esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### SEM Analysis  
In order to investigate the morphology and microstructure of the prepared film composites, SEM images of the cross-sectional area were obtained, as shown in Figure 7.  
The SEM image of pure PW revealed a rough and porous structure with multiple microcracks, indicating poor structural integrity. This can be attributed to the heterogeneous composition of potato peels, particularly the presence of non-starch substances, such as cellulose, which prevents uniform film formation [47]. The incorporation of SA improved structural integrity by reducing small gaps and forming a denser and more cohesive structure [20]. However, minor cracks were still present, which may be due to the lower proportion of SA compared to PW in the compound. When comparing the impact of low- and high-viscosity SA, PAhv films exhibited a slightly denser structure than PAlv. However, the difference was not significant, and some voids remained in both composites. This could be due to the dual effect of hvSA. While it enhances binding and promotes uniformity, its longer polymer chains may hinder homogeneous dispersion with PW, increasing the risk of phase separation or agglomeration [18]. As a result, potential cohesion gains may be offset by microstructural defects.  
In general, crosslinking with calcium chloride improves structural cohesion [61]. It resulted in a more uniform structure with fewer microcracks in both PAlvx and PAhvx composites compared to their non-crosslinked counterparts. The reduction in microfractures correlated with the improved tensile strength observed in mechanical tests, where crosslinked samples exhibited higher tensile strength than their non-crosslinked counterparts.
###### Abstract  
Multilevel security (MLS) is particularly intended to secure information against unauthorized access. An MLS security DBMS allows users with different security levels to access and share a database. For this purpose, the study creates a model that includes a restricted access authentication prototype with multilevel security in a database management system (MLS/DBMS). Accordingly, the model has been designed to emphasize the highest level of authorized security. The system ensures that users can only access information that they are permitted to view, fully adhering to the newly established MLS framework. In addition, the model also integrates cryptographic algorithms, such as RSA and AES, to enhance its functionality and demonstrate the scalability and security of the model. These criteria are defined based on the perspective of the database provided to users, determined by their respective authorization levels. An informal security framework for a multilevel secure DBMS is defined. It includes a classification strategy and explains the implementation of operations like insertion and deletion, addressing the complexity of models with novel methods. The metric evaluation of this model assesses the performance of the authentication process and how operations are implemented across three authentication group types. It also calculates the key generation time and encryption types in cryptographic algorithms. The results confirm that the RSA model requires less time for evaluation while maintaining multilevel security. Furthermore, the type 2 authentication group is more complex and requires more memory and time for generation. Based on the classification, the results highlight notable differences, which designers should consider when selecting authentication methods. Lastly, the study presents various conclusions, explores possible future directions, and discusses its limitations.  
Article
## 4 Conclusions  
In this paper, an improved end-to-end autoencoder based on reinforcement learning by using Decision Tree for optical transceivers is proposed and experimentally demonstrated. Transmitters and receivers are considered as an asymmetrical autoencoder, combining the deep neural network and Adaboost algorithm. Experimental results show that 48 Gb/s with 7% HD-FEC threshold under 65 km SSMF is achieved with the proposed scheme. Compared to the autoencoder based on a Fully Connected Neural Network, our algorithm uses addition operations instead of multiplication operations, which can reduce computational complexity from \(10^{8}\) to \(10^{7}\) in multiplication and \(10^{6}\) to \(10^{8}\) in addition on the training phase.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **Training** & **Running** \\ \hline ANN(CPU) & 287.24 s & 259.64 s \\ ANN(GPU) & 36.94 s & 143.13 s \\ Adaboost Classifier & 30.27 s & 7.79 s \\ \hline \hline \end{tabular}
\end{table}
Table 4: The complexity comparison between ANN and the Adaboost classifier.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **Complexity** & **Calculation (\(\times\)/\(\star\)/\(\star\)/\(\star\)/\(\star\)/\(\star\)/\(\star\))** \\ \hline ANN & \(C_{ANN}=(n_{i}n_{hid2}+n_{hid1}n_{hid2}+n_{out})+(n_{out})+(n_{out})\) & \(\begin{array}{c}C_{ANN}=(84\times 32+32\times 32+16)+16+16+\\ 16=3728+16+16\end{array}\) \\ \hline Adaboost Classifier & \(C_{Adaboost}=n_{n}+n_{n}\times\log_{2}n_{out}\) & \(C_{Adaboost}=30+120\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The complexity comparison between ANN and the Adaboost classifier.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **Complexity** & **Calculation (\(\times\)/\(\star\)/\(\star\)/\(\star\)/\(\star\)/\(\star\)/\(\star\)/\(\star\))** \\ \hline ANN & \(C_{ANN}=(n_{i}n_{hid2}+n_{hid1}n_{hid2}+n_{out})+(n_{out})+(n_{out})\) & \(\begin{array}{c}C_{ANN}=32\times 10^{4}\times\\ (84\times 32+32\times 32+16)+16+16\end{array}\) \\  & \(\begin{array}{c}1.193\times 10^{8}+5.12\times 10^{6}+5.12\times 10^{6}\end{array}\) \\ \hline Adaboost Classifier & \(C_{Adaboost}=n[n_{n}+n_{n}\times\log_{2}n_{out}]\) & \(C_{Adaboost}=9.6\times 10^{6}+3.84\times 10^{8}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: The training complexity comparison between ANN and the Adaboost classifier (20,000 steps).  
