esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Results
### Preprocessing of Data and Parameters
#### 4.2.2 Analysis of Predictive Performance

Table 10: The evaluation indexes of digital twin model for pulverizing system. Figure 15: The predictive results (part 1) of furnace based on different models. Figure 16: The predictive results (part 2) of furnace based on different models. Figure 17: The predictive results (part 1) of drum based on different models. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Indexes** & **Output** & **MMNP** & **EM-LSTM** & **EM-GRU** & **HMPIO** & **HM** \\ \hline \multirow{4}{*}{AOP (\%)} & \(p_{b}\) & 5.23 & 0.80 & 0.63 & 3.99 & 0.01 \\  & \(T_{ss}\) & 9.64 & 0.07 & 0.07 & 0.28 & 0.02 \\  & \(\dot{P}_{b}\) & 3.68 & 0.53 & 0.38 & 2.69 & 0.01 \\  & \(O_{cp}\) & 61.55 & 0.4 & 0.36 & 0.24 & 0.04 \\  & \(Q_{sl}\) & 164.94 & 0.13 & 0.07 & 0.56 & 0.05 \\  & \(p_{b}\) & 0.0018 & \(7.09\times 10^{-4}\) & \(8.39\times 10^{-4}\) & 0.0011 & \(7.63\times 10^{-4}\) \\  & \(T_{gs}\) & 0.0074 & 0.0012 & 0.0012 & 0.0012 & 0.0013 \\  & \(\dot{P}_{b}\) & 0.0014 & \(4.99\times 10^{-4}\) & \(5.94\times 10^{-4}\) & \(8.05\times 10^{-4}\) & \(5.41\times 10^{-4}\) \\  & \(O_{cp}\) & 0.1985 & \(4.22\times 10^{-5}\) & \(4.69\times 10^{-5}\) & \(7.22\times 10^{-5}\) & \(4.51\times 10^{-5}\) \\  & \(Q_{sl}\) & 0.6221 & 0.0025 & 0.0025 & 0.0028 & 0.0025 \\ GDTA (\%) & global & 49.01 & 0.39 & 0.30 & 1.55 & 0.03 \\ GAVAR & global & 0.1662 & \(9.90\times 10^{-4}\) & 0.0010 & 0.0012 & 0.0010 \\ ART (s) & global & \(3.25\times 10^{-6}\) & 0.0048 & 0.0045 & \(2.69\times 10^{-6}\) & 0.0024 \\ RTVAR & global & \(4.51\times 10^{-9}\) & \(1.42\times 10^{-6}\) & \(1.10\times 10^{-6}\) & \(2.01\times 10^{-9}\) & \(3.15\times 10^{-7}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 11: The evaluation indexes of digital twin model for furnace. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Indexes** & **Output** & **MMNP** & **EM-LSTM** & **EM-GRU** & **HMPIO** & **HM** \\ \hline  & \(M_{dl}\) & 35.72 & 3.07 & 4.80 & 11.92 & 0.48 \\ AOP (\%) & \(\rho_{v}\) & 3.20 & 0.27 & 0.19 & 0.22 & 0.09 \\  & \(H_{w}\) & 9.14 & 0.42 & 0.52 & 2.48 & 0.62 \\  & \(H_{r}\) & 2.24 & 0.24 & 0.22 & 0.85 & 0.18 \\  & \(M_{dl}\) & 0.0350 & 0.0022 & 0.0029 & 0.0035 & 0.0021 \\ ACVAR & \(\rho_{v}\) & 0.0022 & 0.0016 & 0.0016 & 0.0017 & 0.0017 \\  & \(H_{w}\) & 0.0080 & 0.0010 & 0.0010 & 0.0038 & 0.0014 \\  & \(H_{r}\) & 0.0041 & 0.0039 & 0.0038 & 0.0039 & 0.0038 \\ GDTA (\%) & global & 12.58 & 1.00 & 1.43 & 3.87 & 0.34 \\ GAVAR & global & 0.0123 & 0.0022 & 0.0023 & 0.0032 & 0.0023 \\ ART (s) & global & \(4.75\times 10^{-6}\) & 0.0044 & 0.0046 & \(7.24\times 10^{-6}\) & 0.0022 \\ RTVAR & global & \(1.45\times 10^{-9}\) & \(6.41\times 10^{-7}\) & \(8.70\times 10^{-6}\) & \(5.19\times 10^{-9}\) & \(1.04\times 10^{-7}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 13: The flexibility index for digital twin model. Figure 18: The predictive results (part 2) of drum based on different models.
