esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Noise Pollution Monitoring
For the third, the period between when the sensor node detects the noise until it stops was around 18 s.  
Figure 9 shows the highest peak of the noise level that was measured. In this case, the noise was caused by a light aircraft which fle  
Figure 8: A-weighting equivalent noise level values measured by the sensor node during the first three minutes. Figure 9: The highest peak of the A-weighting equivalent noise level. The noise indicators defined by the European Commission directives can be calculated by the sensor nodes. However, the noise perception is affected by subjective factors and there is not a direct correlation between the indicators and the subjective perception of noise. The calculated noise levels are by no means adequate indicators for the effects of noise on humans. There are other factors that determine the way in which people perceive noise as, for example, the duration of the noise. In this way, we present a Fuzzy Noise Indicator (FNI) that allows sensor nodes to infer the degree of subjective noise annoyance. The proposed FRBS has two inputs (the A-weighting equivalent noise level value and its persistence in time calculated as the average of the last 10 values) and one output fuzzy variable (FNI). Figure 10 shows the KB variables and their membership functions, and Table 6 presents the set of rules used. Figure 11 shows the input-output surface which relates the values of the noise level and its persistence with the FNI, and Figure 12 presents the inferred values for the noise data collection that is shown in Figure 9. \begin{table}
\begin{tabular}{|c|c|c|c|c|} \hline _Fuzzy Noise_ & \multicolumn{4}{c|}{_Persistence_} \\ _Indicator_ & \multicolumn{4}{c|}{_L_} & **M** & **H** \\ \cline{2-5} \multirow{2}{*}{_Level_} & **L** & **VL** & **VL** & **VL** \\ \cline{2-5}  & **M** & **L** & **L** & **M** \\ \cline{2-5}  & **H** & **M** & **H** & **VH** \\ \hline \end{tabular}
\end{table} 
Table 6: Set of rules used. Figure 10: Membership functions of the inputs and output variables fuzzy sets. The use of this FNI can help to distinguish between situations with noise annoyance, characterized by a high level and persistence, and other situations less annoying. It is to stand out that using the FRBS, only the FNI values that surpass a threshold level are transmitted, reducing communication and battery consumption.
# Exploiting Mobile Edge Computing for Enhancing Vehicular Applications in Smart Cities
## 5 Performance Evaluation  
To evaluate the performance of our proposed protocol, we provide a simulation-based implementation for vehicular data dissemination. We expect that the inclusion of traffic condition information in disseminated messages with the aid of MEC can improve the global data coverage, decrease the total dissemination overhead and minimize the dissemination delay. This improvement is expected due to the potential of utilizing traffic condition information in controlling the suppression in multi-hop delay-based broadcast. Traffic condition detection with the aid of MEC is characterized by its high accuracy, due to the global or semi-global network knowledge that is offered by edges. In contrast, VANET environment can only provide an estimated traffic condition for individual vehicles based on local data, which is sometimes misleading due to direction changes in the urban environment. In the remaining of this section, we describe our performance evaluation scenario from the following perspectives: the considered dissemination protocols, performance metrics, simulation setup and performance results.
## 2 Fundamental Models and Literature Search
### System Model  
#### 2.1.1 Distributed Network  
The distributed network comprises individual sensor nodes that are positioned across the water, whether it be in water bodies or seabed, as depicted in Figure 3. To facilitate the acquisition of data from these sensor nodes, the AMVs need to establish connections through acoustic waves with all the nodes while remaining within their communication range. Consequently, this approach has the potential to decrease energy consumption in UWSNs, as a substantial portion of operational tasks can be offloaded onto the AMVs. Nonetheless, the deployment of distributed networks presents certain challenges. On the one hand, the relatively slow movement of AMVs leads to extended travel times, resulting in increased data collection delays and a reduction in network throughput. On the other hand, the limited power capacity of AMVs places constraints on the overall efficiency of the data collection process. The necessity for AMVs to access all sensor nodes makes it challenging to accomplish their mission without supplementary recharging methods.
## 3 Multi-Scope State Area (MSSA)
### State Programming
#### 3.3.1 State Storage and Update  
However, even though the \"type, offset, length\" representing the state does not specify the state area, the type can be used to determine the state area, because, after matching the entry, the packet can access up to four state areas, each of which is of a different type. Moreover, offset and length can also be used to determine the state's position within the state area. Once the location of the state has been determined, the switch's assignment instructions can be reused to store or update the state in a specific location.  