**Author Contributions:** Conceptualization, Z.W.; Data curation, J.C.; Formal analysis, Q.Z.; Funding acquisition, Y.W.; Investigation, Q.Z.; Methodology, Z.W. and S.D.; Project administration, B.C.; Resources, B.C.; Software, Z.W.; Supervision, M.W.; Validation, Z.W.; Visualization, Z.W.; Writing--original draft, H.Z.; Writing--review & editing, Q.Z. and Z.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported in part by the Science and Technology Commission of Shanghai Municipality (Project No. 20511102400, 20 ZR1420900).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Deep Learning Framework for Signal Detection and Modulation Classification
## 3 Deep Learning Framework for Signal Detection and Modulation Recognition  
DL networks aim at learning different hierarchies of features from data. As one of the branches of DL techniques, the CNNs perform well in the field of image recognition. It performs feature learning via non-linear transformations implemented as a series of nested layers. Each layer consists of several kernels that perform a convolution over the input. Generally, the kernels are usually multidimensional arrays which can be updated by some algorithms [39]. Our DL framework achieves multi-signals detection and modulation recognition. We use different deep neural networks for different tasks. For signal detection, we use SSD networks. For modulation recognition, we design a multi-inputs CNNs.
## 6 Extension with Side-Lobes/Back-Lobes as Well as the Path Loss Effect
### Antenna Model with Side-Lobes and Back-Lobes  
To measure the _directivity_ of an antenna, we often consider the three-dimensional spatial distribution of _antenna gains_, which is called the _radiation pattern_ of an antenna. Figure 9 shows the radiation pattern of a realistic directional antenna in 3-D space, which typically consists of the _main lobe_ (or _beam_) with the largest _radiation intensity_ and the _side-lobes_ and _back-lobes_ with smaller radiation intensity.  
As shown in Figure 9, we use vector **r** to represent the direction of the radiation intensity in 3-D space. In particular, we use \(\theta\) to represent the angle between the vector **r** and the \(z\)-axis (\(\theta\in(0,\pi)\)), and \(\phi\) to represent the angle between the \(x\)-axis and the projection of the vector **r** into the \(xy\) plane (\(\phi\in(0,2\pi)\)). We then define the gain of an antenna as  
\[G(\theta,\phi)=\eta\frac{U(\theta,\phi)}{U_{o}} \tag{3}\]  
where \(\eta\) is the efficiency factor, which is usually set to be 1 since all the antennas in this paper are assumed to be lossless, \(U(\theta,\phi)\) is the _radiation intensity_, which is defined as the power radiated from an antenna per unit solid angle, and \(U_{o}\) denotes radiation intensity of an _isotropic_ antenna with the same radiation power \(P_{rad}\) as a directional antenna. Note that an _isotropic_ antenna is a point that radiates/collects radio power uniformly in all directions in 3-D space. In this paper, we regard an isotropic antenna as being equivalent to an omni-directional antenna since both of them have the same projected _radiation pattern_--a circular area in a 2-D plane.  
We next analyze the antenna gain of omni-directional antennas and the antenna gain of directional antennas, which will be used in the physical channel model in Section 6.2.  
Figure 9: Radiation pattern of a realistic directional antenna.