# Ensuring Energy Efficiency of Air Quality Monitoring Systems Based on Internet of Things Technology
## 5 Discussion
### Energy Efficiency Estimation of the Proposed IoT Air Quality Monitoring System
At the same time, the Smart Air model is an economically viable alternative, due to its having the lowest price (-USD 16) and the widest range of measured parameters. Its IEI is 0.014, which is an acceptable compromise between functionality and energy consumption. Thus, the use of the _IEI_ allows not only qualitative but also quantitative justification for choosing the optimal sensor for an energy-efficient IoT air quality monitoring system. According to the _IEI_, the Ajax LifeQuality achieves the highest efficiency score due to its extremely low power consumption and long battery life, despite its high cost. Smart Air, although consuming more energy, maintains a competitive _IEI_ due to its affordability and broader parameter range, making it a viable budget-friendly option. Finally, the SaveEcoSensor demonstrates low energy efficiency relative to cost. This combined metric supports decision-making when selecting sensor models that balance functionality, sustainability, and cost, which is especially important during large-scale deployment in smart city or environmental monitoring projects. The IEI provides a quantitative tool for sensor selection, supporting decision-making in IoT air quality monitoring projects. Future work could validate the IEI through stakeholder surveys to refine parameter weights and apply it to a broader range of sensors, including international models [45].
# Interference Challenges and Management in B5G Network Design: A Comprehensive Review
## 4 Interference in B5G Networks
### Heterogeneous Networks
#### 4.1.4 Related Works in HetNet
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Issue** & **Methodologies** & **Advantages** & **Limitations/Future Work** & **Ref.** \\ \hline \multirow{6}{*}{Mitigate uplink ICI and improve uplink coverage performance.} & Non-uniform SBS deployments (NU-SBD) with fractional power control (FPC) and reverse frequency allocation (RFA) in the MBS coverage area. & Enhance the edge users’ coverage and ICI significantly with FPC and RFA as compared with U-SBD. & Enhancing the edge users’ coverage probability because of high path loss and interference, which caused a degradation in the SINR of the proposed system. & Increasing the value of the fractional path loss compensation factor resulted in a reduction in uplink coverage probability because of high path loss and interference, which caused a degradation in the SINR of the proposed system. & [68] \\ \hline \multirow{6}{*}{Mitigate interference and maximize the total network’s throughput.} & Distributed parallel iterative water-filling algorithm. & Deliver a significant improvement in overall performance in terms of total throughput. & The impact of the ICI was not considered. & [69] \\ \cline{2-4}  & Interference cancellation & & & \\ \cline{2-4}  & interference and inter-cell interference & & & \\ \cline{2-4}  & inter-cell interference in HetSCNets. & & & \\ \cline{2-4}  & & & & \\ \cline{2-4}  & & & & \\ \hline \multirow{6}{*}{Increase the network’s capacity and range by reducing co-tier interference in downlink HetNets.} & Fractional frequency reuse (FFR) and coordinated multi-point transmission (CoMP). & Give the best ratios of standard deviation for the precise coverage and ergodic capacity. & This study did not take into consideration the association probability for a variable number of SBS in each cluster that affects the ergodic capacity and data rate of the proposed system. & [71] \\ \cline{2-4}  & & & & \\ \cline{2-4}  & & & & \\ \cline{2-4}  & & & & \\ \hline \multirow{6}{*}{Alleviate RUI and IRI between the relay and user link to enhance the capacity of the system.} & The stochastic geometry-based PPP approach. & Enhance the probability of success and ergodic capacity of the user by deploying more MIMO antenna configurations. & The users’ mobility that increases the power consumption of the proposed approach was not considered in this study. & The users’ mobility that increases the power consumption of the proposed approach was not considered in this study. & [72] \\ \cline{2-4}  & & & & \\ \cline{2-4}  & & & & \\ \cline{2-4}  & & & & \\ \cline{2-4}  & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The summary of related works of HetNets in the literature.
