esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Dark Channel Prior Dehazing with Polarization
## 2 Theoretical Analysis
### Polarization Detection Theory  
According to the Fresnel reflection law [23], the object's own properties (such as surface morphology, texture, water content, dielectric constant, and incident angle) determine its characteristic polarization. Using the sensitivity of polarization to physical properties can strengthen the target characteristics, which is conducive to target recognition.  
The polarization state of light is usually characterized by the Stokes vector:  
\[S=\left[\begin{array}{c}S_{0}\\ S_{1}\\ S_{2}\\ S_{3}\end{array}\right]=\left[\begin{array}{c}\frac{1}{2}(i_{0}+i_{45}+i_{90 }+i_{135})\\ i_{0}-i_{90}\\ i_{45}-i_{135}\\ i_{r}-i_{l}\end{array}\right], \tag{10}\]  
\(S_{0}\) is the intensity of the scene total light, \(S_{1}\) indicates the difference between \(0^{\circ}\) and \(90^{\circ}\) polarizations, \(S_{2}\) represents the difference between \(45^{\circ}\) and \(135^{\circ}\) polarizations, \(S_{3}\) denotes the difference between right-handed and left-handed polarizations, and \(i\) means the polarization detection channel.  
Under natural conditions, the Stokes vector can be simplified to a vector with only linear polarization components \(S_{0}\), \(S_{1}\) and \(S_{2}\) as the circular polarization characteristics are negligible. The light wave is measured for its polarization information, and the linear polarization degree image _DoLP_ is used for visualization. We can use the Stokes vector to measure the linear polarization degree of the scene, which indicates the ratio of polarized components in the total radiance of the scene. The formula for calculating the linear polarization degree (Equation (11)) is as follows:  
\[DoLP=\frac{1}{S_{0}}\sqrt{S_{1}^{2}+S_{2}^{2}}. \tag{11}\]
## 3 Results and Discussion
### The Assessment of Health Risk-Sediments
#### 3.3.3 Excessive Risk for Cancer (ECR)  
It is possible to calculate the ECR values for toxic metals, such as Cr and Pb, in sediments, and there are many studies focusing on the concentrations of metals in the sediments of water reservoirs [61; 62; 63]. If the ECR ranges between \(10^{-6}\)-\(10^{-4}\), there is a low risk of cancer. Carcinogenic risk is considered to occur when ECR > \(10^{-6}\). For the studied elements that are recognized by EPA as carcinogenic, the risk of cancer development related to the sediments was calculated, and the results are summarized in Table 11. An apparent carcinogenic health risk was observed at all the sampling sites. In our study, the highest ECR values for Cr were found in S2 (for children (2.23 \(\times\)\(10^{0}\)) and adults (8.3 \(\times\)\(10^{0}\))), as the highest Cr concentration was found at this site. In other studies, the sediments in the former area of the slag heap have never been studied, although soil samples were assessed in terms of their cancer risk. It was found that the ECR values for Pb in the soil samples near the slag heap were high; however, for Cr, the risk was negligible [30]. The situation is completely different in the studies of [PERSON] et al. [45], wherein the carcinogenic risk was extremely high for Cr for occupational exposure scenarios. In the studies of [PERSON] et al. [56], the high ECR values for Cr for children were obtained from the soil samples taken from the former ferrochromium plant area. Surprisingly, lower values for Pb were obtained in these studies.  
The obtained results for ECR prove that the former ferrochromium plant is still a significant environmental hazard and could have a critical impact on the surrounding environment. Old environmental practices and outdated technologies can still have a key role in maintaining the good health of the whole ecosystem as they could serve as a reservoir for pollutants that can be dangerous to all living organisms, and they can be released again into the environment. Therefore, it is extremely important to assess past environmental burdens to protect the environment. Future studies should track the possible changes in the Cr and Pb concentrations, as these elements pose a carcinogenic risk to humans and other living organisms.
## 3 Results and Discussion  
### Errors in Designation of Habitats in Annex I of the Habitats Directive  
The Habitats Directive obliges EU member states to designate special areas of conservation for habitats and species considered to be of community interest, in a network known as Natura 2000. The Nature 2000 network affects 30% of the Spanish territory and the country has the largest area of land included in the network in the EU. Although this is very positive from an environmental point of view, it has led to difficulties in the execution of development plans in rural areas, particularly in the implementation of projects (e.g., wind parks and fish farms) where the location affects a Nature 2000 network space. The absence of clear guidelines for the correct, standardized interpretation of habitats included in Annex I has generated confusion and errors during the identification of areas that should be protected [27; 28].  
