esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Analysis and Discussion
### Privacy and Ethical Considerations  
Both the development and deployment of speech emotion recognition (SER) systems, particularly in educational contexts, raise important privacy and ethical concerns that must be addressed. As SER technology involves the processing and analysis of potentially sensitive personal data, such as emotional states, it is crucial to prioritize data protection and ensure transparency regarding how this information is collected, used, and stored.  
In diverse cultural contexts, the perception and expression of emotions may vary significantly. Therefore, it is essential to consider cultural nuances and develop SER systems that are culturally sensitive and respectful of individual privacy. Future research and development efforts should involve collaboration with experts in ethics, privacy, and cultural studies to establish guidelines and the best practices for the responsible deployment of SER technology.  
To address privacy concerns, several measures can be implemented:  
Data Anonymization: Ensuring that personal identifiers are removed from the speech data and that individuals cannot be directly linked to their emotional data.  
Secure Data Storage: Implementing robust security measures to protect the collected emotional data from unauthorized access or breaches.  
Informed Consent: Obtaining explicit consent from individuals before collecting and processing their speech data for emotion recognition purposes, after clearly communicating how the data will be used and stored.  
Transparency and Control: Providing individuals with transparency about the SER system's functionalities, the types of data being collected, and how these data are being used, as well as offering individuals control over their data, including the ability to access, modify, or delete their emotional data.  
Ethical Guidelines: Developing and adhering to ethical guidelines that govern the use of SER technology, ensuring that it is not misused or employed in ways that could harm or discriminate against individuals.  
By proactively addressing privacy and ethical concerns, researchers and developers can foster trust in SER systems and promote their responsible deployment in educational settings. Future work should prioritize the development of privacy-preserving techniques, such as federated learning [39] or differential privacy, to enable the training of SER models without compromising individual privacy.  
Moreover, ongoing dialogue and collaboration among researchers, educators, policymakers, and the public are necessary to navigate the ethical implications of SER technology and develop frameworks that balance the benefits of emotional intelligence with the protection of individual rights and cultural values.
## 2 Materials and Methods
### Data Reduction and Model Learners
#### 2.1.3 Multi-Layer Perceptron  
Neural Networks are a class of nonlinear regressors. Given a set of inputs \(X\) and a set of outputs \(Y\), one can easily use a Neural Network to compute a nonlinear function \(f\) such as  
\[Y\approx f(X). \tag{8}\]  
When an NN contains more than one hidden layer of neurons, it is called Multi-Layer Perceptron. Figure 2 schematizes the architecture of a MLP with one hidden layer consisting of \(p\) neurons. The MLP takes as inputs vectors of size \(n\) and transforms them into output vectors of size \(m\).  
In the MLP illustrated in Figure 2, coefficients \(\omega_{i,j}\) and \(h_{i,j}\) are the weights of the neurons connectors, and \(k\) refers to the \(k^{th}\) data in the Design of Experiments -DoE-. The function that generates the \(i^{th}\) component of an output \(Y_{k}\) from the input \(X_{k}\) reads  
\[Y_{k,i}=f_{i}(X_{k})=\phi\left(\sum_{l=1}^{p}h_{l,i}\;\phi\left(\sum_{j=1}^{n} \omega_{l,j}\;X_{k,j}+b_{1}\right)+b_{2}\right), \tag{9}\]  
Figure 2: Example of a multi-layer perceptron.  
with \(b_{1}\) and \(b_{2}\) the bias introduced at each connection and \(\phi\) the activation function. The weights of \(f_{i}\) are obtained by minimizing the difference between the output prediction and the known output that is \(\|Y_{kj}-f_{i}(X_{k})\|\), for all \(i,k\).  
The trickiest concern when using the MLP is the choice of the so-called hyper-parameters, e.g., number of layers, number of neurons in the hidden layers, etc. For additional information, the interested reader can refer to [22; 23; 24; 25].
## 2 Materials and Methods
### Transmission Phase
#### 2.3.3 Selection of Optimum Route  
In the PECR calculation, if CHs are situated far away, they do not advance collected data straightforwardly to the MCH. In the multi-hop transmission mode, a CH sends route demands to CHs situated in a forward way to the MCH, and the BS and chooses one which takes less time to reply. The minimum reply time indicates the minimum distance among the CHs. The selected CH then receives the data, combines them with its own, and forwards them in a compacted form to the MCH as displayed in Figure 1.
