esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Simulation Examples
### Channel Mismatch Errors Simulation  
The influence of channel mismatch errors on the resolution probability of the eight-element R-MUSIC and five-element MUSIC is shown in Figure 8. The channel mismatch errors range from 0 to 40\({}^{\circ}\), SNR is 6 dB and the snapshots number is 100. The two DOAs are the same as the simulation in Subsection 5.2. Every data is averaged over 200 Monte Carlo simulations. Table 1 shows the RMSE  
Figure 6: RMSE _versus_ SNR.  
Figure 7: RMSE _versus_ snapshots.  
_versus_ the channel mismatch errors. Because the statistical characteristics of the two DOAs are the same, both Figure 8 and Table 1 shows the estimation results of DOA = (90\({}^{\circ}\), 85\({}^{\circ}\)).  
It can be seen from the Figure 8 that the proposed R-MUSIC algorithm can provide better robustness than that of the classical MUSIC in large channel mismatch errors. Table 1 shows the estimation accuracy performance of R-MUSIC is much better than that of the classical MUSIC, when the channel mismatch errors exist. This is because the R-MUSIC algorithm only needs two elements; the number of elements of R-MUSIC is much less than that of the classical MUSIC. Furthermore, the problem of element channel calibration is easier to solve too.
# Bee Swarm Metropolis-Hastings Sampling for Bayesian Inference in the Ginzburg-Landau Equation
## 3 The Proposed BeeSwarm-MH Algorithm
### Hybrid Sampling Framework Integrating Bee Swarm Intelligence with MH
#### 3.1.5 Convergence Diagnosis and Results Output  
The convergence diagnosis module provides a clear and automated criterion for assessing the convergence of the BeeSwarm-MH sampling process while standardizing the recording of the best samples at each iteration. This ensures the sufficient exploration of the posterior space and avoids unnecessary computational overhead.  
At each iteration \(t\), the algorithm computes the maximum log-posterior value among all swarm individuals:  
\[Q_{t}=\max_{i}f_{i},\]  
and appends it to the historical best-value sequence  
\[Q=\{Q_{1},Q_{2},\ldots,Q_{t}\},\]  
which characterizes the temporal evolution of the global optimal posterior and is used to monitor the convergence trend.  
Once the iteration count exceeds the sliding window length \(W\), the variance of the latest \(W\) best values is calculated:  
\[v=\text{Var}(Q_{t-W+1},Q_{t-W+2},\ldots,Q_{t}).\]  
If this variance satisfies  
\[v<\epsilon,\]  
the sampling process is considered converged, indicating that the optimal posterior value has stabilized and the swarm search has reached a steady state. This criterion, based on the stationarity of the best-value sequence, is straightforward and computationally efficient, making it especially suitable for complex multimodal posterior distributions where traditional diagnostics such as the Gelman-Rubin statistic may be difficult to apply.  
Simultaneously, the algorithm records the parameter vector of the current best individual at each iteration:  
\[\mathcal{S}_{t}=\arg\max_{i}f_{i},\]  
forming a time-series sample matrix (with columns corresponding to time steps):  
\[\mathcal{S}=\Big{[}\mathcal{S}_{1}\quad\mathcal{S}_{2}\quad\cdots\quad\mathcal{ S}_{T}\Big{]},\]  
which serves as the data foundation for subsequent Bayesian parameter estimation and uncertainty quantification. The detailed procedure is summarized in Algorithm 5.  
```
0: Current iteration \(t\), log-posterior values of swarm individuals \(\{f_{i}\}\), historical best value sequence \(Q\), variance threshold \(\epsilon\), sliding window length \(W\), sample matrix \(\mathcal{S}\)
0: Convergence flag converged, updated sample matrix \(\mathcal{S}\)
1: Compute current best log-posterior value: \(Q_{t}\leftarrow\max_{i}f_{i}\)
2: Append \(Q_{t}\) to \(Q\)
3: Record current best parameter vector: \(\mathcal{S}_{t}\leftarrow\arg\max_{i}f_{i}\)
4: Append \(\mathcal{S}_{t}\) to sample matrix \(\mathcal{S}\)
5:if\(t>W\)then
6: Compute variance: \(v\leftarrow\text{Var}(Q_{t-W+1},\ldots,Q_{t})\)
7:if\(v<\epsilon\)then
8:return converged = true
9:endif
10:endif
11:return converged = false
```  
**Algorithm 5** Convergence Diagnosis and Results Output  
Following the completion of core modules such as adaptive step-size adjustment and convergence diagnosis, these components will be integrated to construct the complete BeeSwarm-MH main procedure, enabling efficient closed-loop control from initialization to convergence output.
