esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Results and Discussions
### Battlefield 2:
#### Battlefield 3:  
For the next experimental test related to the third battlefield consisting of twenty threats, the proposed algorithm outperforms all its competitors in the best, worst, and mean values, as shown in Table 7. In regards to the obtained standard deviation values, the AHA algorithm performs better for the first-dimension case and the BWO algorithm for both the second- and third-dimension cases. The SMOBHC has a relatively slow convergence speed, but its curve continuously converges to a better fitness value compared to all other algorithms. As shown in Table 8, the SMOBHC displays superior performance to all other algorithms except the SMO, where there is no significant difference between them. The boxplot illustrations in Figure 21 indicate the superiority of SMOBHC over its  
Figure 16: Boxplot graphs of mean values relative to all algorithms for battlefield 2 with \(D=30\), 60, and 90.  
Figure 17: Comparison between SMOBHC and other seven algorithms for battlefield 2 with \(D=90\).  
competitors, while Figure 22 shows the flight paths produced by SMOBHC, SMO, and six other algorithms for the second-dimension problem with a small modification to the starting and ending points. Despite the change in flight conditions, SMOBHC and SMO still produced optimal flight paths of good quality.  
In particular, the optimized flight paths of SMOBHC and SMO have minimal collisions with the battlefield threat areas, unlike some cases like DMOA and PDO, which have a clear insufficiency in problem treatment as seen in their optimized flight paths. Meanwhile, the paths related to the ASO, SO, DO, and EO algorithms are all trapped in local optima.  
Figure 19: Comparative convergence curves of algorithms for battlefield 3 with \(D=60\).  
Figure 18: Comparative convergence curves of algorithms for battlefield 3 with \(D=30\).  
Figure 21: Boxplot graphs of mean values relative to all algorithms for battlefield 3 with \(D=30\), 60, and 90.  
Figure 20: Comparative convergence curves of algorithms for battlefield 3 with \(D=90\).  
The integration of the basic SMO algorithm with the BHC optimizer has resulted in the creation of a new SMO variant, referred to as SMOBHC. This hybrid algorithm boasts a simple yet effective structure and has been shown to outperform a recent class of meta-heuristic algorithms in solving the path planning problem for UCAVs of varying scales.  
The combination of these two algorithms results in a powerful tool for addressing the complex and challenging task of UCAV path planning. The significance of the SMOBHC algorithm lies in its ability to provide optimal solutions for the path planning problem with a high degree of accuracy and efficiency, outperforming other contemporary methods. These findings make SMOBHC a valuable contribution to the field of UCAV path planning and optimization.
## 4 Experiment and Analysis
### The Trial of the Neural Network Dynamic Detection Model  
In summary, this paper integrates the background subtraction algorithm into the enhanced YOLOv5 model for the selection of moving targets and employs a monocular vision algorithm for the measurement of the targets' dimensions. This approach results in a dynamic detection model that incorporates machine vision with neural networks. This section will apply the model to test its efficacy in real-world railway slope scenarios.  
In practical engineering scenarios, the mobility of an object is crucial in assessing its level of danger. In existing railway slopes, instances of singular rolling rockfall invasions are rare; most scenes are complex, featuring stationary objects that can interfere with risk detection. These are non-moving objects in the neural network that share origins with the detection targets, which a single neural network cannot avoid recognizing. Therefore, filtering out these less risky stationary objects is critical. As illustrated in Figure 10, interference targets exist in both scenes 1 and 2. A solitary neural network indiscriminately identifies targets, leading to potential misguidance, whereas models employing background subtraction algorithms can effectively filter out stationary objects, yielding more accurate detection results. The aforementioned images represent the detection outcomes of our model for specific slope scenarios.  
On the other hand, slope foreign object intrusion detection is dedicated to rapidly alerting of risks during the construction process along existing railway slopes. The scale  
Figure 10: The detection performance of combining background subtraction algorithms with neural networks.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Strategies** & **Metrics/mAP\_0.5** & **fps** \\ \hline FReLU & 0.743 & 44.63 \\ LeakyReLU & 0.769 & 79.10 \\ MetaAconC & 0.751 & 27.49 \\ SiLU with Data Augmentation & 0.806 & 83.49 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of four schemes.  
information obtained through monocular vision technology will facilitate a comprehensive understanding of the intrusion target information by the warning system. Hence, based on the model described earlier, a machine vision algorithm based on monocular imaging has been added to estimate the size information of moving foreign objects in the target scene.  
