esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
In this paper, the transmission of the spike information, such as the spike rate and the probability of symbolic patterns embedded in the LFF dynamics in a cascaded laser system, are numerically demonstrated. In the last decades, there have been works studying the LFF synchronization in an open-loop configuration [28; 29]. Most works utilize CC to measure the performance of LFF synchronization. However, when using the laser with optical feedback as a photonic neuron, CC is insufficient to describe the spike information because it can hardly reflect the timing of the spikes of LFF dynamics. Therefore, we analyzed the transmission of the LFF dynamic through both CC and the timing of spikes such as the spike rate and the probabilities of symbolic patterns. Firstly, the results show that the performance of the LFF transmission can be improved through increasing the injection strength, which is identical to the existing numerical and experimental results. Moreover,  
Figure 10: Effect of injection strength on the LFF transmission performance. The pump current and linewidth enhancement factor are 15 mA and 5, respectively. (**a**) Correlation coefficient, (**b**) average spike rate difference.  
Figure 9: Effect of external parameter mismatch on (**a**) the correlation coefficient and (**b**) the difference of the average spike rate.  
we find that to make the response laser generate the LFF dynamic, the injection strength should be larger than the feedback strength of the drive laser, and the pump current of the response laser should be smaller than that of the drive laser. Meanwhile, the mismatch of the pump current always has a more significant influence on the transmission of LFF waveforms than the internal physical parameters. For the transmission of the timing of spikes, we find that when there is a mismatch of the system parameters, the spike rate of the response laser is smaller than that of the drive laser. In addition, the symbolic pattern 210 always has a large transmission error when the parameters are mismatched. As the LFF waveform of the semiconductor laser is similar to what is produced by the neuron, and the results of this paper are beneficial for the practical application of photonic neurons and offer promising prospects for brain-like optical information transmission. Future work will focus on the LFF waveform of other laser systems, such as mutually coupled laser systems.  
[PERSON], [PERSON] and [PERSON] contributed to the writing of the manuscript. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China (No. 62001077) and in part by the Dalian high-level talent innovation support plan (No. 2021 RQ063).  
Not applicable.  
The authors declare no conflict of interest.
# Slope Stability Prediction Based on Incremental Learning Bayesian Model and Literature Data Mining
###### Abstract  
In predicting slope stability, updating datasets with new cases necessitates retraining traditional machine learning models, consuming substantial time and resources. This paper introduces the Incremental Learning Bayesian (ILB) model, combining incremental learning theory with the naive Bayesian model, to address this issue. Key slope parameters\(-\)height; slope angle; unit weight; cohesion; internal friction angle; and pore water ratio\(-\) are used as predictive indicators. A dataset of 242 slope cases from existing literature is compiled for training and evaluation. The ILB model's performance is assessed using accuracy, area under the ROC curve (AUC), generalization ability, and computation time and compared to four common batch learning models: Random Forest (RF), Gradient Boosting Machine (GBM), Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP). Variable importance and partial dependence plots are used to explore the relationship between prediction results and parameters. Validation is performed with real slope cases from the Lala Copper Mine in Sichuan Province, China. Results show that (1) The ILB model's accuracy and AUC improve as the dataset grows. (2) The ILB model outperforms GBM, SVM, and MLP in accuracy and AUC, similar to RF. (3) It demonstrates superior generalization and lower computation time than batch learning models. (4) Internal friction angle, slope angle, and pore water ratio are the most important predictors.  
s +
Footnote †: journal: applied sciences
## 4 Proposed PSO Variant
### Ic-Vaindiwposo Algorithm
#### 4.2.4 The Specific Steps of the IC-VAINDIWPSO Algorithm  
The fundamental steps are as follows:  
1. Modeling the threat environment for drone flight;* Initializing the particle swarm with improved chaos theory;
* Evaluating particles: according to the constraints in the threat environment, the fitness value of the particles is evaluated;
* Updating particles: the particle positions and velocities are updated according to (11) and (19)-(21);
* Calculating the rate of change of fitness (FCR) according to (25). If \(k<2T_{max}/3\), and FCR is less than the set threshold, the local position of the particle changes slightly, and it falls into the local optimum. Go to (f), otherwise, go to (g);  
\[FCR=\frac{F(k)-F(k-1)}{F(k-1)} \tag{25}\]
* According to the logistic chaotic map (23), the mutation operation is performed on the global best position;
* Stop condition; by iterating continuously, adjust their velocity and position to keep them inside the feasible range until the algorithm reaches the maximum number of iterations and obtains the optimal solution; otherwise, return to (c).  
