esa-sceva/satcom-chunk-collection huggingface dataset:
# Near-Field Aeroacoustics of Spanwise Forcing on a Transonic Wing: A DNS Study
## 5 Discussion
Finally, the broadband noise in the aforementioned branches at \(\theta\approx 135^{\circ},\theta\approx 225^{\circ}\), and \(\theta\approx 315^{\circ}\) is slightly intensified; this is also reflected in the higher OASPL values perceived at the same angles (see Figure 6). It is worth highlighting that the scale of these differences is tiny. Once again, the presence of tonal noise specifically related to the forcing can be ruled out, since the Fourier coefficients at the frequency corresponding to StTWs in cases C10 and R10 (\(f=5.3c/U_{\infty}\)) are negligible. In view of the importance of applying StfWs at the reduced incidence to obtain the lift of the uncontrolled wing with less drag, we also compute the directional Fourier transform for the controlled R10 simulation, in which \(\alpha=3.45^{\circ}\) while the control parameters match those of case C10. We plot in Figure 12 the difference between the magnitudes of the Fourier coefficients between cases R10 and REF (labeled as \(\Delta|\widehat{p^{\prime}}|/p_{\infty}\)), with the same rationale behind curve RREF of Figure 7 in Section 4. The largest effects are observed at low frequencies, as above \(f\approx 3U_{\infty}/c\) the two transforms are basically identical regardless of \(\theta\). In terms of directions, the probes located upstream of the leading edge detect little to no difference in the frequency content of noise. In fact, the larger effects are observed on symmetric branches at \(\theta\approx 45^{\circ},\theta\approx 135^{\circ},\theta\approx 225^{\circ}\), and \(\theta\approx 315^{\circ}\). This pattern resembles indeed what was previously described in Figure 7 for the curve RR10. Furthermore, we note a general reduction in the low-frequency content below the pressure side of the wing (\(\theta\approx[225\div 315]\)), which could be the cause of the lower OASPL values depicted in Figure 6 in the same region. Finally, we wish to remark that the values of \(\Delta|\widehat{p^{\prime}}|\) induced by StTWs, combined with those caused by the reduction in the angle of attack, are tiny from the point of view of the frequency content, too. Figure 12: Difference between the absolute values of the Fourier coefficients of the controlled simulation at \(\alpha=3.45^{\circ}\) (R10) and the uncontrolled simulation at \(\alpha=4^{\circ}\) (REF). The three circles denote the same frequencies described in Figure 11.  
In conclusion, this preliminary near-field analysis has determined only a slight intensification of the aeroacoustics noise when StTWs are employed on a transonic wing slab to reduce drag. The frequency content of noise is minimally altered, too. Once the angle of attack of the wing is reduced to take into account the increased lift due to the forcing and to actually achieve drag reduction, both the noise intensification and the modification of its frequency content become negligible.
