esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Verification and Analysis
### Constant Current Discharge Test  
The experiment was performed with a constant discharge current of 1 A. The initial SOC value is set to be 0.8, rather than the real SOC of 1.0. The process noise covariances are set as \(Q_{k}^{x}=1\times 10^{-6}I_{2}\) and \(Q_{k}^{\theta}=1\times 10^{-6}I_{3}\). The measurement noise variances used for DEKF are \(\Sigma_{k}^{x}=0.001\) and \(\Sigma_{k}^{\theta}=0.0005\). The scale factors for VB-ADEKF are set to \(1\times 10^{-4}\). The initial parameters \(\alpha_{0}\) and \(\beta_{0}\) for battery parameters and SOC are both set as 10 and 0.001, respectively.  
The estimated values of the battery model parameters by VB-ADEKF are presented in Figure 4. The ohmic resistance is stable at the beginning of the discharge and increases at the end of the discharge. The polarization resistance decreases at first and increases later with the depth of the discharge and again decreases in the last 1000 s. The polarization capacitance shows a declining trend overall in the process of discharge.  
Figure 4: Results of parameter identification using variational Bayesian approximation-based adaptive dual extended Kalman filter (VB-ADEKF) in the constant current discharge test.  
Figure 3: The relationship curve of open circuit voltage (OCV) versus state of charge (SOC).  
Furthermore, to verify the accuracy of online identification of the battery parameters by the VB-ADEKF algorithm, the measured terminal voltage and the estimated terminal voltage by VB-ADEKF were compared, as shown in Figure 5. The maximum absolute error was 0.023 V, except for the first big error caused by an incorrect initial SOC value. The mean absolute error was 0.0050 V and the relative mean absolute error was 0.052%. It is clear that the estimated terminal voltage agrees well with the measured voltage. This illustrates the effectiveness of the battery model whose parameters are identified by the proposed VB-ADEKF method.  
The SOC estimation results using VB-ADEKF and DEKF are shown in Figure 6. Clearly, VB-ADEKF has a much more accurate SOC estimation than DEKF. The SOC estimation error of VB-ADEKF is bounded within \(\pm\)1% for most of the time, but DEKF goes outside of this interval. The detailed error values are shown in Table 2. The maximum absolute estimation error of VB-ADEKF is 1.28% and the mean absolute error is 0.64% after discharge for 12 min. Both are significantly smaller than the errors of DEKF, which are correspondingly 2.76% and 1.39%. Meanwhile, from the figure it can be seen that VB-ADEKF converges much faster than DEKF under initial SOC errors. This is also verified in Table 2. The convergence time, which is defined as the first time instant at which the SOC estimation error decreases to \(\pm\)5%, are 10 s and 335 s for VB-ADEKF and DEKF, respectively.
# Nanomaterials in Photocatalysis: An In-Depth Analysis of Their Role in Enhancing Indoor Air Quality
## 2 Indoor Environments
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Key Points** & **Ref.** \\ \hline \multirow{8}{*}{Residential} & Key factors include heating systems, building materials, age of buildings, & \\  & and ventilation systems. & \\ \cline{2-3}  & Urban homes are prone to outdoor pollutants (e.g., NO\({}_{\times}\), SO\({}_{2}\), PM) & \\  & infiltrating indoor spaces. & \\ \cline{2-3}  & Poor Indoor Air Quality (IAQ) results from both indoor and outdoor & \\  & pollutants, including vehicle emissions, dust, and combustion fumes. & \\ \cline{2-3}  & COVID-19 lockdowns increased indoor pollutant exposure (e.g., VOCs, & \\  & particulate matter, microbes). & \\ \cline{2-3}  & Prolonged confinement and remote work highlighted the need for healthy & \\  & IAQ and pollutant control in homes & \\ \hline \multirow{8}{*}{Offices and commercial settings} & Like for residential buildings, IAQ in offices affects health and well-being, & \\  & but also productivity. & \\ \cline{2-3}  & Common pollutants include VOCs from office equipment (e.g., printers, & \\  & copiers), PM, CO\({}_{2}\), and biological contaminants. & \\ \cline{2-3}  & Pollutant sources include furnishings, cleaning agents, air fresheners, and & \\ \cline{2-3}  & external urban pollutants. & [44; 45; 46; 47; 48; 49; 50; 51] & \\ \cline{2-3}  & Poorly maintained HVAC systems, more common in commercial