esa-sceva/satcom-chunk-collection huggingface dataset:
# Cooperative Path Planning for Multiple Mobile Robots via HAFSA and an Expansion Logic Strategy
## 3 Local Path Planning Based on an Expansion Logic Strategy  
In this section, an expansion logic strategy is presented to avoid collisions between multiple mobile robots and an environment with obstacles. It plays a decisive role in real-time navigation of mobile robots. Figure 1 shows the obstacle information in an unknown environment. For any polygonal obstacle, the minimum circumscribed circle (MCCI) method and wire envelopes method can be used to perform obstacle expansion operations. In this article, as we sought a rapid expansion method, we adopted the endpoint connection method to generate a circular equation instead of the MCCI method. The expansion logic strategy is described as follows:  
If the vertices of n-sided polygonal obstacles are denoted as \(p_{i}(x_{i},y_{i})\), the Euclidean distance \(d_{ij}\) between two vertices \(p_{i}(x_{i},y_{i})\) and \(p_{j}(x_{j},y_{j})\) can be expressed by the following equation:  
\[d_{ij}=\sqrt{\left(x_{i}-x_{j}\right)^{2}+\left(y_{i}-y_{j}\right)^{2}}. \tag{12}\]  
Therefore, the diameter of a range circle is given by  
\[d_{\max}=\max\bigl{\{}d_{ij}\bigr{\}},i,j=1,2,\cdots,n \tag{13}\]  
and the circle equation is given by Equation (10).  
\[\left(x-\frac{x_{i}+x_{j}}{2}\right)^{2}+\left(y-\frac{y_{i}+y_{j}}{2}\right)^ {2}=\left(\frac{d_{\max}}{2}\right)^{2} \tag{14}\]  
In this paper, an environmental map with n-sided polygonal obstacles is shown in Figure 1a. Following the circle Equation (14), two concentric circles with diameters \(1.2d_{\max}\) and \(1.4d_{\max}\) could be obtained (Figure 1b). As the figure shows, the collision probability was 0.85 for the complementary set area of the intersection between the first circle and the polygonal obstacles. Furthermore, the collision probabilities were 0.65 and 0.45 when the robots moved in the other two grid areas.  
Figure 1: (**a**) Environmental map information; (**b**) expansion logic operation for obstacles.  
To evaluate the grid-based environment map, a kind of scoring function is given by Equation (15), which shortens the time of local path planning and improves decision-making ability.  
\[score=100-\lceil\frac{dist}{l}\rceil, \tag{15}\]  
where _dist_ denotes the Euclidean distance between the starting point and the target point, and \(l\) represents the Euclidean distance between the starting point and the current position of the robot.  
From the vertical line of the motion direction, we scored the surrounding grids by Equation (15) and the obtained grid scores are shown in Figure 2a. Then, in accordance with the current position information and Equation (12), the mobile robot selected the grid that was the closest to the target point as the position of the next moment. If the distance between the grids was equal, preference was given to the high score grid. Finally, the local path planning trajectory, which is indicated by a dotted line, could be obtained using the expansion logic strategy (Figure 2b).
## 3 Case Study
### Ship Knowledge Graph Construction  
The ontology construction based on the graph semantic rules can form the ship base graph model, which includes the ship's owner company, ship type, key tracking or not, ship certificate validity, ship port call, etc. After the completion of the ontology construction, the initial prototype of the ship knowledge graph is formed, as shown in Figure 10.  
Figure 9: Fake ship license plates inference.  
Ontologies include Ship, Historyship(HShip), Shiptype(St), Company(Com), Cert, Key Track, Key Type(Kt), Port, Berth, etc. Relations include type, renew, cert, belong, keytracking, reason, have, etc.  
Also included is a description of the category that a ship belongs to by establishing a type of entity link relationship; a description of the ship's change history by establishing a renewed relationship; the ship certificate information is included by establishing a cert relationship; a description of the ship company that the ship belongs to by establishing a Belong_to relationship; a description of whether a ship is a key tracking ship by establishing a key tracking relationship; a description of the specific reason why a ship is a key tracking ship by establishing a reason relationship. By establishing the relationship to describe the berth information under the port, the corresponding ship relationship graph is shown in Figure 11.  
