esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Calculation of Adsorbed Asphal Film Thickness Based on Micromechanical Model  
### Hashin Model  
The Hashin model, based on the variational principle of linear elasticity, utilizes an approximate approach to derive the elastic modulus expression for two-phase or multiphase heterogeneous materials. This model assumes that inclusions are spherical and uniformly distributed within an isotropic, homogeneous matrix, with no interaction between the inclusion particles. This configuration is illustrated schematically in Figure 1.  
The effective shear modulus of the model is calculated using Equation (1):  
\[\frac{G_{\rm c}}{G_{\rm m}}=1+\frac{15(1-v_{\rm m})(G_{\rm f}/G_{\rm m}-1)\phi _{\rm f}}{7-5v_{\rm m}+2(4-5v_{\rm m})[G_{\rm f}/G_{\rm m}-(G_{\rm f}/G_{\rm m }-1)\phi_{\rm f}]} \tag{1}\]  
where \(G_{\rm c}\) represents the complex shear modulus of the asphalt mastic, \(G_{\rm m}\) is the complex shear modulus of the asphalt, \(G_{\rm f}\) is the complex shear modulus of the mineral filler, and \(v_{\rm m}\) denotes the Poisson's ratio of the asphalt. When rigid mineral filler particles are  
Figure 1: Schematic diagram of the Hashin model.  
dispersed in a viscoelastic medium, it is reasonable to assume that \(G_{\rm f}\gg G_{\rm m}\), implying that \(G_{\rm m}/G_{\rm f}\gg 1\)[31]. Under this assumption, Equation (1) can be simplified to Equation (2):  
\[\frac{G_{\rm c}}{G_{\rm m}}=1+\frac{15(1-v_{\rm m})\phi_{\rm f}}{2(4-5v_{\rm m} )(1-\phi_{\rm f})} \tag{2}\]  
By substituting the experimental value of \(G_{\rm c}/G_{\rm m}\) into Equation (2), the effective volume fraction of the mineral filler (\(\phi_{\rm eff}\)) can be determined, as expressed in Equation (3):  
\[\phi_{\rm eff}=\frac{2(G_{\rm c}/G_{\rm m}-1)(4-5v_{\rm m})}{15(1-v_{\rm m})+2 (G_{\rm c}/G_{\rm m}-1)(4-5v_{\rm m})} \tag{3}\]  
[PERSON] et al. [33] proposed a micromechanical approach to calculate the thickness of the adsorbed asphalt film, as shown in Equation (4):  
\[d=\frac{\phi_{\rm eff}-\phi_{\rm f}}{\phi_{\rm f}\times G_{\rm s}\times S} \tag{4}\]  
where \(d\) represents the adsorbed asphalt film thickness, \(\phi_{\rm eff}\) is the effective volume fraction of the mineral filler, \(\phi_{\rm f}\) is the volume fraction of the mineral filler, \(G_{\rm s}\) is the apparent density of the mineral filler, and \(S\) is the specific surface area of the mineral filler. By incorporating \(\phi_{\rm eff}\) into Equation (4), the thickness of the adsorbed asphalt film based on the Hashin model can be accurately determined.
# Research on the Optimization Method of Bus Travel Safety Considering Drivers' Risk Characteristics
## 2 Driver Scheduling Model  
### Quantitative Evaluation of Driver Driving Safety  
To compare the differences in driving safety among drivers under various working environments, this paper quantitatively evaluates the driving safety of bus drivers based on their historical warning data from bus operations. Assuming that the set of warning types contained in the historical bus warning data is denoted as \(A=\{a_{1},a_{2},\cdots a_{m}\}\), then the probability of risk events occurring for driver \(d_{k}\) in different scenarios can be obtained based on the naive Bayes formula. The calculation formula is as follows:  
\[\begin{array}{ll}P_{a|c}^{k}&=P^{k}(a\big{|}c)\\ &=\frac{P^{k}(c\big{|}a)\cdot P^{k}(a)}{P^{k}(c)}\end{array} \tag{1}\]  
In this context, \(c\) denotes the driving scenario, \(a\) denotes the driving warning event, and thus, \(P(a|c)\) represents the probability of the warning event \(a\) occurring under the driving scenario \(c\).  