settings, & \\ \cline{2-3}  & exacerbate IAQ issues. & \\ \cline{2-3}  & Good IAQ improves cognitive function, reduces absenteeism, and aligns & \\  & with corporate social responsibility goals. & \\ \cline{2-3}  & Investment in IAQ offers health and economic benefits & \\ \hline \multirow{8}{*}{Industrial Environments} & High pollutant concentrations due to manufacturing, machinery, & \\  & and chemical use. & \\ \cline{2-3}  & Key pollutants: PM, VOCs, heavy metals, CO, SO\({}_{2}\), NO\({}_{\times}\), and & \\ \cline{2-3}  & biological contaminants. & \\ \cline{2-3}  & PM originates from material handling, grinding, cutting, and welding. & [52; 53; 54; 55; 56; 57] \\ \cline{2-3}  & VOCs are released during painting, coating, and solvent use. & \\ \cline{2-3}  & Combustion processes emit CO and NOx, posing severe health risks. & \\ \cline{2-3}  & Strategies include compliance with occupational health standards and & \\ \cline{2-3}  & improved air quality management. & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Category of indoor environments, key points, and relevant references.
## 1 Introduction
However, all these tests have some limitations; the data are potentially subject to bias due to the subjectivity of the examiner, the patient's collaboration attitude, and the characteristics of the physical space where the assessments are conducted. Additionally, multidimensional frailty assessment requires a considerable amount of time, which is limited in daily clinical practice. The use of electronic devices, wearable sensors and other technologies to measure physical activity or evaluate gait functions is relatively new but with a growing interest in the past ten years [26; 27; 28; 29]. These technologies have obtained interesting results regarding falls evaluation [26; 30; 31; 32] and frailty assessment [33; 34; 35; 31], improving clinical information with additional advantages as they are cheap, easy-to-use and accurate [36]. They also provide continuous detailed data on multiple variables and higher sensitivity and specificity for detecting frailty or falls [32; 34; 37; 38]. But they require complex data analysis, so at present, they are used in investigation settings as they need standardization in clinical practice. In conclusion, while wearable sensors show promise in the early detection of frailty, they complement rather than replace traditional tests like TUG, gait speed or SPPB right now [30]. The purpose of this study is to examine spatiotemporal gait parameters with a wearable sensor system (G-STRIDE) in participants with and without falls according to frailty status. The objectives of the study are: 1--To compare clinical characteristics, functional performance and gait parameters (estimated with the G-STRIDE inertial sensor) between different frailty groups in older adults with and without falls. 2--To identify variables that stratify participants according to frailty status and falls. 3--To verify the sensitivity, specificity and accuracy of the model that stratifies participants according to frailty status and falls.
## 3 Numerical Calculation
### Meshing  
For finite element analysis, the characteristic length of acoustic mesh elements must be no greater than one-sixth (1/6) of the smallest acoustic wavelength to guarantee the precision of the acoustic simulation. The formula is given below.  
\[\lambda_{\text{min}}=\frac{c_{0}}{f_{\text{max}}}\Rightarrow 1\leqslant\frac{c_{0}}{6f_{ \text{max}}} \tag{35}\]  
Given that the exhaust noise frequency is below 5000 Hz, the maximum characteristic length of the acoustic mesh elements, as determined by the formula, should be less than 19 mm. Consequently, the maximum acoustic mesh element size for the muffler was defined as 8 mm, and the minimum size was 5 mm. The acoustic domain exhibits low sensitivity to boundary data, and thus it was configured with two layers, featuring a first layer wall boundary layer thickness of 0.3 mm. Concurrently, the mesh around the four rushing holes on the muffler's inner tube was locally refined, with a maximum acoustic mesh element size of 3 mm. To enhance the acoustic mesh quality, a swept meshing technique was applied to the regular cylindrical volumes at the inlet and outlet of the muffler, resulting in hexahedral elements. After the above operations, the resulting mesh comprised a total of 209,157 elements, as shown in Figure 8.
