esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Works
### Dual Image Based Reversible Data Hiding Using Center Folding Strategy  
Dual image based reversible data hiding using center folding strategy was proposed by [PERSON] et al. (2019). In the method, the number of k bits is determined during data hiding process. After the secret data is parsed into k-bit d packets, it is processed for each of these packets as presented in Equation (3).  
\[d^{\prime}=d-2^{k-1} \tag{3}\]  
Figure 2 shows an example of \(k=3\) for this transformation process at center  
Equation (4) is used to calculate the change in pixel values in the stego image during data hiding process. These values are used in obtaining the stego image pixel values by using the Equation (5).  
\[d_{1}^{\prime}\ =\ \left\lfloor\frac{d^{\prime}}{2}\right\rfloor\text{ and }d_{2}^{ \prime}\ =\ \left\lceil\frac{d^{\prime}}{2}\right\rceil \tag{4}\]  
\[X_{i,j}^{\prime}=X_{i,j}+d_{1}^{\prime}\text{ and }X_{i,j}^{\prime\prime}=X_{i,j}-d_{2 }^{\prime} \tag{5}\]  
To avoid the fall of the boundary problem (FOBP), the data hiding process is not performed in cases where the value \(d^{\prime}\) is between \(\left\lceil-2^{(k-1)},2^{(k-1)}-1\right\rfloor\). In briefly, the data hiding range in the method is \(\left\lceil 2^{(k-1)},256-(2^{(k-1)}-1)\right\rceil\).  
In the data extraction process of the method, essentially, the difference between two stego image pixel values is calculated; the formula showing this process is presented in Equation (6).  
\[d=\left[X_{i,j}^{\prime}-X_{i,j}^{\prime\prime}\right]+2^{(k-1)} \tag{6}\]  
To recover the original cover image, the average of the two stego image pixel values must be computed and rounded up as shown in Equation (7).  
\[X_{i,j}=\left\lceil\frac{X_{i,j}^{\prime}+X_{i,j}^{\prime\prime}}{2}\right\rceil \tag{7}\]  
In the data hiding process, for an example, the cover image pixel values are \(X=\left\{45,\ 46\right\}\), \(k=3\), Secret data (S), \(S\)\(=001111\), \(S_{1}=001\), \(S_{2}=111\) (binary system), \(S_{1}=1\), \(S_{2}=7\) (decimal system), \(d=\left\{1,7\right\}\), we apply Equation (3), \(d^{\prime}=\left\{1,\ 7\right\}-4=\left\{-3,3\right\}\), then, we use folding strategy in Equation (4), \(d_{1}^{\prime}=\left\{-2,1\right\}\), and \(d_{2}^{\prime}=\left\{-1,2\right\}\), finally we apply Equation (5), \(X_{i,j}^{\prime}=X^{\prime}=X+d_{1}^{\prime}=\left\{45,\ 46\right\}+\left\{-2,1 \right\}=\left\{43,\ 47\right\}\), \(X^{\prime\prime}=X-d_{2}^{\prime}=\left\{45,\ 46\right\}-\left\{-1,+2\right\}=\left\{46,\ 44\right\}\).  
In the data extraction process, for an example, \(X^{\prime}=\left\{43,47\right\}\) and \(X^{\prime\prime}=\left\{46,44\right\}\), \(X^{\prime}-X^{\prime\prime}+2^{(k-1)}=\left\{43,\ 47\right\}-\left\{46,\ 44\right\}+4=\left\{-3,3\right\}+4=\left\{1,7\right\}\), \(S_{1}=1\), \(S_{2}=7\), Secret data (S) = 001111. To recover the original cover image, \(X=\left\lceil(X^{\prime}+X^{\prime\prime})/2\right\rceil=\left\lceil(\left\{ 43,\ 47\right\}+\left\{46,\ 44\right\})/2\right\rceil=\left\lceil\left\{89,\ 91\right\}/2\right\rceil\), \(X=\left\lceil\left\{89,\ 91\right\}/2\right\rceil=\left\{45,46\right\}\).