For a clearer understanding of the IC-VAINDIWPSO algorithm, pseudocode is added, as shown in Algorithm 1.  
```
/* Initialization: */
1 Obtain search map and initial path planning information;
2 Set swarm parameters, swarm_size;
3 for each particle i in swarm do
4 Create a random path;
5 Assign to particle's position;
6 Compute fitness of the particle;
7 Set local_best of the particle to its fitness;
8 end
9 Chaos initialization
10 Set global_best to the best-fit particle; /* evolutions: */
11 fork \(\leftarrow\) 1 to max_generation do
12 for each particle i in swarm do
13 Compute velocity; /* Equation (21)*/
14 if Particles become stuck in local optima
15 Compute velocity; /* Equation (20)*/
16 end
17 Compute new position; /* Equation (11)*/
18 Update fitness; /* Equation (9)*/
19 ifk is less than 2/3 * max_generation
20 Compute FCR
21 if FCR is less than the set threshold
22 Compute new position; /* Equation (23)*/
23 end
24 end
25 Update local_best;
26 end
27 Update global_best;
28 Update inertia weights /* Equation (19)*/
29 Save best_position associated with global_best; /* the best path */
30 end
```  
**Algorithm 1** IC-VAINDIWPSO algorithm  
To evaluate the three algorithms extra clearly, Table 1 compares the three algorithms.
###### Abstract  
The optical bistability, tristability and multistability are explored in arrays of graphene. The arrays are periodically arranged spatially by single sheets of graphene. Optical bistability could be achieved with a strong enough incident intensity of light wave. The thresholds of optical bistability and the intervals between the upper and lower thresholds change with the surface conductivity of graphene and the incident wavelength. By increasing the intensity of incident light, tristability and multistability can be induced as well. Furthermore, the thresholds of bistability, tristability and multistability can be regulated via the chemical potential of graphene. This study may have potential applications in optical logic gates, all-optical switches and photomemory.  
Keywords: nonlinearity; graphene; array; optical bistability; all-optical switch +
Footnote †: journal: Applied Physics Letters
## 5 Results
### Pseudocode, Flowchart, and Example  
The SSO is very easy to code using any computer language and its pseudocode is provided below [17, 18, 35, 36, 37, 38, 39]:  
**STEP S0.**: Generate \(P_{i}=X_{i}\) randomly, calculate \(F(P_{i})=F(X_{i})\), find \(gBest\), and let \(t=1\) and \(k=1\) for \(i=1,\,2,\,\ldots\), \(N_{\text{sol}}\).
**STEP S1.**: Update \(X_{k}\) based on Equation (9).
**STEP S2.**: If \(F(X_{k})>F(P_{k})\), let \(P_{k}=X_{k}\). Otherwise, go to STEP S5.
**STEP S3.**: If \(F(P_{k})>F(P_{gBest})\), let \(gBest=k\).
**STEP S4.**: If \(k<N_{\text{sol}}\), let \(k=k+1\) and go to STEP S1.
**STEP S5.**: If \(t<N_{\text{gen}}\), let \(t=t+1\), \(k=1\), and go to STEP S1. Otherwise, halt.  
The flowchart of the above pseudocode is given in Figure 2:  
STEP S0 initializes all solutions randomly because the SSO is a population-based algorithm. STEP S1 implements the SSO stepwise function shown in Equation (9) to update the solution. STEPs S2 and S3 test whether \(P_{k}\) is replaced with \(X_{k}\) and \(P_{gBest}\) is replaced with \(P_{k}\), respectively. STEP S5 is the stopping criteria, which is the number of generations. Note that the stopping criteria are changed to the runtime in this study and the details are discussed in Section 4.  
For example, let \(C_{g}=0.4\), \(C_{p}=0.7\), \(C_{w}=0.9\), and \(\rho=(\rho_{1},\,\rho_{2},\,\rho_{3},\,\rho_{4},\,\rho_{5})=(0.53,\,0.78,\,0.16, 0.97,\,0.32)\). Assume that we have the solution in the second generation, i.e., \(X_{15}=(4,\,3,\,2,\,1,\,4)\), \(P_{15}=(1,\,4,\,2,\,3,\,2)\), and \(P_{gBest}=(1,\,4,\,2,\,3,\,2)\), which are the 15 solutions in the second generation of the evolutionary, the best 15 solutions before the second generation, and the best solution before the second generation, respectively. Now, we are going to update \(X_{15}\) to obtain the new \(X_{15}\) of the third generation, which is presented in Table 2, based on the stepwise function of the SSO update mechanism provided in Equation (9).  