In the same two scenarios mentioned above, as the detection model filters out moving targets and identifies them, the monocular vision-based detection simultaneously calculates the corresponding size information based on the basic data of the target in the scene.  
As shown in Figure 11, by incorporating terrain data acquired earlier and leveraging neural networks and background subtraction algorithms, the final detection results not only effectively identified moving rocks but also calculated the estimated size of the target rocks using monocular vision technology. This has further enriched the existing railway slope rockfall intrusion detection system and essentially achieved a scientific and effective recognition of potential slope disaster risks.
# Flaw Size Quantification for Cable Flaw Inspection System with Inductive Search Coil Sensor
## 4 Experimental Result Analysis
### Analysis of Flaw Length Based on Flaw Position  
To investigate and quantify the mapping relation between flaw axial length and response curve width, the differentials of the response curves were calculated to analyze the shape change of the signal curves. Figure 9 presents typical differential curves of different  
Figure 8: Inductance variation of different length flaws at different situations (**a**) Result without PE sheath under 1000 Hz (**b**) Result without PE sheath under 10,000 Hz (**c**) Result with PE sheath under 1000 Hz (**d**) Result with PE sheath under 10,000 Hz.  
length flaws at 10,000 Hz excitation frequency under \(D_{2-6}\). The peak-valley distances, which are defined as the interval between the minimum and maximum extreme points of each curve, clearly clarify the relationship between flaw axial length and inductance response width. Therefore, the peak-valley distances of \(D_{1-1}\) were obtained, and the experiment was repeated five times to calculate the standard deviation and add error bars. All results are assembled in Figure 10a, where the x-axis adopts the logarithmic form of the anchor frequencies, and the y-axis represents the peak-valley distances.  
Furthermore, in order to minimize the influence of the flaw location, a follow-up experiment was carried out under \(D_{2-1}\). The experiment was repeated five times to obtain the mean and standard deviation values, which are presented as error bars in Figure 10b. Both Figure 10a,b demonstrate a downward trend in the peak-valley distance as the excitation frequency increases.  
The standard deviations under both \(D_{1-1}\) and \(D_{2-1}\) exhibit a relative high dispersion level, particularly for short flaw length situations. Based on this analysis, the peak-valley distances of \(D_{1-1}\) and \(D_{2-1}\) were compiled in Figure 11. It can be observed that there is a relatively good overlap for longer flaw lengths, whereas some slight deviation is apparent for shorter flaw lengths. However, distinct boundaries between the situations of different  
Figure 10: Comparison of peak\(-\)valley distances for one\(-\)column flaw on different position (**a**) \(D_{1-1}\) _situation_ (**b**) \(D_{2-1}\) _situation_.  
Figure 9: Typical response differential curves at 10,000 Hz excitation frequency for different axial lengths.  
-flaw lengths remain clearly visible, which indicates that this deviation does not significantly affect boundary determination. Consequently, it can be concluded that the self-inductive coil consistently responds to changes in the axial length of the flaw, regardless of its location.
# Recent Advances in Si-Compatible Nanostructured Photodetectors
## 5 Conclusions  
The performance of various types of photodetectors based on silicon, germanium, and tin is rapidly improving due to the dedicated efforts of many research groups. The bandwidth and quantum efficiency of such devices are comparable to those of their competitors in the face of devices based on III-V compounds and are sufficient for a number of applications. Thus, at present, no serious barriers are foreseen for the widespread use of Si-compatible group-IV-based nanostructured photodetectors.  
The main advantages of silicon-based electronics and photonics, including their relative cheapness, highly-developed technology of synthesis, compatibility with silicon integrated circuits and CMOS-compatibility, opportunity to create multi-spectral detectors, and many others, make the research and industrial community confident in the long and bright future of this material system.  
The reviewed devices may play a pivotal role in the establishment of anticipated low-cost and high-performance Si-based photonic-integrated circuits. All these results allow us to count on solving the problem of integrating all optoelectronic components on a single silicon chip in the nearest future.  
