esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In order to effectively solve the inefficient path planning problem of mobile robots traveling in multiple destinations, a multi-destination global path planning algorithm is proposed based on the optimal obstacle value. A grid map is built to simulate the real working environment of mobile robots. Based on the rules of the live chess game in Go, the grid map is optimized and reconstructed. This grid of environment and the obstacle values of grid environment between each two destination points are obtained. Using the simulated annealing strategy, the optimization of multi-destination arrival sequence for the mobile robot is implemented by combining with the obstacle value between two destination points. The optimal mobile node of path planning is gained. According to the Q-learning algorithm, the parameters of the reward function are optimized to obtain the q value of the path. The optimal path of multiple destinations is acquired when mobile robots can pass through the fewest obstacles. The multi-destination path planning simulation of the mobile robot is implemented by MATLAB software (Natick, MA, USA, R2016b) under multiple working conditions. The Pareto numerical graph is obtained. According to comparing multi-destination global planning with single-destination path planning under the multiple working conditions, the length of path in multi-destination global planning is reduced by 22% compared with the average length of the single-destination path planning algorithm. The results show that the multi-destination global path planning method of the mobile robot based on the optimal obstacle value is reasonable and effective. Multi-destination path planning method proposed in this article is conducive to improve the terrain adaptability of mobile robots.  
mobile robot; multi-destination path planning; obstacle value; simulated annealing strategy; Q-learning algorithm +
Footnote †: journal: Applied Sciences
## Basic Theory
### Dual-Tree Complex Wavelet Transform Based on Bivariate Shrinkage Function
The signal variance \(\sigma_{\omega}^{2}\) of this region is estimated by the wavelet coefficients in the \(N(k)\) region, and the noise variance \(\sigma^{2}\) is estimated by the median filtering method:  
\[\hat{\sigma^{2}}=\frac{Median(|y_{i}|)}{0.6745},\ y_{i}\in subband\ HH, \tag{9}\]  
where _subband_\(HH\) is an aggregate of wavelet coefficients in the \(N(k)\) region. From the observation model, we can obtain: \(\sigma_{y}^{2}=\sigma_{\omega}^{2}+\sigma^{2}\), the empirical estimation of \(\sigma_{y}^{2}\) is:  
\[\hat{\sigma_{y}^{2}}=\frac{1}{M}\sum_{y_{i}\in N(k)}y_{i}^{2}, \tag{10}\]  
\(M\) is the size of the rectangle region \(N(k)\), and the \(\sigma_{\omega}\) is estimated to be:  
\[\hat{\sigma_{\omega}}=\sqrt{\left(\hat{\sigma_{y}^{2}}-\hat{\sigma^{2}}\right) _{+}}, \tag{11}\]  
After the noisy image processed by DTCWT, the wavelet coefficients of DTCWT is handled with the local adaptive bivariate shrinkage model which has local variance estimation, and then the denoised image can be obtained via inverse DTCWT of new wavelet coefficients.
## 2 Isolation and Characterization of the Sub-6 GHz Multi-Port MIMO Antenna
### Derivation of Isolation Using the S-Parameters  
In a MIMO antenna, inter-element coupling critically affects overall performance. The scattering parameters (S-parameters) are commonly used to quantify this. Among them, the forward transmission coefficient S\({}_{21}\) is used to evaluate the isolation between the ports of a two-port network. The forward transmission coefficient \(S_{21}\) is defined when no signal is injected into port 2 (\(a_{2}=0\)):  
\[S_{21}=\left.\frac{b_{2}}{a_{1}}\right|_{a2=0} \tag{1}\]  
where  
* \(a_{1}\) is the incident wave at port 1;
* \(b_{2}\) is the outgoing wave at port 2;
* \(a_{2}=0\) indicates that port 2 is matched and that there is no input signal.  
The input power at port 1 is given by  
\[P_{in}=\left|a_{1}\right|^{2} \tag{2}\]  
Figure 1: Two representative antenna integration configurations in commercial smartphones that differ in terms of the printed circuit board (PCB) and battery arrangements; (**a**) a central PCB with the battery on top, enabling lateral edge antenna deployment, (**b**) a split-level sub-PCB with the antenna modules at the bottom. The new sub-6 GHz antenna is applicable to both configurations because the frame-coupling approach is flexible.  