Figure 1: Flowchart of driving safety analysis and scheduling optimization method.  
Then, using the entropy weight method, the characteristic weight \(W_{a|c}\) corresponding to the warning event \(a\) under the driving scenario \(c\) is obtained.  
\[W_{a|c}=\frac{1-e_{a|c}}{\sum_{u=1}^{n}\left(1-e_{a|c}\right)} \tag{2}\]  
\[e_{a|c}=-\frac{1}{\ln n}\sum_{k=1}^{n}P_{a|c}^{k}\ln(P_{a|c}^{k}) \tag{3}\]  
In the formula, \(n\) represents the total number of drivers; \(m\) represents the number of driving warning event categories; and \(e_{a|c}\) denotes the information entropy corresponding to the warning event \(a\) under scenario \(c\).  
In this paper, the driving scenario \(c\) is mainly divided into driving periods \(p\) and driving weather \(w\). Assuming that they are independent of each other, the combined risk of driver \(d_{k}\) in different environments can be expressed as:  
\[\begin{array}{l}t_{ij}^{k}\\ \\ =\frac{\sum\limits_{a=1}^{m}\sum\limits_{p\in p_{ij}:w\in w_{ij}}\left(W_{a|p} +W_{a|w}\right)\cdot P_{a|p:w}^{k}}{2N}\\ \\ =\frac{\sum\limits_{a=1}^{m}\sum\limits_{p\in p_{ij}:w\in w_{ij}}\left(W_{a|p} +W_{a|w}\right)\cdot P_{a|p}^{k}\cdot P_{a|w}^{k}/P_{a}^{k}}{2N}\end{array} \tag{4}\]  
In the formula, \(r_{ij}^{k}\) represents the combined risk value of driver \(d_{k}\) under \(s_{i,j}\); \(p_{ij}\) denotes the risk value and safety evaluation value corresponding to driver \(k\) under the shift chain \(s_{i,j}\); and \(N\) represents the length of the shift chain.
## 4 Discussion
### TMDs Prevalence and Etiology
#### 4.1.6 Behavioral Habits  
Behavioral habits are significant contributors to TMDs risk, with various oral behaviors playing a crucial role in the development and exacerbation of symptoms. Bruxism, both awake and during sleep, is a primary contributor to TMJ overloading, resulting in muscle fatigue and joint stress. [PERSON] et al. (2016) demonstrated that bruxism significantly increases the odds of painful TMDs, particularly when combined with other parafunctional habits, such as nail biting or gum chewing [57]. Similarly, [PERSON] et al. (2024) found that specific oral behaviors, such as clenching teeth or holding the jaw in a rigid position, were strongly associated with painful TMDs (OR: 4.478 and 3.209, respectively) [58].  
Parafunctional habits, such as excessive yawning, jaw propping, and biting objects, have also been identified as risk factors for TMDs. [PERSON] et al. (2023) reported a strong correlation between harmful oral habits like nail and lip biting and the development of TMJ pain and dysfunction [59]. Additionally, [PERSON] et al. (2006) highlighted that female adolescents are more likely to engage in parafunctional activities, such as intense gum chewing, which may partly explain the higher prevalence of TMDs symptoms in females compared to males [60].  
Behavioral modifications, including reducing bruxism and other parafunctional activities, are essential components of TMDs prevention and management [61]. Addressing underlying psychosocial stressors is equally critical, as psychological factors often interplay with physical habits to exacerbate symptoms. These findings underline the need for comprehensive interventions targeting both behavioral and psychosocial aspects of TMDs risk [57; 58; 59; 60].
# Modeling and Application of Dynamic Soaring by Unmanned Aerial Vehicle
## 2 Dynamic Soaring Application Model
### UAV and the Environment Wind Model  
An aircraft that has a high aspect ratio is suitable for soaring. Hence, this paper chose the glider Fox as the UAV model. The details of this UAV are described in Table 1 and Figure 3.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Variable, Unit** & **Range** \\ \hline Mass, kg & 4.7 \\ Wing Span, m & 3 \\ Wing Area, m\({}^{2}\) & 0.75 \\ Aspect Radio & 11.57 \\ \(C_{D0}\) & 0.0223 \\ \(L/D_{max}\) & 27.96 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The Fox’s Parameters.  