Conceptualization, K.L., A.K.; writing--original draft preparation, R.D., K.L., H.D., V.D. and K.K.K.; writing--review and editing, K.L.; supervision, A.K.; project administration A.K.; funding acquisition, A.K. All authors have read and agreed to the published version of the manuscript. This research was funded by the Ministry of Science and Higher Education of the Russian Federation, grant number FSWM-2020-0048.  
Not applicable.  
The authors declare that the data supporting the findings of this study are available within the article.  
The reported study was supported by the Tomsk State University Development Programme (Priority 2030, No. 2.0.6.22).  
The authors declare no conflict of interest.
## 1 Introduction
The recordings are independent of one another because the dye sensitizer in each layer is matched to one of the recording laser wavelengths. It is demonstrated here for two layers but has the potential to be developed for additional layers (e.g., red, green, blue). The stacking/multiplexing approach to correcting for chromatic dispersion is conceptually simple and relies on fabrication and combination of devices tailored to specific wavelength ranges. It has been exploited to multiplex or de-multiplex in diffusers [15] and lenses [16], in stacking meta-surfaces [17], and more recently, to wavelength multiplexing in lens arrays for full color imaging [18] with each layer fabricated of different materials and with different design parameters to optimize it for a specific frequency band. However, these devices are fabricated through lithographic processes or holographically recorded \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Surface DOEs** & **Volume DOEs** \\ \hline \multirow{2}{*}{**Fabrication**} & Multiple steps, including wet & Single step (self-processing \\  & processing & photopolymers) \\  & More difficult since the & Easier, the pattern is in the \\
**Protection and Stacking** & pattern is on the surface & volume \\
**Diffraction Efficiency** & For sinusoidal profiles & \\  & theoretical limit 33\% & Up to 100\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparing surface and volume diffractive optics elements. into a single layer of a pan-chromatic material through multiplexing. Here, we utilize the limited wavelength sensitivity range in the pre-stacked photopolymer layers to exploit the simple full field exposure step to holographically record a compound element at two separate wavelengths, without the need for complex modeling and fabrication techniques. In the literature, a variation of dye concentration in multilayer photopolymer was used to improve the uniformity in volume gratings [19]. Three dyes were combined in a single layer of photopolymer material for full color display holography [20]. In this work, two photopolymer layers sensitive to two different wavelengths were laminated together and a lens was recorded in each layer by their designed wavelength in sequential manner. Firstly, a blue-sensitive photopolymer formulation, previously developed for the recording of reflection holograms (at high spatial frequency), was optimized for the low spatial frequency and high layer thickness needed in the proposed off-axis collimating lens transmission-format element. Next, the design considerations for stacked lenses were discussed for the application of off-axis, high-diffraction efficiency collimating HOE elements. Finally, the dual-wavelength sensitive multi-layer structure was prepared by stacking two photopolymer layers and used to successfully record collimating elements for blue and green wavelengths. The local diffraction characteristics of the recorded microstructure were tested and limitations discussed.
## 4 Experimental Results
### Effectiveness of Dual Mutual Feedback  
For evaluating the effectiveness of dual mutual feedback, we performed ablation studies for dual feedback, mutual feedback, and feedback. The ablation architectures are shown in Figure 6. First, to realize the ablation of dual feedback, we used feedback with down sampling, as shown in Figure 6b. Therefore, it perfomed two more up-sampling steps and one more down-sampling step than the proposed DMFN in every feedback session. Second, to realize the ablation of mutual feedback, the architecture has one more up-sampling and down-sampling step than the proposed DMFN in each feedback, as shown in Figure 6c. Finally, to realize the ablation of feedback, we used a down-sampling operation after the up-projection unit to build the feedforward architecture, and up-sampled all features at the last layer, as shown in Figure 6d. All the ablation architectures have more parameters and a higher computational complexity than the proposed DMFN.  