In Galicia, several areas degraded by anthropogenic activities were designated priority habitats. For instance, different _Sphagnum acid bogs_ (habitat 7150) were located in a zone dominated by rocky outcrops or forest areas strongly affected by anthropogenic activities during a period of more than 30 years [29]. Likewise, the main Galician dune systems [30], which are often characterized by sediments with more than 10% of CaCO\({}_{3}\), were designated as Atlantic decalcified fixed dunes (_Calluno-Ulliceta_) (priority habitat 2150) [31].  
The coastal lakes, i.e., the focus of this study, represent another example of incorrect habitat designation, as all were identified as Coastal lagoons (priority habitat 1150). This applies to the Xuno and Bodeira lakes, but probably also applies to other coastal lakes in Galicia (e.g., Doninos), which do not fit within this habitat category in relation to the water composition, contrary to the definition given in the manuals published in the last few decades (e.g., [30]).  
A significant weakness that appeared at the outset of the establishment of the Natura 2000 network was the lack of standardized criteria for interpreting the habitats and the inadequate scientific-technique knowledge of the work teams involved [28,32,33]. This weakness is, to a large extent, responsible for the problems identified in the present study.
## 3 Results and Discussion
### Coating Formation Stages
#### 3.5.1 Surface Conversion to Hydroxycarbonates  
The results demonstrate that carbonatation coating inhibits the formation of a magnesium hydroxide layer. This is the first coating stage, namely the rapid deposition from the solution, supersaturated in bicarbonate ions, of carbonates groups that combine with hydroxyl groups on the surface of Mg. Gradually, the formation of stable, non-soluble hydroxycarbonates stabilizes the Mg surface, while soluble OH\({}^{-}\) groups enter the solution.  
We can resume this stage as surface conversion to hydroxycarbonates. Our explanation of the substitution of hydroxyl groups with carbonates is related to the locally increased pH associated with the oxidized Mg surface, according to the hydroxylation reaction (1), which favours the decomposition of bicarbonate into carbonates groups and hydroxyl substitution by carbonates at the surface:  
\[\text{Mg}^{0}+2\text{H}_{2}\text{O}\leftrightarrow\text{Mg}(\text{OH})_{2}+ \text{H}_{2} \tag{1}\]  
\[\text{xMg}(\text{OH})_{2}+\text{yHCO}_{3}{}^{-}\leftrightarrow\text{Mg}_{ \text{x}}(\text{CO}_{3})_{\text{y}}(\text{OH})_{2(\chi-\gamma)}+\text{y}\text{H} _{2}\text{O}+\text{yOH}^{-} \tag{2}\]  
The chemical Equation (2) inhibits the formation of a hydroxide layer promoting the nucleation of amorphous hydroxycarbonate hydrates, according to the Raman and FTIR results. The first stage is key for the formation of the carbonate coating, not only by impeding the formation of a hydroxide layer but also because hydroxycarbonate hydrates are a propitious surface for the deposition of calcium carbonate.  
This coating stage presents the advantage of full coverage of the Mg surface, explained because the nucleation of carbonate and deposition is favored at the surface of Mg wherever hydroxide groups have been formed, and their substitution with non-soluble carbonates is promoted. Once lateral growth is complete, the growth of the coating is hampered and limited to nanoparticle attachment on growing calcite crystals.  
The pH values of the solution measured during immersion evidenced a gradual increase in time toward more alkaline values (Figure 13). However, measurements of the pH of the solution with time in the absence of Mg also revealed a similar increase of the pH values (Figure 13), attributed to reaction (4) of dissolved hydrogen carbonate ions in carbonated water, accompanied by the evolution of CO2. Therefore, the main cause of the pH increase can be attributed to the free evolution of CO2 at atmospheric pressure. A further increase in pH may occur associated with the release of hydrogen at the metal surface according to reaction (1) and the subsequent release of hydroxide ions into the solution following reaction (2), leading to higher pH values at the Mg surface. However, the effect is relatively small in the bulk solution.
## 3 Implementation of Vehicular VLC System  
In this section, we provide detailed descriptions of the hardware components, communication protocols, and the overall system architecture of the proposed vehicular VLC system installed in each vehicle. This system facilitates real-time data exchange between vehicles, ensuring synchronized movement and improved road safety in tunnel environments.
## 5 Field Experimental Results and Discussion
### Analysis of Experimental Results  
Figure 13 shows experimental results, where the red line is the actual pressure. The pressure tracking curves of traditional PID, traditional PID with compensation correction and adaptive fuzzy PID with compensation correction control algorithm are shown in Figure 13a,b,c, respectively.  