# Bearing Fault Diagnosis Method Based on Improved VMD and Parallel Hybrid Neural Network
## 2 Model Construction Principle
### Convolutional Neural Network  
CNN is a widely used network model. Its basic theory is to extract features from image, voice, or text data through convolution operation [27]. Its basic structure is composed of the convolution layer, pool layer, and full connection layer. The convolution layer is used to extract features. Local features are extracted by the convolution operation between the convolution kernel and input data, and different features are extracted by multiple convolution kernels. The pooling layer is used to compress features. The full connection layer is used to classify or regress the extracted features [28].  
1 DCNN is a variant of CNN, which is mainly used for the processing of sequence data. Compared with traditional CNN, 1 DCNN can better capture the local patterns in the sequence data [29]. The FC layer in the traditional CNN network may lead to the problems of overfitting and there being too many model parameters, and it is unable to retain the spatial information of the feature map. To solve this problem, the GAP layer is employed to replace the last FC layer. The GAP layer averages and pools the feature map extracted from the convolution layer, and then directly connects the pooling result to the classifier as the input of the classifier [30]. By compressing the feature map while preserving the spatial information of the feature map, the addition of the GAP layer improves the generalization ability of the model, reduces the overfitting in the training process, and reduces the number of parameters and calculation of the model. Specifically, for channel \(c\) of the input characteristic graph, its size is \(H\times W\) and the global average pooled output \(GAP_{c}\) is shown in Formula (11), where \(x_{i,j,c}\) is the value of channel \(c\) input at position \((i,j)\). The 1 DCNN-GAP pooling process is shown in Figure 2.  
\[GAP_{c}=\frac{1}{H\times W}{\sum_{i=1}^{H}\sum_{j=1}^{W}x_{i,j,c}} \tag{11}\]
## 3 Analytical Methods
### Zircon U-Pb and Hf Isotope Analyses  
Zircon crystals were selected from rhyolitic samples and embedded in epoxy resin, and then polished down to expose the interior of the crystals. Cathodoluminescence (CL) images were taken and used to demonstrate the internal texture of the zircons and to select optimum spot locations during the laser-ablation inductively coupled plasma mass spectrometry (LA-ICP-MS). Zircon U-Pb isotopic data were collected from 32 \(\upmu\)m diameter regions of single grains by use of ICP-MS (Agilent 7700) and a laser-ablation system at the Key Laboratory for the Study of Focused Magmatism and Giant Ore Deposits, MNR, Xi'an. The adopted laser-ablation system is a GeoLas 200M equipped with a 193 nm laser. Harvard zircon 91,500 was used as the external standard for isotopic fractionation, NIST SRM610 glass was used as the external calibration standard, and \({}^{29}\)Si was used as the internal standard for the elemental analyses. Isotopic ratios and element concentrations for zircons were calculated with the software Glitter 4.0 (Macquarie University). The ages were calculated with the program Isoplot (ver. 2.94), an add-in for Microsoft Excel [39]. The detailed analytical procedures for age and trace element determinations of zircons were similar to those described in the references [40].  
Zircon Hf isotope compositions were examined simultaneously from the same spot. The 193 nm laser ablation system attached to a Neptune Plasma Multicollector-ICP-MS at the same laboratory. The analysis was undertaken under a laser repetition rate of 10 Hz at 200 mJ and an ablated spot size of 60 \(\upmu\)m. The measured \({}^{176}\)Lu/\({}^{177}\)Hf ratios and the \({}^{176}\)Lu decay constant of 1.867 \(\times\) 10\({}^{-11}\) yr\({}^{-1}\) were used to calculate initial \({}^{176}\)Hf/\({}^{177}\)Hf ratios [41]. The present-day \({}^{176}\)Hf/\({}^{177}\)Hf = 0.28325 and \({}^{176}\)Lu/\({}^{177}\)Hf = 0.0384 values were used to calculate the depleted mantle model age (T\({}_{\rm DM}\)) [42]. The chondritic values of \({}^{176}\)Hf/\({}^{177}\)Hf = 0.0332 and \({}^{176}\)Lu/\({}^{177}\)Hf = 0.282772 were used for the calculation of \(\epsilon_{\rm Hf}\) values [43]. The detailed analytical procedures were described by reference [44].