The wind field is vital for dynamic soaring. The typical wind field models are the linear wind field model, exponential wind field model, and logarithmic wind field model. Compared with the linear and logarithmic wind field models, the exponential wind field model is the closest to the real situation. Hence, for the dynamic soaring application, this paper chose the exponential wind field model to carry out the following study. The model is shown in Figure 4.
## 6 Steady State Performance Analysis
### Relationship between Performance and \(R_{\rm xv}\)  
Figure 2 shows the relationship between \(R_{\rm xv}\) and \(\epsilon_{\rm rms}\) for \(a_{\rm D}^{2}=0.01\) and \(0.1\), and both cases exhibit the same trend. For both proposed and RA filters, better performance is achieved with better velocity measurement accuracy. The performance of the proposed filter is better than that of the best \(\alpha\)-\(\beta\) filter including relatively small \(R_{\rm xv}\) (the velocity measurement accuracy is low). In contrast, the performance of the RA filter is worse than that of the best \(\alpha\)-\(\beta\) filter for small \(R_{\rm xv}\), because the covariance matrix of the RA filter is limited to Equation (16). Moreover, by comparing Figure 2a,b, we see the superior effectiveness of the proposed filter for relatively large \(a_{\rm D}\). These results indicate that the proposed filter is effective when the velocity measurement accuracy and/or target acceleration is relatively high.
# Generating Fingerings for Piano Music with Model-Based Reinforcement Learning
## 4 Learning Algorithm
### Model-Based Reinforcement Learning in Fingerings Annotation  
In earlier studies on piano fingering annotation using reinforcement learning, model-free algorithms such as Q-learning [19] or deep Q network (DQN) [20] were commonly employed. Model-free algorithms do not estimate the probability distribution of state transitions and the reward function of the MDP. They can only learn through interaction with the environment.  
Instead, model-based algorithms can learn by predicting the rewards and state transitions after executing actions. This process is referred to as model learning [17]. In this task, since the environmental model is deterministic, meaning the state transition probability \(P_{a}(s,s^{\prime})\) is always 1, utilizing model-based reinforcement learning algorithms can reduce the interaction between the agent and the environmental model compared to model-free algorithms, thereby improving the sampling efficiency. Therefore, a model-based reinforcement learning approach like prioritized sweeping is more suitable for this task.  
Prioritized sweeping is essentially an improvement to the Dyna architecture [21]. It introduces a priority queue, during the planning step of Dyna, which utilizes the experience in model learning to prioritize learning state-action pairs with a larger TD-error. In Algorithm 2, we replaced the Q-table and \(Model(s,a)\) in the original algorithm with implementations using hash tables instead of 2D matrices. This is intended to avoid the enumeration of the state space and action space.  
```
procedurePrioritized Sweeping  Initialize empty hash table \(Q(s,a)\), \(Model(s,a)\) and empty priority queue \(PQueue\). while\(true\)do \(s\gets current(nonterminal)state\) \(a\gets policy(s,Q)\)  Execute action \(a\), observe resultant state, \(s^{\prime}\), and reward, \(r\) \(Model(s,a)\gets s^{\prime},r\) \(p\leftarrow|r+\gamma max_{s^{\prime}}Q(s^{\prime},a^{\prime})-Q(s,a)|\) \(push(PQueue,(s,a),p)\) while\(isemptyQueue\)do \(s,a\gets pop(PQueue)\) \(s^{\prime},a\gets Model(s,a)\) \(Q(s,a)\gets Q(s,a)+a[r+\gamma max_{s^{\prime}}Q(s^{\prime},a^{\prime})-Q(s,a)]\) for\(s,a\) predicted to lead to \(s\)do \(r\leftarrow\) predicted reward \(p\leftarrow|r+\gamma max_{s^{\prime}}Q(s^{\prime},a^{\prime})-Q(s,a)|\) if\(p>\delta\)then \(push(PQueue,(s,a),p)\) endif endfor endwhile endwhile endwhile endprocedure
```  
**Algorithm 2** Prioritized sweeping (key-value storage)/  
Figure 5: Traditional tabular Q-learning and key-value storage Q-learning.