## 3 Proposed Algorithm
### Evaluation Metrics  
The success rate [55] reflects the estimation accuracy of the predicted bounding box, which is the percentage of all frames in which the overlap area of the target and the ground truth bounding boxes is above a threshold. The overlap score is calculated as  
\[O_{s}=\frac{|Y_{t}\cap Z_{t}|}{|Y_{t}\cup Z_{t}|} \tag{38}\]  
where \(Y_{t}\) is the target bounding box area of our algorithm, and \(Z_{t}\) is the area of the ground truth. As discussed, when the overlap between the bounding boxes exceeds a threshold, the tracking is considered successful. Specifically, for the coordinate system of the success rate curve, the \(x\)-axis denotes a series of continuous thresholds from 0 to 1, with different thresholds set. Similarly, the \(y\)-axis is the success rate. The precision rate [56] reflects positioning precision, which is the percentage of all frames in which the distance between the center of the predicted target bounding box and the ground truth is less than a threshold. The distance \(F\) between the center point of the target bounding box center and the ground truth value is calculated as  
\[F=\sqrt{(x_{t}-x_{0})^{2}+(y_{t}-y_{0})^{2}} \tag{39}\]  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Sequences** & **car11** & **car78** & **ider11** & **ider17** & **board** & **kangaroo** & **ball\&mirro9** & **nairstreet10** \\ \hline Frames & 101 & 119 & 142 & 165 & 471 & 117 & 250 & 275 \\ Resolution & 473 \(\times\) 248 & 493 \(\times\) 256 & 389 \(\times\) 222 & 471 \(\times\) 248 & 277 \(\times\) 140 & 385 \(\times\) 206 & 389 \(\times\) 222 & 512 \(\times\) 272 \\ Initial Size & 20 \(\times\) 15 & 20 \(\times\) 10 & 122 \(\times\) 121 & 9 \(\times\) 13 & 36 \(\times\) 68 & 22 \(\times\) 41 & 18 \(\times\) 20 & 21 \(\times\) 45 \\ Challenges & OCC, SV, FM & OCC, SV, FM & SV & OCC, SV, LR & IPR, OPR, OCC, BC, SV & BC, SV, DEF, OPR, MB & REF & OCC \\ \hline \hline \end{tabular}
\end{table}
Table 1: Frames, Resolution, Initial Size, Challenges of the test sequences.  
Figure 4: Eight sequences in the HOT2023.  
where \((x,y)\) is the center point of the ground truth, and \((x_{0},y_{0})\) is the predicted center point of the target bounding box. Specifically, for the coordinate system of the precision rate curve, the \(x\)-axis is a series of continuous thresholds from 0 to 50, and the \(y\)-axis is precision. The average distance precision rate is reported at a threshold of 20 pixels (DP@20). Additionally, we used the area under the curve (AUC) to measure the performance of each tracking algorithm.
## 7 Conclusions  
We first analyzed the characteristics of time delay complex Chen systems, outlining their randomness and unpredictability. Then, we proposed an innovative communication scheme based on CMPS for wireless body area networks and discussed its advantages. As the complex scale factor is more complicated than the real scale factor and the calculation of complex numbers is more complicated, the scheme can greatly increase the security of communication systems. Finally, we verified the effectiveness of the proposed communication system by conducting simulation experiments. The system could quickly and securely transmit information with strong robustness against noise. From the beginning, when [PERSON] discovered and proposed chaos theory based on weather changes in nature, to the current chaos theory being applied in many fields, chaos theory has been constantly enriched and developed. The application of chaos communication theory in the field of WBANs is an important branch. The proposed chaotic communication scheme based on CMPS for WBANs only has its feasibility verified at the theoretical stage. It will be realized in the future, thus enhancing WBAN development.  
Conceptualization, F.Z.; methodology, F.Z.; software, S.L.; validation, Z.L.; formal analysis, F.Z. and S.L.; investigation, Z.L.; writing-original draft preparation, S.L.; resources, Z.L.; data curation, S.L. and Z.L.; visualization, F.Z.; writing-review and editing, F.Z.; supervision, C.J.; project administration, C.J.; funding acquisition, F.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the International Collaborative Research Project of Qilu University of Technology (QULTGJHZ018020), the Young Doctorate Cooperation Fund Project of Qilu University of Technology (Shandong Academy of Sciences; 2018 BSHZ001), the National Natural Science Foundation of China (61773010), Major Scientific and Technological Innovation Projects of Shandong Province (2019 JZZY010731), the Key Research and Development Program of Shandong Province (2019 GCX104037), and the Research Leader Studio Project of Jinan City (2019 GXRC062).  
The authors would like to thank the anonymous reviewers for their valuable suggestions and questions.  
The authors declare that they have no conflict of interest.