## 5 Numerical Modelling for Form-Finding of TEL-Gridshells
### Form-Finding Stages  
The main aspects of each erection stage defined in the numerical model are described below:  
* _Stage 1. Grid bending_ (Figure 14a,b). In this stage, the grid bending is modelled from its initial position at the top of the structure until its complete deformation (Figure 3). For this purpose, the grid is modelled plane and positioned at the top of the built structure. For reasons of computation time, the grid is modelled semi-open instead of closed. The grid is linked to a support located in the centre of the grid, which prevents its displacement but allows it to rotate around a vertical axis. At this stage, rotation around the longitudinal axis of the couplings is allowed (see Figure 7), but the springs at their ends remain deactivated. The ends and points of the transverse midplane of the grid are pulled to their final coordinates by means of very low stiffness contraction cables which are shortened to almost zero length by an imposed axial deformation \(\varepsilon=-0.999\) on each of them. For reasons of numerical stability, \(\varepsilon\) cannot be one and therefore the length of the cables is never completely zero;
* _Stage 2. Grid support._ The cables from the previous stage are deactivated, and the definitive grid supports located at the ends of the laths are activated. Provisional supports are also activated at the internal crossing points of the deformed grid. This is essential to keep the grid position fixed until the transverse laths are added;
* _Stage 3. Bending of transverse laths_ (Figure 14c,d). The multilayer transverse members are modelled straight and placed tangent to the grid in the appropriate position. For the bending of these transverse members, contraction cables are also used, which pull the nodes of the three laths forming each of them into their deformed position in the grid. The coordinates of the target points of the nodes of each of the laths are easily obtained by placing auxiliary bars perpendicular to the grid at the corresponding nodes;* _Stage 4. Connection._ The cables from the previous stage are deactivated and a new coupling system is activated to connect the transverse members to the grid, and the couplings of the three layers of the transverse members to each other. The springs at the ends of the couplings corresponding to the _Type 1_, _Type 2_ and _Type 3_ joints, described in the previous section, are also activated;
* _Stage 5. Relaxation._ Finally, all the provisional supports used in previous stages are deactivated. The gridshell relaxes and reaches a new equilibrium position as a result of the internal forces of elastic deformation and the slipping produced in the joints.
## 5 Conclusions
The data presented in this study are openly available in these webpages: Sentinel-IA ([[https://dataspace.copernicus.eu/](https://dataspace.copernicus.eu/)]([https://dataspace.copernicus.eu/](https://dataspace.copernicus.eu/)), accessed on 10 May 2023), DEM ([[http://strm.csi.cgi.org/strmdata](http://strm.csi.cgi.org/strmdata)]([http://strm.csi.cgi.org/strmdata](http://strm.csi.cgi.org/strmdata)), accessed on 10 May 2023), satellite precise orbit data ([https://s1 qc.asf.alaska.edu/aux_poeorb/](https://s1 qc.asf.alaska.edu/aux_poeorb/), accessed on 10 May 2023), stratigraphic lithology data ([[https://www.ngac.cn/](https://www.ngac.cn/)]([https://www.ngac.cn/](https://www.ngac.cn/)), accessed on 12 May 2023), Fault zone data ([[https://www.ngac.cn/](https://www.ngac.cn/)]([https://www.ngac.cn/](https://www.ngac.cn/)), accessed on 10 June 2023), land use data ([[https://viewer.esa-worldcover.org/worldcover/](https://viewer.esa-worldcover.org/worldcover/)]([https://viewer.esa-worldcover.org/worldcover/](https://viewer.esa-worldcover.org/worldcover/)), accessed on 20 May 2023), soil types data ([[https://www.resdc.cn/](https://www.resdc.cn/)]([https://www.resdc.cn/](https://www.resdc.cn/)), accessed on 23 May 2023), river data ([[https://www.openstreetmap.org/](https://www.openstreetmap.org/)]([https://www.openstreetmap.org/](https://www.openstreetmap.org/)), accessed on 10 June 2023), road data ([[https://www.openstreetmap.org/](https://www.openstreetmap.org/)]([https://www.openstreetmap.org/](https://www.openstreetmap.org/)), accessed on 10 June 2023), Landsat data ([[http://www.gscloud.cn/](http://www.gscloud.cn/)]([http://www.gscloud.cn/](http://www.gscloud.cn/)), accessed on 5 June 2023), rainfall data ([[http://data.cma.cn](http://data.cma.cn)]([http://data.cma.cn](http://data.cma.cn)), accessed on 12 June 2023). Thanks to the Sentinel-IA image and satellite precise orbit data provided by ESA, DEM data provided by NASA, stratigraphic lithology and fault zone data provided by NGAC, land use type data provided by ESA, soil type data provided by Resource and Environmental Science Data Platform, river and road data provided by OpenStreetMap, Landsat data provided by Geospatial Data Cloud, rainfall data provided by China National Meteorological Science Data Center, disaster points data provided by China Geological Environment Monitoring Institute. The authors declare no conflicts of interest.
