esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theoretical Preliminaries  
The \"Tsallis entropy\", denoted here as \(S_{q}\), for a variable \(X\) with a probability distribution \(p_{i}\) for the occurrence of the value \(X_{i}\), is defined as [17]:  
\[S_{q}=k_{B}\frac{1}{q-1}\left(1-\sum_{i=1}^{w}p_{i}^{q}\right) \tag{1}\]  
where \(k_{B}\) is the Boltzmann's constant, \(w\) is the total known multitude of the system's microstates (all possible values of \(X_{i}\)), and \(q\) is the entropic index which expresses the non-additivity degree of the system [17; 18; 19; 20; 21]. When the index \(q\) is greater than unity (\(q>1\)) the system is characterized by sub-additive processes (sub-additivity), while when the index \(q\) is less than unity (\(q<1\)), the system is characterized by super-additive processes. Equation (1), for \(q\to 1\), changes to the familiar expression of Boltzmann-Gibbs entropy formulation (\(S_{BG}\)): \(S_{BG}=-k_{B}\sum_{i=1}^{w}p_{i}\ln p_{i}\).  
Tsallis entropy constitutes a generalization of Boltzmann-Gibbs entropy; therefore, they have several properties in common, some of which are positivity, concavity, and Lesche experimental stability (for a more comprehensive comparison, readers should refer to Table3.10 of Reference [21]). A key difference between Tsallis entropy and Boltzmann-Gibbs entropy is that the latter highlights the short-range correlations of a given physical system, with the total system's entropy depending on the size and the number of subsystems of the total microstates that comprise the system. On the other hand, as indicated by Equation (1), the Tsallis entropy \(S_{q}\) can show long-range correlations and may be more suitable for describing complex dynamical systems [17].  
The cumulative distribution function (CDF) of parameter \(X\) is described in terms of a \(q\)-exponential function as follows [25]:  
\[P(>X)=\exp_{q}\left(-\beta_{q}X\right) \tag{2}\]  
The term \(\exp_{q}(X)\) denotes the \"\(q\)-exponential function\" (q-exponential function), which is defined as:  
\[\exp_{q}(X)=\left\{\begin{matrix}\left[1+\left(1-q\right)X\right]^{\frac{1}{1- q}}&\text{when}&\left[1+\left(1-q\right)X\right]\geq 0\\ 0&\text{when}&\left[1+\left(1-q\right)X\right]<0\end{matrix}\right. \tag{3}\]  
The inverse form of the \(q\)-exponential function is called the \"\(q\)-logarithmic function\" and is given by the following expression: \(\ln_{q}(X)=\frac{1}{1-q}\left(X^{1-q}-1\right)\). In the case of Boltzmann-Gibbs statistical mechanics, when the index \(q\) tends to unity (\(q\to 1\)), Equation (3) changes to the usual exponential function (so does the \(q\)-log function, which transitions to the usual \(q\)-logarithmic function). The \(q\)-exponential function is a generalization of the Zipf-Mandelbrot distribution, which is recovered for index values index less than unity. \((q<1)\). However, in the case of sub-additivity \((q>1)\) Equation (3) shows an asymptotic power law tail, while in the case of super-additivity \((0<q<1)\), it exhibits a cutoff at \(X_{c}=\frac{1}{(q-1)}\)[34; 35].
# Enhancing Route Guidance with Integrated V2X Communication and Transportation Systems: A Review
###### Abstract  
Due to its anticipated impacts on the performance of transportation systems, intelligent transport systems (ITS) have emerged as one of the most extensively investigated topics. The U.S. Department of Transportation has defined route guidance systems (RGSs) as one of the main categories within ITS. Systems like these are essential components when managing travel and transportation. While RGSs play a pivotal role in both present and future transportation, there has been limited research on evaluating the effectiveness and dependability of integrating them with vehicular communication frameworks. Therefore, this paper aims to evaluate the RGS architectures proposed to date in the literature, providing comparisons and classifications based on their structures and requirements for communication systems. Moreover, it explores existing, next generation, as well as prospective choices for V2X communication technologies, evaluating how well they contribute to the development of RGS applications by integrating them with potential communication systems. Specifically, this study assesses the suitability of communication technologies in meeting the requirements of RGS applications. In conclusion, it suggests a framework for integrating RGS and V2X systems and offers directions for future research in this area.  