The ship knowledge graph database established by setting up ship characteristic rules [40] and combining the ship maritime supervision and navigation protection business model can realize the presentation of the overall relationship network of ships and the ship portrait of the static relationship of specific ships [34]. Take the ship named \"Ning Shuangshun 5568\" as an example. By drawing its portrait, we can see that it is a river ship, the type of ship is a bulk carrier, it belongs to the maritime key tracking ship, it has violated  
Figure 11: Ship relationship graph.  
Figure 10: Ship semantic model.  
regulations in Nanjing and disobeyed the authorities or evaded them, its certificate is valid (valid until 10 January 2021), and it belongs to [PERSON], a self-employed person.
# Robust Hand Shape Features for Dynamic Hand Gesture Recognition Using Multi-Level Feature LSTM
## 6 Experiments and Discussion
### Experiment 4: Comparison with Related Works  
We made a comparative survey of the previous works on the dynamic hand gesture recognition, as shown in Table 9. [PERSON] et al. [12] built the DHG dataset and conducted their experiments based on the Fisher vector to extract features from the Shape of Connected Joints (SoCJ), built temporal pyramid features, and classified by SVM. Their works achieved state-of-the-art performances of 86.86% and 84.22% on DHG 14 and 28, respectively, with the traditional approach. Using deep learning with the dynamic graph and attention mechanism, [PERSON] at al. successfully achieved the highest accuracy of 91.9% and 88% on DHG 14 and 28 by the deep learning approach.  
Due to the multi-modal features between enhancing traditional features and integrating the joint point-cloud model for exploiting the 3D geometric transform, our method gave better results than the other two. The proposed method gave 93.69% and 90.11% on DHG 14 and 28 using skeleton data, as well as 96.07% as 90.11% using both depth and skeleton data.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**14 Classes**} & \multicolumn{3}{c}{**28 Classes**} \\ \hline
**No.** & **Features** & **MLF LSTM** & \begin{tabular}{c} **MLF LSTM** \\ **Conv1-2D** \\ \end{tabular} & \
\begin{tabular}{c} **MLF LSTM** \\ **Conv1-2D** \\ \end{tabular} &
\begin{tabular}{c} **MLF LSTM** \\ **Conv1-2D** \\ \end{tabular} \\ \hline
1 & Motion & 80.23 & 82.50 & 72.85 & 70.23 \\ \hline
2 & Motion + Skeleton-Shape & 86.07 & 84.52 & 82.02 & 81.19 \\ \hline
3 &
\begin{tabular}{c} Motion + skeleton shape \\ + joint point-cloud \\ \end{tabular} & 89.52 & 90.59 & 85.47 & 86.78 \\ \hline
4 &
\begin{tabular}{c} Motion + skeleton shape \\ + joint point-cloud + points \\ \end{tabular} & 93.45 & 93.69 & 89.40 & 90.11 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance results on the skeleton data.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**14 Classes**} & \multicolumn{3}{c}{**28 Classes**} \\ \hline
**No.** & **Input** & **MLF LSTM** & \
\begin{tabular}{c} **MLF LSTM** \\ **Conv1-2D** \\ \end{tabular} &
\begin{tabular}{c} **MLF LSTM** \\ **Conv1-2D** \\ \end{tabular} \\ \hline
1 & Skeleton & 93.45 & 93.69 & 89.4 & 90.11 \\
2 & Depth2D & 92.26 & 90.71 & 87.61 & 88.33 \\
3 & All & 96.07 & 94.28 & 94.4 & 92.38 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Overall performance results.  
Figure 16: _Cont_.  
Figure 15: Confusion matrix of the best prediction results on the skeleton data: MLF LSTM Con1-2D with skeleton data (accuracy of 93.69%) (**a**) on DHG-14 and MLF LSTM Conv1-2D with skeleton data (accuracy of 90.11%) (**b**) on DHG-28.
## 2 Related Works  
### Heuristic and Approximate Approaches  
This subsection introduces studies using heuristic, approximation, or simulation-based approaches. Research focusing on pricing or inventory management problems for perishable goods often emphasizes customer demand models. For instance, [PERSON] et al. [5] use a simulation-based approach to analyze an inventory management problem where the policy dynamically switches between FIFO and LIFO depending on the presence of discount strategies. Furthermore, [PERSON] et al. [6] propose an adaptive approximation approach for an inventory management problem that incorporates a demand model dependent on the age of products. Similarly, [PERSON] and [PERSON] [7] introduce a mixed FIFO-LIFO demand model and design heuristics tailored for inventory management problems utilizing this model. [PERSON] et al. [8] focus on developing optimal pricing strategies for perishable goods with age-dependent demand. Beyond demand models, other studies address critical factors such as lead time. For example, [PERSON] et al. [9] propose an approximation approach for inventory management problems that explicitly account for lead time. Considering the diversity of demand patterns, as reflected in these studies, is crucial because customer preferences often render the assumption of FIFO consumption unrealistic in retail environments.  