The experimental results are shown in Table 2. First, the ablation of dual feedback did not perform well. This is because the input of the two units is so similar that the information exchange between them cannot function adequately. Second, the ablation  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{\(\mathbf{w}\)} & \multirow{2}{*}{**Scale**} & \multirow{2}{*}{**Params**} & \multicolumn{2}{c}{**MRI13**} & **ADNI100** & **OASIS100** \\ \cline{4-6}  & & & **PSNR/SSIM** & **PSNR/SSIM** & **PSNR/SSIM** \\ \hline
0 & & & 583 k & 36.58/0.9480 & 30.13/0.8716 & 34.22/0.9475 \\
0.01 & \(\times\)3 & & 583 k & 36.61/0.9482 & 30.15/0.8718 & 34.36/0.9479 \\
0.1 & & 583 k & 36.72/0.9485 & 30.17/0.8719 & 34.35/0.9478 \\
1 & & 583 k & 36.54/0.9480 & 30.14/0.8715 & 34.33/0.9477 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of different weights assigned to unity feedback loss on DMFN.  
Figure 6: The ablation architectures. (**a**) is the DMFN. (**b**) represents the ablation of dual feedback. (**c**) represents the ablation of mutual feedback. (**d**) represents the ablation of feedback.  
of mutual feedback also exhibited a poorer performance than DMFN. This is because it has less information exchange for its self-feedback architecture, and the additional up sampling and down sampling are not directly used for SR reconstruction. Finally, DMFN with feedback manner has fewer parameters but performs better than the feedforward manner. This is because the feedback mechanism enables the previous layers to perceive high-level information of latter layers.
## 5 Simulations Results and Discussions
### Case Study 1: Home Is Not Equipped with DGs
#### 5.1.1 Electricity Cost Optimization  
Figure 5 depicts the net electricity cost consumed by the consumer's appliances having unscheduled loads without DGs for case study 1. In the case of the unscheduled load, the highest cost of electricity recorded is 70 cents in slots 9-10, and the combined electricity cost is 730 cents for 24 h. In PSO, the maximum energy cost is 65 cents at the 19-20, 21-22, and 23-24 time slots, whereas the combined 24 h slot is 638. In WDO, the maximum electricity cost recorded is 53 cents at the 8-9 time slot, and the 24 h combined cost is 646. While considering the results of ACO, it is 67 cents at the 7-8 h slot, and at 24 h it is 638. Similarly, BFA has 58 cents in the 21-22 time slot and 659 for 24 h. In GA, it is 56 cents within the time slot within the time slot of 22-23 and 623 for 24 h. In GPSO, it is 48 cents within the time slot of 9-10, while in GACO, it is 49 cents in the 9-10 time slot. Additionally, in GBFA, it is 57 cents in the 8-9 time slot. In GWDO-based hourly electricity cost, the highest is 41 cents at a 9-10 h slot. Whereas 620, 601, 614, and 571 cents for GPSO, GACO, GBFA, and GWDO, respectively, for 24 h.  
The execution of the GWDO algorithm while minimizing the electricity cost outperforms the benchmark heuristic algorithms, whether simple or hybrid. By evaluating simulations, the GWDO technique gives the best outcomes in terms of the average cost for 24 h, which is a 21.78% reduction compared with the case of unscheduled operation. Additionally, there is a 14.6%, 13.9%, 9.72%, 12.60%, 11.5%, 15%, and 17.6% reduction in case of GA, PSO, BFA, ACO, WDO, GPSO, GBFA, and GACO, respectively, compared with the unscheduled case. Moreover, GWDO also gave a low hourly peak electricity cost compared with other optimization algorithms: ACO, PSO, WDO, GA, BFA, GACO, GPSO, and GBFA.  
Figure 4: Pricing signal for energy management.