## 5 Case Study
### Analysis of Identification Effect
#### 5.2.1 Setting of Model Parameters  
(1) **Computing operations**. According to the sample size, the kernel sizes were \(3\times 3\), \(5\times 5\), \(7\times 7\) in 2D DSCB, \(3\times 3\), \(5\times 5\) in 2D DiSCB, \(3\times 3\) in 2D MaxPool, and 2D AvgPool. Featuring the void operation, eight elements were present in the alternative computing operation set of the cell edges.  
(2) **CAS**. The number of stacked cells was eight, the channel number for the Init Conv was six, the batch size of each iteration was 128, and the number of training epochs was 50. The other parameters were in accordance with the DARTS basic setting [38]. Moreover, the number of intermediate nodes in the cells was four. The loss function was the cross-entropy loss function [53]. The inner optimization algorithm used was the stochastic gradient descent (SGD) [57], and the learning rate was updated via the non-restart cosine annealing method [58], with an initial value of 0.025, momentum of 0.9, and weight decay of 0.004. The outer optimization algorithm was Adam [59] with a learning rate of 0.004, momentum of \([0.5,0.999]\), and a weight decay of 0.001.  
(3) **FMV**. Different numbers of stacked cells and training epochs were set in this study to analyze the impact of these hyperparameters on the model performance, which is described in detail in later sub-sections. The number of channels for the Init Conv was six, and the batch size of each iteration was 512. The other parameters were in accordance with the DARTS basic settings [38]. The loss function employed was the cross-entropy loss function, and SGD was utilized as the optimization algorithm. To improve the training efficiency, cutout [60] and path dropout [61] with a probability of 0.2 were employed.  
Figure 9: Schematic of dataset division.  
(4) **Implementation and computing**. The models in this case study were all implemented via the PyTorch [62] ML framework and were trained on a single NVIDIA GeForce GTX 1080 8 gigabyte GPU.
# User-Centric Cell-Free Massive MIMO with Low-Resolution ADCs for Massive Access
## 1 Introduction
### Organization and Notations  
The remainder of this paper is organized as follows. In Section 2, we introduce the system model of user-centric cell-free massive MIMO with fronthaul capacity constraints. In Section 3, we derive the closed-form expressions for uplink SE. Section 4 proposes the AP-UE association algorithm, while Section 5 analyzes the simulation results. Finally, in Section 6, we conclude our study.  
Notation: In boldface, lowercase **x** represents a vector, while uppercase **X** represents a matrix. \(\mathbf{X}_{i,j}\) denotes the entry \(i,j\), and the superscripts \({}^{-1}\), \({}^{T}\), \({}^{\star}\), and \({}^{H}\) denote inverse, transpose, conjugate, and conjugate transpose, respectively. The \(N\)-dimensional identitymatrix is denoted by \(I_{N}\) and the \(N\)-dimensional zero matrix is denoted by \(\mathbf{0}_{N}\). The symbol \(\mathbb{E}\) represents the expectation operator. \(\mathbf{R_{xy}}\) denotes the correlation matrix between \(\mathbf{x}\) and \(\mathbf{y}\). The symbol \(\mathbb{C}\) denotes the set of all complex numbers, and the symbol \(\mathcal{CN}(0,1)\) denotes the complex Gaussian distribution with a mean of 0 and a variance of 1.