The output power measured at port 2 is  
\[P_{out}=|b_{2}|^{2}=|S_{21}a_{1}|^{2}=|S_{21}|^{2}|a_{1}|^{2} \tag{3}\]  
The ratio of the transmitted to incident power is therefore  
\[\frac{P_{out}}{P_{in}}=|S_{21}|^{2} \tag{4}\]  
To express the power isolation in decibels (dB), the following logarithmic transformation is applied:  
\[\textit{Isolation}\ (\mathrm{dB})=-10\ \mathrm{log}_{10}\bigg{(}\frac{P_{out}}{P _{in}}\bigg{)}=-10\ \mathrm{log}_{10}\bigg{(}|S_{21}|^{2}\bigg{)} \tag{5}\]  
Using the logarithmic identity \(\mathrm{log}_{10}\ x^{2}=2\mathrm{log}_{10}\ x\), the final expression becomes  
\[\textit{Isolation}\ (\mathrm{dB})=-20\ \mathrm{log}_{10}|S_{21}| \tag{6}\]  
Here, \(S_{21}\) denotes the transmission coefficient from port 1 to 2. This is the amount of power that is coupled from one antenna to another. A higher isolation value, typically above 15 dB, is desirable to minimize interference and ensure optimal MIMO operation [11]. The equation quantifies the extent of isolation between MIMO antennal elements. A larger value reflects reduced mutual coupling, thereby improving spatial diversity and system performance.
## 4 Discussion
### Influence of Vibration of 2D Moving Stages  
In reality, the vibration of the 2D moving stage had little influence on the displacement measurement. Through analysis, we found that the vibration of the 2D moving stage in different directions did not affect the measurement system completely. For example, only the vibration of the 2D moving stage in the same direction affected the measurement system when the measuring direction was along with X axis. Vibration in the other two directions will have a slight effect on the stability of the measurement system. To eliminate or reduce the influence, some measures might be taken in the experiment. Firstly, the 2D moving stage with high accuracy and stability has priority in applications to reduce vibration from the source. In addition, the optical slope sensor in the measurement system requires a vibration reduction design, such as precise mechanical design and a good vibration isolation material (like ultraviolet ray glue). Finally, multiple measurements and median filtering techniques are used in the image processing of light spots to improve stability.
## 4 Traffic Filtering-Based Techniques
### Port Locking (PL)
#### 4.3.2 PL Operations  
The PL consists of two stages, the learning stage and working stage:  
1. Learning stage: In the learning stage, QuadBoxes check location of the destination node and perform port locking if needed. When the source node sends the first frame to the destination node, copies of the first frame are flooded into the entire network, as under the standard HSR protocol. QuadBoxes will check location of the destination of the frame, and then lock their DANH side's ports if DANH rings connecting to those ports do not contain the destination of the frame.
2. Working stage: After the learning stage, all rings that do not contain the destination node are pruned. The working stage starts from the second frame. In the stage, frames are sent and received on paths through unlocked ports.  
Figure 7 shows the process of filtering unicast traffic for DANH rings under the PL approach in the sample network.  
In that case, the source node in DANH ring 1 sends unicast frames to the destination node in DANH ring 6. In the leaning stage, QuadBoxes forward the first frame into DANH rings through their DANH side's ports. QuadBox nodes 2-5 then lock their DANH side's ports connected to DANH rings 2-5 respectively since they found that these DANH rings do not contain the destination of the frame. In the working stage, the frames are delivered from source node in DANH ring 1 to destination node in DANH ring 6 through all QuadBox rings, even unused QuadBox rings, such as QuadBox ring 2. Additionally, the frames are still doubled and circulated in all QuadBox rings.