# Solar-Supplied Satellite Internet Access Point for the Internet of Things in Remote Areas
## 1 Introduction
### Service Plan  
One the other hand, the service plans of the SIM cards for satellite phones have to be considered. Table 1 compares several popular prepaid subscriber identification module (SIM) cards [9]. As can be seen, satellite Internet is inherently low bandwidth (especially when choosing a cheap data plan), so it is recommended to deploy IoT applications with satellite networks for humanitarian relief [10]--for example:  
* Issue a disaster warning;
* Request emergency services;
* Push information about the weather;
* Push information related to agriculture and fisheries.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Card**} & **Entry Plan Cost** & **Cost (USD) per MB/mins** & \multirow{2}{*}{**Connection Speed**} & **Free Short Message Service (SMS)** \\ \hline \multirow{2}{*}{IstHone Pro (GEO)} & \multirow{2}{*}{550 (50 unit)} & \multirow{2}{*}{51/min} & \multirow{2}{*}{2.4 kbps} & Yes \\  & & & & \\ \hline \multirow{2}{*}{Iridium GO (LEO)} & \multirow{2}{*}{5600 (400 unit)} & \multirow{2}{*}{51.5 min} & \multirow{2}{*}{2.4 kbps} & Yes \\  & & & & \\ \hline \multirow{2}{*}{Iridium Phone} & \multirow{2}{*}{5100 (50 unit)} & \multirow{2}{*}{52/min} & \multirow{2}{*}{2.4 kbps} & Yes \\  & & & & \\ \hline \multirow{2}{*}{Thuraya Pre-Paid} & \multirow{2}{*}{570.00 (20 unit)} & \multirow{2}{*}{517.5/MB} & \multirow{2}{*}{60 kbps (GmPRS)} & N/A \\ \multicolumn{2}{c}{Inmarsat BGAN} & \multirow{2}{*}{550 (50 unit)} & \multirow{2}{*}{53.6/min} & \multirow{2}{*}{32 kbps} & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 1: The price of a prepaid SIM card for connecting to a satellite network.
## 2 Problem Formulation
### Kinematic Model of Surface Vessels  
An exact model of surface vehicles is with six degrees of freedom, where three parameters indicate the position of the center of mass of the vessel and the other three parameters determine the orientation of the body coordinate frame. However, the model of surface vehicles has been considered as underactuated in this paper in which, the number of controlled degree of freedoms are more than of independent input controls. Three variables that we work on it in this article is shown in Figure 2.  
Consider a group of number N ASVs. The kinematic model for \(i\)-th surface vehicle is described in [25]:  
\[\begin{array}{l}\dot{x}_{i}=u_{i}\cos\varphi_{i}-v_{i}\sin\varphi_{i}\\ \dot{y}_{i}=u_{i}\sin\varphi_{i}+v_{i}\cos\varphi_{i}\\ \dot{\varphi}_{i}=r_{i}\end{array} \tag{1}\]  
where \(x_{i}\) and \(y_{i}\) indicate position and \(\varphi_{i}\) indicates the orientation of the \(i\)-th surface vehicle in the earth fixed frame. \(u_{i}\), \(v_{i}\), and \(r_{i}\) respectively surge, sway, and heave velocities. Considering the state vector as \(\eta_{i}=[x_{i},y_{i},\varphi_{i}]^{T}\in\mathcal{R}^{3}\) and the velocity vector as \(V_{i}=[u_{i},v_{i},r_{i}]^{T}\), the model in Equation (1) can be rewritten as follow:  
\[\dot{\eta}_{i}=S_{i}(\eta_{i})V_{i}(t),(i=1,2,\ldots,N) \tag{2}\]  
where  
\[S_{i}(\eta_{i})=\left[\begin{array}{ccc}\cos\varphi_{i}&-\sin\varphi_{i}&0 \\ \sin\varphi_{i}&\cos\varphi_{i}&0\\ 0&0&1\end{array}\right]. \tag{3}\]
# Research on Polarization Coding Cooperative Communication Scheme for FSO System
###### Abstract  
To solve the problems of interruption events and the high bit-error rate of the FSO system caused by atmospheric turbulence, an FSO cooperative communication scheme based on system polarization code is proposed. In this scheme, the upper limit of the average bit-error rate of the atmospheric turbulence channel is used to construct the frozen bit set of the system polarization code, and the information bit set of the \(S-R\) link is recovered by using the ownership relationship between the frozen bit set of the \(S-R\) link and the \(S-D\) link at the destination node. Finally, the information bits of the \(S-R\) link and \(S-D\) link are combined using equal gain combination, and the original information is recovered by decoding. The simulation results show that the FSO cooperative communication system can overcome the influence of atmospheric turbulence and improve the system performance, and the bit-error rate performance of the FSO cooperative communication system can be improved by at least 0.5 dB; the outage probability of the FSO cooperative communication system can be reduced to less than \(10^{-7}\), and it shows a stable inhibitory effect on strong turbulence conditions.  
system polarization code; cooperative communication; outage probability; bit-error rate +
Footnote †: journal: Electronics Letters  
[[https://doi.org/10.3390/electronics11101597](https://doi.org/10.3390/electronics11101597)]([https://doi.org/10.3390/electronics11101597](https://doi.org/10.3390/electronics11101597))