From Figure 13a, it can be seen that the system response is fast, reaching 1.5 MPa in about 2 s, but the pressure tracking curve fluctuates greatly, which fluctuates between the minimum 0.7 MPa and the maximum 2.1 MPa, and the system robustness is poor when the traditional PID algorithm without compensation correction is adopted.  
From Figure 13b, it can be seen that the system pressure reaches 1.5 MPa in 2.5 s, and when the system is stable, the pressure tracking curve fluctuates between the minimum 1.2 MPa and the maximum 1.7 MPa under the control of traditional PID algorithm with compensation correction.  
From Figure 13c, it can be seen that the system pressure only reaches 1.5 MPa in 1.5 s, and when the system is stable, the pressure tracking curve fluctuates between the minimum 1.3 MPa and the maximum 1.6 MPa under the control of adaptive fuzzy PID algorithm with compensation correction. The fluctuation is caused by the forced flow caused by the uncertain parameters of the valve and other factors, which cannot be fully compensated. The dynamic performance of the system is better than others.  
Figure 12: Pressure control experimental platform for the tractor. (**1**) Pressure controller; (**2**) Host computer; (**3**) CAN card; (**4**) Power supply; (**5**) Pressure sensor; (**6**) Displacement sensor.  
control system with the proposed algorithm has a favourable dynamic response character, high tracking accuracy, and sufficient robustness to meet the field performance requirements of the tractor.
## 3 Results and Discussion
### No Turbidity Interference
#### 3.1.2 PE_PLS  
We use the original spectrum to detect the COD concentration, and the PE algorithm was adopted to process the original spectral data directly. PE was used to find \(\gamma_{i}(i=1,2,3)\) in the original spectrum. PLS was used to establish the prediction model. According to the division of calibration set and validation set in Table 1, \(\gamma_{i}(i=1,2,3)\) in the calibration set were used as independent variables, and the COD concentrations were used as dependent variables to establish COD prediction model. The model is shown as Equation (18).  
\[C=-0.0669+9.882*\gamma_{1}-412.2077*\gamma_{2}+300.5518*\gamma_{3} \tag{18}\]  
The fitting result of the model is shown in Figure 13, where black dots and red dots represent the fitting results of the calibration set and validation set, respectively. The performance of the fitting results is shown in Table 6.
## 1 Introduction
### Design Consideration of HG-SIQBC Configuration
#### 2.6 Efficiency Analysis of HG-SIQBC Configuration  
To determine the efficiency of the proposed HG-SIQBC converter, the power losses due to the inductors, capacitors, diodes, and switch are calculated as described below.  
The power losses of the inductor can be derived as  
\[\sum_{i=1}^{3}P_{L_{i}}=R_{L_{1}}*I^{2}{}_{L_{i},rms}. \tag{41}\]The power losses of the capacitor can be derived as  
\[\sum_{i=0}^{3}P_{C_{i}}=R_{C_{1}}*I^{2}_{C_{i}rms}. \tag{42}\]  
The power losses of the capacitor can be derived as  
\[\sum_{i=1}^{6}P_{D_{i}}=\sum R_{FD_{1}}*I^{2}_{D_{i}rms}+\sum V_{FD_{1}}*I_{D_{i}}. \tag{43}\]  
The total power loss of the converter is given by  
\[P_{Total}=P_{R_{DS}}+P_{SW}, \tag{44}\]  
\[P_{R_{DS}}=I^{2}_{sw}*R_{DS}, \tag{45}\]  
\[P_{SW}=V^{2}_{sw}*C_{s}*f_{s}, \tag{46}\]  
where \(P_{RDS}\) is the conduction loss, \(P_{SW}\) is the switching loss, \(V_{sw}\) is the voltage stress of the switch, \(I_{sw}\) is the current stress of the switch, \(f_{s}\) is the switching frequency, \(R_{DS}\) is the ON state resistance of the switch, and \(C_{s}\) is the parasitic switch capacitance.  
Then, the efficiency of the proposed HG-SIQBC converter can be calculated as follows:  
\[\eta_{\text{Proposed HG-SIQBC}}=\frac{P_{0}}{P_{0}+P_{L}+P_{C}+P_{D}+P_{Total}}. \tag{47}\]  
The converter loss breakdown chart for the HG-SIQBC topology is shown in Figure 10.