From the above pseudocode, flowchart, and example, we can find that the update mechanism of the SSO is simple, convenient, and efficient.
# Improved JPS Path Optimization for Mobile Robots Based on Angle-Propagation Theta* Algorithm
## 3 JPS Path Optimization Based on Angle-Propagation Theta* Algorithm
### Angle-Propagation Theta* Algorithm
#### 3.2.2 AP-Theta* Algorithm for Visual Angle Constraint  
The AP Theta* algorithm sets the viewable angle \([lb(x),ub(x)]\) of the current node to \([-\infty,+\infty]\) when expanding from the current node \(x\) to other nodes, and then, it constrains \(lb(x)\) and \(ub(x)\) by the viewable angle constraints. We set the current node as \(x\), the parent of the current node as \(parent(x)\), the path start point as \(s\), and the end point as \(e\). The specific constraints are as follows:  
Figure 6: Visual angle of x node in AP theta*.  
Figure 7: The viewable angle range of node \(x\).  
After the angle constraint is completed, the LOS reachability of \(x^{\prime}\) and \(parent(x)\) can be determined by checking whether \(lb(x)\leq\theta(x,parent(x),x^{\prime})\leq ub(x)\) is satisfied when expanding from the current node \(x\) to the neighboring node \(x^{\prime}\). The pseudo code for visual angle constraint is shown below.  
```
Input: Current Node \(\{x\}\), Neighbor node \(\{x^{\prime}\}\) Output:\(lb(x)\), \(ub(x)\)
1 if\(x\
eq x_{start}\) AND \(x^{\prime}\) is the vertex of the same obstacle as \(x\)then
2\(z=x^{\prime}\);
3 if\(parent(x)=z\) OR \(\theta(x,parent(x),z)<0\) OR
4\((\Theta(x,parent(x),z)=0\) AND \(cparent(x),z)\leq c(parent(x),x))\)then
5\(lb(x)=0\);
6
7 end if
8 if\(parent(x)=z\) OR \(\theta(x,parent(x),z)>0\) OR
9\((\Theta(x,parent(x),z)=0\) AND \(cparent(x),z)\geq cparent(x),x))\)then
10\(ub(x)=0\);
11
12 end if
13 if\(x\
eq x_{start}\) AND \(x^{\prime}\) is the neighbor node of \(x\)then
14 if\(x^{\prime}\in\) closedlist AND \(parent(x)=parent(x^{\prime})\) AND \(x^{\prime}\
eq x_{start}\)then
15 if\(lb(x^{\prime})+\theta(x,parent(x),x^{\prime})\leq 0\)then
16\(lb(x)=max(lb(x),lb(x^{\prime})+\[PERSON](x,parent(x),x^{\prime}))\)
17 end if
18 if\(ub(x^{\prime})+\theta(x,parent(x),x^{\prime})\geq 0\)then
19\(lb(x)=max(lb(x),lb(x^{\prime})+\[PERSON](x,parent(x),x^{\prime}))\)
20 end if
21 if\(c(parent(x),x^{\prime})<c(parent(x),x)\) AND \(parent(x)=x^{\prime}\) AND
22\((x^{\prime}\
otin\) closedlist OR \(parent(x)\
eq parent(x^{\prime}))\)then
23 if\(\theta(x,parent(x),x^{\prime})<0\)then
24\(lb(x)=max(lb(x),\theta(x,parent(x),x^{\prime}))\)
25 end if
26 if\(\theta(x,parent(x),x^{\prime})>0\)then
27\(ub(x)=min(ub(x),\theta(x,parent(x),x^{\prime}))\)
28 end if
29
30 end if
31 return\(lb(x)\) and \(ub(x)\);
```  
**Algorithm 1**Visual Angle Constraint.
## 2 Study Area and Data
### Optical Remote Sensing Data  
The Environment and Disaster Monitoring and Forecasting by Small Satellite Constellation A and B satellites (HJ-1 A/B satellites) were launched by China in 2008. The charge-coupled device (CCD) camera is one of the instruments that operate on the HJ-1A and HJ-1B satellites. Each satellite provides a spatial resolution of 30 m, a swath width of 700 km, and a four day return period. The combination of the two satellites is capable of providing a two day return period. The HJ-1 CCD cameras have three visible bands (0.43-0.52 um, 0.52-0.60 um, and 0.63-0.69 um) and one near-infrared (NIR) band (0.76-0.9 um).  
