esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Data and Methods  
The dataset used in this study comprises a 3D seismic survey covering 1100 km\({}^{2}\) in the eastern Northern Structural Belt, supplemented by 2D seismic profiles, data from 24 boreholes, three outcrop sections (Kuchehe, Kezilenvergou, and Yangxia Coal Mine), and 507 m of cores. A total of 35 mudstone samples were collected from the Yangxia and Kezilenuer Formations at the Kuchehe outcrop. Comprehensive geochemical testing of these samples was performed by Beijing Cration Rock Innovation Technology Co., Ltd. (Beijing, China), providing detailed major and trace element compositional data. Major elements were quantified using X-ray fluorescence spectroscopy (XRF), while trace elements were analyzed by inductively coupled plasma mass spectrometry (ICP-MS).  
Lithofacies classification is based on sedimentary structures, grain size, lithology, and color. Lithofacies associations and seismic facies were identified by analyzing core data, well-logging characteristics, and calibrated seismic data to elucidate features of the depositional systems. Seismic facies are also useful to explain the architecture and distribution of the depositional systems in the area without wells. Sequence stratigraphic analysis is based on sequence boundaries, including unconformities and sedimentary transition surfaces, derived from seismic, well-logging, and outcrop observations to delineate se  
Figure 2: Sequence classification and depositional evolution of the Jurassic in the Kuqa depression.  
quences. However, accurate identification of key sequence boundaries and their associated stratigraphic hierarchy, particularly for fourth-order and higher-resolution sequence units, remains challenging in conventional sequence stratigraphy [35; 36]. Recent advancements in wavelet analysis of well logs have provided a quantitative approach to delineate sequence boundaries across multiple scales, effectively addressing the limitations of traditional methodologies [35; 37]. Hierarchical correspondence between wavelet coefficient periodicities at varying scales and sequence orders provides a robust framework for stratigraphic subdivision, where low-frequency components correlate with prolonged geological cycles defining sequence stratigraphic frameworks [35; 36], while high-frequency components align with Milankovitch band cycles, facilitating high-resolution cyclost stratigraphic analysis [19; 38]. Within an established framework, depositional systems' temporal and spatial distribution is determined through integrated analysis of well-to-well cross-sections and well-seismic correlation sections. The Chemical Index of Alteration (CIA) and the logarithmic ratio of Al\({}_{2}\)O\({}_{3}\)/Na\({}_{2}\)O have been established as reliable indicators of paleoclimate conditions [39; 40; 41; 42].
## 3 Performance Analysis for Single SWIPT Relaying System
### Optimization for the PS Factor \(\rho\)  
Note that \(P_{B,\rm id}^{\rm Asym}\) in (19) depends on \(\rho\), \(\eta\), and the channel conditions. To optimize the PS factor \(\rho\), we define  
\[\rho_{\rm opt}=\arg\min_{0<\rho<1}P_{B,\rm id}^{\rm Asym}(\rho) \tag{22}\]  
which can be efficiently determined via numerical methods using the asymptotic BER expression in (19).
## 2 Methods and Tools
### Drone-Based Photogrammetry  
Drone-based photogrammetry has proven to be a powerful tool for monitoring geomorphological changes, particularly in response to flood events and base-level lowering [9; 26; 27; 35; 36; 28]. Researchers conducted photogrammetric surveys using drones, kites, and balloons to monitor the geomorphological responses of alluvial streams to flood events over several years. These surveys facilitated the generation of high-resolution Digital Elevation Models (DEMs) and orthophoto maps, enabling accurate quantification of elevation changes and detailed analysis of subsidence, sinkholes, and ground failures within stream channels [36]. These studies highlighted the critical role of peak discharge and flood timing in influencing sediment removal and channel incision in the Dead Sea's alluvial streams.  
The application of drone-based photogrammetry provided detailed spatial and temporal data, which would have been challenging to obtain using traditional methods, showcasing the potential of drones in geomorphological research within extreme environments.