## 2 Results and Discussion
### VOCs of Cling-Films for Retail Use  
The different VOC profiles characterized PE-based cling-film (PE200) and PVC-based cling-films (812 and 818) are presented in Table 1 and Figure 2. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{3}{*}{**Rt a**} & \multirow{3}{*}{**Volatile Compounds b**} & \multicolumn{6}{c}{**Cling-Films for Retail Use c**} \\ \cline{3-8}  & & \multicolumn{2}{c}{**812 PVC Based**} & \multicolumn{2}{c}{**818 PVC Based**} & \multicolumn{2}{c}{**PE 200 PE Based**} \\ \cline{3-8}  & & **mean** & **s.d.** & **mean** & **s.d.** & **mean** & **s.d.** \\  & & **(_n_ = 3_)** & **(\(\pm\))** & **(_n_ = 3_)** & **(\(\pm\))** & **(_n_ = 3_)** & **(\(\pm\))** \\ \hline \multicolumn{8}{c}{_Hydrocarbons_} \\ \hline
1.63 & \multicolumn{2}{c}{Heptane} & 4.13 & 0.09 & nd & - & nd & - \\
1.71 & \multicolumn{2}{c}{Cyclohexane} & nd & - & nd & - & 34.89 & 0.79 \\
1.86 & \multicolumn{2}{c}{Methylcyclohexane} & 0.80 & 0.02 & nd & - & 20.38 & 0.46 \\
2.28 & \multicolumn{2}{c}{1-Chlorobutane} & 29.06 & 0.66 & 8.14 & 0.18 & nd & - \\
4.16 & \multicolumn{2}{c}{2,2,4,6,6-} & \multicolumn{2}{c}{nd} & - & 7.52 & 0.17 & nd & - \\
5.17 & \multicolumn{2}{c}{Decane} & nd & - & 0.00 & 0.00 & 28.76 & 0.65 \\
6.1 & \multicolumn{2}{c}{Toluene} & nd & - & 3.79 & 0.09 & 5.56 & 0.13 \\
10.35 & \multicolumn{2}{c}{Ethylbenzene} & 3.33 & 0.08 & 3.51 & 0.08 & 21.33 & 0.48 \\
10.95 & \multicolumn{2}{c}{\(p\)-Xylene} & 1.40 & 0.03 & 2.21 & 0.05 & 7.34 & 0.17 \\
10.99 & \multicolumn{2}{c}{\(m\)-Xylene} & 10.80 & 0.24 & 24.05 & 0.54 & 1.47 & 0.03 \\
11.05 & \multicolumn{2}{c}{\(Oo\)-Xylene} & 4.25 & 0.10 & 13.20 & 0.30 & 0.78 & 0.02 \\
16.18 & \multicolumn{2}{c}{Styrene} & 4.41 & 0.10 & 14.29 & 0.32 & 1.01 & 0.02 \\ total & \multicolumn{2}{c}{58.18} & \multicolumn{2}{c}{76.72} & \multicolumn{2}{c}{121.51} \\ \hline \multicolumn{8}{c}{_Aldehydes_} \\ \hline
2.61 & \multicolumn{2}{c}{Butanal} & 2.09 & 0.05 & 1.60 & 0.04 & nd & - \\
4.33 & \multicolumn{2}{c}{3-Methylbutanal} & 2.75 & 0.06 & 1.74 & 0.04 & nd & - \\
8.25 & \multicolumn{2}{c}{Hexanal} & 22.95 & 0.52 & 12.40 & 0.28 & 4.66 & 0.11 \\
13.38 & \multicolumn{2}{c}{Heptanal} & 11.32 & 0.26 & 8.15 & 0.18 & 3.82 & 0.09 \\
17.44 & \multicolumn{2}{c}{Octanal} & 4.22 & 0.10 & 3.67 & 0.08 & 11.61 & 0.26 \\
20.47 & \multicolumn{2}{c}{Nonanal} & 66.48 & 1.50 & 46.29 & 1.05 & 51.77 & 1.17 \\
21.22 & \multicolumn{2}{c}{2-octenal} & 2.63 & 0.06 & 1.29 & 0.03 & nd & - \\
23.04 & \multicolumn{2}{c}{Decanal} & 13.45 & 0.30 & nd & - & nd & - \\ total & \multicolumn{2}{c}{125.88} & \multicolumn{2}{c}{75.15} & \multicolumn{2}{c}{71.86} \\ \hline \end{tabular}
\end{table} 
Table 1: VOC profile by HS-SPME-GC/MS of cling-films for retail use.
