  & HH02 mps3 & 4 & 2.00 & 1.20 & 0.80 & 2.40 \\  & HH05 mps2 & 10 & 0.85 & 2.49 & 0.85 & 2.12 \\  & HH1 mps2 & 20 & 0.25 & 5.38 & 0.50 & 1.35 \\  & HH2 mps2 & 40 & 0.13 & 9.65 & 0.52 & 1.25 \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._  
Figure 14: Typical stress versus nominal strain-rate profiles of soft ice with different loading rates at \(-5\)\({}^{\circ}\)C (results derived from the upper load cell are shown as dotted lines and results derived from the lower load cell are shown as solid lines).  
The compressive strength results of soft ice are shown in Table 3. In the strain-rate range of 2.0-40.0 s\({}^{-1}\), uniaxial compressive strength values vary from 1.2 to 7.0 MPa with an average strength value of 4.0 MPa at \(-5\)\({}^{\circ}\)C. Uniaxial compressive strength values vary from 2.0 to 10.0 MPa with an average strength value of 6.0 MPa at \(-10\)\({}^{\circ}\)C. Compared with hard ice, the strength of soft ice is weaker under the same testing condition, however, the characteristics showed by soft ice and that of hard ice are similar. Compressive strength increases with increasing loading rate and the strength along the length of the columns is slightly greater than that perpendicular to the length of
# Comparison of L1 and L5 Bands GNSS Signals Acquisition
## 3 Assessment of Processing Time and Memory Requirements  
This section aims to determine the expression of the processing time and of the amount of memory for the different implementations. For this, it discusses first the acquisition of a data channel, and then the acquisition of a pilot channel, without assistance (where the correlation over the secondary code should be performed) and with assistance (where the delay of the secondary code is known, allowing a simple secondary code removal). For each implementation, the timing diagram is provided and the processing time is directly deduced from it, as done in Reference [17; 23]. Note that these expressions are fully deterministic and exact since the processing time for one frequency bin is given, not the mean acquisition time [30], which is sufficient to compare the implementations.
## 3 Simulation Model and Simulation Methods
### Numerical Method  
The numerical simulation of the flow and heat transfer process is conducted using Ansys Fluent v2022, which allows for the calculation of basic physical quantities such as velocity, temperature, and pressure in the flow field through computational fluid dynamics (CFD) calculations. The control equations are discretized using the finite volume method to handle complex geometric shapes and boundary conditions. To deal with the coupling problem of pressure and velocity, the SIMPLE algorithm was adopted. The pressure term is discretized using the standard difference format, while the second-order upwind method is used for other terms during the difference process. The \(k\)-\(\varepsilon\) turbulence model was introduced in numerical simulations to capture the regularity of fluid flow and applied in transient calculations. In solution controls, the under-relaxation factors were set to 0.8 for turbulent kinetic energy, 0.4 for momentum, and 0.3 for pressure. To ensure the accuracy and reliability of the results, the convergence residuals of the energy equation and other variables were required to be less than \(10^{-6}\).
# A Self-Supervised Tree-Structured Framework for Fine-Grained Classification
## 3 Proposed Tree-Structured Framework for Fine-Grained Classification
### Initial Classification Tree Construction
Once belonging clusters of the subclasses have been determined, we can dynamically construct the initial classification tree by performing a preorder traversal, which allows us to decide the number of levels in the classification tree and the number of clusters split from the cluster in the previous level. The construction process is shown in Algorithm 2. ```
0: Expected number of levels in the classification tree (denoted as \(n_{l}\)); Number of clusters split from the cluster in the previous level (denoted as \(n_{chd}\)); Extracted features for each image (denoted as \(F\));
0: an initial classification tree (denoted as \(t_{int}\))
1:function CreateClassificationTree (feature, cluster)
2:while levels of classification tree \(<n_{l}\)do
3: determine the belonging cluster for the subclass (Algorithm 1)
4:for\(i=1\) to \(n_{chd}\)do
5: cluster \(\leftarrow\) cluster \(i\);
6: feature \(\leftarrow\) feature of cluster \(i\) in \(F\);
7: CreateClassificationTree (feature, node)
8:endfor
9:endwhile
10:endfunction
```  
**Algorithm 2** Construction of the initial classification tree. In Algorithm 2, the initial classification tree is constructed recursively based on the image features. First, the inputs are specified as the expected number of levels in the classification tree, the number of clusters split from the cluster in the previous level, and extracted features for each image. A clustering algorithm is used to segment clusters of image features, and majority voting is used to determine a belonging cluster for each subclass (Line 3). Construction of initial classification trees is built using preorder traversal with recursion (Lines 2-9). The cluster is set to the current cluster \(i\) (Line 5), and the feature is updated to the values in the current cluster \(i\) (Line 6). The recursion ends and outputs the initial classification tree only when the expected number of levels and the desired number of clusters resulting from the split in the previous level have been reached.