## 3 Implementation of RUL Prediction Based on TCNMABG
### Model Verification and Comparison  
#### 3.3.1 Denoising of Raw Signals  
The DBOVMD algorithm is used to denoise the raw vibration signals based on the XJTU-SY dataset. The initialization settings of modal component \(k\) are [3; 10], and the penalty factor \(\alpha\) is [100, 2500]. The fitness curve in the noise reduction process of the algorithm is shown in Figure 5.  
When the fitness function reaches the minimum value, the optimal parameter combination can be determined by DBOVMD, and \(k\) is taken as 10, and \(\alpha\) is taken as 2338. Then, by calculating the correlation coefficient between the intrinsic mode function and the raw signals, the standard deviation of the correlation coefficient is taken as the threshold value, and the IMF component whose correlation coefficient is larger than the threshold value is retained for signal reconstruction, and the signal after noise reduction is obtained.  
Figure 5: Fitness function value curve of DBOVMD algorithm.
## 4 Results and Discussion
### Analysis of Heavy Metal Sources
The average concentration of As exceeded the background values of the Guangxi soils [18], and the coefficient of variation reached 63.56%, suggesting that As was produced by a combination of natural and anthropogenic sources. Relevant data have indicated that the ones in the upper gold mining area of the study site predominantly consist \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Item**} & \multicolumn{3}{c}{**Principal Components**} \\ \cline{2-4}  & **PC1** & **PC2** & **PC3** \\ \hline Ni & 0.872 & \(-\)0.169 & \(-\)0.113 \\ Pb & 0.843 & 0.298 & \(-\)0.004 \\ Zn & 0.804 & 0.325 & \(-\)0.192 \\ Cd & 0.691 & \(-\)0.472 & 0.168 \\ Cr & 0.664 & \(-\)0.475 & 0.3620 \\ Cu & 0.577 & 0.272 & \(-\)0.285 \\ As & 0.103 & 0.797 & \(-\)0.019 \\ Hg & 0.079 & 0.471 & 0.826 \\ Eigenvalue & 3.387 & 1.602 & 0.972 \\ Variance contribution rate (\%) & 42.335 & 20.019 & 12.154 \\ Cumulative variance & 42.335 & 62.354 & 74.509 \\ contribution rate (\%) & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Factors matrix of soil heavy metals. Figure 2: Results of correlation analysis of soil’s heavy metal content in the study area. of sulfide pyrite and exhibit a high concentration of arsenopyrite, an iron arsenic sulfide mineral [33]. The waste rock and tailings left behind by mining activities contain elevated levels of As, which gradually migrate into the surrounding soil over time, leading to pollution. The historical data indicate that the water quality of Pingshui flushing tributaries in the study area has exceeded the As standard (exceeding the limit value of As \(\leq\) 0.05 mg/L in Class III standard of Environmental Quality Standard for Surface Water (GB3838-2002)), which is potentially attributed to ore separation and grinding during mining and smelting processes. This leads to the introduction of arsenic-containing powder into the water system, while particles containing arsenic in river water infiltrate surrounding soil through mechanisms such as flow, sedimentation, and adsorption, consequently resulting in soil pollution [34]. The contribution rate of factor 3 (PC3) was 12.154%, and Hg had a large loading with a contribution rate of 82.60%, with an average concentration exceeding the background value of Guangxi soils [18], while also displaying a high coefficient of variation. Correlation analysis results indicate that there exists a weak correlation between Hg and the other seven heavy metals. This suggests that Hg may have originated from the irregular selection and smelting methods employed in early small private workshops [35]. Since the 1980s, the Jinzhuzhou gold mine has been in a state of chaotic and disorderly mining for a long time, and small private workshops often use the low-cost amalgamation method to add metallic mercury in the process of extracting gold, and the wastewater, waste gas and solid waste produced are discharged and discarded without treatment, resulting in serious Hg contamination of Pingshui flushing and downstream cultivated land.
## 1 Introduction
### _1.1. The Emergence of 6G in Maritime Communications_
This advancement is particularly critical for intelligent MCN resource management, autonomous shipping, real-time oceanographic data collection, remote port operations, and AI-driven decision-making for fleet management. Furthermore, the inclusion of smart, energy-efficient communication solutions will significantly reduce the operational costs of maritime connectivity [9].