# Named Entity Recognition Method Based on Multi-Feature Fusion
## 2 Related Work
In the feature extraction layer, a parallel approach using both BiLSTM and DGCNN is adopted: BiLSTM captures comprehensive temporal information, while a DGCNN with residual connections and gating mechanisms is introduced to capture a broader range of spatial information, thus enhancing the capture of text features and further improving model performance.
# A Study of Cellular Traffic Data Prediction by Kernel ELM with Parameter Optimization
## 3 Experimental Preparation
### Experimental Setup  
The kernel limit learning machine has different flow prediction performance depending on the kernel function selected. Even if it is the same kernel function with different parameters, the results of flow prediction will be different. Therefore, it is necessary to optimize the kernel function and its parameter when the kernel limit learning machine is used for flow prediction. In this study, in order to maximize the optimal flow prediction of the kernel limit learning machine, we applied the metaheuristic optimization algorithm for parameter optimization of kELM.  
The parameter settings of the metaheuristic optimization algorithm in the experiment are shown in Table 3. In kernel function parameter optimization, for the same kernel function, the variable boundary settings of all optimization algorithms are the same, as shown in Table 4.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Kernel Function** & **Upper** & **Lower** \\ \hline Gaussian [C k\({}_{1}\)] & [1000, 1000] & [0.001, 0.001] \\ Linear [C] & 1000 & 0.001 \\ Polynomial [C k\({}_{1}\) k\({}_{2}\)] & [1000, 10, 6] & [0.001, 0, 0] \\ Sigmoid [C k\({}_{1}\) k\({}_{2}\)] & [1000, 10, 10] & [0.001, \(-10\), \(-10\)] \\ \hline \hline \end{tabular}
\end{table}
Table 4: Variable boundaries.  
Figure 3: Sequence Processing.  
When the metaheuristic optimization algorithm searches for the parameters of kELM, the performance evaluation mechanism of kELM is the mean absolute percentage error (MAPE), as shown in Equation (14). To prevent overlearning, we used MAPE of the test set as the fitness function of the optimization algorithm.  
\[MAPE\ =\ \frac{1}{n}\sum\
olimits_{i=1}^{n}\left|\frac{y_{i}-\hat{y}_{i}}{y_{i}} \right|\times 100\% \tag{14}\]  
where \(y_{i}\) is the actual cellular traffic value, and \(\hat{y}_{i}\) is the predicted value.
## 5 Conclusions  
This paper presented a novel method for communication in quarantined areas, e.g., due to the new COVID-19 pandemic, between isolated areas and in areas with technical damage, using UAV networks with a flight mission plan on triangular cells and with a well-established mobility schedule. Two main scenarios of drone battery management using charging and swapping battery stations were investigated. The following conclusions are drawn:  
* A novel method for mapping an area using regular polygons was proposed. The proposed network of cells to cover a geographical area is hexagonal, each having three UAVs.
* A new methodology based on experimental preliminary flight tests for a network cell was proposed to simulate a UAV cell network.
* A new TD-UAV Dijkstra algorithm and well-known DTN algorithms were analyzed to simulate UAV networks with a well-established mobility schedule.
* A delivery rate of 0.146 to 0.644 in the UAV network with a respective battery charge of 0.179 to 0.973 with battery swapping was found. The best results were obtained for the TD-UAV Dijkstra algorithm, which delivered most of the data packages in the shortest delivery time. The average latency was 1.48 h for the UAV network with battery recharge and 0.45 h for the UAV network with battery swapping.
* The Epidemic, Spray and Wait, and MaxDelivery algorithms produced poorer results due to the small number of contacts between nodes and a low number of message exchanges.