# A Robust Localization System Fusion Vision-CNN
## 6 Discussion  
Both simulations and experiments prove the robustness, and the real-time and the high precision of the localization algorithm proposed in this paper. When collecting images to train the neural network, we not only collected the observation images during the translational movement of the robot, but also increased the observation images of the rotation movement at certain points. Compared to translational motion, in the case of rotational motion, the camera can obtain richer information in the environment. In the experimental test, it was found that, except for the case where the camera's field of view was severely obscured, the system could be repositioned correctly in almost all possible positions and orientations of the robot. In the simulation environment, the use of nonlinear optimization methods is very beneficial to the precise repositioning of the system. However, when testing in an actual environment, it was found that in a very small number of test positions, after using the optimized method, the pose score did not improve. The main reason may be that there are non-structural parts in the actual environment and the approximate method of interpolation of the map occupancy probability is used  
Figure 22: Comparison of the calculation time of the two test trajectories (**a**) trajectory 1 (**b**) trajectory 2.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Trajectory**} & \multirow{2}{*}{**Pose**} & \multicolumn{2}{c}{**AMCL**} & \multicolumn{2}{c}{**The System**} & \multicolumn{2}{c}{**Improvement**} \\ \cline{3-8}  & & **RMSE** & **MAE** & **RMSE** & **MAE** & **RMSE** & **MAE** \\ \hline \multirow{3}{*}{1} & pose\_x(m) & 0.0310 & 0.0293 & 0.0304 & 0.0290 & 1.94\% & 7.52\% \\  & pose\_y(m) & 0.0192 & 0.0175 & 0.0185 & 0.0157 & 3.65\% & 10.28\% \\ \cline{1-1}  & pose\_x(m) & 0.0240 & 0.0225 & 0.0214 & 0.0204 & 10.8\% & 9.33\% \\ \cline{1-1}  & pose\_y(m) & 0.0237 & 0.0230 & 0.0207 & 0.0192 & 12.65\% & 16.52\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: The results of the pose estimation errors in the \(x\) and the \(y\) directions, respectively.  
in the optimization process. Therefore, this article combines the two methods to achieve very good results in precise relocation. In the pose tracking stage, the dynamic object will also affect the tracking and the positioning accuracy of the system, and then affect the navigation of the robot.
# Energy-Efficient Opportunistic Routing Algorithm for Post-Disaster Mine Internet of Things Networks
## 5 Performance Evaluation
### Effect of Control Parameters  
According to Formula (19), the routing quality is the multiplication product of three factors, i.e., \(\tilde{\theta}_{ij}\), \(\tilde{d}_{ij}\), and \(\tilde{\epsilon}_{ij}\). Therefore, increasing the value of any control parameter will enhance the impact of the corresponding factors on routing performance. The control parameters (\(\alpha_{\theta},\alpha_{d},\alpha_{c}\)) can be adjusted to meet the requirements of the various applications for post-disaster MIoT. However, there may be a factor that is ineffective in routing in some cases. In order to select the optimal forwarders in the routing process, the setting of control parameter values should ensure that they have a certain effect on the three factors. In addition, in order to maximize the lifetime of the post-disaster network, the value of the energy control parameter \(\alpha_{c}\) should be larger than the other two parameters (\(\alpha_{\theta},\alpha_{d}\)). In the simulation, the number of nodes is set to 200, the communication range of nodes varies from 30 m to 50 m, the two control parameters are set to \(\alpha_{\theta}=\alpha_{d}=1\), and the value of \(\alpha_{c}\) varies from 1 to 6. Figure 8 shows the effect of the control parameter \(\alpha_{c}\) on the network lifetime. We can clearly see that when the communication range \(r\) is fixed, the network lifetime increases the larger \(\alpha_{c}\) is. When \(\alpha_{c}=5\), the growth of network lifetime tends to be stable. Therefore, the DEOR algorithm ensures good performance in terms of network lifespan for different control parameters' \(\alpha_{c}\) values.
# Performance Analysis of BDS-5G Combined Precise Point Positioning
## 2 Methodology
### Observation Model for BDS-5G PPP
#### 2.1.2 Observation Model for 5G Millimeter Wave  
The 5G millimeter wave positioning uses a time of arrival observation model that takes into account the receiver-base station clock difference, as shown in Equation (3):  
\[cT^{(b)}=d^{(b)}-c\cdot\delta_{lb}+\varepsilon_{T}, \tag{3}\]  
where \(b\) is the base station number; \(T\) is the 5G millimeter wave observation; \(c\) is the speed of light; \(d\) is the geometric distance between the receiver and the base station; \(\delta_{lb}\) is the 5G receiver's clock difference; \(\varepsilon_{T}\) represents other measurement noise.