## 4 Results and Discussion
### Latencies and Delays of the Seismic Network  
Latency, delay, and packet size are critical factors in EEWS. While these terms are often used interchangeably, there are important physical differences between them that must be taken into account in early warning studies. Here, latency refers to the time between the P-wave onset at a station and the time data was packaged and transmitted along the signal path until it arrives at the data center. On the other hand, delay refers here only to the time it takes for the packaged signal to travel from the sensor to the data center along the transmission line. Packet size refers to the number of samples that are prepared for each transmission. These factors can have a significant impact on the speed and accuracy of early warning alerts. Understanding and minimizing latency and delay while optimizing packet size is essential for creating a fast and reliable EEW system [11; 47; 48].  
The necessary data for the EEWS in Bursa and its surroundings are provided to YUBAM via AFAD and Bursagaz. However, since the data in the AFAD center passes several data servers and GSM technology is used for communication, latencies of the data are high. The delay time of some stations is more than 800 s due to the malfunction of the GPS. Therefore, when using an overall latency, the most accurate calculation would be to use the median time, measured as 4.6 s. Latencies of the data transmissions in the real-time analysis are given in Figure 8.  
Figure 7: Histograms of errors for play-backed catalog dataset (**a**) magnitude errors using default equations for \(Pd\), (**b**) magnitude errors using newly developed for \(Pd\), (**c**) magnitude errors using default for \(T_{p}^{max}\), (**d**) magnitudes errors using newly developed for \(T_{p}^{max}\).
## 4 Optimization Design Algorithm
### Results
#### 4.5.2 Structural Parameter Optimization Results  
To further verify the effectiveness of the optimization solutions shown in Figure 12a, two groups of solutions with the minimum and maximum bearing capacity are selected from 50 groups of Pareto solutions, and eight groups of solutions are randomly selected from the remaining 48 groups of Pareto solutions according to the bearing capacity from small to large. The static performance corresponding to these ten groups of solutions is calculated using the simulation model and then compared with that of the aerostatic bearing before optimization. The comparison results are shown in Table 4.  
Group 1 is the structural parameters before optimization, and Group 2 to Group 11 correspond to the ten groups of structural parameters obtained using the optimization algorithm. It can be seen intuitively from Table 4 that compared with the structural parameters before optimization, the ten groups of parameters optimized by the optimization algorithm guarantee the effect of improving bearing capacity and reducing gas consumption. Group 11 shows the largest increase in bearing capacity. It increases bearing capacity by 23.07% and reduces gas consumption by 1%. Group 2 shows the largest reduction in air consumption. It reduces gas consumption by 25.59% and increases bearing capacity by 2.96%. Therefore, it can be proved that the multi-objective particle swarm optimization algorithm can effectively optimize the structural parameters of aerostatic bearings, thereby improving the static performance.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Group** & \(d\) **(mm)** & \(l\) **(mm)** & \(h_{av}\) **(\(\mu\)m)** & \(n\)** & \(W\) **(N)** & **Q (g/s)** \\ \hline
1 & 0.2000 & 15.00 & 20.0 & 8 & 94.68 & 0.2157 \\
2 & 0.2055 & 16.52 & 15.6 & 8 & 97.48 & 0.1605 \\
3 & 0.2049 & 17.15 & 15.0 & 8 & 99.49 & 0.1720 \\
4 & 0.2017 & 16.97 & 17.7 & 8 & 101.35 & 0.1814 \\
5 & 0.2079 & 16.25 & 18.0 & 8 & 104.97 & 0.1861 \\
6 & 0.2088 & 15.93 & 15.7 & 8 & 106.17 & 0.1887 \\
7 & 0.2004 & 15.06 & 16.1 & 8 & 108.29 & 0.1960 \\
8 & 0.2025 & 14.86 & 18.3 & 10 & 110.55 & 0.1970 \\
9 & 0.2040 & 14.27 & 16.9 & 10 & 112.46 & 0.2021 \\
10 & 0.2068 & 13.89 & 17.3 & 10 & 113.08 & 0.2076 \\
11 & 0.2098 & 13.14 & 17.4 & 10 & 116.52 & 0.2136 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of optimization results.  