* The fastest communication was obtained for a UAV triangular network with a battery charge. It was found that the battery swapping scenario led to an increase of \(\sim\)46% for the delivery rate against the battery charge scenario.  
A future direction of research for this type of UAV network with a well-established mobility schedule would be to use them for parcel delivery in emergencies in remote quarantined zones. In addition, a square cell network could be considered instead of the triangular one since both networks can be constructed starting from the idea of equidistantly covering a given 2D area with points that are vertices of some regular polygons (see Figure 1a,b). Both methods could be compared to determine which is better under various conditions.  
Conceptualization, A.M.D. and R.U.; methodology, R.U. and A.M.D.; software, A.M.D. and C.-S.N.; validation, R.U. and A.M.D.; formal analysis, A.M.D. and R.U.; investigation, R.U. and A.M.D.; resources, R.U., C.-S.N. and A.M.D.; data curation, C.-S.N. and A.M.D.; writing--original draft preparation, R.U., A.M.D. and C.-S.N.; writing--review and editing, R.U. and A.M.D.; visualization, A.M.D. and R.U.; supervision, A.M.D. and R.U.; project administration, R.U. and A.M.D.; funding acquisition, A.M.D., C.-S.N. and R.U. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The authors acknowledge Transilvania University of Brasov for providing the infrastructure used in this work.  
The authors declare no conflict of interest.
# Monitoring Cropland Abandonment in Hilly Areas with Sentinel-1 and Sentinel-2 Timeseries
## 3 Results
### Spatial Distribution and Statistics of Cropland Abandonment  
Visually, the cropland was mainly located near rivers and residential areas, with a greater concentration in urban centers and greater dispersion in remote areas. Small cropland fields (<1 ha) and small-holder agriculture were prevalent in the study area (Figure 5).  
The areas and percentages of active cropland, intermittent irrigated cropland, intermittent rainfed cropland, abandoned irrigated cropland, and abandoned rainfed cropland were calculated (Table 3). In general, the area of abandoned cropland had a high proportion, and the abandoned rainfed cropland was the most obvious. In addition, the intermittently abandoned cropland had a high proportion, indicating that the area of cropland abandonment tended to expand in the study area.  
Figure 4: Monthly median and standard deviation of NDVI from 2019 to 2021.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Area (km2)** & **Percentage (\%)** \\ \hline Active cropland & 137.4 & 77.8 \\ Intermittent irrigated cropland & 3.0 & 1.7 \\ Intermittent rainfed cropland & 19.8 & 11.2 \\ Abandoned irrigated cropland & 0.9 & 0.5 \\ Abandoned rainfed cropland & 15.5 & 8.8 \\ Total & 176.6 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The area and percentage of active cropland, intermittent cropland, and abandoned cropland.
# Research on Application of Improved Quantum Optimization Algorithm in Path Planning
###### Abstract  
For the building emergency evacuation path planning problem, existing algorithms suffer from low convergence efficiency and the problem of getting trapped in local optima. The Bloch Spherical Quantum Genetic Algorithm (BQGA) based on the least-squares principle for single-robot path planning and Bloch Spherical Quantum Bee Colony Algorithm (QABC) for multi-robots path planning are studied. Firstly, the characteristics of three-dimensional path planning are analyzed, and a linear decreasing inertia weighting approach is used to balance the global search ability of chromosomes and accelerate the search performance of the algorithm. Then, the application algorithm can generate a clear motion trajectory in the raster map. Thirdly, the least squares approach is used to fit the results, thus obtaining a progressive path. Finally, multi-robots path planning approaches based on QABC are discussed, respectively. The experimental results show that BQGA and QABC do not need to have a priori knowledge of the map, and they have strong reliability and practicality and can effectively avoid local optimum. In terms of convergence speed, BQGA improved by 3.39% and 2.41%, respectively, while QABC improved by 13.31% and 17.87%, respectively. They are more effective in sparse paths.  
Bloch sphere; path planning; Quantum Optimization Algorithm; quantum genetic algorithm; quantum bee colony algorithm +
Footnote †: journal: Applied Physics Letters  
1
Footnote 1: institutetext: School of Computer and Information Engineering, Harbin University of Commerce, Harbin 150028, China; [EMAIL_ADDRESS]  
2
Footnote 2: institutetext: Correspondence: [EMAIL_ADDRESS]