route guidance systems; traffic assignment; V2X; vehicular communication +
Footnote †: journal: smart cities
# Hierarchical Resource Management for Mega-LEO Satellite Constellation
## 5 Detailed Elaboration of Each Layer
### Functions of User Terminals
#### 5.3.2 Random Access Strategy  
Opportunistic spectrum access is a promising technology for users, especially low-priority users accessing SBSs. When users' service streams arrive, they enter a random access status. When there is an opportunity, the SBS allocates time-and-frequency resources to the users for their service accesses.  
Another approach is random access based on NOMA protocols. In this method, users transmit signals to the SBS on the same time-and-frequency resource block. The SBS distinguishes these signals using multi-user detection (MUD), which relies on unique signatures for each user. The MUD technologies include successive interference cancellation (SIC), despreading, descrambling, deinterleaving, compressed sensing, and machine learning, etc.  
In previous satellite communication systems, grant-based random access protocols with multiple handshakes were commonly used. However, as the number of users increases, grant-free random access protocols are gradually being introduced to reduce handshake overhead and access delay. Figure 6 illustrates the grant-based random access procedure, while Figure 7 shows the grant-free random access procedure.  
Figure 6: Grant-based random access procedure.  
Figure 7: Grant-free random access procedure.
## 5 Estimation and Simulations
### Simulation  
Here, in this sub-section, a comprehensive MCSS is conducted to assess the performance of the estimators of the NGLog-Weib distribution by using the R package. The simulation results are calculated for six sets of parameter values: (i) Set \(1\)\((\delta=0.5,\ \beta=2.4,\ \alpha=3.6)\), (ii) Set II \((\delta=3.2,\ \beta=2.7,\ \alpha=3.8)\), (iii) Set III \((\delta=1.2,\ \beta=3.3,\ \alpha=4.5)\), (iv) Set IV \((\delta=1.5,\ \beta=3.9,\ \alpha=4.6)\), (v) Set V \((\delta=1.4,\ \beta=3.5,\ \alpha=1.0)\), and (vi) Set VI \((\delta=1.2,\ \beta=0.7,\ \alpha=1.0)\). For each set of the above parameters values, the MCSS replicates are made 1000 times under different sample sizes, \(n=25,50,\ldots,1000\). We obtained the average MLEs, MSEs (mean square errors), and biases for each sample size. The numerical values of the biases and the MSEs of the model are calculated as:  
\[\frac{1}{n}\sum_{i=1}^{n}(\hat{\delta}_{i}-\delta)\]  
and  
\[\frac{1}{n}\sum_{i=1}^{n}(\hat{\delta}_{i}-\delta)^{2}\]  
For the numerical values, the same process is repeated for \(\Theta\). The SRs (simulation results) corresponding to Set I and Set II are shown in Table 2. Similarly, the results of Set III and Set IV are provided in Table 3, beside the results of Set V and Set VI, which are provided in Table 4. It is observed from Tables 2-4 that the estimated values of \((\hat{\delta},\hat{\Theta})_{MLE}\) are stable, and the MSE of \((\hat{\delta},\hat{\Theta})_{MLE}\) decreases as the sample size increases. Similarly, the values of the biases, \((\hat{\delta},\hat{\Theta})_{MLE}\), are also decay to zero as the sample size increases.