It is worth noting that when the state is a matching field, it can only be in one of three state areas, as shown in Figure 7. Because the packet has not yet matched an entry, we cannot get the state of the matching entry's flow state area in advance. As a result, the state can only be in the global state, table state, or packet state areas when used as a matching field. When the state is used as an instruction parameter, it can be in one of four states because the packet has already matched the entry.  
This method necessitates network applications that manually plan the state area's space. Although it is unfriendly to programmers, each state area can only store a limited number of states. Directly specifying the location can reduce the time overhead of finding the state within the state area even further. In the future, we will improve the friendliness of programmers and shield the low-level details of state locations by using network programming languages.
## 4 Artificial Intelligence (AI)-Enabled 6G Networks
### Traffic Prediction  
Network traffic predictions focus on predicting future traffic using previous traffic data. This aids in proactively managing the network and computing resources, improving the QoS, making the network operations cost-effective, and detecting anomalies in the data traffic. DL has shown potential in predicting network traffic accurately in real time. Figure 7 shows an overview of the DL-based predictions of the network traffic data. [PERSON] et al. [50] evaluated the performance of different RNNs, namely the simple RNN, Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Identity Recurrent Unit (IRNN), and Feed-forward Neural Network (FNN) to predict network traffic using a GEANT backbone networks dataset. The experimental results showed that LSTM predicts the network traffic with the least MSE. [PERSON], [PERSON], and [PERSON] [51] used Bi-directional LSTM (Bi-LSTM) and Bi-directional GRU (Bi-GRU) to predict the network traffic matrix. To increase the prediction accuracy, the authors combined a CNN with Bi-LSTM or Bi-GRU. The authors found that the prediction performance of DL algorithms depends on the configuration of the neural network parameters.
## 2 Theoretical and Numerical Scheme  
### Control Equations  
In a two-phase flow, the phase field parameter (\(\Phi\)) is a measure used to describe the distribution of two immiscible fluids, such as liquid and gas. This concept is typically used to indicate the presence of two different phases in a system, such as the distribution of droplets in the gas or bubbles in the liquid. In a two-phase flow, the phase field parameter method can be utilized to simulate the evolution of phase interfaces and the distribution of the two phases. The phase field parameter (\(\Phi\)) usually takes values between \(-1\) and \(1\), indicating the proportion of the two different phases. For instance, when \(\Phi=-1\), it indicates a point or region entirely occupied by one phase (e.g., liquid). While \(\Phi=1\) indicates the other phase (e.g., gas), with intermediate values representing the extent of the mixed phase. The specific equations are formulated as [30]:  
\[v(\Phi)=\frac{(1+\Phi)}{2}v_{1}+\frac{(1-\Phi)}{2}v_{2} \tag{1}\]  
where \(\
u(\Phi)\) is described as a property (such as velocity) of the phase field parameter \(\Phi\) function, with \(v_{1}\) as the property of phase \(1\) and \(v_{2}\) as the property of phase \(2\).  
To simulate the flow of immiscible two-phase, the interface changes in the two-phase flow can be described by the incompressible Navier-Stokes equations, the continuity equation, and the phase field equation [31]. The Navier-Stokes equations are the fluid momentum conservation equations, which are used to describe the transport characteristics of fluid mass and momentum. In micropores, capillary forces play a significant role in the CH\({}_{4}\)-water process; thus, surface tension must be considered. The immiscible two-phase flow with surface tension can be described by the Navier-Stokes equations, with the specific formula as follows [32; 33]:  
\[\rho\frac{\partial u}{\partial t}+\rho(u\
abla)u=-\
abla p+\
abla\left[\mu \left(\
abla u+\
abla u^{T}\right)\right]+F \tag{2}\]  
\[\
abla\;u=0 \tag{3}\]  
where \(\rho\) is the density of the fluid, \(u\) is the velocity vector of the fluid, \(p\) is the pressure of the fluid, and \(T\) is the temperature, \(\bigtriangledown\) is the Laplace operator, which is used to describe the diffusion process of the phase field, and \(\mu\) is the viscosity of the fluid.  
\(F\) characterizes the contribution of capillary forces to the fluid momentum due to the two-phase fluid interface and can be expressed as [21]:  
\[F=G\
abla\Phi \tag{4}\]  
\[G=\lambda\;\left|\;-\
abla^{2}\Phi+\frac{\Phi\left(\phi^{2}-1\right)}{\varepsilon ^{2}}\;\right| \tag{5}\]  
where \(G\) is the chemical potential, \(\lambda\) is the mixing energy density, and \(\varepsilon\) is the capillary width with respect to the interface thickness.