# Machine Learning-Assisted Secure Random Communication System
## 4 Transmission  
The proposed ML-RCS model is shown in Figure 4. It consists of a transmitter 'T', legitimate receiver 'LR', and illegitimate receiver 'ILR'. The ML-RCS operates in the AWGN channel. Unlike the ILR, the T and LR are assumed to be perfectly synchronized by the method given in [46] and have predetermined pulse lengths and parameters, which are utilized to generate the Sk\(\alpha\)S noise as random carriers. In contrast, the ILR is used to analyze the covertness of the ML-RCS from the perspective of an eavesdropper. To depict the worst-case scenario in this context, the ILR can presumably operate via the same CART-based classifier. However, it does not have knowledge of the predetermined pulse length or the parameters utilized by the T and LR.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Configuration** & **Model Flexibility** & **Description** \\ \hline \multirow{3}{*}{FT} & \multirow{3}{*}{Low} & Many leaves to make many fine distinctions between classes. \\  & & (maximum splits = 100) \\ \hline \multirow{3}{*}{MT} & \multirow{3}{*}{Medium} & Medium number of leaves for finer distinctions between classes. \\  & & (maximum splits = 20) \\ \hline \multirow{3}{*}{CT} & \multirow{3}{*}{High} & Few leaves to make coarse distinctions between classes. \\  & & (maximum splits = 4) \\ \hline \hline \end{tabular}
\end{table}
Table 1: CART classifier.  
Figure 3: Basic structure of the decision tree.
# CNN-Based Illumination Estimation with Semantic Information
###### Abstract  
For more than a decade, both academia and industry have focused attention on the computer vision and in particular the computational color constancy (CVCC). The CVCC is used as a fundamental preprocessing task in a wide range of computer vision applications. While our human visual system (HVS) has the innate ability to perceive constant surface colors of objects under varying illumination spectra, the computer vision is facing the color constancy challenge in nature. Accordingly, this article proposes novel convolutional neural network (CNN) architecture based on the residual neural network which consists of pre-activation, atrous or dilated convolution and batch normalization. The proposed network can automatically decide what to learn from input image data and how to pool without supervision. When receiving input image data, the proposed network crops each image into image patches prior to training. Once the network begins learning, local semantic information is automatically extracted from the image patches and fed to its novel pooling layer. As a result of the semantic pooling, a weighted map or a mask is generated. Simultaneously, the extracted information is estimated and combined to form global information during training. The use of the novel pooling layer enables the proposed network to distinguish between useful data and noisy data, and thus efficiently remove noisy data during learning and evaluating. The main contribution of the proposed network is taking CVCC to higher accuracy and efficiency by adopting the novel pooling method. The experimental results demonstrate that the proposed network outperforms its conventional counterparts in estimation accuracy.  
Keywords:human visual system (HVS); color constancy; residual neural network; semantic information; local and global information; image dataset +
Footnote †: journal: Applied Sciences
## 7 Robustness
### Security Analysis
#### 7.5.1 Trusted Parties and Threat Model  
The system design assumes two principal trusted entities. First, Investigator Administrator [85] is responsible for the embedding and management of the watermarking  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Metric** & **DCT Only** & **DWT Only** & **DCT+DWT** \\ \hline Mean PSNR (dB) & 48.2 & 47.9 & **49.1** \\ BER @ DF60 & 0.041 & 0.057 & **0** \\ BER @ rot 90\({}^{\circ}\) & 0.118 & 0.012 & **0** \\ Payload (bits) & 64 & 64 & **128** \\ \hline \hline \end{tabular}
\end{table}
Table 8: Dual-domain versus single-domain baselines (same datasets).  
infrastructure. Second, law enforcement agencies and courts are trusted to verify watermarks and authenticate image provenance during investigative and judicial processes.
## 2 Curtain Workshop Scenario
### Radio Analysis of the Workshop  
In preparation for the deployment of the WSN within the Galeo Enrollables Company's workshop, radio planning measurements were taken in order to study the viability of the proposed ZigBee-based deployment in such a scenario. Specifically, the aim of these initial measurements was to detect potential radio interferences and their sources, such as specific machinery (i.e., laser or soldering machines) or present wireless communication systems operating at interfering frequency bands. The measurements were carried out with the aid of a portable spectrum analyzer on the RF band of 2.4-2.5 GHz, as the WSN to be deployed is based on the 2.4 GHz ZigBee communication technology. No interfering signals produced by the machinery were detected, but the Wi-Fi access points present at the workshop produced RF signals/noise at the desired band, as can be seen in Figure 2. However, the band close to 2.5 GHz was still available, so high-frequency ZigBee channels can be used to avoid potential interference. Additionally, the ZigBee protocol can automatically choose the best operation frequency channel (which is narrower than WiFi channels: 3 MHz vs. 20 MHz) depending on the scenario conditions (e.g., if the channel is free of interferences or not), and the protocol also provides three retransmissions for each lost packet by means of ACKs, minimizing the harmful effect of wireless interferences.  