Moreover, while many studies assume zero lead time for simplicity, lead time is a significant factor that complicates inventory management problems and should be incorporated into the analysis. Next, studies addressing joint pricing and inventory management for perishable goods are introduced. [PERSON] and [PERSON] [10] model and analyze pricing and inventory management problems for perishable goods with age- and price-dependent demand using DP. [PERSON] et al. [11] employ DP to develop dynamic pricing strategies for multi-batch perishable goods and propose four heuristics. [PERSON] and [PERSON] [12] develop a mathematical model for pricing and inventory management of perishable goods that considers social learning, analyzing its structural properties.  
Additionally, [PERSON] et al. [13] apply optimal control theory to derive optimal dynamic pricing and shipment consolidation policies across multiple stores. [PERSON] and [PERSON] [14] focus on developing optimal dynamic pricing and inventory management strategies for non-perishable goods, while [PERSON] et al. [15] address pricing and inventory replenishment policies specifically for perishable goods. Finally, [PERSON] and [PERSON] [16] construct a stochastic optimization model to devise optimal dynamic pricing and production control strategies for perishable goods. These studies collectively underscore the importance of accounting for diverse demand behaviors and operational constraints, such as lead time and product perishability, to develop robust and efficient inventory and pricing strategies.
## 2 Materials and Methods
### The Electromotor-Driven Parallel External Fixator System
#### 2.4.2 The Electromechanical System  
(1) Hardware of the electromechanical system  
The hardware of the electromechanical system mainly includes the motor, incremental encoder, motor driver, and microprocessor, as shown in Figure 4. The power supply voltage of the motor driver (Maxon EPOS2 24/2 390438) is 9-24 V, and the maximum output power is 48 W. The communication interface can be provided in three ways: RS232, CAN (Controller Area Network), and USB 2.0/3.0. In the actual design, the power supply voltage is selected as 12 V, which is less than the human body safety voltage of 36 V. The emergency stop button is set to avoid emergencies, which can ensure the safety of electrical appliances during the use of the system. Furthermore, the CAN bus, mounted with 6 motor drivers (CAN slave station), is used for communication. The STM32 (STM32F103 ZET6) chip with the CAN interface is selected as the processor chip. The chip is used as the master station in the CAN network to send control commands to multiple motor drivers (CAN slave stations) when controlling the robot's motion.  
(2) Mode of the motion control  
The incremental encoder is installed at the end of the motor, which can read and feed back the length information of each strut of the external fixator in real-time and obtain the position information of the bone end through the position inverse solution. Then, according to the correction prescription, the microcontroller sends motion instructions to the motor to form a semi-closed loop control mode. Before the correction is implemented, we set the initial position to the zero position. Then, the current correction state can be determined according to the position information feedback from the encoder until the target position is reached.  
The external fixator provides three motion control modes: manual adjustment, mechanism reset, and automatic trajectory tracking. Manual adjustment is mainly based on the speed control mode of the CANopen DS402 protocol for motion control of the motor. The automatic trajectory tracking  
Figure 4: The hardware composition of the electromechanical system.  
is realized based on the position interpolation mode of the motor driver. In the master computer software, the initial position of the mechanism and the increment of each degree of freedom (DOF) are given. The position data are sent to the microcontroller to perform trajectory tracking. The two consecutive trajectory points obtained by the inverse kinematic solution are defined as the reference points \(p_{i}\) and \(p_{i+1}\) and the velocity V and time T are given based on the two reference points. Spline interpolation is used to calculate the functional relationship of the two consecutive points by the motor driver. Then, the position, velocity, and acceleration values are set.