## 1 Introduction
Furthermore, the approximated equations of not only the outage probability but also the ergodic capacity are derived and compared in both scenarios: perfect and imperfect SIC. The contributions of this paper can be summarized as follows:  
* The downlink NOMA system with the base station (BS) of multiple antennas and the user of single antenna is analyzed. Multiple users become an one cluster according to their location and Zero-forcing based precoding is applied at the BS to mitigate the inter-cluster interference. * The approximated equations of OP and EC are derived for every user for evaluating the proposed method based on perfect and imperfect SIC. The proposed approximated expressions are verified by simulation results. * The EC of proposed method is calculated and compared with the OMA method to indicate the better one via both theoretical analysis and simulation in cases of perfect and imperfect SIC. The organization of the rest of paper is as follows. Section 2 briefly depicts the downlink NOMA MU-MISO system and its channel model. The approximation of EC and OP is proposed in Section 3. The proposed method is evaluated by numerical results that are depicted in Section 4. Finally, the conclusion of paper is given in Section 5. _Notations_: The notations which are used in this paper are denoted as: Bold upper letters are the matrices, whereas bold lower letters indicate the column vectors, the conjugate transpose operation is denoted by \((\cdot)^{H}\) and \((\cdot)^{*}\). A vector norm is denoted by \(\|\cdot\|\), whereas \(|\cdot|\) denotes the absolute value. Furthermore, the \(\mathbb{E}\{\cdot\}\) and \([\mathbf{X}]^{\mathbf{T}}\) denote the average operator and the transpose matrix of \(\mathbf{X}\), respectively. \(f_{X}(\cdot)\) and \(F_{X}(\cdot)\) respectively denote the probability density function (PDF) and the cumulative distribution function (CDF).
# Multiservice Loss Models in Single or Multi-Cluster C-RAN Supporting Quasi-Random Traffic
## 4 The Generalized f-MC-MC Model
### The Analytical Model
Based on (19), we can compute the TC probabilities of service class \(u,w,s\) calls: (a) as the result of RRUs unavailability only, \(B^{\mathrm{TC}}_{\mathrm{r},u,w,s}\), and (b) as the result of CRUs unavailability, \(B^{\mathrm{TC}}_{\mathrm{c},u,w,s}\). Additionally, we can calculate the total TC probability, \(B^{\mathrm{TC}}_{\mathrm{tot},u,w,s}\), via:  
\[B^{\mathrm{TC}}_{\mathrm{tot},u,w,s}=B^{\mathrm{TC}}_{\mathrm{r},u,w,s}+B^{  
Having computed \(q^{\prime}_{t,u,w,s}(j)\), we compute the aggregated o.d. of the \(u,w\)-th RRH without taking into consideration the first service class, \(Q^{u,w}_{t,(-1)}\):  
\[Q^{u,w}_{t,(-1)}=q^{\prime}_{t,u,w,2}*\ldots*q^{\prime}_{t,u,w,s}*\ldots*q^{ \prime}_{t,u,w,5,u,r}, \tag{26}\]  
where the operation between \(q^{\prime}_{t,u,w,x}\equiv q^{\prime}_{t,c}\) and \(q_{t,u,w,y}\equiv q^{\prime}_{t,d}\) takes the following form:  
\[q^{\prime}_{t,c}*q^{\prime}_{t,d}=\Bigg{\{}q^{\prime}_{t,c}(0)\cdot q^{\prime }_{t,d}(0),\sum_{i=0}^{1}q^{\prime}_{t,c}(i)\cdot q^{\prime}_{t,d}(1-i),\ldots,\sum_{i=0}^{C_{n}}q^{\prime}_{t,c}(i)\cdot q^{\prime}_{t,d}(C_{u}-i)\Bigg{\}}. \tag{27}\]  
Finally, the computation of, \(q^{\prime}_{t,u,w}\), is based on \(q_{t,u,w}=Q^{u,w}_{t,(-1)}*q^{\prime}_{t,u,w,1}\) and the normalization \(q^{\prime}_{t,u,w}(j)=q_{t,u,w}(j)/G_{t,u,w}\) with \(G_{t,u,w}=\sum_{j=0}^{C_{n}}q_{t,u,w}(j)\). Step B  
In this step, we proceed with the calculation of the aggregated o.d. of all RRHs without taking into consideration the \(u,w\)-th one, via:  
\[Q_{t,(-(u,w))}=q^{\prime}_{t,1,1}*\ldots*q^{\prime}_{t,u,w-1}*q^{\prime}_{t,u,w +1}*\ldots*q^{\prime}_{t,U,w_{l}}. \tag{28}\]  
The operation of \(q^{\prime}_{t,o}\) and \(q^{\prime}_{t,w}\) is given by (12), while the normalized values of \(Q_{t,(-(u,w))}(j)\), \(Q^{\prime}_{t,(-(u,w))}(j)\), can be determined via \(Q^{\prime}_{t,(-(u,w))}(j)=Q_{t,(-(u,w))}(j)/G_{t,(-(u,w))}\) where  