# Numerical Study of Heat Transfer Enhancement for Laminar Nanofluids Flow
## 2 Mathematical Formulation
### Grid Independence Test  
The governing equations of mass, momentum and energy were solved using control volume finite difference approach. The semi-implicit method for pressure linked equations was employed to couple pressure and velocity in equations. A second-order upwind scheme was employed for interpolating the parameters. The structured grid distribution was used to discretize the computational domain. To ensure the accuracy and the consistency of computational results, various uniform grids were tested as shown in Table 5. The selected grid for the study calculations consisted of 532 and 30 nodes in the axial and radial directions, respectively. The numerical computations were considered converged, when the residual summed over all the computational nodes at \(i\)th iteration, \(R_{j}^{i}\), satisfies the following criterion: \(\left(R_{j}^{i}/R_{j}^{m}\right)\leq 10^{6}\), where \(R_{j}^{m}\) denotes the maximum residual value of \(j\) variable after \(m\) iterations, and \(j\) is applied for pressure, velocity and temperature.  
The local and average HTC's were calculated using Equations (8) and (9), respectively.  
\[h_{x}\ =\ \frac{q^{\prime\prime}}{T_{w}(x)-T_{m}(x)} \tag{8}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Nanoparticle Type** & **Density (kg/m3)** & **Specific Heat (J/kg-K)** & **Thermal Conductivity (W/m-K)** \\ \hline Al\({}_{2}\)O\({}_{3}\) & 3970 & 791 & 40.00 \\ TiO\({}_{2}\) & 3900 & 692 & 8.40 \\ CuO & 6400 & 551 & 32.90 \\ SiO\({}_{2}\) & 2200 & 745 & 1.40 \\ ZnO & 5600 & 495 & 13.00 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Thermophysical properties of different nanoparticles types.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Base Liquid Type** & **Density (kg/m3)** & **Specific Heat (J/kg-K)** & **Viscosity (kg/m-s)** & **Thermal Conductivity** & **Pr** \\ \hline Water & 997 & 4170 & \(1.00\times 10^{-3}\) & 0.606 & 6.96 \\ Ethylene-Glycol (EG) & 1111 & 2415 & \(1.57\times 10^{-2}\) & 0.252 & 150.46 \\ Turbine Oil & 868 & 2000 & \(2.70\times 10^{-2}\) & 0.120 & 462.55 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Thermophysical properties of different base liquid types.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Grid Size** & **Nu** & **Grid Size** & **Nu** & **Grid Size** & **Nu** & **Grid Size** & **Nu** \\ \hline
534 \(\times\) 10 & 5.80 & 534 \(\times\) 20 & 5.85 & 534 \(\times\) 30 & 5.94 & 534 \(\times\) 40 & 5.94 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Mesh independency test.  
\[h_{avg}\ =\ \frac{1}{L}\int_{0}^{L}h(x)dx \tag{9}\]  
The Nusselt number (_Nu_), was defined as:  
\[Nu(x)\ =\ \frac{(h_{x}D)}{k} \tag{10}\]  
The wall and fluid temperatures and the heat flux were computed to calculate the convective HTC. For constant heat flux, the fluid mean temperature (\(T_{m}\)) was computed as follows:  
\[T_{m}\ =\ \frac{1}{v_{avg}A}\int_{0}^{A}uTdA \tag{11}\]
## 4 Experimental Results
### Propeller Measurement Results
#### 4.2.2 Stability in Continuous Measurement Mode  
One-way ANOVA was performed to assess the measurement stability of the developed system. The results revealed nonsignificant differences in the repeated measurements for the first, second, and third blades (\(F\) = 0.004, \(p\) > 0.05; \(F\) = 0.090, \(p\) > 0.05; and \(F\) = 0.027, \(p\) > 0.05, respectively). This confirmed that the data collected across the different groups were statistically consistent, reflecting a high level of measurement stability. Therefore, the measurement platform of the developed system does not introduce bias under repeated sampling (Table 5).  