# Narrative Review on Methods of Activating Irrigation Liquids for Root Canal Treatment
## 4 Irrigants Activation Techniques
### Manual Activation  
Manual dynamic activation (MDA) is an activation using a gutta percha cone inserted 1 mm before the working length. A moderate pumping motion is introduced into the irrigant-filled root canal. It is comprised of brief vertical strokes (2 mm amplitude at 100 strokes per minute). Dynamic movements generate increased intracanal pressure, which disrupts the vapor lock effect and significantly enhances irrigant replacement. This enables the use of fresh portions of the solution and their positive effect on a larger portion of the root canal system. Activation (either NaOCl or EDTA solutions) is recommended following root canal preparation [38]. Activating EDTA or citric acid solutions with MDA increases their affinity for removing the smear layer and debris [17]. Especially in one-third of the root canal apex in comparison to irrigating with EDTA without activation [39; 40]. MDS demonstrated a superior ability to remove organic tissue from the apical region than conventional irrigation. However, none of these techniques achievedcomplete decontamination [41].  
The study suggested that manual activation causes a risk of post-treatment pain, occurring 24 h after the procedure. MDA was compared to irrigation with a needle without any activation and passive ultrasonic irrigation. The pain was evaluated after endodontic treatment of teeth with irreversible pulp inflammation [42]. Additionally, the penetration depth of irrigants into root canal dentin is lower with MDA compared to sonic, ultrasonic, and laser-induced activation techniques [39; 40].
###### Abstract  
This paper describes a system, named \"pan-and-tilt hyperspectral radiometer system\" (PANTHYR) that is designed for autonomous measurement of hyperspectral water reflectance. The system is suitable for deployment in diverse locations (including offshore platforms) for the validation of water reflectance derived from any satellite mission with visible and/or near-infrared spectral bands (400-900 nm). Key user requirements include reliable autonomous operation at remote sites without grid power or cabled internet and only limited maintenance (1-2 times per year), flexible zenith and azimuth pointing, modularity to adapt to future evolution of components and different sites (power, data transmission, and mounting possibilities), and moderate hardware acquisition cost. PANTHYR consists of two commercial off-the-shelf (COTS) hyperspectral radiometers, mounted on a COTS pan-and-tilt pointing system, controlled by a single-board-computer and associated custom-designed electronics which provide power, pointing instructions, and data archiving and transmission. The variable zenith pointing improves protection of sensors which are parked downward when not measuring, and it allows for use of a single radiance sensor for both sky and water viewing. The latter gives cost reduction for radiometer purchase, as well as reduction of uncertainties associated with radiometer spectral and radiometric differences for comparable two-radiance-sensor systems. The system is designed so that hardware and software upgrades or changes are easy to implement. In this paper, the system design requirements and choices are described, including details of the electronics, hardware, and software. A prototype test on the Acqua Alta Oceanographic Tower (near Venice, Italy) is described, including comparison of the PANTHYR system data with two other established systems: the multispectral autonomous AERONET-OC data and a manually deployed three-sensor hyperspectral system. The test established that high-quality hyperspectral data for water reflectance can be acquired autonomously with this system. Lessons learned from the prototype testing are described, and the future perspectives for the hardware and software development are outlined.  