\(G_{t,(-(u,w))}=\sum_{j=0}^{T}Q_{t,(-(u,w))}(j)\). Step C  
Herein, we calculate \(Q^{\prime}_{t,(-(u,w))}*q^{\prime}_{t,u,w}\). The latter leads to the un-normalized \(Q_{t,u,w}(j)\) which can be normalized via: \(G^{*}_{t,u,w}=\sum_{j=0}^{T}Q_{t,u,w}(j)\):  
\[Q^{\prime}_{t}(j)=\frac{Q_{t,u,w}(j)}{G^{*}_{t,u,w}}. \tag{29}\]  
To determine the values of \(Q^{\prime}_{t}(j)\), one may choose any of the \(u,w\) RRHs since all of them have the same capacity and o.d. Based on (29), we can now calculate the TC probabilities due to the CRUs and RRUs unavailability, via: (30) and (31), respectively:  
\[B^{\text{TC}}_{\text{c},u,w,s}=\sum_{j=T-\hat{b}_{\text{c},u,w}+1}^{T}Q^{ \prime}_{t}(j), \tag{30}\]  
\[(N_{u,w,s}-n_{u,w,s})v_{u,w,s}P_{\text{g,fin}}(\mathbf{n})=(n_{u,w,s}+1)\mu_{u,w,s} P_{\text{g,fin}}(\mathbf{n}^{+}_{u,w,s}). \tag{18}\]0
# Impact of VR Application in an Academic Context
###### Abstract
There are also a couple of other voice recognition models, such as neural networks (NN) and fuzzy models, but compared to HMM, they do not have the same accuracy in noisy environments [17], In this study, the virtual assistant application developed using Unity is able to search based on voice input provided by the user. To ensure a high-level understanding of language variances for non-native speakers, the confidence level was set to \"low\". Unity provides four levels of confidence: low, medium, high, and rejected [16]. In addition to the aforementioned features, the space and interactivity of a teaching application are essential considerations. Therefore, we believe that the integration of multimedia elements represents a fundamental component for enhancing the learning experience. One of the hypotheses of the study is that students are more inclined to participate in collaborative interactions with a learning application when they feel comfortable and confident using it. In this regard, a whiteboard placed into a virtual amphitheater was the option to create the environment that elicits comfort and provides an immersive experience for the end user. Figure 1: Adapted [PERSON]’s and [PERSON]’s reality–virtuality continuum for educational fields. Thus, an immersive amphitheater featured with a whiteboard is considered the appropriate solution for creating a dynamic and engaging learning environment for the end-user. Overall, the primary objective of this study is to evaluate the potential impact of VR technology on teaching and learning paradigms, with an aim to determine how it may significantly transform and enhance current educational practices. Our research aims to bring a great contribution in the field by exploring the impact of using multimedia VR applications for academic purposes and providing insights on the advantages and limitations of adopting this in the classroom. In this context, the key features of a virtual assistant tool are described and evaluated, including gamification, interactive simulations, and collaborative experiences. The potential impact of our virtual assistant tool on students' learning outcomes and engagement is also assessed. To achieve the objective, the research question that guides this study is: \"Can the integration of multimedia VR applications, such as the virtual assistant tool, enhance students' engagement level, improve their knowledge acquisition efficiency, and foster collaboration and interaction among participants in academic settings?\". The paper is structured as follows: Section 2 describes the software and hardware equipment and the materials and methods used to evaluate the impact of virtual reality in academic context, followed by the results of the evaluation process in Section 3, and by the discussion, conclusions, and future work in Section 4.