# Detecting and Responding to Concept Drift in Business Processes
## 4 Methodology  
In this section, we introduce the proposed approach, called the hierarchical Markov model-based Markov decision process (HMDP). Using the hospital billing data as an example (as demonstrated in Table 1 and Figure 1), an illustration of the main logic structure of the method is given in Figure 2.  
Given the old and new data/event log, we first represent customer behaviour by using an SMP separately. Customer transition behaviour is characterized using discrete-time transition probabilities, and the duration between every activity pair is modelled using gamma mixture models (ga-mm). We thereafter calculate the mean of these gamma mixture models and create a duration matrix that has the same size as the transition matrix, with each element referring to the average customer time spent on a specific transition. The transition and duration matrices are the low-level representation of the data with continuous elements. Consequently, the representation has uncountable variations, causing challenges in learning the MDP Q-table [39]. We, therefore, extract a high-level customer representation/pattern using the K-means technique [46]. Specifically, we combine the transition and duration matrices as a row vector and use it as the input of K-means. The output clustering centre is its high-level representation that has a categorical value. The high-level pattern of the old and new data contains information about the business environment evolution, and we combine them as the state of the MDP to make actions, detect concept drift and update the SMP model. Details of the approach are given in the following sub-sections.
# Delay-Sensitive Service Provisioning in Software-Defined Low-Earth-Orbit Satellite Networks
## 6 Experimental Result  
In this section, we first introduce an approximation algorithm based on differential evolution (DE) as a comparison algorithm to the LCP algorithm and then evaluate the performances of the algorithms on the satellite testbed described above. We solve the ILP in the IBM ILOG CPLEX optimization studio solver, version 12.6.3.0, to solve the traffic routing optimization model.
# Design, Modeling, and Analysis of IEEE Std 80 Earth Grid Design Refinement Methods Using ETAP
## 4 Refinement Methods
### Optimized Earth Grid Using the Refinement Methods  
To assess which refinement method would yield the best solution for problematic earth grid design conditions without altering the available earth grid surface area and input soil data, the results with the lowest tolerable thresholds for each method were subjected to the ETAP's Ground Grid Optimization tool. For the current division method, the results obtained from the 60% division factor simulation were used. For the fault duration method, the results obtained from the fault duration of 0.3 s were used, and, lastly, from the current limitation method, results obtained using the 400 A-rated NER were used for the optimization comparison. Table 6 shows the results of the optimized earth grid using each of the refinement methods and how they compare against each other.
# Satellite-Based Localization of IoT Devices Using Joint Doppler and Angle-of-Arrival Estimation
## 6 Experiment and Results
### Localization Simulation and Performance
measurement sample points, the achievable median localization error is less than 150 m. Note that for non-stationary IoT devices, taking measurements over long periods of time might result in _target migration_ and thus reduce the localization accuracy. Furthermore, to observe the effect of the AoA estimation error on the localization performance, we increased the AoA standard deviation to a range of 0.01 to \(1^{\circ}\) as indicated in Figure 20. The figure shows that as the AoA error increases, the localization accuracy reduces. From the plot, it can also be observed that the effect of AoA standard deviation of more than 0.05\({}^{\circ}\) is more prominent when the Doppler standard deviation is more than 15 Hz. In contrast, when the AoA standard deviation is very small, such as 0.01\({}^{\circ}\), the Doppler standard deviation has little effect on the localization performance. From