Figure 1: Location of study region and the distribution of sampling plots in the study area. The background image is a charge-coupled device (CCD) composite image of bands 4 (near-infrared), 3 (red), and 2 (green) (corresponding to R, G, B color space) showing the study area.  
The HJ-1A CCD1 image was collected during virtually cloud-free conditions on 4 August 2013. The following steps were used to preprocess the HJ-1 CCD image.  
(1) Geometric correction: The images were geometrically referenced using a set of 30 ground control points measured by a differential global positioning system (GPS) unit. The root mean square error (RMSE) of the geometric correction was less than one pixel, which meets the geometry correction requirement.  
(2) Radiometric calibration: First, the DN values of the raw image were converted to the at-satellite radiance using calibration coefficients (Table 1) provided by the China Center for Resources Satellite Data and Application. The calibration equation used is  
\[L=DN\,/\,\alpha+L_{o} \tag{1}\]  
where \(L\) is the apparent radiance, \(DN\) is the digital number of the raw image, \(\alpha\) is the absolute calibration coefficient, and \(L_{o}\) is the offset.  
The apparent radiance was then converted to apparent reflectance according to  
\[\rho_{\lambda}=\frac{\pi\times L_{\lambda}\times D^{2}}{E_{\text{sun, }}\times\cos\theta} \tag{2}\]  
where \(\rho_{\lambda}\) is the apparent reflectance of the band \(\lambda\) at the top of the atmosphere, \(D\) is the distance of the astronomical unit from the earth to the sun, \(\theta\) is the solar zenith angel, and \(E_{sun}\) is the mean exoatmospheric solar irradiance in the \(\lambda\). The exoatmospheric solar irradiance for HJ-1A CCD1 is listed in Table 1.  
(3) Atmospheric correction: the algorithm of 6S (the second simulation of the satellite signal in the solar spectrum) [28], an atmospheric radiative transfer model, and HJ-1 CCD spectral response function were used to convert sensor radiance to directional surface reflectance. The study area is at the mid-latitude in summer and the aerosol type is continental. The parameters were inputted into the 6S code with surface assumption of non-uniform Lambertian to correct HJ-1 CCD image, and the outcome was atmospherically corrected reflective radiation.
# Communication Technologies for Smart Grid: A Comprehensive Survey
## 4 Challenges of Smart Grid Communication
### Denial of service attack  
It has been claimed that DoS attacks are one of the greatest concerns for service providers. Smart Grids consists of a number of measurement devices such as smart meters, smart appliances, data aggregators, PMUs, IEDs, RTUs, PLCs, etc. Attacks on SGs can result in loss of data availability, loss of communication control, compromised data integrity, and loss of power [137].
## 3 Results
### Experimental Results  
Experimental measurements were performed in a microwave anechoic chamber, where a pair of horn antennas (1-9 GHz) connected to a calibrated Vector Network Analyzer (N9918A 26.5 GHz FieldFox Vector Network Analyzer, Keysight Technologies, Santa Rosa, CA, USA) were used to transmit and receive the plane wave, respectively [41]. To attain  
Figure 5: Results of the transmission coefficient of the designed bandpass FSS filter under an impinging TE plane wave with incident angles equal to \(0^{\circ}\), \(20^{\circ}\), \(40^{\circ}\), and \(60^{\circ}\): (**a**) simulated, and (**b**) measured.  
the transmission coefficient, the two antennas were placed on both sides of the prototype, equally spaced 60 cm from it. The measurements with a specific angular inclination of the plane wave were carried out by positioning the antennas according to the required orientation.  
Furthermore, the prototype of the presented filter was fabricated by using printed circuit board (PCB) technology. This is represented in detail in Figure 6a and consists of a 12 \(\times\) 12 unit cell panel, with an overall size of 288 mm \(\times\) 288 mm. The SMD resistors were in the 0603 package and soldered on each unit cell via printed circuit board (PCB) technology. The measurement environment is reported in Figure 6b.  
A slab of FR4 with dielectric permittivity \(\varepsilon_{r}\) equal to a 4.26 and 0.46 mm thickness was employed for the prototype substrate. The FSS layers were realized in copper and printed on opposing sides of the substrate with a 35-\(\upmu\)m etching process, as specified in Section 2.  
In particular, this bandpass FSS filter exhibited a low weight of approximately 0.925 kg/m\({}^{2}\), making it an extremely lightweight solution, ideal for applications where reduced mass and thickness are essential as airborne radomes.  
The experimental results are provided in Figure 5b. It was evident that the wideband bandpass feature of the proposed FSS filter, along with the flat plateau response and low insertion loss, was also well-maintained experimentally.  