# _remote sensing_
## 2 Methodology  
### Reference Frame and Time Difference  
The BeiDou coordinate system (BDCS) was adopted by the BDS-3 broadcast ephemeris, while the precise orbit of IGS is referred to as the international terrestrial reference frame (ITRF) [1; 5; 26; 27; 28]. The difference between BDCS and ITRF is about 4.0 cm [10]. The impact of this deviation is not considered in this paper, because it is negligible with regard to theorbit accuracy of navigation. The BDS-3 broadcast ephemeris time was based on BeiDou time (BDT), and the precise ephemeris of IGS was based on GPS time (GPST) [1; 5; 29; 30; 31]. In order to assess the clock error accuracy of the BDS-3 broadcast ephemeris, the time was unified as GPST.
# Survey on 5G Physical Layer Security Threats and Countermeasures
## 7 Location-Based Techniques for Enhancing PHY Layer Security
### Network-Assisted Methods  
In order to localize base stations (either legitimate or rogue ones), network-assisted methods can also be used. In addition to the above-mentioned DL-empowered method [162], more straightforward solutions can be used, without the need to exploit machine learning. The Timing Advance (TA) parameter sent from the base station to the UE, or the Measurement Reports (MR) collected by the BS, have been considered in the literature [163]. The tri-lateration approach is widely used in such methods.  
The exploitation of the TA parameter for base station location has already been studied thoroughly, starting from network simulations in [164] employing a Gaussian Mixture Filter, to practical field tests in Austria as described in [165]. As an example of network-assisted methods relying on measurement reports, [166] should be mentioned.
## 2 Tests and Methods
### Preprocessing of Gas Concentration Data
#### 2.3.2 Outlier Recognition Based on K-Means Algorithm  
K-means algorithm, also known as the K-means algorithm, is a typical distance-based clustering algorithm, which is widely used in clustering and outlier identification problems due to its clear structure and fast convergence speed [28; 29].  
The basic idea of the K-means algorithm is that in the gas concentration data set D = {\(X_{1}\), \(X_{2}\),..., K samples were selected as cluster centers in \(X_{m}\)}, namely, the center of mass \(Z_{j}\) (\(j\) = 1, 2, 3,..., \(k\)). Calculate the Euclidean distance dij between each sample \(X_{i}\) (\(i\) = 1, 2,..., m) and K centroids. The samples were divided into clusters with the nearest centroid to obtain K clusters \(C_{j}\) (\(j\) = 1, 2, 3,..., \(k\)). The centroid of the new cluster is calculated step by step, and the above steps are repeated until the clustering criterion function converges. The clustering criterion function is shown in Equation (2).  
\[\sum_{j=1}^{k}\sum_{X_{i\in C_{j}}}\left\|X_{i}-z_{j}\right\|_{2}^{2} \tag{3}\]  
The E value represents the minimum square error of each cluster after clustering. The smaller the E value is, the closer the gas concentration data in the cluster is around the center of mass. The higher the similarity within the cluster, the better the clustering effect.
# A Novel Self-Forming Virtual Sub-Nets Based Cross-Layer MAC Protocol for Multihop Tactical Network
## 3 Proposed VSCL-MAC Architecture
### Data Unit  
In the data transmission unit, SDRs of each sub-net send data in time slots allocated on corresponding data channels. It enables all SDRs to execute collision-free and interference-free data transmission with efficient use of bandwidth. After completion of data transfer, all reserved time slots and frequencies are released and reserved again accordingly.
# Wind Turbine Gearbox Fault Diagnosis Based on Improved EEMD and Hilbert Square Demodulation
## 2 Review of Ensemble Empirical Mode Decomposition and Hilbert Square Demodulation  
### Ensemble Empirical Mode Decomposition  
EEMD is an adaptive data-driven signal processing method, which substantially improves on EMD in overcoming the problem of mode mixing. The procedures of EEMD are as follows:Given that \(x(t)\) is an original signal, add random white noise signal \(randn_{j}(t)\) to \(x(t)\): \[x_{j}(t)=x(t)+randn_{j}(t)j=1,2,\ldots,M\] (1) where \(x_{j}(t)\) is the noise-added signal and \(M\) is the number of trials.
2. Decompose \(x_{j}(t)\) into a series of IMFs \(c_{ij}(t)\) utilizing EMD as follows: \[x_{j}(t)=\sum_{i=1}^{N}c_{ij}(t)+r_{N_{j}}(t)\] (2) where \(c_{ij}(t)\) stands for the IMF obtained each trial, \(r_{N_{j}}(t)\) denotes the residue of the \(j\)-th trial and \(N_{j}\) is the IMFs number of the \(j\)-th trial.