## 4 Results
### Ship Target Tracking Experiment
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Video**} & \multirow{2}{*}{**Detector**} & \multirow{2}{*}{**Tracker**} & \multicolumn{4}{c}{**Evaluation Metrics**} \\ \cline{4-6}  & & & **MOTA/\%** & **IDFI/\% (\(\uparrow\))** & **IDs (\(\downarrow\))** & **FPS/Hz (\(\uparrow\))** \\ \hline \multirow{4}{*}{Scene-1} & \multirow{4}{*}{Bi-YOLO} & StrongSORT & 90.4 & 95.7 & 0 & 31 \\  & & OC-SORT & 96.3 & 98 & 0 & 65 \\  & & (ours) & 96.3 & 97.0 & 78 \\  & & ByteTrack & 88.6 & 93.7 & 0 & 78 \\  & & BOT-SORT & 94.1 & 97.3 & 0 & 18 \\ \hline \multirow{4}{*}{Scene-2} & \multirow{4}{*}{Bi-YOLO} & StrongSORT & 89.7 & 94.1 & 1 & 26 \\  & & OC-SORT & 93.3 & 96.4 & 0 & 57 \\  & & (ours) & 96.3 & 98.0 & 0 & 75 \\  & & ByteTrack & 91.8 & 95.9 & 0 & 75 \\  & & BOT-SORT & 90.9 & 94.9 & 0 & 15 \\ \hline \multirow{4}{*}{Scene-3} & \multirow{4}{*}{Bi-YOLO} & StrongSORT & 93.7 & 95.1 & 0 & 29 \\  & & OC-SORT & 97.7 & 98.5 & 0 & 41 \\  & & ByteTrack & 92.5 & 95.4 & 0 & 74 \\  & & BOT-SORT & 96.2 & 96.3 & 0 & 14 \\ \hline \multirow{4}{*}{Scene-4} & \multirow{4}{*}{Bi-YOLO} & StrongSORT & 95 & 95.5 & 1 & 36 \\  & & OC-SORT & 93.8 & 97.2 & 0 & 78 \\ \cline{1-1}  & & ByteTrack & 93.6 & 96.7 & 0 & 86 \\ \cline{1-1}  & & BOT-SORT & 94.7 & 94.2 & 2 & 28 \\ \hline \multirow{4}{*}{Scene-5} & \multirow{4}{*}{Bi-YOLO} & StrongSORT & 90.8 & 93.7 & 0 & 24 \\  & & OC-SORT & 92.7 & 91.8 & 0 & 55 \\ \cline{1-1}  & & ByteTrack & 88.5 & 86.4 & 0 & 66 \\ \cline{1-1}  & & BOT-SORT & 88 & 89.9 & 4 & 10 \\ \hline \multirow{4}{*}{Scene-6} & \multirow{4}{*}{Bi-YOLO} & StrongSORT & 87.3 & 93.2 & 1 & 27 \\ \cline{1-1}  & & OC-SORT & 90.6 & 96.2 & 0 & 52 \\ \cline{1-1}  & & ByteTrack & 85.1 & 91.5 & 0 & 67 \\ \cline{1-1}  & & BOT-SORT & 86.7 & 89.3 & 6 & 12 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Multi-ship tracking evaluation results in six scenarios. Figure 15: Tracking performance comparison of four state-of-the-art object trackers in Scene-2.
# Phosphorus Mobilizing Enzymes of _Alnus_-Associated Etomycorrhizal Fungi in an Alaskan
###### Abstract  
Because of its high phosphorus (P) demands, it is likely that the abundance, distribution, and N-fixing capacity of _Alnus_ in boreal forests are tightly coupled with P availability and the mobilization and uptake of soil P via ectomycorrhizal fungi (EMF). We examined whether _Alnus_ shifts EMF communities in coordination with increasingly more complex organic P forms across a 200-year-old successional sequence along the Tanana River in interior Alaska. Root-tip activities of acid phosphatase, phosphodiesterase, and phytase of _A. tenuifolia_-associated EMF were positively intercorrelated but did not change in a predictable manner across the shrub, to hardwood to coniferous forest successional sequence. Approximately half of all _Alnus_ roots were colonized by _Alnicola_ and _Tomentella_ taxa, and ordination analysis indicated that the EMF community on _Alnus_ is a relatively distinct, host-specific group. Despite differences in the activities of the two _Alnus_ dominants to mobilize acid phosphatase and phosphodiesterase, the root-tip activities of P-mobilizing enzymes of the _Alnus_-EMF community were not dramatically different from other co-occurring boreal plant hosts. This suggests that if _Alnus_ has a greater influence on P cycling than other plant functional types, additional factors influencing P mobilization and uptake at the root and/or whole-plant level must be involved.  