Figure 14: Performance metrics of the multi-objective optimization algorithm.
## 2 Mathematical Model and Inversion Method
### Coordinate Transformation  
Before model inversion, the geographical coordinates \((\varphi,\theta,r)_{S}\) and geomagnetic component \(\left(N,E,U\right)_{S}\) of the observation data must be transformed from the spherical coordinate reference frame to the cone coordinate reference frame [45], as shown in Figure 1.  
Figure 1: Sketch of the spherical cap coordinate system: Magnetic field represented in a conical domain \(\Omega\) bounded by a lower and an upper spherical surfaces with radii \(a\) and \(b\), respectively. \(\theta_{0}\) is half aperture of the cone. \(P\) is pole of the cone. \(\left(X,Y,Z\right)_{S}\) are geocentric spatial rectangular coordinate system based on sphere. \(\left(X,Y,Z\right)_{\text{C}}\) are geocentric spatial rectangular coordinate system based on spherical cap. \(N,S,O\) are the north pole, the south pole, and the center of the earth, respectively.
## 9 Application of the TFME Technique in the Analysis of Organic Compounds
They enter the aquatic environment as a result of wastewater discharges directly into the environment or as an effect of incomplete removal from contaminated water in wastewater treatment plants [145]. The presence of glucocorticoids in concentrations at ng L\({}^{-1}\) levels is a factor that adversely affects aquatic organisms [146]. In the determination of glucocorticoids in aqueous matrices, the authors of the publications used TF-SPME, in which they used SDS-MWCNTs/PP [93] as a sorption phase (Table 13). Antibiotics are widely used in both human and veterinary medicine. Global consumption of antibiotics in industrial animal husbandry is increasing year by year. Through animal manure, which is used as fertilizer in agricultural production, antibiotics enter the soil in small amounts and then into post-surface water. Ineffective degradation of pharmaceuticals during wastewater treatment can lead to contamination of surface water, groundwater, and, consequently, drinking water. Residues of antibiotics in water can enter the food chain and adversely affect human health, including causing allergic reactions. As a consequence, there is a need to determine the level of antibiotic concentrations in environmental waters [147]. In the determination of antibiotics in aqueous matrices, the researchers used TF-SPME, in which they used sorption phases: p-Poly-(MMA-IL)FP [147] and PVA-SA-\(\beta\)CD [148] (Table 13). sorption phases in the form of aptamer/cellulose [106; 108], Ni(DMG)2-NiO-Cell [156], and octyl-cyanopropyl/PAN [157] (Table 15)  
Beta-blockers are essential clinical drugs that are used to treat cardiovascular diseases such as hypertension, irregular heart rhythms, strokes, and angina. Additionally, \(\beta\)-blockers improve cardiac function by relaxing muscles and reducing heart rate, which is often abused by athletes competing in professional sports [87]. Procedures for the determination of beta-blockers in biological matrices have been developed using TF-SPME with sorption phases such as GO/PEG [87] and g-C3N4/N6 NC [158] (Table 15). Tables 13-15 provide details of the quantification of analytes in aqueous, food, and biological matrices using TF-SPME reported by researchers in the last few years.
## 3 PPO Resource Scheduling
The training goal is to minimize the loss function based on mean square error, and its parameters are updated by the following formula. \[\phi_{g+1}=\underset{\phi}{\arg\min}\;\frac{1}{T}\sum\limits_{l=0}^{T}\Big{(} V_{\phi}(s_{t})-\tilde{R}_{t}\Big{)}^{2}, \tag{10}\]  
where \(\tilde{R}_{t}\) is an accumulated reward calculated from state transition trajectory data. The Actor network has an input layer, a hidden layer, and two output layers to complete the mapping of the state to the mean and standard deviation of the policy Gaussian distribution. The activation function used by the hidden layer is the Rectified Linear Unit (ReLU) function, the output layer is divided into two parts, the mean and the standard deviation. The mean part first limits the output to through the Tanh activation function, then maps to the entire action space. The standard deviation part limits the output to through the Softplus activation function. The Critic network has an input layer, two hidden layers, and an output layer. Critic network completes the mapping from state to state value. The hidden layer uses the ReLU activation function, and the output layer outputs the state value directly without using the activation function. The considered resource scheduling of heterogeneous edge computing networks has been modeled as an MDP, and the PPO network architecture has been built. Based on PPO, we complete the resource scheduling of heterogeneous edge computing networks with the Algorithm 1 shown as follow. ```
0: Edge computing network task parameters, edge computing parameters, PPO network parameters, other parameters in Table 1.