## 4 Materials and Methods  
### Data Collection  
We collected data from actual SNS users in South Korea where most people heavily use various SNSs. We distributed a total of 3000 questionnaires to SNS users in public places such as libraries, train stations, college campuses, and downtown areas. Of those, we collected 592 (response rate: 19.73%) over the subsequent three months, discarding 13 responses because of missing values. Thus, we used 579 responses in our analysis. As recommended in the literature related to determining appropriate sample sizes e.g., [44; 45], we used the ratios of measures and latent variables to determine if we had collected enough data. In so doing, we found that the minimum required sample in this study was 138 responses, meaning the collected data was sufficient to test the proposed research model.  
Among the 579 respondents, 272 (46.98%) were male and 307 (53.02%) were female. Thus, female respondents' participation rate was slightly higher. Regarding the length of SNS usage, 33.68% reported having used an SNS for seven years or more, 23.66% reported five years or more, and 19.69% reported nine years or more. The most popular SNS was Facebook (74.09% of the respondents), followed by YouTube (66.49%), Instagram (65.11%), and Qzone (53.20%). Furthermore, many users used more than one SNS. Regarding daily SNS usage time, 34.20% of the respondents reported using SNSs for one to three hours a day, followed by three to five hours (31.95%), and less than one hour (16.41%). Interestingly, 17.44% of the respondents reported using SNSs more than five hours a day, which could indicate addiction considering people's average daily schedules. Finally, individuals reported using SNSs for a variety of purposes. The primary reason users reported for using SNS was to express themselves (73.06%), followed by sharing common interests with others (62.52%), and maintaining relationships (55.61%). These reasons may explain the large amounts of data generated in the Web 2.0 environment. Table 2 provides profiles of the respondents.
## 3 Results and Discussion
### Fatigue Performance  
This research takes for granted the homogeneity of asphalt mastic and uses the S-VECD model developed for asphalt to predict the behavior of asphalt mastic [38; 39]. The stress-strain results acquired from the LAS test are presented in Figure 12, along with data points before and after the discovered viscous damage locations. Compared to the stress-strain curve of plain asphalt mastic, the curve of fiber-modified asphalt mastic exhibits a higher breaking stress and a lower strain. This indicates that the addition of fibers increases the material's strength while reducing its deformation. The enhanced elastic properties of the asphalt material are associated with its higher yield strain. However, it should be noted that the inclusion of fiber additives can potentially diminish the material's elasticity.  
Figure 11: Evaluation of flow characteristics index in fiber-modified asphalt mastics.  
Figure 12: Curves illustrating the relationship between strain and stress in fiber-modified asphalt mastics.  
It is important to recognize that the value of failure strain alone provides an indication of the asphalt mastic's performance under repetitive loads and does not necessarily reflect its fatigue resistance. For a more accurate assessment of fatigue behavior, additional evaluations of fatigue damage and failure characteristics need to be conducted.  
According to the S-VECD model's calculations, Figure 13 shows the damage characteristic curves (DCC) for plain and fiber asphalt mastic. Incorporating natural fibers into the asphalt mastic was found to have a significant impact on the fatigue damage evolution. The various damage qualities of different asphalt mastics are represented by the relevant results of each DCC using the correlation between \(C\) and \(S\) as an input to predict fatigue resistance. Significantly, the material's stiffness, which rises with fiber content, mainly determines where the \(C(S)\) curve is located. Usually, a lower stiffness corresponds to a lower position for the curve.  
The present research employs a criterion for asphalt mastic failure estimation based on a 35% decrease in \(\mid G^{*}\mid*\sin\delta\)[32]. Figure 14 shows the results of a simulation under cyclic fatigue loading with strain control that used the measured material parameters to calculate the fatigue life after fiber modification. The results indicate that the fiber-modified asphalt mastic has a lower fatigue life than common asphalt mastic. Furthermore, the fatigue life decreases as the fiber content continues to increase, indicating uneven dispersion of fibers in the asphalt mastic can occur. Bamboo fibers negatively affect the asphalt mastic's fatigue life, likely due to their short particle size, which causes stress concentration and surface damage, resulting in reduced load-carrying capacity and diminished fatigue performance of the asphalt material.