Apart from the interference analysis, it is essential to note that industrial environments exhibit very specific characteristics in terms of radio propagation phenomena. Reflection, diffraction, and scattering produced by different structures and obstacles such as walls, machinery, production lines, shelves, highly reflective surfaces (i.e., big metallic objects), the presence of human beings, etc. result in relevant multipath propagation and, therefore, in a very complex environment in terms of radio propagation [22].  
In this context, wireless channel characterization is compulsory before deploying a wireless communication system in this type of environment. For that purpose, an in-house-developed 3D ray launching technique was used to characterize the wireless channel in the presented factory workshop scenario. This technique provides a better understanding of the wireless channel's behavior and facilitates the deployment of an optimized WSN. By ensuring that the wireless communication system is characterized correctly, the factory will be better equipped to ensure the success of its WSN deployment, leading to a more efficient and productive manufacturing process.
# Enhancing Efficiency of Electromagnetic Simulation in Time Domain with Transformation Optics
## 3 Simulation System and Courant Condition
Note that the power flux remains the same in both numerical and physical coordinates because: according to Equation (3), the electric and magnetic field in the numerical coordinate should be transformed to that in the physical coordinate while the cell size is also correspondingly changed. Since both the field change and cell size change cancel each other, the power flux is not affected by the coordinate transformation.
## 5 Experimental Analysis
* Overall, the amplitude of vibration shapes from the experiment is slightly larger than the theoretical calculation, and meanwhile the measured position of each peak and trough has a certain deviation from their theoretical values. However, the deviation is relatively small and completely acceptable, in addition the trend of consistency does not change under the two voltages. * The maximum deviation of the 200 V excitation voltage is 9 mm, which is slightly larger than that of the 100 V excitation voltage of 8 mm. The two experimental curves are basically in accordance with the theoretical calculation. The experimental deviation value increases slightly with the increase of the excitation voltage.
# Pilot Study of Microplastics in Snow from the Zhetysu Region (Kazakhstan)
## 2 Materials and Methods  
### Research Area  
The object of the study is the territory of the Zhetysu region, located in the southeastern part of Kazakhstan. The Zhetysu region occupies an important physiographic position within Central Asia. Its climatic features are formed under the influence of inland continental climate, of mountain systems, and of the path of transboundary air masses transport. Large parts of Central Asia are characterized by aridity, high temperature amplitudes, and a strong continental climate. The Zhetysu region stands out against this background due to its proximity to the northern slopes of the Tien Shan and Zhongar Alatau mountains. These mountain massifs play a key role in the accumulation of precipitation, including snow, especially in the winter-spring period.  
The relief of the region varies from flat areas (around lakes Alakol and Balkhash) to high-mountainous zones, where stable snow covers are formed. In its mountainous and foothill areas (for example, in the area of Ketmen and Zhetysu Alatau ridges), precipitation falls in the form of snow from late autumn to early spring. The thickness of snow cover in these areas can exceed 50-80 cm, while on the plains it is usually insignificant and unstable. Thus, snow cover in the Zhetysu region has a mosaic character, with mountainous areas acting as natural depositing environments [34, 35]. The plains are largely treeless and therefore wind-open, covered by steppe and desert-steppe vegetation.  
Central Asia in recent years has been increasingly considered as a region prone to transboundary pollution, including microplastics, which can be transported by air masses over long distances. Due to its location between China, Siberia, and the southern regions of Kazakhstan, the Zhetysu region falls within the zone of potential deposition of atmospheric pollutants coming from both the west (via Almaty region and north-eastern regions) and the east (via Dzhungarian Gate and Xinjiang/China). Local sources such as agriculture, road transport, tourism, and activities along large water bodies can also contribute to snow cover pollution. The Zhetysu region is part of the accelerated infrastructure development within the Chinese Belt and Road Initiative of the so-called New Silk Road.  
Consequently, the geographical location of the Zhetysu region within Central Asia, its relief, and climatic conditions create prerequisites for both microplastic input through atmospheric precipitation and its local accumulation, especially in mountainous and foothill areas with stable snow cover. That is why this region becomes an important object for snow cover monitoring as an indicator of transboundaries and local pollution.
# Design and Test of Vertical Axis Rotating Cutters for Cutting Corn Roots and Crown
## 3 Results and Discussion
### Results of the Full-Factor Test
#### 3.2.3 Effect of Different Factors on the Power Consumption of Device  
According to Table 4, the mathematical model relating the forward speed and rotational speed of the cutter shaft to the power consumption of the device was significant (\(p<0.01\)). The effect of both the forward speed and the rotational speed of the cutter shaft on the power consumption of the device was significant (\(p<0.01\)), and there were interactions between them (\(p<0.01\)).  