# A Review of Methods and Applications for a Heart Rate Variability Analysis
## 7 Applications of HRV Analysis
### Blood Pressure/Hypertension Detection  
Blood pressure (BP) refers to the force of pumping blood against the walls of the arteries. The condition is called hypertension when this pressure is too high [146]. Two numbers are used to express someone's blood pressure. The first number, the systolic pressure, indicates the pressure in the blood arteries whenever the heart beats or contracts. The second number, the diastolic pressure, indicates the pressure in the blood vessels while the heart rests between the beats [147]. If the systolic BP and the diastolic BP are \(\geq\)140 mmHg and \(\geq\)90 mmHg, respectively, on two separate days, the patient is said to be suffering from hypertension [148]. Numerous structural and functional alterations of the cardiac muscles are induced in individuals with hypertension due to more workload on the heart. These alterations might increase the cardiovascular risk of hypertensive persons over and above the danger caused by an increase in blood pressure on its own. In recent years, many researchers have studied the feasibility of an HRV analysis as a marker for hypertension detection [149]. In a study by [PERSON] et al. (2018), it was shown that an HRV analysis can predict hypertension at an early stage [150]. The study used 3 months of PPG-based HRV statistics from hypertensive and non-hypertensive participants to calculate HRV features. Six HRV features were utilized in data mining to predict hypertension. It was found that the SDNN feature had the best predictive potential with an accuracy of 85.47%. In light of the findings, the researchers recommended that the PPG-based HRV statistics composed via the wearable devices can be a potential candidate for hypertension prediction with acceptable accuracy. An HRV analysis was tested as a prognostic tool for identifying high-risk patients suffering from hypertension by [PERSON] et al. (2021) [151]. Their study used a hybrid method founded on the dual-tree complex wavelet packet transform (DTCWPT) and other time-domain and non-linear HRV analysis methods to extract features. These features were then fed to the cost-sensitive RUSBoost algorithm, which produced a G-mean and F1 score of 0.9352 and 0.9347, respectively. Table 3 shows selected recent articles on an HRV analysis for hypertension detection.
# Investigation into Recognition Technology of Helmet Wearing Based on HBSYOLOX-s
## 3 Methodology
### Improve Loss Function  
To further improve the accuracy of the helmet-wearing detection task, this paper uses the SIOU loss function to replace the bounding box loss function of the predicted Reg. In 2022, [PERSON] et al. [26] proposed the SIOU loss function algorithm to consider further the vector angle between the actual frame and the predicted frame and redefine the associated loss function. The SIOU loss function contains four components, angle cost, distance cost, shape cost, and iou cost. Compared with the original GIOU loss function,  
Figure 2: BiFPN structural diagram.  
SOU is more consistent with the regression mechanism of the prediction box and makes the generation of bounding boxes more stable. The following formula defines SIOU:  
\[LOSS_{SIOU}=1-IOU+\frac{\Delta+\Omega}{2} \tag{1}\]  
\[\Omega=\sum_{T=W.H}\Bigl{(}1-e^{-Wt}\Bigr{)}^{\theta}=\Bigl{(}1-e^{-Ww}\Bigr{)} ^{\theta}+\Bigl{(}1-e^{-Wh}\Bigr{)}^{\theta} \tag{2}\]  
\[Ww=\frac{\left|\mathbf{w}-w^{gt}\right|}{\max(w-w^{gt})},Wh=\frac{\left| \mathbf{h}-h^{gt}\right|}{\max(h-h^{gt})} \tag{3}\]  
\[\Delta=\sum_{t=x,y}\Bigl{(}1-e^{-\gamma\ell_{t}}\Bigr{)}=2-e^{-\gamma\ell_{x}}- e^{-\gamma\ell_{y}} \tag{4}\]  
\[\ell_{x}=\left(\frac{b_{cx}^{gt}-b_{cx}}{cw}\right),\ell_{y}=\left(\frac{b_{ cy}^{gt}-b_{cy}}{ch}\right) \tag{5}\]  
\[\Lambda=\cos\Bigl{(}2\times\Bigl{(}\arcsin\Bigl{(}\frac{c_{h}}{\sigma}\Bigr{)} -\frac{\pi}{4}\Bigr{)}\Bigr{)} \tag{6}\]  
\[\sigma=\sqrt{\left(b_{cx}^{gt}-b_{cx}\right)^{2}+\left(b_{cy}^{gt}-b_{cy} \right)^{2}} \tag{7}\]  
\[c_{h}=\max\Bigl{(}b_{cy}^{gt},b_{cy}\Bigr{)}-\min\Bigl{(}b_{cx}^{gt},b_{cx} \Bigr{)} \tag{8}\]  
In the preceding equation, IOU represents the cross-entropy loss function, whereas \(\Delta\) and \(\Omega\) represent the distance and shape costs, respectively. In Equation (2), \(w,h,\mathbf{w}^{gt},\mathbf{h}^{gt}\) are the predicted and real box widths and heights, which regulate the degree of attention to the shape, which is normally between 2 and 6. In Equation (4),\(\gamma=2-\Lambda\), \(c_{w},c_{h}\) represent the minimum outer rectangle width and height of the real frame and the predicted frame, respectively, where \(\sigma\) is the distance between the center points of the real frame and the predicted frame, which is calculated in the code using the Pythagorean theorem. \(b_{cx}^{gt},b_{cy}^{gt}\) are the coordinates of the real frame's center. \(b_{cx},b_{cy}\) are the center of the prediction frame's coordinates.  