# IMU-Aided High-Frequency Lidar Odometry for Autonomous Driving
## 3 The Proposed Approach
### Pre-Processing Step 2: Intra-Frame Correction
#### 3.4.1 Rectification with the Aid of IMU  
In this method, we try to use IMU to rectify the raw points. Let the pose at time \(t^{\text{L}}_{\text{k}}\) and \(t^{\text{L}}_{\text{k}-1}\) be \(T^{\text{WB}}_{\text{q}}\) and \(T^{\text{WB}}_{\text{p}}\). Since the IMU data is generated at a frequency of 100 Hz and the lidar point clouds are generated at a frequency of 10 Hz, there are always nine frames of IMU data between the pose \(T_{\rm P}^{\rm WB}\) and \(T_{\rm q}^{\rm WB}\). Therefore, ten relative transformations \(\triangle\,T_{\rm m-1,m}^{\rm B}\) (\(m\in(p,q]\)) can be obtained by Equation (1) and each \(\triangle\,T_{\rm m-1,m}^{\rm B}\) represents the relative transformation during \([t_{\rm m-1}^{\rm B},t_{\rm m}^{\rm B}]\), where \(t_{\rm m}^{\rm B}-t_{\rm m-1}^{\rm B}\) equals to 10 ms.  
Since the rotational velocity of the lidar is constant during \([t_{\rm k-1}^{\rm L},t_{\rm k}^{\rm L}]\), the rotation angle of the lidar per 10 ms is also fixed. We can separate all points of a frame \(f_{\rm k}^{\rm L}\) into ten sets, and the ID of points in each set can be denoted as \(\mathrm{i}\in[\frac{m-1-p}{q-p}\,\times\,S_{b},\frac{m-p}{q-p}\,\times\,S_{b}]\). Let the total number of points in each set be \(S_{e}\), which is equal to \(S_{b}/10\). We further assume that the motion of the vehicle is constant during \([t_{\rm m-1}^{\rm B},t_{\rm m}^{\rm B}]\). Each set of points now correspond to a unique relative transformation \(\triangle\,T_{\rm m,m-1}^{\rm B}\) which can also be denoted as \(exp\left(\triangle\,\xi_{\rm m-1,m}^{\wedge}\right)\).  
We linearly interpolated \(exp\left(\triangle\,\xi_{\rm m-1,m}^{\wedge}\right)\) to transform point \(\mathbf{X}_{\rm(i,b,k-j)}^{\rm B}\) to \(\mathbf{X}_{\rm(i,b,m-1)}^{\rm B}\). Then, \(\mathbf{X}_{\rm(i,b,m-1)}^{\rm B}\) will be transformed to \(\mathbf{X}_{\rm(i,b,k)}^{\rm B}\) by \(\triangle\,T_{\rm q,m-1}^{\rm B}\). The whole rectification process can be denoted as:  
\[\mathbf{\hat{X}}_{\rm(i,b,k)}^{\rm B}=\triangle\,T_{\rm q,m-1}^{\rm B}\cdot\exp \left(\triangle\,\xi_{\rm m-1,m}\cdot\frac{i^{\prime\prime}_{0}S_{e}}{S_{e}} \right)^{\wedge}\cdot\mathbf{X}_{\rm(i,b,k-j)}^{\rm B}\,, \tag{13}\]  
where \(A\%B\) is used to compute the remainder when \(A\) is divided by \(B\), and \(\mathbf{\hat{X}}_{\rm(i,b,k)}^{\rm B}\) represents the undistorted point after rectification.
# A Novel RBFNN-CNN Model for Speaker Identification in Stressful Talking Environments
## 4 Model Architecture
### Deep Learning Experiments
#### 4.3.3 Bidirectional LSTM (BILSTM)  
There are three LSTM layers present within the proposed BILSTM model. The neural network input contained a vector of 96 MFCC features. There were 64 filters present within each of the three LSTM layers. A fully connected layer and dense layer were included, which attained an activation function Softmax with 16 nodes.