## 2 Operational Technology Networks Specifics
### Information Technology and Operational Technology Convergence
It is the automatic protocol recognition that will enable the creation of devices that automatically recognize the protocol in the network and can use this knowledge to, for example, automatically inspect and set firewall rules. In order to best secure the OT network, these approaches need to be combined. It is necessary to use tools for detecting security incidents, classifying the protocols used, as well as educating the human factor. The emergence of automated tools would enable effective control (auditing) and also the supervision of critical network elements. Industrial protocol classification can be used at individual industrial facilities (factories, plants, etc. ), but also within various SaaS service providers, which can monitor and classify traffic within the network. Last but not least, this method can be used to perform non-invasive security checks of industrial protocols without the need to access the data themselves directly. It is thus possible to use the encrypted form of the messages and to perform protocol classification on this basis, including its cipher suite. The early detection of security incidents helps to activate adequate countermeasures. Using the knowledge of the type of anomaly, it is possible to activate appropriate countermeasures so that the impact on the industrial process itself is minimized. This is related to the critical nature of the industrial processes themselves, where system shutdown can mean potential damage. It is thus advisable to perform a timely, safe system shutdown. However, the aim is to prevent such safety incidents. To do just that, it is advisable to use industrial protocol classification in the form of a security audit and implement appropriate countermeasures to minimize the likelihood of a security threat.
# Identification of 3D Lip Shape during Japanese Vowel Pronunciation Using Deep Learning
## 2 Materials and Methods  
The five Japanese vowels are phonetically distinct and rely on well-known and easy-to-practice changes in mouth shape [14]. Hence, simple resonant vocalization can be used to create the basic phonemes. The characteristics of speech resonance can be extracted from recorded speech signals by manipulating the amplitude spectrum. Peak resonance frequency measurement is the norm [15]. The formant frequencies are called the first formant (F1), second formant (F2), and third formant (F3) in descending order, and it has been reported previously that Japanese vowels can be distinguished mainly by F1 and F2 [16]. As the formant frequency of Japanese vowels is determined by the shape of the mouth, a three-dimensional (3D) interpretation of mouth shape should be capable of discriminating vowels. In the proposed method, the mouth shapes of the five vowels pronounced by a healthy Japanese subject were acquired as 3D point clouds. Next, using a model that can learn 3D point clouds, we divided the 3D point clouds acquired by learning into five classes. Finally, the unknown mouth shapes of the vowels were identified and evaluated.
# A Sparse Multiclass Motor Imagery EEG Classification Using 1D-ConvResNet
## 2 Materials and Methods
### Feature Extraction and Dictionary Construction  
We took each trial of the raw EEG data (t = 0 s to t = 6 s) and extracted epochs every half second, as MI only lasts for half a second [10]. We extracted wavelet features (energy and entropy) using discrete wavelet transform (DWT) from each epoch, and for each class, as it produces a great distinction between the MI classes, which results in improved model accuracy [15; 16; 17; 24]. In DWT, a signal s [n] was passed through high-pass and low-pass filters to extract the respective frequency components. The low-pass filtered signal was downsampled at each level with a factor of 2 (see Figure 4) [18]. The coefficients from the low-pass filtered signal are known as approximate coefficients, and those from the high-pass filtered signal are referred to as detailed coefficients. We used the fourth-order Daubechies wavelet (db4) for wavelet feature extraction, as it is appropriate for analyzing EEG signals because of its smoothing [16]. Energy and entropy were calculated from the decomposed wavelet coefficients of the subbands and concatenated as dictionaries for each class. The training sample is the transpose of each epoch's feature vector.  
Figure 4: Wavelet feature extraction. A\({}_{1}\), A\({}_{2}\),..., A\({}_{n}\) are approximate coefficients. D\({}_{1}\), D\({}_{2}\),..., D\({}_{n}\) are detailed coefficients. We estimated the energy and entropy of all detailed coefficients and A\({}_{n}\).  
Figure 3: Timing paradigm of the dataset.  
Figure 2: Overview of the methodology used in the present work.  
We calculated wavelet dictionaries for each class separately and concatenated them into a single dictionary (D) as follows:  
\[D=[D_{1}\|D_{2,\ldots}\|D_{i}]\in\mathbb{R}^{m\times n} \tag{1}\]  
where \(D_{1}\), \(D_{2}\),..., \(D_{i}\) are the dictionaries of each class (\(i\) = 0, 1, 2, 3, as we have four classes), and D is overcomplete.