Alnus; phosphorus; ectomycorrhizae; acid phosphatase; phosphodiesterase; phytase; boreal forest; Alaska +
Footnote †: journal: Accepted: 28 June 2019; Published: 2 July 2019
# Keypoint-Based Automated Component Placement Inspection for Printed Circuit Boards
## 3 Experimental Results
### Performance Metrics  
The performance metrics considered in this study include the quality of component placement inspection, network size, and the computation time of the proposed model. The component inspection accuracy metrics, such as the average precision (AP) [31; 32] value and F1 score [31; 32], are used to indicate the quality of component placement inspection in experiments. Images of PCBs not belonging to training set are adopted as the test set for the evaluation of the AP value and F1 score. The network size is defined as the number of weights in the network. The network size indicates the memory resources required for the deployment of the network. The computation time is the inference latency for the model. It reveals the promptness of the model for inspection.  
Both the AP value and F1 score are evaluated by precision and recall rates. For a given component class (\(c\)), let TP (true positive) and FN (False negative) be the number of components of class \(c\) in the test set that are detected and missed, respectively. Let FP (false positive) be the number of components from other classes in the test set that are falsely identified as components of class \(c\). The precision and recall [31] rates are then defined as  
\[\text{Precision}=\frac{\text{TP}}{\text{TP+FP}},\ \ \ \text{Recall}=\frac{\text{TP}}{ \text{TP+FN}}. \tag{10}\]  
The measurements of precision and recall rates are based on the testing images extracted from the PCBs shown in Figure 9, which are different from the training images.  
Because different threshold values for detection may result in different pairs of precision and recall rates, a precision-recall curve could be obtained by sweeping the threshold values. The AP value is then defined as the area under the precision-recall curve. Higher AP values imply better precision-recall performance.  
Given a pair of precision and recall rates, the computation of the corresponding F1 score [31] is given by  
\[\text{F1 Score}=\frac{2}{\frac{1}{\text{Precision}}+\frac{1}{\text{Recall}}}. \tag{11}\]  
The score provides a comprehensive evaluation based on precision and recall. Given a precision-recall curve, the corresponding F1 score is obtained by finding the pair of precision and recall values on the curve attaining the maximum F1 score.
## 3 Results and Discussion
### Characterization of Nanocom
#### 3.1.3 Magnetic Properties of AC/CFO  
The magnetic properties of the AC/CFO was evaluated by VSM at room temperature; the magnetization curve of AC/CFO displayed a ferromagnetic character (Figure 3).  
In addition to ferromagnetic properties, the room temperature specific magnetization (M) versus applied magnetic field (H) curve measurement of the AC/CFO indicates that the values of coercive force [38], saturation magnetization and remnant magnetization are 433.02 Oe, 22.03 emu/g and 7.16 emu/g, respectively; a similar result was reported earlier by [PERSON] et al. [39].
## 5 Simulation Results
### Trajectory Tracking Control
As a summary, all three control approaches are compared in Table 1. It can be observed that the LMI-based output feedback controller (Case 2) has the smallest deviations between the actual control signals \(u_{\mathcal{S}}(t_{k})\) and the ideal noise-free system inputs \(u_{\text{d}}(t_{k})\). This is shown by a reduction of the RMS values  
\[\Delta u_{\mathcal{S},\text{RMS}}=\sqrt{\frac{1}{N}\sum_{k=1}^{N}(u_{ \mathcal{S}}(t_{k})-u_{\text{d}}(t_{k}))^{2}} \tag{71}\]  
of the system inputs by more than \(30\%\) in all scenarios \(\mathcal{S}\in\{S1,\dots,S4\}\), in comparison with the LQG implementation. These RMS values are evaluated on an equidistant discretization grid with \(t_{k-1}-t_{k}=100\,\text{ms}\) leading to \(N\approx 10^{4}\). Moreover, the full state feedback according to Case 1 has a quality comparable to that of the classical LQG in terms of the control amplitudes as well as the output RMS values  
\[\Delta y_{\mathcal{S},\text{RMS}}=\sqrt{\frac{1}{N}\sum_{k=1}^{N}(x_{1, \mathcal{S}}(t_{k})-y_{\text{d}}(t_{k}))^{2}}, \tag{72}\]  