# Reliable Internet of Things: Challenges and Future Trends
## 3 Major Use Cases of IoT
### Internet of Medical Things  
Internet of Medical Things (IoMT) is a vital enabler for the future health industry. IoMT can provide applications such as remote patient monitoring, automatic monitoring of emergency patients, medical supply chain control, and contact tracing in pandemic situations [48; 49; 50; 51; 52; 53; 54; 55; 56].  
In IoMT, the sensed data are related to patient body parameters such as heart rate, blood pressure, and sugar level. Several sensors can also help in the early detection of severe diseases such as cancers. Moreover, infectious diseases can also be detected earlier to control their spread [57; 58; 59].  
The data dissemination in IoMT can be managed by long-range communication technologies such as 5G. The data, such as patient body parameters, must be shared periodically and reliably with the hospital monitoring room. In supply chain monitoring or contact tracing applications, security while disseminating the data also becomes a key concern.  
The analyzed data in IoMT remote patient monitoring application is about patient health status. In case of a sudden change in a parameter, alarms can be generated, and doctors can be informed. Moreover, for supply chain monitoring, the temperature of the medicines or vaccines can be continuously checked [60; 61; 62].
## 4 Results and Discussion
### Variation Characterization of Pressure Gradient and Amplitude of Compression Waves
These factors result in a higher propagation speed of the wave tail, resulting in a shorter wavelength of the compression waves and an increase in the pressure gradient. The nonlinear steepening of compression waves during propagation is the fundamental reason for the formation of micro-pressure waves. As compression waves continue to propagate in the tunnel, the friction effects gradually strengthen, dominating the state and suppressing the nonlinear effects of compression wave propagation, leading to a decrease in pressure gradient. Therefore, there must exist a steepening critical distance at which the pressure gradient of the compression waves reaches its maximum value, and  
Figure 15: The compression waves waveform and pressure gradients at different measurement points during the propagation of the compression waves: (**a**) compression waves waveform; (**b**) pressure gradient. this length is defined as the steepening critical distance. The pressure amplitude of the compression waves continuously decreases due to the friction effects, with the attenuation ratio decreasing as the propagation distance increases. Under the conditions of no tunnel entrance hood, type A tunnel entrance hoods, and type B tunnel entrance hoods, the steepening critical distances are reached at 5 km, 6 km, and 16 km, respectively. With the type A tunnel entrance hoods, the steepening extent during propagation is the highest, with a maximum steepening rate of 15.6, meaning the maximum pressure gradient increases to 15.6 times the initial compression wave's pressure gradient. Different types of tunnel entrance hoods not only reduce the maximum pressure gradient of the initial compression waves but also change the steepening critical distance.
# A Deep Reinforcement Learning Approach for Microgrid Energy Transmission Dispatching
## 3 Problem Formulation
### Objective Function  
The goal of the optimization problem is to search the optimal power flow of the transmission line at each time period under the given state variables and calculate the minimum cost based on Formulas (5)-(13). Its expression is realized by  
\[\min(C_{t})=\sum_{t}^{+\infty}C_{dg,t}(x,u)+C_{m,t}(x,u)+C_{l,t}(x,u) \tag{14}\]  
The operation cost of microgrid \(C_{t}\) mainly consists of the fuel cost \(C_{dg,t}\), the operation and maintenance cost \(C_{m,t}\) and the line loss cost \(C_{l,t}\). These costs can be calculated by  
\[C_{dg,t}(x,u)=U_{p}\times P_{dg,t} \tag{15}\]  
\[C_{m,t}(x,u)=(\eta_{1}\times P_{pv,t}+\eta_{2}\times P_{dg,t}) \tag{16}\]  
\[C_{l,t}(x,u)=\theta_{1}\times P_{l,t}+\theta_{2}\times P_{b,t} \tag{17}\]  
The price of the diesel is usually set as \(U_{p}\) = USD 1.2, \(\eta_{1}\) = 0.0096, \(\eta_{2}\) = 0.088, \(\theta_{1}\) = 0.08, \(\theta_{2}\) = 0.01 [29]. From Formulas (12), (14) and (17), we can find that both the constraints and the objective function contain quadratic terms. Therefore, the problem can be understood as a quadratically constrained quadratic programming (QCQP) problem.  