\begin{table}
\begin{tabular}{c c c c c} \hline  & & **Mean** & **Variance** & **Levene Statistic** \\ \hline \multirow{2}{*}{First blade} & Second decimal place & 1.21 & 0.750 & \multirow{2}{*}{508.036 ***} \\ \cline{2-2}  & Third decimal place & 4.34 & 7.974 & \\ \hline \multirow{2}{*}{Second blade} & Second decimal place & 1.14 & 0.696 & \\ \cline{2-2}  & Third decimal place & 4.32 & 7.287 & \\ \hline \multirow{2}{*}{Third blade} & Second decimal place & 1.31 & 0.823 & \multirow{2}{*}{343.317 ***} \\ \cline{2-2}  & Third decimal place & 4.83 & 6.671 & \\ \hline \end{tabular}
\end{table}
Table 4: Homogeneity test results for the continuous measurement mode (*** \(p\) < 0.001).  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & & **Sum of Squares** & **df** & **Mean Square** & **F** \\ \hline \multirow{4}{*}{First blade} & Between-group & 0.010 & 3 & 0.003 & \multirow{4}{*}{0.004} \\ \cline{2-5}  & Within-group & 297.180 & 396 & 0.750 & \\ \cline{2-5}  & Total & 297.190 & 399 & & \\ \hline \multirow{4}{*}{Second blade} & \multicolumn{4}{c}{Sum of squares} & df & Mean square & F \\ \cline{2-5}  & Between-group & 0.188 & 3 & 0.063 & \multirow{4}{*}{0.090} \\ \cline{2-5}  & Within-group & 275.590 & 396 & 696 & 0.090 \\ \cline{1-1} \cline{2-5}  & Total & 275.778 & 399 & & \\ \hline \end{tabular}
\end{table}
Table 5: Stability analysis of measurement values in the continuous measurement mode.
# Neighbor-Aware Non-Orthogonal Multiple Access Scheme for Energy Harvesting Internet of Things
## 4 Constrained Stochastic Game
### Reward Function  
The aggregated data rate of IoT devices is used as the reward function \(r(S_{i},A_{i})\). Therefore, \(r(S_{i},A_{i})\) can be defined as  
\[r(S_{i},A_{i})=\sum_{i}\log_{2}(1+\gamma_{i}) \tag{9}\]  
where \(\gamma_{i}\) is the expected SINR of IoT device \(i\).  
\(\gamma_{i}\) can be calculated by  
\[\gamma_{i}=\frac{|h_{i}|^{2}A_{i}}{\sum\limits_{i^{\prime}}|h_{i^{\prime}}|^{ 2}\overline{A_{i^{\prime}}}+\sigma^{2}} \tag{10}\]  
where \(\overline{A_{i^{\prime}}}\) is the expected power of IoT device \(i^{\prime}\). In addition, \(|h_{i}|^{2}\) and \(|h_{i^{\prime}}|^{2}\) are the channel gain of IoT device \(i\) and \(i^{\prime}\), respectively. \(\sigma^{2}\) is the noise power.
# A Survey of Routing Protocols in WBAN for Healthcare Applications
## 2 Introduction of Wireless Body Area Network
### Characteristics of the Wireless Body Area Network  
WBAN is a body network where a lot of sensor nodes are placed in, on or around the human body, to sense data, which is then transmitted through wireless means, to provide continuous and uninterrupted services [7]. WBAN is a special application branch of WSN--they have similarities but many differences. A detailed comparison of the two is shown in Table 1[8].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Problem** & **WSN** & **WBAN** \\ \hline range & environmental & body range(cm/m) \\ number of nodes & hundreds & dozens \\ node size & no special requirements & the smaller the better \\ node task & single or scheduled tasks & many \\ data rate & homogeneous & heterogeneous \\ data loss & tolerable & intolerable \\ node placement & easily & difficult \\ biocompatibility & not considering & consider \\ node life & months/years & the longer the better \\ topological & unchanged & changed \\ node energy & limited, but replaceable & limited and irreplaceable \\ safety & low & very high \\ standard & IEEE 802.11.4 & IEEE 802.15.6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison between WSN and WBAN.  