# Digitalization Processes in Distribution Grids: A Comprehensive Review of Strategies and Challenges
## 3 Grid Operations
### Asset Management and Maintenance in Operation
#### 3.5.1 Predictive Maintenance Strategies for Grid Infrastructure  
The advancement of predictive maintenance in power grid infrastructure utilizes advanced data analytics, sensor technologies, and ML to pre-empt potential failures and refine maintenance schedules. Study [190] explores statistical, ML, and AI methods for time series data analysis, highlighting their effectiveness in pre-emptive maintenance strategies to boost grid performance and cost efficiency. Simultaneously, Ref. [97] examines advanced sensor technologies for monitoring switchgear, focusing on thermal, mechanical, and discharge detections integrated with ML for accurate maintenance predictions.  
Additionally, Ref. [98] tackles the challenges posed by DERs and rapid power electronics, promoting real-time predictive maintenance and failure prediction using cyber physical systems (CPSs) and digital twin models, which is essential for modern power distribution complexities. Study [191] addresses the surge in EV charging demand, suggesting a deep reinforcement learning approach for timely distribution transformer replacements to meet operational demands and maintain grid reliability amid changing energy landscapes.  
These discussions underline the pivotal role of predictive maintenance, driven by digital innovations, in ensuring grid reliability and efficiency.
# Traditional Bangladeshi Sports Video Classification Using Deep Learning Method
## 3 Proposed Approach
### Spatial Feature Extraction  
In videos, spatial features or elements can be defined as the characteristics relevant to the context of the scenes. However, in the case of sports videos, spatial features include surrounding aspects of the sports ground, characteristics of the athletes, and all about the event view. In recent times, CNN has proven its prosperity to a great extent in extracting complex spatial features of the images. The integral component of the CNN is the convolutional layer that takes after the name of this network. Convolutional layers can learn local and translation invariant patterns from the images through \"convolution\". It denotes a linear mathematical operation that performs matrix multiplication between the filter of a specific dimension and the portion of the image on which the filter hovers. The outcome of a convolutional layer is termed as \"feature map\", passed to an activation function that introduces nonlinearity in the output. Another integral element of CNN is the pooling layer that has come up with a downsampling strategy. The key purpose of this layer is to accumulate the most activated presence of a feature by gradually decreasing the spatial  
Figure 1: Workflow of the proposed TBSV classification approach.  
size of the representation. However, for extracting the spatial features from traditional Bangladeshi sports videos, a TimeDistributed CNN architecture has been constructed. The input dimension of a TimeDistributed CNN layer is (samples, frames per video, height, width, channels). This architecture employs the same layer of this architecture to all the picked frames of each video one by one through sharing same weights and thus finally produces feature maps for the frames.
## 6 Experimental Results and Discussion
### Ablation Study  
In the proposed model, we utilize in-context learning on LLMs for knowledge graph reasoning. The in-context triples are extracted by the extractor, which is trained by the logical rule-guided supervisory data as well as the reasoning feedback of LLMs. So, in this section, we study the performance when different in-context triples are incorporated on the validation sets of WN18 RR and FB15k-237. The results are displayed in Table 4.  
In Table 4, row 1 displays the results when only the query triple is fed into the LLMs for reasoning. The method of row 2 incorporates the triples which are directly connected to the query entity, but the in-context query may not necessarily be effective for the reasoning of a given query task. In row 3, the in-context triples are obtained by the extractor, which is trained by the logical rule-guided supervision data. Row 4 shows the performance when both the logical rule-guided triples and the LLM's feedback are all used to train the extractor.  
Comparing the results of rows 1 and 2, all the results of rows 3 and 4 are better than those of rows 1 and 2. These results confirm that refining in-context triples into the prompt can enhance the performance of knowledge graph reasoning. When the in-context triples are obtained by the extractor which is trained by both the logical rules-guided triples and the LLM's feedback (row 4), we can obtain the best performance. This performance indicates that the in-context triples obtained based only on the logical rules may not always be useful for LLM reasoning. Therefore, it is necessary to further optimize the extractor using feedback from the LLMs on top of the logical rules, so that it can extract triples that are more suitable for LLM reasoning. Comparing the results in row 4 and row 1, the greatest improvement is 0.317 on Hits@1 for WN18 RR. These results demonstrate the effectiveness of our proposed method.
# Semantic-Aware Low-Light Image Enhancement by Learning from Multiple Color Spaces
## 3 Overall Architecture  
Semantic-aware knowledge-guided low-light image enhancement aims to restore low-light images to their normal-light counterparts by integrating semantic information with image enhancement techniques. This approach effectively enhances low-light images, improving both their visual quality and semantic perception. Traditional low-light image enhancement algorithms often overlook the inherent semantic information within images. In contrast, the Semantic-Aware Knowledge-Guided Framework (SKF) incorporates semantic information into the low-light image enhancement network, thereby providing richer contextual information and greater robustness. This section first introduces the original LLFlow network, detailing its normalized flow model and training process, along with a comparative analysis against conventional algorithms based on deterministic processes. Subsequently, the technical design of the semantic-aware knowledge-guided low-light image enhancement algorithm is elaborated, focusing on the three key components of the SKF framework: the Semantic Feature Enhancement (SE) module, the Semantic-guided Color Histogram (SCH) loss, and the Semantic-guided Adversarial (SA) loss. Finally, a macro-level overview of the low-light image enhancement problem is provided.