# Multipath Data Storage by Third-Order Nonlinear Optical Properties in Zinc Oxide Nanostructures
## Materials and Methods
### Nanosecond Third-Order Nonlinear Optical Studies  
Multiphotonic phenomena associated with third-order nonlinear optical properties were conducted in the samples by using the linearly polarized second harmonic of a Nd-YAG laser system (Continuum Model SL II). A vectorial two-wave mixing experiment and a single-beam transmittance setup were employed with a pulsed energy of 110 mJ maximum, an optical wavelength at 532 nm and single-shot mode of pulses with 4 nanoseconds of FWHM duration. A beam waist of 1 mm of diameter was focused in the sample with a relation of optical irradiance 1:1 between the incident beams. The probe and pump beams were making a geometric angle of approximately 10\({}^{\circ}\) at the surface of the film. The polarization of the transmitted probe beam was analyzed by a calcite polarizer located behind the sample. PIN photodetectors were employed for acquiring the nonlinear transmittance and the orthogonal component of the polarization of the transmitted probe beam. The vectorial nature of this experiment was evaluated by the assistance of a half-wave plate able to rotate the polarization of the pump beam during the measurements while the polarization of the probe beam was fixed. The calibration of the two-wave mixing experiment was carried out by a CS\({}_{2}\) liquid solution deposited in a 1 mm length quartz cuvette. The error bar in our measurements was approximately \(\pm\)10%.  
The wave equation was employed for the mathematical representation of the amplitudes of the electric fields in propagation through the samples [24]:  
\[\
abla^{2}E_{\pm}=-\frac{n_{\pm}^{2}\omega^{2}}{c^{2}}E_{\pm}. \tag{1}\]  
Here the optical frequency is \(\omega\) and \(c\) is the speed of the light. \(E_{+}\) and \(E_{-}\) correspond to the circular components of the right and left polarized electric fields, respectively. The refractive index, \(n_{\pm}\), can be approximated by [24]  
\[n_{\pm}^{2}=n_{0}^{2}+4\pi\big{(}A|E_{\pm}|^{2}+(A+B)|E_{\mp}|^{2}\big{)} \tag{2}\]  
where \(n_{0}\) is the weak-field refractive index, the refractive index dependent on optical irradiance in optically isotropic materials involves independent components of the third-order susceptibility tensor, \(\chi^{(3)}\), described by \(A=\chi^{(3)}_{1122}\) and \(B=\chi^{(3)}_{1212}\). We described \(\chi^{(3)}\) by [24]  
\[\chi^{(3)}_{1111}=\frac{n_{0}c}{7.91\times 10^{2}}n_{2}+i\frac{n_{0}^{2}c \lambda}{\pi^{2}}\beta. \tag{3}\]In addition, the transmitted optical irradiance \(I(L)\) is [25]  
\[I(L)=\frac{I_{o}\exp(-\alpha_{0}L)}{1+\beta I_{o}L_{eff}}, \tag{4}\]  
Considering initial incident irradiance \(I_{o}\), and \(\alpha_{0}\) as the optical absorption coefficient at low irradiance, then the effective length \(L_{\textit{eff}}\) is  
\[L_{\textit{eff}}=\frac{(1-\exp(-\alpha_{0}L))}{\alpha_{0}}. \tag{5}\]
## 3 Results
### JudgED System Test
#### 3.1.1 Decision Accuracy  
The results of the field experiment showed a mean decision accuracy of 43.011% (minimum = 27.047%, maximum = 61.517%, \(\sigma=\pm 12.898\%\)). A Shapiro-Wilk test showed the normal distribution of the users' decision accuracy, \(W(16)=0.889\), \(p=0.053\). Figure 1 visualises the decision accuracy grouped by discipline, which accents the higher decision accuracy of Tatami disciplines (50.460%) compared to Ring disciplines (30.596%).
## 3 Results and Discussion
### Heavy Metal Accumulation and Ecological Risk Assessment  
The contamination factor, geoaccumulation index, and environmental risk for Pb, Zn, and Cd are presented in Table 6. According to the contamination coefficients [21] for the soils containing Pb, higher figures were noted for samples taken at a depth of 5 cm, with six samples being characterized by a low level of contamination, and the only samples shown as moderately contaminated were those taken from a depth of 5 cm for Point 1 (which was on the leeward side) and Field 3 (the closest site to the mine tailings). The results for Cd contamination showed a similar trend, with the samples from the fields characterized by moderate contamination. For Zn, there was a significant degree of contamination in the field samples and a predominance of moderate contamination among the other soil samples.  