3. If \(j<M\), repeat Steps 1 and 2, and add different random white noise signals each time.
4. Since the noise series in each decomposition step is statistically different and low correlation exists among the various series, the noise will be canceled out in the ensemble means, provided that the sufficient number of steps has been taken. The ensemble means of the corresponding IMFs can be obtained as the final IMFs: \[c_{i}(t)=\left(\sum_{j=1}^{M}c_{ij}(t)\right)/M\] (3) where \(i=1\), \(2\), \(3\),..., \(I\) and \(I=\min(N_{1},N_{2},\ldots,N_{M})\).
5. \(c_{i}(t)\) is the ensemble mean of corresponding IMF of the decomposition.
## 3 Results
### Generation of tps1 Deletion and Overexpression Transformations  
To generate the _tps1_-deletion mutant, the selective marker gene _hph_ was located in the middle of the _tps1_ gene through protoplast transformation. The stability testing of 24 transformants was conducted on hygromycin resistance plates. The initial PCR screening, using primers TPSF/TPSR (Table S1), provided preliminary evidence indicating that the _tps1_ gene deletion cassette had integrated into the _T. atroviride_ HB20111 genome through homologous recombination in two transformants (Figure 3a). A subsequent qRT-PCR analysis confirmed the absence of _tps1_ transcript expression in the deletion mutant \(\Delta\)_tps1_-16, indicating the successful replacement of the _tps1_-coding region through homologous recombination (Figure 3b).  
The _tps1_-gene-overexpression strains were also obtained through the transformation of HB20111 protoplast with linearized plasmids pOf_tps1_ using _Notl_. Fourteen transformants resistant to hygromycin (150 \(\mathrm{\SIUnitSymbolMicro\SIUnitSymbolMicro}\)/mL) were screened using primers Hyg-648F/Hyg-939R (Sangyo Biotech (Shanghai) Co., Shanghai, China). All transformants tested positive for the _hph_ gene. The _tps1_ expression cassette from plasmid pO_tps1_ may have been randomly integrated into the genome of _T. atroviride_ HB20111 in these overexpressing transformants. Transformant O_tps1_-15 was selected for further characterization and analyzed by qRT-PCR. Expression of the _tps1_ gene in _Otps1_-15 was elevated 22-fold in comparison with WT (Figure 3b). Finally, two deletion mutants and overexpression strains were obtained and designated as \(\Delta\)_tps1_ and _Otps1_ for subsequent experiments.
# An Interactive Transient Model Correction Active Sonar Target Tracking Method
## 3 Simulation Verification and Offshore Testing
### Simulation Verification
#### 3.1.1 Simulation Conditions  
The underwater target trajectory is composed of five segments, and the motion parameters for each segment are shown in Table 1[36]. The initial speed of the target is 2.06 m/s, the heading is 90\({}^{\circ}\), and the total duration of motion is 1140 s. At the end of the trajectory, the target speed is 13.35 m/s, and the heading is 47.6\({}^{\circ}\).  
Assuming an active sonar detection scan period of 20 s, the root mean square error (RMSE) of the target's radial distance measurement is 100 m, and the RMSE of the azimuth measurement is 0.5\({}^{\circ}\). The true trajectory of the target's motion and the sonar measurements are shown in Figure 3. Specifically, in Figure 3a, when converting the target's radial distance and azimuth measurements into X- and Y-axis distance measurements in a Cartesian coordinate system, the debiasing process described in Section 2.1.3 was applied.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{\begin{tabular}{c} **Motion** \\ **Parameters** \\ \end{tabular} } & \multicolumn{2}{c}{**Initial Speed**} & \multicolumn{2}{c}{**Initial Heading**} & \multicolumn{2}{c}{**Acceleration**} & \multirow{2}{*}{\
\begin{tabular}{c} **Motion Duration** \\ **(s)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Motion** \\ **Type** \\ \end{tabular} } \\ \cline{2-2} \cline{5-6}  & \(v_{\pi}\) & & \(v_{\eta}\) & & & \(a_{\pi}\) & \\ \hline Trajectory 1 & 2.06 & 0.00 & 90.00 & 0.0500 & 0.1000 & 120 & CV \\ \hline Trajectory 2 & 2.06 & 0.00 & 90.00 & 0.0500 & 0.100 & 120 & CA \\ \hline Trajectory 3 & 8.06 & 12.00 & 33.88 & 0.0025 & \(-\)0.0750 & 240 & CA \\ \hline Trajectory 4 & 8.66 & \(-\)6.00 & 117.60 & 0.0025 & \(-\)0.0250 & 120 & CA \\ \hline Trajectory 5 & 8.96 & \(-\)9.00 & 135.13 & 0.0025 & 0.0500 & 360 & CA \\ \hline \end{tabular}
\end{table}
Table 1: Simulation parameters for underwater target motion.