0: Subtask scheduling strategy set, network energy consumption, network load balancing. 1: Initialize the Actor network and Critic estimate parameters \(\theta\) and \(\phi\), initialize the old-Actor network. 2: Clear the experience buffer. 3:for each episode do
4: Agent obtains current sensor network state \(s_{t}\). 5: Actor inputs \(s_{t}\) and output strategy Gaussian distributed, agent recoveries strategy and select an action \(a_{t}\). 6: Agent gets reward value \(r_{t}\) performs action \(a_{t}\) and updates to next state \(a_{t+1}\). 7:for for 1 : \(T\) do do
8: According to \(V(s_{t})\) and the reward value of each time step in the buffer, the state value of the corresponding time step is calculated. 9: Update the parameters of actor network and critic network according to (9) and (10), respectively. 10:endfor
11:endfor
12:return Subtask scheduling strategy set, network energy consumption, network load balancing. ```  
**Algorithm 1** PPO Resource Scheduling Algorithm.
# A Lifetime Maximization Relay Selection Scheme in Wireless Body Area Networks
## 4 Optimization Problem Formulation
### Variables and Matrix  
In the formulation, \(x_{i,j}\) is denoted as an optimization binary variable to represent relay selection results. \(x_{i,j}=1\) represents the \(i\)th sensor node using cooperative transmission with the help of the \(j\)th sensor node, which is acting as a relay node; otherwise, \(x_{i,j}=0\). It should be noticed that when \(i=j\), \(x_{i,j}\) (or \(x_{i,j}\)) = 1, this means that the \(i\)th (or \(j\)th) sensor node using direct transmission without any help from other nodes. Having known that there are \(l\) sensor nodes in the network, all of the binary variables \(x_{i,j}\), \(i,j=1,2,\cdots,l\) form a relay selection matrix \(\mathbf{X}\) defined as follows:  
\[\mathbf{X}=\left[\begin{array}{ccccc}x_{1,1}&x_{1,2}&\cdots&x_{1,l-1}&x_{1,l} \\ x_{2,1}&x_{2,2}&\cdots&x_{2,l-1}&x_{2,l}\\ \vdots&\vdots&\ddots&\vdots&\vdots\\ x_{l-1,1}&x_{l-1,2}&\cdots&x_{l-1,l-1}&x_{l-1,l}\\ x_{l,1}&x_{l,2}&\cdots&x_{l,l-1}&x_{l,l}\end{array}\right].\]  
In this matrix, the \(i\)th row represents the \(i\)th sensor node's transmission strategy. That is to say, if \(x_{ij}=1,i\
eq j\), the \(i\)th sensor node is to use cooperative transmission with relay of the \(j\)th sensor node; otherwise, if \(x_{i,i}=1\), the \(i\)th sensor node is to use direct transmission. Due to the principle specified in IEEE 802.15.6 that each node can only transmit with the help of one relay node or by itself, \(\sum_{j=1}^{l}x_{i,j}=1,\forall i=1,2,\cdots,l\) always holds. In addition, in order to comply with the topology restriction, relay nodes only use direct transmission.  
We also denote \(d_{i,j},i\
eq j\) as the distance between the \(i\)th sensor node and the \(j\)th sensor node. It should be noticed that when \(i=j\), \(d_{i,i}\)(or \(d_{j,j}\)) represents the distance between the \(i\)th(\(j\)th) sensor node and the coordinator.