When the energy supply of the distributed energy source can meet the energy consumption demand of the microgrid, the energy storage battery can perform a charging operation to store energy. Correspondingly, when the energy supply is insufficient, the diesel power generation and energy storage system can generate enough energy to meet the operation needs of the microgrid.
## 2 Hybrid Numerical Model
### Thermodynamic Model
\tag{12}\]  
where \(\varphi_{v}\) is the semi-empirical flow coefficient that accounts for the non-idealities of valve; \(A_{v}\) is the flow area through the compressor valve; \(y\) is displacement from the closed orientation of valve plate; \(\rho\) denotes density; \(s\) and \(d\) subscripts are suction and discharge, respectively. Heat exchange due to convection in the cylinder is given by the well-known formulation [38]:  
\[\frac{dQ_{cv}}{d\theta}=\frac{dQ_{c}}{d\theta}=\frac{1}{\omega}\alpha A_{hex} (T_{w}-T_{c}) \tag{13}\]  
Here, \(A_{hex}\), \(T_{w}\) and \(T_{c}\) are the wall surface area in contact with the gas, wall temperature and in-cylinder gas temperature, respectively; \(\alpha\) is the convective heat transfer coefficient and it is defined in this work by the representative correlation formulated by [PERSON] [18; 39]:  
\[\alpha=3.26p_{c}^{0.8}T_{c}^{-0.546}D_{c}^{-0.2}u^{0.8} \tag{14}\]  
In the above equation, \(u\) is the characteristic velocity in which \(u=6.18u_{p}\) for suction and discharge processes and \(u=2.28u_{p}\) for compression and expansion processes. Here, \(u_{p}\) is the average speed of the piston. For the suction chamber, mass flow rates can be expressed by:  
\[\frac{dm_{in}}{d\theta}=\rho_{s_{pipe}}u_{s_{pipe}}A_{s_{pipe}} \tag{15}\]  
\[\frac{dm_{out}}{d\theta}=\frac{dm_{s}}{d\theta} \tag{16}\]  
and for the discharge chamber, mass flow rates can be expressed by:  
\[\frac{dm_{in}}{d\theta}=\frac{dm_{d}}{d\theta} \tag{17}\]  
\[\frac{dm_{out}}{d\theta}=\rho_{d_{pipe}}u_{d_{pipe}}A_{d_{pipe}} \tag{18}\]  
It is noteworthy that suction and discharge chambers are assumed to be adiabatic and work is regarded as zero due to the fixed control volume:  
\[\frac{dQ_{cv}}{d\theta}=0,\frac{dW_{cv}}{d\theta}=0 \tag{19}\]  
For the valve dynamic simulation, the valve is assumed as a one-degree-of-freedom mass-spring-damper system, and valve placement \(y\) is restricted by the valve seat (\(y=0\)) and limiter (\(y=y_{max}\)). Based on [PERSON]'s second law, the general equation of valve plate motion is written by a 2 nd-order ordinary differential equation:  
\[m_{eq}\omega^{2}\frac{d^{2}y}{d\theta^{2}}+c_{eq}\omega\frac{dy}{d\theta}+k_{ eq}(y+y_{0})=C_{D}A_{D}\Delta p \tag{20}\]  
where \(m_{eq}\), \(k_{eq}\), \(A_{D}\) and \(y_{0}\) are the equivalent mass of the valve, equivalent stiffness of the spring, surface area of the valve plate and pre-load displacement, respectively; \(c_{eq}\) is the damping coefficient that is often neglected [18]; \(C_{D}\) is the drag coefficient that is obtained from previous investigation [4]; \(\Delta p\) is the pressure difference described by \(p_{s_{chamber}}-p_{c}\) for the suction valve and described by \(p_{d}-p_{d_{chamber}}\) for the discharge valve. In order to represent the collision impact between the valve plate and valve limiter/seat, a so-called rebound coefficient is defined here [38]:  
\[\frac{dy}{d\theta}\bigg{|}_{reb}=-C_{r}\left.\frac{dy}{d\theta}\right|_{imp} \tag{21}\]  
where \(C_{r}\) is the rebound coefficient and is considered as 0.3 in this work.