Figure 3: Structure of improved YOLOX-s.
## 2 Graph-Based Smoothness Harmonic
### Temporal Smoothness Index and Spatial Smoothness Brain Map  
The previously defined smoothness harmonic (\(\mathbb{E}_{\text{temp}}\)) captures the slowly varying harmonic in terms of the time-by-time graph, revealing how these harmonics propagate over time. To obtain a simplified measure of the temporal smoothness harmonic, we extract the diagonal component from the time-by-time graph. This yields a scalar indicator that conveniently represents the level of temporal smoothness harmonic (see Figure 2, upper plot).  
Figure 2: Temporal and spatial smoothness harmonics to reflect the spatiotemporal pattern of cortical dynamics. Upper plot: the production process of temporal smoothness index. The temporal smoothness index of a graph can be obtained from the diagonal elements of a time-by-time graph, which illustrates how slowly varying cortical dynamics evolve over time. Lower plot: the production process of the spatial smoothness brain map. The spatial smoothness brain map is obtained by projecting a region-of-interest (Rol)-by-Rol graph onto a brain surface map. The diagonal elements of this graph indicate how slowly varying cortical dynamics propagate across the cortex.  
Interestingly, since the graph signal \(f\) contains both spatial and temporal information regarding the recorded cortical dynamics, we can calculate a spatial version of the smoothness harmonic (\(\mathbb{E}_{\text{spat}}(f)\)) using the following procedure:  
\[\mathbb{E}_{\text{spat}}(f)=(\mathbf{U}_{k=1}\otimes f)\cdot f^{T}. \tag{2}\]  
where the broadcastle operator \(\otimes\) ensures element-wise multiplication between \(\mathbf{U}_{k=1}\) and \(f\); we broadcast \(\mathbf{U}_{k=1}\) to match the dimensions of \(f\), resulting in a matrix \((\mathbf{U}_{k=1}\otimes f)\) with a size of \([N,T]\).  
The diagonal elements of spatial smoothness harmonics unveil the spatial distribution of smoothness harmonics across the cortex, represented as a region-of-interest (RoI)-by-RoI graph that illustrates their spatial propagation pattern. This graph can be projected onto a brain surface to generate (a) brain map(s) for visualization purposes (see Figure 2, lower plot). The full Python notebook showcasing the computational steps for producing \(\mathbb{E}_{\text{temp}}(f)\) and \(\mathbb{E}_{\text{spat}}(f)\) is available at [[https://github.com/[PERSON]/SH](https://github.com/[PERSON]/SH)]([https://github.com/[PERSON]/SH](https://github.com/[PERSON]/SH)) (accessed on 1 April 2023).
## 2 Materials and Methods
### Intensity Processing Procedures
#### 2.5.2 Z-Score Intensity Normalization  
Z-score (ZS) normalization is a technique designed to standardize the intensity values of MR images to a set range, making images from different subjects or scanning sessions comparable. By calculating the mean \(\mu_{\text{zs}}\) and standard deviation \(\sigma_{\text{zs}}\) of the intensities within the image, Z-score normalization adjusts each voxel value \(I(x)\) as follows:  
\[I_{\text{zs}}(x)=\frac{I(x)-\mu_{\text{zs}}}{\sigma_{\text{zs}}} \tag{1}\]  
This formula ensures that the resulting image \(I_{\text{zs}}\) has a mean intensity and a standard deviation set to one [31]. However, because Z-score normalization can generate an intensity distribution with negative intensity values, which violates image requirements for some brain extraction procedures, the distribution was shifted with _fslmaths_ to have a minimum intensity value of zero after normalization [18].