where \(x_{1,\mathcal{S}}(t_{k})\) is the simulated first state variable in the respective scenario \(\mathcal{S}\). However, in comparison to the LQG, it possesses a strict proof of stability despite the polytopic parameter uncertainty, which makes the separation principle invalid as discussed in Section 2. Concerning Case 2, which is less accurate concerning output tracking than the full state feedback, it should be pointed out that the RMS output values are significantly smaller than the standard deviations of the measurement noise, except for scenario S1, where a large additive disturbance acts directly on the control input. From a practical point of view, it is necessary to make a trade-off between tracking accuracy and control effort. Using the presented approach, the partial state feedback (Case 2) reduces the control effort in comparison to the full state feedback (Case 1), however, with the cost of a slight decrease in tracking accuracy. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Scenario** & \multicolumn{2}{c}{**Case 1**} & \multicolumn{2}{c}{**Case 2**} & \multicolumn{2}{c}{**LQ**} & \multicolumn{2}{c}{**Control Improvement in \%**} \\  & \(\Delta y_{\text{RMS}}/\text{deg}\) & \(\Delta u_{\text{RMS}}/\text{m}\) & \(\Delta y_{\text{RMS}}/\text{deg}\) & \(\Delta u_{\text{RMS}}/\text{m}\) & \(\Delta y_{\text{RMS}}/\text{deg}\) & \(\Delta u_{\text{RMS}}/\text{m}\) & ** ment in \%** \\ \hline S1 & 0.945 & 0.0083 & 1.332 & 0.0027 & 0.713 &Figure 8: _Cont._  
\[u_{\text{ff},i+1}(t_{k})=u_{\text{ff},i}(t_{k})+S_{k}\cdot(y_{\text{d}}(t_{k})- \hat{y}_{i}(t_{k})) \tag{67}\]0 Comparison of the control signal \(u(t)\) for 10 iterations of the proposed LMI-based output feedback controller (Case 2).
# Impact of Childhood Food Allergy on Quality of Life: A Systematic Review
## 3 Results  
Based on the studies reviewed, the findings have been organized according to the quality of life indicators identified by [PERSON] [22]. They are grouped into three sections corresponding to the dimensions affected by childhood food allergy: the intrapersonal (the child), the interpersonal (caregivers and their interaction with the child), and the extrapersonal (academic environment). For more information, Appendix A can be consulted.  
Of the studies included in this review, fifteen employed experimental designs, primarily characterized by a cross-sectional approach and the use of self-report measures as the primary assessment tool. The remaining studies are systematic reviews. The Food Allergy Quality of Life Questionnaire (FAQLQ) [23] was the most commonly used instrument, followed by the Food Allergy Independent Measure (FAIM) [24] and researcher-developed surveys focused on sociodemographic and clinical variables. The most frequently assessed variables related to quality of life included psychological health (e.g., emotional impact), social well-being (e.g., limitations or experiences of bullying), parental burden, perceived severity or risk of exposure, parental self-efficacy, coping strategies, school performance, allergy severity, expectations, clinical history, type of food allergen, and sociodemographic factors (e.g., age, gender, family income, parental education level) [25; 26; 27; 28].  
The primary construct assessed was perceived health-related quality of life, although this is often referred to as simply \"quality of life\" within the studies. While some studies include children, they do not exclusively focus on this age group, instead often comparing findings with adolescent populations. In studies involving children, the questionnaires are typically completed by parents.  
In terms of geographical distribution, most experimental studies (\(n=8\)) were conducted in Europe, with a notable concentration in France, the United Kingdom, and Turkey. There is a limited representation in Mediterranean countries such as Italy, Greece, and Spain. Additional studies were conducted in Asia and the United States, but none were found in Latin American populations. Only one study used a multi-country approach. Regarding the types of allergies evaluated, the most common were egg, milk, and peanut allergies. Generally, however, the studies addressed allergies collectively, without specific distinctions by type, except in the case of peanut allergy, for which specific studies were conducted exclusively on this condition.