# Sum Rate Optimization Scheme of UAV-Assisted NOMA under Hardware Impairments
## 3 Optimization of UAV Position and Power Allocation
### Uav Position Optimization  
For the non-convex constraint (7a) which is equivalent to:  
\[0\leq\alpha_{k,n} \leq 1,\forall k,n, \tag{10a}\] \[\alpha_{k,n}-\alpha_{k,n}^{2} \leq 0,\forall k,n. \tag{10b}\]  
the inequality (10a) is affine, but the constraint (10b) is still non-convex, which can be transformed into a convex one through the use of SCA. Constraint (10b) can be rewritten as:  
\[\alpha_{k,n}\leq\tilde{\alpha}_{k,n}^{2}+2\tilde{\alpha}_{k,n}(\alpha_{k,n}- \tilde{\alpha}_{k,n}),\forall k,n. \tag{11}\]  
The objective function in P1 with a fixed power allocation is still hard to handle because it does not have a strictly concave-convex form that can be directly handled by fractional programming. Thus we introduced non-negatively auxiliary variables \(\mathbf{R},\mathbf{T},\forall n\). It can be rewritten as:  
\[\text{P2}:\] (12a) \[\max_{x,y,\mathbf{A},\mathbf{T},\mathbf{R}}\,\sum_{n=1}^{N}R_{n}\] \[s.t.\,\frac{1}{2}\text{log}_{2}\bigg{(}1+\frac{p_{n}}{t_{n}} \bigg{)}\geq R_{n},\forall n,\] (12b) \[t_{n}\geq\sum_{k\
eq n}^{N}\alpha_{k,n}p_{k}+\frac{A_{n}B_{n}+C_ {n}}{{A_{n}}^{\prime}},\forall n,\] (12c) (7b)-(7d), (9f), (10a), (11). P2 is hard to solve due to the non-convex constraints (9f), (12b), and (12c). By performing mathematical operations to (12b) and applying a first-order Taylor expansion, (12b) can be given as follows:  
\[\log_{2}(t_{n}+p_{n})-\log_{2}(\bar{t}_{n})-\frac{\text{log}_{2}e}{\bar{t}_{n} }(t_{n}-\bar{t}_{n})\geq 2R_{n},\forall n. \tag{13}\]  
With some simple mathematical transformations and the SCA technique at the first term on the right side of (12c), we obtained:  
\[\sum_{k\
eq n}^{N}\alpha_{k,n}p_{k}\leq\frac{1}{4}\sum_{k\
eq n}^{N}\Pi_{k,n}, \tag{14}\]  
where  
\[\Pi_{k,n}=(\alpha_{k,n}+p_{k})^{2}-(\bar{\alpha}_{k,n}-p_{k})^{2}-2(\bar{ \alpha}_{k,n}-p_{k})(\alpha_{k,n}-\bar{\alpha}_{k,n}).\]  
By substituting \(A_{n}\), \({A_{n}}^{\prime}\), \(B_{n}\), \(C_{n}\) into the second term on the right side of (12c), it could be formulated through the following:  
\[\alpha_{k,n}-\alpha_{k,n}^{2} \leq 0,\forall k,n. \tag{10b}\]0  
where  
\[\alpha_{k,n}-\alpha_{k,n}^{2} \leq 0,\forall k,n. \tag{10b}\]1 \[\alpha_{k,n}-\alpha_{k,n}^{2} \leq 0,\forall k,n. \tag{10b}\]2 \[\alpha_{k,n}-\alpha_{k,n}^{2} \leq 0,\forall k,n. \tag{10b}\]3 \[\alpha_{k,n}-\alpha_{k,n}^{2} \leq 0,\forall k,n. \tag{10b}\]4 \[\alpha_{k,n}-\alpha_{k,n}^{2} \leq 0,\forall k,n. \tag{10b}\]5 \[\alpha_{k,n}-\alpha_{k,n}^{2} \leq 0,\forall k,n. \tag{10b}\]6  
Since the second term appeared to be the convex multiplied by the convex, we employed the scaling method to solve this problem.