As the first study that leveraged spatial information from high-resolution UAV imagery for \(V^{25}_{cmax}\) and GPP estimation, we extracted the spatial information of the underlying surface from high-resolution images using various representation methods, including texture features and deep learning, to quantify the macroscopic feature, field-scale \(V^{25}_{cmax}\). The UAV-imagery-driven \(V^{25}_{cmax}\) model was integrated into a classical leaf biochemical properties-based model for better GPP estimation. Our findings demonstrate the critical role of spatial information and provide new insight into monitoring GPP. The specific objective of this study is to (1) evaluate whether spatial information can improve the accuracy of field-scale \(V^{25}_{cmax}\), (2) build a UAV-imagery-driven hybrid framework for GPP estimation and validate its performance on the paddy rice field, and (3) see how spatial information contributes to field-scale \(V^{25}_{cmax}\) estimation.
### Related Work
If an entry in the original matrix changes, it is possible to restart the factorisation at its corresponding column in the **LU** matrix and retain all columns to the left of it. Here, it should be mentioned that the **LU** matrix is a single matrix that stores the nonzero entries of the **L** and **U** matrices. A drawback of the algorithm in [11] is that it does not consider the column dependencies within the **LU** matrix. It refactorises all columns to the right of the first numerically varied one in the **LU** matrix, regardless of whether they need to be updated or not, which is a key difference to the approach presented in this paper. Instead of completely refactorising all columns to the right, one can determine the so-called factorisation path [12; 13], which contains the column-wise dependencies of the decomposed matrix. It is assumed that the symbolic pattern of the matrix does not change. Given an LU decomposition and a list of entries in the original matrix that holds information about which entries are numerically changing, one can determine the so-called factorisation path encompassing the columns to be refactorised. This leads to a smaller number of columns that have to be recomputed in order to factorise the numerically varying matrix. This concept of partial refactorisation is adopted within this paper and is described in more detail in Section 3.1. To the knowledge of the authors, no libraries currently implement a factorisation path based refactorisation approach.
# Swarm Intelligence-Based Multi-Robotics: A Comprehensive Review
## 3 Applications of Swarm Intelligence in Multi-Robotics
### Space Exploration
#### 3.4.1 Planetary Exploration  
* Surface Mapping and Analysis [44; 45]: Swarm robots can be deployed on planetary surfaces to map and analyze the terrain collaboratively. Each robot in the swarm can be equipped with different sensors to collect data on soil composition, topography, and environmental conditions. The benefits of this application are that it ensures comprehensive and high-resolution mapping, reduces the risk of mission failure by distributing tasks among multiple robots, and enhances data accuracy through collective measurements.
* Resource Discovery and Utilization [46]: A swarm robot can explore planetary surfaces to locate and assess resources such as water, minerals, and other materials. This information is crucial for in situ resource utilization (ISRU), which aims to use local resources for mission sustainability. The benefits are that it increases the efficiency of resource discovery, reduces the need for transporting materials from Earth, and supports long-term human and robotic presence on other planets.
# A Study on the Electromagnetic-Temperature Coupled Analysis Method for In-Wheel Motors
## 4 Coupling Analysis of the Electromagnetic-Temperature Field
### Coupling Characteristic Analysis Based on Unidirectional Coupling Method
#### 4.1.3 Establishment of the Temperature Analysis Model  
After the establishment of the electromagnetic analysis model, the temperature analysis model was established, which is mainly about the determination of heat source and boundary conditions of the temperature field.  
Figure 8: Grid diagram.  
Figure 9: Schematic diagram of winding configuration.  
The equation for calculating the surface heat dissipation coefficient is as follows [27]:  
\[\alpha_{\rm e}=\alpha_{0}\Big{(}1+k_{\rm c}\sqrt{v_{\rm e}}\Big{)}^{3}\sqrt{ \frac{\theta}{25}}. \tag{12}\]  
Through calculation, the heat dissipation coefficient of the outer surface of the casing circumferentially was 41.7 W/(m\({}^{2}\cdot\)K), and that of the two side surface of the casing axially was 37.1 W/(m\({}^{2}\cdot\)K). The air temperature on the outer surface was 22 \({}^{\circ}\)C.  