The geoaccumulation index proposed by [PERSON] [33] was used to identify and determine the metal contamination of soil. The HM geoaccumulation factor indicated high levels for all metals at all sampling points. The highest index was 11.32 units for Zn in Field 3 at a depth of 5 cm and was characterized as extreme Zn contamination. This was also seen in the other soil samples with values of 3.2 and 2.26 (as shown in Table 7), which are higher than those identified by other authors [57; 58]. Extreme Pb contamination was noted in Fields 1 and 3, for which the indices were 5.26 and 7.32, respectively, also higher concentrations than those reported by other authors [59; 60; 58]. Excessive concentrations of Cd were also found in Field 3. The environmental risk values associated with Pb and Zn showed a low level of risk. However, the values for Cd in Field 3 were described as a moderate level of risk, consistent with the findings of other researchers [24; 61].
# Soft Iterative Decoding Algorithms for Rateless Codes in Satellite Systems
## 5 Conclusions  
This paper introduced two promising applications of rateless codes for satellite systems, with efficient soft iterative decoding algorithms for performance enhancement. By utilizing the inherent incremental redundancy capability of rateless codes, they can be applied to the HARQ scheme in satellite systems. Because many recent satellite broadcasting systems adopt LDPC codes which do not have inherent rate compatibility, direct application to HARQ may come with a number of problems. The study results presented in this paper showed that combining existing LDPC codes with rateless codes either in the PHY or MAC layer can be an effective solution. Related studies with extensive simulations with various system configurations and scenarios, including raptor-like LDPC codes adopted in the 5G NR, should be conducted in the future.  
Conceptualization and methodology, M.Z. and S.K.; software and data curation, M.Z. and S.C.; validation, S.K.; writing--original draft preparation, review and editing, S.K.; funding acquisition, M.Z. and S.K.  
This research was supported by National Natural Science Foundation of China (Grant No. 61601403) and Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education(2017R1 DIA1B03027939)  
The authors declare no conflict of interest.
## 3 Sliding Mode Control with Sliding Perturbation Observer (SMCSPO)
### Estimation of Reaction Force Using Sliding Perturbation Observer (SPO)  
The SPO can be used to estimate the perturbation, leading to the determination of the reaction forces. The estimation of perturbations consists of two factors: the external disturbance, and the dynamic error due to nonlinearities of gravity and friction. Equations (2) and (3) can be used to estimate the perturbation.  
\[\hat{\psi}_{s1}=-\frac{1}{J_{s1}}(\hat{\tau}_{e1})-\frac{1}{J_{s1}}(0.5M_{s1}L_ {1}g\sin\theta_{1})-(\frac{\Delta J_{s1}}{J_{s1}})\ddot{\theta}_{1}-\frac{1}{ J_{s1}}(\Delta B_{s1}+\beta_{1})\dot{\theta}_{1} \tag{24}\]  
\[\hat{\psi}_{s2}=-\frac{1}{J_{s2}}(\hat{\tau}_{e2})-\frac{1}{J_{s2}}(M_{s2}L_{2} g\cos\theta_{2})-(\frac{\Delta J_{s2}}{J_{s2}})\ddot{\theta}_{1}-\frac{1}{J_{s2}}( \Delta B_{s2}\dot{\theta}_{2})-\frac{1}{J_{s2}}(\beta_{2}\dot{x})-\frac{1}{J_ {s2}}(\lambda) \tag{25}\]  
Equations (24) and (25) can be rewritten as  
\[\hat{\tau}_{e1}=J_{s1}\hat{\psi}_{s1}+0.5M_{s1}L_{1}g\sin\theta_{1}+\Delta J_{ s1}\ddot{\theta}_{1}+(\Delta B_{s1}+\beta_{1})\dot{\theta}_{1} \tag{26}\]  
\[\hat{\tau}_{e2}=J_{s2}\hat{\psi}_{s2}+M_{s2}L_{2}g\cos\theta_{2}+\Delta J_{s2} \ddot{\theta}_{1}+\Delta B_{s2}\dot{\theta}_{2}+\beta_{2}\dot{x}+\lambda \tag{27}\]  
where \(\hat{\tau}_{e1}\) and \(\hat{\tau}_{e2}\) are the estimated reaction torques of the end effector and the \(2^{\mathrm{nd}}\) link, respectively. It is worth mentioning that if the parameter is well estimated, then the parameter of uncertainty could be considered as well.