## 4 Discussion
### Limitations of the Proposed Methodology  
Some limitations of the proposed method need to be highlighted. First of all, difficulties may arise in identifying the exact base of the tree and the endpoint of the shadow, especially where shadows may blend with other ground elements. Therefore, this method is most effective in areas where there is sufficient distance between trees to ensure that shadows are fully visible on the ground without overlapping other elements (a condition often met in modern orchards, for example, to facilitate mechanized operations). Additionally, the identification of segment vertices representing shadow length in the image, which follows a predetermined orientation (dependent on the Sun's azimuth), may be influenced by the operator. The irregular shape of olive tree canoise varied shadow projections on the ground, introducing a degree of subjectivity into tree height calculation. However, the results remain encouraging, as similar limitations exist when using traditional photogrammetry (e.g., from stereo pairs). Regarding Test 1b, we assume that the longest shadow corresponds to the maximum height of the trees. This assumption may affect the height estimation because, based on the canopy shape, it is not certain that the longest shadow corresponds to the highest point. Nonetheless, the shape of the olive tree canopy can be considered appropriate in this case, as the maximum height of the canopy is typically found at the outer perimeter.  
In any case, this methodology can be considered a rapid assessment tool, although drone or ground-based measurements could achieve slightly better accuracy but would be significantly more time-consuming for larger areas.
## 6 Concluding Remarks
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Panel** & \(\sigma_{\mathbf{y}}\) & **W** & \(a\) & \(\ell\) & **L** & \(\sigma_{\text{Exp}}\) & \(\beta_{\text{P-config}}\) & \(\beta_{a}\) & \(\beta_{\ell}\) & \(\sigma_{\text{LU}}\) \\
**ID** & **MPa** & **mm** & **mm** & **mm** & **mm** & **MPa** & **(\(\beta_{\text{LU}}\))** & \(\beta_{a}\) & \(\beta_{\ell}\) & **MPa** \\ \hline LJ-1 & 268.9 & 610 & 106.52 & 3.65 & 16.83 & 126.73 & 0.843 & 0.922 & 2.506 & 146.46 \\ \hline LJ-2 & 268.9 & 610 & 106.52 & 4.92 & 15.56 & 115.77 & 0.846 & 0.935 & 2.323 & 137.17 \\ \hline LJ-3 & 268.9 & 610 & 107.79 & 3.65 & 15.56 & 126.73 & 0.821 & 0.904 & 2.574 & 141.51 \\ \hline LJ-4 & 268.9 & 610 & 107.79 & 4.92 & 14.29 & 113.35 & 0.826 & 0.918 & 2.393 & 131.79 \\ \hline LJ-5 & 268.9 & 610 & 107.79 & 6.19 & 13.02 & 106.53 & 0.834 & 0.934 & 2.268 & 122.30 \\ \hline LJ-6 & 268.9 & 610 & 109.06 & 3.65 & 14.29 & 119.84 & 0.810 & 0.889 & 2.656 & 135.86 \\ \hline LJ-7 & 268.9 & 610 & 109.06 & 4.92 & 13.02 & 113.56 & 0.815 & 0.905 & 2.466 & 125.64 \\ \hline LJ-8 & 268.9 & 610 & 109.06 & 6.19 & 11.75 & 105.77 & 0.818 & 0.923 & 2.346 & 115.69 \\ \hline LJ-9 & 268.9 & 610 & 109.06 & 7.46 & 10.48 & 101.49 & 0.821 & 0.944 & 2.294 & 105.38 \\ \hline LJ-10 & 268.9 & 610 & 131.92 & 3.65 & 16.83 & 107.29 & 0.828 & 0.948 & 2.853 & 128.10 \\ \hline LJ-11 & 268.9 & 610 & 131.92 & 4.92 & 15.56 & 102.6 & 0.831 & 0.961 & 2.642 & 120.00 \\ \hline LJ-12 & 268.9 & 610 & 133.19 & 3.65 & 15.56 & 105.91 & 0.810 & 0.930 & 2.930 & 123.87 \\ \hline LJ-13 & 268.9 & 610 & 133.19 & 4.92 & 14.29 & 97.29 & 0.812 & 0.941 & 2.720 & 115.66 \\ \hline LJ-14 & 268.9 & 610 & 133.19 & 6.19 & 13.02 & 90.32 & 0.816 & 0.958 & 2.572 & 107.41 \\ \hline LJ-15 & 268.9 & 610 & 134.46 & 3.65 & 14.29 & 102.18 & 0.793 & 0.915 & 3.034 & 118.92 \\ \hline LJ-16 & 268.9 & 610 & 134.46 & 4.92 & 13.02 & 96.25 & 0.799 & 0.930 & 2.799 & 110.25 \\ \hline LJ-17 & 268.9 & 610 & 134.46 & 6.19 & 11.75 & 88.26 & 0.802 & 0.949 & 2.663 & 101.50 \\ \hline LJ-18 & 268.9 & 610 & 134.46 & 7.46 & 10.48 & 81.91 & 0.807 & 0.971 & 2.613 & 92.34 \\ \hline LJ-19 & 268.9 & 610 & 157.32 & 3.65 & 16.83 & 89.29 & 0.816 & 0.992 & 3.314 & 111.70 \\ \hline LJ-20 & 268.9 & 610 & 157.32 & 4.92 & 15.56 & 86.26 & 0.820 & 1.007 & 3.028 & 104.84 \\ \hline LJ-21 & 268.9 & 610 & 158.59 & 3.65 & 15.56 & 87.84 & 0.802 & 0.979 & 3.403 & 107.63 \\ \hline LJ-22 & 268.9 & 610 & 158.59 & 4.92 & 14.29 & 81.29 & 0.806 & 0.994 & 3.113 & 100.53 \\ \hline LJ-23 & 268.9 & 610 & 158.59 & 6.19 & 13.02 & 75.02 & 0.812 & 1.012 & 2.952 & 93.26 \\ \hline LJ-24 & 268.9 & 610 & 159.86 & 3.65 & 14.29 & 84.46 & 0.787 & 0.964 & 3.503 & 103.38 \\ \hline LJ-25 & 268.9 & 610 & 159.86 & 4.92 & 13.02 & 79.78 & 0.793 & 0.981 & 3.218 & 95.89 \\ \hline LJ-26 & 268.9 & 610 & 159.86 & 6.19 & 11.75 & 74.12 & 0.796 & 1.001 & 3.059 & 88.26 \\ \hline LJ-27 & 268.9 & 610 & 159.86 & 7.46 & 10.48 & 69.23 & 0.801 & 1.025 & 2.988 & 80.39 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Datasets for the lap-joint 2024-T3 panels (experimental data and calculated assistance parameters) [32].
# An Aeromagnetic Compensation Algorithm Based on a Residual Neural Network
###### Abstract
Residual connection changes the structure of the network and makes it have the ability of identification mapping. It effectively retains the gradient characteristics during backpropagation, alleviates the gradient disappearance problem of traditional neural networks to a certain extent, and improves the accuracy of aeromagnetic compensation. The algorithm is applied to the data collected by UAVs to verify its effectiveness. The remainder of this article is organized as follows. In Section 2, we briefly introduce the T-L model and analyze the reason for the gradient disappearance of the primitive neural network, as well as the principle of the algorithm proposed in this paper. In Section 3, we apply real compensation flight data to conduct experiments and analysis.
# Real-Time SLAM and Faster Object Detection on a Wheeled Lifting Robot with Mobile-ROS Interaction
## 4 Experiments
### Ablation Study  
An ablation study was conducted to demonstrate the effectiveness of the introduced module. YOLOv8n served as the baseline and uses C2f modules in its backbone. To ensure fairness, all variants of the detector were configured with the same environmental settings except for the studied module. As presented in Table 3, replacing the C2f modules in the backbone with Star Blocks significantly reduces both the parameter count and FLOPS. Substituting the detection head in YOLOv8n with the LSCDH further decreases the complexity of the model and improves FPS. Finally, the application of the LAMP pruning technique to the detector demonstrates a dramatic reduction in parameters and computational requirements while substantially boosting the FPS. The ablation study results indicate that the implemented improvements contribute significantly to reducing model complexity while enhancing detection speed. Consequently, these enhancements enable real-time detection capabilities in resource-constrained environments, demonstrating the practical applicability of the proposed YOLOv8-R.  