At the same time, as an object of heat conduction, the velocity of heat conduction is closely related to the thermal conductivity. Thermal conductivity is a parameter that characterizes the heat transfer of an object under a stable temperature. Components with different materials have different thermal conductivity, especially the stator and rotor cores with silicon steel sheets that have anisotropic thermal conductivity. The material properties of each part of IWM can be obtained, as shown in Table 5.
## 5 Analysis and Discussion  
The evaluation of the efficacy of the proposed model involved conducting experiments using not only the diverse EMO-DB, RAVDESS, SAVEE, and IEMOCAP datasets, but also the purpose-built SHEIE dataset. Table 8 presents the performance of the model across these datasets. Its performance also improved with the DA strategies of adding noise to, time stretching, and shifting the audio data, which diversified the training datasets, providing the model with more robust features and enabling the generalizability of new data. Table 9 compares the precision of the model developed in this study to that of other models used in previous studies.  
The developed model recognizes the input sequence link using several transformer blocks and a multi-head self-attention mechanism. DA, preprocessing, and the complicated model design further increase the reliability of this model. The ensemble model outperformed those developed in previous studies [11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23] and demonstrably increased the accuracy from 56.41% to 95.25% compared to a multiclass SVM model that included the MFMC, MFCC, LFPC, and LPCC features. For the first EMO-DB dataset, the ensemble model recorded an average accuracy of 99.8%, as well as average accuracies of 96.3% for the RAVDESS dataset, 96.5% for the SAVEE dataset, 85.3% for the IEMOCAP dataset, and 83% for the SHEIE dataset. Thus, this model enhanced the SER. The model's average accuracies for anger, boredom, excitement, happiness, and sadness were 84.13%, 81.07%, 84.13%, 82.10%, and 79.87%, respectively. Thus, the model can accurately classify numerous emotions. Voice-assisted emotion detection and online teaching tools can benefit from these features. Figure 17 summarizes the model's accuracy and F1 scores across the datasets, demonstrating its strong performance. The ROC curves in Figure 18 visualize the ensemble model's performance in terms of classifying emotions from the five datasets, with the \(x\)-axis representing the FPR and the \(y\)-axis representing the TPR. This indicates that the model has a high TPR (90%) and a low FPR (10%), indicating that it can effectively identify emotions.  
Figure 17: Summary of model accuracy and F1 scores across the datasets.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Model** & **Testing Accuracy** & **F1 Score** \\ \hline EMO-DB & Ensemble model & 99.86\% & 99.71\% \\ RAVDESS & Ensemble model & 96.3\% & 95.9\% \\ SAVEE & Ensemble model & 96.5\% & 95.2\% \\ IEMOCAP & Ensemble model & 85.3\% & 73.6\% \\ SHEIE & Ensemble model & 83\% & 81.71\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: The ensemble model’s overall accuracy.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
# Impact of Groundtrack Pattern of a Single Pair Mission on the Gravity Recovery Quality
## 3 Results  
We simulate different inline (GRACE-like) mission scenarios (Table 1). The scenarios are taken from [8] which try to cover a range of orbits from drifting to fast-skipping with altitudes around 260 to 360 km. The simulations are based on a simplified version of the ll-SST acceleration approach using nominal orbits and the Hill equations [28]. The input models are the relevant time-variable gravity fields of hydrology, ice and solid Earth (HIS) and the difference of two ocean tide models as well as a model for Atmosphere-Ocean (AO) error (see Section 2). The sampling interval of the measurementsis \(\Delta t=1\) s. The error is represented as the difference between the gravity recovery (output) and the mean of the HIS time-variable gravity models (input) of the simulations for the same time duration. In all the simulations, the coefficients \(\Delta C_{00}\), \(\Delta C_{10}\), \(\Delta C_{20}\), \(\Delta C_{11}\) and \(\Delta S_{11}\) are neglected, since the II-SST measurement concepts for these coefficients are poorly sensitive (although depends on the recovery approach). A white noise model of tracking laser interferometer on the level of range acceleration with a PSD (power spectral density) of \(10^{-10}\) ms\({}^{-2}/\sqrt{\mathrm{Hz}}\) is applied in our RST simulation software.