## 3 Results and Analysis  
### Ridgeline and Valley Line Mapping Analysis  
From Figures 2 and 5, the significant ridgelines and valley lines in the study area show a clear linear pattern of double rings. The inner and outer rings are almost like a \"heart.\" The ridgelines and valleys exhibit a dense distribution, particularly in the topography of the ring zone between the inner and outer rings, which is characterized by radiating ridges and valleys. ArcGIS was employed for a more detailed characterization of the outer and inner rings, encompassing parameters such as area, perimeter, width, and others (Table 3).  
According to the statistics in Table 3, the area ratio of the inner ring to the outer ring is 0.403, the perimeter ratio is 0.539, the east-west width ratio is 0.557, and the north-south length ratio is 0.616.
# Screening of Scaffolds for the Design of G-Quadruplex Ligands
## 3 Results and Discussion
### In Silico Studies  
Taking into account that G4 ligands present poor drug-like properties, a computational study using [PERSON]'s rule and [PERSON]'s parameters was performed to predict in vitro permeation and oral absorption [23; 24]. The in silico parameters are presented in Table 1. All tested ligands presented MW (210.236-395.615), Log \(P\) (0.657-4.093), n-ON acceptors (3-6) and n-OHNH donors (0-5), in the range defined by [PERSON]'s rule [23]. The n-ROTB between 2 and 13 and TPSA from 38.92 to 181.68, revealed that most of the tested scaffolds comply with [PERSON]'s parameters (n-ROTB \(<\) 10 and TPSA \(<\) 10 A\({}^{2}\)) [24]. None of the tested ligands violated [PERSON]'s rule of five, indicating that they have favorable properties that achieve the criteria of drug-likeness.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Ligand** & **TPSA** & **MW** & **Log \(P\)** & **n-ROTB** & \begin{tabular}{c} **n-ON** \\ **Acceptors** \\ \end{tabular} & \
\begin{tabular}{c} **n-OHNH** \\ **Donors** \\ \end{tabular} &
\begin{tabular}{c} **[PERSON]’s** \\ **Violations b** \\ \end{tabular} \\ \hline Ter-1 & 181.68 & 212.208 & 2.997 & 2 & 4 & 0 & 0 \\ ISO-1 & 52.06 & 212.208 & 2.997 & 2 & 4 & 0 & 0 \\ Quin-1 & 38.92 & 210.236 & 3.198 & 1 & 3 & 0 & 0 \\ Napht-3 & 50.08 & 243.354 & 1.48 & 7 & 3 & 3 & 0 \\ Napht-5 & 74.13 & 329.492 & 0.657 & 13 & 5 & 5 & 0 \\ Phen-1 & 77.85 & 314.304 & 4.093 & 2 & 6 & 0 & 0 \\ DPPZ & 51.57 & 260.3 & 3.343 & 0 & 4 & 0 & 0 \\ PhenCOOCH\({}_{3}\) & 78.39 & 296.282 & 2.356 & 2 & 6 & 0 & 0 \\ C\({}_{8}\)NH\({}_{2}\) & 35.50 & 395.615 & 2.3372 & 10 & 3 & 1 & 0 \\ C\({}_{5}\)NH\({}_{2}\) & 35.50 & 353.534 & 1.1669 & 7 & 3 & 1 & 0 \\ \hline \hline \end{tabular}  
* TPSA, topological polar surface area (A\({}^{2}\)); molecular weight (g/mol); Log \(P\) (n-octanol:water partition coefficient); n-ROTB (number of rotatable bonds); n-ON acceptors (number of hydrogen bond acceptors); n-OHNH donors (number of hydrogen bond donors); b Lipinski rule of five: MW \(<\) 500 Da; Log \(P\leq\) 5; n-OHNH acceptors \(\leq\) 10; n-OHNH donors \(\leq\) 5. A maximum of 1 violation is generally permitted.  
\end{table}
Table 1: In silico molecular descriptors of in vitro evaluated compounds a.