Figure 5: Visual analysis of the prediction results. The figure demonstrates YOLOv8-R’s capability to accurately detect objects in real scenarios.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Model** & **Parameters/M \(\downarrow\)** & \(\Delta_{Param}\) & **FLOPS/B \(\downarrow\)** & \(\Delta_{FLOPS}\) & **FPS \(\uparrow\)** & \(\Delta_{\text{FPS}}\) \\ \hline Baseline (YOLOv8n) & 3.0 & - & 8.1 & - & 283.0 & - \\ + Star Block & 2.2 & \(-\)26.7\% & 6.5 & \(-\)19.8\% & 286.2 & +1.1\% \\ + LSCDH & 1.4 & \(-\)53.3\% & 4.5 & \(-\)44.4\% & 314.9 & +11.3\% \\ + LAMP Pruning (YOLOv8-R) & 0.1 & \(-\)96.7\% & 1.0 & \(-\)87.7\% & 639.8 & +126.1\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ablation study for the proposed YOLOv8-R. The up arrow (\(\uparrow\)) indicates that higher values are better, and the down arrow (\(\downarrow\)) indicates that lower values are better.
# SRv6-Based Edge Service Continuity in 5G Mobile Networks +
## 7 Conclusions  
In the ETSI MEC environment, edge service migration may be required to support user mobility. Leveraging the SRv6-based implementation of the 5 GC network, in this work, we proposed a solution that guarantees service continuity in the case of workload migration between MEC Hosts. Our method preserves the IPv6 service address, enabling relocation transparency for the client, achieved through SRv6 to steer packets to the new service location. We introduced new SRv6 behaviors to support service continuity in the 5G-MEC environment, namely, End.Buffer and End.M.GTP6.D.Buffer. Those behaviors allow being able to flexibly place the buffer within the network and integrate it as part of the SID processing. We set up a small-scale testbed environment to assess our proposed solution. The results outline that our approach has superior scalability compared to a non-SRv6 alternative relying on standard IPv6 routing. The configuration time for the SRv6 approach is at most 52% less than the non-SRv6 alternative. The number of rules to be installed for the SRv6 approach scale with the number of clients, while the non-SRv6 alternative scales with the number of clients multiplied by the number of core devices. Additionally, the experimental analysis illustrates that our system ensures lossless workload migration through the utilization of a packet buffer. The packet buffer allows the throughput's standard deviation to be 10% lower compared to the No Buffer solution for all the considered topologies.  
As future work, we plan to extend the analysis by including the RAN in order to have a more complete view of the performance of our system. We also intend to study orchestration solutions following the CATS paradigm.  
Conceptualization, L.L., C.P., A.V. and E.M.; methodology, L.L., C.P., A.V. and E.M.; software, L.L.; writing, L.L. and A.V.; writing--review and editing, L.L., C.P., A.V. and E.M. All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by the Italian Ministry of Education and Research (MUR) in the framework of the CrossLab and the FoReLab projects (Departments of Excellence), and by the European Union--NextGenerationEU--under the National Sustainable Mobility Center (CN00000023, Italian Ministry of University and Research Decree n. 1033--17/06/2022, Spoke 10), partnership on \"Telecommunications of the Future\" (PE0000001--program \"RESTART\"), and PRIN 2022 project TWINKLE (project ID: 20223N7 WCJ and CUP: E53D23007770001).  
The data presented in this study are available in this article.  
The authors declare no conflicts of interest.
# A Review of DSP-Based Enabling Technologies for Cloud Access Networks
###### Abstract  
Optical access networks, metro networks and mobile data networks are facing rapidly evolving demands, not only is it essential to satisfy the unyielding need for increased user bandwidths, but future networks must also support the growing wide variation in traffic dynamics and characteristics, due to various emerging technologies, such as cloud-based services, the Internet-of-Things (IoT) and 5G mobile systems, and due to growing trends, such as the proliferation of mobile devices and the rapidly increasing popularity of video-on-demand services. To be cost-effective and commercially sustainable, future optical networks must offer features, such as, dynamic reconfigurability, highly efficient use of network resources, elastic bandwidth provisioning with fine granularity, network sliceabilty and software defined networking (SDN). To meet these requirements Cloud Access Networks (CANs) are proposed which require a number of flexible, adaptive and reconfigurable networking elements. By exploiting digital signal processing (DSP) we have proposed a digital orthogonal filter-based multiplexing technique to implement CANs with multiplexed, independent optical channels at the wavelength, sub-wavelength, and orthogonal sub-band levels. This paper reviews the overall CAN concept, the operating principles of the various CAN network elements and presents an overview of the research work we have undertaken in order to validate the feasibility of the proposed technologies which includes real-time DSP-based demonstrations.  
fiber optic communications; digital signal processing; cloud access networks; reconfigurable optical transceivers; soft-ROADMs; spectral converters +
Footnote †: journal: Future Internet
# Soil Classification by Machine Learning Using a Tunnel Boring Machine's Operating Parameters
## 2 Soil Classification
### Soil Classification by Particle Size  
Soil classification aims to categorize soils by their different engineering properties: grouping soils with similar engineering properties is advantageous when considering their behavior during construction. Soil classification methods include the Unified Soil Classification System (USCS) and that of the American Association of State Highway and Transportation Officials (AASHTO), with the former being the most widely used in Korea.  