Figure 2: Applications of WBAN.
## 2 Results and Discussion
### Principal Component Analysis (PCA)  
PCA allowed us to differentiate between the different treatments of strawberry according to their volatile constituents. The PCA score and loading plots, including relationships between strawberry samples and volatile components, are reported in Figure 3. The sum of the first two principal components (PCs) explained 70.72% of data variability. Graphs showed that the fruit pretreatment had a significant effect on the volatile profile of strawberry, resulting in four different groups. The variability of data was produced mostly by variance of (\(E\))-nerolidol (values of eigenvectors: 8.27; \(-\)2.21) and \(\gamma\)-decalactone (8.39; \(-\)2.45) in the first PC, and by ethyl hexanoate (0.10; 4.91) and (\(E\))-2-hexenyl acetate (\(-\)2.43;\(-\)4.78) in the second PC. The group located in the upper side of the score plot was composed of shade air-dried or oven-dried (45 and 60 \({}^{\circ}\)C) samples. They were mostly correlated with a high release of ethyl hexanoate, acetoin (3-hydroxy-butanone), and mesifurane. Another group present in the middle upper side of the score plot was composed of microwave-dried samples and characterized by mesifurane. On the other hand, fresh, freeze-dried, and frozen samples grouped separately in the lower side of the score plot, with the former being characterized by (_E_)-2-hexenyl acetate, 1-hexanol, ethyl hexanoate, and hexyl acetate, the latter being characterized by (_E_)-nerolidol and \(\gamma\)-decalactone, and the freeze-dried samples being characterized by intermediate composition.  
According to PCA, we can conclude that the retention of fruit volatiles in dried strawberry fruits varied according to the type of drying method and freezing. A previous study classified the strawberry aroma active volatiles into six aroma groups according to their primary aroma characteristics as follows: fruits (comprising more than 50% of the total aroma intensity, represented by esters and lactones), sweet (about 18%, covered by furanones), floral (the least intense strawberry aroma class, 7%, featuring terpen), volatile acids (9%), green (aldehydes, 10%), and miscellaneous [32]. Accordingly, the PCA (Figure 3) and obtained results (Table 1), concerning the contents of esters, lactones, and furanones, showed that shade air-dried, followed by frozen, freeze-dried, and oven-dried 45 \({}^{\circ}\)C samples, retained more of the fruity and sweet aroma of strawberry fruits, representing more than 68% of the total aroma intensity according to the literature [32]. In contrast, the microwave-drying method showed a drastic loss of fruity esters volatiles, while the oven-dried 60 \({}^{\circ}\)C sample demonstrated an intermediate loss.
# Path Loss Model for 3.5 GHz and 5.6 GHz Bands in Cascaded Tunnel Environments
## 5 Verification and Comparison  
To verify the accuracy of the proposed model, 3.5 GHz, and 5.6 GHz bands are simulated in cascaded tunnels with different curvatures (\(R\) = 500 m and 1000 m). The ELC for different scenarios is calculated through the empirical formula, and the results are given in Table 7. To ensure that the curvatures are entirely in the far-field region, the propagation distances \(d\) at 3.5 GHz and 5.6 GHz in the straight tunnel are set as 400 m and 500 m, respectively.  
The path loss of a typical RT model relying on high-precision 3D tunnel models [32] at 3.5 GHz and 5.6 GHz for different radii of curvature is also given for comparison. As shown in Figures 13 and 14, the proposed model matches well with the results of the computationally intensive RT model [32]. For the 3.5 GHz band, the RMSE in the cascaded tunnels with R = 500 m and 1000 m are 2.19 dB and 1.68 dB, respectively. Meanwhile, for the 5.6 GHz band, the RMSEs are only 1.76 dB and 1.71 dB, showing a good agreement with the results of the RT model.  
\begin{table}
\begin{tabular}{c c c c} \hline \(f\) (GHz) & \(A\) & \(b\) & RMSE (dB) \\ \hline
3.5 & 1.75 & 1618 & 0.104 \\
5.6 & 1.97 & 1612 & 0.230 \\ \hline \end{tabular}
\end{table}
Table 6: Path-loss model parameters at 3.5 GHz and 5.6 GHz bands.  
\begin{table}
\begin{tabular}{c c c} \hline \(f\) (GHz) & \(R\) (m) & ELC (dB/100 m) \\ \hline
3.5 & 500 & 5.00 \\
3.5 & 1000 & 3.38 \\
5.6 & 500 & 5.20 \\
5.6 & 1000 & 3.58 \\ \hline \end{tabular}
\end{table}
Table 7: Calculation of ELC using empirical formula.  