As shown in Figure 9, when the forward speed of the device was kept, the power consumption of the device increased with the increase in the rotational speed of the cutter shaft. The reason was that as the rotational speed of the cutter shaft increased, the kinetic energy of the cutters significantly increased, thereby improving energy conversion efficiency and resulting in an enhancement in the qualification rate of cutting the root-crown length. However, it also led to a significant increase in the power consumption of the  
Figure 8: Effect of different factors on the rate of soil disturbance.  
device [35]. Additionally, with the increase in the rotational speed of the cutter shaft, the soil resistance acting on the cutters for cutting roots also increases. To overcome soil resistance, a corresponding increase in power consumption was required, which was another reason for the increase in power consumption. When the rotational speed of the cutter shaft was kept, the power consumption of the device increased with the increase in the forward speed of the device. The reason was that as the forward speed of the device increased, the cutters for cutting roots must overcome greater soil resistance, resulting in an increase in power consumption of the device [40]. Future studies could focus on jointly optimizing the rotational speed of the cutter shaft and the forward speed of the cutter shaft while incorporating soil characteristics to establish dynamic resistance models, aiming to minimize power consumption while maintaining compliance with the qualified rate of root-crown length. When the forward speed was 1.43 m\(\cdot\)s\({}^{-1}\) and the rotational speed of the cutter shaft was 540 r\(\cdot\)min\({}^{-1}\), the power consumption of the device reached the highest value of 8.239 kW. When the forward speed was 0.71 m\(\cdot\)s\({}^{-1}\) and the rotational speed of the cutter shaft was 380 r\(\cdot\)min\({}^{-1}\), the power consumption was lowest, the value of which was 3.835 kW.
# Challenges, Threats, Security Issues and New Trends of Underwater Wireless Sensor Networks
## 3 Threats and Challenges in Underwater Wireless Sensor Networks (UWSNs)
### DoS Attacks  
DoS attacks can be launched in different ways that do not exist in other wired or wireless networks and which can be launched at any layer of the protocol stack [16; 17]. Even if UWSNs are well protected by encryption technology, they can still be threatened by DoS attacks, which can disrupt communication and cooperation between nodes and decrease availability of the whole network. Moreover, DoS attacks are low cost, deadly, and even worse, hard to detect and defend. Malicious attackers can cause severe damage at very low cost, impersonate as a legitimate node to deceive neighbor nodes, or impose particularly high power cost tasks to legitimate nodes to shorten the nodes lifetime. According to the UWSNs network model, DoS attacks and the corresponding defense technologies are discussed as below.
# Frequency Domain Full Waveform Inversion Method of Acquiring Rock Wave Velocity in Front of Tunnels
## 3 Numerical Simulation
### Fault Model  
A fault model with a thickness of 30 m is established (Figure 7a). The size of the forward model is 160 m \(\times\) 80 m, and grid spacing is \(\Delta x=\Delta y=1m\). The CFS-PML absorption boundary around the model is set. Referring to the tunnel size in the actual project, the tunnel size in the forward model is set to length 50 m \(\times\) width 12 m and the tunnel cavity is filled with air. The inclination angle of the fault is 75\({}^{\circ}\) and the distance from the tunnel face is 30 m. The rock wave velocity on one side of the tunnel is set to 4000 m/s and the rock wave velocity inside the fault is set to 2000 m/s. Another fault model with a thickness of 110 m is established (Figure 7b). The size of the forward model is 175 m \(\times\) 80 m, and the inclination angle of the fault is 60\({}^{\circ}\).  
Figure 6: (a) FWI result of 90\({}^{\circ}\) lithologic interface rock wave velocity numerical model. (b) FWI result of 45\({}^{\circ}\) lithologic interface rock wave velocity numerical model.  
Figure 8 shows the CSP seismic records of the common shot gather of all the lower wall geophones with the lower wall 5 m away from the tunnel face selected as the seismic source in the 30 m thick fault model. The original seismic records of no anomalous body in front of the tunnel face are also subtracted in order to remove the interference of scattered waves from the tunnel cavity and the tunnel face. From Figure 8, it can be seen that the response characteristic of the fault is a strong event axis and a set of weaker event axes. The strongest event axis corresponds to the front interface of the fault. There are multiple weaker event axes. After analysis, it is believed that it is caused by the mixing of the reflected wave at the back interface of the fault and the secondary reflected wave at the front interface.  
The frequency domain FWI is carried out via the observation mode shown in Figure 3. The results are shown in Figure 9a,b. In addition, only the wave velocity of the front of the tunnel face is updated. The white dashed line in the figure represents the position and shape of the real lithologic interface. The front and back interfaces of the fault are retrieved and the position and inclination of the interface are more accurate. The wave velocity inside the fault is close to the real wave velocity except for the low wave velocity in some regions.  
Figure 7: **(a)** A 30 m thick fault rock wave velocity numerical model. **(b)** A 110 m thick fault rock wave velocity numerical model.