# Node Depth Adjustment Based Target Tracking in UWSNs Using Improved Harmony Search
## 3 Problem Formulation
### Multi-Sensor Particle Filter for Target Tracking in UWSNs  
The Particle Filter (PF) is an efficient way to solve nonlinear and non-Gaussian problems. Readers can refer to references [30; 31] for detailed PF. In this paper, multi-sensor PF will be utilized for tracking issues.  
It is assumed that \(N_{\mathrm{s}}\) sensing nodes take part in tracking the underwater target and transmit their measurements to the fusion center at each time step. Once receiving measurements vector \(\mathbf{Z}_{k+1}=\left[Z_{k+1}^{1},\cdots,Z_{k+1}^{N_{\mathrm{s}}}\right]^{ \mathrm{T}}\), the fusion center merges measurements into a single multi-sensor measurement likelihood. The measurement likelihood over all \(N_{\mathrm{s}}\) nodes is given by  
\[p(\mathbf{Z}_{k+1}|\mathbf{x}_{k+1}^{i})=\prod_{s}^{N_{\mathrm{s}}}p(Z_{k+1}^ {s}|\mathbf{x}_{k+1}^{i}), \tag{6}\]  
where  
\[p(Z_{k+1}^{s}|\mathbf{x}_{k+1}^{i})=\frac{\exp[-\frac{1}{2}(Z_{k+1}^{s}-h_{k+ 1}^{s}(\mathbf{x}_{k+1}^{i}))^{\mathrm{T}}(R)^{-1}(Z_{k+1}^{s}-h_{k+1}^{s}( \mathbf{x}_{k+1}^{i}))]}{\sqrt{2\pi R}} \tag{7}\]  
is probability density function (pdf) of the measurement likelihood regarding the measurement acquired by the \(s_{th}\) sensing node at time \(k+1\); \(\mathbf{x}_{k+1}^{i}\) is the \(i\)th particle at time \(k+1\). Then, adopting the prior the transition prior \(p(x_{k}|x_{k-1})\) as the proposal distribution, the importance weights of particles are calculated as:  
\[w_{k+1}^{i}=w_{k}^{i}p(\mathbf{Z}_{k+1}|\mathbf{x}_{k+1}^{i}). \tag{8}\]
## 4 Results and Discussions
### Impacts on Solar Power Generation  
The developed mathematical model has been solved for 24 h in a day (17 January) in the middle of winter in Oxford, UK. In particular, the main reason for choosing a day at the middle of winter is to assess the performance of the system and ESS when the energy input (solar irradiance) is at its minimum values [4]. The solar irradiance is calculated using the model presented in Section 3.1. In addition, the simulation has considered 3600s as the time step. Figure 10 shows the aggregation of solar beam and diffuse radiations transmitted through the atmosphere installed on the tilted plane (PV panels).  
Figure 11 demonstrates the current output of the PVs to the system during the day for different scenarios. As mentioned before, a switch, controlled by the batteries' SoC parameter is adopted to prevent the batteries from overcharging. Therefore, the switch will break the connection between the PVs and the battery storage system when the batteries are overcharged. The activation time of the switch can be figured out in Figure 11. For the base scenario (black line), in which only residential building demand is applied to the system, the switch is turned to active mode right after reaching the peak current value at 1 PM. The extra power generated by PVs would not be directed to the system afterward resulting in a sharp increase in PV current flow through the system after 1 PM. An increase of second life battery pack size by an increment of the number of modules from 2 to 4, when extra EVCS load is applied, leads to an increased PV system current flow to the system after reaching peak hours. This is due to an increase in the overcharging limit of the energy storage system and higher amounts of stored energy. Accordingly, the switch activation time is delayed by an increase in the number of packs.  
Figure 10: Solar irradiance (clear-sky radiation) at various times during the day on 17 January.