Figure 12: The relationship between ELC and \(R\) at 3.5 GHz and 5.6 GHz.  
Since a prerequisite of the RT model is the 3D tunnel model constructed to describe the specified tunnel environments, the complexity per channel instance of the RT model is \(O(NT^{N+1})\), where \(N\) is the number of reflections and \(T\) is the total number of the basic surface units in the 3D model [46]. Considering typical values of \(N\) and \(T\) in the order of 2-6 and 100-10,000, respectively, the RT model requires excessive memory and CPU resources to run large-scale numerical electromagnetic simulations.  
Compared with the RT model, the simplified model proposed in this paper can predict path loss in cascaded tunnels with comparable accuracy but significantly lower complexity, which eases the demands on computational resources and facilitates propagation modeling for cascaded tunnels.
## 2 Economy-Quality Bi-Objective Optimization Model
### Objectives
#### 2.2.2 Objective 2: To Enhance Power Quality  
PQ of ME-VPP involves real power, reactive power, voltage magnitude, voltage angle, etc. This paper mainly concentrates on power index \(\Theta_{\rm P}\) and voltage index \(\Theta_{\rm U}\).  
\[\textit{Maximize}\ F_{2}=\beta\cdot\Theta_{\rm P}+(1-\beta)\cdot\Theta_{\rm U}, \tag{5}\]  
where \(F_{2}\) represents the degree of users' satisfaction. Note that power index of satisfaction degree \(\Theta_{\rm P}\) and voltage index of satisfaction degree \(\Theta_{\rm U}\) are derived from real power losses \(P_{\rm loss}\) and voltage stability index \(U_{\rm\%}\) by the fuzzy membership function as follows:  
\[\Theta_{X}=\left\{\begin{aligned} & 0,\ X\geq X^{0}\\ &\frac{X^{0}-X}{X^{0}-X^{*}},\ X^{*}<X<X^{0},\\ & 1,\ X\leq X^{*}\end{aligned}\right. \tag{6}\]  
where \(X\in\left\{\begin{aligned} & P_{\rm loss}^{\prime},\ U_{\rm\%}^{ \prime}\end{aligned}\right\}\), \(X^{0}\) and \(X^{*}\) denote initial value and optimal value of \(X\) respectively. \(\beta\) determines the relative weight between two indexes.  
* Real power losses \(P_{\text{loss}}\) \[P_{\text{loss}}=\sum_{i=1}^{N_{\text{B}}}\sum_{j=1}^{N_{\text{B}}}G^{ij}\Big{[} \left(U^{i}\right)^{2}+\left(U^{j}\right)^{2}-2U^{i}U^{j}\cos\theta^{ij}\Big{]},\] (7)  
where \(N_{\text{B}}\) represents the number of the buses; \(G^{ij}\) denotes the conductance of the transmission line connecting Bus \(i\) and Bus \(j\); \(U^{i}\) and \(U^{j}\) refer to the voltages of Bus \(i\) and Bus \(j\); \(\theta^{ij}\) denotes the phase angle difference of the voltages of Bus \(i\) and Bus \(j\).
* Voltage stability index \(U_{\%}\) \[U_{\%}=\sum_{i=1}^{N_{\text{B}}}\left[\frac{2U^{i}-\left(U_{\text{max}}^{i}+U _{\text{min}}^{i}\right)}{2\left(U_{\text{max}}^{i}-U_{\text{min}}^{i}\right) }\right]^{2},\] (8)  
where \(U_{\text{min}}^{i}\) and \(U_{\text{max}}^{i}\) respectively denote the minimal and maximal voltages of Bus \(i\).