The USCS was developed by the American Corps of Engineers during World War II in 1942. The procedures are stipulated in standards such as ASTM D-2487/2488 and KS F 2324. Classification first involves a sieve analysis: if at least 50% of the soil passes through a No. 200 sieve (particle diameter 0.075 mm), the soil is classified as fine-grained soil comprising silt or clay; otherwise, it is coarse-grained soil comprising gravel or sand. After establishing whether the soil is fine- or coarse-grained, it is further categorized by its physical properties. The tests include measuring its passage through a No. 4 sieve (particle diameter 4.75 mm), liquid limit, and plasticity limit. Table 1 summarizes the classificationsof soil by particle size following the USCS, and Figure 1 shows the overall classification method of the USCS.
## 3 Results
It should also be noted that the obtained results substantiate the correct choice of the threshold value \(M_{\rm R}\) based on the assessment of the completeness magnitude \(M_{\rm c}\) in the catalog. epicenters of earthquakes with \(M\geq 5.5\) and \(M\geq 6.0\). Interpreting FCAZ zones as areas prone to earthquakes with \(M\geq 5.5\), and substantiating their reliability, it should be noted that they can also be interpreted as areas prone to events with \(M\geq 6.0\). This is confirmed by the statistics of \"missed target\" errors given in the paper. For the first time, the high seismicity zones determined by the FCAZ method were interpreted for two thresholds \(M_{0}\) when recognizing in the Crimean Peninsula and the northwestern Caucasus [41]. In the same paper [41], a mathematical theory was constructed which makes it possible to represent FCAZ zones as a fuzzy set to consider areas in which earthquakes with a magnitude of \(M_{0}^{1}\leq M<M_{0}^{2}\) can occur. Another important result was obtained in the present paper. The classification shown in Figure 3 turns out to be stable for the variation of the threshold \(M_{0}\) in the interval \(\Delta M_{0}\approx 1\) (\(5.0\leq M_{0}\leq 6.0\)). Such stability of the solution for the \(M_{0}\) threshold, which was not observed in previous FCAZ regions, serves as a weighty argument in favor of the reliability of the solution presented in Figure 3.  
Note that the results of this paper point to the need to use the completeness magnitude estimate \(M_{\rm c}\) in the catalog as the magnitude threshold \(M_{\rm R}\) (starting from which the earthquake epicenters are used as FCAZ recognition objects). This will make it possible to use in FCAZ recognition the data set that is the most complete and contains the minimum number of errors in the values of the seismicity parameters of the considered high seismicity region. Conceptualization, A.D.G. ; data curation, B.A.D., B.V.D., A.A.S. and I.M.N. ; formal analysis, A.D.G., B.A.D., B.V.D., E.O.K. and A.A.S. ; investigation, B.A.D., E.O.K. and I.M.N. ; methodology, A.D.G. and B.A.D. ; software, B.A.D. ; supervision, A.D.G. ; validation, B.V.D., E.O.K., A.A.S. and I.M.N. ; visualization, B.V.D., E.O.K. and I.M.N. ; writing--original draft, A.D.G., B.A.D., B.V.D., E.O.K., A.A.S. and I.M.N. All authors have read and agreed to the published version of the manuscript. The reported study was funded by the Russian Science Foundation, project number 21-77-30010, System analysis of geophysical process dynamics in the Russian Arctic and their impact on the development and operation of the railway infrastructure. Not applicable. This work employed data provided by the Shared Research Facility, the Analytical Geomagnetic Data Center of the Geophysical Center of RAS ([[http://ckp.gcras.ru/](http://ckp.gcras.ru/)]([http://ckp.gcras.ru/](http://ckp.gcras.ru/)) accessed on: 23 October 2022). The authors declare no conflict of interest.