# Bidirectional Sliding Window for Boundary Recognition of Pavement Construction Area Using GPS-RTK
## 3 Experiments
### Single-Point Positioning Accuracy and the Measurement Error Distribution Model
#### 3.2.2 The GPS-RTK Measurement Error Distribution Model  
To analyze the measurement error distribution model, we collected the GPS-RTK measurement errors of four vertex coordinates in the Gaussian plane coordinate system, as shown in Figure 8.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Coordinate Parameter** & **Value Range/m** & **Coordinate Parameter** & **Value Range/m** \\ \hline X1 & (0.5717,0.5728) & Y1 & (0.9659,0.9666) \\ X2 & (2.0927,2.0934) & Y2 & (\(-\)15.9304,\(-\)15.9299) \\ X3 & (6.3364,6.3374) & Y3 & (\(-\)16.9209,\(-\)16.9201) \\ X4 & (4.4021,4.4029) & Y4 & (1.5304,1.5315) \\ \hline \end{tabular}
\end{table}
Table 2: Value range of coordinate parameters at the 95% confidence interval.  
Figure 7: Four vertex coordinates in the Gaussian plane coordinate system respectively, where the horizontal axis represents the number of data (**a**)38, (**b**)30, (**c**)29, (**d**)23, the left vertical axis represents the x-axis in meters and the right vertical axis represents the y-axis in meters.  
The Lilliefors test can check whether the sample data come from a normally distributed population. It is specifically implemented as follows: when the population mean and variance are unknown, they are replaced with the sample mean and variance, and then the Kolmogorov-Smirnov test is performed. In Matlab, the lillietest function can directly perform the Lilliefors test on the input vector, which was used to test the GPS-RTK measurement error, at the 0.05 significance level. The experimental results show that the data conform to a normal distribution, and their histogram and normal distribution curve are shown in Figure 9. The mean value of the random variable is \(-1.11\times 10^{-10}\), and the variance of the random variable is \(1.41\times 10^{-6}\).
## 5 Results and Discussions
### System Performance Optimization and Comparison
As for the expense, the exergy efficiencies \(\eta_{cx}\) of sCO\({}_{2}\) system, sCO\({}_{2}\)/DH system, sCO\({}_{2}\)/ammonia-water AHP system, and sCO\({}_{2}\)/LiBr-H\({}_{2}\)O AHP system for the COD case dropped by about 3.14%, 3.35%, 4.09%, and 4.13%, respectively. In both the COD case and the EOD case, the sCO\({}_{2}\)/LiBr-H\({}_{2}\)O AHP system has the highest \(\eta_{cx}\) with a lowest cost \(c_{total}\), indicating that the overall system performances are greatly improved by combining the LiBr-H\({}_{2}\)O AHP with sCO\({}_{2}\) cycle. In the the EOD case, \(\eta_{cx}\) of sCO\({}_{2}\)/LiBr-H\({}_{2}\)O AHP system is 13.39%, 3.53% and 1.13% higher than those of the sCO\({}_{2}\) system, sCO\({}_{2}\)/DH system, and sCO\({}_{2}\)/ammonia-water AHP system, respectively. In the the COD case, \(c_{total}\) of sCO\({}_{2}\)/LiBr-H\({}_{2}\)O AHP system is 8.66%, 1.27%, and 0.42% lower than those of sCO\({}_{2}\) system, sCO\({}_{2}\)/DH system and sCO\({}_{2}\)/ammonia-water AHP system, respectively. In addition, the \(COHP\) of the sCO\({}_{2}\)/LiBr-H\({}_{2}\)O AHP system is much larger than those of the sCO\({}_{2}\)/DH system and \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Items** & **sCO\({}_{2}\)** & **sCO\({}_{2}\)/DH** & **sCO\({}_{2}\)/Ammonia-Water sCO\({}_{2}\)/LiBr-H\({}_{2}\)O** \\  & & & **AHP** & **AHP** \\ \hline \(PRc\) & 2.2–3.3 & 2.2–3.3 & 2.2–3.3 & 2.2–3.3 \\ \(T_{5}\) (\({}^{\circ}\)C) & 500–600 & 500–600 & 500–600 & 500–600 \\ \(T_{\text{A8}}\) (\({}^{\circ}\)C) & / & / & 92–125 & 87–110 \\ \(T_{\text{A1}}\) (\({}^{\circ}\)C) & / & / & 23–28 & 23–28 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Boundary conditions of decision variables for the PSO algorithm. the sCO\({}_{2}\)/ammonia-water AHP system. It indicates that the LiBr-H\({}_{2}\)O AHP can maximize the recovery of waste heat from sCO\({}_{2}\) cycle to provide heat for users. Table 14 shows the optimized results for the exergy efficiency optimal design case. Table 15 shows the optimized results for the cost optimal design case.