Hyperspectral reflectance; validation; autonomous measurements; ground-truth data; system design +
Footnote †: journal: Remote Sensing
# An In-Depth Assessment of the New BDS-3 B1C and B2a Signals
## 4 Carrier-To-Noise Density Ratio
### C/N\({}_{0}\) Statistical Characteristics of MEO Satellites
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**System**} & \multirow{2}{*}{**Satellite**} & \multicolumn{2}{c}{**Elevation**} & \multirow{2}{*}{**B1I**} & \multirow{2}{*}{**B1C**} & \multirow{2}{*}{**B2a**} & \multirow{2}{*}{**B2I**} & \multirow{2}{*}{**B3I**} \\  & & **(Average)** & & & & & & \\ \hline \multirow{4}{*}{**BDS-2**} & average & 32.41 & 40.68 & & & 43.02 & 44.93 \\  & C11 & 24.82 & 39.32 & & & 41.15 & 43.43 \\  & C12 & 25.36 & 38.48 & & & 42.00 & 43.49 \\  & C14 & 47.05 & 44.25 & & & 45.92 & 47.87 \\  & average & 39.33 & 44.42 & 43.87 & 47.23 & & 44.67 \\  & C19 & 46.03 & 45.50 & 45.29 & 48.68 & & 45.96 \\  & C20 & 35.78 & 43.75 & 43.12 & 46.39 & & 43.65 \\  & C21 & 44.54 & 46.14 & 45.61 & 48.78 & & 46.21 \\  & C22 & 43.58 & 46.59 & 45.98 & 48.64 & & 46.25 \\  & C23 & 48.82 & 46.91 & 46.38 & 50.11 & & 46.77 \\  & C24 & 40.28 & 44.60 & 44.26 & 48.15 & & 45.13 \\  & C25 & 35.58 & 43.71 & 42.88 & 45.75 & & 43.72 \\  & C26 & 54.39 & 46.99 & 46.09 & 49.21 & & 47.11 \\  & C27 & 45.17 & 45.88 & 44.46 & 47.65 & & 45.08 \\  & C28 & 44.87 & 45.47 & 44.39 & 47.10 & & 44.90 \\  & C29 & 45.47 & 45.35 & 44.34 & 48.04 & & 45.25 \\  & C30 & 43.35 & 45.98 & 44.94 & 47.87 & & 45.38 \\  & C32 & 30.87 & 42.96 & 42.73 & 46.19 & & 43.77 \\  & C33 & 32.20 & 42.57 & 42.32 & 46.43 & & 43.51 \\  & C34 & 22.87 & 40.82 & 40.18 & 43.02 & & 41.16 \\  & C35 & 43.24 & 45.56 & 44.67 & 47.30 & & 45.23 \\  & C36 & 29.44 & 42.52 & 41.97 & 46.12 & & 43.72 \\  & C37 & 46.37 & 44.91 & 44.70 & 48.76 & & 45.53 \\  & C41 & 25.93 & 40.86 & 40.89 & 44.77 & & 42.33 \\  & C42 & 51.39 & 46.10 & 45.92 & 49.96 & & 46.94 \\  & C43 & 35.44 & 44.05 & 43.17 & 45.87 & & 43.86 \\  & C44 & 31.37 & 43.32 & 42.68 & 45.51 & & 43.08 \\  & C45 & 32.36 & 42.65 & 42.77 & 46.59 & & 43.93 \\  & C46 & 34.68 & 42.92 & 43.06 & 46.70 & & 43.61 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Statistics of \(\rm C/N_{0}\) values of all MEO satellites. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & **B1I** & **B1C** & **B2a** & **B2b** & **B2I** & **B3I** \\ \hline \multirow{2}{*}{GEO} & BDS-2 & 37.59 & & & 40.34 & 42.62 \\  & BDS-3 & 42.82 & & & & 44.57 \\  & BDS-2 & 43.35 & & & 43.76 & 46.08 \\  & BDS-3 & 45.36 & 45.76 & 49.31 & & 46.18 \\  & BDS-2 & 40.68 & & & 43.02 & 44.93 \\  & BDS-3 & 44.42 & 43.87 & 47.23 & & 44.67 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: \(\rm C/N_{0}\) statistical results of all types of satellites.
# Reconfigurable Formation Control of Multi-Agents Using Virtual Linkage Approach
## 3 Formation Control: Virtual Linkage Approach
### Moving in Formation Using Virtual Linkage
#### 3.3.1 Fitting the Virtual Linkage
\({}^{R}T_{vj}^{d}\) is a fixed homogeneous transform from the coordinate frame of \(j\)th virtual link \(\{vj\}\) to the virtual linkage's reference coordinate frame \(\{R\}\). It is determined when the formation shape is configured with \(\theta_{d}\). In order to implement \(D(\cdot)\), the error between \({}^{w}T_{vj}(X_{vj}^{*})\) and \({}^{w}T_{R}(X_{R})\cdot{}^{R}T_{vj}^{d}(\theta_{d})\) are transformed into the distance between four pair of points. In details, a homogeneous transform is a moving coordinate frame \(\{vj\}\), and the position and orientation error between two coordinate frames can be calculated by moving and rotating a tetrahedron. Here the shape of the tetrahedron is determined by four vertices in the \(\{vj\}\) coordinate frame: \(\left\{{}^{vj}r_{p1}=(1,0,0,1)^{T},{}^{vj}r_{p2}=(0,1,0,1)^{T},{}^{vj}r_{p3}= (0,0,1,1)^{T},{}^{vj}r_{p4}=(0,0,0,1)^{T}\right\}\) (See Figure 9). Hence, the objective function \(D(\cdot)\) is converted into the distance between four pairs of points:  
\[F(X_{R})=\sum_{vj=1}^{k}\sum_