## 3 Results
### Changes in PM According to Mist Spraying at CRP
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Time**} & **Mist** & \multicolumn{2}{c}{**PM\({}_{\mathbf{10}}\)**} & **PM\({}_{\mathbf{2.5}}\)** & **PM\({}_{\mathbf{1.0}}\)** & **PM\({}_{\mathbf{1.0}}\)**/**PM\({}_{\mathbf{10}}\)** & **PM\({}_{\mathbf{1.0}}\)**/**PM\({}_{\mathbf{2.5}}\)** \\ \cline{3-8}  & **Spray** & & **\(\mu\)g/m\({}^{3}\)** & & & & \\ \hline \multirow{2}{*}{\begin{tabular}{c} Evening \\ Rush hour \\ \end{tabular} } & Before & 23.9(\(\pm\)4.1) b & 6.9(\(\pm\)0.8) b & 3.4(\(\pm\)0.3) b & 0.30(\(\pm\)0.01) a & 0.15(\(\pm\)0.01) a & 0.49(\(\pm\)0.01) a \\  & During & **82.9(\(\pm\)30.5) a** & **23.4(\(\pm\)8.0) a** & **9.6(\(\pm\)3.2) a** & 0.31(\(\pm\)0.02) a & 0.13(\(\pm\)0.01) a & **0.44(\(\pm\)0.02) b** \\  & After & 29.2(\(\pm\)4.6) b & 9.0(\(\pm\)1.4) b & 4.2(\(\pm\)0.6) b & 0.31(\(\pm\)0.01) a & 0.15(\(\pm\)0.01) a & 0.47(\(\pm\)0.01) a \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} Morning \\ Rush hour \\ \end{tabular} } & Before & 16.9(\(\pm\)2.8) b & 4.8(\(\pm\)0.6) b & 2.1(\(\pm\)0.3) b & 0.30(\(\pm\)0.00) a & 0.13(\(\pm\)0.00) a & 0.44(\(\pm\)0.01) a \\  & During & **117.1(\(\pm\)42.2) a** & **13.9(\(\pm\)4.0) a** & **6.4(\(\pm\)1.9) a** & **0.21(\(\pm\)0.03) b** & **0.09(\(\pm\)0.01) b** & 0.45(\(\pm\)0.01) a \\  & After & 45.4(\(\pm\)25.8) b & 6.8(\(\pm\)2.5) b & 3.2(\(\pm\)1.3) b & 0.27(\(\pm\)0.02) a & 0.12(\(\pm\)0.01) a & 0.44(\(\pm\)0.01) a \\ \hline \multirow{2}{*}{
\begin{tabular}{c} Lunch \\ \end{tabular} } & Before & 12.7(\(\pm\)0.6) b & 3.5(\(\pm\)0.1) b & 1.5(\(\pm\)0.0) b & 0.27(\(\pm\)0.01) a & 0.12(\(\pm\)0.00) a & 0.43(\(\pm\)0.01) a \\  & During & **102.4(\(\pm\)38.8) a** & **11.1(\(\pm\)3.5) a** & **5.0(\(\pm\)1.5) a** & **0.20(\(\pm\)0.03) b** & **0.09(\(\pm\)0.01) b** & 0.44(\(\pm\)0.01) a \\  & After & 16.2(\(\pm\)1.3)b & 4.1(\(\pm\)0.2) b & 1.7(\(\pm\)0.1) b & 0.27(\(\pm\)0.01) a & 0.11(\(\pm\)0.00) a & 0.43(\(\pm\)0.00) a \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The concentration (mean \(\pm\) S.D., \(\mu\)g/m\({}^{3}\)) of PMx and PM concentration ratio (mean \(\pm\) S.D.) among three experiments (before, during, and after spraying) during the evening rush hour and lunch hour in the Cheongdam Road Park (CRP). Figure 4: PMx concentrations (mean \(\pm\) S.D., \(\mu\)g/m\({}^{3}\)) at the upper part (**a**) and PM concentration ratio (mean \(\pm\) S.D.) at the lower part (**b**) in the Cheongdam Road park (CRP) before and after mist spraying.
## 5 Results and Discussion  
### Identification Results  
Table 3 presents the results of the identification evaluation. The results show an average accuracy above 90% for almost all cases except for those involving a combination of image transformations. It can be seen that noisy speech samples do not have a great impact on the accuracy since the accuracy does not change significantly at different decibel values. It is also noted that the image transformations had a significant impact on the identification performance because of the difference of accuracy values between them.
# A Comprehensive Review of Micro UAV Charging Techniques
## 6 Simultaneous Wireless Information and Power Transfer (SWIPT)
Some recent works have focused on mmWave UAV-based relaying systems for SWIPT because it offers benefits such as significant array gains and short-distance transmission. Figure 15: Multi-UAV-enabled SWIPT scenario. However, it also raises security challenges and physical layer security analysis for mmWave UAV-based relay networks for SWIPT has become an interesting research topic [112]. In [112], the authors proposed a secure transmission technique for mmWave UAV-based relay networks for SWIPT, in which they derived a close-form expression for the lower side of the average capacity rate. In [113], the authors used the power-splitting technique for UAV-aided SWIPT for IoT. The authors designed a mathematical model through an optimization problem which includes the AUV's speed and power budget constraints. Similarly, the authors in [114] used the time switching technique for a UAV-aided SLIPT system in which source nodes charged the UAV and then the UAV sent the information to the intended receiving node. From these studies, we find that theoretically, TS is a special form of PS based on binary splitting power ratios.