the results, it is evident that at lower errors in AoA measurements, the localization performance improves significantly even when the Doppler measurements have large errors. Figure 19: The median localization error for a varying number of measurements taken at intervals separated by 5 s and different curves represent the number of satellites with Doppler standard deviation, \(\sigma_{\rm d}=10\) Hz and AoA standard deviation \(\sigma_{\rm\Phi}\), \(\sigma_{\rm\Theta}=1^{\circ}\); the results are for 300 runs. Figure 14:0 The median localization error for varying Doppler standard deviation, \(\sigma_{\rm d}\) and azimuth, \(\sigma_{\rm\Phi}\) and elevation, and \(\sigma_{\rm\Theta}\) AoA deviation using six satellites and 75 s of measurements; the results are deduced from 300 runs. To observe the merit of the joint utilization of Doppler and AoA, we depict in Figure 21 three cases: (i) when only using Doppler measurements, (ii) when only using AoA measurements, and (iii) when using both Doppler and AoA jointly for localization. Given a Doppler standard deviation, \(\sigma_{\text{d}}\) at 5 Hz and AoA deviation, \(\sigma_{\Phi},\sigma_{\Theta}\) at 0.01\({}^{\circ}\), the median localization error for Doppler is 64.29 m, AoA is 43.78 m, and the joint measurement is 34.55 m. From the results, it can be noted that combining the AoA measurements with the Doppler measurements has a positive effect on the localization performance. It not only reduces the median localization error but also narrows the error distribution. Therefore, it is clearly beneficial to utilize both the Doppler and AoA measurements for ground IoT device localization using a constellation of satellites.
# An Overview of Indoor Localization System for Human Activity Recognition (HAR) in Healthcare
## 3 Signal Measurement Techniques
### Time-Based Methods
#### 3.1.2 Time Difference of Arrival (TDOA)  
With this technique, the arrival times of various reference signals are evaluated, and, from the difference in these times, it is possible to determine the location of the target node [25]. The difference in signal arrival time at two reference points is used to calculate the distance difference between the target and the reference points [26]:  
\[\Delta d=v*(\Delta t), \tag{3}\]  
where \(v\) is the speed of the signal employed, and \(\Delta t\) is the difference in arrival time at each reference point. In two dimensions, this leads to the following form:  
\[\Delta d=\sqrt{\left(x_{2}-x\right)^{2}-\left(y_{2}-y\right)^{2}}-\sqrt{\left( x_{1}-x\right)^{2}-\left(y_{1}-y\right)^{2}}, \tag{4}\]  
where \(\left(x_{1},y_{1}\right)\) and \(\left(x_{2},y_{2}\right)\) are the known positions of the beacons.  
Unlike the TOA, the transmitted messages do not convey a timestamp because the difference in the time of receipt is already sufficient information to guarantee localization. Instead, through the multilateration technique, by comparing the arrival times of the signals in pairs, it is possible to build a system of hyperboles whose intersection determines our position (Figure 5). Unlike TOA, in the TDOA method, not all nodes need to be synchronized; it is sufficient that the anchor nodes are synchronized.  
Figure 4: Time of arrival.
## 3 Results and Discussion
### WVP Analysis
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Composites**} & **Tensile Strength** & **Elongation at Break** & **WVP** & \multirow{2}{*}{**Ref.**} \\  & **(MPa)** & **(\%)** & **(\(\times\)10\({}^{-12}\) g/Pa\(\cdot\)cm\(\cdot\)s)** & \\ \hline
**Current study** & & & & \\
**(PAhvx)** & 6.47 & 43 & 1.41 & \\
**PW + 5\% bacterial** & & & & \\
**cellulose (5–10)** & 13.87 & 0.11 & 306 & [PERSON] [22] \\
**PW + sweet lime pomace** & & & & \\
**(1:0.5)** & 2.4 & 7.6 & 0.02 & [PERSON] [59] \\ PW (3–0.3–0)** & 9.48 & 5.33 & 8.31 & [PERSON] [23] \\ PW starch/curcumin & 14.46 & 8.79 & 13.2 & Liu [60] \\
**(CS-DES-Cur5)** & & & & \\
**Starch/SA/ground coffee** & & & & \\
**(TPS-Alg/SCG-10)** & & & & \\
**Starch/SA/MMT** & & & & \\
**(S-6\%M)** & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of tensile properties and WVP of the present study and the previous literature on PW, PW starch, or SA/starch composites.