## 3 Results and Discussion
### Stability of TiO\({}_{2}\) Dispersion upon Adding Surfactants
In the case of Tween 20, unlike the other surfactants, the viscosity increased at low concentrations (0-0.5 wt%) of the treated surfactants but decreased at their higher concentrations (1-5 wt%). The non-ionic but hydrophilic ions of the surfactant do not repel when adsorption occurs on the surface of the particles; therefore, the viscosity increases at low concentrations. However, at high concentrations, micelles are formed between the surfactants. Meanwhile, the amount of surfactant that can be adsorbed onto the particles decreases, reducing the viscosity [5; 6; 32; 37; 38]. Figure 3: (**a**) Viscosity of TiO\({}_{2}\) dispersed in non-polar oil (DC345) using different surfactants. (**b**) Viscosity of TiO\({}_{2}\) dispersed in polar oil (CEH) using surfactants. The viscosity of the dispersion was recorded at 10 rpm. For polar oil (CEH), similar to the trend of viscosity for non-polar oil, ionic surfactants (anionic SDS, cationic CTAB, and zwitterionic DDAPS) showed a decreasing trend at low concentrations (0-1 wt%) of treated surfactants, but increased at their higher concentrations (2-5 wt%) (Figure 3b). In the case of Tween 20, unlike other surfactants, the viscosity increased at low concentrations (0-0.5 wt%) of the treated surfactants but increased at their higher concentrations (1-5 wt%).
## 3 Bisection Constraint Method for Topology Optimization Considering Multiple-Loading Conditions
### The Bisection Constraint Method
where \(c_{cj}^{k}\) is the critical value (boundary value) of the \(j\)th loading condition. The constraint function is expressed as follows:  
\[c_{j}(\mathbf{x})\leq\overline{c_{cj}^{k}},\ j\in N_{L}\backslash\{m_{k}\}. \tag{12}\]  
Equations (11) and (12) explain the name of the proposed bisection constraint method. After assigning the objective function and constraints, a sub-TO problem is solved based on constrained TO techniques (see Step 7 in Figure 1). The objective is to minimize the compliance under the worst load case \(m_{k}\) at step \(k\) as follows:  
\[\min_{\mathbf{x}}c_{m_{k}}(\mathbf{x}),\ m_{k}\in N_{L},\] (13a) subject to \[\frac{V(\mathbf{x})}{V_{0}}=f, \tag{13b}\] \[c_{j}(\mathbf{x})\leq\overline{c_{cj}^{k}},\ \ j\in N_{L}\backslash\{m_{k}\},\] (13c) \[\mathbf{K}(\mathbf{x})\mathbf{u}_{j}(\mathbf{x})=\mathbf{p}_{j},\ j=1,2,\ldots,n_{L}\] (13d) \[\mathbf{K}\mathbf{u}_{j}=\mathbf{p}_{j}, \tag{9}\]0  
The sub-TO Problem (13) is a typical single-objective TO problem under one volume constraint and \((n_{L}-1)\) compliance constraints. Various methods can be used to solve this sub-TO problem. For simplicity, in the next section of this article, sequential linear programming (SLP) [65] is used to solve the example problems. After solving the sub-TO Problem (13), a set of new cost function values \(c_{j}\) is defined at a new design variable vector \(\mathbf{x}_{k+1}\). The processes of cost function comparison, assigning objective and constraint functions, calculating boundary values for the constraints, and sub-TO problem solving are repeated until convergence. An example of the compliances' evolution during Stage 2 of the optimization process is shown in Figure 2 (with three loading conditions), where the bar symbols \(\overline{c_{1}^{k}}\), \(\overline{c_{2}^{k}}\), and \(\overline{c_{3}^{k}}\) represent the critical values (boundary values) of compliances associated with the compliance functions \(c_{1}\), \(c_{2}\), and \(c_{3}\), respectively. \[\mathbf{K}\mathbf{u}_{j}=\mathbf{p}_{j}, \tag{9}\]1 An example of evolution of compliances during the optimization process with three loading conditions using the proposed bisection constraint method. By finding the maximum compliance at every outer loop and reducing it while keeping other compliances from exceeding the boundary values (constrained), the proposed method can obtain the true solution of the min-max compliance formulation.
## 2 The Time-Varying Performance Function of In-Service Transmission Towers  
### Resistance Degradation Model for Steel Members  
The resistance of in-service transmission towers in corrosive atmospheric environments degrades over time. As transmission towers are primarily composed of circular steel tubes or angle steel, the resistance degradation model of steel members must be examined first in order to describe their time-varying performance. However, most studies on resistance degradation models focus on reinforced concrete, with relatively few studies centering around steel members [18; 19]. In this paper, the degradation process of steel members is described based on the idea of the attenuation function method [20; 21]. The initial value is \(R_{0}\), and the resistance \(R(t)\) at any time can be expressed by the following equation:  
\[R(t)=R_{0}\xi(t) \tag{1}\]  
where \(R_{0}\) is usually considered a random variable, and \(\xi(t)\) is the function that describes resistance degradation over time.