By executing measurements for increasing oblique incidence angles, it is noteworthy to point out that the transmission bandwidth diminished but consistently preserved at least a 60% fractional bandwidth (FBW). Therefore, this trend showed consistency with the simulated results, confirming an excellent wideband flat bandpass property.
## 3 Experimental Results
### Comparison of Location Results in Different Scenarios
#### 3.1.3 Surrounding Occluded Scene  
The positioning results are shown in the figure below after setting the azimuth and mask angle of the Surrounding occluded scene in Section 2.2.  
As can be seen from Figure 13, under the influence of the increase of mask angle, the LEO/INS positioning accuracy becomes worse, but the accuracy after GNSS/INS filtering is improved. The overall accuracy after federated filtering is about 2 m. As can be seen in Figure 14, in the Semi-occlusion scenario, the geometric configuration of the LEO  
Figure 11: Position error of the Semi-occluded scene.  
Figure 12: DOP value of the Semi-occluded scene.  
constellation is greatly limited due to the high mask angle limit. The results of velocity error and attitude error of the Surrounding occluded scene can be seen from Figures 14 and 15 in Appendix A. The error value of the sub-filter will be reduced after using federated filtering.
## 4 Conclusions  
In this paper, a broadband 3D absorber based on a notch-slot resonator with a lossy coupled microstrip line is proposed. The coupled microstrip line eliminates the reflection band of the original lossy notch-slot resonator and merges the two separating absorption bands; hence, a wide absorption band is obtained. The proposed absorber can also operate without the reflecting plate, which is also demonstrated in this paper. A dual-polarized design is also presented, and some shorting pins were added in order to eliminate the undesirable resonances introduced by the securing slits cut between two adjacent unit cells. The measured results show that the proposed dual-polarized 3D absorber exhibits an absorption band from 2.74 GHz to 11.08 GHz (120.7% FBW).  
Conceptualization, Y.Y.; methodology, G.X. and Q.L.; validation, G.X. and Q.L.; formal analysis, G.X. and D.G.; data curation, G.X. and Q.L.; writing--original draft preparation, D.G. and G.X.; writing--review and editing, Y.Y. and L.P.; supervision, Y.Y. and L.P. funding acquisition, Y.Y. and L.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of Zhejiang Province (ZJNSF) under Grants LY22F010020 and LR21F010002; and the National Natural Science Foundation of China under Grants 62171168, 61875051 and 62271439.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Thickness (\(\lambda_{L}\) 1)** & **FBW (\%)** &
\begin{tabular}{c} **Number of Lumped** \\ **Components in a Unit Cell** \\ \end{tabular} \\ \hline
[4] & 0.088 & 126.8 & 8 \\
[6] & 0.096 & 159.1 & 16 \\
[7] & 0.113 & 137.1 & 8 \\
[10] & 0.091 & 59.3 & 6 \\
[11] & 0.100 & 112.0 & 3 \\
[12] & 0.101 & 148.0 & 16 \\ This work & 0.164 & 120.7 & 2 \\ \hline \hline \end{tabular}  
* \(\lambda_{L}\) is the wavelength at the lowest frequency of the absorption band.  
\end{table}
Table 1: Comparison of the proposed 3D absorber and other absorbers.  
Figure 10: Simulated and measured \(\mid\!S_{11}\!\mid\) of the proposed 3D absorber under the oblique incidences.
## 4 Experimental Setup  
The experiment is based on a dual laser asynchronous optical sampling (ASOPs) system [16; 17]. It controls two femtosecond pulsed lasers (\(\sim\)100 fs pulse width) with a repetition rate of 80 MHz and allows the delay between the pulses to be set and swept from 0 to 12.5 ns every 100 \(\upmu\)s (10 kHz). The wavelength of the probe laser is tunable from 700 to 850 nm. The sample is scanned by moving electromechanical stages with a minimum step motion of 100 nm. Typically, 30,000 averages per point are taken during scanning, which takes around 9 s to acquire. The system uses a maximum of 2 mW average power in the probe and 1 mW in the pump measured at the sample corresponding to pulse energies of 25 and 10 pJ and peak powers of 250 and 100 W, respectively. The response of the transducer is detected by monitoring the low frequency (\(<\)30 MHz) intensity variation of the probe light on a photo-detector, which is captured and averaged on a digital oscilloscope (DSO). With this arrangement, a signal detected at delay of T on the DSO corresponds to a real delay between the laser pulses of T/8000 and a frequency measured on the DSO of \(f_{scope}\) corresponds to a real frequency of 8000 \(f_{scope}\).